The aim of this retrospective study was to compare the clinical outcomes of treating isolated surgical neck fractures of the humerus using either the intramedullary nail or the MIROS (Minimally Invasive Reduction and Osteosynthesis System®) system. The study included 42 patients who underwent surgical treatment at the Polyclinic from January 2014 to June 2020. The results indicated that both treatments were effective; however, the MIROS system exhibited a higher complication rate. Intramedullary nailing was more reliable, thus recommended as the primary treatment method. This study underscores the importance of selecting the appropriate fixation technique to minimize complications and enhance patient outcomes.

OBJECTIVE: Cervical cancer is a major cause of cancer-related mortality, necessitating effective screening and diagnostic methods. This study aimed to assess the performance of cervicovaginal smear (CVS) and Human papilloma virus (HPV)-DNA cotest. STUDY DESIGN: The pathology results of 225 female patients who underwent HPV-DNA testing with CVS between 2014 and 2022 and were subsequently diagnosed by colposcopic cervical biopsy or second CVS were retrospectively analyzed. RESULTS: CVS samples showed Atypical Squamous Cells of Undetermined Significance (ASCUS), Low-grade squamous intraepithelial lesion (LSIL), High-grade squamous intraepithelial lesion (HSIL) and cervical cancer. Concordance between the first and second diagnoses demonstrated moderate agreement for LSIL. ASCUS cases exhibited a significant correlation with HPV-DNA positivity and higher-grade cervical lesions. In biopsy, sensitivity and specificity for CIN 2 were 67.8% and 67.9%, respectively, while for CIN 3, they were 81.8% and 58.5%. HPV testing showed significant correlation with histopathologic results. In women over 40 years, more intraepithelial lesions were diagnosed compared to younger women (p<0.005). The conventional smear technique proved reliable in detecting high-grade lesions. CONCLUSION: Despite the limitations of our study, our results emphasize the value of HPV-DNA testing to avoid unnecessary interventions and to establish appropriate follow-up strategies.

Honey is a sweet substance extensively consumed in the world for its nutritional and healthy properties. However, residues of pesticides and environmental contaminants can compromise its quality. For this reason, the physicochemical parameters, and the organic contamination of monofloral and multifloral honeys from three regions of Algeria (Tiaret, Laghouat and Tindouf) were monitored to evaluate the quality of honey and the safety for consumers. In general, the results obtained from the physicochemical analyses were in line with the EU standards. In terms of contamination, pesticides authorised and used in Algerian agriculture (matalaxyl-M and cyromazine), as well as banned pesticide (carbaryl), were found in almost all the samples. However, only the concentration of cyromazine was higher than the relative EU maximum residue levels. PCB 180, PCB 189, anthracene, fluorene and phenanthrene were mainly detected. All the honeys show traces of DiBP, DBP, DEHP and DEHT, but no traces of bisphenols were found. Moreover, the dietary exposure assessment shows that a small amount of Algerian honey can be safely consumed. Overall, data from this study should motivate the Algerian government to enhance their monitoring activities in beekeeping and to find solutions for implementing more sustainable agricultural practices harmonizing with the international legislation.

Cardiopulmonary resuscitation (CPR) is a widely practiced lifesaving skill. Conventional CPR is based on manual chest compressions and delivering mouth-to-mouth breaths. There has been advancement in biomedical technology over the last few decades which has led to the introduction of new techniques and devices in CPR as well. In light of these advancements, we propose SMEAR (Standard, Mechanical, ECMO Assisted, Augmented virtual and Remote) classification system to provide structured overview from traditional to novel methods and devices. The standard or Manual CPR, widely practiced by the general public during out-of-hospital cardiac arrests, combines active chest compressions with or without mouth breathing. However, to enhance CPR’s efficacy, various mechanical devices have been developed, encompassing Active Chest Compression, Passive Decompression, and those integrated with Automated External Defibrillators. Feedback sensor-equipped devices, include the Metronome, Accelerometer, Barometer, and Triaxial Field Induction. Mechanical devices, classified by their compression mechanisms, can be isolated – like the Piston-driven, Load-distributing, and Active Compression-Decompression (ACD) – or combined, as in ACD-ITV (Impedance Threshold Device), ACD-IAC (Interposed Abdominal Compression), and ACD-ITV-EECP (Enhanced External Counter Pulsation). Beyond mechanics, Extracorporeal CPR offers veno-arterial Extracorporeal Membrane Oxygenation in refractory cardiac arrest and can improve outcomes. Augmented Virtual Reality usage in training and provision of CPR is at the early stages. It has the potential of expanded usage in training public to provide high-quality CPR. A provider can be remotely guided step by step via telephone or smartphone applications, ensuring continuous support until professional assistance arrives.

Cervical cancer is a worldwide public health problem with a high rate of illness and mortality among women. In this study, we proposed a novel framework based on Faster RCNN-FPN ar-chitecture for the detection of abnormal cervical cells in cytology images from cancer screening test. We extended the Faster RCNN-FPN model by infusing deformable convolution layers into the feature pyramid network (FPN) to improve scalability. Furthermore, we introduced a global contextual aware module alongside the Region Proposal Network (RPN) to enhance the spatial correlation between the background and the foreground. Extensive experimentations with the proposed deformable and global context aware (DGCA) RCNN were carried out using the cer-vical image dataset of “Digital Human Body" Vision Challenge from the Alibaba Cloud TianChi Company. Performance evaluation based on the mean average precision (mAP) and receiver operating characteristic (ROC) curve has demonstrated considerable advantages of the proposed framework. Particularly, when combined with tagging of the negative image samples using tra-ditional computer-vision techniques, 6-9% increase in mAP has been achieved. The proposed DGCA-RCNN model has potential to become a clinically useful AI tool for automated detection of cervical cancer cells in whole slide images of Pap smear.

The implementation of sentinel lymph node (SLN) biopsy is changing the scenario in the surgical treatment of early-stage cervical cancer and the oncologic safety of replacing bilateral pelvic lymphadenectomy with SLN biopsy is currently under investigation. One of the undisputed values of SLN biopsy is its diagnostic accuracy in detecting low-volume metastases (LVM) via pathologic ultrastaging. In early-stage cervical cancer, the reported incidence of LVM ranges from 4 to 20%. The prognostic impact and the role of adjuvant treatment in patients with LVM is still unclear. Some non-prespecified analyses in prospective studies showed no impact on the oncologic outcomes compared to node-negative disease. However, the heterogeneity of the studies, the differences in the disease stage and the use of adjuvant treatment, and the concomitant pelvic lymphadenectomy (PLND) make any conclusion on this topic hard. Current guidelines suggest considering micrometastasis (MIC) as a node-positive disease, while isolated tumor cells (ITC) as a node-negative disease, with a low level of evidence. This review aims to highlight the unanswered questions about the definition, identification, prognostic and therapeutic role of LVM and underline the present and future challenges we are facing. We hope this review will guide further research, giving robust evidence on LVM and their impact on clinical practice.

In August 2020, the World Health Assembly launched a global initiative to eliminate cervical cancer by 2030, setting three primary targets. One key goal is to achieve a 70% screening coverage rate for cervical cancer, primarily relying on the precise analysis of Papanicolaou (Pap) or digital Pap smears. However, the responsibility of reviewing Pap smear samples to identify potentially cancerous cells primarily falls on pathologists, a task known to be exceptionally challenging and time-consuming. This paper proposes a solution to address the shortage of pathologists for cervical cancer screening. It leverages the OpenAI-GYM API to create a deep reinforcement learning environment utilizing liquid-based Pap smear images. By employing the Proximal Policy Optimization algorithm, autonomous agents navigate Pap smear images, identifying cells with the aid of rewards, penalties, and accumulated experiences. Furthermore, the use of a pre-trained convolutional neuronal network like Res-Net54 enhances the classification of detected cells based on their potential for malignancy. The ultimate goal of this study is to develop a highly efficient, automated Papanicolaou analysis system, ultimately reducing the need for human intervention in regions with limited pathologists.

Inherited platelet disorders (IPDs) are rare diseases featured by low platelet count and/or defective platelet function. Patients have variable bleeding diathesis and sometimes additional features that can be congenital or acquired. Identification of an IPD is desirable to avoid misdiagnosis of immune thrombocytopenia and use of improper treatments. Diagnostic tools include platelet function studies and genetic testing. The latter can be challenging as the correlation of its outcomes with phenotype is not easy. The immune-morphological evaluation of blood smear (by light- and immunofluorescence microscopy) represents a reliable method to phenotype subjects with suspected IPD. It is relatively cheap, not excessively time-consuming, and applicable to shipped samples. In some forms, it can provide diagnosis by itself, as for MYH9-RD, or in addition to other first-line tests as aggregometry or flow cytometry. In regard to genetic testing, it can guide specific sequencing. Since only minimal amounts of blood are needed for preparation of blood smears, it can be used to further characterize thrombocytopenia in pediatric patients and even newborns. In principle it is based on visualizing alterations in the distribution of proteins, which result from specific genetic mutations, by using monoclonal antibodies. It can be applied to identify deficiencies in membrane proteins, disturbed distribution of cytoskeletal proteins, and alpha as well as delta granules. On the other hand mutations associated with impaired signal transduction are difficult to identify by immunofluorescence of blood smears. This review summarizes technical aspects and the main diagnostic patterns achievable by this method.

According to cancer death rates for women worldwide, this form of cancer ranks fourth after breast, bronchopulmonary, and colorectal cancer, affecting around 570,000 women annually. About 270,000 women each year pass away from this illness, 85% of them are from underdeveloped nations where cervical cancer claims more lives than any other kind of cancer. In Romania, cervical cancer ranks fourth in terms of death, behind breast, bronchopulmonary, and colorectal cancers, and second in terms of incidence, behind breast cancer. Endometriosis in the cervical region is quite uncommon. Only a tiny fraction of women had the condition upon diagnosis. It might be challenging to detect cervical endometriosis since it rarely manifests any symptoms or markers. Women who are asymptomatic can manage the condition expectantly; however, if the symptoms return often, surgery may be required. As many patients have concurrent diseases such fibroids, adenomyosis, ovarian cysts, and pelvic endometriosis, cervical endometriosis therapy appears to be straightforward. There ought to be surgical intervention. Symptomatic cervical endometriosis, such as irregular or post-coital bleeding, may be treated surgically using the LLETZ surgery or another cervical ablation approach. Background and Objectives: (1) To assess one's degree of knowledge on the signs, causes, and methods of preventing cervical cancer or cervical endometriosis, as well as screening for these conditions. (2) To ascertain how the responder behaves about cervical cancer screening and prevention, as well as endometriosis in the cervical region. Materials and Methods: A case report about cervical cancer or cervical endometriosis was conducted among a patient from Romania, Timisoara. Results: In this study, we report the patient with cervical endometriosis, although he was initially diagnosed with cervical cancer. Conclusions: Cervical cancer, a condition that can have serious repercussions on the patient's health, even if there is no clear picture of paraclinical investigations, to raise the suspicion of cervical endometriosis and not cervical cancer.

Cervical cancer is one of the ailments that endangers the health of women all over the world and causes infertility. Fortunately, this disease can be avoided. Both the results and participation rates of the current preventative strategy remain poor. Hence, preventative or early detection methods remain challenging and open. In gynecology and computer science fields, few studies on identifying cervical cancer are based on behavior risk factor and machine learning. Moreover, various social and behavioral aspects might often make it more difficult to detect cervical cancer especially in low‑ and middle‑income countries (LMICs). Accordingly, focus on predicting the presence of cervical cancer through behavioral risk factors is the basic structure of this research. We proposed an approach to classify cervical cancer from a social-behavioral perspective using logistic regression to predict cervical cancer. We got significantly improved outcomes than the existing methods. Moreover, we used feature engineering to formulate less dimensionality as a pre-processing step and optimized parameters to be prepared for classification phase. The results proved that using the logistic regression with regularization type L1 had a promising performance. The accuracy of Logistic Regression (LR) L1 was 97.2%. Such results support the proposed work as a reliable classification predictive tool.

Background: Sexual dysfunction is the most common side effect encountered by cervical cancer patients during and after treatment, and rehabilitation strategies are needed to improve their quality of life. A variety of rehabilitation strategies have been reported in previous studies, varying by country, race and even continent. However, the common rehabilitation strategies used by cervical cancer survivors in Tanzania and their outcomes are unknown. The study aimed to unfold sexual rehabilitation strategies and outcome practiced by cervical cancer survivors who experienced sexual dysfunction during and after treatment in North-western Tanzania. Methods: Descriptive qualitative study were employed. Phenomenology study design used with a sample of 7 participants and data corrected via in-depth interview guided by 11 questions. Audiotaped and transcribed to get main themes and sub themes through deductive analysis. Results: Most participants reported inability to achieve an orgasm and low libido and dyspareunia after treatment. Most participants were eagerly to have their sexual activity recovered. However, they were not familiar with any rehabilitation strategy after the experience of sexual dysfunction. Even though, some of the participants attempted various interventions, but all were unsatisfactory leading to a significant decline in sexual interest and activity. Conclusion: Cervical cancer survivors are not familiar with any sexual rehabilitation strategies. Health education concerning various sexual rehabilitation strategies should be provided to survivors during and even after treatment.

Kisspeptin, a key neuropeptide derived from the KISS1 gene, is renowned for its critical role in regulating the hypothalamic-pituitary-gonadal axis and reproductive hormone secretion. Beyond its primary function in reproductive biology, emerging research has illuminated its influence in various cancers, mediating significant effects through its interaction with the G-protein coupled receptor, Kisspeptin receptor. This interaction has been implicated in modulating cellular pro-cesses such as proliferation and metastasis, making it a potential target for therapeutic interven-tion. Our study initially screened ten kisspeptin-10 analogs through cytotoxic effects of kisspep-tin-10 (KP10) and its analogs in several cancer types, including cervical, prostate, breast, and gastric cancers, with a particular focus on cervical cancer where the most profound effects were observed. Further exploration using kinase array assays revealed that these analogs specifically alter key kinases involved in cancer progression. Migration assays demonstrated a substantial decrease in cell motility, and Bioluminiscence Resonance Energy Transfer assays confirmed these analogs strong interactions with the kisspeptin receptor. Overall, our results indicate that these KP10 analogs not only hinder cervical cancer cell proliferation but also curtail migration through targeted modulation of kinase signaling, suggesting their potential as therapeutic ayfgents in managing cervical cancer progression. This comprehensive approach underscores the therapeutic promise of exploiting kisspeptin signaling in cancer treatment strategies.

The aim of this randomized controlled clinical trial was to compare the 2-year clinical performance of resin composite restorations placed at non-caries cervical lesions (NCCL) with one-step self-etch, total-etch, and selective enamel etch & self-etch adhesive techniques. Thirty-two patients received three resin composite restorations each at NCCLs (Tetric EvoCeram/Ivoclar/Vivadent), bonded with a total-etch adhesive agent (ExciTE F/Ivoclar/Vivadent) and a self-etch (AdheSE One F/Ivoclar/Vivadent) without and with selective enamel etching. All restorations were evaluated by two examiners at baseline, 6-, 12-, 18- and 24-months post-operation regarding retention, caries occurrence, marginal adaptation, and marginal staining. A logistic regression analysis with generalized estimating equations was used. After 2 years, the retention rate was 86,8% for total-etch, 92.26% for self-etch, and 93.63% for selective enamel etching & self-etch. No caries were detected during all the restorations. Concerning marginal adaptation, the clinically perfect restorations were 26.9% for the total-etch technique, 16% for self-etch, and 25,9% for selective enamel etch & self-etch. All three adhesive strategies provided restorations with no significant differences in the retention rate and the marginal adaptation, whereas the total etch yielded better performance for marginal staining. All the restorations were assessed as clinically acceptable after 2 years.

(1) Background: The prediction of cervical lesions evolution is a challenge for clinicians. The prospective study aimed to determine and compare the predictive accuracy of cytology, HPV genotyping, and p16/Ki67 dual staining alone or in combination with personal risk factors for the prediction of progression, regression or persistence of cervical lesions in human papillomavirus (HPV)-infected patients; (2) Methods: This prospective study included HPV-positive patients with or without cervical lesions that underwent follow-up. Descriptive statistics and sensitivity analysis for individual or combined index tests were used for data analysis; (3) Results: The highest predictive performance for the progression of cervical lesions was achieved by a model that comprised a Pap smear suggestive of high-grade squamous intraepithelial lesion (HSIL), the presence of 16/18 HPV strains, a positive p16/Ki67 dual staining result along with the presence of at least 3 clinical risk factors, which had a sensitivity (Se) of 74.42%, a specificity of 97.92%, an area under the receiver operating curve (AUC) of 0.961, and an accuracy of 90.65%. The prediction of cervical lesions regression or persistence was modest; (4) Conclusions: Multiple testing or new biomarkers should be used to improve HPV-positive patient surveillance, especially for cervical lesion regression or persistence prediction.

HPV51 belongs to group I high-risk HPV (hrHPV) genotypes with a high prevalence of infection in numerous epidemiological studies, although its occurrence varies across geographical regions. To better understand the presence of variants and genetic variation of HPV51, this study aimed to perform a phylogenetic analysis of the E6, E7 and L1 regions to detect the hrHPV variants circulating in women referred to colposcopy in two different Italian regions (Lombardy and Sardinia). Cervical samples were tested using Anyplex™ II HPV HR Detection. HPV51 positive samples were further analysed using previously described PCR protocols to obtain sequences of E6, E7 and L1. A positivity rate for HPV51 of 7.4% (26/351) and 13.6% (11/81) was found in Lombardy and Sardinia, respectively. The majority (34 out of 35) of the analyzed sequences clustered into the A lineage and only one sequence was grouped into B lineage. L1 capsid gene showed the presence of different mutations but none under positive selective pressure. Three nucleotide polymorphisms T6311G, A6312T and G6313A related to the two aminoacidic mutations V264G and G265S were found among all analysed sequences. This study contributes to implement data regarding the molecular epidemiology of HPV but additional analysis on a larger number of sequences is necessary to better understand the role of HPV variants in cervical lesions development.

Carotid atherosclerotic lesions are a major cause of cerebrovascular disease (CVD). Identification and quantification of carotid plaques are important for categorizing the vulnerability of plaques for rupture and assessing the impact of treatments. The irregularity of plaque surface is associated with previous plaque rupture and plays an important role in the risk of stroke. Thus, the aim of this study is to develop and validate novel vulnerability biomarkers from three-dimensional ultrasound (3DUS) images by analyzing the surface morphological characterization of carotid plaque using fractal geometry features. 3D box-counting and 3D blanket are the two types of 3D fractal dimension that were employed to describe the smoothness of plaques. This fractal dimension analysis tool was used to evaluate the effect of atorvastatin using 3DUS carotid images, which were acquired from 6 patients treated with atorvastatin with 80 mg daily and 5 patients with placebo. The Student's T Test results showed that those two fractal features were effective for detecting the statin-related changes in carotid atherosclerosis with p<0.0068 and p<0.015 respectively, suggesting that 3D fractal dimension measurements can be used effectively to analyze the surface characteristics of carotid plaques, especially for evaluating the impact of the treatment.

Cervical Cancer affects a large part of female population that makes the prediction of this disease based on Machine Learning (ML) by outmost importance. ML algorithms can be integrated in complex intelligent agent-based systems that can offer decision support to the resident medical doctors or even to experienced medical doctors. For instance can be mentioned the situation when an experienced medical doctor diagnose a case but he/she needs expertise support that is related to another medical specialty. Data imbalance is frequent in healthcare data and has a negative influencing effect in making predictions using ML algorithms. Cancer data generally and cervical cancer data particularly are frequently imbalanced. Based on this fact the study of data imbalance impact on diverse state-of-the-art ML prediction algorithms is important. This research subject is also motivated by the fact that in many research are presented experimental evaluations of algorithms without characterization of the data on that they have been applied. Such characterizations could give clear indication to other researchers regarding the applicability of the algorithms on their specific data. Specifically, if the data have the respective characteristics than are expectable the same performance evaluation results like those in the reported research. For the study we chosed a messy real-life Cervical Cancer dataset available in a recognized data repository included a large amount of missing and noisy values. To identify the best imbalanced technique for this medical dataset, it is compared the performance of eleven important resampling methods combined with the following state-of-the-art ML models: K-Nearest Neighbours (with k values of 2 and 3), binary Logistic Regression, and Random Forest, that are frequently applied in prediction types of researches in healthcare. The studied resampling methods includes seven undersampling methods namely Condensed Nearest Neighbour, Tomek Links, Edited Nearest Neighbours, Repeated Edited Nearest Neighbours, All K-Nearest Neighbours, NearMiss, Neighbourhood Cleaning Rule, and Instance Hardness Threshold, and four oversampling methods namely Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling Approach for Imbalanced Learning SMOTE, Support Vector Machine SMOTE and Borderline SMOTE. In the case of the dataset for the confidence interval with 95% confidence level was between 9.23 and 16.22, while the imbalance ratio is 12.73. The obtained results show that resampling methods help the learning models to improve the classification ability of cervical cancer. The applied oversampling techniques generally showed better results than undersampling methods. In the case of Logistic Regression classifier had the highest impact on balanced techniques, while Random Forest had promising performance, even before balancing techniques and KNN2 was generally better than KNN3.

Cervical cancer is 4th the most common cancer in women worldwide. Due to the prevalence of HPV in the population (80-90%), the scientists are likely to discover even more associations of this pathogen with other diseases in the future. In recent years, ISH assays that use automated signal-detecting methods in ormalin-fixed, paraffin-embedded (FFPE) cervical tissue, such as the enzyme-categorized signal-detecting system, have shown a higher sensitivity. To evaluate automatic signal detection of in situ hybridization (ISH) assay for detecting human papillomavirus (HPV) DNA, we compared the ability of an ISH probe, Inform HPV II and III (Ventana Medical Systems, Tucson, AZ), to that of PCR assays to detect HPV DNA in cervical tissue specimens with cervical intraepithelial neoplasia (CIN; CIN 1, 28 cases; CIN 2, 22 cases; and CIN 3, 20 cases) and normal cervix (2 cases). Our findings showed significant relation was confirmed between ISH III level and HPV outcome (positive/negative). Patients with positive HPV outcomes had significantly lower ISH III levels, MD = -7961.82 CI95 [-17230.00; -199.21], p = 0.005. Automatic signal detection of ISH assay is not particularly applicable to cervical tissue material. A more useful method of confirming the presence of HPV in the cervix is ​​the HPV test with genotyping, as it allows for collecting a larger amount of material from the cervical disc and canal. The interpretation of a positive or negative ISH test must be guided in the context of clinical history and morphology.

Background. Cervical esophageal reconstruction is vital for improving the quality of life in cancer surgery patients. Microsurgery is crucial, providing vascularized tissue for defect repair, particularly in secondary cases with a higher risk of failure due to larger defects and damage from previous surgery and radiotherapy. The purpose of this study was to describe the clinical characteristics of a series of patients who underwent secondary repair of esophageal defects and provide practical information for the management and treatment of such cases based on the authors' experience and the literature review. Methods. We retrospectively reviewed the electronic medical records of the Plastic Surgery Clinic at the University of Trieste to identify cases of patients who underwent secondary esophageal microsurgical reconstructions following oncological surgery. Patient demographics, etiology of esophageal defects, previous surgical history, and preoperative assessments were collected from medical records. Surgical techniques employed for reconstruction, such as pedicled flaps or free tissue transfers, were documented along with intraoperative details. Postoperative outcomes, including complications, graft viability, and functional outcomes, were evaluated during follow-up. Results. We treated 13 cases of secondary esophageal reconstructions from 2011 to 2022. Most commonly, Antero-Lateral Thigh (ALT) flaps were used in 10 cases, while 2 cases employed Radial Forearm Flap (RFF), and 1 a chimeric parascapular flap. No flap failures occurred during a median 50-month follow-up. One ALT flap patient experienced post-op stricture but maintained swallowing ability. A single tracheoesophageal fistula occurred in a RFF patient with a history of radiotherapy and completion lymph node dissection. Conclusions. Cervical esophageal reconstruction significantly impacts patients' quality of life by restoring oral feeding and phonation. When local flaps fall short, microsurgical reconstruction with intestinal flaps is valuable, but burdened by limitations. For challenging secondary cases, ALT or RFF flaps emerge as safer options due to their robust pedicles, yielding low complication rates and positive functional outcomes.

Cloud-induced atmospheric extinction and occlusion have a major effect on the effectiveness and quality of telescope observations. Real-time cloud cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. At ground-based astronomical telescope sites, cloud cover distribution is currently analyzed using manual observation methods. However, the main disadvantages of manual observation methods are human subjective, heavy workloads and poor real-time performance. Therefore, a real-time automatic cloud images classification method is desperately needed. This paper presents a new cloud identification method, which is named PSO+XGBoost model by combining eXtreme Gradient Boosting (XGBoost) and Particle Swarm Optimization (PSO). The entire cloud image is divided into 37 sub-regions in order to more precisely identify the distribution of the clouds. 19 features are then extracted, including the sky background, star density, lighting conditions, and subregion grayscale values. The experimental results have shown that the overall classification accuracy of 96.91% and our model is able to outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.

ABSTRACT Purpose: Tongue image analysis for disease diagnosis is an ancient traditional non-invasive diagnosis technique widely used by traditional medicine practitioners. Deep Learning based multi-label disease detection models offer tremendous potential to clinical decision support systems, by facilitating preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline, where in observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. All images are voluntarily given by subjects consulting collaborating physicians. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolution neural network (MobileNetV2) model for binary classification. Once the diseased label is predicted, the image is used to diagnose multiple diseases using the prediction algorithm based on DenseNet121. Results: Average accuracy of 93 % was achieved in classifying diseased from normal healthy tongue by detection model with MobileNetV2 architecture. Multilabel disease classification produced more than 90% accurate results for the seven class labels considered. Conclusion: AI based image analysis shows promising results and an extensive dataset could provide further improvements to this approach. Rather than employing high-cost sophisticated image capturing setup, experimenting with smartphone images opens opportunity to provide preliminary health status to individuals on smartphone prior to further line of treatment and diagnosis.

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small targets in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small target objects. Finally, we propose the Smooth Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model's focus on the attributes of detection boxes and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach's efficacy, we conduct comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the [email protected] metric, and 72.03%, 65.37%, and 38.91% in the more stringent [email protected]:0.95 metric, respectively.

Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.

Objective: The aim of our study was to perform a retrospective analysis of the volume of cervical screening tests, the number of patients treated with an excision method, and the incidence of invasive and non-invasive cervical during a pandemic and pre-pandemic period of 24 months. Results: There was a statistically significant age difference between the two study periods, both by the average age of patients and by age group. The mean difference was 32 years before the pan-demic and 35 years during the pandemic (p-value >0.05). The majority of patients presenting for investigations before and during the pandemic were in the 30-39-year-old age group (31.95%, respectively; 34.2% (p-value = 0.003). The biggest patient loss ratio identified by age group was in the 50–59 years: 14,53% in the pre-pandemic period and 9,1% in the pandemic period. In the pan-demic period, patients from rural areas presented in the clinical trial with a lower rate of 39.52% (83 patients) vs. 60.47% (127 patients) in urban areas. A higher percentage of patients experiencing cervicorrhagia as a clinical manifestation in the pandemic period vs. the prepandemic period, with an increase in more severe lesions in the pandemic period, has a statistical significance of 8% more newly diagnosed compared to the pre-pandemic period. Conclusion: The addressability of the patients during the COVID period was not affected in a drastic way in our study. We encountered a decrease in appointments in the age group 50–59 years and a decrease in patients with rural residence. In our study, we found an increase in cervicorrhagia as a reason for consultation in the pandemic period with a higher lesion degree, both on a pap smear and on a cervical biopsy.

Transcatheter Aortic Valve Implantation (TAVI) now represents the mainstay in the treatment of severe aortic stenosis. Owing to its exceptional procedural efficacy and safety, TAVI has been extended to include patients at lower surgical risk, thus now encompassing a diverse patient population receiving this treatment. Yet, long-term outcomes also depend on optimal medical therapy for secondary vascular prevention, with antithrombotic therapy serving as a cornerstone. Leveraging data from multiple randomized controlled trials, current guidelines generally recommend single antithrombotic therapy, with either single antiplatelet therapy (SAPT) or oral anticoagulation (OAC) alone in those patients without, or with atrial fibrillation respectively. Yet, individualization of this pattern, as well as specific case uses, may be needed based on individual-patient characteristics and concurrent procedures. This review aims to discuss the evidence supporting antithrombotic treatments in patients treated with TAVI, indications for a standardized treatment, as well as specific considerations for an individualized approach to treatment.

Pavement crack detection is one of the key links in highway pavement maintenance management. In the current road crack detection, the network model easily ignores the shallow geometric features of cracks and cannot extract the key feature information of cracks. Aiming at the above problems, an EC-YOLOX network model is proposed. The CFPN is constructed to cross-fuse different scale features to solve the feature scale invariance. In the strengthen feature extraction layer, ECANet is fused to enhance the ability to pay attention to key information. The WEIoU loss function is proposed, which assigns different penalty terms to the target box in the vertical and horizontal directions. In terms of data, geometric distortion constraint correction is performed on the single-point perspective pavement image. The experimental data show that the accuracy of EC-YOLOX on the self-built data set reaches 76.34% [email protected], which is 4.45% higher than that of YOLOX. The loss curve of EC-YOLOX is smoother than that of YOLOX, and the minimum training loss value is 2.136, which is 0.648 lower than the minimum loss value 2.784 of YOLOX. Many experiments have verified the effectiveness of EC-YOLOX in improving the detection effect in pavement crack detection.

Many have speculated that events of personal and financial loss in Rembrandt’s life caused depression and that this is revealed by examination of his self-portraits particularly those painted in old age. Some report detecting various physiological diseases associated with aging, including vision impairment, which may have affected his mood and work. Aging and neurodegenerative disease which often accompanies it, are both associated with depression. Recent advances in imaging and computer technology make it possible to empirically examine changes in artistic style which can contribute to understanding artists’ physical and mental health. In the current study changes in contrast, colour and fractal dimension were measured in the entire corpus of Rembrandt’s painted self-portraits and portraits to determine whether changes in style indicate depression or disease. Productivity was also examined as an indirect indicator. Given the high salience attributed to self-images, it also examined whether differences in style could be detected between self-portraits and portraits of related and unrelated others. The results indicate that it is unlikely that Rembrandt suffered from unipolar or bipolar depression, age-related cognitive decline, or neurodegenerative disease. The data are consistent with someone experiencing episodes of low mood consistent with normal grieving and adversity followed by resilient recovery. There is evidence of a gradient in saliency and complexity between self-portraits and related and unrelated portraits, of a less formal style in portraits of related others and of a ‘late’ style identified by leading art historians consistent with macular degeneration. The results are also consistent with an association of aesthetic preference with mid-level fractal values tending towards the Golden Mean long associated with beauty in nature and in art.

Ocean exploration is crucial for utilizing its extensive resources. Images captured by underwater robots suffer from issues such as color distortion and reduced contrast. To address the issue, we propose an innovative enhancement algorithm that integrates Transformer and Convolutional Neural Network (CNN) in a parallel fusion manner. Firstly, a novel transformer model is intro-duced to capture local features, employing peak-signal-to-noise ratio (PSNR) attention and linear operations. Subsequently, to extract global features, both temporal and frequency domain features are incorporated to construct convolutional neural network. Finally, the Fourier’s high and low-frequency information of the original image are utilized to fuse different features. To demon-strate the algorithm's effectiveness, underwater images with various levels of color distortion are selected for both qualitative and quantitative analyses. The experimental results demonstrate that our approach surpasses other mainstream methods, achieving superior PSNR and structural sim-ilarity index measure (SSIM) metrics and leading to a detection performance improvement of over ten percent.

Background Cervical insufficiency, marked by recurrent painless cervix dilation leading to second-trimester pregnancy losses, is traditionally managed through cerclage placement and vaginal progesterone administration. However, a recent experimental approach targeted intra-amniotic inflammation or infection in a selected group of women with cervical dilation over 1 cm, intact membranes, and no uterine contractions in the second or third trimester. Case presentation We report a case of a 21 year old primigravid woman who experienced resolution of mid-trimester cervical insufficiency after the administration of triple antibiotic therapy, as confirmed by ultrasound monitoring. Remarkably, the patient successfully carried the pregnancy to term and delivered a healthy baby. Conclusions This case highlights the potential effectiveness of a novel combination of antibiotics, even in the presence of several high-risk pregnancy conditions, and underscores the possibility of a favorable outcome.

Expression of miR-21 has been found to be altered in almost all types of cancers and it has been classified as an oncogenic microRNA. In addition, expression of tumor suppressor gene RECK is associated with miR-21 overexpression in high-grade cervical lesions. In the present study we analyze the role of miR-21 in RECK gene regulation in cervical cancer cells. To identify the downstream cellular target genes of upstream miR-21, we silenced endogenous miR-21 expression using siRNAs. We analyzed the expression of miR-21 and RECK, as well as functional effects on cell proliferation and migration. We found that in cervical cancer cells there was an inverse correlation between miR-21 expression and RECK mRNA and protein expression. SiRNAs to miR-21 increased luciferase reporter activity in construct plasmids containing the RECK-3′-UTR microRNA response elements MRE21-1, MRE21-2, and MRE21-3. The role of miR-21 in cell proliferation was also analyzed and cancer cells transfected with siRNAs exhibited a markedly reduced cell proliferation and migration. Our findings indicate that miR-21 post-transcriptionally down-regulates the expression of RECK to promote cell proliferation and cell migration inhibition in cervical cancer cell survival. Therefore, miR-21 and RECK may be potential therapeutic targets in gene therapy for cervical cancer.

Train fault detection primarily relies on the comparative analysis of collected images with standard reference images, making the accurate identification of the collected image types crucial for effective fault detection. Most current systems achieve indirect classification of carriage images by using Automatic Equipment Identification (AEI) devices to recognize carriage identification number information while capturing the carriage images. However, damaged RF tags or blurred characters can hinder these devices, obstructing further train fault analyses. The unique features of carriage linear array images, such as high resolution, extreme aspect ratios, and local non-linear distortions, pose significant challenges to recognition algorithms. This paper proposes a method specifically designed to recognize these types of images. First, we implement an object detection algorithm to locate the positions of key components in carriage linear array images. By leveraging the spatial layout mapping between these key components and the carriage type classification, we simplify the complex image recognition problem into a sparse point set alignment task. To ensure effective registration despite local distortions in the linear array images, we propose a weighted radial basis function. To address the issues of unknown matching relationships and quantity mismatches during point set alignment, the proposed objective function is designed to maximize the similarity between Gaussian mixtures of point sets, thereby determining the RBF weights. Extensive experiments have demonstrated that our algorithm achieves 100% recognition accuracy under local nonlinear scale distortions of less than 15%. The algorithm remains effective even in the presence of some false positives or missed detections in key component object detection results. It can accurately identify the target category from 79 image categories within 24 milliseconds on an i7 CPU without GPU support. It is of great significance to reduce system cost and promote automatic exterior fault detection for train rolling stock.

Equine Asthma Syndrome (EAS) is a common respiratory problem that affects horses of any age. In the severe EAS form (historically referred to as recurrent airway obstruction or RAO) the diagnosis, based mainly on history and clinical signs, it is definitively confirmed by the cytological examination of the bronchoalveolar lavage (BAL), also very useful in monitoring lower airway inflammation in response to environmental management and medication. Cytocentrifugated preparation is usually the staging method for the BAL cytological interpretation. To evaluate whether the BAL cytology in horses with severe EAS under different environmental conditions and before and after treatment can undergo significant changes, and at the same time to investigate whether in the cytological evaluation of the BAL there are interpretative differences between two methods of smear preparation of the collected fluid, a study was carried out on a series of BAL samples (n=48) collected in 8 withdrawals from 6 EAS-affected horses subjected to process exacerbation through environmental stimuli and then to pharmacological treatment. Each BAL fluid collected was equally divided into duplicate portions: one set up by cytocentrifugation and one by sediment from simple centrifugation. The cytological examination revealed any significant difference between the EAS-affected horses in all experimental phases (asymptomatic, early exacerbation phase, late exacerbation phase, and remission phase). Diagnostic interpretive comparison between the two BAL preparations then showed no significant differences in results, suggesting how they can be used indifferently in the evaluation of BAL under conditions of airway inflammation in the horse. Thus, the concentrated smear preparation appears to be an equally diagnostically useful method in conditions where there is no possibility of using an appropriate cytocentrifuge.

Setting A high proportion of notified tuberculosis cases in the Philippines are clinically diagnosed (63%) as opposed to bacteriologically confirmed. Better understanding of this phenomenon is required to improve tuberculosis control. Objectives To determine the percentage of Smear Negative Presumptive Tuberculosis patients that would be diagnosed by GeneXpert; compare clinical characteristics of patients diagnosed as tuberculosis cases; and review the impact that the current single government physician and a reconstituted Tuberculosis Diagnostic committee (Expert Panel) may have on tuberculosis over-diagnosis. Design This is a cross-sectional study of 152 patients 15-85 years old with two negative Direct Sputum Smear Microscopy results, with abnormal chest X-ray who underwent GeneXpert testing and review by an Expert Panel. Results 31% (48/152) of the sample were Xpert positive. 93% (97/104) of GeneXpert negatives were clinically diagnosed by a Single Physician. Typical symptoms and X-ray findings were higher in bacteriologically confirmed tuberculosis. When compared to GeneXpert results, the Expert panel’s sensitivity for active tuberculosis was high (97.5%, 39/40) but specificity was low (40.2%, 35/87). Conclusion Using the GeneXpert would increase the level of bacteriologically confirmed tuberculosis substantially among presumptive Tuberculosis. An Expert panel will greatly reduce over-diagnosis usually seen when a decision is made by a Single Physician.

With the rapid developments that have been made in computer vision and computer technology, semantic segmentation of high-resolution images has emerged as a mainstream and challenging task. The, segmentation of water areas remains a complex task due to the similar features between water areas or between water areas and other land objects. Meanwhile, since coastlines and riverbanks are typically presented in irregular shapes, it is difficult to obtain the categories of pixels at boundaries. This paper proposed the use of a Water-Land Boundary Attention Network (WLBANet) to improve the accuracy of pixels at boundaries between water and land. This network also reinforced the performance of contextual information extraction into distinguishing between water areas and other land objects. To prove the validity of this proposed network, WLBANet was performed on the Water Land Dataset (WLD), which is also by this paper, and which contains water areas and other land objects categories. The results demonstrates that the proposed method has significant effectiveness.

This study proposes and validates a novel combustion control system for Oil-Fired Boilers aimed at reducing air pollutant emissions through flame image prediction. The proposed system is easily applicable to existing ships. Traditional proportional combustion control systems supply fuel and air at fixed ratios according to the set steam load, without considering the emission of air pollutants. To address this, a stable and immediate control system is proposed, which adjusts the air supply to modify the combustion state. The combustion control system utilizes oxygen concentration predictions from flame images via SEF+SVM as control inputs, and applies Internal Model Control (IMC)-based Proportional-Integral (PI) control for real-time combustion control. Due to the complexity of modeling the image-based system, IMC parameter tuning through experimentation is essential for achieving effective control performance. Experiments conducted on a 3000 kg/h marine oil-fired boiler in actual operation optimized the controller parameters for stability and responsiveness, and validated their effectiveness. The results demonstrate the potential of the proposed system to improve combustion efficiency and reduce emissions of air pollutants. This study provides a feasible and effective solution for enhancing the environmental performance of marine oil-fired boilers. Given its ease of application to existing ships, it is expected to contribute to sustainable air pollution reduction across the maritime environment.

Cancer ranks second among the causes of mortality worldwide,following cardiovascular diseases.Brain cancer, in particular, has lowest survival rate of any kind of cancer. Brain tumours vary in their morphology, texture, and location, which determine their classification. Accurate diagnosis of the tumour category enables physicians to select optimal treatment strategies and potentially prolong patients’ lives. Researchers who also implemented deep learning models for diagnosing diseases in recent years largely focused on deep neural network optimization to enhance neural network performance. This implicates implementing models with the best performance and incorporating various network architectures by configuring their hyperparameters. This paper presents a novel hybrid approach method for improved brain tumour classification by combining CNN and EfficientNetV2B3 as feature extraction, followed by (KNN) for classification. To evaluate the recommended method’s efficacy, two widely known benchmark MRI datasets were utilized in the experiments. The initial dataset consisted of 3064 MRI images depicting meningiomas,pituitary,and gliomas tumours.Images from two classes of normal brain and brain tumour were included in the second dataset, which was obtained from Kaggle.In order to enhance performance even more, the study concatenates the CNN and EfficientNetV2B3 flattened outputs before feeding them into the KNN classifier. The proposed framework run on two different dataset and demonstrates outstanding performance with an accuracy of 99.51% and 99.8% on each dataset.

Cervical cancer (CC) is the most common cancer in women worldwide and is almost always associated with repeated infections by human papillomavirus (HPV). Screening by traditional tests combined with biomarker identification techniques for low- or high-grade injuries, are becoming increasingly important in diagnosis and prognosis, avoiding countless deaths. This article explores existing literature on the main serum biomarkers and the identification of biomarkers associated with the oncogenesis of HPV expression in the identification of pre-cancerous lesions and of CC for an effective treatment, with reduction of recurrence, as well as we explain strategies for the development of biomarkers based on DNA, proteins and other markers. We also describe how markers of chromosomal instability host DNA, promoting hypo- or hypermethylation of DNA, as well as polymorphisms and epigenetic events in the p53 gene. Finally, we discuss changes in gene expression using cDNA microarray techniques and changes in the expression of proteins and markers identified through mass spectrometry.

Smear-ripened cheeses host complex microbial communities that play a crucial role in the ripening process. Although bacteriophages have been frequently isolated from dairy products, their diversity and ecological role in such type of cheese remain underexplored. In order to fill this gap, the main objective of this study was to isolate and characterize bacteriophages from the rind of a smear-ripened cheese. Thus, viral particles extracted from cheese rind were tested against a collection of bacterial isolates through a spot assay. In total, five virulent bacteriophages infecting Brevibacterium aurantiacum, Glutamicibacter arilaitensis, Leuconostoc falkenbergense and Psychrobacter aquimaris species were obtained. All exhibit a narrow host range, being only able to infect a few cheese-rind isolates within the same species. The complete genome of each phage was sequenced using both Nanopore and Illumina technologies, assembled and annotated. Sequence comparison with known phages revealed that four of them may represent at least new genera. The distribution of the five virulent phages into the dairy-plant environment was also investigated by PCR and three potential reservoirs were identified. This work provides new knowledge on the cheese rind viral community and an overview of the distribution of phages within a cheese factory.

Fluorescence microscopic images of cells contain a large number of morphological features that are used as an unbiased source of quantitative information about cell status, through which researchers can extract quantitative information about cells and study the biological phenomena of cells through statistical and analytical analysis. As an important research object of phenotypic analysis, images have a great influence on the research results. Saturation artifacts present in the image result in a loss of grayscale information that does not reveal the true value of fluorescence intensity. From the perspective of data post-processing, we propose a two-stage cell image recovery model based on generative adversarial network to solve the problem of phenotypic feature loss caused by saturation artifacts. The model is capable of restoring large areas of missing phenotypic features. In the experiment, we adopt the strategy of progressive restore to improve the robustness of the training effect, and add the contextual attention structure to enhance the stability of the restore effect. We hope to use deep learning methods to mitigate the effects of saturation artifacts to reveal how chemical, genetic, and environmental factors affect cell state, providing an effective tool for studying the field of biological variability and improving image quality in analysis.

Background: Cervical laminoplasty is a useful for treatment of cervical myelopathy. However, this procedure has limitations for kyphotic cervical alignments. We used the finite element (FE) analysis and investigated the biomechanical changes in an intact and laminoplasty models with lordosis, straight, and kyphosis cervical alignments. Methods: A three-dimensional FE model of the cervical spine (C2-C7) with ligaments was created from computed tomography. The model was modified with the following cobb angles and the C3-C6 laminoplasty was conducted; a) laminoplasty-lordotic model (LM-L; C2-C7 angle: -10°), b) laminoplasty-straight model (LM-S; C2-C7 angle: 0°), and c) laminoplasty-kyphotic model (LM-K; C2-C7 angle: 10°). A pure moment with a compressive follower load of 100N to represent the weight of the head/cranium and cervical muscle stabilization was applied to these models. The range of motion (ROM), annular stress, nucleus stress and facet forces were analyzed. Results: ROM of LM-K increased when compared to the other models except for flexion. The LM-K had the highest mobility with 49% increase in ROM observed under extension, compared to the intact model. In all motion except for flexion, LM-L models’ ROM decreased by more than 10%, and LM-S and LM-K models’ ROM increased by more than 10% at C2-C7 compared to the intact model. The annular stresses and nucleus stresses in LM-K were higher compared to the other models. The maximum increase in annular stresses was about 194% in LM-K compared to the intact model, observed at the C3-C4 segment. The facet contact forces were lowest in the LM-K, compared to the other models.Conclusions: Patients with a cervical kyphosis alignment are at a disadvantage of increased kyphosis compared to cases with lordosis or straight alignment and should be treated with caution.

Introduction: Cervical cancer is the fourth most common cancer in women globally and the most common cancer in Tanzania, resulting in about 9772 new cases and 6695 deaths each year. Research has shown an association between low levels of risk perception and knowledge of the prevention, risks, signs, etiology, and treatment of cervical cancer and low screening uptake, as contributing to high rates of cervical cancer-related mortality. However, there is scant literature on the perspectives of a wider group of stakeholders (e.g., policymakers, healthcare providers (HCPs), and women at risk), especially those living in rural and semi-rural settings. Methods: We adapted Risso-Gill and colleagues’ framework for a Health Systems Appraisal (HSA), to identify HCPs' perspective of the extent to which health system requirements for effective cervical cancer screening, prevention, and control are in place in Tanzania. We adapted interview topic guides for cervical cancer screening using the HSA framework approach. Study participants (69 in total) were interviewed between 2014-2018 –participants included key stakeholders, HCPs, and women at risk for cervical cancer. The data was analyzed using reflexive thematic analysis methodology. Results: Seven themes emerged from our analysis of semi-structured interviews and focus groups: 1) knowledge of cervical cancer risk and the role of screening and preventive care/services (e.g., prevention, risks, signs, etiology, and treatment), 2) training and knowledge of HCPs, 3) knowledge of cervical cancer screening among women at risk, 4) beliefs about cervical cancer screening, 5) role of traditional medicine, 6) risk factors, and 7) symptoms and signs. Conclusion: It is evident that there is a low level of knowledge of cervical cancer risk and the role of screening and preventive services among stakeholders, HCPs, and women living in rural and semi-rural locations in Tanzania. There is a critical need to implement more initiatives and programs to increase the uptake of screening and related services and allow women to make more informed decisions on their health.

This paper explores the application of machine learning (ML) in predicting functional recovery in patients with ischemic stroke. As technology advances, ML shows significant potential in the field of stroke medicine, especially in the areas of big data analytics and personalized medicine. Studies have shown that ML algorithms can improve the accuracy of stroke image analysis, subtype classification, risk assessment, treatment guidance, and prognosis prediction. However, the widespread use of ML still faces challenges such as data standardization, model validation, privacy, and bias. This paper reviews the current application status of ML in the field of stroke, discusses the challenges faced, and looks forward to the future development direction, aiming to promote the practical application of ML technology in the diagnosis and treatment of stroke to improve the prognosis and quality of life of patients.

Introduction: Anterior cervical discectomy and fusion (ACDF) for cervical disc herniation (CDH) is commonly performed. Specific postoperative complications include dysphagia, dysphonia, cervicalgia, adjacent segment disorder, cage subsidence, and infections. However, interscapular pain is commonly reported by these patients after surgery, although its mechanisms have been not clarified yet. Methods: This retrospective series of 31 patients undergoing ACDF for CDH at a single Academic Hospital. Baseline and postoperative clinical, radiological, and surgical data were analyzed. The linear regression analysis was conducted to identify any factor independently influencing the occurrence rate of postoperative interscapular pain. Results: The mean age was 57.6±10.8 years, and the M:F ratio was 2.1. The preoperative mean VAS-arm was 7.15±0.81 among the 20 patients reporting brachialgia, and the mean VAS-neck was 4.36±1.43 among those 9 patients reporting cervicalgia. At 1 month interscapular pain was still reported by 8 out of the 17 patients who experienced it postoperatively, and it was recovered in all patients after 2 months. The regression analysis showed how the interscapular pain was not directly associated to age(p=0,74), gender(p=0,46), smoking status(p=0,44), diabetes(0,42), preoperative brachialgia(p=0,21) or cervicalgia(p=0,48), symptoms duration(p=0,13), baseline VAS-arm(p=0,11), VAS-neck(p=0,93), or mJOA(p=0,63) scores, or disc height modification(p=0,90). However, the postoperative increase of the mean zygapophyseal joint rims distance was identified as an independent factor in determining interscapular pain (p=0,02). Conclusions: Our study revealed that the onset of interscapular pain following ACDF may be determined by over-distraction of the zygapophyseal joints rim. Then, proper sizing of prosthetic implants could reduce this painful complication.

Reservoirs created by damming rivers significantly modify the environment and problems may arise: the storage of nutrients and organic matter in reservoirs often leads to decreased water quality due to eutrophication. Reservoirs built on lowland rivers are exposed to significant nutrient loads, often leading to eutrophication. In this article, the Sulejów Reservoir, in Central Poland, was the subject of the research, which focused on the use of Sentinel-2 remote sensing data to detect phytoplankton blooms, and on understanding the pattern of wind-driven surface currents using the CCHE2D model (a depth-averaged hydrodynamic model). The calculation of the total phosphorus load has shown that the reservoir mainly acts as nutrient storage. Still, during the low flow and intensive phytoplankton bloom, it can be a source of nutrients. The distribution of phytoplankton bloom on the Sulejów Reservoir was documented using eight Sentinel-2 satellite images from the vegetation season of 2020 and the Normalized Difference Chlorophyll Index (NDCl). Coupling remote sensing data and numerical modelling helps to interpret the hydrodynamic model results and understand nutrients and sediment dynamics within the reservoir. Hydrodynamic modelling combined with remote sensing data can be a valuable tool for selecting appropriate locations for water quality measurements at monitoring stations.

We assessed the impact of social determinants of health (SDOH) and psychosocial factors on screening behavior in a nationally representative sample of women in the US. A retrospective population-level cross-sectional sample was extracted from the 2022 Health Information National Trends Survey. Descriptive statistics were conducted for demographic characteristics and covariates. Population-based estimates with 95% confidence intervals (95% CI) were produced for Pap testing behaviors across study years. We used logistic regression models to assess Pap testing differences based on SDOH and psychosocial factors, adjusting for covariates, with a significance level of 0.05 for hypothesis testing. Women (n=2,224) with a mean age of 46.96 participated in the study. Our study found that 90% of women were interested in cervical cancer screening, with an 80% actual screening rate. Screening rates differed by age group and rurality. SDOH and psychosocial factors were associated with participants’ interest in screening and in actual screening behavior. The impacts of SDOH and psychosocial factors on screening behavior were 3% and 1%, respectively. The SDOH and psychosocial factors are associated with the uptake of cervical cancer screening among women in the United States. Policies must be directed at bridging the gap created by these SDOHs.

Human Papillomavirus (HPV) self-sampling has the potential to increase cervical cancer screening (CCS) and reduce the cervical cancer burden in Medically Underserved Women (MUW). However, interventions promoting self-sampling are limited. We examined the effectiveness of an intervention study in increasing CCS among MUW. We conducted a quasi-experimental intervention study. Face-to-face verbal approach was to recruit MUW (n=85, mean age 48.57±11.02) living in a small city in the US. Behavioral intervention based on reframing, reprioritizing, and reforming (3R model) was used to educate the women about CCS in a group format. The women completed pre-and post-intervention assessments and followed-up interviews. The primary outcome was CCS uptake. Mixed methods analyses were conducted using a t-test for the primary outcome, PROCESS for mediation analysis, and NVivo for interview data. Majority of women (75%) completed self-testing. High-risk HPV prevalent among the women was 11%, and of those, 57% followed-up with physicians for care. We found that the significant increase in the women’s post-intervention screening behaviors was mediated by the increase in knowledge (Indirect Effect [IE] = .1314; 95% CI, .0104, .4079) and attitude (IE = .2167; 95% CI, .0291, .6050) scores, (p&lt;0.001). Interview analyses offered further explanations (see the explanations in parenthesis) why MUW found the intervention messages acceptable (encourages proactive behavior), feasible (simple and easy to understand), and appropriate (helpful and informative). Barriers including lack of trust and fear of results were identified. The findings suggest that an intervention that combines the 3R model and self-sampling may increase CCS among MUW.

The differentiation between Neuromyelitis optica spectrum disorder (NMOSD) and cervical spondylotic myelopathy (CSM) is necessary, because both diseases have similar symptoms, such as motor weakness and sensory disturbances. The challenge arises when NMOSD presents with short-segment spinal cord lesions, which can mimic the clinical presentation of CSM. This case report presents an NMOSD misdiagnosed as CSM. A 66-year-old woman presented with neck pain, left arm pain, and motor weakness. She was initially diagnosed with CSM based on MRI findings that revealed the increased signal intensity of the spinal cord at the C3 to 4 levels and underwent surgery. Three years later, she had sudden left upper extremity motor weakness and mild gait disturbance. Physical exam showed left-side weakness and altered reflexes, with longitudinally extensive transverse myelitis (LETM) findings on cervical MRI. The CSF study revealed a 2+ AQP4-Ab finding. Finally, NMO was diagnosed. Steroid pulsed therapy and immunosuppression therapy led to improved motor function and gait after 20 days. We present a case report of a NMO patient who initially exhibited clinical and radiological features CSM but was later diagnosed with NMO. This case underscores the diagnostic challenge in distinguishing NMO from CSM, especially in elderly patients who commonly experience spinal degeneration leading to cord compression and associated cord signal changes on MRI. The misinterpretation or confusion with transverse myelitis can occur as a result. This case highlights the importance of considering NMO as a potential differential diagnosis in elderly patients with spinal degeneration, emphasizing the significance of precise and timely diagnosis for guiding optimal treatment decisions.

Cervical cancer is the fourth common cancer amongst women worldwide. Environmental factors such as smoking and obesity, and recurrent infection by high-risk human papillomavirus subtypes are known to promote progression towards invasive cervical disease. Infection by high-risk HPV is necessary in most cases but not sufficient to develop invasive cervical cancer. Despite a predicted genetic heritability of between 27-36%, known genetic susceptibility loci that may be tumorigenic or influence host response to infection, only account for a small fraction of heritable risk factors so far. Various biobank-driven population based studies and meta-analyses have found corroborative evidence for several risk variants at the 6p21.3 locus (HLA), while many reports of variants outside the HLA region remain to be validated in other cohorts. Here, we review cervical cancer susceptibility variants arising from recent genome-wide association studies and meta-analysis in large cohorts and propose 2q14 (PAX8), 17q12 (GSDMB), and 5p15.33 (CLPTM1L) as consistently replicated non-HLA cervical cancer susceptibility loci. We further discuss the available evidence for these loci, knowledge gaps, future perspectives, and the potential impact of these findings on precision medicine strategies to combat cervical cancer.

Cervical fusion has been a standard procedure for treating the abnormalities associated with the cervical spine. However, the reliability of anterior cervical discectomy and fusion (ACDF) has become arguable due to its adverse effects on the biomechanics of adjacent segments. One of the drawbacks associated with ACDF is adjacent segment degeneration (ASD) which has served as the base for the development of dynamic stabilization systems (DSS) and total disc replacement (TDR) devices for cervical spine. However, the hybrid surgical technique has also gained popularity recently but their effect on the biomechanics of cervical spine is not well researched. Thus, the objective of this FE study was to draw the comparison among single, bi-level and hybrid surgery with DCI implant with traditional fusion. Reduction in range of motion (ROM) for all the implanted models was observed for all the motions except extension, compared to intact model. The maximum increase in ROM of 42% was observed at C5-C6 level in Hybrid-DCI model. The maximum increase in adjacent segment’s ROM of 8.7% was observed in multilevel fusion model. The maximum von Mises stress in the implant was highest for the multilevel DCI model. Our study also showed that the shape of DCI implant permits flexion/extension relatively more compared to lateral bending and axial rotation.

Human papillomavirus (HPV) is a well-known cause of cervical cancer, with HIV infection exacerbating the prevalence of high-risk HPV (HR-HPV) and cervical intraepithelial neoplasia (CIN). ART has been shown to reduce the likelihood of developing squamous intraepithelial lesions (SILs) and HR-HPV prevalence, although its impact on invasive cervical cancer remains inconclusive. In a study in Accra, 250 sexually active HIV-positive women exhibited a high HPV prevalence, emphasising the need for regular screening. HPV testing, visual inspection with acetic acid (VIA), and cytology-based tests are primary screening methods, each with specific advantages and limitations. Cervical cancer rates in sub-Saharan Africa are alarmingly high, necessitating targeted vaccination and prevention strategies.

Catastrophic mass flows originating from the high mountain cryosphere often cause hazard cascades. With increasing human activities in the alpine region and the sensitivity of glaciers, snow and permafrost to climate warming, the cryospheric hazard risks tend to be more active in the Tibetan Plateau(TP). The remoteness and inaccessibility of high mountain regions make the remote sensing technology the most effective approach for monitoring and investigating the cryospheric hazards. Exploring the evolution process and impact of ice avalanches and subsequent hazards are crucial to understand drivers and nature of mass flow process chain in order to prevent and mitigate potential hazard consequences. In this study, the glacier and ice-rock avalanches occurred in Arutso and Sedongpu basins in Tibet were investigated on the basis of Sentinel-2 time-series images and in-situ investigation, the evolution process of these events were reconstructed, and impacts and driving factors were analyzed. The massive Chamoli rock-ice avalanche occurred in February 2021 in the western Himalayas was also reviewed using using same methods. The study shows that Arutso glacier No. 53 (Arutso-53) avalanche completely melted away in July 2018 after two years from occurrence to final disappearance, while Arutso glacier No. 50 (Arutso-50) avalanche completely melted by the end of August 2023 after lasting for seven years. Four large-scale ice-rock avalanche and debris flow events in the Sedongpu basin in 2017 and 2018 not only had significant impacts on the river flow, landscape and geomorphologic shape in the basin, but also caused serious disasters in the basin and downstream. In March 2021, a small-scale ice-rock avalanche and debris flow event occurred again in the Sedongpu basin. The glacier and ice-rock avalanches occurred in Tibet area were mainly induced by climate warming, heavy precipitation and seismic activity, which acting on specific topographic and geomorphic structure of glacier properties in the high mountain regions. Catastrophic hazard events originating from the high-elevation glacier and ice-rock collapses in the mountain cryosphere and subsequent hazard cascades in the TP and Himalayas highlight the importance of mountain hazard monitoring, early warning systems and sustainable mountain development.

The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection which detects the patient's exhaled VOCs combined with sensor arrays of Convolutional Neural Network (CNN) algorithms as a new lung cancer detection is attracting more researchers' attention. However, due to low accuracy, high complexity computation and large number of parameters make the CNN algorithms difficult transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models such as EfficientNet, LTNet has fewer parameters (32K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than that of the five existing models’, and it also offers the shortest training (844.38s and 584.67s) and inference time (23s and 14s) in the same validation set. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.

For multi-sensor integrated systems, such as a mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, though the latter is more reliable.

Surface water distribution extracted from remote sensing data has been used in water resource assessment, coastal management, and environmental change studies. Traditional manual methods for extracting water bodies cannot satisfy the requirements for mass processing of remote sensing data; therefore, accurate automated extraction of such water bodies has remained a challenge. The histogram bimodal method (HBM) is a frequently used objective tool for threshold selection in image segmentation. The threshold is determined by seeking twin peaks, and the valley values between them; however, automatically calculating the threshold is difficult because complex surfaces and image noise which lead to not perfect twin peaks (single or multiple peaks). We developed an operational automated water extraction method, the modified histogram bimodal method (MHBM). The MHBM defines the threshold range of water extraction through mass static data; therefore, it does not require the identification of twin histogram peaks. It then seeks the minimum values in the threshold range to achieve automated threshold. We calibrated the MHBM for many lakes in China using Landsat 8 Operational Land Imager (OLI) images, for which the relative error (RE) and squared correlation coefficient (R2) for threshold accuracy were found to be 2.1% and 0.96, respectively. The RE and root-mean-square error (RMSE) for the area accuracy of MHBM were 0.59% and 7.4 km2. The results show that the MHBM could easily be applied to mass time-series remote sensing data to calculate water thresholds within water index images and successfully extract the spatial distribution of large water bodies automatically.

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, extracting the surface area affected by precipitation using a threshold method constrained by confidence intervals derived from microwave remote sensing images; Second, collecting disaster information from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms was proposed (TextCNN-Attention Disaster Classification); Finally, matching traffic road information on social media with basic geographic data, visualizing the classification of traffic road disaster risk levels, and completing the assessment of traffic road disaster levels based on multi-source heterogeneous data. Using the "7.20" rainstorm event in Henan Province as an example, this study categorizes the disaster impact on traffic roads into five levels: particularly severe, severe, moderate, mild, and affected, as interpreted from remote sensing images. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

One of the most prevalent malignancies in males is prostate cancer, which most often metastasizes to the liver, thorax, bone, and local lymph nodes. The current case describes development of cervical lymphadenopathy; which remains a rather rare initial presentation, seen in <0.1% of the patients diagnosed with prostatic carcinoma. The current report describes a 55-year-old man with complaints of lower back pain and swelling on the left side of his neck for the previous two months. Physical examination revealed several swollen cervical lymph nodes that measured 2 cm, were nontender, firm, and adhered to the skin above. A poorly differentiated adenocarcinoma was found during cytological examination of the lymph nodes in the left lower cervical region. Findings from the ensuing Computed Tomography scan pointed to the possibility of spread prostatic cancer. PSA values were determined to be 364.762 ng/ml after additional analysis. The patient was then planned for hormone ablation therapy as per Institute protocol, in view of metastatic prostate cancer. The current report highlights the importance of timely detection of malignancies, particularly those such as Prostate cancer, that are slow growing and have well-established screening tools (such as PSA assays). Involvement of the cervical lymph nodes in prostate cancer, particularly for older individuals, is nearly always linked to extensive metastatic disease. As a result, it is a poor prognostic factor for prostate cancer patients, whose survival rates are markedly lower. Although hormone therapy has advanced significantly as the cornerstone of care for metastatic prostate cancer, the prognosis for these cases is still quite poor, especially when there has been distant lymph node metastases.

During the last 25 years the scientific community has coexisted with the most fascinating protocol due to Quantum Physics: quantum teleportation (QTele), which would have been impossible if quantum entanglement, so questioned by Einstein, did not exist. In this work, a complete architecture for the teleportation of Computational Basis States (CBS) is presented. Such CBS will represent each of the possible 24 classical bits commonly used to encode every pixel of a 3-color-channel-image (red-green-blue, or cyan-yellow-magenta). For this purpose, a couple of interfaces: classical-to-quantum (Cl2Qu) and quantum-to-classical (Qu2Cl) are presented with two versions of the teleportation protocol: standard and simplified.

Semantic segmentation of high-resolution remote sensing images provides detailed and precise feature information, playing a pivotal role in decision-making and analysis in sectors such as water management, agriculture, military, and environmental protection. However, most methods merely combine features from various branches directly, without a mechanism for spatial location feature screening, and treat all extracted features as equally important. To overcome these limitations, we introduce a novel spatially adaptive interaction network (SAINet) for dynamic interaction across different features in remote sensing semantic segmentation task. Specifically, we propose a spatial refined module that leverages local context information to filter spatial locations and extract salient regions. Following this, we introduce an innovative spatial interaction module that uses a spatial adaptive modulation mechanism to dynamically select and allocate spatial position weights. This facilitates interaction between local salient areas and global information, allowing the network to focus more on relevant regions. This flexible approach enables the network to capture more informative features, thereby enhancing segmentation accuracy. Experiments conducted on the DeepGlobe, Vaihingen, and Potsdam public datasets confirm the effectiveness and capability of SAINet. Our code and models will be publicly available

Dystonias, characterized by excessive muscle contractions resulting in involuntary postures and movements, impact 3 million people globally, making them the third most common movement disorder. Often accompanied by tremors, dystonias have epidemiological links and non-motor features shared with isolated tremor, such as essential tremor. Both dystonia and tremor present with balance dysfunction and abnormal involuntary movements, potentially linked to abnormal cerebellar function. This study explores the perception of one’s own linear movement, heading, particularly discrimination of heading direction, in isolated cervical dystonia, isolated tremor, and their combination. The focus is on the perception of heading direction during isolated visual star-cloud movement (visual heading perception) and en bloc body movement (vestibular heading perception). Nineteen participants underwent a two-alternative forced-choice task in the vestibular and visual domains. Results reveal elevated vestibular heading perception thresholds in cervical dystonia with or without tremor, and isolated tremor compared to healthy controls. Vestibular heading perception threshold was comparable in cervical dystonia with tremor and isolated tremor, but it was even worse in isolated cervical dystonia. Visual heading perception, however, remained less affected all three conditions – isolated cervical dystonia, isolated tremor, and their combination. These findings indicate shared deficits and distinctions in the perception of linear translational heading across movement disorders, such as isolated cervical dystonia, tremor, or their combination, offering insights into their pathophysiology, particularly the involvement of cerebellum regions responsible for vestibular processing.

Background: Few studies have investigated the longitudinal association between cervical spondylosis (CS) and migraine by using a nationwide population-based database. Methods: We conducted a retrospective cohort study from 2000 to 2011 identifying 27,930 cases of cervical spondylosis and 111,720 control subjects (those without cervical spondylosis) from a single database. The subjects were frequency-matched on the basis of sex, age, and diagnosis date. The non- cervical spondylosis cohort was four times the size of the cervical spondylosis cohort. To quantify the effects of cervical spondylosis on the risk of migraine, univariate and multivariate Cox proportional hazard regression analyses were used to calculate the hazard ratio (HR) and 95% confidence interval (CI). Results: After a 10-year follow-up controlling for potential confounding factors, overall migraine incidence was higher in the cervical spondylosis cohort than in the non- cervical spondylosis cohort (5.16 and 2.09 per 1,000 people per year, respectively; crude hazard ratio = 2.48, 95% confidence interval = 2.28–2.69) with an adjusted hazard ratio of 2.03 (95% confidence interval = 1.86–2.22) after accounting for sex, age, comorbidities, and medication. Individuals with myelopathy in the cervical spondylosis cohort had a 2.19 times (95% confidence interval = 1.80–2.66) higher incidence of migraine compared than did those in the non- cervical spondylosis cohort. Conclusion: Individuals with cervical spondylosis exhibited a higher risk of migraine than those without cervical spondylosis. The migraine incidence rate was even higher among individuals with cervical spondylotic myelopathy.

We apply the Canny Edge algorithm to the Utqiaġvik coastal sea ice radar system (CSIRS) to quantify open water and sea ice (landfast or drifting) concentration. The radar-derived sea ice concentration is compared against the 25-km resolution NSIDC Climate Data Record (CDR, one pixel closest to the radar field of view) and the 1-km merged MODIS-AMSR2 sea ice concentrations in the ∼11 km field of view for the year 2022-2023, when improved image contrast was first implemented. The algorithm was first optimized using sea ice concentration from 14 different images and 10 ice analysts (140 analysis in total) covering a wide range of ice condition with landfast ice, drifting ice and open water. The algorithm is also validated quantitatively against high-resolution MODIS-Terra in the visible range. Results show a correlation coefficient and mean bias error between the optimized algorithm, the CDR and MODIS-AMSR2 daily SIC of 0.18 and 0.54, and ∼ -1 and 0.9%, respectively, with an averaged inter-analyst error of ± 3%. In general, the CDR captures the melt period correctly and overestimates the SIC during the winter and freeze-up period, while the merged MODIS-AMSR2 better captures the punctual break-out events in winter including those during the freeze-up events (reduction of in SIC). Remnant issues with the detection algorithm include the false detection of sea ice in the presence, fog or precipitation (up to 20%), quantified from the summer reconstruction with known open water conditions. Coastal marine radars provide prove very useful in bridging the gap between (lower) resolution satellite-derived and in-situ (visual) sea ice conditions, and to quantify land contamination from lower resolution satellite-derived SIC.

The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for classifying brain tumor from Magnetic Resonance Imaging (MRI) scans. However, despite their impressive performance, the opaque nature of DL models poses challenges in understanding their decision-making processes, particularly crucial in medical contexts where interpretability is essential. This paper explores the intersection of medical image analysis and DL interpretability, aiming to elucidate the decision-making rationale of DL models in brain tumor classification. Leveraging state-of-the-art DL frameworks with transfer learning, we conduct a comprehensive evaluation encompassing both classification accuracy and interpretability. Using state-of-the-art DL frameworks with transfer learning, we conduct a thorough evaluation covering both classification accuracy and interpretability. We employ adaptive path-based techniques to understand the underlying decision-making mechanisms of these models. Grad-CAM and Grad-CAM++ highlight critical image regions where the tumors are located.

This study introduces SkinLiTE, a lightweight supervised contrastive learning model, tailored to enhance the detection and typification of skin lesions in dermoscopic images. The core of SkinLiTE lies in its unique integration of supervised and contrastive learning approaches, which leverages labeled data to learn generalizable representations. This approach is particularly adept at handling the complexities and imbalances inherent in skin lesions datasets. The methodology encompasses a two-phase learning process. In the first phase, SkinLiTE utilizes an encoder network and a projection head to transform and project dermoscopic images into a feature space where contrastive loss is applied, focusing on minimizing intra-class variations while maximizing inter-class differences. The second phase freezes the encoder's weights, leveraging the learned representations for classification through a series of dense and dropout layers. The model was evaluated using three datasets from Skin Cancer ISIC 2019-2020, covering a wide range of skin conditions. SkinLiTE demonstrated superior performance across various metrics—accuracy, AUC , and F1 score—particularly when compared with traditional supervised learning models. Notably, SkinLiTE achieved an accuracy of 0.9087 using AugMix augmentation for binary classification of skin lesions. It also showed comparable results with the state-of-the-art approaches of ISIC challenge without relying on external data, underscoring its efficacy and efficiency. The results highlight the potential of SkinLiTE as a significant step forward in the field of dermatological AI, offering a robust, efficient, and accurate tool for skin lesion detection and classification. Its lightweight architecture and ability to handle imbalanced datasets make it particularly suited for integration into Internet of Medical Things environments, paving the way for enhanced remote patient monitoring and diagnostic capabilities. This research contributes to the evolving landscape of AI in healthcare, demonstrating the impact of innovative learning methodologies in medical image analysis.

Manifestation of cystic hygroma in adulthood is very rare. In this study, we reported a rare case of Cervical Cystic Hygroma in Adults. We report 20 years old male with a diagnosis of left-side neck cystic Hygroma and discuss the presentation, diagnosis, and clinical, radiological, and operative aspects of it.

Sentinel-2 can provide multi-spectral optical remote sensing images in RGBN bands with a spatial resolution of 10m, but the spatial details provided are not enough for many applications. WorldView can provide HR multi-spectral images less than 2m, but it is a commercial paid resource with relatively high usage costs. In this paper, without any available reference images, Sentinel-2 images at 10m resolution are improved to a resolution of 2.5m through super-resolution (SR) based on deep learning technology. Our model, named DKN-SR-GAN, uses degradation kernel estimation and noise injection to construct a dataset of near-natural low-high-resolution (LHR) image pairs, with only low-resolution (LR) images and no high-resolution (HR) prior information. DKN-SR-GAN uses the Generative Adversarial Networks (GAN) combined of ESRGAN-type generator, PatchGAN-type discriminator and the VGG-19-type feature extractor, using perceptual loss to optimize the network, so as to obtain SR images with clearer details and better perceptual effects. Experiments demonstrate that in the quantitative comparison of the non-reference image quality assessment (NR-IQA) metrics like NIQE, BRISQUE and PIQE, as well as the intuitive visual effects of the generated images, compared with state-of-the-art models such as EDSR8-RGB, RCAN and RS-ESRGAN, our proposed model has obvious advantages.

Medical brain image analysis is a necessary step in the Computers Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of White Matter Hyperintensities (WMHs) of brain MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out of a total of 140 documents we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and proposal of new models of deep learning to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however with little practical application due to the uses of small datasets and lack of reproducibility. Therefore, the main conclusion is to establish multidisciplinary research groups to overcome the gap between CAD developments and their complete utilization in the clinical environment.

Human papillomavirus testing is increasingly central to screening programs for cervical cancer. Although HPV testing has excellent clinical sensitivity it suffers from modest clinical specificity. Cytology has taken an important role as a triage test, while more recently HPV16/18 genotyping has provided modest incremental benefits. Despite such progress issues remain to be addressed, some of which are how to: 1) improve access to the best methods, 2) decrease costs so more women can be screened, 3) simplify management algorithms, 4) Recognize the evolving diagnostic landscape created by widespread HPV vaccination. Part of the solution lies in improving the positive predictive value of the initial screen and reflex triage. An aspirational goal should be that most women attending colposcopy have true precancer; DNA methylation testing has a role to play here. Cervical cancers and CIN2/3 generally have high levels of methylation in target human and HPV genes. In contrast normal and healthy women with no intraepithelial lesions or malignancy usually have low methylation. Furthermore, there is an interesting and useful secular gradient of increasing cervical DNA methylation as women progress toward precancer and cancer. Recent results from clinical studies using commercially available routine qPCR-based DNA methylation tests have been encouraging, demonstrating the potential for higher sensitivity and specificity than current triage methods.

Necrotizing fasciitis is a life-threatening soft-tissue infection affecting the deep fascia and subcutaneous tissue. It is characterized by a fulminant course and high mortality rates. NF of the head and neck is very rare, most cases being odontogenic in origin. Streptococcus spp. and Staphylococcus spp. are the most common isolated organisms. If the infection progresses to descending mediastinitis the prognosis becomes very poor. Since the initial clinical features can be similar to those of a non-necrotizing deep cervical infection, a high degree of suspicion is critical for an early diagnosis. Computed tomography scan can be useful in the diagnosis and to define the extent of the infection. Early and aggressive surgical debridement of all compromised tissue and antibiotic therapy and fluid resuscitation are essential and should not wait for bacterial culture results. Despite prompt and adequate treatment, mortality of CNF can be as high as 35%.

To date, the development of unmanned technologies using autonomous underwater vehicles (AUV) has become an urgent demand for solving the problem of inspection of industrial sub-sea structures. A key issue here is precise localization of AUVs relative to underwater objects. However, the well-known challenges of recognizing and coordinating underwater objects (impossibility to use GPS, undercurrents, etc.) do not allow high-precision navigation if based solely on a standard suite of AUV navigation tools (sonars, etc.). An alternative is a technology of processing of optical images that, at short distances, can provide higher accuracy of AUV navigation compared to a technology of acoustic measurement processing. In this study, in the context of the problem of subsea production system inspection, we propose a method to recognize underwater objects and provide coordinate reference of AUV to them, based on stereo-image processing. Its distinctive features are the use of a non-standard technique for generating a geometric model of an object from its views (foreshortening) taken from positions of a pre-made overview trajectory, the use of various characteristic geometric elements when recognizing objects, and the original algorithms for comparing visual data of the inspection trajectory with an a priori model of the object.

(1) Background: The stylohyoid syndrome, known as classical Eagle Syndrome (cES), is characterized by calcificatication of the stylohyoid chain with numerous nonspecific symptoms, mainly pain. This study introduces Minimally Invasive Cervical Styloidectomy (MICS). (2) Methods: MICS was performed on sixty-five patients diagnosed with classical Eagle syndrome. Patients underwent meticulous differential diagnosis. Surgical plans were established based on the findings from neck angioCT. (3) Results: The healing process was uneventful without significant complications. The overall success rate was 97.0%, with a follow-up of a minimum of six months. In one case, the surgery did not yield the desired improvement. In one case, a partial relapse of symptoms was observed. (4) Conclusions: MICS is a straightforward and efficient surgical treatment technique for stylohyoid syndrome.

(1) Background: High-grade cervical dysplasia is primarily caused by human papillomavirus (HPV) infection. Conservative surgery is the preferred treatment approach for this condition. The most commonly employed technique is the loop electrosurgical excision procedure (LEEP), which involves removing the affected portion of the cervix. Excisional treatments are often curative, and complications are typically rare and minor; (2) Methods: The loop electrosurgical excision procedure (LEEP) is the standard method used for conservative surgery in high-grade cervical dysplasia. It entails the excision of the specific area of the cervix where the abnormal cells are present. The procedure employs a wire loop carrying an electrical current to remove the affected tissue; (3) Results: Excisional treatments, such as LEEP, have shown to be effective in treating high-grade cervical dysplasia. They have a high success rate in eliminating abnormal cells and reducing the risk of cervical cancer. Complications associated with LEEP are infrequent and usually minor. Short-term complications may include bleeding, which can be managed easily. Long-term complications may involve cervical canal stenosis, which can impact fertility; (4) Conclusions: Conservative surgery, particularly the loop electrosurgical excision procedure (LEEP), is the preferred and effective treatment for high-grade cervical dysplasia caused by HPV infection. It offers a high cure rate with rare and minor complications. While short-term bleeding is a common occurrence, it is manageable. Long-term complications such as cervical canal stenosis may impact fertility. However, an extremely rare and possibly unique complication described in this case is the development of a vaginorectal fistula. This complication is likely due to indirect thermal injury resulting from compromised tissue. Further research is needed to better understand and prevent such complications.

Neck lymphangiomas are rare, benign congenital malformations that predominate in childhood. Most present before the age of two. There are no risk factors related to sex, ethnic origin or predominance on either side, and they can occur in any anatomical location, although the most frequent is in the head and neck. The clinical presentation depends on its location. The most frequent symptom is the existence of a painless mass that grows slowly. Available therapeutic tools include surgery, systemic treatment, and injection of sclerosing agents.

This paper provides orthogonal moments (OM) such as, Zernike Moments(ZM), Psuedo Zernike Moments(PZM) and Orthogonal Fourier Mellin Moments(OFMM) for the analysis of melanoma images. The moment invariants may vary with respect to geometric variations. For the analysis of orthogonal moments hundred random melanoma images and hundred non-melanoma images have been taken into consideration from the database of 570 melanoma images and 250 non-melanoma images respectively. Orthoganal moments have been computed by varying the phase angles from 10° to 40° with an equal interval of 10° degree for the orders 2, 4,8,16,32,64,128,256 respectively. For the optimal OMs Particle Swarm Optimization (PSO) technique have been used. These set of extracted optimal OMs have been further applied to classify melanoma images. Support Vector Machine (SVM) has been used for the classification of [1]sensitivity=88.78%.

The application of deep learning algorithms to predict the molecular profiles of various cancers from digital images of hematoxylin and eosin (H&E)-stained slides has been reported in recent years, mainly in gastric and colon cancers. In this study, we investigated the potential use of H&E-stained endometrial cancer slide images to predict the associated mismatch repair (MMR) status. H&E-stained slide images were collected from 127 cases of the primary lesion of endometrial cancer. After digitization using a Nanozoomer virtual slide scanner (Hamamatsu Photonics), we segmented the scanned images into 5,397 tiles of 512 × 512 pixels. The MMR proteins (PMS2, MSH6) were immunohistochemically stained, classified into MMR proficient/deficient, and annotated for each case and tile. We trained several neural networks, including convolutional and attention-based networks, using tiles annotated with the MMR status. Among the tested networks, ResNet50 exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.91 for predicting the MMR status. The constructed prediction algorithm may be applicable to other molecular profiles and useful for pre-screening before implementing other, more costly genetic profiling tests.

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioural as-sessments, enabling a comprehensive evaluation of the hens' health, behaviour, and population counts. To address the issue of insufficient data samples in the health detection process for indi-vidual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environ-ments, various data enhancement methods such as rotation, shearing, colour enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for ap-plying visible light images and corresponding thermal infrared images in the health detection, behavioural analysis, and counting of caged laying hens under large-scale farming.

Background and Objectives: Intramedullary spinal cord tumors (IMSCT) are rare entities. Especially, a location in the upper cervical spine as a highly eloquent region carries the risk of postoperative neurological deficits, such as tetraparesis or respiratory dysfunction. Evidence for respiratory dysfunction is scarce. The aim of this study was to describe the early and late postoperative clinical course of this highly eloquent tumours. Patients and Methods: This is a single-centre retrospective cohort study. We included 35 patients with IMSCT at levels of craniocervical junction to C4 who underwent surgical treatment between January 2008 and December 2022. The authors analysed the patients' preoperative status, tumour- and surgery-specific characteristics, and follow-up functional status. Results: The study cohort included 22 patients with grade II ependymoma (62.9%), 2 low-grade astrocytomas (5.7%), 2 glioblastomas (5.7%), 6 hemangioblastomas (17.1%), 2 metastases (5.7%), and 1 patient with partially intramedullary schwannoma (2.9%). Gross total resection was achieved in 76% of patients. Early dorsal column related symptoms and motor deterioration occurred in 64% and 44% of patients, respectively. At a follow-up of 3.27 ± 3.83 years, 43% and 33% of patients preserved postoperative sensory and motor deterioration, respectively. The median McCormick Scale grade was 2 in the preoperative and late postoperative periods, respectively (p=0.0897, α=0.05). Only two out of 35 patients (5.7%) developed new intermittent respiratory dysfunction, of whom two were transient. There were no surgery-related deaths. Conclusions: The surgery of IMSCT of the upper cervical spine could be performed without major procedure-related risk. Roughly a half of the patients develop new neurological deficit in the immediate postoperative period. However, most of the patients still maintain functional independence. The rate of respiratory insufficiency is low and is strongly influenced by the rapid neurological deterioration in high-grade tumours.

Nowadays, cervical cancer (CC) is treated as the leading cancer among women throughout the world. Despite effective vaccination and improved surgery and treatment, CC remains its fatality rate about half of the infected populations globally. The major screening biomarkers and therapeutic target identification have now become a global concern. The present study, we have employed systems biology approaches to retrieve the potential biomarkers and pathways from the transcriptomic profiling. Initially, we have identified 76 of each up-regulated and down-regulated gene from a total of 4,643 differentially expressed genes. The up-regulatory genes are mainly concentrating on immune-inflammatory response and the down-regulatory genes are on receptor binding and gamma-glutamyltransferase. The involved pathways associated with these genes were also assessed through pathway enrichment and they were mainly focused on different cancer pathways, immunoresponse, and cell cycle pathways. After the subsequent enrichment of these genes, we have identified 12 hub genes, which play a crucial role in CC. Furthermore, the survival of the hub genes was also assessed, and among them, finally, CXCR4 has identified as one of the most potential differentially expressed gene that might play a vital role to the survival of CC patients. Thus CXCR4 could be used as a prognostic biomarker and development of a drug target for CC.

Background: Cervical myelopathy (CM) is a common cause of morbidity and disability in patients with mucopolysaccharidosis (MPS) and, therefore, early detection is crucial for best surgical intervention and follow-up. Transcranial Magnetic Stimulation (TMS) non-invasively evaluates the conductivity along the cortico-spinal tract, also allowing preclinical diagnosis and monitoring. Methods: motor evoked potentials (MEPs) to TMS were recorded in a group of 8 patients with MPS-related CM. Responses were obtained during mild tonic contraction through a circular coil applied over the “hot spot” of the first dorsal interosseous and tibialis anterior muscles, bilaterally. Central motor conduction time was estimated as the difference between MEP cortical latency and the peripheral motor conduction time by cervical or lumbar magnetic stimulation. Peak-to-peak MEP amplitude to cortical stimulation and right-to-left difference of each parameter were also measured. Results: TMS revealed abnormal findings from both upper and lower limbs compatible with axonal damage and demyelination in 6 of them. Notably, a subclinical cervical spinal disease was detected before the occurrence of an overt CM in two patients, whereas TMS signs compatible with a CM of variable degree persisted despite surgery in all treated subjects. Conclusions: TMS screening should be performed in MPS patients, before and after surgery.

Abstract: Cervical cancer, which is the fourth most common malignancy and the fourth greatest cause of cancer-related deaths in women globally, continues to be a serious issue for global health. Despite advancements in treatment techniques, particularly the implementation of concomitant chemoradiation therapy, predicting the prognosis of locally advanced cervical cancer, especially for stage III, remains challenging. In recent years, neoadjuvant chemotherapy (NACT) has gained interest as a viable treatment strategy. The objective of this review paper is to scrutinize the clinical data and outcomes associated with NACT in the management of stage 3 cervical cancer. Electronic databases were used to perform a comprehensive literature search, and research up to 2023 were included. The findings suggest that NACT followed by irradiation may have advantages such as lowering tumor size, pelvic architectural distortion, and subclinical distant metastases, therefore enhancing radiation delivery. However, several studies have produced conflicting findings, raising questions regarding toxicity and finding no appreciable survival benefit over radiation alone. NACT followed by radical surgery has demonstrated more promising outcomes compared to surgery alone in terms of increasing rates of overall survival (OS) and progression-free survival (PFS). Additionally, NACT has been associated with decreased tumor growth, parametrial infiltration, nodal, metastases, and vascular space invasion. These findings suggest adjuvant radiation treatment. Although the overall survival rate has been markedly increased by concomitant chemoradiation treatment, metastatic disease continues to be a major factor in the death rate from cervical cancer. Therefore, improving outcomes in locally advanced cervical cancer requires investigating novel systemic treatments, such as NACT. The study also emphasizes how crucial it is to research factors like cisplatin dose-intensity and cycle lengths in order to maximize NACT's efficacy. To prevent the need for subsequent treatments and cumulative toxicity, more study should concentrate on patient selection for NACT followed by surgery.

The Fractal Analysis of Medical Images is an investigation field in those that numerous results of important value have been obtained. This work describes the basic foundations of the Fractal Analysis and its application has more than enough medical images, leaning on in five scientific investigations.

An image consisting of (m, n) pixels can be seen as a matrix of m × n size. Based on the formula of angles between two subspaces, a pair of angles can be defined between two matrices, by utilizing column spaces and row spaces of the two matrices. The singular values of each matrix can be used to calculate distances. Thus, a distance representation is obtained, and a pair of angles between 2 matrices of the same size.

Hyperspectral imaging provides vital information about the objects and elements present inside the image. That’s why they are very useful in satellite imagery as well as image forensics. Hyperspectral document analysis (HSDI) can be used for document authentication using ink analysis which can provide sufficient information about the composition and type of ink. In this project, we have implemented HSDI based ink classification technique using Principle Component Analysis for dimensionality reduction and K-means clustering for ink classification. This is unsupervised learning approach and it is very simple and efficient in order to classify limited number of bands. We have used this technique to classify 33 different bands of ink.

Background: Previous investigations have found a correlation between abnormal curvatures and a variety of patient complaints such as cervical pain and disability. However, no study has shown that loss of the cervical curve is a direct result of exposure to a motor vehicle collision (MVC). This investigation presents a retrospective consecutive case series of patients with both a pre-injury cervical lateral radiograph (CLR) and a post-injury CLR after exposure to a MVC. Computer analysis of digitized vertebral body corners on CLRs was performed to investigate the possible alterations in the geometric alignment of the sagittal cervical curve. Methods: Three spine clinic records were reviewed, over a 2-year period, looking for patients where both an initial lateral cervical x-ray and an examination were performed prior to the patient being exposed to a MVC; afterwards an additional exam and radiographic analysis were required. A total of 41 patients met the inclusion criteria. Examination records of pain intensity on numerical pain rating scores (NPRS) and neck disability index (NDI), if available, were analyzed. The CLRs were digitized and modeled in the sagittal plane using curve fitting and the least squares error approach. Radiographic variables included total cervical curve (ARA C2-C7), Chamberlain’s line to horizontal (skull flexion), horizontal translation of C2 relative to C7, segmental translations (retrolisthesis and anterolisthesis), and circular modelling radii. Results: There were 15 males and 26 females with an age range of 8-65 years. Most participants were drivers (28) involved in rear end impacts (30). The pre-injury NPRS was 2.7 while the post injury was 5.0; p &lt; 0.001. The NDI was available on 24 /41 (58.5%) patients and was found to increase following exposure to the MVC: 15.7% to 32.8%, p &lt; 0.001. An altered cervical curvature was identified following exposure to MVC, characterized by an increase in radius of curvature and an approximate 8° reduction of lordosis from C2-C7; p &lt; 0.001. The mid cervical spine (C3-C5) showed the greatest curve reduction with localized mild kyphosis at these levels on average. Several participants developed segmental translations that approached the instability value of translation &gt; 3.5mm. Conclusions: Abnormalities of cervical lordosis occurred following exposure to a MVC. The average loss of cervical curvature was 8° from C2-C7, with mild flexion of the mid cervical segments. Snap through type or dynamic buckling during the MVC may explain our findings. The primary contribution of this study to the motor vehicle collision literature is that altered cervical lordosis may result from MVC exposures and the consequences of an altered cervical alignment, in turn, may lead to future pain, disability and decreased health-related quality of life.

Cervical cancer remains a major public health problem. Despite the screening programs, cervical cancer is the second most commonly diagnosed cancer. Most of the cases occur in less developed countries, and it is the third leading cause of cancer death among females. In 2000–2007, the five-year relative survival rate for European women diagnosed with cervical cancer was 62%.[1] Standard treatment for locally advanced cervical cancer reveals external beam radiotherapy (EBRT) with concurrent chemotherapy followed by image-guided adaptive brachytherapy (IGABT) boost [2, 3]. In the last years, the Gynaecological (GYN) working group composed of Group Européen de Curiethérapie-European Society for Therapeutic Radiology and Oncology (GEC-ESTRO) has developed new recommendations for the treatment of cervical cancer based on IGABT. The treatment plan includes 3-dimensional (3D) imaging based on magnetic resonance imaging (MRI) or computed tomography (CT), adaptive to the target volume and organ at risk [4–6]. The brachytherapy insertion is made under anaesthesia. At the time of the application insertion, the ultrasound is used to guide the dilatation of the uterine canal and assess the depth of the uterine cavity. In this paper, we review the data available in international literature about the use of ultrasound (US) in brachytherapy treatment of cervical cancer.

Abstract Background: Most cardiac metastases emerge from primary lung, breast, and hematologic malignancies. The clinical manifestations of cardiac metastasis vary depending on tumor location and size. Cardiac metastasis from cervical squamous cell carcinoma is extremely rare and is mostly found on autopsy. We report a case of cervical cancer metastasis to the interventricular septum. Case Summary. This report discusses the case of a 48-year-old woman with interventricular septal metastases, originating from squamous cell carcinoma of the cervix. The woman came to our hospital after experiencing a fainting spell. Her hospital stay was notable for a brief syncopal event during a 30-second asystole episode, which ended spontaneously. Upon awakening, she reported severe chest pain. In response, she was quickly taken to the catheterization laboratory. There, a coronary angiography revealed an 80% blockage in her left anterior descending artery. Two years prior, our patient was diagnosed with invasive squamous cell cervical carcinoma with PET/CT showing no evidence of metastatic disease. A repeat PET/CT scan was done following cardiac catheterization and was significant for a mass along the interventricular septum of the heart. Discussion. Cardiac metastasis from primary cervical squamous cell carcinomas is scarcely reported in medical literature. Among these rare cases, the majority involved the right ventricle, with only three involving the left ventricle. There are no documented instances of metastasis to the interventricular septum. To our knowledge, this would be the first such case.

Endoscopic medical images can suffer from uneven illumination, low contrast, and lack of texture information due to the use of point directional light sources and the presence of narrow tissue structures, posing diagnostic difficulties for physicians. In this paper, a deep learning-based su-pervised illumination enhancement network is designed for low-light endoscopic images, aiming to improve both global illumination and local details. Initially, a global illumination enhancement module is formulated utilizing a higher-order curve function to improve global illumination. Sec-ondly, a local feature extraction module incorporating dual attention is designed to capture local detailed features. Considering the significance of color fidelity in biomedical scenarios, the designed loss function prioritizes introducing color loss to alleviate image color distortion. Compared with seven state-of-the-art enhancement algorithms on Endo4IE endoscopic datasets, experimental re-sults show that the proposed method can better enhance low-light endoscopic images and avoid image color distortion. It provides an efficient method to enhance images captured by endoscopes which can effectively assist clinical diagnosis and treatment.

Cervical cancer (CC) remains a significant global health challenge, characterized by its genetic heterogeneity and complex molecular landscape. The current study investigates the germline variants in proto-oncogenes and tumor suppressor genes among patients diagnosed with cervical cancer. Utilizing a custom NGS panel targeting 48 genes implicated in oncogenesis, we identified 148 nucleotide sequence alterations across the cohort. Of these, 35 variants (23.6%) were classified as benign, while 105 (70.9%) were categorized as variants of uncertain significance (VUS), whose clinical relevance remains to be elucidated. Moreover, we discovered seven pathogenic or likely pathogenic mutations, as well as a polymorphic variant rs1042522 in the TP53 gene, which has been previously associated with an increased risk of CC. Our findings contribute to the understanding of the molecular-genetic landscape of cervical cancer, highlighting the potential impact of rare germline mutations on the disease's pathogenesis and progression. This study underscores the importance of comprehensive genetic screening in improving the diagnostic and therapeutic strategies for cervical cancer patients.

Cervical mesonephric ductal adenocarcinoma (MA) is a HPV-independent adenocarcinoma that originates from remnants of the wolffian duct in the male reproductive system during fetal development and occurs in middle-aged women. MA is a rare disease that accounts for less than 1% of all cervical adenocarcinomas. We report a case of MA in which abdominal radical hysterectomy(ARH) was performed after neoadjuvant chemotherapy (NAC). The patient was a 66-year-old woman with abnormal genital bleeding. A colposcopy examination revealed macroscopic invasive cancer. A pelvic MRI scan revealed a 53 × 26 mm tumor in the cervix, and the histological diagnosis of the cervix was endometrioid carcinoma, with the diagnosis being cervical adenocarcinoma cT1b3N0M0. One course of NAC with paclitaxel-carboplatin (PC) was administered to shrink the tumor and stop the bleeding, and ARH was performed. Postoperative histopathological diagnosis was MA. The surgical margins of the resected specimen were negative, and NAC had been effective, so the patient underwent five courses of PC therapy after surgery. There has been no recurrence 12 months after surgery. There is no established standard treatment, but there are reports that PC therapy is effective. It is necessary to search for effective treatments by following up and accumulating further cases.

Cervical cancer is preventable through vaccination, early detection, and treatment of precancerous lesions. However, global inequalities mean that the disease remains a leading cause of cancer death around the world, with over 80% of new cases and 90% of deaths occurring in low and middle-income countries (LMICs). In El Salvador, joint efforts between the Ministry of Health (MoH) and the non-profit organization Basic Health International (BHI) have been in place since 2008 with the goal of reducing the country’s disease burden. While the World Health Organization’s (WHO) call to action to eliminate cervical cancer provided worldwide momentum to implement new public health initiatives, the COVID-19 pandemic disrupted ongoing programs and jeopardized plans for the future. The purpose of this manuscript is to describe the progress that El Salvador has achieved in improving cervical cancer prevention, the impact of the pandemic on current strategies, and potential solutions that can help the country meet the WHO’s strategic targets by 2030 to accelerate the elimination of cervical cancer.

With the development of economy, maritime transportation has increased rapidly, which has had a significant impact on the marine environment and resources. In recent years, Synthetic Aperture Radar (SAR) has been widely used to observe maritime environmental incident. Through the large coverage area of SAR, marine environmental emergencies, especially oil pollution, can be better monitored and responded to. This study first proposed an improved Full-scale Aggregated MobileUNet (FA-MobileUNet) model to achieve more complete detection results of oil spill areas. The convolutional block attention module (CBAM) in FA-MobileUNet was modified based on morphological concepts. By introducing the morphological attention module (MAM), the improved FA-MobileUNet model can reduce the fragments and holes in the detection results, providing complete oil spill areas which were more suitable for describing the location and scope of oil pollution incidents. In addition, to overcome the inherent category imbalance of the dataset, label smoothing was applied in model training to reduce the model’s overconfidence in majority class samples while improving the model’s generalization ability. The detection performance of the improved FA-MobileUNet model reached 84.55% mIoU (mean Intersection over Union), which was 17.15% higher than that of the original U-Net model. The improved FA-MobileUNet model was then examined by some oil pollution incidents. According to the detection results, if the oil spill was suspected to be caused by a ship, the corresponding Automatic Identification System (AIS) data were collected based on the acquisition time and geo-location of the SAR image to search for the ship suspected of causing oil pollution. Finally, some oil pollution incidents in Taiwan marine area were collected to verify the effectiveness of the proposed model for marine oil pollution monitoring. Experimental results showed that the extent of the detected oil spill was consistent with the oil pollution area recorded in the incident reports.

While there are millions of reptile images available online, they are not well organized and not easily findable, accessible, interoperable, or reproducible (FAIR). More importantly, they are not standardized and thus hardly comparable. Here we present a reference database of more than 14,000 standardized images of 1,045 reptile species (969 lizard and 76 snake species), that are based on preserved specimens in 20 different collections, including 533 type species of genera and type specimens of 72 species. All images were taken with standardized views, including dorsal and ventral body shots as well as dorsal, ventral, and lateral views of the heads and other body parts. Although only 11 out of the 20 collections are cross-referenced in Vertnet, some others are indexed in GBIF, and this fraction will certainly grow in the near future. The utility of this and similar image collections will further grow with additional material and further cross-referencing, e.g., to DNA sequence databases or citizen science projects. The images are searchable and freely available on Morphobank.org (Project 5121) and on Figshare.com.

The evolution in imaging technologies and artificial intelligence algorithms, coupled with improvements in UAV technology, has enabled the use of unmanned aircraft in a wide range of applications. The feasibility of this kind of approach for cattle monitoring has been demonstrated by several studies, but practical use is still challenging due to the particular characteristics of this application, such as the need to track mobile targets and the extensive areas that need to be covered in most cases. The objective of this study was to investigate the feasibility of using a tilted angle to increase the area covered by each image. Deep Convolutional Neural Networks (Xception architecture) were used to generate the models for the experiments, which covered aspects like ideal input dimensions, effect of the distance between animals and sensor, effect of classification error on the overall detection process, and impact of physical obstacles on the accuracy of the model. Experimental results indicate that oblique images can be successfully used under certain conditions, but some practical limitations need to be addressed in order to make this approach appealing.

Breast and Cervical cancer are significant causes of morbidity and mortality among women worldwide. Usually, conventional therapies and early detection has shown success in metastasis and relapse rates and improved overall survival of the patients. The elimination of the efficacy of chemotherapies and target therapies because cancer stem cells create resistance against chemotherapy, metastasis, and relapse. Studies reveal that cancer stem cell markers are important for improving efficiencies of targeted therapies. Our study aims to analyze the stem cell markers and target the most significant stem cell marker by phytochemicals and their derivates. Through the analysis, it was observed that CXCR4 and SOX-2, mir-1, mir-125, and mir-429 may have a crucial role in stemness of breast and cervical cancer respectively, while BPMD & MD simulations revealed Di-valinoyl curcumin as a potential compound for CXCR4 targeting.

Event-based vision is an emerging field of computer vision that offers unique properties such as asynchronous visual output, high temporal resolutions, and dependence on brightness changes to generate data. These properties can enable robust high-temporal-resolution object detection and tracking when combined with frame-based vision. In this paper, we present a hybrid, high-temporal-resolution, object detection and tracking approach, that combines learned and classical methods using synchronized images and event data. Off-the-shelf frame-based object detectors are used for initial object detection and classification. Then, event masks, generated per each detection, are used to enable inter-frame tracking at varying temporal resolutions using the event data. Detections are associated across time using a simple low-cost association metric. Moreover, we collect and label a traffic dataset using the hybrid sensor DAVIS 240c. This dataset is utilized for quantitative evaluation using state-of-the-art detection and tracking metrics. We provide ground truth bounding boxes and object IDs for each vehicle annotation. Further, we generate high-temporal-resolution ground truth data to analyze the tracking performance at different temporal rates. Our approach shows promising results with minimal performance deterioration at higher temporal resolutions (48 – 384 Hz) when compared with the baseline frame-based performance at 24 Hz.

Treating central giant cell granuloma (CGCG) is challenging due to high recurrence rates and variable therapy responses. This study examines the efficacy of various treatments in reducing CGCG recurrence. A literature review explored outcomes of surgical excision, curettage, intralesional corticosteroid injection, and adjuvant therapy, considering factors like lesion location, size, and histological features. Aggressive surgical techniques such as en bloc resection were found to potentially lower recurrence rates compared to conservative approaches. However, treatment should be tailored to individual patient needs. Further research is needed to confirm these findings and improve treatment strategies. A concise literature review was conducted using PubMed, MEDLINE, and Google Scholar, focusing on papers published from 1986 to 2024. Search terms included "central giant cell granuloma," "recurrence," "treatment modalities," and "surgical excision". Studies reporting recurrence rates and treatment outcomes for CGCG were analyzed. Twenty-nine studies were reviewed, including six studies on surgical excision and curettage, eight studies on intralesional corticosteroid injections, six studies on calcitonin therapy, five studies on interferon alpha therapy and four studies on the therapy with denosumab. Analysis indicated that aggressive surgical treatments like en bloc resection were associated with lower recurrence rates compared to conservative methods. Predictors of recurrence included lesion size (&gt;3 cm), location (mandible), and aggressive histopathological features. Aggressive surgical excision combined with nonsurgical methods may lower recurrence rates, while conservative techniques remain viable in some cases. Further prospective research is needed to validate these findings and enhance CGCG treatment options.

The visualization of hyperspectral images in display devices, having RGB colour composition channels is quite difficult due to the high dimensionality of these images. Thus, principal component analysis has been used as a dimensionality reduction algorithm to reduce information loss, by creating uncorrelated features. To classify regions in the hyperspectral images, K-means clustering has been used to form clusters/regions. These two algorithms have been implemented on the three datasets imaged by AVIRIS and ROSIS sensors.

Recent state-of-the-art scene text recognition methods have primarily focused on horizontal text in images. However, in several Asian countries, including China, large amounts of text in signs, books, and TV commercials are vertically directed. Because the horizontal and vertical texts exhibit different characteristics, developing an algorithm that can simultaneously recognize both types of text in real environments is necessary. To address this problem, we adopted the direction encoding mask (DEM) and selective attention network (SAN) methods based on supervised learning. DEM contains directional information to compensate in cases that lack text direction; therefore, our network is trained using this information to handle the vertical text. The SAN method is designed to work individually for both types of text. To train the network to recognize both types of text and to evaluate the effectiveness of the designed model, we prepared a new synthetic vertical text dataset and collected an actual vertical text dataset (VTD142) from the Web. Using these datasets, we proved that our proposed model can accurately recognize both vertical and horizontal text and can achieve state-of-the-art results in experiments using benchmark datasets, including the street view test (SVT), IIIT-5k, and ICDAR. Although our model is relatively simple as compared to its predecessors, it maintains the accuracy and is trained in an end-to-end manner.

Cervical cancer is known as one of the most important cancers in women worldwide. Chemotherapy is a standard treatment for advanced/recurrent cervical cancer in which the prognosis of the disease is really poor and the 1-year survival chance in these patients is maximally 20%. However, resistance to anticancer drugs is a major problem in treating cancer. Cervical cancer stem cells are considered as a fundamental cause of chemo and radio-resistance and also relapse after primary successful treatment. Signaling pathways include a wide range of molecular mechanisms contribute to drug resistance. Recently, microRNAs (miRNAs) are announced as a group of molecular biomarkers involving in response to chemotherapy in cancer patients. As the miRNAs, there are some long non-coding RNAs (LncRNAs) which their aberrant expression is considered as a biomarker for monitoring chemoresistance. In this review, we summarized current reports about the involvement of signaling pathways during chemoresistance in cervical cancer. Then, genes that have been demonstrated their involvement during drug resistance in cervical cancer were tabulated. Further, miRNAs that have been reported as biomarkers during treatment are listed. By bioinformatic analysis, we predictedmiR-335-5p and miR-16-5p as the most potential biomarkers for monitoring resistance to chemotherapy. Finally, long non-coding RNAs that have been introduced in recent studies as novel biomarkers during the response to chemotherapy were mentioned.

In sub-Saharan Africa (SSA), cervical cancer is a critical public health issue; it is the second leading cause of cancer among women and the leading cause of female cancer deaths. Incidence and mortality rates are substantially higher than in high-income countries with population-based screening programs, yet implementing screening programs in SSA has so far proven to be challenging due to financial, logistical and sociocultural factors. Human Papillomavirus (HPV) vaccination is an effective approach for primary prevention of cervical cancer and presents an opportunity to reduce the burden from cervical cancer in SSA. With a number of SSA countries now eligible for GAVI support for vaccine introduction, it is timely to consider the factors that impede and facilitate implementation of vaccine programs in SSA. This article reviews the epidemiological and clinical features of cervical cancer in SSA and describes the current status of HPV vaccine implementation in SSA countries. The review considers the challenges that will need to be addressed, and effective approaches to the design and implementation of HPV vaccination programs, using Rwanda as a case study. The review aims to provide suggestions and guidance to those involved in the development and implementation of HPV vaccination programs in SSA.

This review of the meaningful data from 2021 on cervical, endometrial, and ovarian cancers aims to provide an update of the most clinically relevant studies presented at important oncologic congresses during the year [the American Society of Clinical Oncology (ASCO) Annual Meeting, the European Society for Medical Oncology (ESMO) Congress and the Society of Gynecologic Oncology (SGO) Annual Meeting]. Despite the underlying existence of the COVID-19 pandemic, the last year has been notable in terms of research, with significant and promising advances in gynecologic malignancies. Several major studies reporting the effects of innovative therapies for patients with cervical, endometrial, and ovarian cancers might change the medical practice in the future.