Extensive growth in the volume of irregular brain cells is known as brain tumor. Human brain is surrounded by stiff skull. There are various issues that occur due to the growth of any tumor inside this restricted space. The malignant and benign are two main categories of the brain tumor. The skull is pressurized to enlarge from inside in case of growth of any benign or malignant tumor. This tumor leads to harm in brain and it may be dangerous to life also. The brain tumor is divided into two kinds - primary or secondary. The brain tumor detection techniques have various phases. In this paper, comparative study of CNN with GLCM approach and superpixel based spectral clustering is done tumor. This work takes into account metrics like accuracy, sensitivity and specificity for drawing the comparison between both the techniques.

Admission and discharge diagnoses of in-hospital patients are often in discord. Incorrect admission diagnoses are related to increased cost of care and patient safety. Additionally, due to the seasonality of many conditions, this discord may vary across the year. In this paper, we used medical claims data to develop a methodological framework that examines these differences, for Medicare beneficiaries. We provide examples for pneumonia, a condition with seasonal implications, and aneurysm, where early detection can be life-saving. Following a Bayesian approach, our work quantifies and visualizes with time series plots the degree that any clinical condition is correctly diagnosed upon admission. We examined differences in weekly intervals, during a calendar year. The mean length of stay and hospital charges were furthermore compared between matching and non-matching {admission, discharge Dx} pairs, and 95% confidence intervals of the difference of means were estimated. We applied Statistical Process Control methods and then visualized differences, for the hospital charges and the length of stay, per week, with time series plots. Our methodology and the visualizations underline the importance of a rigorous and non-delayed diagnostic process upon admission since there are significant implications in terms of hospital outcomes and cost of care.

Background: Despite its global eradication in 1977, smallpox remains a concern owing to its potential as a biological agent, thereby prompting the ongoing development and utilization of its vaccine. Vaccination with the Vaccinia virus induces immunity against variola virus, the causative agent of smallpox; however, this immunity does not extend to viruses of different genera within the Poxviridae family. In this study, we aimed to assess the efficacy of an enzyme-linked immunosorbent assay (ELISA) method utilizing Vaccinia virus and recombinant A27L antigen for detecting antibodies against smallpox. Methods. Analysis of serum from 20 individuals pre- and post-vaccination with the CJ strain (CJ50300) revealed neutralizing antibodies, which were confirmed using the plaque reduction neutralization test (PRNT). The ELISA method, validated with a PRNT50 cut-off value of >4, exhibited a sensitivity and specificity of >95% and was particularly reactive with the inactivated virus. Furthermore, adherence to the smallpox vaccination policy revealed significant differences in Orthopoxvirus antibody levels among 300 individuals of different age groups. These findings highlight the reliability and efficacy of the ELISA method in detecting post-vaccination antibodies and contribute significantly to diagnostic methods to prepare for potential smallpox resurgence and bioterrorism threats.

Preventing the occurrence of secondary caries serves as one of the significant issues in dental clinic, thus make it indispensable to improving the properties of conventional composite resin (CR) by developing a novel CR. In present study, two groups of experimental CRs loaded with different contents of fluoride-doped nano-zirconia fillers (25 wt% and 50 wt%) were fabricated. The surface topography, mechanical performance, fluoride release, antibacterial effect, aging property and cytotoxicity of the experimental CRs were evaluated subsequently. A uniform distribution of the F-zirconia fillers over the whole surface of resin matrix could be observed. The experimental CRs showed continuous fluoride release within 28 days, which was positively correlated with the content of F-zirconia fillers. Moreover, the amount of fluoride release increased in the acidic buffer. Addition of F-zirconia fillers could improve the color stability, wear resistance and microhardness of the experimental CRs, without reducing the flexure strength. Furtherly, the fluoride ions released continuously from the experimental CRs resulted in effective contact and antibacterial properties, while they showed no cytotoxicity. As a consequence, considerations can be made to employ this new kind of composite resin loaded with fluoride-doped nano-zirconia fillers to meet clinical requirements when the antimicrobial benefits are desired.

Recently, cases of antifreeze poisoning in companion animals, particularly cats, have surged in the Republic of Korea. Ethylene glycol (EG), the toxic primary component of antifreeze, is metabolized into glycolic acid (GA), leading to severe conditions such as metabolic acidosis and acute renal failure. Traditional detection methods, although effective, are often time-consuming owing to complex sample preparation. This study involved a novel analytical method utilizing GC-MS for EG and LC-MS/MS for GA detection, which streamlined the detection process by eliminating the need for derivatization. The method was validated for accuracy and reliability, enabling the rapid and precise identification of EG and GA in biological samples. The study also included the successful application of this method in a case where initial exposure to antifreeze was not apparent, which highlighted the effectiveness of this method in diagnosing poisoning even in cases where clinical history is unclear. The development of this rapid diagnostic approach addresses the urgent need for the efficient detection of antifreeze poisoning, improving animal welfare and supporting forensic investigations.

Purpose: The purpose of this study is to apply the Composite Radiographic Index modified (CRIm) in CBCT scans of patients with Medication related osteonecrosis of the jaws (MRONJ) in order to detect its validity, and corelate the radiologic findings with the clinical staging of the disease. Methods: This study included 43 patients with MRONJ diagnosis and CBCT scans performed in the School of Dentistry and approved by the Research Ethics Committee. Clinical staging for every patient (0-3) was provided according to AAOMS 2022 guidelines. In cases of patients with both jaws affected, each jaw was evaluated independently resulting to 52 CBCT scans. The 52 CBCT scans were assessed under standardized viewing conditions by 2 observers who evaluated independently the following 8 radiologic features: lytic changes, sclerosis, periosteal reaction, sequestration, non-healing extraction sockets and other findings (sinus implication, inferior alveolar canal involvement, jaw fracture). The CRIm index was constructed to assess the extent of osseous changes. The radiologic features were scored as absent (0), localized/single (1), extensive/multiple (2), yielding a theoretical range of 0–12. For statistical analysis Fisher’s exact test and Spearman’s correlation coefficient were performed. Results: A statistically significant association of clinical stage with lytic changes, sequestration and inferior alveolar canal involvement was found. Also, a statistically significant correlation of clinical stage with CRIm index classification was obtained. Conclusions: The CRIm index tends to increase as the clinical stages of MRONJ advance, suggesting a correlation between the clinical stage of MRONJ and the CRIm index.

Background/Objectives: Periodontitis (P), a chronic inflammatory condition that affects the supportive tissues around the teeth, is 3 to 4 times more prevalent in individuals with diabetes mellitus (DM), with a direct correlation between its severity and the levels of glycosylated haemoglobin (HbA1c). This study aimed to evaluate the periodontal clinical parameters following non-surgical periodontal treatment (NSPT) in patients with and without type 2 DM and P. Methods: Forty patients with P were divided into two groups: Group DM/P and Group P. All patients were assessed at baseline, and 90 and 180 days after receiving NSPT. The parameters evaluated included HbA1c levels, plaque index (PI), probing pocket depth (PPD), clinical attachment level (CAL), and bleeding on probing (BoP). Statistical analysis was performed with a significance level set at α = 5%. Results: There were significant differences in HbA1c levels between the DM/P and P groups at baseline, 90, and 180 days, as expected. Importantly, HbA1c levels did not change after NSPT. Group P showed a significant reduction in both PI and BoP values at 90 and 180 days (p < 0.05). In contrast, group DM/P demonstrated a significant increase in the percentage of sites with PPD ≥ 5mm at 180 days (p < 0.05). Additionally, group P exhibited an increase in sites with PPD ≤ 4mm and a decrease in sites with PPD ≥ 5mm at both 90 and 180 days (p < 0.05). Conclusions: Our findings suggest that DM may compromise the effectiveness of NSPT, potentially hindering favorable outcomes during the follow-up period.

Clostridial infections in cattle are a significant concern for Brazilian livestock. These diseases are caused by various species of Clostridium, which are known for their ability to produce potent toxins. Botulism in cattle is a serious and often fatal condition caused by the ingestion of neurotoxins produced by C. botulinum. This bacterium thrives in decomposing organic matter, such as spoiled feed, carcasses, and contaminated water. Tetanus while less common, tetanus is a serious disease following contamination of wounds with Clostridium tetani spores. It results in muscle stiffness, spasms, and often death due to respiratory failure. Blackleg (C. chauvoei) is this disease primarily affects young cattle, leading to acute lameness, swelling, and high fever. Malignant edema (C. septicum and others) is characterized by rapid onset of swelling at wound sites, malignant edema can occur after injuries or surgical procedures. Enterotoxemia is triggered by the rapid growth of C. perfringens in the gut following excessive carbohydrate intake. This leads to toxin production that causing sudden death. In conclusion, clostridial bovine infections remain a persistent challenge for Brazilian cattle farmers. With continued focus on vaccination, good management practices, and research, the impact of these diseases can be minimized, safeguarding the livestock industry's economic viability.

Background/Objectives: Multisystem Inflammatory Syndrome in children (MIS-C) is a rare but severe post-infectious complication of COVID-19, that often requires admission to the Pediatric Intensive Care Unit (PICU). The present study aimed to compare the demographic, clinical and laboratory characteristics between children diagnosed with MIS-C who were admitted to the PICU or did not require PICU admission. Methods: Children diagnosed with MIS-C from September 2020 to April 2023 were included in this case-control study. Patient’s demographic, clinical and laboratory data, were collected from medical records. Results: Fifty children with MIS-C were included in the study [median (IQR) age: 7.5 (4.3, 11.4) years, 28/50 (56%) males]. Twenty-two (22/50, 44%) children required admission to the PICU. In the multivariate regression analysis, hepatic (OR:12.89, 95%CI: 1.35-123.41, p-value=0.03) and cardiological involvement (OR:34.55, 95%CI: 2.2-541.91, p-value=0.01) were significantly associated with hospitalization at the PICU. Regarding the laboratory and imaging parameters during the first 48 hours from admission, D-dimer levels higher than 4μg/mL and decreased Left Ventricular Ejection Fraction were associated with an increased risk of PICU admission (OR:7.95, 95%CI:1.48-42.78, p-value=0.02 and OR=1.28, 95%CI:1.07-1.53, p-value=0.01). Children who were admitted to the PICU were more likely to develop complications during their hospitalization (10/22, 45.5% vs. 3/28, 10.7%, p-value=0.005) and were hospitalized for more days, than children in the pediatric wards (median length of stay (IQR): 20 (15,28) days vs. 8.5 (6, 14) days, p-value<0.001). Conclusions: In the present study, cardiovascular, hepatic involvement and increased D-dimer levels in children with MIS-C were associated with admission to the PICU.

: Microplastics and nanoplastics (MNPs) are becoming an increasingly severe global problem due to their widespread distribution and complex impact on living organisms. Apart from their environmental impact, the effects of MNPs on living organisms have also continued to attract attention. The harmful impact of MNPs has been extensively documented in marine invertebrates and larger marine vertebrates like fish. However, the research on the toxicity of these particles on mammals is still limited, and their possible effects on humans are poorly understood. Considering that MNPs are commonly found in food or food packaging, humans are primarily exposed to them through ingestion. It would be valuable to investigate the potential harmful effects of these particles on gut health. This review focuses on recent research exploring the toxicological impacts of micro- and nanoplastics on the gut, as observed in human cell lines and mammalian models. Available data from various studies indicate that the accumulation of MNPs in mammalian models and human cells may result in adverse consequences, in terms of epithelial toxicity, immune toxicity, and disruption of gut microbiota. The paper also discusses the current research limitations and prospects in this field, aiming to provide a scientific basis and reference for further studies on the toxic mechanisms of micro- and nanoplastics.

Background: Open AI developed ChatGPT, a language model based on the GPT architecture, designed for text-based communication. Trained on diverse internet texts, ChatGPT generates contextually appropriate responses using machine learning. It understands input through analysis and context interpretation, generating coherent and contextually relevant responses. Interaction is possible through messaging platforms. Materials and Methods: In November 2023, we utilized ChatGPT 3.5, the default version at that time, to answer questions from the Italian National Residency Program Admission Tests (SSMs) of 2021, 2022, and 2023. The questions cover clinical, diagnostic, analytical, therapeutic, and epidemiological scenarios, sometimes accompanied by images. The study compared ChatGPT's answers to the official corrections on the Italian Ministry of University and Research (MUR) website. The scoring method used for evaluation was 1 point for correct answers, 0 points for unanswered questions, and -0.25 points for incorrect answers, reflecting the SSM test scoring system. Results: In summary, ChatGPT was tested with a total of 420 questions, 140 for each test. It achieved an overall accuracy of 80.48%, providing correct answers for 338 questions and incorrect answers for 82 questions. When faced with questions containing both text and images, it answered 55% correctly and 45% incorrectly. The model's performance varied over time, with an 82.14% accuracy rate in 2021 and 2022 (115 correct out of 140) and a 77.14% accuracy rate in 2023 (108 correct out of 140). Applying thisscoring method to the SSM test, ChatGPT would have scored 105 points in 2021 and 2022, and 100 points in 2023. Conclusions: ChatGPT has exhibited above-average performance in the last three SSM tests, highlighting its robust capability to interpret clinical scenarios and offer precise diagnostic and therapeutic guidance. Despite this, some limitations persist, notably the software's inability to interpret non-textual information.

Neonatal mortality remains a public health concern and timely identification and prompt intervention in the delivery room could improve the outcome in neonatal morbidity and mortality. We performed a retrospective study to help us finding the profile of the high-risk newborn regarding the need for NICU admission, necessary to establish a protocol regarding the mobilization of human and material resources for the urgent management of these situations. All deliveries and neonates born in Maternity of Bihor County Emergency Clinical Hospital from Romania were reviewed, including relevant maternal, foetal and perinatal data to identify risk factors that result in birth of newborns needing resuscitation at birth between 2012-2019. The number of births showed a downward trend but around 90% of the births were on term. The incidence of neonatal emergencies is increasing, the most frequently were respiratory, sepsis and other infections, cardiovascular emergencies, and extreme prematurity. It is noted that in 2021 the incidence of these neurological emergencies increased by three times compared to previous years. The neonatal and maternal risk factors associated with admission to the NICU were mother's age over 35 years, low level of education, second/more gestation, weight under 2500 g, Apgar score under 7, premature rupture of membranes, macrosomia and mother's infectious pathology. Accurate identification of risk factors and anticipation at the birth of a high-risk neonate would result in adequate preparation and prompt resuscitation of neonates who need some level of intervention and thus, reducing neonatal morbidity and mortality.

Incidental findings (IFs) depicted in imaging tests during diagnostic evaluation of patients with cleft lip and/or palate (CL/P) can guide clinicians during treatment-planning procedures for effective, comprehensive cleft care. Evidence regarding IFs in different anatomical regions distant from the dentition are scarce. The aim of this study was to compare the prevalence of IFs in various anatomical areas between patients with non-syndromic CL/P and unaffected subjects. Orthopantomographs (OPT), lateral cephalometric (LC) radiographs and cone-beam computed tomography (CBCT) scans of 120 subjects (case group: n = 40, 18 females, age 14.5 ± 5.0 years; control group: n = 80, 36 females, age 14.6 ± 4.9 years) were examined and IFs were assessed by four observers. Significantly higher prevalence of IFs (p < 0.001) was reported in the case group (97.5%) compared with the control group (62.5%). The most prevalent location of IFs in the case group was the maxilla (92.5%), followed by the nasal cavity (75.8%) and the mandible (63.2%), while in the control group, the highest rates of IFs occurred in the maxilla (43.8%), mandible (41.3%) and temporomandibular joint (TMJ) (14.7%). No gender-dependent pattern for IF occurrence was detected overall. Orofacial clefts may affect the mandible. However, the reported IFs were limited compared to those in the maxilla. In conclusion, individuals with CL/P are more likely to present with a range of IFs than their unaffected peers. Understanding the association between CL/P and IFs is critical for successful interdisciplinary treatment, raising awareness of the potential need for future dental care for cleft patients, and managing extra-dental aberrations. A systematic assessment of diagnostic records is required to eliminate the risk of overlooking clinically significant IFs.

Degnala is one of the main mycotoxicoses that affects buffaloes, and the fungus Fusarium spp. is the main causative agent. This disease is strongly related to the supply of rice straw to buffaloes and is considered endemic to regions where this crop is cultivated. Cases of the disease are concentrated in the winter, when conditions favour fungal growth in inadequately stored rice straw. Degnala is characterized by necrosis and gangrene of the extremities, including the tail, lower limbs, ears, tongue, muzzle and teats. The pelvic limbs are more affected than the thoracic limbs. A tortuous appearance of the tail is very common, and cracks or corneal loss of the hooves may occur, with exposure of the blades and even the bones. There is no diagnostic method for identifying the disease in animals other than the clinical and epidemiological criteria associated with fungal culture of rice straw samples. There is no treatment that neutralizes the effects of the toxins, and the treatment currently used is a palliative and supportive method consisting of pentasulfate solution, anti-Degnala liquid, 2% nitroglycerin ointment and broad-spectrum antibiotics for secondary infections. In addition, the management of drying straw and proper storage are essential for controlling this disease.

Immunohistochemistry (IHC) helps identify different types of tissues and provides important information about the pattern, shape, and structure of cells in a tissue sample. It is a very useful tool and widely used to uncover histology and pathology diagnosis uses. The IHC is a very useful tool to conduct research in the developmental biology area. Some insects, like butterflies, bees, beetles, ants, wasps, and moths, undergo a unique life cycle called complete metamorphosis, which has four distinct stages: egg, larva, pupa, and adult. Larvae are often wingless and have different habits and forms than adults, which are better suited for growth and development. During the metamorphosis, how does the insect legs change is a really an interesting issue.To better understand insect development, agriculture pest control, and insect pathogen interaction, here we use an insect Bombyx mori (domestic silk moth) as a model to detail how to conduct the IHC assays, such as HE staining, TUNEL to see the cell apoptosis, pH3 and BrdU staining to check cell division. The reagents can be easily found, and this step-by-step protocol is easily reproduced.

Candidiasis is a significant fungal infection caused by various species of the genus Candida, posing health challenges to a wide range of animals, including Choloepus species (two-toed sloths). This review article aims to provide a comprehensive understanding of candidiasis in Choloepus sp., highlighting the etiology, epidemiology, pathogenesis, clinical manifestations, diagnosis, treatment, and prevention strategies.The article begins by examining the causative agents, primarily focusing on Candida albicans, which is the most commonly implicated species in candidiasis. The epidemiological aspects are discussed, emphasizing the prevalence of candidiasis in wild and captive Choloepus populations and identifying predisposing factors such as immunosuppression, stress, poor nutrition, and environmental conditions. Pathogenesis is explored, detailing the mechanisms through which Candida species invade host tissues and evade immune responses. Clinical manifestations in Choloepus sp. are described, including oral thrush, cutaneous lesions, and gastrointestinal infections, and their impact on the health and behavior of affected individuals. Diagnostic methods, including culture techniques, histopathology, and molecular assays, are reviewed to highlight their roles in accurately identifying Candida infections. The article also covers treatment options, focusing on antifungal therapies and supportive care tailored to the unique physiology of Choloepus sp. Finally, prevention and management strategies are discussed, emphasizing the importance of maintaining optimal husbandry practices, regular health monitoring, and early intervention to reduce the incidence and impact of candidiasis in Choloepus populations. This review underscores the need for further research to enhance our understanding of candidiasis and improve health outcomes for these unique and vulnerable animals.

Studying models of healthy aging and exceptional longevity is crucial to understand a possible longevity signature as most of them show resistance to age-related diseases. In particular, semi- and supercentenarians are a highly selected group, having survived significant adversities, including the Spanish Flu and COVID-19 pandemics, indicating distinctive immune system characteristics. This paper analyse the inflammatory scores (INFLA-score, Systemic Inflammation Response Index (SIRI)) and Aging-Related Immune Phenotype (ARIP) indicators calculated from the dataset of the DESIGN project, including 249 participants aged 19-111 years, trying to understand the immune-inflammatory (IMFLAM) role to achieve longevity. Statistical analyses, including linear regression and one-way ANOVA, were performed to explore the correlations between these parameters and age. Both INFLA-score and SIRI showed a significant increase with age. However, no statistical difference is observed when analysing the values for semi- and supercentenarians with those for other age groups, whose values are similar to that of adults and less than younger centenariansRegarding ARIP values, it is noteworthy that when comparing the CD8 Naive/Effector scores between groups, no significant differences were observed between the semi- and supercentenarian group and the other groups. These results support the idea that the control of IMFLAM response can promote extreme longevity.

Background: breastfeeding is the optimal nourishment for infants and recommends that children commence breastfeeding within the first hour of birth and be exclusively breastfed for the initial 6 months of life. Our objective was to determine which factors related to mothers could influence the degree of exclusive breastfeeding during hospitalization, as well as to assess breastfeeding mothers' attitudes towards breastfeeding. Methods: A multicenter cross-sectional study was undertaken in the healthcare area of Santiago de Compostela, Spain. The reduced Iowa Infant Feeding Attitude Scale (IIFAS-s) was employed to gauge maternal attitudes toward feeding their baby. Results: 64 women were studied. The overall score of IIFAS-s (mean ± standard deviation) was 36.95 ± 5.17. No use of pacifier by the newborn was associated with a positive attitude for breastfeeding. Having previous children (ORa=6,40; IC95% 1,26 – 32,51) and previous experience with breastfeeding (ORa=6,70; IC95% 1,31 – 34,27) increase the likelihood of exclusive breastfeeding during admission. Conclusions: It is important to identify the factors that contribute to increasing the probability that mothers choose exclusive breastfeeding. We must guide our training efforts to these factors.

Background According to some authors, a single isolated measurement of serum BNP executed on hospital admission would not be a sufficiently accurate method to predict the outcome of patients with ADHF. Aims For verifying this assumption, a retrospective study was conducted on patients hospitalized for ADHF. Our main objective was to ascertain whether there was any difference in midterm mortality among patients with rising BNP at discharge as compared to those with decreasing BNP at discharge. Methods Medical records were examined so as to make a partition of the ADHF patient population into two groups, the former characterized by a rise in BNP during hospitalization, and the latter exhibiting a decrease in BNP in the measurement taken at hospital discharge. Results 177 patients were enrolled in a retrospective study. Among them, 53 patients (29.94%) had increased BNPs at the time of discharge, whereas 124 (70.06%) showed decreases in serum BNP during their hospital stay. The group with patients who exhibited BNP increases at the time of discharge had higher degree of congestion evident in the higher frequency of persistent jugular venous distention and persistent orthopnea at discharge. Moreover, patients with increased BNP at the time of discharge had a lower reduction in inferior vena cava maximum diameter [1.58 ± 2.2 mm vs. 6.32 ± 1.82 mm; p (one-way ANOVA)=0.001]. In contrast, there was no significant difference in weight loss when patients with increased BNP at discharge were compared to those with no such increase. A total of 14 patients (7.9%) died during the six-month follow-up period. Cox proportional hazard analysis revealed that BNP increase at the time of discharge was an independent predictor of six-month all-cause mortality after adjustment for age, sodium at discharge, creatinine at discharge and New York Heart Association (NYHA) class at discharge (hazard ratio 34.49; 95% confidence intervals: 4.55–261.06; P =0.001). Conclusions Among patients with history of ADHF, more elevated BNP levels at the time of discharge from the hospital compared to those detected at admission identify a patient subset with higher grade of congestion and higher six-month mortality.

In the industrial sector, intelligent sensors in fault diagnosis are becoming more critical in recent technological improvements. The prediction accuracy can be enhanced in fault diagnosis using 3-dimensional, sequential, real-time, and image data. Sensors that capture the vibration, sound, and image data are more critical in predicting unbalancing, tool wear, crack, misalignment, etc, in the rotating machinery to increase productivity and to provide an effective maintenance management system. Due to the fast development of industry 4.0 techniques, monitoring of mechanical machinery is experiencing explosive growth and getting more attention in the area of fault diagnosis (FD). Machine learning and Deep learning methods give promising results and accuracy in predicting faults in rotating machinery on shop floors. The success of AI-based models is due to the availability of comprehensive labeled data. Federated Learning (FL) is the machine learning subfield aiming to train an algorithm with a heterogeneous dataset. Data transmission from local facilities to a central server in AI models creates data privacy and security issues. Heterogeneous data analysis is a complicated process in predicting the machine fault in the central server because of millions of data during real-time condition monitoring. Decentralized data handling and analysis is mandatory in condition monitoring because of heterogeneity in data processing, data privacy, and security advantages. The application of federated learning in fault diagnosis has been getting more attention in recent days, and this study is a review of FL applications that address fault diagnosis in rotating machinery in the first phase. A comparison between the types of FL approaches in FD and the use of aggregation algorithms and their applications will also be discussed in Phase 1. In phase 2, a novel methodology has been proposed using Federated learning to diagnose rotating machinery faults. The proposed method, FLOACOS, addresses how prediction challenges are solved using federated learning approaches by optimizing and standardizing the data at local facilities. This work will be helpful for future condition monitoring researchers and gives an overview of a novel method of the FL technique used in predicting faults and the progress made in the maintenance management of rotating machinery.

Background/Objectives: Endometriosis represents substantial direct and indirect healthcare costs impacted by absent accurate, non-invasive uniformly diagnostic tools. We endeavored to demonstrate gastrointestinal myoelectrical activity (GIMA) biomarkers unique to endometriosis allow noninvasive, uniformly accurate diagnosis or exclusion of endometriosis. Methods: Prospective open-label comparative study of 154 patients, age >18, with or without diagnosed endometriosis. Population included 62 non-endometriosis controls (Cohort 1), 43 subjects with surgically histologically-confirmed endometriosis (Cohort 2), and 49 subjects with abdominal pain and negative imaging (Cohort 3). Electroviscerography (EVG) recorded GIMA biomarkers from three abdominal electrodes before and 30-minutes post water-load protocol. Cohort 2 had postoperative EVG and Cohort 3 had preoperative EVG. Calculated specificity, sensitivity, NPV, PPV and predictive probability or C-Statistic used univariate, multivariate, linear and logistical regression analyses of the area under the curve (AUC) at all frequency and time points, including age and pain covariants. Results: Non-endometriosis cohort differed significantly from endometriosis cohorts (p<0.001) for median (IQR), and AUC percent frequency distribution of power at baseline, 10-minute, 20-minute, and 30-minute post water-load at all frequency ranges 15-20cpm, 30-40cpm and 40-50cpm. Endometriosis cohorts were statistically similar (p>0.05). GIMA biomarker threshold scoring demonstrated 95%/91% sensitivity and PPV, 96%/95% specificity and NPV and C-statistic of >99%/98% respectively for age subsets. GIMA biomarkers in Cohort 3 predicted 47/49 subjects positive and 2/49 negative for endometriosis, confirmed surgically. Hormonal therapy, surgical stage, nor pain score affected diagnostic accuracy. Conclusions: Non-invasive electroviscerography with GIMA biomarker detection distinguished participants with and without endometriosis based upon endometriosis specific GIMA biomarkers threshold scoring.

Although ovarian cystic teratoma is the most common ovarian tumor, complications are quite rare. However, it is important to be recognized by the radiologist in order to avoid inaccurately diagnosing them as malignant lesions. This case report describes a 61-year-old postmenopausal woman, who presented to the emergency room with abdominal pain following a minor blunt abdominal trauma. In this context, a CT scan was performed, which showed the presence of round, hypodense masses randomly distributed in the peritoneum, with coexisting ascites in moderate amount; ovarian carcinoma with peritoneal carcinomatosis was suspected. The patient was hospitalized and an MRI of the abdomen and pelvis was recommended for a more detailed lesion characterization. Following this examination, the patient was diagnosed with mature cystic ovarian teratoma complicated by rupture. Surgery was performed, and the outcome was favorable. The cases of ruptured cystic teratomas are rare, and to our knowledge, this is the first occurrence described in literature. Special attention must be paid when confronting with such a case in medical practice, since it can easily misdiagnosed as peritoneal carcinomatosis

In patients with primary central nervous system lymphoma (PCNSL), the choice of surgical strategy for histopathologic assessment is still controversial, particularly in terms of preoperative corticosteroid (CS) therapy. To provide further evidence for clinical decision-making, we retrospectively analyzed data from 148 consecutive patients who underwent surgery at our institution. Although patients treated with corticosteroids preoperatively were significantly more likely to require a second or third biopsy (p=0.049), it was only necessary in less than 10% of the cases with preoperative corticosteroid treatment. The choice of surgical procedure did not affect diagnostic accuracy (p>0.05). Preoperative CSF sampling did not provide additional information but was associated with delayed surgery (p=0.02). Surprisingly, diagnostic accuracy was significantly lower when patients were treated with anticoagulation or dual antiplatelet therapy (p=0.015). In conclusion, preoperative CS therapy can challenge the histological diagnosis of PCNSL. At the same time, our data suggest that preoperative CS treatment only presents a relative contraindication for an early surgical intervention. The standard surgical approach is stereotactic biopsy, but other approaches can be individually considered. If a definitive diagnosis cannot be made after the first surgical intervention, the timing of a repeat biopsy after discontinuation of CS remains a case-by-case decision.

Lynch syndrome (LS) is an inherited cancer predisposition disorder associated with an elevated risk of various epithelial cancers. Despite sharing the same pathogenic variant (PV), Lynch syn-drome variant heterozygotes (LSVH) exhibit considerable phenotypic variability in cancer risk. The role of Human Leukocyte Antigen (HLA) in modifying cancer risk prompted our investiga-tion into whether HLA variations act as genetic modifiers influencing age at cancer diagnosis in a unique cohort of LSVH carrying a PV in the hMLH1 gene in South Africa. Within our extensive LS cohort, 426 individuals carried the same hMLH1 PV (MLH1:c.1528C>T). We selected 100 indi-viduals with the greatest diversity in age at cancer diagnosis and the oldest unaffected individu-als for high-throughput HLA genotyping of 12 HLA class I and II loci using next-generation se-quencing. Statistical analyses employed Kaplan-Meier survival analyses with Logrank tests and Cox proportional hazards. Following the robust application of statistical correction methods, six HLA alleles (3.2%) were significantly associated with a young age at cancer diagnosis. Notably, HLA-B*15:17 and HLA-DPB1*55:01 correlated significantly with very young colorectal cancer (CRC) diagnosis (Mean age: 21y [17-25]; HR = 71.59; q

Chronic granulomatous disease (CGD) is a group of rare primary inborn errors of immunity characterised by a defect in the phagocyte respiratory burst, which leads to severe and life-threatening infective and inflammatory complications. Despite recent advances in our understanding of the genetic and molecular pathophysiology of X-linked and autosomal recessive CGD, and growth in the availability of functional and genetic testing, there remain significant barriers to early and accurate diagnosis. In the current review, we provide an up-to-date summary of CGD pathophysiology, which underpins methods of diagnostic testing for CGD and closely related disorders. We present an overview of the benefits of early diagnosis and when to suspect and test for CGD. We discuss current and historical methods for functional testing of NADPH oxidase activity, as well as assays for measuring protein expression of NADPH oxidase subunits. Lastly, we focus on genetic and genomic methods employed to diagnose CGD, including gene-targeted panels, comprehensive genomic testing and ancillary methods. Throughout, we highlight general limitations of testing, and caveats specific to interpretation of results in the context of CGD and related disorders, and provide an outlook for newborn screening and the future.

Background: Although several studies have been conducted on the use of Artificial Intelligence (AI) in mammography (MG), research focusing on the diagnosis of metachronous bilateral breast cancer (BC), which is typically more challenging to diagnose, remains limited. This study assessed whether AI could enhance the detection of BC, achieving earlier or more accurate diagnoses than radiologists in cases of metachronous contralateral BC. Methods: The study included patients who underwent unilateral BC surgery and subsequently developed contralateral BC during follow-up. We evaluated the capability of the AI-supported MG diagnostic system FxMammo™ (FathomX Pte Ltd, Singapore) to diagnose BC more accurately or earlier than radiologists' assessments. This evaluation was supplemented by reviewing MG readings by radiologists. Results: Out of 1,101 patients who underwent surgery, 10 patients who had initially received a partial mastectomy and later developed contralateral BC were analyzed. The AI system identified malignancies in 6 cases (60%), while radiologists identified 5 cases (50%). Notably, two cases (20%) were diagnosed solely by the AI system. Additionally, for these cases, the AI system had identified malignancies a year prior to the conventional diagnosis. Conclusions: This study highlights the AI system's effectiveness in diagnosing metachronous contralateral BC via MG. In some cases, the AI system consistently diagnosed cancer at earlier than radiological assessments.

Healthcare professionals and resource planners can use healthcare delivery process mining to ensure the optimal utilisation of scarce healthcare resources when developing policies. Within hospitals, patients' Length of Stay (LOS) and volume of admitted patients, in terms of number and characteristics (age, gender, and social deter-minants), are significant factors determining daily resource requirements. In this study, we used Coxian phase-type Distribution (C-PHD) based Phase-Type Survival (PTS) trees for analysing how covariates such as admission date, gender, age, district, and admissions source influence the admission rate and LOS distribution. PTS trees. This study used a two-year data set (2011-2012) of patients admitted to the Emergency Department at Mater Dei Hospital to generate models and an independent one-year data set (2013) of patients admitted to the Emergency Department at Mater Dei Hospital to evaluate. The PTS tree effectively clusters patients based on their LOS, considering the prognostic significance of different covariates related to patients' characteristics. Charac-terising these covariates provided meaningful results about LOS. Similarly, the PTS tree was used to effectively cluster patients based on the admission rate, considering the prognostic significance of these covariates.

(1) Introduction: The unprecedented coronavirus disease 2019 (COVID-19) epidemic has caused millions of infections worldwide and represents a significant challenge facing modern health care systems. This study was conducted to investigate the impact of lockdown measures on regional neonate services, which might be used to predict the long-term effects of medical behavior. (2) Methods: Using hospital information system (HIS) Statistics data from 4 January 2019 to 27 August 2020, an interrupted time-series analysis was employed to compare changes and trends in hospital admissions and disease spectrum before and after the lockdown interventions. Furthermore, this study was designed to evaluate whether the pandemic influenced newborns' healthcare behavior. (3) Results: Overall, 13,540 infants were admitted to the NICU during the pre-COVID period (n = 12082) and COVID-impacted period (n =4558). The patients' age at admission were younger than that of the pre-COVID-19 period (median age 5d vs. 6d after birth, p＜0.001). The overall number of neonate visits consistently decreased from the first days of the lockdown measures (24 January 2020). The disease spectrum for respiratory system, infectious diseases, and gastrointestinal disease indicated no declined immediately after intervention(p =0.079, p =0.113, p =0.060, respectively). There was an immediate decline in the volume of Jaundice-related conditions (p<0.001) after lockdown measures. The percentage of patients who suffer from respiratory system and infectious diseases has decreased (p =0.005 and p =0.002). However, a relatively high percentage of patients admitted to the neonatal intensive care unit (NICU) presented with Jaundice-related conditions (p <0.001). (4) Conclusions: In summary, the COVID-19 pandemic profoundly impacted the regional neonate services. However, it is still unclear what might be the effect of long-term effects from pandemic.

This study aimed to learn disturbance information to effectively control multiple small unmanned surface vehicles (USVs) for marine research purposes at sea. The learned disturbance information was used to investigate the control of multiple USV swarms based on the leading–following formation technique. Numerous studies have already been conducted on swarm control using drones in the air; however, there has been a paucity of studies on swarm control in the sea. Contrary to swarm control in aviation, the fluid density of the sea is high, and the environment is constantly changing; thus, a robot system would be significantly affected by disturbances. This ultimately influences the maintenance of swarm formations and the accomplishment of missions [1,2]. Therefore, it is difficult to control numerous moving objects while maintaining a formation at sea by using the existing aerial drone swarm control technology. To solve this problem, we learned a long short-term memory (LSTM) model with time-series marine environmental data to predict maritime disturbances. Thereafter, the model was applied to a mission-based position control algorithm of each USV constituting the swarm to design a marine unmanned floating vessel swarm control system that can be applied to various marine environments. Specifically, to effectively control numerous small USVs for marine research purposes, ocean currents are learned using the LSTM algorithm, and the predicted ocean currents are used to generate a swarm USV control system for USV formations. In this study, a control system model of several small USVs equipped with two rear thrusters and a front lateral thruster was designed. The LSTM algorithm was learned using the previous ocean current data, and the velocity of the following ocean currents was predicted. The predictions were then used as a system disturbance to determine the thrust of the controller. To measure ocean currents in the sea when each USV moves, the velocity, azimuth, and position (latitude, longitude) data from the GPS mounted on the USV were used to measure the velocity and direction of the hull's movement. Further, the flow rate was measured using a flow rate sensor on a small USV. The USV movement and position were controlled using an artificial neural network-PID (ANN-PID) controller. This study entailed a comparative analysis of the designed USV model results and those generated by the simulator, including the behavior control rule of the USV swarm and the path of the actual USV swarm at sea. It was verified that the effectiveness of the USV mathematical model and behavior control rules. By comparing the movement path of the swarm USV with and without the disturbance learning algorithm and ANN-PID control algorithm applied to the designed simulator, the position error and maintenance performance of the swarm formation were analyzed, and the application results were compared.

HIV early detection (CD4 counts ≥350 cells/μL) is correlated with higher life expectancy among people living with HIV (PLHIV). Several factors including physical, cultural, structural, and financial barriers have contributed to HIV late detection. This is the first-of-its-kind study on the regional differences in early detection of HIV within Tajikistan and any country in the Central Asia region. Utilizing Tajikistan Ministry of Health's national HIV data (N=10,700) spanning 2010 to 2023, we developed median regression models with median CD4 cell count as the outcome and as predictors: time (years), region, age, gender, and area (urban/rural status). Individuals younger than 19 years old are more likely to be detected early for HIV, whereas those older than 39 years are more prone to late detection. Given their active engagement in age-related and sexual activities, those older than 39 years face a higher risk of HIV transmission to others if diagnosed late. Therefore, they require further attention and targeted interventions. Females are detected earlier compared to their male counterparts, regardless of region of residence. Rural populations are detected earlier in most years compared to their urban counterparts. The COVID-19 pandemic accelerated HIV early detection in 2021 but most regions have returned to near pre-pandemic levels of detection in 2022 and 2023. These fluctuations in early detection may signify a shift in attention and resource allocation, potentially affecting efforts to address access gaps during the initial year of the COVID-19 pandemic. There are significant health inequities among different demographic and geographic groups which warrant further attention.

To address the issue of intelligent optimization algorithms being prone to local optima, resulting in insufficient feature extraction and low fault type recognition rates when optimizing Variational Mode Decomposition (VMD) and Support Vector Machine (SVM) parameters, this paper proposes a fault diagnosis method based on an improved Artificial Gorilla Troops Optimization (GTO) algorithm. The GTO algorithm is enhanced using Logistic chaotic mapping, a linear decreasing weight factor, the global exploration strategy of the Osprey Optimization Algorithm, and the Levy flight strategy, improving its ability to escape local optima, adaptability, and convergence accuracy. This algorithm is used to optimize the parameters of VMD and SVM for fault diagnosis. Experiments on fault diagnosis with two datasets of different sample sizes show that the proposed method achieves a diagnostic accuracy of no less than 98% for samples of varying sizes, with stable and reliable results.

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from January 1, 2013, to December 22, 2023. Articles were collected using key search terms such as “artificial intelligence” and “machine learning.” Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. 8 studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

Mitochondria play a significant role in the functioning of the cell, acting both as an intracellular energy source and a signaling organelle. In the heart, mitochondria produce more than 95% of the ATP in addition to regulating the redox state, calcium homeostasis, apoptosis, and lipid synthesis. However, there is also evidence that mitochondria exist outside of cells where they have been observed to mediate cell-to-cell communication, repair, and participate in the immune response. Cardiometabolic disease (CMD) has a complex pathogenesis closely associated with mitochondrial dysregulation, inflammation, and metabolic abnormalities. Because of this metabolic interdependence between the myocardium and the intracellular mitochondria, circulating extracellular mitochondria (CEMI) are being identified as possible regulators of CMD. Outside of the cell, mitochondrial functions are determined by the mitochondrial form, either as whole functional structures (e.g., small extracellular mitochondria) or as mitochondrial components with or without membrane encapsulation. Here, we explore the different forms of CEMI, their role in CMD pathogenesis, their potential therapeutic utility in treating diseases, and their role as biomarkers.

This is a single tertiary center in southwest Romania, population-based, study. We retrospectively compared data obtained in two periods: January 2008 - December 2013 and January 2018 - De-cember 2023, respectively. The global incidence of transposition of great arteries (TGA) - the ter-minated cases added to the ones resulting in live-born pregnancies, remained almost constant. The live-born incidence of TGA decreased (0.34% to 0.013%). The prenatal detection rate increased slightly (71.42% to 87.5%). The median gestational age at the diagnosis decreased, from 29.3 ges-tational weeks (mean 25.4) to 13.4 weeks (mean 17.2). The proportion of terminated pregnancies significantly increased (14.28% to 75%, p = 0.019).

Background: Microscopic examination of acid-fast stained sputum smears for detecting mycobacterial acid-fast bacilli (AFB) remains the most economical and readily available method for laboratory diagnosis of tuberculosis (TB). However, this conventional approach has limitations, including low sensitivity and labor-intensive procedures. Methods: An automated microscopy system incorporating artificial intelligence (AI) and machine learning for AFB identification was evaluated. The study was conducted at an Infectious Disease Hospital in Jiangsu Province, China, utilizing an intelligent microscope system (TB-Scan, Wellgen Medical, Kaohsiung). A total of 1,000 sputum smears were included in the analysis, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFB. Referee technicians provided the gold standard for discrepant results. Results: The automated system demonstrated an overall accuracy of 95.00% (950/1,000), sensitivity of 91.24% (177/194), and specificity of 95.91% (773/806). Notably, the system identified 21 smears as positive that were previously reported as negative, with referee technicians confirming 17 of these as true positive and retracting the test results accordingly. Recalculating the performance, the accuracy increased to 96.70% (967/1,000), sensitivity to 91.94% (194/211), and specificity to 97.97% (773/789), with a false negative rate of 8.06% (17/211) and a false positive rate of 2.03% (16/789). Conclusions: The incorporation of AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

The research aims to present an innovative methodology to execute the Heritage conservation processes in a collaborative and interdisciplinary BIM project, with an effective management of the deterioration suffered over time, emphasizing the structures and coatings. The research begins with the architectural survey using scanning techniques (TLS) and terrestrial photogrammetry (SfM) and taking as a study the Duomo of Molfetta (Italy), a unique Romanesque architecture of Puglia (Italy). The methodological process is mainly aided by the precise semantic segmentation of global point clouds, a semi-automatic process assisted by classification algorithms implemented in the post-processing software, and which has allowed the classification of the unstructured information provided by the remote sensing equipment when identifying the architectural-structural systems of a building with high historical values. Subsequently, it has been possible to develop an efficient Scan-to-HBIM workflow, where the Heritage-BIM (HBIM) project has fulfilled the function of a database by incorporating and organizing all the information (graphic and non-graphic) to optimize the tasks of auscultation, identification, classification, quantification and, in turn, facilitating the parametric modeling of unique structures and architectural elements. The results have shown great effectiveness in the processes of characterization of architectural and artistic heritage, focusing on the deformations and deterioration of the masonry in columns, and making multidisciplinary conservation work in an HBIM project more flexible. But there are still some difficulties for the automatic interpretation of 3D point cloud data, related to specific systems of the historical architecture. In conclusion, human action and interpretation continues to be a fundamental pillar to achieve precise results in a heritage environment.

This study explores the optimization of the admission process at the Escuela Colombiana de Ingeniería Julio Garavito through data anal- ysis. By integrating advanced tools like Power BI, Python Flask, and data mining techniques, a model has been developed to improve student selection and retention, enabling more informed and effective decisions. The results indicate that applying data science not only facilitates efficient data management but also promotes more inclusive and equitable educational poli- cies. This pioneering approach in the field of higher education in Colombia offers a frame- work for future research and technological de- velopments, highlighting the vital importance of data science in improving educational and administrative processes.

Pulmonary embolism (PE) is one of the leading causes of cardiovascular mortality, therefore new parameters regarding risk stratification are sought after. In patients admitted for acute PE we investigated associations between the initial coagulation impairment, expressed by prothrombin time international normalised ratio (INR), and parameters reflecting PE severity. Furthermore, in-hospital, 30-day and long-term mortality were also evaluated. The analysis included 848 patients who were divided into two groups: with normal INR≤1.2, and elevated INR>1.2 (252 patients, 29.7%). The group with elevated INR presented higher incidence of tachycardia and lower systolic blood pressure, higher CRP, d-dimer, and NT-proBNP. This group presented higher estimated systolic pulmonary artery pressure (49 IQR39-62mmHg vs 43 IQR32-53mmHg, p<0.001) and shorter pulmonary artery acceleration time (65 IQR55-85ms vs 81 IQR63-102ms; p<0.001). Patients with elevated INR had more often a sPESI of 1 or higher (78%vs60%, p=0.003). Cox regression model revealed that age, leukocyte level, SBP, neoplasm, and INR are associated with higher risk of death (p<0.001). Finally, elevated INR was associated with higher in-hospital (13%vs3%; p<0.001), 30-day (19%vs6%; p<0.001), and long-term mortality (p<0.001). Summing up, elevated INR on admission is frequent in patients with PE, reflects worse clinical condition and is related to PE severity and prognosis.

Health management in industrial systems is more critical in maintenance management and it plays an important role in productivity, fault diagnosis, safety, efficiency, and economy in manufacturing industries. Early detection of faults in machinery may increase the effectiveness of maintenance actions and will avoid unwanted consequences in process operations and maintenance. Existing fault diagnosis methods have limitations such as insufficient accuracy, slow detection rate, and handling large and complex data sets. In this digital age, Industry 4.0 techniques have been applied across all fields, including the condition monitoring of machines. This research addresses the gaps in traditional fault diagnosis by using deep learning, a modern AI technique effective for diagnosing faults in various machines. Deep learning algorithms Multilayer Perceptron (MLP), Coevolutionary Neural Network (CNN), and Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) are tested for fault diagnosis using vibration datasets collected from Spectra Quest Machinery Fault Simulator (SQMFS). In this research work, NI-DAQ (National Instruments- Data Acquisition) system, accelerometer, and LabVIEW software are used to collect vibration signals. Preprocessing of the signals has been done using a sampling strategy, shuffling, standardization, and reshaping data augmentation. The result shows that MLP accuracy in the prediction of fault is 0.9, CNN reached 0.95, and RNN and LSTM with 0.57 and 0.45 respectively. The high performance of CNN is due to its ability to effectively capture spatial patterns in vibration data which is crucial for fault diagnosis in rotating machinery followed by MLP due to its faster convergence during training. However, when scaling the data, MLP outperformed CNN, demonstrating superior adaptability to increased data complexity and volume. Due to the need for larger datasets and temporal patterns in the vibration data, which RNN and LSTM are designed to handle, they resulted in a lower accuracy. This study shows that CNN has given better results than other deep learning algorithms MLP, RNN, and LSTM in fault diagnosis of rotating machinery. Future research could explore the application of these techniques to different types of machinery and fault conditions.

Peri-implant infective diseases seem to be the most common cause for the late dental implant failure. Therefore, peri-implant microbiota has been intensively studied in modern implantology. Many studies showed a presence of extremely varied microflora in cases of peri-implant infective diseases. The main scope of this observational single-blind study was to analyze the presence of some microbial species in a group of patients with clinical signs of peri-implant mucositis compared to two other groups (patients treated with dental implants but without any clinical signs of peri-implant inflammation and patients treated with no dental implants). The identification of microorganisms was followed by an antibiogram in order to find their sensitivity to various antibiotics. The results showed a microbiota not necessarily specific for each situation (peri-implant infective diseases, healthy tissues around dental implants and healthy natural teeth and gums) with species like Candida albicans, Neisseria mucosa, Prevotella, Staphylococcus aureus, Streptococcus and others. Peri-implant infective diseases presented more bacterial species than other conditions.

Aim: To investigate inflammation indices and erythropoietin (EPO) levels for their potential role in distinguishing polycythemia vera (PV) from secondary polycythemia (SP), and to compare different parameter combinations in terms of diagnostic accuracy. Methods: This retrospective cohort was created from patients assessed for polycythemia from January 2020 to December 2023. PV diagnosis was made according to the 2016 WHO criteria (n=145). Those who did not fulfill the criteria were defined as having SP (n=84). Results: NLR, PLR and SII were significantly higher in the PV group (p

Bearing fault diagnosis is crucial for ensuring the stable operation of mechanical equipment. With the continuous development of deep learning technology, Convolutional Neural Networks (CNNs) have demonstrated significant advantages in the field of fault diagnosis. This paper proposes a new method that combines various CNN architectures to improve the accuracy of bearing fault diagnosis. We designed five different convolutional network structures, including SerConv, ResConv, One-Shot Aggregation Convolution (OSAConv), Cross-Stage Aggregation Convolution (CSAConv), and MD-DAConv. Experimental results on the Case Western Reserve University (CWRU) bearing dataset show that the proposed method exhibits high accuracy and robustness in fault diagnosis. The results indicate that strategies such as multi-directional, multi-scale, and residual connections play a crucial role in enhancing the depth and breadth of feature extraction, while simple and effective feature fusion and information transmission mechanisms are key to ensuring the robustness and generalization ability of the model.

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.

Abstract: Ovarian cancer (OC) ranks as the fifth most common cancer among women in the United States and globally, posing a significant health threat. Reactive oxygen species (ROS) have emerged as critical factors in the pathophysiology of this malignancy. ROS, characterized by their instability due to an unpaired electron, are involved in essential cellular functions and play a crucial role in the immune response under normal physiological conditions. However, an imbalance in ROS homeostasis, leading to excessive ROS production, results in oxidative stress (OS), which can cause indiscriminate damage to cellular structures and contribute to the pathogenesis of specific diseases, including OC. OC is primarily classified based on the originating cell type into epithelial, stromal, and germinal tumors, with epithelial tumors being the most prevalent. Despite advancements in medical technology, early detection of OC remains challenging, often leading to delayed treatment initiation. Current therapeutic approaches include surgical excision of tumor tissue, radiotherapy and chemotherapy. While these treatments are effective in early-stage OC, high mortality rates and frequent relapse underscore the urgent need for novel diagnostic and therapeutic strategies. This review aims to elucidate the role of ROS in OC, emphasizing the potential for developing innovative diagnostic tools and treatments that target ROS-mediated pathways. Given the critical impact of early detection and effective treatment, advancing our understanding of ROS in the context of OC could significantly enhance patient outcomes.

Pancreatic cancer is one of the most aggressive and lethal forms of cancer, with a five-year survival rate of less than 10%. Despite advances in treatment modalities, the prognosis for pancreatic cancer patients remains poor, highlighting the urgent need for innovative approaches for early diagnosis and targeted therapies. In recent years, there has been significant progress in understanding the molecular mechanisms underlying pancreatic cancer development and progression. This paper reviews the current knowledge of molecular targets for the diagnosis and treatment of pancreatic cancer.

In New Orleans, Louisiana the population’s poor baseline health led to its establishment as an early epicenter for severe acute COVID-19. Antici-pating future outbreaks of COVID-19 and other respiratory viruses, we need to identify correlates of outcome, from real clinical experience. 89 patients were recruited into the ClinSeqSer acute COVID-19 longitudinal observational study, from the beginning of the outbreak in March to July 2020. Patients admitted for acute COVID-19 were enrolled in person. The cohort is unique as it is 68% Black, 53% female, of average age of early 60s, and prevalence of obesity and hypertension respectively of 55% and 83%. The outcomes are: 53% severe (20% fatal, 33% non-fatal) and 47% non-severe, with severe defined as death or requiring mechanical ventila-tion or high flow oxygen. Obesity and admit systolic blood pressure (SBP) >140mmHg are each associated with severe outcome and, despite respective sensitivity of 71% and 76%, specificity ~66% for both, and ac-curacy of 60% and 70% by ROC analysis, would likely provide useful predictors of outcome in critically stressed health care systems. We discuss pathophysiologic hypotheses to explain why high admit SBP is observed only in half of patients with pre-COVID hypertension and is associated with severe outcome.

Venous and arterial thromboembolism (VTE/ATE) often coexist with onco-hematologic diagnosis. The study aimed to assess the time relationship between the diagnosis of VTE/ATE and blood cancers. The second aim was to identify VTE/ATE risk factors related to the type of hematology disease and cardiac history. 1283 patients underwent cardio-oncology evaluation at the Institute of Hematology and Transfusion Medicine in Warsaw from March 2021 through March 2023 (2 years), and 101 (7.8%) cases were identified with VTE/ATE. ATE compared to VTE significantly more often occurred before the diagnosis and treatment of hematologic malignancy: 33/47 (70.2%) vs. 15/54 (27.8%), p<0.0001. The risk of a VTE episode is exceptionally high in the first months after the diagnosis of an onco-hematological disease. The higher frequency of VTE was associated with acute myeloid leukemia (17 cases/270 patients/6.30%/p = 0.055), acute lymphocytic leukemia (7 cases/76 patients/9.21%/p = 0.025) and chronic myeloproliferative disease (7 cases/48 patients/14.58%/p=0.0003). Only the risk of VTE was significantly increased before (OR=6.79; 95%CI: 1.85-24.95; p=0.004) and after diagnosis of myeloproliferative disease (OR=3.12; 95%CI: 1.06-9.16; p=0.04). ATEs occur more often than VTE before a diagnosis of blood cancer. The risk of VTE is exceptionally high before and after diagnosis of chronic myeloproliferative disease.

The implementation of systems condition monitoring is of utmost importance in ensuring effective maintenance, optimal efficiency, and adherence to established design criteria. This research presents a novel and economically efficient Digital Twin Model (DTM) designed for the purpose of Live Condition Monitoring (LCM) and Predictive Lifecycles. This study discusses the enhancement of DTs modelling by decreasing the number of dimensions from five to three: Physical, Digital, and Connection entities. The resulting model exhibits a notable improvement in accuracy and efficiency. The suggested Dynamic Time Model (DTM) enhances the empirical pre-set average load for both simulation and experimentation by 35.7%, which is equivalent to a 1.6-fold increase. The DTM demonstrates a significant enhancement in the empirical predefined average lifecycles of the system, as shown in the real-life case study. Specifically, the DTM achieves a 12-fold improvement compared to the simulated results and a nine-fold improvement compared to the experimental findings. The suggested Dynamic Time Management (DTM) demonstrates a significant enhancement in the average lifecycles of the system, with a 19.7% improvement (equivalent to 1.2 times greater) compared to the wireless DTM results obtained through the application of the real system load. The suggested Dynamic Time Warping (DTW) method's accuracy and efficiency are shown via a practical case study using the suspension system of a Peugeot 3008 vehicle.

Number of studies claim that tobacco control (TC) regulations are associated with reductions in smoking-related hospitalisation rates, however very few have estimated the impact of TC laws (TCL) at both countrywide and regional levels, and none of them - in relation to compliance with TC regulations. This study evaluates the effects of Russian TCL on hospital admission (HA) rates for pneumonia countrywide and in 10 Russian regions and the extent of these effects in connection with the compliance to TCL. Methods: HA rates for pneumonia during 2005–2019 were analysed to compare the periods before and after the adoption of TCL in 2013. An interrupted time series design and a Poisson regression model were used to estimate the immediate and long-term effects of TCL on pneumonia annual hospitalisation rates after the TCL adoption, compared with the pre-law period. The 10 Russian regions were compared using the TCL Implementation Scale (TCIS) developed on the basis of the results of Russian TC policy evaluation survey; Spearman’s rank correlation and linear regression models were employed. Results showed a 14.3% reduction in HA rates for pneumonia (RR 0.88; p=0.01) after the adoption of TCL in Russia with significant long-term effect after 2013 (RR 0.86; p=0.006). Regions with better enforcement of TCL exhibited greater reductions in pneumonia HA rates (rsp=−0.55; p=0.04); (=−4.21; p=0.02). Conclusions: TCL resulted in a sustained reduction in pneumonia hospitalisation rates, but these effects, varying by region, may depend on the scale of the TCL enforcement.

Primary ciliary dyskinesia (PCD) is an inherited disorder that impairs motile cilia, essential for respiratory health, with a reported prevalence of 1 in 16,309 within Hispanic populations. Despite having the RSPH4A [c.921+3_921+6del (intronic)] founder mutation in 70% of Puerto Rican patients, the characterization of the ciliary dysfunction remains unidentified due to the unavailability of advanced diagnostic modalities like High-Speed Video Microscopy Analysis (HSVA). Our study implemented HSVA for the first time on the island as a tool to better diagnose and characterize the RSPH4A [c.921+3_921+6del (intronic)] founder mutation in Puerto Rican patients. By applying HSVA, we analyzed the ciliary beat frequency (CBF) and pattern (CBP) in native Puerto Rican patients with PCD. Our results showed decreased CBF and a rotational CBP linked to the RSPH4A founder mutation in Puerto Ricans, presenting a novel diagnostic marker that could be implemented as an axillary test into the PCD diagnosis algorithm in Puerto Rico. The integration of HSVA technology in Puerto Rico substantially enhances the framework for PCD evaluation and diagnosis, facilitating prompt detection and early intervention for improved disease management. This initiative, demonstrating the potential of HSVA as an adjunctive test within the PCD diagnostic algorithm, could serve as a blueprint for analogous developments throughout Latin America.

Breast cancer remains a global health problem requiring effective diagnostic methods for early detection in order to achieve WHO’s ultimate goal of breast self-examination (BSE). A literature review indicates the urgency of improving diagnostic methods and identifies thermography as a promising, cost-effective, and non-invasive and adjunctive and complementary detection method. This research explores the potential of using machine learning (ML) techniques, specifically Bayesian Networks (BN) combined with Convolutional Neural Networks (CNN) to improve breast cancer diagnosis at early stages. Explainable artificial intelligence (XAI) aims to clarify the reasoning behind any output of artificial neural networks based models. The proposed integration adds interpretability of the diagnosis, which is particularly significant for a medical diagnosis. We have constructed two diagnostic expert models. In research model A, combining thermal images after XAI process together with medical records, an accuracy of 84.07% has been achieved, while model B, that includes also CNN prediction, achieved an accuracy of 90.93%. These results demonstrate the potential of XAI to transform breast cancer diagnosis, increasing accuracy and reducing the risk of misdiagnosis.

Background: The skin prick test (SPT) is used to diagnose sensitization to antigens. This study proposes a deep learning approach to infer wheal dimensions, aiming to reduce dependence on human interpretation. Methods: A dataset of SPT images (n=5844) was used to infer a convolutional neural network for wheal segmentation (ML model). Three methods for inferring wheal dimensions were evaluated: the ML model; the standard protocol (MA1); and approximation of the area as an ellipse using diameters measured by an allergist (MA2). The results were compared with assisted image segmentation (AIS), the most accurate method. Bland-Altman, distribution analyses, and correlation tests were applied to compare the methods. The study also compared the percentage deviation among these methods in determining the area of wheals with regular geometric shapes (n = 150) and with irregular shapes (n=150). Results: The Bland-Altman analysis showed that the difference between methods was not correlated with the absolute area. The ML model achieved a segmentation accuracy of 85.88% and a strong correlation with the AIS method (ρ=0.88), outperforming all other methods. Additionally, MA1 showed significant error (13.44±13.95%) for pseudopods. Conclusions: The ML protocol can potentially automate the reading of SPT, offering greater accuracy than the standard protocol.

There is limited evidence for the association between malnutrition and hospital mortality as well as Intensive Care Unit length-of-stay (ICU-LOS) in critically ill patients. We aimed to examine the aforementioned associations by conducting a prospective cohort study in an ICU of a Singapore tertiary hospital. Between August 2015 and October 2016, all adult patients with ≥24 h of ICU-LOS were included. The 7-point Subjective Global Assessment (7-point SGA) was used to determine patients’ nutritional status within 48 hours of ICU admission. Multivariate analyses were conducted in two ways: 1) presence versus absence of malnutrition, and 2) dose-dependent association for each 1-point decrease in the 7-point SGA. There were 439 patients of which 28.0% were malnourished, and 29.6% died before hospital discharge. Malnutrition was associated with an increased risk of hospital mortality [adjusted-RR 1.39 (95%CI: 1.10–1.76)], and this risk increased with a greater degree of malnutrition [adjusted-RR 1.09 (95%CI: 1.01–1.18) for each 1-point decrease in the 7-point SGA]. No significant association was found between malnutrition and ICU-LOS. Conclusion: There was a clear association between malnutrition and higher hospital mortality in critically ill patients. The association between malnutrition and ICU-LOS could not be replicated and hence requires further evaluation.

In the conservation of monumental heritage, the collection and utilization of information are of primary importance. The Heritage Building Information Modeling (HBIM) procedure harnesses the potential of three-dimensional models, offering significant advantages in accessing documentation, interoperability, multidimensionality of intervention design, cost evaluation and maintenance management. Attention here is focused on the Certosa di Pisa, a large historical complex built in the 14th century as a monastery of the Carthusian order, currently in a state of deterioration and in need of restoration and re-functionalization. The multifaceted nature of this monumental complex, with its intricate interplay of architectural elements spanning different historical periods and featuring diverse techniques, poses a significant challenge for structural safety assessment. This case study presents an opportunity to explore an HBIM approach to streamline the diagnostic process and facilitate the intervention design phase. The goal is achieved by utilizing an accurate 3D model enriched with data from multiple sources and automating certain operations for structural assessment. The usefulness of the HBIM methodology is highlighted as a valuable tool in the realm of Cultural Heritage structures for both technicians and scholars alike.

Background/Objectives: Vitamin B12 deficiency can cause variable symptoms, which may be irreversible if not diagnosed and treated in a timely manner. We aimed to develop a widely accepted expert consensus to guide the practice of diagnosing and treating B12 deficiency. Methods: We conducted a scoping review of the literature published in PubMed since January 2003. Data were used to design a two-round Delphi survey to study the level of consensus among 42 experts. Results: The panelists agreed on the need for educational and organizational changes in the current medical practices for diagnosing and treating B12 deficiency. Recognition of clinical symptoms should receive the highest priority in establishing the diagnosis. There is agreement that the serum B12 concentration is useful as a screening marker, whereas methylmalonic acid or homocysteine can support the diagnosis. Patient lifestyle, disease history and medications can provide clues to the cause of B12 deficiency. Regardless of the cause of the deficiency, initial treatment with parenteral B12 was regarded as the first choice for patients with acute and severe manifestations of B12 deficiency. The use of high-dose oral B12 at different frequencies may be considered for long-term treatment. Prophylactic B12 supplementation should be considered for specific high-risk groups. Conclusions: There is a consensus that clinical symptoms need to receive more attention in establishing a diagnosis. B12 laboratory markers can support the diagnosis. The severity of clinical symptoms, the causes of B12 deficiency and the treatment goals govern decisions regarding the route and dose of B12 therapy.

The detection of Fasciola eggs in ruminant fecal samples typically occurs around 15 weeks post-infection during patent infections. The recent introduction of coproantigen ELISA (cELISA) diagnostics, which capture antigenic enzymes in fecal material, holds promise for monitoring Fasciola fecal egg count (FFEC) to implement control programs, particularly targeted treatments. This study compared two study groups, those with positive FFEC (FFEC+ve) and negative FFEC (FFEC-ve), to assess the sensitivity of Flukefinder sedimentation and a cELISA kit. This study reports that the coproantigen ELISA detection limit was 4.5 eggs per gram (epg) for a 100% positivity rate. Additionally, a moderate, statistically significant positive correlation (r²=0.716, p-value < 0.01) and an odds ratio of 1.96 (p-value < 0.01) were observed between FFEC and Fasciola coproantigen from cELISA. This implies that for every single unit increase in FFEC, there is a 96% likelihood of obtaining a higher optical density (OD) reading from the cELISA test. The results suggest that cELISA can complement fecal sedimentation tests, serving as a valuable tool for detecting cattle with high FFEC. This capability facilitates targeted treatment strategies, thereby aiding in the control and prevention of the spread of fasciolosis within the studied area.

Tumid Lupus Erythematosus has been the subject of heated debate as to its correct nosographic classification. In particular, its definition has changed over time, depending on the different studies performed. In this review we address the initial definition of TLE, the changes that have taken place in the understanding and placement within the classification of Lupus Erythematosus (LE), and focus on clinical, histopathological, immunophenotypical and differential diagnosis aspects.

Advances in early detection of Breast cancer and treatment improvements have significant-ly increased survival rates. Traditional screening methods, including mammography, MRI, ultrasound, and biopsies, while effective, often come with high costs and risks. Recently, thermal imaging has gained attention due to its minimal risks compared to mammography, although it is not widely adopted as a primary detection tool since it depends on identifying skin temperature changes and lesions. The advent of machine learning (ML) and deep learning (DL) has enhanced the effectiveness of breast cancer detection and diagnosis using this technology. In this study a novel methodology for developing an interpretable computer-aided diagnosis (CAD) system for breast cancer detection, leveraging explaina-ble Artificial Intelligence (XAI) throughout its various phases. To achieve these goals, we proposed a new multi-objective optimization approach named Hybrid Particle Swarm Optimization algorithm (HPSO) and Hybrid spider Monkey Optimization algorithm (HSMO). These algorithms simultaneously combine the continuous and binary representations of PSO and SMO to effectively manage trade-offs between Accuracy, feature selection and hyperparameter tuning. We evaluate several CAD models and investigate the impact of handcrafted methods such as Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gabor filters, and edge detection. We further shedding light on the effect of feature selection and optimization on feature attribution and model decision-making processes using the SHapley Additive exPlanations (SHAP) framework, with a particular emphasis on cancer classification using the DMR-IR dataset. The results of our experiments demonstrate in all trials that the performance of the model is improved. Also with HSMO our models achieved a high accuracy of 98.27% and F1- score of 98.15% while selecting only 25.78% of the HOG features. This approach not only boosts the performance of CAD models but also ensures comprehensive interpretability. This method emerges as a promising and transparent tool for early breast cancer diagnosis.

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.

The optimal functionality and dependability of mechanical systems are important for the sustained productivity and operational reliability of industrial machinery which has direct impact on it’s longevity and profitability. Therefore, failure of a mechanical system or any of it component would be detrimental to production continuity and availability. Consequently,this study proposes a robust diagnostic framework for analyzing the blade conditions of shot blast industrial machinery. The framework involves analyzing the spectral characteristics of the vibration signals generated by Industrial Shot Blast. Additionally, a peak detection algorithms is introduced to identify and extract the unique features present in the peak magnitudes of each signal spectrum. A feature importance algorithm is then deployed as the feature selection tool, and these selected features are fed into 10 machine learning classifier, with Extreme gradient boosting (XGB) as the core classifier. Results show that the XGB classifier achieved the best accuracy of 98.05%, with a cost-efficient computational cost of 0.83 seconds. Other global assessment metrics were also implemented in the study to further validate the model.

The purpose of the study is to analyse and to identify specific structural characteristics of melanocytic nevi, to youth pacients. Using the optical microscope, could be possible a proper describtion related melanocytic nevi. In this study play a significant role, different factors, such as genetic, epigenetic, microbiomic and proteomic factors. Future directions refers to preventive and prophylactic methods. Disease diagnostic and management directions are also important key points.

Obtaining a genetic diagnosis of a primary mitochondrial disease (PMD) is often framed as a diagnostic odyssey. Yet, even after receiving a diagnosis, parents of affected children experience ongoing therapeutic and prognostic uncertainty, and considerable psychosocial challenges. Semi-structured interviews (N= 24) were conducted with parents of 13 children (aged 2-19 years) with a genetically confirmed PMD. Paternal (N=11) and maternal (N=13) perspectives were obtained, and thematic analysis performed on all interviews. A genetic diagnosis was valuable and empowering for parents, despite eliciting varied emotional responses. While the diagnosis helped focus management decisions, families often felt overwhelmed and unsupported in navigating the healthcare system. Most parents reported a serious impact on their romantic relationship. The sources of social support varied with a preference for established friendship and family support networks over disease-specific community support groups. Most parents favored prenatal genetic testing in the event of a future pregnancy. This study provides insight into the lived experiences of parents after a genetic diagnosis of PMD in their children. The findings draw awareness to supportive care needs and highlight important gaps that should be addressed to ensure that parents feel supported within a holistic framework of management for PMDs.

Properties of time domain parameters of the vibration signal have been extensively studied for the fault diagnosis of rolling element bearings (REB). Parameters like kurtosis and Envelope Harmonic-to-Noise Ratio are most widely applied in this field and some important progress has been made. However, since only one-sided information is contained in these parameters respectively, problems still exist in practice when the signals collected are of complicated structure and/or contaminated by strong background noises. A new parameter, named Shock pulse index (SPI), is proposed in this paper. It integrates the mutual advantage of both parameters above and can help effectively identify fault related impulse components under the interference of strong background noises, unrelated harmonic components and random impulses. The SPI optimizes the parameters of Maximum Correlated Kurtosis Deconvolution (MCKD), which is used to filter the signals under consideration. Finally, the interested transient information contained in the filtered signal can be highlighted through demodulation with Teager Energy Operator (TEO). Fault related impulse components can therefore be extracted accurately. Simulations and experiment analyses verify the effectiveness and correctness of the SPI.

Background and objective: Iliopsoas abscess (IPA) is a rare condition with varied symptomology and etiology. Less than one-third of patients with IPA present on the emergency department (ED) admission with the traditional triad of fever, back pain, and restricted hip motion (or limp), leading to delays in diagnosis and management. This case report aims to bring awareness among healthcare professionals regarding the atypical presentation and introduce a potential differential diagnosis when evaluating patients with fever and back pain. The intention is to provide insights into the signs and symptoms that may indicate the presence of an iliopsoas abscess and prompt additional investigations. Case report: Here, we describe a case of primary iliopsoas abscess associated with drug-induced acute liver failure in our ED. The patient complained of pain in the left lumbar region and fatigue that started two weeks before this presentation, claiming that, during the previous night, the pain suddenly worsened. She had a fever, pain at palpation in the right hypochondriac and left lumbar regions, accompanied by fever and vomiting, jaundiced skin, and sclera at the examination in ED. On abdominal ultrasonography, the diagnosis of acute cholangitis was suspected. The laboratory test shows leukocytosis with neutrophilia, thrombocytosis, hepatocytolysis syndrome, and hyperbilirubinemia with the predominance of indirect bilirubin. We performed then abdominal and pelvic computed tomography, which confirmed the diagnosis of cholelithiasis observed with the diameter of the bile duct within normal limits but also showed an abscess-like collection fused to the interfibrillar level of the left iliopsoas muscle; a diagnosis we most likely would have been missed. After that, the patient was hospitalized in the General Surgery Department, and surgery drainage of the abscess was performed. The patient's evolution was excellent; she was discharged after 11 days. Conclusions: The case presented here exemplifies how iliopsoas abscess, a rare cause of back pain, can quickly go unrecognized, especially in the emergency department. Our experiences will raise awareness among doctors in emergency departments about this uncommon but essential diagnosis. With advancements in diagnostic tools and techniques, we aspire that more cases of iliopsoas abscess will be accurately diagnosed.

A recent study has suggested that the age profiles for deaths due to COVID-19 variants differs between variants and shows male/female specificity. This study demonstrates the age and sex dependency is common among human pathogens. The often-reported higher susceptibility to infections among the young and elderly is true in general but does not apply to individual pathogens. Even among different types of pneumonia there are subtle differences in the age profile. The gender ratio between pathogens likewise shows wide variation from as low as 10% female admissions for leptospirosis to around 90% for gonococcal admissions. The observed age/sex variation observed for mortality due to COVID-19 variants is an expression of a far wider phenomenon with implications to the age/sex response to vaccines. We propose that such differences are likely to be controlled by small noncoding RNAs which act as potent regulators of gene expression leading to altered cell morphology, metabolism, immune function, and response to vaccines.

Alzheimer’s is a degenerative brain condition brought on by intricate brain changes after cell injury. The prevalence of Alzheimer’s disease is highest in those over 65. Presently, AD is detected by manual examination of a patient’s Magnetic Resonance Imaging (MRI) scan and neuropsychological assessments. Due to its subpar accuracy and performance, solutions based on artificial intelligence (AI) have not been made more widely available. This paper proposes a deep Convolutional Neural Network (CNN) with the pre-trained MobileNet-V2 architecture as the model’s base for feature extraction and reducing the parameter size and training time. The proposed model accurately classifies various early stages of AD. The proposed approach visualizes the class activation maps in the form of a heat map on brain MRI scans via the Grad-CAM algorithm that lifts the black-box nature of the deep model. To handle the imbalance problem of the AD data set acquired from Kaggle, the oversampling algorithm SMOTE interpolates new images to even the class samples. The proposed multi-class classification deep model is named Bio-AD and evaluated using different quality metrics. The Bio-AD model has the following values for evaluation metrics: 97.92%, 99.87%, 97.87%, 98%, 98%, and 0.0646 for accuracy, Area Under the Curve (AUC), F1-score, Precision, Recall, and loss respectively. The proposed Bio-AD model is compared with DenseNet-169, VGG-19, and InceptionResNet-V2 using different quality metrics and outperformed in comparison with all other models using the AD data set.

Background: Bronchopulmonary dysplasia (BPD) is the most common respiratory disease in pre-term and is still associated with increased mortality and morbidity. The great interest lies in iden-tifying early biomarkers that can predict the development of BPD. Objectives: This pilot study explores the potential of e-nose for early identification of BPD risk in premature infants by analyzing volatile organic compounds (VOCs) in the exhaled breath con-densate (EBC). Materials and Methods: Fourteen mechanically ventilated very preterm infants were included in the study. Clinical parameters and EBC were collected within the first 24 hours of life. The dis-criminative ability of breath-prints between preterms who did and did not develop BPD was in-vestigated by pattern recognition, machine learning algorithm, and standard statistical methods. Results: We found that e-nose probes can significantly predict the outcome of “no-BPD” vs “BPD”. Specifically, a subset of probes (S18, S24, S14, and S6) were found to be significantly predictive, with an AUC of 0.87, 0.89, 0.82, 0.8 and p=0.019, 0.009, 0.043, 0.047, respectively. Conclusion: The E-nose is an easy-to-use, handheld, non-invasive electronic device that quickly samples breath. Our preliminary study has shown that it has the potential for early prediction of BPD in preterms.

Purpose: Investigation for the co-occurrence of two unrelated genetic disorders of muscular dystrophy and Prader-Willi syndrome (PWS) (OMIM # 176270) using joint whole genome sequencing (WGS). Methods: Trio WGS joint analysis was performed to investigate the genetic etiology in a proband with PWS, prolonged muscular hypotonia associated hyperCKemia, and early-onset obesity. The parents were unaffected. Results: Results showed maternal isodisomy uniparental disomy (UPD) in chromosome 15 expanding from 15q11.2 to 15q22.2, including PWS regions at 15q11.2-15q13. Maternal heterodisomy was detected from 15q22.2 to 15q26.3. A pathogenic variant, NM_000070.3(CAPN3):c.550del (p.Thr184fs), was identified at 15q15.1 in a heterozygous state in the mother that was homozygous in the proband due to maternal isodisomy. Conclusion: This is the first study of the concurrent molecular etiology of PWS and calpainopathy (OMIM # 253600) in the same patient. This report highlights the utility of joint analysis and need for the assessment of autosomal recessive disease in regions of isodisomy in patients with complex and unexplained phenotype.

We evaluated the dentofacial characteristics and temporomandibular disorder (TMD) symptomatology of patients with orthopedic instability before and after deprogramming with a stabilization splint. Sixty patients with the signs and symptoms of TMD were assessed using the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) and underwent stabilization splint therapy to place the condyles in a more stable musculoskeletal position. The amount of condylar displacement was evaluated using the condylar position indicator (CPI). Six-teen angular and linear hard tissue landmarks were traced and compared from lateral cephalo-grams taken in the maximum intercuspation (MI) position before and in the centric relation (CR) position after the splint therapy. Following the splint therapy, the signs and symptoms of TMD were significantly reduced or completely eliminated in more than 90% of the patients. Compared with the values registered before the splint therapy, a significant reduction in the mean values of condylar displacement was observed on both sides of the vertical, horizontal, and transverse planes of space after the splint therapy. A comparison of pre- and post-splint lateral cephalograms revealed that, following the splint therapy, the mandible moved more posteriorly and rotated more in a clockwise direction. In patients with orthopedic instability and signs and symptoms of TMD, deprogramming of the neuromuscular system with a stabilization splint is highly recommended to provide a correct orthodontic diagnosis and improve the health of temporomandibular joint structures.

Coronaviruses cause infections in humans and diverse species of animals and birds with a global distribution. Bovine coronavirus (BCoV) produces predominantly two forms of disease in cattle, a respiratory form, and a gastrointestinal form. All age groups of cattle are affected by the respiratory form of coronavirus whereas the gastroenteric form causes neonatal diarrhea or calf scour in young cattle and winter dysentery in adult cattle. The tremendous impacts of bovine respiratory disease and the associated losses are well-documented and underscores the importance of this pathogen. Beyond this, studies have demonstrated significant impacts on milk production associated with outbreaks of winter dysentery, with up to a 30% decrease in milk yield. Research data so far have shown that the viral structure or strain is not a determinant of tissue tropism and clinical form of the disease. In North America, BCoV was identified for the first time in 1972, and it continues to be a significant economic concern for the cattle industry. The virus is classified under Coronaviridae family, Betacoronavirus genus and Betacorovirus 1 species. A number of conventional and molecular diagnostic assays are available for the detection of BCoV from clinical samples. Conventional assays for BCoV detection include virus isolation, which is challenging from clinical samples, electron microscopy, fluorescent antibody assays, and various immunoassays. Molecular tests are mainly based on nucleic acid detection and predominantly include conventional and real-time polymerase chain reaction (PCR) assays. Isothermal amplification assays and genome sequencing have also been used increasingly in recent years for the detection and identification of BCoV. Loop-mediated isothermal amplification, helicase-dependent amplification, recombinase polymerase amplification, and multienzyme isothermal rapid amplification are some examples of the isothermal amplification assays for used BCoV. Point-of-care testing has gained some popularity recently as they can offer user-friendly testing on the farm (barn-side). The present study reviewed literature on coronavirus infections in cattle from the last three and a half decades and presents information mainly on the current and advancing diagnostics in addition to epidemiology, clinical presentations, and the impact of the disease on the cattle industry.

Neurocutaneous disorders, also known as phakomatoses, are congenital and acquired syndromes resulting in simultaneous neurologic and cutaneous involvement. In several of these conditions, the genetic phenomenon is understood, providing a pivotal role in the development of therapeutic options. This review encompasses the discussion of the genetic and clinical involvement of neurocutaneous disorders and examines clinical management and treatment options. With the current advances in genetics, the role of precision medicine and targeted therapy plays a substantial role in addressing management of these conditions. The interconnectedness between therapeutic options highlights the importance of precision medicine in treating each disorder’s unique molecular pathway. This review provides an extensive synthesis of ongoing and current therapeutics in the management of such clinically unique and challenging conditions.

Polycystic Ovarian Syndrome (PCOS) is a common endocrine condition that affects 8–13% of women of reproductive age and is one of the leading causes of anovulation and infertility. In Kazakhstan, the prevalence of this syndrome is particularly high compared to other countries and the global average. Currently, the diagnosis of polycystic ovarian syndrome is based on internationally established Rotterdam criteria, using hyperandrogenism, i.e. high testosterone levels, as a key parameter. These criteria are applied to diagnose PCOS in all female patients, although obese patients may have excess testosterone produced by adipose tissue. To avoid possible false PCOS diagnoses, an additional criterion could be considered to diagnose PCOS among obese women. Anti-Müllerian hormone (AMH) is promising as a more effective diagnostic criterion, since its level represents the state of the ovarian reserve and adipose tissue does not produce AMH. Therefore, the use of AMH instead of hyperandrogenism as a diagnostic criterion for PCOS among obese women might improve diagnostic precision and patient treatment. In this study, data on the levels of various hormonal indicators such as estrogen, progesterone, and others were analyzed for a large cohort of female patients with and without PCOS to explore possible correlations between these indicators, AMH, and testosterone. The findings suggest that the absence of a negative correlation between AMH and follicle-stimulating hormone (FSH), and a positive correlation of AMH with testosterone could support the use of AMH as a reliable additional criterion for PCOS diagnosis among obese women.

Although heart failure with preserved ejection fraction (HFpEF) has become the predominant heart failure subtype, it remains clinically under-recognized. This, has been attributed to the complex pathophysiological mechanisms that accompany individuals with several co-morbidities and symptoms and signs of HFpEF. Natriuretic peptides have been recognized to have important role in the diagnosis and monitoring of patients with heart failure with reduced ejection fraction (HFrEF), but their role in HFpEF remains controversial. Its seems that the role of NtproBNP in the diagnosis of HFpEF, is mainly driven by the pathophysiological characteristics of the patients.

(1) Background: The aim of this study was to investigate the circadian rhythms of tongue features according to the effects of physiological phases over a 24-hour period. (2) Methods: Fifteen healthy participants aged 20 to 69 years were recruited. The participants did not have current chronic diseases or past diseases and had to meet the inclusion and exclusion criteria. The participants stayed at the Gil Hospital for a duration of 2 nights and 3 days. On the first day, at 18:00, they consumed their allocated portions of food and water and then completed a questionnaire. At approximately 21:00, their tongue images were acquired using a computerized tongue image acquisition system, following which they slept for 8 hours, commencing at 23:00. Measurements were taken from 07:00 through 21:00 on the second day, and the final acquisition was taken at 07:00 on the following morning, resulting in a total of eight images. The circadian rhythm was authenticated and quantified utilizing the single cosinor analysis, a technique for periodic regression analysis for fitting a 24-hour cosine curve. (3) Results: Cosinor analysis revealed that all tongue features were significantly related to circadian rhythm. (4) Conclusions: The results of this study may be important for considering the time of day at which the tongue is observed and tongue status is evaluated.

This survey of research literature is an overview of the current state of science addressing DSM-5 diagnostic criteria as it relates to identifying Autism Spectrum Disorder (ASD) in young children. Selected sources are of high quality and have been published since 2020 in peer-reviewed journals relevant to children’s health. The narrative is organized from the earliest to the most recent publications and is divided into sections around categories and themes that highlight the main arguments, findings, and controversies identified by researchers. There are indications in the literature that while ASD can be identified and differentiated from other developmental disorders in young children, doing so requires that clinicians who have relied on the DSM-5 should also use multiple measures, including other formalized screening tools as assessments that gather observational input from parents and caregivers, as well as findings from physical examinations, laboratory investigations, and magnetic resonance imagining (MRI). This high level of corroboration points to an emerging diagnosis paradigm through which transdiagnostic approaches to early diagnosis and intervention may exist.

Augmented Reality (AR) and 3D printing (3DP) are novel technologies in the orthopedic field. Over the past decade, enthusiasm for these new digital applications is driving new perspectives in improving diagnostic accuracy and sensitivity in the field of traumatology. Currently, however, it is still difficult to quantify the value and impact in the medical-scientific field especially in the improvement of diagnostic in complex fractures. Acetabulum fractures have always been a challenge among orthopedics, for volumetric complexity and low diagnostic reliability.Background/Objectives: The goal of this study is to determine whether these methods can improve the learning aspect and diagnostic accuracy of complex acetabulum fractures compared to gold standard CT (computed tomography). Methods: Orthopedic residents of our department have been selected and divided into Junior (JUN) and Senior (SEN) groups. Associated fractures of acetabulum have been included in the study, obtaining : CT-scans, 3DP model, and AR model displayed on a tablet screen. In a double-blind questionnaire, each resident classified every fracture according to methods. Diagnostic accuracy (DA), response time (RT), agreement (R), and confidence (C) were measured. Results: 20 residents (JUN=10, SEN=10) classified five fractures. Overall DAs were 26% (CT), 18% (3DP), and 29% (AR). AR-DA resulted to be superior to 3DP-DA (p=0,04). DA means (JUN vs SEN respectively) were :CT-DA was 20% vs 32% (p&lt;0.05), 3DP-DA was 12% vs 24% (p=0.08), and AR-DA was 28% vs 30% (p=0,80). Overall RT was 61.2 sec (±24,6) for CT, 35.8 sec (±20,1) for 3DP, and 46,7 sec (±20,8) for AR. R was fairly poor between methods and groups. 3DPs resulted to have superior overall C (65%). Conclusions: AR resulted to have the same Overall DA as CT, independent from experience, 3DP has minor DA and R, but it resulted to be the most confident and fast method. Intra and interobserver R between methods remain very poor in residents.

Cytologic diagnosis of extra‐adrenal paraganglioma presenting as a peripancreatic mass is challenging with a high rate of diagnostic error. We present a case of a peripancreatic mass identified by radiology as a gastrointestinal stromal tumor. Endoscopic ultrasound‐guided fine needle aspiration (FNA) of the mass showed a moderately cellular tumor composed of small to medium sized neoplastic cells with round to oval nuclei arranged singly and in loose clusters. The cells were positive for neuroendocrine markers (synaptophysin and chromogranin) and negative for CD117. A diagnosis of a neoplasm with a neuroendocrine tumor (NET) was made on FNA cytology. The subsequent surgical resection of the tumor revealed peripancreatic paraganglioma with immunohistochemistry positive for synaptophysin, chromogranin, and S100. The latter delineates the sustentacular cells. Although paraganglioma is a well recognized tumor, a detailed comparison of peripancreatic paraganglioma versus pancreatic/gastrointestinal NET is still lacking.

Optimizing intensive care resources using predicting modeling is paramount for fighting the COVID-19 pandemic. In this paper, we model the admission of COVID-19 patients in intensive care units (ICU) in Colombia using openly available data gathered from 18 March 2020 to 14 October 2020. After an intensive preprocessing of the data, we trained four different machine learning models using four different strategies for handling the imbalanced features. Our findings show that our best model (XGBoost) effectively predicts an Area Under the Curve (AUC-ROC) of 0.94, in line with the state-of-the-art results obtained in other predictive models obtained with medical data.

PURPOSE: To investigate the oxylipin profiles in aqueous humor of primary open angle glaucoma (POAG) patients. METHODS: Aqueous humor samples were collected from 17 POAG patients and 15 cataract subjects and subjected to the liquid chromatography-mass spectrometry (LC-MS) analysis to detect the oxylipins. The prediction potential of the differential abundant oxylipins was assessed by the receiver operating characteristic (ROC) curves. Pathway and correlation analyses on the oxylipins and clinical and biochemical parameters were also conducted. RESULTS: The LC-MS analysis detected a total of 76 oxylipins, of which 29 oxylipins reached the detection limit. The multivariate analysis identified 5 differential abundant oxylipins, 15-keto-prostaglandin F2 alpha (15-kPGF2α), Leukotriene B4 (LTB4), 12,13-Epoxyoctadecenoic acid (12,13-Epome), 15-Hydroxyeicosatetraenoic acid (15-HETE) and 11-Hydroxyeicosatetraenoic acid (11-HETE). The 5 oxylipins are enriched in the arachidonic acid metabolism and linoleic acid metabolism pathways. Pearson correlation analysis showed that 11-HETE was positively correlated with intraocular pressure and central corneal thickness and negatively with cup/disk ratio in the POAG patients. In addition, 15-kPGF2α was positively correlated with visual field defect, and LTB4 was negatively correlated with macular thickness.  CONCLUSIONS: This study revealed the oxylipin profile in the aqueous humor of POAG patients. Oxylipins involved in arachidonic acid metabolism pathway could play a role in POAG and anti-inflammatory therapies could be potential treatment strategies for POAG.

We describe an interesting case report about a patient with hemophilia A with severe chronic elbow arthropathy who underwent multiple surgical because of his condition. We collected all the clinical and imaging findings, and we compared the decision-making flow chart and the surgery performed with the most recent indications, treatments and complications reported in the literature. The aim of this case report is to show how usually the hemophilic patient even if young usually comes to our attention from the beginning with complex and advance musculoskeletal disease. Elbow hemophilic arthropathy is a complex disease and the management in these patients could be more difficult than others because of the higher risk of complications rate after all surgical procedures, for this reason it’s very important to treat these patients in hub centers with expertise in well-coordinated multidisciplinary teams. The path from diagnosis to surgical treatment of hemophilic elbow arthropathy can be a risky one, as it happened in this case.

Cancer develops from abnormal cell growth in the body, causing significant mortalities every year. To date, potent therapeutic approaches have been developed to eradicate tumor cells, but intolerable toxicity and drug resistance can occur in treated patients, limiting the efficiency of existing treatment strategies. Therefore, searching for novel genes critical for cancer progression and therapeutic response is urgently needed for successful cancer therapy. Recent advances in bioinformatic and proteomic techniques have allowed the identification of a novel category of peptides encoded by non-canonical open reading frames (ncORFs) from historically non-coding genomic regions. Surprisingly, many of the ncORFs express functional microproteins that play a vital role in human cancers. In this review, we provide a comprehensive description of different ncORF types with coding capacity and technological methods in discovering ncORFs among human genomes. We also summarize the diagnostic and prognostic value of these ncORF as well as their carcinogenic role in cancer progression and underestimated potential as therapeutic targets.

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

This study examines a case involving a 7-year-old child who developed dengue symptoms following Qdenga vaccination. Despite initial negative diagnostic results, molecular analysis confirmed an infection with DENV4. Next-generation sequencing detected viral RNA from both DENV2 and DENV4 serotypes, which were identified as vaccine-derived strains using specific primers. Phylogenetic analysis further confirmed that these sequences belonged to the Qdenga vaccine rather than circulating wild-type viruses. This case underscores the critical need for precise diagnostic interpretation in vaccinated individuals to avoid misdiagnosis and to strengthen public health surveillance. A comprehensive understanding of vaccine-induced viremia is essential for refining dengue surveillance, improving diagnostic accuracy, and informing public health strategies in endemic regions.

The objective of this study was to investigate the transcriptomic profiling of differentially ex-pressed genes between a commercial layer breed and an indigenous Indian breed. RNA was ex-tracted from NDV-infected and uninfected embryo lung tissue samples of Aseel, Kadaknath, and White Leghorn chickens. RNA-Seq was performed, and reads were aligned to the chicken refer-ence genome (Galgal7). A total of 594 genes were significantly differentially expressed, with 264 upregulated and 330 downregulated in infected embryos. Highly overexpressed genes in infect-ed conditions included C8A, FGG, PIT54, FETUB, APOC3, FGA, SPIA1, AvBD13, APOH, AMBP, TTR, PLA2G12B. Additionally, 29 novel transcripts and 20 lncRNAs were upregulated. White Leghorn embryos showed distinct expression patterns for CAV1 and GATA3, while Kadaknath healthy embryos had higher levels of CYTB, HBBA, ND4, ND4L, and ND5, indicating breed uniqueness. The analysis revealed significant gene involvement through Gene Ontology and KEGG pathways, highlighting alterations in gene expression related to immune function, me-tabolism, cell cycle, nucleic acid processes, and mitochondrial activity due to NDV infection. ALDOB, PRPS2, XDH, DCK2, and TK1 genes, which were involved in several metabolic path-ways, clearly displayed an altered expression pattern in the infected embryo. Additionally, ALB, TLR4, TLR2, TLR21, IL1R2, IL22RA2, HSP90AA, HSPB9(p60), and HSPB8 were significantly up-regulated in infected lung samples, while CXCR4, CXCL14, GATA3, IL17REL, and IL22RA1 were downregulated. Notably, higher expression of HSPs in Kadaknath suggests heat tolerance. This research provides insights into the genetic differences between commercial and local breeds and reveals the impact of NDV infection on metabolic processes, potentially identifying candidate genes for breed improvement.

The article discusses early diagnosis and intervention for developmental delays in children, emphasizing the role of specialized services and personnel in supporting recovery and development. It presents findings from a pilot project initiated in Bucharest, aiming to assess the incidence of developmental delays in children aged 1-3 years in selected kindergartens, including ASD. The study involved children, parents, and specialists, utilizing systematic behavior observation (SBO) and semi-structured interviews. Results indicate that out of 613 evaluated children, 81 received specialized control recommendations, with 4 cases showing potential risk of ASD. Additionally, 8 children were diagnosed with autism. The incidence of autism in children aged 1-3 years was 1 in 51. Although exploratory, these findings suggest a model of good practice for future early diagnosis campaigns nationally. Conclusively, the study underscores the importance of early diagnosis and intervention for developmental delays, advocating for a responsive community and emphasizing the significance of national-level efforts to address these issues effectively.

Malaria and Typhoid fever are prevalent diseases in tropical regions, exacerbated by unclear protocols, drug resistance, and environmental factors. Prompt and accurate diagnosis is crucial to improve accessibility and reduce mortality rates. Traditional diagnosis methods cannot effectively capture the complexities of these diseases due to the presence of confusable symptoms. Although machine learning (ML) models offer accurate predictions, they operate as "black boxes" with non-interpretable decision-making processes, making it challenging for healthcare providers to comprehend how the conclusions are reached. This study employs explainable AI (XAI) models such as Local Interpretable Model-agnostic Explanations (LIME), and Large Language Models (LLMs) like ChatGPT, Gemini, and Perplexity, to clarify diagnostic results for healthcare workers, building trust and transparency in medical diagnostics. The models were implemented on Google Colab and Visual Studio Code because of their rich libraries and extensions. Results showed that the Random Forest model outperformed Extreme Gradient Boost and Support Vector Machines and important features for predicting Malaria and Typhoid were identified with the LIME plots. Among LLMs, ChatGPT 3.5 demonstrates a comparative advantage over Gemini and Perplexity, hence highlighting how crucial it is to use a hybrid strategy that combines ML, XAI, and LLMs to improve diagnostic performance and reliability in healthcare applications. The study recommends implementing the integrated ML, LIME, and LLM processes because it streamlines the development and maintenance process's workflow overall, makes use of resources more effectively, and improves explainability by allowing the LLM to consider the complete context that LIME provides.

Tinea incognito is a kind of skin infection that is caused by a fungus which has been changed by the use of topical or systemic steroids or other immunosuppressive medications. This alteration in the typical clinical presentation of the infection makes it difficult to diagnose for general practitioners and dermatologists. Delayed diagnosis, spread of the infection to critical body surfaces, resistance to antifungal drugs, and increased costs due to prolonged hospitalization and multiple treatment regimens often complicate tinea incognito. This condition can affect individuals of all ages and genders, but it is more common in children. Tinea incognito is a type of fungal skin infection that can present with various and non-specific clinical features, such as sudden onset of itching in an area that was previously unaffected, making it challenging to differentiate from other diseases such as eczema, seborrheic dermatitis, lupus erythematosus, psoriasis, or other non-fungal skin conditions. The treatment of tinea incognito usually involves stopping the use of topical steroids or other immunosuppressive medications. Preventive measures and management of the underlying fungal infection are necessary and can be achieved with antifungal drugs. Patients should wear loose cotton clothes, use boiling water for laundry, and iron their clothing before wearing them. Additionally, they should avoid sharing bed linens, towels, clothes, and shoes. This review aims to raise awareness of tinea incognito among health practitioners, provide tips for suspecting the disorder, include it in the differentials, and evaluate the available diagnostic procedures. The therapy utilized in published studies is also reviewed.

Hepatic encephalopathy (HE) is a form of brain dysfunction that is specifically caused by liver insufficiency and/or portal-systemic shunt. The exact nature of HE is debated, so that conflicting uses of the term HE may cause inconsistencies in its detection and, in turn, issues with its management. This review highlights the meaning of the term HE on the basis of both its historical origins and current consensus. It also provides criteria for the diagnosis of the condition, on the basis of its phenotypes and the risk factors for its occurrence. The procedure for differential diagnosis from other conditions which result in similar phenotypes is considered, together with precipitants and confounders. Finally, the current multidimensional approach for the correct clinical recording of HE episodes is discussed.

COVID-19 was identified in Wuhan, China in in December 2019, and rapidly spread worldwide, being declared global pandemic one month later on 30 January 2020. Since its emergence, COVID-19 has raised global concerns associated with drastic measures that were never adopted in any previous outbreak, to contain the situation as early as possible. The 2019 novel corona virus (2019-nCoV) or SARS-CoV-2 is the causative agent of COVID-19. 2019-nCoV genetic sequence was rapidly identified within few days since the first reported cases and RT-PCR kits became available for COVID-19 diagnosis. However, RT-PCR diagnosis carries a risk of false-negative results, therefore additional serologic test are needed. The most important approach in the battle against COVID-19 is rapid diagnosis of suspicious cases, timely therapeutic intervention and isolation to avoid community spread. In this review, we summarize the clinical scenario that raises suspicion of COVID-19 and available laboratory diagnostics.

Recent advancements in mass spectrometry have significantly enhanced our understanding of complex lipid profiles, opening new avenues for oncological diagnostics. This review highlights the importance of lipidomics in the comprehension of certain metabolic pathways and its potential for the detection and characterization of various cancers, in particular melanoma. Through detailed case studies, we demonstrate how lipidomic analysis has led to significant breakthroughs in the identification and understanding of cancer types and its potential for detecting unique biomarkers that are instrumental in its diagnosis. Additionally, the review addresses the technical challenges and future perspectives of these methodologies, including their potential expansion and refinement for clinical applications. The discussion underscores the critical role of lipidomic profiling in advancing cancer diagnostics, proposing a new paradigm in how we approach this devastating disease, with particular emphasis on its application in comparative oncology.

Elevated resting heart rate is frequently observed in cancer patients, and is associated with increased mortality. Although specific chemotherapeutic agents can induce cardiotoxicity, the presence of sinus tachycardia in chemotherapy-naive patients suggests other factors likely contribute to this clinical presentation. Despite its prevalence, cancer-associated resting sinus tachycardia has not been fully recognized and comprehensively described as a separate clinical entity. Secondary effects of cancer, especially structural cardiac changes, secretory factors (inflammatory cytokines), and thromboembolic disease can cause resting tachycardia. Alternatively, rapid heart rate may reflect compensatory mechanisms responding to increased metabolic demands, raised cardiac output states, and even pain. Hence, cancer-associated tachycardia presents a clinical dilemma; acute life-threatening conditions (such as sepsis, pulmonary embolism, etc.) must be ruled out, but cancer itself can explain resting sinus tachycardia and more conservative management can avoid unnecessary testing, cost and patient stress. Furthermore, identification and management of cardiac conditions associated with cancer may improve survival and the quality of life of cancer patients.

Tuberculosis (TB) is an infectious disease declared a global emergency by the World Health Organization and continues as one of the world’s top ten causes of death. TB diagnosis is more critical in developing countries where demanded infrastructure for detection, and treatment complicates the efforts against the disease. These aspects related to limited resources are significant, especially in areas away from the main cities, with few mechanisms to make a timely diagnosis that contributes to successfully addressing the possible patients. Artificial intelligence has begun to be essential in providing additional strategies in the diagnosis processes for health professionals’ support. This paper uses natural language processing (NLP) and machine learning (ML) techniques to create models that can supply TB diagnosis when the needed infrastructure is unavailable. Two different sources were explored: text extracted from electronic medical records (EMR) and patient clinical data (CD). Four proposals using five different machine learning models were implemented. The first two models employed ML and each data source independently. Then, two additional approaches developed a data fusion from both sources. This strategy’s employment was analyzed with physicians according to their pertinence in the process and understanding of the EMR. Finally, the results of the data fusion were compared to each source, obtaining better performance at using only the CD, where an area under the ROC curve of 69.9±2.3% was obtained. However, the advantage of analyzing physician’s reports is the availability of this information contrasted to clinical-specific data, which can be more useful in places far from the main cities without enough basic structure for its obtaining.

Despite the available literature on traumatic brain injury (TBI) biomarkers elsewhere, data are limited or non-existent in sub-Saharan Africa (SSA). The aim of the study was to analyse associations in acute TBI between admission serum biomarker concentrations and TBI severity, CT-scan findings and outcome as well as explore the influence of concurrent Toxoplasma gondii infection. Concentrations of serum biomarkers (GFAP, NFL Tau, UCH-L1, and S100B) were measured and Toxoplasma gondii detected in samples obtained <24 hours post-injury. The GOSE was used to evaluate 6 months outcome. All biomarkers levels increased with severity of TBI, but this increase was significant only for NFL (P=0.01). GFAP values significantly increased (P=0.026) in those with unfavorable outcome. Tau levels were higher in those who died (P=0.017). GFAP and NFL were sensitive to CT-scan pathology (p values respectively 0.004 and 0.002). S100B levels were higher (P<0.001) in TBI patients seropositive to Toxoplasma gondii. NFL was found to be sensitive to TBI severity while NFL and GFAP were predictive of CT intracranial abnormalities. Increased levels of GFAP and Tau were associated with poorer outcomes 6 months after TBI. S100B levels were significantly higher in T. gondii seropositive TBI patients.

Postprandial hyperlipidemia showing postprandial increase in serum triglyceride (TG) is atherogenic and associated with the development of coronary artery disease (CAD). To diagnose postprandial hyperlipidemia, the oral fat loading test (OFLT) should be performed, however, this test is very time-consuming and is difficult to perform. Ele-vated serum TG levels reflect an increase in TG-rich lipoproteins (TRL) such chylomi-crons (CM), very low-density lipoproteins (VLDL) and their remnants (CM remnant [CMR] and VLDL remnant [VLDLR]). Understanding of elevation in CMR and/or VLDLR can lead us to understand the existence of postprandial hyperlipidemia. The measurements of apo B48 which is a constituent of CM and CMR, non-fasting TG which includes TG content in all lipoproteins including CM and CMR, non-high-density lipoprotein-cholesterol (non-HDL-C) which includes TRL and low-density lipoprotein, and remnant cholesterol, are useful to suspect the existence of postprandial hyperlipidemia. Postprandial hyperlipidemia is observed in patients with familial type III hyperlipoproteinemia, familial combined hyperlipidemia, chronic kidney disease, metabolic syndrome and type 2 diabetes. Postprandial hyperlipidemia is closely related to postprandial hyperglycemia, and insulin resistance may be an in-ducing and enhancing factor for both postprandial hyperlipidemia and postprandial hyperglycemia. Remnant lipoproteins and metabolic disorders associated with post-prandial hyperlipidemia have various atherogenic properties such as induction of in-flammation and endothelial dysfunction. A healthy diet, calorie restriction, weight loss and exercise, positively impact postprandial hyperlipidemia. Anti-hyperlipidemic drugs such pemafibrate, bezafibrate, ezetimibe and eicosapentaenoic acid have shown to improve postprandial hyperlipidemia. Anti-diabetic drugs including metformin, alpha-glucosidase inhibitors, pioglitazone, dipeptidyl-peptidase-4 inhibitors and glu-cagon like peptide 1 analogues, have shown to ameliorate postprandial hyperlipidemia. Although sodium glucose cotransporter-2 inhibitors have not been proven to reduce postprandial hyperlipidemia, they reduced fasting apo B48 and remnant lipoprotein cholesterol. In conclusion, it is important to appropriately understand the existence of postprandial hyperlipidemia and to connect it to optimal treatments.

Since first described by Dr. Tomisaku Kawasaki in 1967, the clinical diagnosis of Kawasaki disease (KD) has remained relatively unchanged. However, studies since then have suggested that diagnoses using only that original clinical criteria may lead to underdiagnosis. In this review, updated KD diagnostic methods, diagnostic methods for “atypical” KD, diagnostic pitfalls, and other differential diagnoses are discussed. Peripheral blood eosinophil showed important in distinguishing KD from febrile children. We review herein the current literature regarding new biomarkers, scoring systems, and microRNA panels that may aid in the diagnosis of KD, the importance of eosinophil and treatment updates.

Immunology is a branch of medicine that studies the immune system and its pathology. There are known scientific informations, that in medical immunology specific tests performed in the laboratory for diagnosis. In addition good to mention that allergies include medical diagnostic methods such as blood tests. Also there are used for detection the presence and the levels of IgE antibodies. These antibodies are produced by the human immune system in contact with certain allergens. Examples of allergens include for example pollen, house dust, some herbs or molds, some animals and certain foods. If a person has an allergy, their immune system overreacts to an allergen by producing antibodies, called Immunoglobulin E (IgE). Currently within the present describtion aim to discuss some aspects reffering to fungal allergen and mold allergy diagnosis, in a briefly following steps of our purposed shortly review.

Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important in order to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. The aim of brain tumor segmentation is to accurately delineate the areas of the brain. A specialist with a thorough understanding of brain illnesses must manually identify the proper type of brain tumor. Additionally, processing a lot of images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in the development of algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. This review discussed various types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, and deep learning, learning through transfer, for the study of brain tumors.

This article investigates the effectiveness of feature extraction and selection techniques for enhancing the performance of classifiers accuracy in Type II Diabetes Mellitus (DM) detection using microarray gene data. To address the inherent high dimensionality of the data by employing three Feature Extraction (FE) methods are used namely, Short-Time Fourier Transform (STFT), Ridge Regression (RR), and Pearson Correlation Coefficient (PCC). To further refine the data, meta-heuristic algorithms like Bald Eagle Search Optimization (BESO) and Red Deer Optimization (RDO) are utilized for feature selection. The performance of seven classification techniques such as Non-Linear Regression - NLR, Linear Regression - LR, Gaussian Mixture Model - GMM, Expectation Maximization - EM, Logistic Regression - LoR, Softmax Discriminant Classifier - SDC, and Support Vector Machine with Radial Basis Function kernel - SVM-RBF are evaluated with and without feature selection. The analysis reveals that the combination of PCC with SVM-RBF achieved a promising accuracy of 92.85% even without feature selection. Notably, employing BESO with PCC and SVM-RBF maintained this high accuracy. However, the highest overall accuracy of 97.14% was achieved when RDO was used for feature selection alongside PCC and SVM-RBF. These findings highlight the potential of feature extraction and selection techniques, particularly RDO with PCC, in improving the accuracy of DM detection using microarray gene data.

Sepsis is a pervasive condition that affects individuals of all ages, with significant social and economic consequences. Early diagnosis of sepsis is fundamental for establishing appropriate treatment and is based on warning scores and clinical characteristics, with positive microbiological cultures being the gold standard. Research has yet to identify a single biomarker to meet this diagnostic demand. Presepsin is a molecule that has the potential as a biomarker for diagnosing sepsis. In this paper, we present a narrative review of the diagnostic and prognostic performance of presepsin in different age groups. Given its particularities, it is identified that presepsin is a potential biomarker for sepsis at all stages of life.

Superior mesenteric artery (SMA) syndrome or Wilkie’s syndrome is a vascular compression disorder that causes abnormal compression of the third portion of the duodenum by the SMA. It has a low incidence rate, higher in young women, and is rarely associated with the Nutcracker phenomenon: a condition of compression of the left renal vein between the SMA and the aorta, which manifests as pain in the left flank and pelvis. Here we report the case of a 54-year-old woman with a history of repeated episodes of abdominal pain caused by the Nutcracker syndrome and Wilkie's syndrome.