Background and Objectives: Fibrillinopathies have different phenotypic expression: Marfan- like skeleton features, MLSF; mitral valve prolapse syndrome, MVPS; MASS phenotype, M= mitral valve prolapse, MVP, A= aortic root dilation, S= skeletal features, and the second S= (cutaneous) striae; Marfan syndrome, MS. These are inherited connective tissue diseases, provoked by a mutation of the encoding gene for a protein(fibrillin 1, FBN1) which has major importance in the formation of elastic fibers.The study had the following main objectives: the correlation between disc displacement, DD( a major sign for temporomandibular joint dysfunction, TMD) on magnetic resonance imaging, MRI, and aortic Z score(a major sign for aortic root dilation) on echocardiography; the predictive value of DD towards aortic Z score. DD had 2 types of severity: DD with reduction(the mild type, DDwR) , and DD without reduction( the severe type, DDwoR). Materials and Methods: The type of fibrillinopathy was precised by clinical exam( systemic score), ophthalmic exam(for ectopia lentis), and echocardiography(aortic Z score, MVP). Medical treatment consisted in betablockers, BB(for patients with tachycardia), or angiotensin II receptor blockers, ARB. Surgical treatment was addressed to aortic root aneurysm, and severe MR. DD benefited by dental conservatory treatment, or surgical restoration.Results: DD- Z score had a powerful correlation in MASS(Rho= 0.787, p< 0.01), and in MS patients(Rho=0.819, p< 0.01). For the entire sample, both DDwR- Z score, and DDwoR- Z score had a moderate correlation(Rho= 0.469, and respectively 0.669). Muchmore, DD was an important predictor for Z score. DDwoR had a double power of prediction for Z score, comparative with DDwR(B coefficient= 1.661 for DDwR, and 3.281 for DDwoR). Conclusions: TMD had a powerful correlation with aortic root dilation, in MASS, and MS patients from the sample. Likewise, TMD was a major predictor for aortic root dilation, in the entire sample. We can utilize in clinical practice, an extracardiac finding, TMD, obtained by a non-invasive technique, MRI, for cardiac severity stratification of fibrillinopathies.

This review is focused on the current status of quantitative MR (qMR) of skeletal muscle. The first section covers the techniques of qMR in muscle with the focus on each quantitative parameter, the corresponding imaging sequence, discussion of the relation of the measured parameter to underlying physiology/ pathophysiology, the image processing and analysis approaches, and studies on normal subjects. We cover the more established parametric mapping from T1 weighted imaging for morphometrics including image segmentation, proton density fat fraction, T2 mapping, diffusion tensor imaging to emerging qMR features such as magnetization transfer including ultralow TE imaging for macromolecular fraction, and strain mapping. The second section is a summary of current clinical applications of qMR of muscle; the intent is to demonstrate the utility qMR in different disease states of the muscle rather than a complete comprehensive survey.

This study aimed to demonstrate the potential role of Dual Energy CT in assessing Bone Mineral Density (BMD) in postmenopausal women. A retrospective study was conducted on 51 postmenopausal female patients who underwent DXA and DECT examinations for other clinical reasons. DECT images were acquired with spectral imaging using a 256-slice system. These images were processed and visualized using an HAP(fat) map. Statistical analysis was performed using the Bland-Altman method to assess agreement between DXA and DECT HAP(fat) measurements. Mean BMD, vertebral and femoral T-scores were obtained. For vertebral analysis, the Bland-Altman plot showed an inverse correlation (R2: -0.042; RMSE: 0.690) between T-scores and DECT HAP(fat) values for measurements from L1 to L4, while a good linear correlation (R2: 0.341; RMSE: 0.589) it was found for measurements at the femoral neck. In conclusion, we demonstrated the enhanced accuracy of BMD calculation through DECT, finding a statistically significant correlation only at the femoral neck where BMD results do not seem to be influenced by the overlap of the measurements on cortical and trabecular bone. This outcome could be beneficial in the future by reducing radiation exposure for patients already undergoing follow-up for chronic conditions.

The conservation of the main protease in viral genomes, combined with the absence of a homologous protease in humans, makes this enzyme family an ideal target for developing broad-spectrum antiviral drugs with minimal host toxicity. GC-376, a peptidomimetic 3CL protease inhibitor, has shown significant efficacy against coronaviruses. Recently, a GC-376-based PROTAC was developed to target and induce the proteosome-mediated degradation of the dimeric SARS-CoV-2 3CLPro protein. Extending this approach, the current study investigates the application of the GC-376 PROTAC to the 3CPro protease of Enteroviruses, specifically characterizing its interaction with CVB3 3CPro through X-ray crystallography, NMR and biophysical techniques. The present structural data show that there are some changes between the binding of CVB3 3CPro and SARS-CoV-2 3CLPro, but the overall similarity is strong. The most notable variation is the orientation of the phenylmethyl ester of GC-376 with the S4 subsite of the proteases. This is linked to the flexibility of the protein structure around the region of the catalytic triad shown by our NMR data. Other structural variations include distinct ligand interactions in subsites S1 and S2, influenced by the presence of Glu71 in CVB3 3CPro. Despite these differences, the GC-376 PROTAC fits well within the binding sites of both proteases, demonstrating its potential as a broad-spectrum antiviral agent.

Objective: The diagnostic efficacy of the apparent diffusion coefficient (ADC) on diffusion-weighted magnetic resonance imaging for lymph node metastasis in biliary tract cancer was investigated in the present study. Methods: Surgically resected 112 lymph nodes from 35 biliary tract cancer (BTC) patients were examined in this study. Values of the ADC mean and minimum of lymph nodes, as well as short-axis and long-axis diameters of lymph nodes were assessed by computed tomography (CT). The relationship between these parameters and the presence of histological lymph node metastasis was evaluated. Results: Histological lymph node metastasis was detected in 31 (27.7%) out of 112 lymph nodes. The short-axis diameter in metastatic lymph nodes was significantly larger than that in non-metastatic lymph nodes (P = 0.002), but the long-axis diameter was not significantly different between metastatic and non-metastatic lymph nodes. Both ADC mean and minimum values in metastatic lymph nodes were significantly lower than those in non-metastatic lymph nodes (P < 0.001, respectively). However, the ADC minimum value showed the highest accuracy for the diagnosis of histological lymph node metastasis, with an area under the curve of 0.877, sensitivity of 87.1%, specificity of 82.7%, and accuracy of 83.9%. Conclusions: The ADC minimum value on diffusion-weighted magnetic resonance imaging was very useful for the diagnosis of lymph node metastasis in BTC.

Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to reduce the defects and accident rates by alerting individuals when they enter a drowsy state. This research proposes an Electroencephalography (EEG) based approach for detecting drowsiness. EEG signals are passed through a preprocessing chain composed of artifact removal and segmentation to ensure accurate detection followed by different feature extraction methods to extract the different features related to drowsiness. This work explores the use of various machine learning algorithms such as Support Vector Machine (SVM) the K Nearest Neighbor (KNN) the Naive Bayes (NB) the Decision Tree (DT) and the Multilayer Perceptron (MLP) to analyze EEG signals sourced from the DROZY database, carefully labeled into two distinct states of alertness (awake, and drowsy). Segmentation into 10-second intervals ensures precise detection, while a relevant feature selection layer enhances accuracy and generalizability. The proposed approach achieves high accuracy rates of 99.84% and 96.4% for intra (subject by subject) and inter (cross-subject) modes, respectively. SVM emerges as the most effective model for drowsiness detection in the intra mode, while MLP demonstrates superior accuracy in the inter mode. This research offers a promising avenue for implementing proactive drowsiness detection systems to enhance occupational safety across various industries.

Background and Objectives: High job strain is worsening the outcome in coronary artery disease, CAD. Severe dissatisfaction on workplace, imbalance between high demands , and low rewards, are affecting the quality of life, and diminishing the duration of life, especially in CAD patients. Our study included two samples : CAD patients (experimental group), and without CAD patients, but with traditional risk factors (control group). The main objective of the study was the role played by high sensitivity C reactive protein, hs CRP, and microalbuminuria, as mediators of job strain and CAD relationship. Secondary objectives were the following: identification of the most vulnerable patients( with significant job strain) and intervention towards this category, for cardiovascular events prevention (primary or secondary). Materials and Methods: The utilized methods were the following : clinical exam; electrocardiogram; echocardiogram; non contrast enhanced computed chest tomography; laboratory findings ( lipid profile, glycemia, hs CRP, microalbuminuria); type D scale, DS 14; depression questionnaire, PHQ-9; and satisfaction with work scale, SWWS. Results: The highest values for hs CRP and microalbuminuria were noticed in patients with the lowest Likert score, for both samples CAD, and without CAD patients. The highest job strain was noticed in females. More than half of CAD patients ,with low Likert scores, revealed myocardial infarction. Conclusions: Microalbuminuria and hs CRP were statistical significant in the study as intercessors, in job strain and CAD relationship. The most vulnerable patients for vascular events were females, in both groups : CAD and “CAD waiting desk”. In CAD sample, the vascular vulnerability was also increased by high job strain.

In this multicenter, retrospective study we evaluated the added value of magnetic resonance dispersion imaging (MRDI) to standard multiparametric MRI (mpMRI) for PCa detection. The study included 76 patients - 51 with clinically significant prostate cancer (csPCa), who underwent radical prostatectomy and had an mpMRI including dynamic contrast enhanced-MRI. Two radiologists performed three separate randomized scorings based on mpMRI, MRDI and mpMRI+MRDI. Radical prostatectomy histopathology was used as the reference standard. Imaging and histopathology were both scored according to the Prostate Imaging-Reporting and Data System V2.0 sector map. Sensitivity and specificity for PCa detection were evaluated for mpMRI, MRDI and mpMRI+MRDI. Inter- and intra-observer variability for both radiologists were evaluated using Cohen’s Kappa. On a per patient level, sensitivity for csPCa for radiologist 1 (R1) for mpMRI, MRDI and mpMRI+MRDI were 0.94, 0.82 and 0.94, respectively. For the second radiologist (R2) these were 0.78, 0.94 and 0.96. R1 detected 4% additional csPCa cases using MRDI compared to mpMRI and R2 detected 20% extra csPCa cases using MRDI. Inter-observer agreement was significant only for MRDI (Cohen’s kappa = 0.4250, p=0.004). The results of this study show the potential of MRDI to improve interobserver variability and the detection of csPCa.

The morphological and compositional complexities of keratinized components make feathers ingenious skin appendages adapted to diverse ecological needs. Frizzling feathers, characterized by their distinct curling phenotypes, offer a unique model to explore the intricate morphogenesis in developing a keratin-based bioarchitecture over a wide range of morphospace. Here, we investigated the heterogeneous allocation of α- and β-keratins in flight feather shafts of homozygous and heterozygous frizzle chickens by analyzing the medulla-cortex integrations using quantitative morphology characterizations across scales. Our results reveal intriguing construction of frizzling feather shaft through modified medulla development, leading to a perturbed balance of the internal biomechanics and, therefore, introducing the inherent natural frizzling compared to those from wild-type chickens. We elucidate how the localized developmental suppression of the α-keratin in the medulla interferes with the growth of the hierarchical keratin organization by changing the internal stress in the frizzling feather shaft. This research not only offers insights into the morphogenetic origin of the inherent bending of frizzling feathers but also facilitates our in-depth understanding of the developmental strategies toward the diverse integuments adapted for ecological needs.

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. Early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings pose challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we developed a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieved the highest performance with accuracy, sensitivity, specificity, and combined AUC of 0.93±0.04, 0.94±0.04, 0.93±0.08 and 0.97 respectively in a 5-fold cross-validation study with pulmonary sarcoidosis (n=126) and LCa (n=93) cases. The model offers promising potential for improving the diagnosis of pulmonary sarcoidosis. A detailed ablation study showing the impact of CNN+ViT compared to CNN or ViT alone, and CT+radiomics input, compared to CT or radiomics alone, is also presented in this work.

The pathology of idiopathic intracranial hypertension (IIH), a disease characterized by papillary edema and increased intracranial pressure (IICP), is not yet understood; this disease significantly affects quality of life due to symptoms, including vision loss, headache, and pulsatile tinnitus. By contrast, superficial siderosis (SS), a disorder in which hemosiderin is deposited on the surface of the cerebral cortex and cerebellum, potentially causes cerebellar ataxia or hearing loss. So far, no cases of IIH with infratentorial and supratentorial cortical SS have been reported. Herein, we report a case of a 31-year-old woman with obesity who developed this condition. The patient suddenly developed headache and dizziness, had difficulty walking, and subsequently became aware of diplopia. Fundus examination revealed bilateral optic nerve congestive papillae and right eye abducens disturbance. Head magnetic resonance imaging (MRI) showed prominent SS on the cerebellar surface and cerebral cortex. Lumbar puncture revealed IICP of 32 cmH2O, consistent with the diagnostic criteria for IIH, and treatment with oral acetazolamide was started; subsequently, the intracranial pressure decreased to 20 cmH2O. Her abduction disorder disappeared, and the swelling of the optic papilla improved. She was now able return to her life as a teacher without any sequelae. SS is caused by persistent slight hemorrhage into the subarachnoid space. In this case, both infratentorial and supratentorial cortical superficial SS was observed. Although cases of IIH complicated by SS are rare, it should be kept in mind that a causal relationship between IIH and SS was inferred from our case. Our findings also suggest that cerebrospinal fluid dynamic analysis using MRI is effective in diagnosing IIH and in determining the efficacy of treatment.

Surgical site infections (SSIs) are a major concern in public healthcare, leading to increased morbidity, pro-longed hospital stays length, and significant economic costs. Understanding the epidemiology and risk fac-tors of SSIs is crucial for developing effective prevention and management strategies. In the last century, CDC ‘s HICPAC improved a bundle for prevention of HAIs based on the use of aseptic procedures, disin-fection of the skin and correct use of antibiotics for SSIs. In our country, Gelli-Bianco law introduced the national health guidelines system that include the prophylaxis antibiotic therapy pre- and post-surgery. The aim of our study is to evaluate adherence and appropriateness of the use of antibiotic-preoperative prophylaxis in a province of southern Italy and to identify risk factors that may influence the development of an SSI. The study found many statistically significant associations among the factors examined, i.e. the timing of administration adherent to guidelines, with 47% of the analyzed files administering antibiotics within 60 min before the intervention and 62.7% using intravenous administration.

Artificial Intelligence (AI) and Machine Learning (ML) approaches that could learn from large data sources have been identified as useful tools to support clinicians in their decisional process; AI and ML implementations have had a rapid acceleration during the recent COVID-19 pandemic. However, many ML classifiers are “black box” to the final user, since their underlying reasoning process is often obscure. Additionally, the performance of such models suffer from poor generalization ability in presence of dataset shift. Here, we present a comparison between an explainable-by-design (“white box”) model (Bayesian Network (BN)) versus a black-box model (Random Forest), both studied with the aim to support clinicians of Policlinico San Matteo University Hospital in Pavia (Italy) during the triage of COVID-19 patients. Our aim is to evaluate whether the BN predictive performances are comparable with those of a widely used but less explainable ML model such as Random Forest and to test the generalization ability of the ML models across different waves of the pandemic.

Hypoxia is a hallmark of solid tumors, including hepatocellular carcinoma (HCC). Hypoxia has proved to be involved in multiple tumor biological processes and associated with malignant progression and resistance to therapy. Transarterial chemoembolization (TACE) is a well-established locoregional therapy for patients with unresectable HCC. However, TACE-induced hypoxia regulates tumor angiogenesis, energy metabolism, epithelial-mesenchymal transition (EMT), and immune processes through hypoxia-inducible factor 1 (HIF-1), which may have adverse effects on the therapeutic efficacy of TACE. Hypoxia has emerged as a promising target for combination with TACE in treatment of HCC. This review summarizes impact of hypoxia on HCC tumor biology, adverse effects of TACE-induced hypoxia on its therapeutic efficacy, highlighting the therapeutic potential of hypoxia-targeted therapy in combination with TACE for HCC.

A series of conditions can mimic musculoskeletal infections on imaging, complicating their diagnosis and affecting the treatment. Depending on the anatomical location, different conditions can manifest with clinical and imaging findings that mimic infections. Herein we present a wide spectrum of the musculoskeletal disorders of the axial skeleton, long bones, peripheral joints and soft-tissue which may manifest as an infectious process, besides focusing on the potential mimics of osteomyelitis, septic arthritis and infectious spondylodiscitis that are common in clinical practice. We present the typical imaging characteristics of each musculoskeletal infection, followed by mimicking conditions.

The aim of study was to investigate epidemiology aspects of magnetic resonance imaging (MRI) during COVID-19 pandemic. The study comprised depersonalized residents of Tomsk and Tomsk Region (n = 1714). Invitations to take online survey were sent to 50,000 residents by target SMS with response rate of 1.2% (n = 727, Cohort 1). Cohort 2 comprised retrospective patients (n = 987) who underwent contrast-enhanced cardiac MRI (CMR) in 2019-2022. Referrals, clinical characteristics, diagnosis, gender, age, past COVID-19, MRI study protocols, and MRI data were analyzed. 29% of respondents in cohort 1 received MRI examination within past two years; 26% of respondents considered MRI the most informative imaging modality for detecting COVID-19 pneumonia; 12% of respondents reported MRI unavailable. Proportion of CMR among MRI studies increased during COVID-19 pandemic, and maximum incidence of cardiac diseases detected by MRI was in 2021. Incidence of myocardial fibrosis increased from ~67% in 2019 to ~84% in 2022. The rate of outpatient MRI studies significantly increased in 2020, but returned to pre-pandemic level in 2021. COVID-19 pandemic increased the need for MRI and CMR. Patients with history of COVID-19 had persistent and newly occurring symptoms of myocardial damage suggesting chronic cardiac involvement requiring continuous follow-up.

Introduction Atherosclerotic disease is a major contributor to heart failure, stroke, and myocardial infarction, significantly lowering the quality of life and life expectancy, with a significant burden on healthcare. Not all lesions deemed non-significant are benign, and conversely, not all significant lesions are causative of ischemia. Fractional flow reserve (FFR) provides a functional assessment of coronary lesions, while optical coherence tomography (OCT) offers detailed imaging of plaque morphology, aiding in therapeutic decision-making. The objective of this study was to evaluate the utility of OCT and FFR as adjunctive tools in the catheterization laboratory for guiding therapeutic decisions in patients with multivessel disease for non-culprit vessels. Specifically, we aimed to assess how OCT and FFR influence therapeutic decision-making in patients with multi-vessel coronary artery disease. Methods 36 patients with acute coronary syndrome (ACS) and multivessel disease were randomized 1:1 into two groups: one guided by FFR alone, and the other by a combination of FFR and OCT. For the FFR group, revascularization decisions for non culprit lesions were based solely on FFR measurements. If the FFR was >0.8, the procedure concluded, and the patient received maximal medical treatment. If the FFR was ≤0.8, a stent was placed. For the FFR + OCT group, if the FFR was >0.8, the revascularization decision was based on OCT findings. If there were no vulnerable plaques (VP), the procedure concluded, and the patient received maximal medical treatment. If OCT imaging indicated VP, then the patient underwent revascularization. If the FFR was ≤0.8, the patient underwent revascularization regardless of OCT findings. Results OCT imaging altered the therapeutic decision in 11 cases where FFR measurements were above 0.8, but the lesions were characterized as VP. Analyzing the total change in the decision to stent, 4 cases in the FFR group and 15 cases in the FFR and OCT group (4 based on FFR and 11 on OCT) revealed a statistically significant difference (p = 0.0006; Relative Risk = 0.2556; 95% CI: 0.1013 to 0.5603). When analyzing the change in the total decision both to stent and not to stent, we observed a statistically significant difference, with Group 1 having 7 cases and Group 2 having 15 cases (p = 0.0153; Relative Risk = 0.4050; 95% CI: 0.2004 to 0.7698) Conclusion Based on the findings of this study, OCT significantly increases the percentage of stenting procedures by identifying vulnerable lesions. The use of intracoronary imaging facilitates the timely identification and treatment of these vulnerable lesions. This underscores the crucial role of OCT in enhancing the precision of coronary interventions by ensuring timely intervention for vulnerable lesions, thereby potentially improving patient outcomes Acknowledgment funding „This work was supported by the University of Medicine, Pharmacy, Science and Technology ,, George Emil Palade’’ of Targu Mures Research Grant number 510/14/17.01.2022”

Dynamic optical structural color is always desired in various display applications and usually involves active materials. Full-color generation, especially bi-directional full-color generation in both reflective and transmissive modes, without any active materials included has been rarely investigated. Herein, we demonstrate a scheme of bi-directional full-color generation based on a plasmonic metasurface modulated by rotation of the polarization angle of the incident light without varying the geometry and the optical properties of the materials and the environment where the metasurface resides. The metasurface unit cell consists of plasmonic modules aligning in three directions and is patterned in a square array. The metasurface structural color device generates full colors in both reflection and transmission. Based on the proposed polarization-dependent structural color, information encoding of three multiplexed animal images and quick-responsive (QR) codes is demonstrated to verify the efficient information encoding and decoding of the proposed scheme. The animals can be seen under different polarization incidence and the QR codes can be successfully decoded by the polarization rotation in transmission. The proposed bi-directional full-color generation metasurface has great potential in applications such as kaleidoscope generation, anti-counterfeiting, dynamic color display, and optical information encoding.

Objective: This study aimed to assess the impact of artificial intelligence (AI)-driven noise reduction algorithms on metal artifacts and image quality parameters in cone-beam computed tomography (CBCT) images of the oral cavity. Materials and Methods: This retrospective study included 70 patients, 61 of whom were analyzed after excluding those with severe motion artifacts. CBCT scans, performed using a Hyperion X9 PRO 13 × 10 CBCT machine, included images with dental implants, amalgam fillings, orthodontic appliances, root canal fillings, and crowns. Images were processed with ClariCT. AI deep learning model (DLM) for noise reduction. Objective image quality was assessed using metrics such as the differentiation between voxel values (ΔVVs), the artifact index (AIx), and the contrast-to-noise ratio (CNR). Subjective assessments were performed by two experienced readers, who rated overall image quality and artifact intensity on predefined scales. Results: Compared with native images, DLM reconstructions significantly reduced the AIx and increased the CNR (p<0.001), indicating improved image clarity and artifact reduction. Subjective assessments also favored DLM images, with higher ratings for overall image quality and lower artifact intensity (p<0.001). However, the ΔVV values were similar between the native and DLM images, indicating that while the DLM reduced noise, it maintained the overall density distribution. Orthodontic appliances produced the most pronounced artifacts, while implants generated the least. Conclusion: AI-based noise reduction using ClariCT. AI significantly enhances CBCT image quality by reducing noise and metal artifacts, thereby improving diagnostic accuracy and treatment planning. Further research with larger, multicenter cohorts is recommended to validate these findings.

Despite the existing effective treatment methods, tuberculosis (TB) is the second most deadly infectious disease globally, its carriers in the latent and active phases accounting for more than 20% of the world population. An effective method to control TB and reduce the mortality is regular population screening and diagnosis of the latent form of TB in order to take preventive and curative measures. Numerous methods allow diagnosing TB and directly detecting Mycobacterium tuberculosis (M.tb) biomarkers, including M.tb DNA, proteins, and specific metabolites, as well as antibodies produced by the host immune system in response to M.tb. PCR, ELISA, immunofluorescence and immunochemical analyses, flow cytometry, and other methods allow the detection of M.tb biomarkers or the host immune response to M.tb by recording the optical signal of fluorescent or colorimetric dyes included in the diagnostic tools. Current research in biosensors is aimed at increasing the sensitivity of detection, which can be achieved by using brighter and more photostable optical tags containing fluorescent quantum dots. Here, we review current methods for detection of M.tb biomarkers using optical sensor systems, primarily quantum dot–based nanosensors, and summarize M.tb biomarkers whose detection can be made significantly more sensitive by using quantum dot–based nanosensors.

Background and objectivesː Physical examination is the predominant method for evaluating pelvic floor defects. Magnetic Resonance Imaging (MRI) is a radiological technique useful to identify the underlying defects of pelvic floor structures that require surgery. The primary aim was to find correspondence between the clinical and radiological staging of pelvic organ prolapse (POP) before and after vaginal surgery. A secondary endpoint was to investigate, through clinical and MRI findings if surgery influences continence mechanisms. Finally, we reported changes in the quality of life in women who underwent surgery for prolapse. Materials and methodsː Twenty-five women with prolapse stage ≥ 2 POP-Q were recruited in this prospective study. They underwent preoperative clinical examination, MRI at rest and under the Valsalva maneuver, and quality of life questionnaires. Three months after vaginal surgery they repeated clinical and radiological evaluation. Resultsː Twenty women completed the study. Both clinical and MRI evaluations showed an improvement in prolapse and symptoms after surgery. There were some discrepancies between clinical and radiological staging. MRI parameters did not show differences between pre and postoperative at rest; under the Valsalva maneuver instead, the measurements changed after surgery. Continence was not worsening by the widening of the vesicourethral angle. Patients reported improvement in quality of life. Conclusionsː MRI is an accurate and objective method to define the stage of prolapse but the clinical evaluation alone is sufficient to stage prolapse before surgery and evaluate the result at follow-up. It is an accurate method to visualize some pelvic structures that can be compromised because of pelvic organ prolapse. MRI showed that vaginal surgery doesn’t affect continence mechanisms.

Amyloid-beta plays an important role in the pathogenesis of Alzheimer’s disease. Aberrant amyloid-beta and tau accumulation induce neuroinflammation, cerebrovascular alterations, synaptic deficits, functional deficits, and neurodegeneration, leading to cognitive impairment. Animal models recapitulating the amyloid-beta pathology such as transgenic, knock-in mouse and rat models have facilitated the understanding of disease mechanisms and development of therapeutics targeting at amyloid-beta. There is a rapid advance in high-field MR in small animals. Versatile high-field magnetic resonance imaging (MRI) sequences such as diffusion tensor imaging, arterial spin labelling, resting-state functional MRI, anatomical MRI, MR spectroscopy as well as contrast agents have been developed for the applications in animal models. These tools have enabled high-resolution in vivo structural, functional, and molecular readouts with a whole brain field-of-view. MRI have been utilized to visualize non-invasively the amyloid-beta deposits, synaptic deficits, regional brain atrophy, impairment in white matter integrity, functional connectivity, cerebrovascular and glymphatic system in animal models of amyloidosis. Many of the readouts are translational in clinical MRI in the brain of patients with Alzheimer’s disease. In this review, we summarize the recent advance of using MRI for visualizing the pathophysiology in amyloidosis animal model. We discuss the outstanding challenges in brain imaging using MRI in small animal and propose future outlook in visualizing amyloid-beta-related alterations in brain of animal models.

Fractional flow reserve (FFR) is well recognized as a gold standard measure for the estimation of functional coronary stenosis. Technological progressions in image processing have empowered the reconstruction of three-dimensional models of the coronary arteries via both non-invasive imaging modalities, such as Coronary Computed Tomography Angiography (CCTA), and invasive coronary angiography or intravascular imaging. The application of computational fluid dynamics (CFD) techniques to coronary 3D anatomical models allows the virtual evaluation of the hemodynamic significance of a coronary lesion with high diagnostic accuracy in comparison to the invasive FFR using pressure wires. These opportunities providing hemodynamic information based on anatomy have given rise to a new era of functional angiography and coronary imaging. However, further validations are needed to overcome several scientific and computational challenges before these methods are applied in everyday clinical practice. This review aims to present all the data on the studies and methodologies that have been presented for the virtual estimation of FFR, and the diagnostic execution of the virtual tools for the hemodynamic assessment of coronary lesions compared to the invasive FFR.

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

This paper presents an overview of tomography and different tomographic imaging modalities. The goal of this paper is to provide an understanding of various imaging modalities; in particular, how the imaging is performed, working principles, and applications. It was not intended to provide the in-depth mathematical principles of these imaging modalities, rather this paper focuses on providing the knowledge in an easy-to-grasp manner for the readers' benefit. Imaging modalities are categorized based on the physics behind image generation. First, the paper explains the general tomography principle. Then explains Ultrasound Imaging, Electrical Impedance Tomography, Computed Tomography, Magnetic Resonance Imaging (MRI), Photoacoustic Tomography (PAT), and Positron Emission Tomography (PET). In the end, the paper provides the fundamentals and review of two relatively new imagining modalities, Diffuse Optical Tomography (DOT) and functional Near-infrared Spectroscopy (fNIRS).

The last wave of Artificial Intelligence is dedicating particular emphasis to the line of explainability research. The potential of Explainable Artificial Intelligence (XAI) in producing trustworthy computer-aided-diagnosis systems and its usage for knowledge discovery are collecting interest in the Medical Imaging (MI) community to support the diagnostic process and the discovery of image biomarkers. Most of the existing XAI applications in MI were focused on interpreting the predictions returned by deep neural networks, typically including attribution techniques with saliency map approaches and other feature visualization methods. However, these are often criticized for providing incorrect and incomplete representations of the black-box models’ behaviour. This raises the attention in proposing models intentionally designed to be self-explanatory. In particular, part-prototype (PP) models are interpretable-by-design computer vision (CV) models that base their decision process in learning and identifying representative prototypical parts from the input images, and they are collecting increasing interest and results in MI applications. This narrative review provides a summary of existing PP networks, their application in MI analysis and current challenges.

Diffusion magnetic resonance imaging (dMRI) is an imaging technique that provides information on the microstructural organization of biological tissue. dMRI allows for non-invasive visualization and quantitative assessment of white matter architecture in the brain by characterizing restrictions on the random motion of water molecules. Ultra-high field MRI scanners, such as those operating at 7 Tesla (7T) or higher, can boost signal-to-noise ratio (SNR) to improve dMRI compared with what is attainable at conventional field strengths such as 3T or 1.5T. However, at 7T wavelength effects cause lower transmit magnetic field efficiency in the human brain, mainly in the posterior fossa region, manifesting as signal dropouts in this region. Recently, we reported a simple approach of using a wireless radiofrequency (RF) surface array to improve transmit efficiency and signal sensitivity at 7T. Here we demonstrate the feasibility and effectiveness of using this RF enhancer for in-vivo dMRI at 7T. The use of the RF enhancer can effectively recover signal dropouts in the regions with intrinsically lower SNR such as cerebellum, leading to a better depiction of principal fiber orientations and enhanced visualization of extended tracts to the brainstem and pons enabling more comprehensive delineation of the full white matter architecture.

The fast and reliable processing of medical images is of paramount importance to adequately generate data to feed machine learning algorithms that can prevent and diagnose health issues. Here we benchmark different compressed sensing techniques applied to magnetic resonance imaging as a means to reduce the acquisition time spent in the collection of data and signals that form the image. We show that by using these techniques, it is possible to reduce the number of signals needed and, therefore, substantially decrease the time to acquire the measurements. To this end, we have considered and compared different algorithms: the Iterative Re-Weighted Least Squares, the Iterative Soft Thresholding Algorithm, the Iterative Hard Thresholding Algorithm, the Primal Dual Algorithm and the Log-Barrier Algorithm. We have implemented such algorithms in different analysis programs that have been used to perform the reconstruction of the images and found that the Iterative Soft Thresholding Algorithm gives the optimal results. We found that the images obtained with this algorithm have less quality than the original ones, but the quality is good enough to distinguish each body structure and detect any health problems.

In this article we present a SCILAB implementation of algebraic iterative reconstruction methods for discretisation of inverse problems in imaging. These so-called row action methods rely on semi-convergence for achieving the necessary regularisation of the problem. We implement this method using SCILAB and provide a few simplified test problems: medical tomography, seismic tomography and walnut tomography.Numerical results show the capability of this method for the original and perturbed right-hand side vector.

Extremely low-frequency electromagnetic fields (ELF-EMF) are ubiquitous in industrial-11 ized environments due to the continuous use of electrical devices. Our previous studies demon-12 strated ELF-EMF affects muscle cells by modulating oxidative stress and enhancing myo-genesis. 13 This pilot study investigated these effects on skeletal muscles of sedentary adult mice, assessing 14 physiological responses to ELF-EMF exposure and potential modulation by antioxidant supplemen-15 tation. Male C57BL/6 mice were exposed to ELF-EMF (0.1 or 1.0 mT) 1 hour/day up to 5 weeks, and 16 fed a standard diet without or with N-acetyl-cysteine (NAC). Results showed transient increases in 17 muscle strength (after 2-weeks’ exposure at 1.0 mT), potentially linked to muscle fiber recruitment 18 and activation, revealed by higher PAX7 and MyH expression levels. After ELF-EMF exposure, ox-19 idative status assessment revealed transient increases in expression levels of SOD1 and catalase en-20 zymes, in total antioxidant capacity and in protein carbonyl levels, marker of oxidative damage. 21 These effects were partially reduced by NAC. In conclusion, ELF-EMF exposure affects skeletal 22 muscle physiology and NAC supplementation partially mitigates these effects, highlighting the 23 complex interactions between ELF-EMF and antioxidant pathways in vivo. Further investigations 24 on ELF-EMF as a therapeutic modality for muscle health are necessary.

Although mammography has been the gold standard for early detection of breast cancer, if a woman has dense breast tissue, a false negative diagnosis may occur. Breast ultrasound, whether hand-held or automated, is a useful adjunct to mammography but adds extra time and cost. The primary aim was to demonstrate that our second-generation Aceso system, which combines full-field digital mammography (FFDM) and automated breast ultrasound (ABUS) in a single platform, is able to produce improved quality images that provide clinically meaningful results. Aceso was first tested using two industry standards: a CDMAM phantom to assess the FFDM images; and the CIRS 054GS phantom to evaluate the ABUS images. In addition, 25 women participated in a clinical trial: 14 were healthy volunteers while 11 were patients referred by the breast clinic at Groote Schuur Hospital. The CDMAM phantom results showed the FFDM results were better than EUREF’s standard of “acceptable” and were approaching “achievable”. The ABUS results showed lateral and axial spatial resolution of 0.5 mm and adequate depth penetration of 80 mm. Our second-generation Aceso system, with its improved quality of clinical FFDM and ABUS images, has demonstrated its potential for early detection of breast cancer in a busy clinic.

The review considers physics and some applications of a new approach that promises to be a breakthrough in efficiency and instrumentation of various methods for nondestructive testing (NDT) of composites. The approach is based on frequency-selective properties of defects which fall into resonance (Local Defect Resonance (LDR)) at certain frequencies. An activation of LDR enables to route the input acoustic energy directly to the defect that dramatically increases its vibration amplitude and thus facilitates defect-selective imaging, rises efficiencies of various derivative acoustic NDT methods (nonlinear, acousto-thermal) and enables to develop some new methodologies, like Resonant Air-Coupled Emission (RACE), machine and remote imaging.

Our aim was to compare the inter-rater agreement of Transvaginal ultrasonography (TVS) with Magnetic Resonance Imaging (MRI) regarding diagnosing adenomyosis and for various predefined imaging features of adenomyosis in the same set of women. MRIs were assessed by four experienced radiologists and 2D TVS videoclips and 3D ultrasound volumes were assessed by five experienced sonographers. Each rater subjectively evaluated the presence or absence of adenomyosis, assessed various imaging. Fleiss kappa (κ) was used to reflect inter-rater agreement for categorical data and the Intraclass correlation coefficient (ICC) to reflect the reliability of quantitative data. Agreement between raters for diagnosing adenomyosis was higher for TVS than for MRI (κ =0.42 vs.0.28). MRI had a higher inter-rater agreement in assessing wall asymmetry irregular Junctional Zone (JZ) presence of myometrial cysts, while TVU had a better agreement for globular shape. MRI showed a moderate-good reliability for measuring the JZ (ICC=0.57-0.82). For TVS, JZ was unmeasurable in &gt; 50% of cases and the remaining cases had a low reliability (ICC=-0.31-0.08). We found that Inter-rater agreement for diagnosing adenomyosis was higher for TVS than for MRI, despite that MRI showed a higher inter-rater agreement in most specific features. Measurements of JZ in the coronal plane with 3D TVS were unreliable and thus unlikely to be useful for diagnosing adenomyosis.

This review focuses on the pivotal role of radiotracers in breast cancer imaging, emphasizing their importance in accurate detection, staging, and treatment monitoring. Radiotracers, labeled with radioactive isotopes, are integral to various nuclear imaging techniques, including positron emission tomography (PET) and positron emission mammography (PEM). The most widely used radiotracer in breast cancer imaging is 18F-fluorodeoxyglucose (18F-FDG), which highlights areas of increased glucose metabolism, a hallmark of many cancer cells. This allows for the identification of primary tumors, metastatic sites, and assessment of tumor response to therapy. In addition to 18F-FDG, this review will explore newer radiotracers targeting specific receptors, such as estrogen receptors or HER2, which offer more personalized imaging options. These tracers provide valuable insights into the molecular characteristics of tumors, aiding in tailored treatment strategies. By integrating radiotracers into breast cancer management, clinicians can enhance early disease detection, monitor therapeutic efficacy, and guide interventions, ultimately improving patient outcomes. Ongoing research aimed at developing more specific and sensitive tracers will also be highlighted, underscoring their potential to advance precision medicine in breast cancer care.

(1) Background: The diagnosis of lumbosacral radiculopathy involves anamnesis, assessment of sensitivity and strength, diagnostic imaging—usually magnetic resonance imaging (MRI)—and electrodiagnostic testing (EDX), typically electromyography (EMG) and electroneurography (ENG). MRI evaluates the structures supporting the spinal cord, while EDX evaluates root functionality. The present study aimed to analyze the concordance of MRI and EDX findings in patients with clinically suspected radiculopathy. Additionally, we investigated the comparison between these two reference tests and various clinical variables and questionnaires. (2) Methods: We designed a prospective epidemiological study of consecutive cases with an observational, descriptive, cross-sectional, and double-blind nature following the STROBE guidelines, encompassing 142 patients with clinical suspicion of lumbosacral radiculopathy. (3) Results: Of the sample, 58.5% tested positive for radiculopathy using EDX as the reference test, while 45.8% tested positive using MRI. The comparison between MRI and EDX in the diagnosis of radiculopathy in patients with clinical suspicion was not significant; the overall agreement was 40.8%. Only the years with symptoms were comparatively significant between the positive and negative radiculopathy groups as determined by EDX. (4) Conclusion: The comparison between lumbar radiculopathy diagnoses in patients with clinically suspected pathology using MRI and EDX as diagnostic modalities did not yield statistically significant findings. MRI and EDX are complementary tests assessing different aspects in patients with suspected radiculopathy; degeneration of the structures supporting the spinal cord does not necessarily imply root dysfunction.

The relationship between epicardial adipose tissue (EAT) and atrial fibrillation (AF) has gained interest in recent years. Previous literature on the topic presents great heterogeneity, focusing especially on computed tomog-raphy imaging. Aim of the present study is to determine whether an increased volume of left atrial (LA) EAT evaluated at routine pre-procedural cardiac magnetic resonance imaging (MRI) relates to AF recurrences after catheter ablation. 50 patients undergoing AF cryoballoon ablation and pre-procedural cardiac MRI allowing quantification of LA EAT were enrolled. In one patient segmentation of LA EAT could not be achieved. After a median follow-up of 16.0 months, AF recurrences occurred in 17 patients (34%). Absolute volume of EAT was not different in patients with and without AF recurrences (10.35 ml vs. 10.29 ml; p-value=0.963), whereas volume of EAT indexed on the LA (EATi) was lower, albeit non statistically significant, in patients free from arrhythmias (12.77% vs. 14.06%; p-value=0.467). Receiver operating characteristic curve testing the ability of EATi to predict AF recurrence after catheter ablation showed sub-optimal performance (AUC: 0.588). The finest identified cut-off of EATi was 10.65%, achieving a sensitivity of 0.5, a specificity of 0.82, a positive predictive value of 0.59 and a negative predictive value of 0.76. Patients with values of EATi lower than 10.65% showed greater survival free from arrhythmias than patients with values above this cut-off (84% vs. 48%; p-value=0.04). In conclusion, indexed LA EAT volume evaluated at cardiac MRI independently relates to arrhythmia recurrence after AF cryoballoon ablation.

Imaging living cells by atomic force microscopy (AFM) promises not only high-resolution topographical data, but additionally, mechanical contrast, which are not obtainable with other microscopy techniques. Such imaging is however challenging, as cells need to be measured with low interaction forces to prevent either deformation or detachment from the surface. Off-resonance modes which periodically probe the surface have been shown to be advantageous, as they provide excellent force control combined with large amplitudes, which help reduce lateral force interactions. However, the low actuation frequency in traditional off-resonance techniques limits the imaging speed significantly. Using photothermal actuation, we probe the surface by directly actuating the cantilever. Due to the much smaller mass that needs to be actuated, the achievable measurement frequency is increased by two orders of magnitude. Additionally, photothermal off-resonance tapping retains the precise force control of conventional off-resonance modes and is therefore well suited to gentle imaging. Here we show how photothermal off-resonance tapping can be used to study live cells by AFM. As an example of imaging mammalian cells, the initial attachment, as well as long term detachment of a human thrombocytes are presented. The membrane disrupting effect of the antimicrobial peptide CM-15 is shown on the cell wall of E. coli. Finally, the dissolution of the cell wall of B. subtilis by lysozyme is shown. Taken together, these evolutionarily disparate forms of life exemplify the usefulness of PORT for live cell imaging in a multitude of biological disciplines.

The Carbonic Anidrase isoform IX (hCA IX) is one of the main player in extracellular tumor pH regulation and it is known to be overexpressed in breast cancer as well as in many common tu-mors. hCA IX has shown to contribute to the growth and survival of tumor cells and its expres-sion is correlated to metastasis and resistance to therapies making it an interesting biomarker for diagnosis and potential image-guided therapy. The aim of this work is the development of an MRI Imaging probe able to target the extracellular non catalytic PG domain of CAIX. A new specific nano probe has been designed by conjugating a peptidic interactor of PG domain on the surface of a liposome loaded with Gd-based contrast agents. Mouse Mammary Adenocarcinoma Cell Line (TS/A ) has been chosen as in vitro breast cancer model to test the efficacy of the de-veloped probe. MRI results display a high selectivity towards hCA IX marker and a good sensi-tivity of the Imaging probe. These findings make this approach promising for future develop-ment of in vivo diagnostic protocols aimed at visualizing this enzyme expression.

The maturity of tomato fruit is normally characterized by external color and it is often difficult to know when fruit have achieved commercial maturity or become over-mature. The internal structure of tomato fruit change during development and this study investigates the utility of nondestructive measurement of tomato fruit structure as a function of maturity using magnetic resonance imaging (MRI). The objective of this work is to use analysis of internal tomato fruit structural measurements to characterize maturity. Intact cherry tomato fruit were harvested at six different maturity stages. At each stage of maturity, the internal structure of the fruit was measured using a series of 2D magnetic resonance (MR) images. Qualitative and quantitative image analyses were performed to correlate internal fruit structure with maturity. Internal structural changes observed in the pericarp region of the tomato fruit are highly correlated with fruit maturity. MR image information combined with classical analysis techniques provides a more complete understanding of structure and physicochemical changes in tomato fruit during maturation. This study demonstrates that MRI is a useful analytical tool to characterize internal changes in agricultural produce as the produce matures. This technique can be applied to almost any agricultural produce to monitor internal physical changes due to external impact, maturity stage, variation in climate, storage time and condition or other factor impacting quality.

Defectively manufactured and deliberately damaged composite laminates fabricated with different continuous reinforcing fibres (respectively, carbon and glass) and polymer matrices (respectively, thermoset and thermoplastic) were inspected in magnetic resonance imaging equipment. Two pulse sequences were evaluated during non-destructive examination conducted in saline solution-immersed samples to simulate load-bearing orthopaedic implants permanently in contact with biofluids. The orientation, positioning, shape, and especially the size of translaminar and delamination fractures were determined according to stringent structural assessment criteria. The spatial distribution, shape, and contours of water-filled voids were sufficiently delineated to infer the amount of absorbed water if thinner image slices than this study were used. The surface texture of composite specimens featuring roughness, waviness, indentation, crushing, and scratches was outlined, with fortuitous artefacts not impairing the image quality and interpretation. Low electromagnetic shielding glass fibres delivered the highest, while electrically conductive carbon fibres produced the poorest quality images, particularly when blended with thermoplastic polymer, though reliable image interpretation was still attainable.

The landscape of medical education has witnessed significant transformations over the past decades, particularly with the advent of active teaching methodologies. However, despite these advancements, the traditional theoretical assessment methods have remained largely unchanged. This lack of evolution in assessment systems poses a challenge, as it is crucial for assessment methods to evolve in tandem with teaching approaches to ensure a comprehensive and effective learning process in medical education. This paper reviews the integration and effectiveness of open-book examinations (OBEs) in medical education, reflecting their growing significance. An integrative review of the literature was conducted, drawing from a range of relevant publications over the last decade, sourced from databases such as PubMed, Web of Science, Scopus, and ERIC. The inclusion criteria focused on full-text articles in English, with search terms including "medicine," "assessment," "open book examination," "open book exam," and "open book assessment," combined using Boolean operators. Thirteen publications were selected and critically appraised using The Critical Appraisal Skills Program checklist. The analysis identified three primary thematic categories: ' Teaching Strategy for Pandemic and Challenging Conditions,” Tool of Learning & Educational Impact” and “Operational Challenges & Future Directions”. These themes were explored to understand the role and impact of OBEs in medical education. The findings indicate that open-book examinations are a crucial component in the evolving landscape of medical education. While certain reservations remain, OBEs have shown significant potential in fostering critical thinking, argumentation skills, and lifelong learning among medical students. They reflect the ongoing evolution of knowledge in the medical field and contribute to the development of professionals’ adept at navigating and applying complex information. Further research is recommended to solidify these findings and to expand the understanding of OBEs in medical education.

This paper aimed to describe an atlas of the head of the crested porcupine (Hystrix cristata), applying advanced imaging techniques such as MRI. Furthermore, by combining the images acquired through these techniques with anatomical gross sections, we obtained an adequate description of the structures that form the CNS and associated structures of this species. This anatomical information could serve as a valuable diagnostic tool for the clinical evaluation of different pathological processes in porcupines such as abscesses, skull malformations, fractures, and neoplasia.

Cystic artery bleeding (CAB), a relatively uncommon entity, can arise from both traumatic and non-traumatic etiologies. This pictorial essay aims to elucidate the computed tomography (CT) findings associated with CAB in these distinct contexts. In traumatic CAB, particularly blunt or penetrating trauma, a cystic artery lesion is the culprit behind the hemoperitoneum characteristically located within the gallbladder fossa. Prompt diagnosis is crucial. Fortunately, trans-arterial embolization offers a successful treatment option for traumatic cystic artery lesions, especially in settings where non-operative management is preferred. Non-traumatic CAB, on the other hand, often stems from inflammation in the hepatobiliary and pancreatic system, gallbladder tumours, or iatrogenic injuries. The underlying mechanism typically involves cystic artery wall damage, either due to inflammation or procedural complications, leading to the formation of a cystic artery pseudoaneurysm (CAP). Notably, CAP bleeding can manifest long after the causative event, posing a diagnostic challenge with a variable clinical presentation. This pictorial essay serves to illustrate the CT signs and features of CAB in both traumatic and non-traumatic scenarios. Additionally, it will delve into the characteristic CT appearance of CAP.

Background: With its impressive natural language processing and comprehension skills, ChatGPT-4 (Chat Generative Pre-Trained Transformer) is a technology that shows great promise for use in a number of medical fields. This article provides a thorough description of the possible uses of OpenAI's state-of-the-art language model, ChatGPT-4, in musculoskeletal imaging. Purpose for review: MSK Radiologists need to evolve about their knowledge and understanding of the roles of ChatGPT and its implications in daily practice. We aim to emphasize the advantages of ChatGPT-4 on the musculoskeletal imaging field and its potential to enhance radiologist working checklist review items, and the overall productivity of radiology reports. Recent findings: The fact of that specialists in radiology, rheumatology, orthopedic surgery, and other fields are conversant with ChatGPT. Furthermore, opinions are generally well, despite the fact that many people have reservations about the new technology, which is still developing ^[1]. Compared to GPT-4V based ChatGPT, GPT-4 based ChatGPT showed noticeably better diagnostic accuracy. Although ChatGPT, which is based on GPT-4, performed diagnostically on par with radiology residents, however, it demonstrated lower performance than a musculoskeletal radiologist ^[2].

Epilepsy is a common neurological condition, typically diagnosed using Electroencephalogram (EEG). Large scale EEG datasets have recently been made publicly available, allowing the use of advanced Machine Learning (ML) algorithms to analyze EEG patterns associated with epilepsy. While most existing studies focus on identifying seizures in the EEG, few have tried to identify preictal EEG segments. Identifying preictal EEG segments are not only useful in developing early warning systems but also helps inform the course of treatment for the patient. In this study, we propose to represent EEG segments as images, instead of time-series data, and identify preictal EEG segments using a preexisting ML algorithm (YOLOv8) designed for image processing. Multiplexed images (containing the original EEG signal represented on a 2D grid, kurtosis, and spectral entropy) achieve the best accuracy of 95.75% on the dataset while images just containing the EEG signal result in an accuracy of 91.25%. Using only spectrograms, generated from the original EEG signal, results in an accuracy of 90.15%.

Background: Studies have tried to identify predictors of functional markers of keratoconus progression, whereas structural outcomes of collagen cross-linking have not been properly investigated. Objective: find and set up a combination of diagnostic modalities and measurements to reliably reflect CXL efficiency. Material and methods: We retrospectively reviewed the medical histories of 107 KC patients (131 eyes) who underwent CXL in a medical center from January 2018 to December 2022. The dataset included preoperative examinations and follow-up results, which provided a total of 796 observations. The cases of other corneal diseases, repeated CXL, pregnancy, and observations without follow-up examinations were excluded. Results: The study findings revealed a significant drop in the central and corneal thickness during 14.01±9.98 months of observations. Linear and polynomial equations suggested different trends in pachymetry change after CXL. The linear model showed corneal thinning, and according to the polynomials, pachymetry findings return to baseline values two years after CXL. Conclusion: The most reliable prognosis of postoperative corneal thickness is achieved with the models trained on keratometry readings and topography indices. BAD indices are also reliable predictors of the corneal thickness after CXL. A combination of the aforementioned structural parameters and their derivatives can reliably predict CXL efficiency.

It is time-consuming for acquiring complete data by fully phase encoding in two orthogonal directions along with one frequency encoding direction. Undersampling in the 3D k-space is promising in accelerating such 3D MRI process. Though 3D undersampling can be conducted according to predefined probability density, the density based method is not optimal. Because of the large amount of 3D data and computational cost, it is challenging to perform data-driven and learning-based 3D undersampling and subsequent 3D reconstruction. To tackle this challenge, this paper proposes a deep neural network called EEUR-Net, realized by optimizing specific undersampling patterns for the fully-sampled 3D k-space data. Innovatively, our undersampling algorithm employs an end-to-end deep learning approach to optimize phase encoding patterns and uses a 3D U-Net for image reconstruction of undersampled data. Through end-to-end training, we obtain an optimized 3D undersampling pattern, which significantly enhances the quality of the reconstructed image under the same acceleration factor. A series of experiments on a knee MRI dataset demonstrated that, in comparison to standard random uniform, radial, Poisson and equispaced Cartesian undersampling schemes, our end-to-end learned undersampling pattern considerably improves the reconstruction quality of undersampled MRI images.

Background: As coronavirus (COVID-19) continues to pose a threat to the entire world, it is critical to developing strategies for containing its spread. The purpose of this study was to assess the demographic characteristics, brain tomography characteristics, and diffusion magnetic resonance findings of COVID-19 positive individuals. Material and Method: Between January 1 and December 31, 2021, 317 patients over the age of 18 were admitted to the emergency department with symptoms consistent with COVID-19. Three groups were formed based on clinical findings in patients, divided into light, medium, and severe, and four groups were formed based on radiological imaging findings. Results: The mean age of the 317 patients included in the study was 67.28±12.06 years, with a range of 28-91 years. The clinical classification of the patients was based on laboratory parameters and radiological imaging, not on their age or gender. Mild cases were classified as CO-RADS 0-4; moderate and severe cases were classified as CO-RADS 5-6 (p=0.001). While 60 (18.9%) of patients were followed outpatient, 144 (45.4%) were admitted to the hospital, 73 (23%) were admitted to the intensive care unit, and 40 (12.7%) died (p=0.001). Direct radiographs revealed bilateral involvement in 224 (70.7%) cases, peripheral involvement in 259 (81.7%) cases, and mid-lower lobe lung involvement in 194 (61.2%) cases (p=0.001). Brain tomography revealed infarction in 42 (13.2%) of the patients who were followed and hospitalized and hemorrhage in 22 (6.9%) of the patients. Magnetic resonance imaging revealed diffusion involvement in 68 (21.5%) of the cases (p=0.001). Conclusion: A standardized reporting system for COVID-19 data is required, and it must be simple to use, quick to understand, and focused on determining the risk of pneumonia. Additionally, the diagnostic role of radiological imaging, prognosis prediction, and severity scoring of lung involvement should be included.

This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It sheds light on the journey of radiology, from the pioneering discovery of X-rays to today’s intricate imaging technologies, infused with machine learning and deep learning in medical image analysis. At the crux of this review lies an in-depth study of AI applications in radiology, elucidating its seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology — data quality, the ’black box’ enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. It concludes by firmly cementing the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.

Objective: To explore the hot spots and development trends of community public health research. Methods: The literature related to this topic was searched in CNKI and PubMed databases, and the trend map of the number of articles was drawn according to the obtained data. The included literature were analyzed with Vosviewer software for keyword and author co-occurrence, and CiteSpace 6.3.R1 Basic software was used for mechanism co-occurrence, key emergence and clustering analysis, and timeline distribution map drawing. Results: 189 Chinese literature and 77 English literature were selected. The Chinese literature focused on “community public health service, emergency management, and community governance”, and the research trend was “community public health emergency management and community governance”; English literature focuses on “nursing education, health education, human qualitative research”. Conclusion: Although the Chinese and English literature shows that scholars in this field have different emphases, the primary community health is the key and difficult point to realize the national public health service project, and improving the basic public health service in the primary community is related to the health of all the people, so it is very important.

Introduction: Contrast-enhanced spectral mammography (CESM) is a novel technique employed in breast cancer screening and detection. In this systematic review, we aim to compare the diagnostic performance of CESM with magnetic resonance imaging (MRI) in the context of breast cancer diagnosis. Material and Method: We have searched for paper in Pubmed and Scopus from July 2023 to February 2024, finding 256 suitable papers according to our selection criteria (perspective or retrospective studies, women diagnosed with breast cancer, comparison of Contrast-enhanced Spectral Mammography and Magnetic Resonance Imaging, sensitivity and specificity metrics provided). Every paper was then checked for quality using the QUADAS 2 assesement score. Sensibility, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and accuracy were recorded. Results: 15 studies were selected for analysis. CESM demonstrated comparable sensitivity to MRI, with values ranging from 36.4% to 100% for CESM and 0% to 100% for MRI. Specificity varied between the modalities, with CESM showing specificity ranges of 17% to 100% compared to 23% to 92.31% for MRI. Discussion and Conclusion: CESM has comparable diagnostic validity to MRI, and is a valuable tool in detecting breast cancer.

Cardiomyopathies (CMPs) are a group of myocardial disorders that are characterized by structural and functional abnormalities of the heart muscle. These abnormalities occur in the absence of coronary artery disease (CAD), hypertension, valvular disease, and congenital heart disease. CMPs are an increasingly important topic in the field of cardiovascular diseases due to the complexity of their diagnosis and management. In 2023, the ESC guidelines on cardiomyopathies were first published, marking significant progress in the field. The growth of techniques such as cardiac magnetic resonance imaging (CMR) and genetics has been fueled by the development of multimodal imaging approaches. For the diagnosis of CMPs, a multimodal imaging approach, including CMR, is recommended. CMR has become the standard for non-invasive analysis of cardiac morphology and myocardial function. This document provides an overview of the role of CMR in CMPs, with a focus on tissue mapping. CMR enables the characterization of myocardial tissues and the assessment of cardiac functions. CMR sequences and techniques, such as late gadolinium enhancement (LGE) and parametric mapping, provide detailed information on tissue composition, fibrosis, edema, and myocardial perfusion. These techniques offer valuable insights for early diagnosis, prognostic evaluation, and therapeutic guidance of CMPs. The use of quantitative CMR markers enables personalized treatment plans, improving overall patient outcomes. This review aims to serve as a guide for the use of these new tools in clinical practice.

Differential diagnosis of solid pancreatic lesions (SPLs) using B-mode endoscopic ultrasonography (EUS) is challenging. Detective flow imaging (DFI) offers the potential for detecting low-flow vessels in the pancreas, thus enhancing diagnostic accuracy. This study aimed to investigate DFI-EUS findings of SPLs and analyze their differential diagnostic accuracy for pancreatic cancer. This retrospective study included patients with pathologically confirmed SPLs who underwent EUS between April 2021 and June 2023. Expert endosonographers, blinded to the patients’ clinical data, evaluated images obtained through B-mode, eFLOW, and DFI-EUS. Frame rate and vessel detection sensitivity between eFLOW and DFI were compared, and diagnostic criteria for pancreatic cancer were established. This study included 104 patients, most diagnosed with pancreatic cancer. DFI-EUS demonstrated a significantly higher vessel visualization rate (96%) in SPLs compared to eFLOW (27%). Additionally, DFI showed a superior frame rate, sensitivity (99%), and accuracy (88%) in detecting pancreatic cancer, although with a modest specificity (43%). On DFI-EUS, characteristics such as hypovascular, peritumoral, or spotty vessel findings were suggestive of pancreatic cancer. DFI-EUS significantly improved the visualization of vascular structures within the SPLs, highlighting its efficacy as a diagnostic modality for pancreatic cancer.

Using two independent speckle imaging systems based on the recently published LSOCI method operating in contact mode on the skin, we assess the reproducibility of in vivo measurements and conduct an inter-instrument comparison. To this aim, we propose a calibration method to handle each imaging system as a comprehensive unit, which includes the laser source, optics, and camera. Our experimental results demonstrate that the microcirculation images produced by both instruments exhibit high accuracy and stability, with microcirculation activity values in excellent agreement, thereby paving the way for clinical applications.

The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores into cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

Pyridoxine is a versatile molecule that forms part of the family of B vitamins. It is used to treat and prevent vitamin B6 deficiency and certain type of metabolic disorders. Besides, pyridoxine molecule has been investigated as a suitable ligand toward metal ions. Nevertheless, the study of the magnetic properties of metal complexes containing lanthanide(III) ions and this biomolecule has been unexplored. We have synthesized and characterized a novel pyridoxine-based GdIII complex of formula [Gd(III)(pyr)2(H2O)4]Cl3 • 2 H2O (1) [pyr = pyridoxine]. 1 crystallizes in the triclinic system with space group Pī. In its crystal packing, the cationic [Gd(pyr)2(H2O)4]3+ entities are linked each other through H-bonding interactions involving non-coordinating water molecules and chloride anions. In addition, Hirshfeld surfaces of 1 were calculated to further investigate their intermolecular interactions in the crystal lattice. The study of the magnetic properties of 1 through ac magnetic susceptibility measurements reveals the occurrence of slow relaxation of magnetization in this mononuclear Gd(III) complex, this fact indicating an unusual single-ion magnet (SIM) behavior. We have also studied the relaxometric properties of 1, as a potential contrast agent for high-field MR imaging, from solutions of 1 prepared in physiological serum (0.0 - 3.2 mM range) and measured at 3 T on a clinical MR scanner. The relaxivity values obtained for 1 are larger than those of some commercial MR imaging contrast agents based on mononuclear Gd(III) systems

The Atlantic puffin is a medium-sized seabird with black and white plumage and orange feet. It is distributed mainly along the northern Atlantic Ocean and due, among other reasons, to human activities, it is in a threatened situation and classified as a vulnerable species according to the International Union of Conservation of Nature (IUCN). In this study, we used a total of 20 carcasses of juvenile Atlantic puffins to perform anatomical cross sections and magnetic resonance images. Thus, an adequate description of the head was made, providing valuable information that could be helpful as a diagnostic tool for veterinary clinicians, who increasingly treat these birds in zoos, rehabilitation centers and even in the wild.

Deep Neutral Networks (DNNs) were initially proposed towards the midst of the 20th century, motivated by the neural structure and mechanism of the human brain. Thanks to major ad-vancements in computational resources, during the past decade DNN-based systems have demonstrated unprecedented performance in terms of accuracy and speed. However, recent work has shown that such models may not be sufficiently robust during the inference process. Furthermore, due to the data-driven learning nature of DNNs, designing interpretable and gen-eralizable networks is a major challenge, especially in critical applications, such as medical Computer Aided Diagnostics (CAD) and other medical imaging tasks, including classification, regression and reconstruction. Within this context, a line of physics-driven approaches for deep learning has recently emerged, aimed at improving the stability and generalization capacity of DNNs for medical imaging applications. In this paper, we review recent work focused on phys-ics-driven or prior-information learning for a variety of imaging modalities and medical applica-tions. We discuss how the inclusion of domain-related knowledge into the learning process and networks’ design supports their stability and generalization capability. In addition, we highlight current and future challenges within this scope.

(1) Background: The aim of the research was to present and emphasize the importance of magnetic resonance imaging (MRI) in diagnosing Achilles tendon (AT) ruptures by presenting case reports. (2) Materials and Methods: The sample of respondents included 7 patients (4 males and 3 females, 2 athletes and 5 non-athletes), aged 25 to 58 years, with AT injuries, who were examined and treated at the Institute for Treatment and Rehabilitation "Niška Banja", Niš, Serbia. Ankle examinations with MRI were conducted using the Esaote O-scan 0.3 T machine. The examinations were performed without the application of a paramagnetic contrast agent and without the use of anesthesia. Results: The MRI method's sensitivity and overall diagnostic accuracy were excellent, with 100% true positives. In our study, the method accurately detected the presence, location, and severity of AT injuries in all cases. MRI is an important and reliable method for detecting AT injuries, classifying them, monitoring the course of recovery, and assessing the effects of therapy. Conclusions: An unrecognized or delayed diagnosis of an AT injury can lead to chronic changes. Timely diagnosis (clinical examination, ultrasonography, magnetic resonance imaging) and appropriate treatment (conservative or operative) enable patients to return to normal activities, including sports. Unfortunately, the use of MRI is limited by restricted access and the cost of the examination.

Many clinical research datasets have a large percentage of missing values that directly impacts their usefulness in yielding high accuracy classifiers when used for training in supervised machine learning. While missing value imputation methods have been shown to work well with smaller percentages of missing values, their ability to impute sparse clinical research data can be problem specific. We previously attempted to learn quantitative guidelines for ordering cardiac magnetic resonance imaging during the evaluation for pediatric cardiomyopathy, but missing data significantly reduced our usable sample size. In this work, we sought to determine if increasing the usable sample size through imputation would allow us to learn better guidelines. We first review several machine learning methods for estimating missing data. Then, we apply four popular methods (mean imputation, decision tree, k-nearest neighbors, and self-organizing maps) to a clinical research dataset of pediatric patients undergoing evaluation for cardiomyopathy. Using Bayesian Rule Learning (BRL) to learn ruleset models, we compared the performance of imputation-augmented models versus unaugmented models. We found that all four imputation-augmented models performed similarly to unaugmented models. While imputation did not improve performance, it did provide evidence for the robustness of our learned models.

A reliable and practical renal-lipid quantification and imaging method is needed. Here, the feasibility of an accelerated MRSI method to map renal fat fractions (FF) at 3T and its repeatability were investigated. A 2D density-weighted concentric-ring-trajectory MRSI was used to accelerate acquiring 48×48 voxels (each of 0.25 ml spatial-resolution) without respiratory navigation implementations. The data was collected over 512 complex-FID timepoints with a 1250 Hz spectral bandwidth. The MRSI sequence was designed with a metabolite-cycling technique for lipid-water separation. The in vivo repeatability performance of the sequence was assessed by conducting a test-reposition-retest study within healthy subjects. The coefficient of variation (CV) in the estimated FF from the test-retest measurements showed a high degree of repeatability of the MRSI-FF (CV= 4.3 ±2.5%). Additionally, the matching level of spectral signature within the same anatomical region was also investigated, and their intrasubject repeatability was also high, with a small standard deviation (8.1 ±6.4%). The MRSI acquisition duration was ~3 minutes only. The proposed MRSI technique can be a reliable technique to quantify and map renal metabolites within a clinically acceptable scan time at 3T that supports the future application of this technique for the non-invasive characterization of heterogeneous renal diseases and tumors.

Lately, radiomic studies featuring the development of a signature to use in prediction models in diagnosis or prognosis outcomes are being published increasingly. While the results are shown to be promising, these studies still have many pitfalls and limitations. One of the main issues of this methodology is that radiomic features depend on how the images are preprocessed before their computation. Since, in widely known and used software for radiomic features calculation, it is possible to set these preprocessing parameters before the calculation of the radiomic feature, there are ongoing studies assessing the stability and repeatability of radiomic features to find the most suitable preprocessing parameters for every used imaging modality. This review examines the impact of preprocessing parameters on the reproducibility and reliability of radiomic features extracted from multimodality imaging (CT, MRI, CBCT, PET/CT). Standardized preprocessing is crucial for consistent radiomic feature extraction. Key preprocessing steps include voxel resampling, normalization, and discretization, which influence feature robustness and reproducibility. From our work, we highlighted the need to harmonize the use of preprocessing parameters and their values, especially in light of future studies of prospective studies, which are still lacking in the current literature.

The aim of this study was to explore the perceived impact of artificial intelligence (AI) on radiographers’ activities and profession in Switzerland. A survey conducted in the UK, translated into French and German, was disseminated through professional bodies and social media. The participants were Swiss radiographers (clinical/educators/ researchers/students) and physicians working within the medical imaging profession (radiology/nuclear medicine/radiation-oncology). The survey covered five sections: demographics, AI-knowledge, skills, confidence, perceptions about the AI impact. Descriptive, association statistics and qualitative thematic analysis were conducted. A total of 242 responses were collected (89% radiographers; 11% physicians). AI is being used by 43% of participants in clinical practice, but 63.8% of them did not feel confident with AI terminology. Participants viewed AI as an opportunity (57.2%), while 18.5% considered it as a threat. The opportunities were associated with streamlining repetitive tasks, minimizing errors, increasing time towards patient-centred care, research, and patient safety. The significant threats identified were reduction on work positions (22.6%), decrease of the radiographers’ expertise level due to automation bias (16.4%). Participants (68.3%) did not feel well trained/prepared to implement AI in their practice, highlighting the non-availability of specific training (87.6%). 93% of the participants mentioned that AI education should be included at undergraduate education program. Although most participants perceive AI as an opportunity, this study identified areas for improvement including lack of knowledge, educational supports/training, and confidence in radiographers. Customised training needs to be implemented to improve clinical practice and understanding of how AI can benefit radiographers.

The optical properties and physiology of biological tissue, as well as how near-infrared (NIR) light interacts with the tissue, both play a significant role in interpreting the tissue probing optical measurements, and in solving the inverse problem of near-infrared spectroscopy (NIRS)-based medical imaging modalities such as diffuse optical tomography and functional near-infrared spectroscopy. This paper discusses the optical properties of tissue, specifically in the NIR wavelength range, which influence NIRS measurements in NIRS-based medical imaging. There is an easy-to-understand explanation given in this paper of the NIR light-tissue interaction phenomenon. The mathematical explanation, the processes involved in the interaction, and the rationale for a few approximations are described. Various types of chromophores present in the tissue, their composition in the tissue, and how these chromophores overall affect the scattering and absorption of NIR light are presented. The absorption spectra of these chromophores are shown. Finally, the paper concludes with the author’s perspective on two NIRS-based medical imaging modalities, diffuse optical tomography, and functional near-infrared spectroscopy.

Background and Objectives: The existing recommendations for female pelvic magnetic susceptibility weighted imaging MRI (FP-SWI) have not been optimized to capture the disease characteristics of the female pelvis. Therefore, it is imperative to explore acquisition parameters before conducting larger-scale studies. To establish optimized flip angle (FA) for acquiring FP-SWI to improve the image quality of female pelvic lesion sites. Materials and Methods: To evaluate signal quality and lesion conspicuity, regions of interest (ROIs) were manually drawn within the lesion on SWI sequences. Signal intensity was measured within the ROIs. Additionally, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated for SWI sequences acquired at three FA: 10°, 15°, and 20°. Finally, a qualitative assessment of anatomical detail visualization, geometric distortion extent, artifacts, and lesion clarity was performed using a four-point scale. The Wilcoxon rank sum test was employed to compare the qualitative evaluation parameters. The intra-class correlation coefficient (ICC) was calculated to determine the consistency of subjective ratings between two observers. Results: FA=10° showed significantly higher SNR than FA=15° and 20° in most anatomical regions (P&lt;0.05), while the CNR was significantly higher than FA=15° and 20° (P&lt;0.05). Compared with FA=15° and 20°, the image quality of FA=10° was significantly higher, with less distortion and ghosting, and better image contrast (P&lt;0.05). Lesions with FA=10° exhibited higher clarity compared to FA=15° and 20° (P&lt;0.05). Conclusions: The image quality of FP-SWI images at FA=10° exceeds that at FA=15° and 20°. We recommend that when conducting SWI examination of the female pelvic cavity, the FA value should be set at 10° to stabilize the SWI sequence and improve image quality.

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

Mesenteric ischemia diagnosis is challenging with an overall mortality in up to 50% of cases despite advances in treatment. The main problem that affects the outcome is the delayed diagnosis because of non-specific clinical presentation. Multi Detector CT Angiography (MDCTA) is the first line investigation for the suspected diagnosis of vascular abdominal pathologies and is the diagnostic test of choice in suspected mesenteric bowel ischemia. MDCTA can accurately detect the presence of arterial and venous thrombosis, determine the extension and the gastrointestinal tract involved, and provide detailed information determining the subtype and the stage progression of the diseases helping clinicians and surgeons for the proper management. CT can differentiate forms that are still susceptible to pharmacological or interventional treatment (NOM= non-operative management) from advanced disease with transmural necrosis in which a surgical approach is required. Knowledge of CT imaging patterns and corresponding vascular pathways is mandatory in Emergency settings to reach a prompt and accurate diagnosis. The aims of this paper are 1. to provide technical information about the optimal CTA protocol; 2. To empathize the CTA arterial and venous supply to gastrointestinal tract and relative ischemic pattern; 3. To describe vascular, bowel and extraintestinal CT findings for the diagnosis of acute mesenteric ischemia.

The nuclear magnetic resonance (NMR) metabolomic analysis was applied to investigate the differences within nineteen Sicilian Nocellara del Belice mono varietal Extra Virgin Olive Oils (EVOO), grown in two zones different for altitude and soil composition. Several classes of endogenous olive oil metabolites were quantified through a nuclear magnetic resonance (NMR) three-experiment protocol coupled with a yet developed data-processing called MARA-NMR. This method, taking around one-hour experimental time per sample, faces the possible quantification of different class of compounds at different concentration’s range which would require at least three alternative traditional methods. NMR results were compared with the data of traditional analytical methods to quantify free fatty acidity (FFA), fatty acid methyl esters (FAMEs), and total phenol content. The presented NMR methodology is compared with traditional analytical practices and its consistency is also tested through slightly different data treatment. Despite the rich literature about the NMR of EVOOs, the paper points out that there are still several advances potentially improving this general analysis and overcoming the other cumbersome and multi-devices analytical strategies. Monovarietal EVOO’s composition is mainly affected by pedoclimatic conditions in turn relaying upon the nutritional properties, quality, and authenticity. Data collection, analysis and statistical processing are discussed touching the important issues related to the climate changes in Sicily and to the specific influence of pedoclimatic conditions.

Zebrafish (Danio rerio) is an important animal model for a wide range of neurodegenerative diseases. However, obtaining the cellular resolution that is essential for studying the zebrafish brain remains challenging as it requires high-spatial resolution and signal-to-noise ratios (SNR). In the current study, we present the first MRI results of the zebrafish brain at the state-of-the-art magnetic field strength of 28.2 T. The performance of MRI at 28.2 T was compared to 17.6 T. A 20% improvement in SNR was observed at 28.2 T as compared to 17.6 T. Excellent contrast, resolution, and SNR allowed the identification of several brain structures. The normative T1 and T2 relaxation values were established over different zebrafish brain structures at 28.2 T. To zoom into the white matter structures, we applied diffusion tensor imaging (DTI) and obtained axial, radial, and mean diffusivity, as well as fractional anisotropy at a very high spatial resolution. Visualization of white matter structures was achieved by short-track track-density imaging by applying the constrained spherical deconvolution method (stTDI CSD). For the first time, an algorithm for stTDI with multi-shell multi-tissue (msmt) CSD was tested on zebrafish brain data. A significant reduction of false-positive tracks from grey matter signals was observed compared to stTDI with single-shell single-tissue (ssst) CSD. This allowed the non-invasive identification of white matter structures at high resolution and contrast. Our results show that ultra-high field DTI and tractography provide reproducible and quantitative maps of fibre organization from tiny zebrafish brains, which can be implemented in the future for a mechanistic understanding of disease-related microstructural changes in zebrafish models of various brain diseases.

Background: Appendicitis is a common cause of emergency department visits and appendectomies in the US, with 250,000 appendectomies performed annually. Current diagnostic methods, such as clinical findings, lead to inaccuracies and missed diagnoses, increasing morbidity and mortality. The only solution is surgery, but current hematological tests are not reliable and may lead to unnecessary surgery if the underlying etiology is self-limiting or requires medical treatment. This study explores the role of MRI as a diagnostic modality that can replace US/CT and supplement traditional clinical diagnostic signs while avoiding ionizing radiation and intravenous contrast medium. The purpose of this meta-analysis is to determine the accuracy of MRI as a diagnostic modality in diagnosing appendicitis, addressing concerns about diagnostic modalities and their adverse effects, and addressing the necessity and success of relevant interventions. Methods: Medical literature was comprehensively searched and reviewed without restrictions to particular study designs, or publication dates using PubMed, Cochrane Library, and Google Scholar databases for all relevant literature formulated in English. The extraction of necessary data proceeded after specific inclusion and exclusion criteria were applied. Meta Analysis was performed for 5206 patients, with 35 RCTs being selected. Analysis was done using the QualSyst tool, wherein two writers independently assessed the caliber of each study as well as the use of the Cochrane tool for bias risk apprehension. The statistical software packages RevMan (Review Manager, version 5.3), SPSS (Statistical Package for the Social Sciences, version 20), and Excel in Stata 14 were used to perform the statistical analyses along with other analytical software. Results: MRI had an overall sensitivity of over 95%, an overall specificity of 94.2% with a PPV of 0.875 in comparison to CT scan in the diagnosis and intervention regarding appendicitis. Conclusion: These findings strongly suggest that MRI is a reasonable alternative to CT for the diagnosis of appendicitis in hospitals with appropriate access to this technology for reasons about accuracy, reliability, and reproducibility as well as addressing safety concerns.

The paramagnetic gadolinium(III) ion is used as contrast agent in magnetic resonance (MR) imaging to improve the lesion detection and characterization. It generates a signal by changing the relaxivity of protons from associated water molecules and creates a clearer physical distinction between the molecule and the surrounding tissues. New gadolinium-based contrast agents displaying larger relaxivity values and specifically targeted might provide higher resolution and better functional images. We have synthesized the gadolinium(III) complex of formula [Gd(thy)2(H2O)6](ClO4)3·2H2O (1) [thy = 5-methyl-1H-pyrimidine-2,4-dione or thymine], which is the first reported compound based on gadolinium and thymine nucleobase. 1 has been characterized through vis-IR, SEM-EDAX and single-crystal X-ray diffraction techniques, and its magnetic and relaxometric properties have been investigated by means of SQUID magnetometer and MR imaging phantom studies, respectively. On the basis of its high relaxivity values, this gadolinium(III) complex can be considered a suitable candidate for contrast enhanced magnetic resonance imaging.

Atrophy quantification is fundamental for understanding brain development and diagnosing and monitoring brain diseases. FSL-SIENA is a well-known fully-automated method that has been widely used in brain magnetic resonance imaging studies. However, intensity variations arising during image acquisition that may compromise evaluation, analysis and even diagnosis. In this work, we study whether intensity standardisation can improve longitudinal atrophy quantification. We considered seven methods comprising z-score, fuzzy c-means, Gaussian mixture model, kernel density, histogram matching, white stripe, and removal of artificial voxel effects by linear regression (RAVEL). We used a total of 330 scans from two publicly-available datasets, ADNI and OASIS. In scan-rescan assessments, that measures robustness to subtle imaging variations, intensity standardisation did not compromise the robustness of FSL-SIENA significantly (p>0.1). In power analysis assessments, that measures the ability to discern between two groups of subjects, three methods led to consistent improvements in both datasets with respect to the original: fuzzy c-means, Gaussian mixture model, and kernel density estimation. Reduction in sample size using these three methods ranged from 17% to 95%. The performance of the other four methods was affected by spatial normalisation, skull stripping errors, presence of periventricular white matter hyperintensities, or tissue proportion variations over time. Our work evinces the relevance of appropriate intensity standardisation in longitudinal cerebral atrophy assessments using FSL-SIENA.

Traumatic vascular injuries consist in direct or indirect damage to arteries and/or veins and accounts for 3% of all traumatic injuries. Typical consequences are hemorrhage and ischemia. Extremities vascular injuries can occur isolated or in association in major trauma with other organ injuries. They account for 1-2% of patients admitted to Emergency Department, for approximately 50% of all arterial injuries. Lower extremities are more frequently injured than the upper ones in adult population. The outcome of vascular injuries is strictly correlated to the environment and the time background. Treatment can be challenging notably in polytrauma because of the dilemma of which injury should be prioritized and treatment delay can cause disability or even death especially for limb vascular injury. Our purposes are to discuss the role of Computed Tomography Angiography (CTA) in the diagnosis of vascular trauma and its optimized protocol to achieve the definitive diagnosis, and to assess the radiological signs of vascular injuries and the possible pitfalls.

Background and Objectives To compare navigator-gated three-dimensional T1-weighted gradient-echo sequences (NG-3D T1W-GRE) with standard breath-hold T1W-GRE (BH T1W-GRE) for the detection of HCC nodules in patients with chronic viral hepatitis which undergo at magnetic resonance imaging (MRI) using gadoxetic acid. Methods and materials We evaluated 58 patients with hepatic nodules at least 1 cm in diameter detected by ultrasound who underwent liver MRI including NG-3D T1W-GRE sequences and breath hold T1-weighted gradient-echo sequences with different flip angles(FA) at 10-25-40° after contrast administration of gadoxetic acid at magnetic scanner operating at1.5 Tesla. Image quality and relative hepatic and nodular enhancement were evaluated by three blinded reviewers. Results In these patients, image quality of NG-3D T1W-GRE was assessed significantly better than quality of BHT1W- GRE (P < 0.005) and relative enhancement was significantly higher than that of BH- T1W-GRE ( P

Search for the new non-invasive methods of investigating the functioning of human internal organs is an urgent task. One of these methods for assessing the functioning of the human res-piratory system is electromagnetic sensing, based on a significant difference in the dielectric permittivity of muscle tissue and air. During breathing, when the lungs are filled with air, the dielectric permittivity of the lungs decreases, which leads to a change in the level of the elec-tromagnetic signal passing through the body. Results of experiments on recording changes in the level of electromagnetic radiation passing through the human body, performed on an experimental device consisting of eight transmitting and receiving antennas located on opposite sides of the chest have been presented in the article. The possibility of visualizing the measured “pulmonograms” in the form of dynamic two-dimensional images showing the process of filling various parts of the lungs with air has been demonstrated.

To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions. This IRB approved prospective study was conducted at a tertiary pediatric center in compliance with the Health Insurance Portability and Accountability Act. We used a General Electric Signa Premier unit (GE Medical Systems, Milwaukee, WI) to acquire two DTI sequences of the left knee on each child at 3T: an in-plane 2.0 x 2.0 mm2 with section thickness of 3.0 mm and a 2 mm3 isovolumetric voxel, neither had an inter-section gap. We used a multi-band DTI acquisi-tion with fat-suppressed single-shot spin-echo echo-planar sequence (20 non-collinear directions; b-values of 0 and 600 sec/mm2). The MR vendor-provided a commercially available DL model applied with 75% noise reduction settings to same subject DTI sequences at different spatial res-olutions. We compared DTI tract metrics from both DL-reconstructed scans and non-denoised scans for femur and tibia, at each spatial resolution. Differences were evaluated using Wilcox-on-signed ranked test and Bland-Altman plots. When comparing DL versus non-denoised diffu-sion metrics in femur and tibia using the 2 mm x 2mm x 3 mm voxel dimension there were no significant differences between tract count (p = 0.1, p =0.14) tract volume (p = 0.14, p = 0.29), or tibial tract length (p=0.16); femur tract length exhibited a significant difference (p

The MAPK signaling pathway with BRAF mutations has been shown to drive the pathogenesis of 40-60% of melanomas. Inhibitors of this pathway's BRAF and MEK components are currently used to treat these malignancies. However, responses to these treatments are not always successful. Therefore, identifying noninvasive biomarkers to predict treatment responses is essential for personalized medicine of melanoma patients. Using noninvasive 1H magnetic resonance spectroscopy (1H MRS), we previously showed that BRAF inhibition reduces lactate and alanine tumor levels at early stages of effective therapy and could be construed as metabolic imaging biomarkers for drug response. The present work aimed to demonstrate if these metabolic changes and those assessed by 31P MRS are also found in preclinical human melanoma models treated with MEK inhibitors. Apart from 1H and 31P MRS, additional supporting in vitro biochemical analyses are described. Our results indicate significant early metabolic correlations with response levels to MEK inhibition in the melanoma models and are consistent with our previous study of BRAF inhibition. Given these results, our study supports the potential clinical utility of noninvasive MRS to objectively image metabolic biomarkers for early predicting melanoma response to MEK inhibition.

Stroke is a major leading cause of chronic disability, often affecting patients’ motor and sensory functions. Functional magnetic resonance imaging (fMRI) is the most commonly used method of functional neuroimaging and allows the non-invasive study of brain activity. The time-dependent coactivation of different brain regions at rest is described as resting-state activation. As a non-invasive task-independent functional neuroimaging approach, resting-state fMRI (rs-fMRI) may provide therapeutically useful information on both the focal vascular lesion and the connectivity-based reorganization and subsequent functional recovery in stroke patients. Considering the role of prompt and accurate prognosis in stroke survivors along with the potential of rs-fMRI in identifying patterns of neuroplasticity in different post-stroke phases, this review provides a comprehensive overview of the latest literature regarding the role of rs-fMRI in stroke prognosis in terms of motor and sensory outcome. Our comprehensive review suggests that with the advancement of MRI acquisition and data analysis methods, rs-fMRI emerges as a promising tool to study the motor and sensory outcome in stroke patients and evaluate the effect of different interventions.

Data sets for medical images are generally imbalanced and limited in sample size because of high data collection costs, time-consuming annotations, and patient privacy concerns. The training of deep neural network classification models on these data sets to improve the generalization ability does not produce the desired results for classifying the medical condition accurately and often overfit the data on the majority of class samples. To address the issue, we propose a framework for improving the classification performance metrics of deep neural network classification models using transfer learning: pre-trained models, such as Xception, InceptionResNet, DenseNet along with the Generative Adversarial Network (GAN) – based data augmentation. Then, we trained the network by combining traditional data augmentation techniques, such as randomly flipping the image left to right and GAN-based data augmentation, and then fine-tuned the hyper-parameters of the transfer learning models, such as the learning rate, batch size, and the number of epochs. With these configurations, the Xception model outperformed all other pre-trained models achieving a test accuracy of 98.7%, the precision of 99%, recall of 99.3%, f1-score of 99.1%, receiver operating characteristic (ROC) - area under the curve (AUC) of 98.2%.

Dynamic contrast-enhanced MRI (DCE-MRI) is increasingly used to quantify and map the spatial distribution of blood-brain barrier (BBB) leakage in neurodegenerative disease, including cerebral small vessel disease and dementia. However, the subtle nature of leakage and resulting small signal changes make quantification challenging. While simplified one-dimensional simulations have probed the impact of noise, scanner drift, and model assumptions, the impact of spatio-temporal effects such as gross motion, k-space sampling and motion artefacts on parametric leakage maps has been overlooked. Moreover, evidence on which to base the design of imaging protocols is lacking due to practical difficulties and the lack of a reference method. To address these problems, we present an open-source computational model of the DCE-MRI acquisition process for generating four-dimensional Digital Reference Objects (DROs), using a high-resolution brain atlas and incorporating realistic patient motion, extra-cerebral signals, noise and k-space sampling. Simulations using the DROs demonstrated a dominant influence of spatio-temporal effects on both the visual appearance of parameter maps and on measured tissue leakage rates. The computational model permits a greater understanding of the sensitivity and limitations of subtle BBB leakage measurement and provides a non-invasive means of testing and optimising imaging protocols for future studies.

The combination of medical imaging and deep learning has significantly improved diagnostic and prognostic capabilities in the healthcare domain. Nevertheless, the inherent complexity of deep learning models poses challenges in understanding their decision-making processes. Interpretability and visualization techniques have emerged as crucial tools to unravel the black-box nature of these models, providing insights into their inner workings and enhancing trust in their predictions. This survey paper comprehensively examines various interpretation and visualization techniques applied to deep learning models in medical imaging. The paper reviews methodologies, discusses their applications, and evaluates their effectiveness in enhancing the interpretability, reliability, and clinical relevance of deep learning models in medical image analysis.

We developed an ovarian cancer-targeted drug delivery system based on a follicle-stimulating hormone receptor (FSHR) peptide. The lipophilic chemotherapeutic drug SN38 and the photosensitizer IR820 were loaded into the phospholipid bilayer of liposomes. Near-infrared laser irradiation stimulated the generation of reactive oxygen species, causing short-term reactive molecular species and spatial-temporal restricted photodamage to the biological targets. The reactive molecular species and spatial-temporal restricted photodamage enhanced the release and uptake of chemotherapy drugs and increased the sensitivity to chemotherapy drugs in ovarian cancer cells. FSH liposomes loaded with SN38 and IR820 (SN38/IR820-Lipo@FSH) were prepared using thin-film hydration-sonication. FSH peptide binding was analyzed using 1H NMR spectrum and Maldi-Tof. The average size and zeta potential of SN38/IR820-Lipo@FSH were 105.1 ± 1.15 nm (PDI: 0.204 ± 0.03) and −27.8 ± 0.42 mV, respectively. SN38/IR820-Lipo@FSH exhibited good drug-loading properties and the in vitro release was slow. FSH significantly increased the uptake of liposomes, inhibited cell proliferation, and induced apoptosis in A2780 cells. Moreover, SN38/IR820-Lipo@FSH exhibited good tumor-targeting ability and anti-ovarian cancer activity in vivo. The SN38/IR820-Lipo@FSH exhibited specific targeting to A2780 ovarian cancer cells. The combination of chemotherapy and photodynamic treatment based on an FSH peptide-targeted delivery system may be an effective approach to treating ovarian cancer.

Quantum correlations, especially time correlations, are crucial in ghost imaging in order to significantly reduce the background noise on the one hand while increasing the imaging resolution. Moreover, the time correlations serve as a critical reference, distinguishing between signal and noise, which in turn enable clear visualization of biological samples. Quantum imaging also addresses the challenge involved in imaging delicate biological structures with minimal photon exposure and sample damage. Here we explore the recent progress in quantum correlation-based imaging, notably its impact on secure imaging and remote sensing protocols as well as on biological imaging. We also exploit the quantum characteristics of heralded single-photon sources (HSPS) combined with decoy-state methods for secure imaging. This method uses Quantum Key Distribution (QKD) principles to reduce measurement uncertainties and protect data integrity. It is highly effective in low-photon-number regimes for producing high-quality, noise-reduced images. The versatility of decoy-state methods with WCSs (WCS) is also discussed, highlighting their suitability for scenarios requiring higher photon numbers. We emphasize the dual advantages of these techniques: improving image quality through noise reduction and enhancing data security with quantum encryption, suggesting significant potential for quantum imaging in various applications, from delicate biological imaging to secure quantum communication.

Artificial intelligence is dramatically transforming medical imaging. We assessed the levels of artificial intelligence use among radiology trainees and explored their perceived impact of artifi-cial intelligence on the radiology workflow and radiology profession, in correlation with the perceived ease of use and behavioral intention to use artificial intelligence. This cross-sectional study enrolled radiology trainees from Saudi Arabia, and an online 5-part-structured question-naire was disseminated via online networks to trainee in July 2021. We included 98 participants (51 male; age 27.59±2.02 years). Level of use was low; few used it in routine practice (7%). The impact of artificial intelligence on the radiology workflow was positively perceived in all radi-ology workflow steps (3.64–3.97 out of 5). A positive impact on the radiology profession was more frequently perceived for technical and performance aspects (81%–85%) compared with prestige and legal aspects (64%–71%). Perceived ease of use and behavioral intention to use arti-ficial intelligence was associated with the current professional activity, level of use artificial in-telligence use, and perceived impact on the profession as well as on radiology workflow (p<0.05). Levels of artificial intelligence use in radiology are very low. The perceived positive impact of ar-tificial intelligence on radiology workflow and profession is correlated with an increase in be-havioral intention to use artificial intelligence. Thus, increasing awareness about the favorable impact can improve the behavioral use.

99mTc is a well-known radionuclide widely used and readily available for medical imaging. Despite this, the 99mTc isotope is rarely utilized for tumour imaging due to the lack of agents that can selectively deliver it to tumours. However, the use of immunologically compatible recombinant proteins, which combine metal-binding sites and tumour-specific ligands, shows promise for utilizing 99mTc in the early detection of tumours. To illustrate this, a protein called E1b, containing a metal binding center with ELP repeats, tumour-specific ligands recognizing integrin αvβ3 and nucleolin, as well as a short Cys-rich sequence, was artificially designed. This protein was produced in E. coli and purified to homogeneity using metal-chelate chromatography. In experiments, C57Bl/6 and Balb/C mice with subcutaneously grafted lung carcinoma (LLC) or mammary gland adenocarcinoma (Ca755, EMT6, or 4T1) were used to test 99mTc-E1b selectivity and the kinetics of isotope retention. The mice were intravenously injected with approximately 80 MBq of 99mTc in complex with E1b, which had been freshly purified using gel-chromatography. The biodistribution of the labeled protein was then studied using the SPECT/CT imaging method within 24 h. The ratio of radionuclide distribution in the tumour versus the adjacent normal tissue (T/N) steadily increased over the 24-h period, reaching 15.7±4.2 for EMT6, 16.5±3.8 for Ca755, 6.7±4.2 for LLC, and 7.5±3.1 for 4T1.

Background: beta blockers of three generations are first-line drugs in the treatment of chronic heart failure (CHF). However, to date there is no clear understanding of the effect of beta blockers on myocardial energy metabolism disorders in CHF. The aim of the study: to conduct a study of beta-blockers of different generations on myocardial energy metabolism in the experimental CHF. Methods: CHF was modeled in white outbred rats by administering doxorubicin. The study drugs were administered intragastrically – new drug Hypertril (1-(β-phenylethyl)-4-amino-1,2,4-triazolium bromide)-3.5 mg/kg, metoprolol - 15 mg/kg, Nebivolol -10 mg/kg, Carvedilol 50 mg/kg, Bisoprolol, 10 mg/kg. In the myocardium, the main indices of energy metabolism were determined - ATP, ADP, AMP, malate, lactate, pyruvate, succinate dehydrogenase (SDH) activity, NAD-dependent malate dehydrogenase (NAD-MDH) activity. Results: traditional second-generation beta-blockers (Metoprolol and Bisoprolol) did not affect the studied indices of energy metabolism, and third-generation beta-blockers with additional properties - Carvedilol and, especially, Nebivalol and Hypertril improved myocardial energy metabolism. Conclusions: obtained results will help to expand our understanding of the effect of beta-blockers of various generations used to treat cardiovascular diseases on energy metabolism, and are also an experimental justification for the practical choice of these drugs in the complex therapy of CHF.

One of the tools for optimal crop production is regular monitoring and assessment of crops. During the growing season of fruit trees, the bloom period has increased photosynthetic rates that correlate with the fruiting process. This paper presents the development of an image processing algorithm to detect peach blossoms on trees. Images of an experimental peach orchard were acquired from the Parma Research and Extension Center of the University of Idaho using an off-the-shelf unmanned aerial system (UAS), equipped with a multispectral camera (Near-infrared, Green, Blue). The orchard has different stone fruit varieties and different plant training system. Individual tree images (high-resolution) and arrays of trees images (low-resolution) were acquired to evaluate the detection capability. The image processing algorithm was based on different vegetation indices. Initial results showed that the image processing algorithm could detect peach blossoms and demonstrate good potential as a monitoring tool for orchard management.

Beta thalassemia is an inherited blood disorder that results in inefficient erythropoiesis due to genetic mutation that leads to reduction or absence of the hemoglobin beta-globulin protein. Approximately 8.5% of UAE residents suffer from β-thalassemia, a significant health and financial problem. The treatment options available for β-Thalassemia major are limited and associated with a wide range of complications. β-thalassemia gene therapy is emerging as a potential novel treatment option that eliminates the complications caused by the current long-term treatment modalities and the associated economic burden. This paper reviews the scientific literature related to emerging gene-therapy for β-Thalassemia by analyzing all the articles published from January 2010 to December 2023 in the English language on Databases like PubMed, Scopus, ProQuest, and CINAHL. The use of gene therapy has demonstrated promising outcomes for a permanent cure of β-Thalassemia. ZYNTEGLO (Betibeglogene autotemcel) is the only cell-based gene therapy which was approved by the FDA on 19th August, 2018. To conclude, Gene therapy is an innovative solution. It demonstrates a promising future, but does come with its own set-backs and is something that must be tackled in-order to revolutionize it in the medical world. FDA approved ZYNTEGLO is a potentially one-time curative treatment for β-Thalassemia. Although cutting-edge, its use is limited because of the high cost; a price of $2.8million per patient.

The purpose of the study was to identify factors characterizing a decrease in LV global systolic function in patients with ST-segment elevation myocardial infarction (STEMI) after revascularization using cardiac magnetic resonance imaging (MRI)-based ischemic injury pattern and laboratory parameters sensu left ventricular global systolic function. A total of 109 STEMI patients were examined. The patients underwent contrast-enhanced cardiac MRI with a 1.5 Tesla GE SIGNA Voyager (GE HealthCare, USA) on the 7th-10th days from the onset of the disease. According to cardiac MRI analysis, the patients were divided into the following groups with regard to left ventricular ejection fraction (LVEF) values: group 1 – patients with LVEF ≥50%; group 2 – patients with mildly reduced LVEF 40-49%; group 3 – patients with low LVEF <40%. A predominance of most parameters of the ischemic injury pattern was noted in patients with mildly reduced and low LVEF versus patient group with LVEF ≥50%. Some risk factors for a decrease in LVEF <50% systolic function in STEMI patients after revascularization were revealed: male gender; time from the onset of the anginal attack to revascularization; coronary artery status; several LV parameters; ischemic injury characteristics; natriuretic peptide and troponin I levels.

Abstract: Accurately predicting a fetus's gestational age (GA) is of utmost importance in prenatal care. This study aimed to develop an artificial intelligence (AI) model that can automatically predict GA using biometric measurements derived from fetal brain mag-netic resonance imaging (MRI). Additionally, we aimed to assess the significance of con-sidering different references when interpreting GA predictions. To achieve this, we obtained measurements such as Biparietal Diameter (BPD), Fron-to-occipital Diameter (FOD), and Head Circumference (HC) from a dataset comprising 52 normal fetal MRI cases with T2 Haste sequences from Rush University. Both manual and AI-based methods were utilized to acquire these measurements. We also employed three reference papers (Garel, Freq, and Bio) for comparison purposes. The results demonstrated a strong correlation between manual and AI measure-ments, indicating consistency between the two methods. The AI-based measurement of HC exhibited a higher correlation with actual values compared to BPD, FOD, and correct-ed BPD (BPDC). When comparing these measurements with GA in the Picture Archiving and Communication System (PACS), the differences varied depending on the reference used. Specifically, the differences ranged from 0.47 to 2.17 weeks for BPD, 0.46 to 2.26 weeks for FOD, and 0.75 to 1.74 weeks for HC. Furthermore, the Pearson correlation coeffi-cient analysis revealed that all correlation coefficients between PACS records and GA pre-dictions using different references were greater than 0.97. In conclusion, the AI model based on fetal brain MRI accurately predicts GA by uti-lizing BPD, FOD, and HC measurements. The AI approach, which involves combining line segments to calculate fetal head circumference, offers improved accuracy and con-venience compared to manual estimation. This study underscores the potential of AI models in accurately estimating gestational age and highlights their utility in prenatal care. By integrating AI as a valuable tool in prenatal care, we can enhance the accuracy, ef-ficiency, and decision-making involved in assessing fetal development and monitoring pregnancies using MRI measurements.

Ectopic eruption of teeth rarely occurs in the mandibular condyle, orbit, nasal cavity, skin, and maxillary sinus. Ectopic eruption of teeth in the maxillary sinus can be accompanied by chronic and recurrent sinusitis, including headache, facial pain, and numbness. Fibro-osseous lesions in the paranasal sinuses are discovered incidentally on X-ray images and are often asymptomatic. Osteoma is the most common fibro-osseous lesion that develops in the paranasal and nasal sinuses. Herein, we report a rare case of osteoma (exostosis) containing supernumerary teeth within the maxillary sinus. A characteristic pedicled bone lesion with a clear border on computed tomography was the undefined orthopantomogram radiopacity in the left maxillary sinus, and the lesion contained teeth. The tumor was excised using the Caldwell–Luc procedure. Pathologically, the surgical specimen revealed an osteoma. The patient’s symptoms of chronic sinusitis disappeared, and the patient had no symptoms 2 years after the surgery. Because the patient had no history of midface trauma or surgery, the supernumerary teeth were speculated to have migrated during a reactive osteogenic process caused by chronic sinusitis.

Abstract Purpose: This study aims to evaluate the potential of multimodal ultrasound techniques in improving the visibility of liver lesions that are indistinct under conventional ultrasound. Specifically, we will explore the application of fusion imaging, contrast-enhanced ultrasound (CEUS), and high-frequency ultrasound in enhancing the visibility of liver masses. These techniques will be utilized to assist in visualizing liver lesions and to accurately localize them for the purpose of performing percutaneous tissue biopsy. Lastly, we will compare the biopsy results with the final diagnosis to assess whether multimodal techniques can enhance the success rate of percutaneous biopsy for liver masses that are not clearly visible under conventional ultrasound. Furthermore, the potential impact of these techniques on the development of personalized treatment strategies for patients was also discussed. Materials and Results: A retrospective analysis was conducted on 144 patients with liver masses who underwent conventional ultrasound examinations at our hospital from October 2018 to January 2024. These patients had poor visibility on conventional ultrasound, but the tumor location was visible on abdominal computerized tomography (CT) or magnetic resonance imaging (MRI) scans. We excluded 71 patients who did not undergo percutaneous liver tumor biopsy and 8 patients who remained non-visualized under multimodal ultrasound. Finally, 95 patients met the inclusion criteria and their clinical and radiological data were selected for analysis. Computerized tomography or magnetic resonance imaging-ultrasound (CT/MR-US) fusion imaging was performed in 55 patients, CEUS was conducted in 95 patients, and high-frequency ultrasound examination was performed in 21 patients. The visibility of the lesions using these three techniques was evaluated, and the consistency between the biopsy pathology and the final diagnosis was analyzed. Results: In the study, a total of 39 cases were identified as solitary liver lesions, 34 cases as multiple lesions, and an additional 23 cases as diffuse hepatocellular carcinoma. Among them, there were 13 cases (13.7%) of lesions located in the left lobe, 73 cases (76.8%) in the right lobe, and 9 cases (9.4%) at the junction of the left and right lobes. The average maximum diameter of the lesions was 2.7 cm (ranging from 0.7 cm to 10.0 cm). The detection rates of lesions using CT/MR-US fusion imaging, contrast-enhanced ultrasound (CEUS), and high-frequency ultrasound examination were 49.1%, 96.8%, and 76.2%, respectively. All patients in the entire cohort underwent percutaneous ultrasound-guided biopsy after confirming the target location of the lesions, including 9 patients underwent percutaneous microwave ablation. No severe complications were observed. The accuracy rate of the biopsies was 91.6%, and the positive concordance rate was 91.1%. The biopsy pathology included 39 cases of hepatocellular carcinoma (HCC), 16 cases of intrahepatic cholangiocarcinoma (ICC), 22 cases of metastatic adenocarcinoma, 3 cases of hepatic adenoma (HA), 1 case of vascular sarcoma, and 1 case of mixed HCC-ICC. Among the 13 patients with negative pathology findings after biopsy, 8 cases were considered false negatives (based on follow-up laboratory tests and imaging results consistent with malignant tumor characteristics), resulting in a false negative rate of 8.9%. Conclusions: The application of multimodal ultrasound technology can significantly improve the success rate of liver lesion biopsies that are difficult to visualize using conventional ultrasound. Furthermore, it will facilitate the guidance of microwave ablation therapy for lesions using multimodal ultrasound imaging, enabling collaborative precision treatment planning for patients with clinical physicians.

Polycystic ovary syndrome (PCOS) is a common condition in reproductive-age women. Women with PCOS are at higher risk of pregnancy complications including gestational diabetes (GDM). The prevalence of PCOS and GDM varies according to the diagnostic criteria used. The mechanism for the occurrence of GDM in women with PCOS is still unclear. Materials &amp; Methods- A systematic search of the electronic database was done using keywords like PCOS, and GDM to include studies between April 2008 and March 2023 where uniform diagnostic criteria were used. Studies were excluded with high-risk of bias after the assessment of bias, and meta-analysis was done with relative risks and confidence intervals. Results &amp; Discussion-Out of 1896 search results, 95 were selected for full-text. The quality of the studies was scrutinized and a total of 28 studies were included as they fulfilled the inclusion criteria. The incidence of GDM in women with PCOS was found to be 10.55 % (2.9- 54.9%) from pooled data involving 1,280,245. The prevalence of PCOS in women diagnosed with GDM was found from pooled data of 36,351 women in retrospective studies to be 2.47% (1.5- 50.1%). Selection predominantly included cohort studies, most commonly from China. The meta-analysis reported the risk of GDM is higher in women with PCOS than in those without PCOS (HR: 1.59, 95% CI: 1.27–1.91, p &lt; 0.001). Family history of diabetes, ethnicity, pre-pregnancy BMI, insulin resistance, gestational weight gain, use of assisted reproductive techniques, and multifetal gestation were the risk factors for GDM in women with PCOS. Conclusion- The prevalence of GDM in patients with PCOS is high, but the causality is complex. The newer predictive models are promising in clarifying the causative relationships, yet use various parameters at different cut-offs. There is a need for the development of universally acceptable parameters for the early prediction of GDM in women with PCOS.

Chronic pancreatitis is a progressive fibroinflammatory disorder with no currently satisfactory treatment. Emerging evidence suggests an association between gut microbial dysbiosis and chronic pancreatitis. Although direct causative evidence is lacking, it is hypothesized that the gut microbiota may play a pivotal role in modulating pancreatic function via the gut-pancreas axis. Thus, modulating the gut microbiota through the administration of probiotics or prebiotics may alleviate pancreatic disorders. In this review, we first propose the potential mechanisms by which specific probiotics or prebiotics may ameliorate chronic pancreatitis, including the alleviation of small intestinal bacterial overgrowth (SIBO), facilitation of short-chain fatty acids (SCFAs) production, activation of the glucagon-like peptide 1 receptors (GLP-1Rs) in the pancreas. Since there are currently no probiotics or prebiotics used for the treatment of chronic pancreatitis, we have discussed research in other disease models that use probiotics or prebiotics to modulate pancreatic endocrine and exocrine functions and prevent pancreatic fibrosis. This provides indirect evidence for their potential application in the treatment of chronic pancreatitis. We anticipate that this research will stimulate further investigation into the gut-pancreas axis and the potential therapeutic value of probiotics and prebiotics in chronic pancreatitis.

Glioblastoma (GBM) is a prevalent type of malignancy within the central nervous system (CNS), associated with a poor prognosis. The standard treatment for GBM includes surgical resection of the tumor, followed by radiotherapy and chemotherapy; yet despite these interventions, overall treatment outcomes remain suboptimal. The blood-brain barrier (BBB), which plays a crucial role in maintaining the stability of brain tissue under normal physiological conditions of the CNS,also poses a significant obstacle to the effective delivery of therapeutic agents to GBM. Recent preclinical studies have demonstrated that nanomedicine delivery systems (NDDS) offer promising results, demonstrating both effective GBM targeting and safety, thereby presenting a potential solution for targeted drug delivery. This review explores the various strategies employed in preclinical studies to overcome the BBB for drug delivery. Subsequently, the results of clinical translation of NDDS has been summarized, highlighting the progress made. Finally, we discuss potential strategies for advancing NDDS development and accelerating their translational research through well-designed clinical trials in GBM therapy.

This study investigated a novel radioimmunotherapy strategy for targeting tumor angiogenesis. We developed a radiopharmaceutical complex by labeling an anti-adenosine triphosphate syn-thase (ATPS) monoclonal antibody (mAb) with the radioisotope 177Lu using DOTA as a chelating agent. 177Lu-DOTA-ATPS mAb demonstrated high labeling efficiency (99.0%) and stability in se-rum. MKN-45 cancer cells exhibited the highest cellular uptake, which could be specifically blocked by unlabeled ATPS mAb. In mice, 177Lu-DOTA-ATPS mAb accumulated significantly in tumors, with a tumor uptake of 16.0 ± 1.5%ID/g on day 7. 177Lu-DOTA-ATPS mAb treatment sig-nificantly reduced the viability of MKN-45 cells in a dose-dependent manner. In a xenograft tumor model, this radioimmunotherapy strategy led to substantial tumor growth inhibition (82.8%). Furthermore, combining 177Lu-DOTA-ATPS mAb with sunitinib, an anti-angiogenic drug, en-hanced the therapeutic efficacy of sunitinib in the mouse model. Our study successfully developed 177Lu-DOTA-ATPS mAb, a radioimmunotherapy agent targeting tumor blood vessels. This ap-proach demonstrates significant promise for inhibiting tumor growth, both as a single therapy and in combination with other anti-cancer drugs.

Gestational trophoblastic diseases (GTDs) encompass a spectrum of conditions characterized by abnormal trophoblastic cell growth, ranging from benign molar pregnancies to malignant trophoblastic neoplasms. This systematic review explores the molecular underpinnings of GTDs, focusing on genetic and epigenetic factors that influence disease progression and clinical outcomes. Based on 65 studies identified through systematic search and selection criteria, key findings include dysregulations in tumor suppressor genes such as p53, aberrant apoptotic pathways involving BCL-2, and altered expression of growth factor receptors and microRNAs. These molecular alterations not only differentiate molar pregnancies from normal placental development but also contribute to their clinical behavior, from benign moles to potentially malignant forms. The review synthesizes insights from immunohistochemical studies and molecular analyses to provide a comprehensive understanding of GTD pathogenesis and implications for personalized care strategies.

The literature study delves into novel therapeutic strategies in transplantation medicine, highlighting unique approaches that could revolutionize current practices. It emphasizes novel patterns that have the potential to transform the current landscape. Commencing with a thorough examination of the historical environment, it traces crucial advances and changes in transplantation. The focus is on traditional techniques, emphasizing their limitations and challenges. An extensive examination of contemporary transplantation therapies offers valuable insights into the most recent progress in organ, cellular, and tissue transplantation. This analysis examines the immunological obstacles and outlines the current advancements and constraints. The exploration further navigates into novel models and concepts such as immunomodulation, developments in organ preservation techniques, gene editing, biomaterials, scaffold-based solutions, and the integration of precision medicine, artificial intelligence, and machine learning applications. Each section analyzes the potential for change and current progress in various areas, backed by relevant scientific sources up to 2023. The subsequent sections delve into innovative cellular therapies, biotechnological progress, current clinical trials, and case studies demonstrating effective applications. This examination delves into the ethical and regulatory considerations of innovative therapeutic procedures, emphasizing ethical implications, regulatory frameworks, and the importance of balancing innovation with patient welfare. The literature review concludes by offering a forward-looking perspective, emphasizing potential progress, anticipated challenges, and advocating for collaboration and interdisciplinary approaches to overcome limitations in the field of transplantation medicine.

High hopes were associated with the new anti-angiogenic medicament, bevacizumab, which due to the possibility of binding to isoform A of vascular endothelial growth factor (VEGF), inhibited the creation of new blood vessels. However, not only blood vessels are responsible for the tumor cells' spread. During the process of tumor growth, lymphangiogenesis is mediated by other isoforms of the VEGF family, specifically VEGF-C and VEGF-D, which are independent of the bevacizumab action. Therefore, based on the mechanism of bevacizumab action and the processes of angio- and lymphangiogenesis, we formed three hypotheses: 1) if the lymph nodes in the primary ovarian cancers are metastatic, the outcome of bevacizumab treatment is worsened; 2) concerning the second-line treatment, bevacizumab will act in a weakened manner if the recurrence occurs in lymph nodes compared to local recurrences; 3) patients treated by bevacizumab are more likely to have recurrences in lymph nodes. These hypotheses raise the issue of the existing gap of knowledge, which concerns the effect of bevacizumab on the metastatic lymph nodes.

In the contemporary world of science, continuous observation of specific fixed regions is required, awaiting the emergence of targets or phenomena within those areas to locate and identify them. Astronomy requires continuous monitoring of stars, swiftly pinpointing occurrences, such as flares, and acquiring their spectral data to aid identification. In military contexts, continuous observation of specific areas is essential for promptly locating enemy movements and obtaining target spectral data for deciphering enemy disguises. Additionally, continuous observation of experimental samples in biochemistry is mandatory to pinpoint localized changes and acquire spectral data from those areas. Hence, this paper develops a novel imaging spectrometer solution to monitor localized regions continuously. Specifically, this paper first elaborates on the developed instrument’s characteristics and optical structure, followed by optical design software simulations. The instrument has an F-number of 5, a focal length of 100mm, and a field of view covering the wavelength range from 400nm to 600nm. The optical path diagram indicates that the system’s dispersion and imaging images can be separated, meeting the system’s requirements. Besides, the Modulation Transfer Function (MTF) graph confirms that the image quality meets the requirements. Furthermore, a region-monitoring type imaging spectrometer was constructed to assess the instrument’s capability for spectral observation of fixed regions. The instrument can detect a fixed area, with the target placed within the detection area. Spectral data were successfully obtained by conducting image processing on the acquired images. The correlation coefficient between this data and reference data was as high as 0.9226, indicating the instrument’s successful measurement of the target’s spectrum. Thus, the developed instrument demonstrated that it can image the observed areas and acquire spectral data from targets within those regions.