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Inflammatory periodontal and peri-implant diseases follow dysbiotic shifts in a susceptible host. A well-established tool for microbial sample collection is the use of paper points. The purpose of this pilot study was to evaluate the use of interdental brushes compared to paper points. Biofilm samples were collected with paper points and later interdental brushes from ten patients. Five patients were represented with a community periodontal index of treatment needs (CPITN) of 0–2 around the teeth and an implant with PPD ≤ 5 mm and no radiological bone loss. The remaining five patients had a CPITN ≥ 3 and one implant with peri-implantitis. Microbial samples were analyzed with quantitative polymerase chain reaction (qPCR) and next-generation sequencing (NGS). The results showed higher amounts of DNA in samples taken by interdental brushes but also higher Ct values. Both methods detected Filifactor alocis, Porphyromonas gingivalis, Prevotella intermedia, Tannerella forsythia, and Treponema denticola in the majority of samples, while Aggregatibacter actinomycetemcomitans was rarely found. A microbial dysbiosis index showed comparable or higher values in sites with no periodontitis/peri-implantitis with interdental brushes. The results of this pilot study indicate that interdental brushes might be a valid technique for microbial sampling and particularly advantageous in the early detection of dysbiotic shifts around teeth and implants. Larger studies with more participants are needed to validate the proposed microbial sampling method with interdental brushes. [ABSTRACT FROM AUTHOR]

The MNS7 (Mia) blood group antigen is found at a different prevalence among different ethnic groups. Anti-Mia can cause hemolytic disease of the fetus and newborn (HDFN) and both acute- and delayed-type hemolytic transfusion reactions (HTR). Mia typing should be performed in donors to prevent life-threatening hemolytic transfusion reactions. The gel card and standard tube methods still need specialized equipment, centrifugation, and expertise for result interpretation. We used a novel paper-based analytical device (PAD) pre-coated with monoclonal IgM anti-Mia for Mia phenotyping. We measured grey pixel intensity in blood typing results for interpretation processing using OpenCV at the sample (SP) and elution parts (EP); furthermore, we used the SP: EP ratio and F-score as analysis criteria. We typed 214 blood EDTA samples with PAD–Mia and then compared with gel card results for setting an analysis criterion. We observed 100% sensitivity, specificity, and accuracy when we applied the SP: EP ratio and F-score with the optimal criterion (1.07 and 0.17 for SP: EP ratio and F-score, respectively). The validation of PAD–Mia typing for blood donor samples (n = 150) via F-score gave 100% sensitivity and specificity when compared with the gel card method; therefore, we argue that PAD–Mia typing can be used for Mia phenotyping without sero-centrifugation. Moreover, to study the correlation between genotype and phenotype, PCR-SSP was performed to identify GYP(B-A-B) hybrids. The results revealed that all Mia+ blood samples gave a positive with GP. Hut, GP. HF, GP. Mur, GP. Hop, and GP. Bun. Results of the gel card method and PCR-SSP were concordant. Hence, using PAD–Mia typing in blood donors would be helpful for creating a phenotype database of blood donors for reducing alloimmunization risks. [ABSTRACT FROM AUTHOR]

Visceral leishmaniasis remains a serious public health issue, and Brazil was among the seven countries with the highest prevalence of this disease worldwide. The measures to control this disease are not easily developed, and the improvement of its diagnosis, surveillance, and control is still needed. This study aimed to carry out the polymerase chain reaction (PCR) diagnosis of Leishmania infantum in vector samples in some municipalities of the State of São Paulo, which included two municipalities with human disease transmission and two with dog transmission only. Vectors were collected in traps with luminous bait. Next, they were killed at −4 °C and kept in 70% alcohol. Groups of ten female insects (pools) were mashed on cation exchange paper (fine cellulose phosphate with 18 µEq/cm² ionic exchange capacity) for DNA extraction. The PCR was carried out to identify the natural infection of the Leishmania genus in female Lutzomyia longipalpis (Lu. Longipalpis). Out of the 3,880 Lu. longipalpis phlebotomines, 1060 were female and 2820 were male (3:1). The method used to extract the DNA in pools of ten phlebotomines and the PCR resulted in sensitivity, specificity, practicality, and faster analyses when compared to the individual analysis method. The procedure described can be used on a large scale in the leishmaniasis epidemiological surveillance, enabling a higher number of analyses and the optimization of human resources because the traditional diagnostic method is carried out via desiccation of the insect digestive system and microscopic examination, which is time-demanding and there is the need of manual skills. [ABSTRACT FROM AUTHOR]

Subclinical Myocardial Dysfunction in Patients with Persistent Dyspnea One Year after COVID-19 - Why Should Screening for Cardiovascular Diseases Be Performed? The presence of persistent dyspnea was observed in one-third of the recovered COVID-19 patients, in line with the current data on the long-term evolution of COVID-19 [[3]]. A total of 66 COVID-19 patients without a previous history of cardiorespiratory diseases were evaluated at 12 months for potential subclinical cardiac dysfunction following the acute infection [[2]]. [Extracted from the article]

This paper introduces a novel one-dimensional convolutional neural network that utilizes clinical data to accurately detect choledocholithiasis, where gallstones obstruct the common bile duct. Swift and precise detection of this condition is critical to preventing severe complications, such as biliary colic, jaundice, and pancreatitis. This cutting-edge model was rigorously compared with other machine learning methods commonly used in similar problems, such as logistic regression, linear discriminant analysis, and a state-of-the-art random forest, using a dataset derived from endoscopic retrograde cholangiopancreatography scans performed at Olive View–University of California, Los Angeles Medical Center. The one-dimensional convolutional neural network model demonstrated exceptional performance, achieving 90.77% accuracy and 92.86% specificity, with an area under the curve of 0.9270. While the paper acknowledges potential areas for improvement, it emphasizes the effectiveness of the one-dimensional convolutional neural network architecture. The results suggest that this one-dimensional convolutional neural network approach could serve as a plausible alternative to endoscopic retrograde cholangiopancreatography, considering its disadvantages, such as the need for specialized equipment and skilled personnel and the risk of postoperative complications. The potential of the one-dimensional convolutional neural network model to significantly advance the clinical diagnosis of this gallstone-related condition is notable, offering a less invasive, potentially safer, and more accessible alternative. [ABSTRACT FROM AUTHOR]

Background: The lack of a clear answer regarding the efficacy of physiotherapy in the treatment of cubital tunnel syndrome (CuTS) has led to attempts to critically assess the scientific studies conducted to date. Materials and Methods: Two databases (MEDLINE via PubMed and PEDro) and Google Scholar were used to search for papers. The inclusion criteria were randomized controlled trials, case series, and case reports that evaluate the effects of physiotherapy in the treatment of patients with CuTS. Results: A total of 18 studies met the eligibility criteria, capturing a total of 425 participants. Seven papers were randomized controlled trials, three more described prospective studies without a control group, and eight papers contained case reports. An analysis of the literature evaluating the effectiveness of various forms of broadly defined physiotherapy indicates that their use can have a beneficial effect in reducing many subjective and objective symptoms and improving function. In the majority of papers included in this review, their authors indicated positive therapeutic effects. Only one randomized controlled trial reported no change following therapy. It can therefore be stated that the results of the research conducted so far are optimistic. However, only 7 of the 18 papers were randomized controlled trials, while 3 were prospective studies, and 8 papers were case studies, in which 23 people with CuTS were studied. Conclusions: The small number of randomized clinical trials and their considerable heterogeneity do not allow firm conclusions to be drawn about the effectiveness of physiotherapy in the conservative treatment of CuTS. [ABSTRACT FROM AUTHOR]

Neck infections are often prone to being underestimated and can manifest insidiously. The spread of infection can lead to translocation into thoracic areas, causing descending necrotizing mediastinitis (DNM). However, the application of the post-mortem approach in such cases is not well-described in the literature. A literature review was carried out according to the PRISMA methods. Nine papers were included in the final review, revealing different levels of involvement of neck layers that can be linked to different causes. Expertise with respect to the anatomy of the fasciae and spaces of the neck enables an understanding of the pathogenesis of DNM. However, a clear autoptic description was not provided in any of the articles. Therefore, we also employed a practical post-mortem approach to cases of death due to DNM. It is fundamental for pathologists to identify the exact head and neck structures involved. Providing dissectors with support from an otolaryngologist could be useful. This paper could help address such difficult cases. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology. [ABSTRACT FROM AUTHOR]

The paper presents a system for analyzing cardiac activity with the possibility of continuous and remote monitoring. The created sensor mobile device monitors heart activity by means of the convenient and imperceptible registration of cardiac signals. At the same time, the behavior of the human body is also monitored through the accelerometer and gyroscope built into the device, thanks to which it is possible to signal in the event of loss of consciousness or fall (in patients with syncope). Conducting real-time cardio monitoring and the analysis of recordings using various mathematical methods (linear, non-linear, and graphical) enables the research, accurate diagnosis, timely assistance, and correct treatment of cardiovascular diseases. The paper examines the recordings of patients diagnosed with arrhythmia and syncope recorded by electrocardiography (ECG) sensors in real conditions. The obtained results are subjected to statistical analysis to determine the accuracy and significance of the obtained results. The studies show significant deviations in the patients with arrhythmia and syncope regarding the obtained values of the studied parameters of heart rate variability (HRV) from the accepted normal values (for example, the root mean square of successive differences between normal heartbeats (RMSSD) in healthy individuals is 24.02 ms, while, in patients with arrhythmia (6.09 ms) and syncope (5.21 ms), it is much lower). The obtained quantitative and graphic results identify some possible abnormalities and demonstrate disorders regarding the activity of the autonomic nervous system, which is directly related to the work of the heart. [ABSTRACT FROM AUTHOR]

Background: In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical exposure must be kept "As Low As Reasonably Achievable". Very few articles on guidelines for radiotherapy-computed tomography scans are available. This paper reviews the current literature on radiation dose optimization based on the effective dose and diagnostic reference level (DRL) for head, neck, and pelvic CT procedures used in radiation therapy planning. This paper explores the strategies used to optimize radiation doses, and high-quality images for diagnosis and treatment planning. Methods: A cross-sectional study was conducted on 300 patients with head, neck, and pelvic region cancer in our institution. The DRL, effective dose, volumetric CT dose index (CTDIvol), and dose-length product (DLP) for the present and optimized protocol were calculated. DRLs were proposed for the DLP using the 75th percentile of the distribution. The DLP is a measure of the radiation dose received by a patient during a CT scan and is calculated by multiplying the CT dose index (CTDI) by the scan length. To calculate a DRL from a DLP, a large dataset of DLP values obtained from a specific imaging procedure must be collected and can be used to determine the median or 75th-percentile DLP value for each imaging procedure. Results: Significant variations were found in the DLP, CTDIvol, and effective dose when we compared both the standard protocol and the optimized protocol. Also, the optimized protocol was compared with other diagnostic and radiotherapy CT scan studies conducted by other centers. As a result, we found that our institution's DRL was significantly low. The optimized dose protocol showed a reduction in the CTDIvol (70% and 63%), DLP (60% and 61%), and effective dose (67% and 62%) for both head, neck, and pelvic scans. Conclusions: Optimized protocol DRLs were proposed for comparison purposes. [ABSTRACT FROM AUTHOR]

However, the GCW and GWI values of the two groups, both with and without dyspnea, do not deviate from the normal reference values, which are based on the NORRE study [[2]] and that were underpinned by the authors. The study reports the following echocardiographic differences: Compared to asymptomatic patients, patients with dyspnea presented lower LV constructive work (GCW): 2183.7 ± 487.9 vs. 2483.1 ± 422.4, I p i = 0.024; and a lower global work index (GWI): 1960.0 ± 396.2 vs. 2221.1 ± 407.9, I p i = 0.030. This study compares 23 patients with self-reported dyspnea and 43 without 1 year after hospital admission for COVID-19. [Extracted from the article]

(1) Background: In digital-technology-assisted nasal defect reconstruction methods, a crucial step involves utilizing computer-aided design to virtually reconstruct the nasal defect's complete morphology. However, current digital methods for virtual nasal defect reconstruction have yet to achieve efficient, precise, and personalized outcomes. In this research paper, we propose a novel approach for reconstructing external nasal defects based on the Facial Mesh Generation Network (FMGen-Net), aiming to enhance the levels of automation and personalization in virtual reconstruction. (2) Methods: We collected data from 400 3D scans of faces with normal morphology and combined the structured 3D face template and the Meshmonk non-rigid registration algorithm to construct a structured 3D facial dataset for training FMGen-Net. Guided by defective facial data, the trained FMGen-Net automatically generated an intact 3D face that was similar to the defective face, and maintained a consistent spatial position. This intact 3D face served as the 3D target reference face (3D-TRF) for nasal defect reconstruction. The reconstructed nasal data were extracted from the 3D-TRF based on the defective area using reverse engineering software. The '3D surface deviation' between the reconstructed nose and the original nose was calculated to evaluate the effect of 3D morphological restoration of the nasal defects. (3) Results: In the simulation experiment of 20 cases involving full nasal defect reconstruction, the '3D surface deviation' between the reconstructed nasal data and the original nasal data was 1.45 ± 0.24 mm. The reconstructed nasal data, constructed from the personalized 3D-TRF, accurately reconstructed the anatomical morphology of nasal defects. (4) Conclusions: This paper proposes a novel method for the virtual reconstruction of external nasal defects based on the FMGen-Net model, achieving the automated and personalized construction of the 3D-TRF and preliminarily demonstrating promising clinical application potential. [ABSTRACT FROM AUTHOR]

This research paper outlines a method for automatically classifying wakefulness and deep sleep stage (N3) based on the American Academy of Sleep Medicine (AASM) standards. The study employed a single-channel EEG signal, leveraging the Wigner–Ville Distribution (WVD) for time–frequency analysis to determine EEG energy per second in specific frequency bands (δ , θ , α , and entire band). Particle Swarm Optimization (PSO) was used to optimize thresholds for distinguishing between wakefulness and stage N3. This process aims to mimic a sleep technician's visual scoring but in an automated fashion, with features and thresholds extracted to classify epochs into correct sleep stages. The study's methodology was validated using overnight PSG recordings from 20 subjects, which were evaluated by a technician. The PSG setup followed the 10–20 standard system with varying sampling rates from different hospitals. Two baselines, T1 for the wake stage and T2 for the N3 stage, were calculated using PSO to ascertain the best thresholds, which were then used to classify EEG epochs. The results showed high sensitivity, accuracy, and kappa coefficient, indicating the effectiveness of the classification algorithm. They suggest that the proposed method can reliably determine sleep stages, being aligned closely with the AASM standards and offering an intuitive approach. The paper highlights the strengths of the proposed method over traditional classifiers and expresses the intentions to extend the algorithm to classify all sleep stages in the future. [ABSTRACT FROM AUTHOR]

Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) are important liver enzymes in clinical settings. Their levels are known to be elevated in individuals with underlying liver diseases and those consuming hepatotoxic drugs. Serum ALT and AST levels are crucial for diagnosing and assessing liver diseases. Serum ALT is considered the most reliable and specific candidate as a disease biomarker for liver diseases. ALT and AST levels are routinely analyzed in high-risk individuals for the bioanalysis of both liver function and complications associated with drug-induced liver injury. Typically, ALT and AST require blood sampling, serum separation, and testing. Traditional methods require expensive or sophisticated equipment and trained specialists, which is often time-consuming. Therefore, developing countries have limited or no access to these methods. To address the above issues, we hypothesize that low-cost biosensing methods (paper-based assays) can be applied to the analysis of ALT and AST levels in biological fluids. The paper-based biodetection technique can semi-quantitatively measure ALT and AST from capillary finger sticks, and it will pave the way for the development of an inexpensive and rapid alternative method for the early detection and diagnosis of liver diseases. This method is expected to significantly reduce the economic burden and aid routine clinical analysis in both developed and underdeveloped countries. The development of low-cost testing platforms and their diagnostic utility will be extremely beneficial in helping millions of patients with liver disorders. [ABSTRACT FROM AUTHOR]

Beyond the challenges of diagnosis, complexity measurement in clients with mental illness is an important but under-recognized area. Accurate and appropriate psychiatric diagnoses are essential, and further complexity measurements could contribute to improving patient understanding, referral, and service matching and coordination, outcome evaluation, and system-level care planning. Myriad conceptualizations, frameworks, and definitions of patient complexity exist, which are operationalized by a variety of complexity measuring tools. A limited number of these tools are developed for people with mental illness, and they differ in the extent to which they capture clinical, psychosocial, economic, and environmental domains. Guided by the PRISMA Extension for Scoping Reviews, this review evaluates the tools best suited for different mental health settings. The search found 5345 articles published until November 2023 and screened 14 qualified papers and corresponding tools. For each of these, detailed data on their use of psychiatric diagnostic categories, definition of complexity, primary aim and purpose, context of use and settings for their validation, best target populations, historical references, extent of biopsychosocial information inclusion, database and input technology required, and performance assessments were extracted, analyzed, and presented for comparisons. Two tools—the INTERMED, a clinician-scored and multiple healthcare data-sourced tool, and the VCAT, a computer-based instrument that utilizes healthcare databases to generate a comprehensive picture of complexity—are exemplary among the tools reviewed. Information on these limited but suitable tools related to their unique characteristics and utilities, and specialized recommendations for their use in mental health settings could contribute to improved patient care. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurological disorder that significantly impairs cognitive function, leading to memory loss and eventually death. AD progresses through three stages: early stage, mild cognitive impairment (MCI) (middle stage), and dementia. Early diagnosis of Alzheimer's disease is crucial and can improve survival rates among patients. Traditional methods for diagnosing AD through regular checkups and manual examinations are challenging. Advances in computer-aided diagnosis systems (CADs) have led to the development of various artificial intelligence and deep learning-based methods for rapid AD detection. This survey aims to explore the different modalities, feature extraction methods, datasets, machine learning techniques, and validation methods used in AD detection. We reviewed 116 relevant papers from repositories including Elsevier (45), IEEE (25), Springer (19), Wiley (6), PLOS One (5), MDPI (3), World Scientific (3), Frontiers (3), PeerJ (2), Hindawi (2), IO Press (1), and other multiple sources (2). The review is presented in tables for ease of reference, allowing readers to quickly grasp the key findings of each study. Additionally, this review addresses the challenges in the current literature and emphasizes the importance of interpretability and explainability in understanding deep learning model predictions. The primary goal is to assess existing techniques for AD identification and highlight obstacles to guide future research. [ABSTRACT FROM AUTHOR]

Retrograde axonal neurodegeneration along the visual pathway—either direct or trans-synaptic—has already been demonstrated in multiple sclerosis (MS), as well as in compressive, vascular, or posttraumatic lesions of the visual pathway. Optical coherence tomography (OCT) can noninvasively track macular and optic nerve changes occurring as a result of this phenomenon. Our paper aimed to review the existing literature regarding hemimacular atrophic changes in the ganglion cell layer identified using OCT examination in MS patients without prior history of optic neuritis. Homonymous hemimacular atrophy has been described in post-chiasmal MS lesions, even in patients with normal visual field results. Temporal and nasal macular OCT evaluation should be performed separately in all MS patients, in addition to an optic nerve OCT evaluation and a visual field exam. [ABSTRACT FROM AUTHOR]

Mesenteric ischemia diagnosis is challenging, with an overall mortality of up to 50% of cases despite advances in treatment. The main problem that affects the outcome is 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 the diagnostic test of choice in suspected mesenteric bowel ischemia. MDCTA can accurately detect the presence of arterial and venous thrombosis, determine the extent and the gastrointestinal tract involved, and provide detailed information determining the subtype and the stage progression of the diseases, helping clinicians and surgeons with appropriate management. CT (Computed Tomography) 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 (CT Angiography) protocol; 2. to explain the CTA arterial and venous supply to the gastrointestinal tract and the relevant ischemic pattern; and 3. to describe vascular, bowel, and extraintestinal CT findings for the diagnosis of acute mesenteric ischemia. [ABSTRACT FROM AUTHOR]

This paper presents a combined optical coherence tomography (OCT) imaging/machine learning (ML) technique for real-time analysis of lung tissue morphology to determine the presence and level of invasiveness of idiopathic lung fibrosis (ILF). This is an important clinical problem as misdiagnosis is common, resulting in patient exposure to costly and invasive procedures and substantial use of healthcare resources. Therefore, biopsy is needed to confirm or rule out radiological findings. Videoscopic-assisted thoracoscopic wedge biopsy (VATS) under general anesthesia is typically necessary to obtain enough tissue to make an accurate diagnosis. This kind of biopsy involves the placement of several tubes through the chest wall, one of which is used to cut off a piece of lung to send for evaluation. The removed tissue is examined histopathologically by microscopy to confirm the presence and the pattern of fibrosis. However, VATS pulmonary biopsy can have multiple side effects, including inflammation, tissue morbidity, and severe bleeding, which further degrade the quality of life for the patient. Furthermore, the results are not immediately available, requiring tissue processing and analysis. Here, we report an initial attempt of using ML-assisted polarization sensitive OCT (PS-OCT) imaging for lung fibrosis assessment. This approach has been preliminarily tested on a rat model of lung fibrosis. Our preliminary results show that ML-assisted PS-OCT imaging can detect the presence of ILF with an average of 77% accuracy and 89% specificity. [ABSTRACT FROM AUTHOR]

Autoimmune pancreatitis (AIP) is a unique form of chronic pancreatitis with a multifactorial pathogenesis. Historically, it has been classified as type 1 and type 2, according to its clinical and histological features. The diagnosis of AIP is challenging and relies on a combination of clinical, histopathologic, serologic, and imaging characteristics. In the available guidelines, the imaging hallmarks of AIP are based on cross-sectional imaging and cholangiopancreatography retrograde endoscopic findings. Endoscopic ultrasound (EUS) is generally used for pancreatic tissue acquisition to rule out pancreatic cancer and diagnose AIP with limited accuracy. Several papers reported the reliability of EUS for providing informative morphologic features of AIP. Nowadays, the improvement in the resolution of EUS conventional images and the development of new ancillary technologies have further increased the diagnostic yield of EUS: contrast-enhanced EUS and EUS elastography are non-invasive and real-time techniques that strongly support the diagnosis and management of pancreatic diseases. In this review article, we will present the role of conventional EUS and ancillary diagnostic techniques in the diagnosis of AIP to support clinicians and endosonographers in managing this condition. [ABSTRACT FROM AUTHOR]

Deep learning has attained state-of-the-art results in general image segmentation problems; however, it requires a substantial number of annotated images to achieve the desired outcomes. In the medical field, the availability of annotated images is often limited. To address this challenge, few-shot learning techniques have been successfully adapted to rapidly generalize to new tasks with only a few samples, leveraging prior knowledge. In this paper, we employ a gradient-based method known as Model-Agnostic Meta-Learning (MAML) for medical image segmentation. MAML is a meta-learning algorithm that quickly adapts to new tasks by updating a model's parameters based on a limited set of training samples. Additionally, we use an enhanced 3D U-Net as the foundational network for our models. The enhanced 3D U-Net is a convolutional neural network specifically designed for medical image segmentation. We evaluate our approach on the TotalSegmentator dataset, considering a few annotated images for four tasks: liver, spleen, right kidney, and left kidney. The results demonstrate that our approach facilitates rapid adaptation to new tasks using only a few annotated images. In 10-shot settings, our approach achieved mean dice coefficients of 93.70%, 85.98%, 81.20%, and 89.58% for liver, spleen, right kidney, and left kidney segmentation, respectively. In five-shot sittings, the approach attained mean Dice coefficients of 90.27%, 83.89%, 77.53%, and 87.01% for liver, spleen, right kidney, and left kidney segmentation, respectively. Finally, we assess the effectiveness of our proposed approach on a dataset collected from a local hospital. Employing five-shot sittings, we achieve mean Dice coefficients of 90.62%, 79.86%, 79.87%, and 78.21% for liver, spleen, right kidney, and left kidney segmentation, respectively. [ABSTRACT FROM AUTHOR]

In recent years, Convolutional Neural Network (CNN) models have demonstrated notable advancements in various domains such as image classification and Natural Language Processing (NLP). Despite their success in image classification tasks, their potential impact on medical image retrieval, particularly in text-based medical image retrieval (TBMIR) tasks, has not yet been fully realized. This could be attributed to the complexity of the ranking process, as there is ambiguity in treating TBMIR as an image retrieval task rather than a traditional information retrieval or NLP task. To address this gap, our paper proposes a novel approach to re-ranking medical images using a Deep Matching Model (DMM) and Medical-Dependent Features (MDF). These features incorporate categorical attributes such as medical terminologies and imaging modalities. Specifically, our DMM aims to generate effective representations for query and image metadata using a personalized CNN, facilitating matching between these representations. By using MDF, a semantic similarity matrix based on Unified Medical Language System (UMLS) meta-thesaurus, and a set of personalized filters taking into account some ranking features, our deep matching model can effectively consider the TBMIR task as an image retrieval task, as previously mentioned. To evaluate our approach, we performed experiments on the medical ImageCLEF datasets from 2009 to 2012. The experimental results show that the proposed model significantly enhances image retrieval performance compared to the baseline and state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Glaucoma is a chronic eye condition that seriously impairs vision and requires early diagnosis and treatment. Automated detection techniques are essential for obtaining a timely diagnosis. In this paper, we propose a novel method for feature selection that integrates the cuckoo search algorithm with Caputo fractional order (CFO-CS) to enhance the performance of glaucoma classification. However, when using the infinite series, the Caputo definition has memory length truncation issues. Therefore, we suggest a fixed memory step and an adjustable term count for optimization. We conducted experiments integrating various feature extraction techniques, including histograms of oriented gradients (HOGs), local binary patterns (LBPs), and deep features from MobileNet and VGG19, to create a unified vector. We evaluate the informative features selected from the proposed method using the k-nearest neighbor. Furthermore, we use data augmentation to enhance the diversity and quantity of the training set. The proposed method enhances convergence speed and the attainment of optimal solutions during training. The results demonstrate superior performance on the test set, achieving 92.62% accuracy, 94.70% precision, 93.52% F1-Score, 92.98% specificity, 92.36% sensitivity, and 85.00% Matthew's correlation coefficient. The results confirm the efficiency of the proposed method, rendering it a generalizable and applicable technique in ophthalmology. [ABSTRACT FROM AUTHOR]

Systemic lupus erythematosus (SLE) is a chronic autoimmune condition that can seriously impair multiple organs including the nervous system, causing neuropsychiatric SLE (NPSLE), which encompasses a broad range of symptoms. Pathogenesis is not completely understood but is thought to involve inflammatory and vascular pathways. This comprehensive review discusses the complex nature and heterogeneity of NPSLE and the challenges in diagnosis and treatment that result from it. Diagnosis often requires a multidisciplinary approach with multiple assessments, including laboratory testing, imaging, and neuropsychological evaluations. Current treatments focus on managing symptoms through immunosuppressive and anti-thrombotic therapies tailored to the inflammatory or vascular nature of the specific NPSLE manifestations. This paper emphasizes the necessity for interdisciplinary approaches and further research to enhance diagnostic accuracy and treatment effectiveness. It also highlights the importance of understanding the underlying mechanisms of NPSLE to develop more targeted therapies, citing the need for high-quality studies and novel treatment agents. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) is an infectious disease caused by Mycobacterium. It primarily impacts the lungs but can also endanger other organs, such as the renal system, spine, and brain. When an infected individual sneezes, coughs, or speaks, the virus can spread through the air, which contributes to its high contagiousness. The goal is to enhance detection recognition with an X-ray image dataset. This paper proposed a novel approach, named the Tuberculosis Segmentation-Guided Diagnosis Model (TSSG-CNN) for Detecting Tuberculosis, using a combined semantic segmentation and adaptive convolutional neural network (CNN) architecture. The proposed approach is distinguished from most of the previously proposed approaches in that it uses the combination of a deep learning segmentation model with a follow-up classification model based on CNN layers to segment chest X-ray images more precisely as well as to improve the diagnosis of TB. It contrasts with other approaches like ILCM, which is optimized for sequential learning, and explainable AI approaches, which focus on explanations. Moreover, our model is beneficial for the simplified procedure of feature optimization from the perspectives of approach using the Mayfly Algorithm (MA). Other models, including simple CNN, Batch Normalized CNN (BN-CNN), and Dense CNN (DCNN), are also evaluated on this dataset to evaluate the effectiveness of the proposed approach. The performance of the TSSG-CNN model outperformed all the models with an impressive accuracy of 98.75% and an F1 score of 98.70%. The evaluation findings demonstrate how well the deep learning segmentation model works and the potential for further research. The results suggest that this is the most accurate strategy and highlight the potential of the TSSG-CNN Model as a useful technique for precise and early diagnosis of TB. [ABSTRACT FROM AUTHOR]

Spreading quickly throughout populations, whether animal or human-borne, infectious illnesses provide serious risks and difficulties. Controlling their spread and averting disinformation requires effective risk assessment and epidemic identification. Technology-enabled data analysis on diseases allows for quick solutions to these problems. A Combinational Data Assessment Scheme intended to accelerate disease detection is presented in this paper. The suggested strategy avoids duplicate data replication by sharing data among edge devices. It uses indexed data gathering to improve early detection by using tree classifiers to discern between various kinds of information. Both data similarity and index measurements are considered throughout the data analysis stage to minimize assessment errors. Accurate risk detection and assessment based on information kind and sharing frequency are ensured by comparing non-linear accumulations with accurate shared edge data. The suggested system exhibits high accuracy, low mistakes, and decreased data repetition to improve overall effectiveness in illness detection and risk reduction. [ABSTRACT FROM AUTHOR]

Technological improvements have introduced significant innovations in dentistry and broadened the array of tools and techniques in dental care. One technological development that has been widely researched over the past 20 years is the use of Near-Infrared Transillumination (NIRT) imaging for the diagnosis of dental caries. This paper aims to introduce a comprehensive collection of NIRT images, intended as a reference tool for routine dental examinations, dental research, pedagogical activities, and forensic odontology. The collection presents pairwise clinical and NIRT images categorized as follows: (a) healthy teeth, (b) carious teeth, (c) restored teeth, (d) enamel defects, and (e) diverse findings. This atlas could be a valuable tool for the dental community as it is designed as an identification guide of NIRT illustrated dental features. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) is a promising technology to enhance image guided surgery and represents the perfect bridge to combine precise virtual planning with computer-aided execution of surgical maneuvers in the operating room. In craniofacial surgical oncology, AR brings to the surgeon's sight a digital, three-dimensional representation of the anatomy and helps to identify tumor boundaries and optimal surgical paths. Intraoperatively, real-time AR guidance provides surgeons with accurate spatial information, ensuring accurate tumor resection and preservation of critical structures. In this paper, the authors review current evidence of AR applications in craniofacial surgery, focusing on real surgical applications, and compare existing literature with their experience during an AR and navigation guided craniofacial resection, to subsequently analyze which technological trajectories will represent the future of AR and define new perspectives of application for this revolutionizing technology. [ABSTRACT FROM AUTHOR]

Computed tomography (CT)-guided lung biopsy is one of the oldest and most widely known minimally invasive percutaneous procedures. Despite being conceptually simple, this procedure needs to be performed rapidly and can be subject to meaningful complications that need to be managed properly. Therefore, knowledge of principles and techniques is required by every general or interventional radiologist who performs the procedure. This review aims to contain all the information that the operator needs to know before performing the procedure. The paper starts with the description of indications, devices, and types of percutaneous CT-guided lung biopsies, along with their reported results in the literature. Then, pre-procedural evaluation and the practical aspects to be considered during procedure (i.e., patient positioning and breathing) are discussed. The subsequent section is dedicated to complications, with their incidence, risk factors, and the evidence-based measures necessary to both prevent or manage them; special attention is given to pneumothorax and hemorrhage. After conventional CT, this review describes other available CT modalities, including CT fluoroscopy and cone-beam CT. At the end, more advanced techniques, which are already used in clinical practice, like fusion imaging, are included. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes. [ABSTRACT FROM AUTHOR]

One of the most challenging problems when diagnosing autism spectrum disorder (ASD) is the need for long sets of data. Collecting data during such long periods is challenging, particularly when dealing with children. This challenge motivates the investigation of possible classifiers of ASD that do not need such long data sets. In this paper, we use eye-tracking data sets covering only 5 s and introduce one metric able to distinguish between ASD and typically developed (TD) gaze patterns based on such short time-series and compare it with two benchmarks, one using the traditional eye-tracking metrics and one state-of-the-art AI classifier. Although the data can only track possible disorders in visual attention and our approach is not a substitute to medical diagnosis, we find that our newly introduced metric can achieve an accuracy of 93 % in classifying eye gaze trajectories from children with ASD surpassing both benchmarks while needing fewer data. The classification accuracy of our method, using a 5 s data series, performs better than the standard metrics in eye-tracking and is at the level of the best AI benchmarks, even when these are trained with longer time series. We also discuss the advantages and limitations of our method in comparison with the state of the art: besides needing a low amount of data, this method is a simple, understandable, and straightforward criterion to apply, which often contrasts with "black box" AI methods. [ABSTRACT FROM AUTHOR]

Vasa previa is a pregnancy complication that occurs when unprotected fetal blood vessels traverse the cervical os, placing the fetus at high risk of exsanguination and fetal death. These fetal vessels may be compromised by fetal movement and compression, leading to poor oxygen distribution and asphyxiation. Diagnostic tools for vasa previa management and preterm labor (PTL) include transvaginal ultrasound, cervical length (CL) surveillance and use of fetal fibronectin (FFN) testing. These tools can prove to be quite useful as they allow for lead time in the prediction of PTL and spontaneous rupture of membranes which can result in devastating outcomes for pregnancies affected by vasa previa. We conducted a literature review on vasa previa management and the usefulness of FFN and CL surveillance in predicting PTL and found 36 related papers. Although there is limited research available to show the impact of FFN and CL surveillance in the management of vasa previa, there is sufficient evidence to support FFN and CL surveillance in predicting the onset of PTL, which can have devastating consequences for the pregnancies affected. It can be extrapolated that these tools, by helping to determine pregnancies at risk for PTL, could improve management and outcomes in patients with vasa previa. Future studies investigating the management of vasa previa with FFN and CL surveillance to reduce the burden of PTL and its associated comorbidities are warranted. [ABSTRACT FROM AUTHOR]

Background and objectives: This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. Method: A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. Results: Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. Conclusions: When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine. [ABSTRACT FROM AUTHOR]

Background: This investigation is both a study of potential non-invasive diagnostic approaches for the bladder cancer biomarker UBC® Rapid test and a study including novel comparative methods for bioassay evaluation and comparison that uses bladder cancer as a useful example. The objective of the paper is not to investigate specific data. It is used only for demonstration, partially to compare ROC methodologies and also to show how both sensitivity/specificity and predictive values can be used in clinical diagnostics and decision making. This study includes ROC curves with integrated cut-off distribution curves for a comparison of sensitivity/specificity (SS) and positive/negative predictive values (PPV/NPV or PV), as well as SS-J index/PV-PSI index–ROC curves and SS-J/PV-PSI index cut-off diagrams (J = Youden, PSI = Predictive Summary Index) for the unified direct comparison of SS-J/PV results achieved via quantitative and/or qualitative bioassays and an identification of optimal separate or unified index cut-off points. Patients and Methods: According to the routine diagnostics, there were 91 patients with confirmed bladder cancer and 1152 patients with no evidence of bladder cancer, leading to a prevalence value of 0.073. This study performed a quantitative investigation of used-up test cassettes from the visual UBC® Rapid qualitative point-of-care assay, which had already been applied in routine diagnostics. Using a photometric reader, quantitative data could also be obtained from the test line of the used cassettes. Interrelations between SS and PV values were evaluated using cumulative distribution analysis (CAD), SS/PV–ROC curves, SS-J/PV-PSI index–ROC curves, and the SS-J/PV-PSI index cut-off diagram. The maximum unified SS-J/PV-PSI index value and its corresponding cut-off value were determined and calculated with the SS-J/PV-PSI index cut-off diagram. Results: The use of SS/PV–ROC curves with integrated cut-off concentration distribution curves provides improved diagnostic information compared to "traditional" ROC curves. The threshold distributions integrated as curves into SS/PV–ROC curves and SS-J/PV-PSI index–ROC curves run in opposite directions. In contrast to the SS–ROC curves, the PV–ROC and the novel PV-PSI index–ROC curves had neither an area under the curve (AUC) nor a range from 0% to 100%. The cut-off level of the qualitative assay was 7.5 µg/L, with a sensitivity of 65.9% and a specificity of 63.3%, and the PPV was 12.4% and the NPV was 95.9%, at a threshold value of 12.5 µg/L. Based on these set concentrations, the reader-based evaluation revealed a graphically estimated 5% increase in sensitivity and a 13% increase in specificity, as compared to the visual qualitative POC test. In the case of predictive values, there was a gain of 8% for PPV and 10% for NPV. The index values and cut-offs were as follows: visual SS-J index, 0.328 and 35 µg/L; visual PV-PSI index, 0.083 and 5.4 µg/L; maximal reader Youden index, 0.0558 and 250 µg/L; and maximal PV-PSI index, 0.459 and 250 µg/L, respectively. The maximum unified SS-J/PV-PSI index value was 0.32, and the cut-off was 43 µg/L. The reciprocal SS-J index correctly detected one out of three patients, while the reciprocal PV-PSI index gave one out of twelve patients a correct diagnosis. Conclusions: ROC curves including cut-off distribution curves supplement the information lost in "traditionally plotted" ROC curves. The novel sets of ROC and index–ROC curves and the new SS/PV index cut-off diagrams enable the simultaneous comparison of sensitivity/specificity and predictive value profiles of diagnostic tools and the identification of optimal cut-off values at maximal index values, even in a unifying SS/PV approach. Because the curves within an SS-J/PV-PSI index cut-off diagram are distributed over the complete cut-off range of a quantitative assay, this field is open for special clinical considerations, with the need to vary the mentioned clinical diagnostic parameters. Complete or partial areas over the x-axis (AOX) can be calculated for summarized quantitative or qualitative effectivity evaluations with respect to single and/or unified SS-J and PV-PSI indices and with respect to single, several, or several unified assays. The SS-J/PV-PSI index-AOX approach is a new tool providing additional joint clinical information, and the reciprocal SS-J indices can predict the number of patients with a correct diagnosis and the number of persons who need to be examined in order to correctly predict a diagnosis of the disease. These methods could be used in applications like medical or plant epidemiology, machine learning algorithms, and neural networks. [ABSTRACT FROM AUTHOR]

Background: Chronic obstructive pulmonary disease (COPD) has higher rates among the general population, so early identification and prevention is the goal. The mechanisms of COPD development have not been completely established, although it has been demonstrated that endothelial dysfunction plays an important role. However, to date, the measurement of endothelial dysfunction is still invasive or not fully established. Nailfold video capillaroscopy (NVC) is a safe, non-invasive diagnostic tool that can be used to easily evaluate the microcirculation and can show any possible endothelial dysfunctions early on. The aim of this review is to evaluate if nailfold microcirculation abnormalities can reflect altered pulmonary vasculature and can predict the risk of cardiovascular comorbidities in COPD patients. Methods: A systematic literature search concerning COPD was performed in electronic databases (PUBMED, UpToDate, Google Scholar, ResearchGate), supplemented with manual research. We searched in these databases for articles published until March 2024. The following search words were searched in the databases in all possible combinations: chronic obstructive pulmonary disease (COPD), endothelial damage, vascular impairment, functional evaluation, capillaroscopy, video capillaroscopy, nailfold video capillaroscopy. Only manuscripts written in English were considered for this review. Papers were included only if they were able to define a relationship between COPD and endothelium dysfunction. Results: The search selected 10 articles, and among these, only three previous reviews were available. Retinal vessel imaging, flow-mediated dilation (FMD), and skin autofluorescence (AF) are reported as the most valuable methods for assessing endothelial dysfunction in COPD patients. Conclusions: It has been assumed that decreased nitric oxide (NO) levels leads to microvascular damage in COPD patients. This finding allows us to assume NVC's potential effectiveness in COPD patients. However, this potential link is based on assumption; further investigations are needed to confirm this hypothesis. [ABSTRACT FROM AUTHOR]

Metastases are complications of primary tumors due to prolonged cancer survival and have become an important issue for oncological patients and the most frequent cause of death and disability. Bone metastases occur at a later stage of cancer disease, and the spine is the most frequent site. To date, the aim of the treatment of metastases remains to be the control of disease and provide a satisfactory quality of life. The decision making of treatment is influenced by several factors such as the status of the primary disease, the number of metastases, site involvement, and the performance status of the patients. For this reason, the treatment of metastases is challenging and undergoes constant development. Therefore, alternative techniques with respect to surgery, which is the first option but not always practicable, and radiochemotherapy are attractive. Lately, electrochemotherapy has emerged as an innovative method for treating various primary and metastatic solid tumors, showing promising outcomes in terms of inducing tumor tissue necrosis and alleviating symptoms. This technique uses electric pulses to increase the uptake of chemotherapy by tumor cells. Despite the initial enthusiasm and good results in the treatment of bone tumors, relatively few papers have described its use in spine metastases. Therefore, we conducted a systemic review of this intriguing topic while also reporting our experience in the use of electrochemotherapy for the treatment of spine metastases. [ABSTRACT FROM AUTHOR]

Atrial septal defects (ASDs) present a significant healthcare challenge, demanding accurate and timely diagnosis and precise management to ensure optimal patient outcomes. Artificial intelligence (AI) applications in healthcare are rapidly evolving, offering promise for enhanced medical decision-making and patient care. In the context of cardiology, the integration of AI promises to provide more efficient and accurate diagnosis and personalized treatment strategies for ASD patients. In interventional cardiology, sometimes the lack of precise measurement of the cardiac rims evaluated by transthoracic echocardiography combined with the floppy aspect of the rims can mislead and result in complications. AI software can be created to generate responses for difficult tasks, like which device is the most suitable for different shapes and dimensions to prevent embolization or erosion. This paper reviews the current state of AI in healthcare and its applications in cardiology, emphasizing the specific opportunities and challenges in applying AI to ASD diagnosis and management. By exploring the capabilities and limitations of AI in ASD diagnosis and management. This paper highlights the evolution of medical practice towards a more AI-augmented future, demonstrating the capacity of AI to unlock new possibilities for healthcare professionals and patients alike. [ABSTRACT FROM AUTHOR]

Our image recognition system employs a deep learning model to differentiate between the left and right upper limbs in images, allowing doctors to determine the correct surgical position. From the experimental results, it was found that the precision rate and the recall rate of the intelligent image recognition system for preventing wrong-site upper limb surgery proposed in this paper could reach 98% and 93%, respectively. The results proved that our Artificial Intelligence Image Recognition System (AIIRS) could indeed assist orthopedic surgeons in preventing the occurrence of wrong-site left and right upper limb surgery. At the same time, in future, we will apply for an IRB based on our prototype experimental results and we will conduct the second phase of human trials. The results of this research paper are of great benefit and research value to upper limb orthopedic surgery. [ABSTRACT FROM AUTHOR]

Sudden cardiac death (SCD) in children is a devastating event, often linked to primary electrical diseases (PED) of the heart. PEDs, often referred to as channelopathies, are a group of genetic disorders that disrupt the normal ion channel function in cardiac cells, leading to arrhythmias and sudden cardiac death. This paper investigates the unique challenges of risk assessment and stratification for channelopathy-related SCD in pediatric patients—Brugada syndrome, catecholaminergic polymorphic ventricular tachycardia, idiopathic ventricular fibrillation, long QT syndrome, Anderson–Tawil syndrome, short QT syndrome, and early repolarization syndrome. We explore the intricate interplay of genetic, clinical, and electrophysiological factors that contribute to the complex nature of these conditions. Recognizing the significance of early identification and tailored management, this paper underscores the need for a comprehensive risk stratification approach specifically designed for pediatric populations. By integrating genetic testing, family history, and advanced electrophysiological evaluation, clinicians can enhance their ability to identify children at the highest risk for SCD, ultimately paving the way for more effective preventive strategies and improved outcomes in this vulnerable patient group. [ABSTRACT FROM AUTHOR]

The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence is linked to several factors, including the use of injection drugs, poor sterilization practices in medical facilities, and low public awareness. This paper introduces a hyOPTGB model, which employs an optimized gradient boosting (GB) classifier to predict HCV disease in Egypt. The model's accuracy is enhanced by optimizing hyperparameters with the OPTUNA framework. Min-Max normalization is used as a preprocessing step for scaling the dataset values and using the forward selection (FS) wrapped method to identify essential features. The dataset used in the study contains 1385 instances and 29 features and is available at the UCI machine learning repository. The authors compare the performance of five machine learning models, including decision tree (DT), support vector machine (SVM), dummy classifier (DC), ridge classifier (RC), and bagging classifier (BC), with the hyOPTGB model. The system's efficacy is assessed using various metrics, including accuracy, recall, precision, and F1-score. The hyOPTGB model outperformed the other machine learning models, achieving a 95.3% accuracy rate. The authors also compared the hyOPTGB model against other models proposed by authors who used the same dataset. [ABSTRACT FROM AUTHOR]

Glaucoma is one of the world's leading causes of irreversible vision loss. It is often asymptomatic until it reaches an advanced stage, which can have a significant impact on patients' daily lives. This paper describes the case of a 50-year-old female patient who presented with acute onset of ocular pain, photophobia, and loss of visual acuity in her right eye (RE). The patient's medical history includes congenital cataracts, surgical aphakia, nystagmus, strabismus, amblyopia, and secondary glaucoma. Ophthalmological examination showed BCVA RE-hand movement, left eye (LE)—0.08 with an intraocular pressure (IOP) of 30 mmHg in RE and 16 mmHg in LE. Biomicroscopic examination of RE showed corneal graft, epithelial and endothelial edema, endothelial precipitates, corneal neovascularization, aphakia, and Ahmed valve superotemporally. Despite maximal topical and systemic treatment, Ahmed valve, and trabeculectomy, secondary glaucoma in the right eye remained refractory. Reimplantation of an Ahmed valve was performed. This resulted in a favorable outcome with increased visual acuity and controlled intraocular pressure. The combination of aphakia, penetrating keratoplasty, and secondary glaucoma is a challenge for any surgeon. It is important that both the perioperative risks and the possible complications are carefully assessed in each patient, especially if associated pathology is present. [ABSTRACT FROM AUTHOR]

Pediatric sarcomas, rare malignancies of mesenchymal origin, pose diagnostic and therapeutic challenges. In this review, we explore the role of radiomics in reshaping our understanding of pediatric sarcomas, emphasizing methodological considerations and applications such as diagnostics and predictive modeling. A systematic review conducted up to November 2023 identified 72 papers on radiomics analysis in pediatric sarcoma from PubMed/MEDLINE, Web of Knowledge, and Scopus. Following inclusion and exclusion criteria, 10 reports were included in this review. The studies, predominantly retrospective, focus on Ewing sarcoma and osteosarcoma, utilizing diverse imaging modalities, including CT, MRI, PET/CT, and PET/MRI. Manual segmentation is common, with a median of 35 features extracted. Radiomics Quality Score (RQS) and Methodological Radiomics Score (METRICS) assessments reveal a consistent emphasis on non-radiomic features, validation criteria, and improved methodological rigor in recent publications. Diagnostic applications dominate, with innovative studies exploring prognostic and treatment response aspects. Challenges include feature heterogeneity and sample size variations. The evolving landscape underscores the need for standardized methodologies. Despite challenges, the diagnostic and predictive potential of radiomics in pediatric oncology is evident, paving the way for precision medicine advancements. [ABSTRACT FROM AUTHOR]

Basic point-of-care ultrasound of the heart—also known as Focused Cardiac Ultrasound (FoCUS)—has emerged as a powerful bedside tool to narrow the differential diagnosis of causes of hypotension. The list of causes of hypotension that a FoCUS provider is expected to be able to recognize includes a compressive pericardial effusion due to hemopericardium (blood in the pericardial sac). But hemopericardium can be difficult to distinguish from a more common condition that is not immediately life-threatening: epicardial fat. This paper reviews illustrative images of both epicardial fat and hemopericardium to provide practice guidance to the FoCUS user on how to differentiate these two phenomena. [ABSTRACT FROM AUTHOR]

A laboratory-based lateral flow (LF) test that utilizes up-converting reporter particles (UCP) for ultrasensitive quantification of Schistosoma circulating anodic antigen (CAA) in urine is a well-accepted test to identify active infection. However, this UCP-LF CAA test requires sample pre-treatment steps not compatible with field applications. Flow, a new low-cost disposable, allows integration of large-volume pre-concentration of urine analytes and LF detection into a single field-deployable device. We assessed a prototype Flow-Schistosoma (Flow-S) device with an integrated UCP-LF CAA test strip, omitting all laboratory-based steps, to enable diagnosis of active Schistosoma infection in the field using urine. Flow-S is designed for large-volume (5–20 mL) urine, applying passive paper-based filtration and antibody-based CAA concentration. Samples tested for schistosome infection were collected from women of reproductive age living in a Tanzania region where S. haematobium infection is endemic. Fifteen negative and fifteen positive urine samples, selected based on CAA levels quantified in paired serum, were analyzed with the prototype Flow-S. The current Flow-S prototype, with an analytical lower detection limit of 1 pg CAA/mL, produced results correlated with the laboratory-based UCP-LF CAA test. Urine precipitates occurred in frozen banked samples and affected accurate quantification; however, this should not occur in fresh urine. Based on the findings of this study, Flow-S appears suitable to replace the urine pre-treatment required for the laboratory-based UCP-LF CAA test, thus allowing true field-based applications with fresh urine samples. The urine precipitates observed with frozen samples, though less important given the goal of testing fresh urines, warrant additional investigation to evaluate methods for mitigation. Flow-S devices permit testing of pooled urine samples with applications for population stratified testing. A field test with fresh urine samples, a further optimized Flow-S device, and larger statistical power has been scheduled. [ABSTRACT FROM AUTHOR]

Groin pain syndrome (GPS) is one of the most frequent injuries in competitive sports. Stresses generated in the lower limbs by quick turns and accelerations, such as in soccer, basketball or hockey, can produce localized regions of increased forces, resulting in anatomical lesions. The differential diagnoses are numerous and comprise articular, extra-articular, muscular, tendinous and visceral clinical conditions and a correct diagnosis is crucial if treatment is to be efficient. MRI is the gold standard of diagnostic techniques, especially when an alternative pathology needs to be excluded and/or other imaging techniques such as ultrasound or radiography do not lead to a diagnosis. This paper, based on the current literature, gives a comprehensive review of the anatomy of the pubic region and of the typical MRI findings in those affected by GPS. Many clinical conditions causing GPS can be investigated by MRI within appropriate protocols. However, MRI shows limits in reliability in the investigation of inguinal and femoral hernias and therefore is not the imaging technique of choice for studying these clinical conditions. [ABSTRACT FROM AUTHOR]

The facet joint injection is the most common procedure used to release lower back pain. In this paper, we proposed a deep learning method for detecting and segmenting facet joints in ultrasound images based on convolutional neural networks (CNNs) and enhanced data annotation. In the enhanced data annotation, a facet joint was considered as the first target and the ventral complex as the second target to improve the capability of CNNs in recognizing the facet joint. A total of 300 cases of patients undergoing pain treatment were included. The ultrasound images were captured and labeled by two professional anesthesiologists, and then augmented to train a deep learning model based on the Mask Region-based CNN (Mask R-CNN). The performance of the deep learning model was evaluated using the average precision (AP) on the testing sets. The data augmentation and data annotation methods were found to improve the AP. The AP50 for facet joint detection and segmentation was 90.4% and 85.0%, respectively, demonstrating the satisfying performance of the deep learning model. We presented a deep learning method for facet joint detection and segmentation in ultrasound images based on enhanced data annotation and the Mask R-CNN. The feasibility and potential of deep learning techniques in facet joint ultrasound image analysis have been demonstrated. [ABSTRACT FROM AUTHOR]

Pregnancy-related complications (PRC) impact maternal and fetal morbidity and mortality and place a huge burden on healthcare systems. Thus, effective diagnostic screening strategies are crucial. Currently, national and international guidelines define patients at low risk of PRC exclusively based on their history, thus excluding the possibility of identifying patients with de novo risk (patients without a history of disease), which represents most women. In this setting, previous studies have underlined the potential contribution of non-coding RNAs (ncRNAs) to detect patients at risk of PRC. However, placenta biopsies or cord blood samples are required, which are not simple procedures. Our review explores the potential of ncRNAs in biofluids (fluids that are excreted, secreted, or developed because of a physiological or pathological process) as biomarkers for identifying patients with low-risk pregnancies. Beyond the regulatory roles of ncRNAs in placental development and vascular remodeling, we investigated their specific expressions in biofluids to determine favorable pregnancy outcomes as well as the most frequent pathologies of pregnant women. We report distinct ncRNA panels associated with PRC based on omics technologies and subsequently define patients at low risk. We present a comprehensive analysis of ncRNA expression in biofluids, including those using next-generation sequencing, shedding light on their predictive value in clinical practice. In conclusion, this paper underscores the emerging significance of ncRNAs in biofluids as promising biomarkers for risk stratification in PRC. The investigation of ncRNA expression patterns and their potential clinical applications is of diagnostic, prognostic, and theragnostic value and paves the way for innovative approaches to improve prenatal care and maternal and fetal outcomes. [ABSTRACT FROM AUTHOR]

MR is a powerful diagnostic tool in the diagnosis and management of most hepatic and pancreatic diseases. Thanks to its multiple sequences, the use of dedicated contrast media and special techniques, it allows a multiparametric approach able to provide both morphological and functional information for many pathological conditions. The knowledge of correct technique is fundamental in order to obtain a correct diagnosis. In this paper, different MR sequences will be illustrated in the evaluation of liver and pancreatic diseases, especially those sequences which provide information not otherwise obtainable with other imaging techniques. Practical MR protocols with the most common indications of MR in the study of the liver and pancreas are provided. [ABSTRACT FROM AUTHOR]

The recent introduction of immunotherapy and targeted therapy has substantially enriched the therapeutic landscape of metastatic melanoma. However, cerebral metastases remain unrelenting entities with atypical metabolic and genetic profiles compared to extracranial metastases, requiring combined approaches with local ablative treatment to alleviate symptoms, prevent recurrence and restore patients' biological and psychological resources for fighting malignancy. This paper aims to provide the latest scientific evidence about the rationale and timing of treatment, emphasizing the complementary roles of surgery, radiotherapy, and systemic therapy in eradicating brain metastases, with a special focus on the distinct response of intracranial and extracranial disease, which are regarded as separate molecular entities. To illustrate the complexity of designing individualized therapeutic schemes, we report a case of delayed BRAF-mutant diagnosis, an aggressive forearm melanoma, in a presumed psychiatric patient whose symptoms were caused by cerebral melanoma metastases. The decision to administer molecularly targeted therapy was dictated by the urgency of diminishing the tumor burden for symptom control, due to potentially life-threatening complications caused by the flourishing of extracranial disease in locations rarely reported in living patients, further proving the necessity of multidisciplinary management. [ABSTRACT FROM AUTHOR]

A Computed Tomography Urography (CTU) scan is a medical imaging test that examines the urinary tract, including the bladder, kidneys, and ureters. It helps diagnose various urinary tract diseases with precision. However, patients undergoing CTU imaging receive a relatively high dose of radiation, which can be a concern. In our research paper, we analyzed the Computed Tomography Dose Index (CTDIvol) and Dose-Length Product (DLP) for 203 adult patients who underwent CTU at one of the most important regional centers in Bosnia and Herzegovina that sees a large number of patients. Our study included the distribution of age and sex, the number of phases within one examination, and different clinical indications. We compared our findings with the results available in the scientific literature, particularly the recently published results from 20 European countries. Furthermore, we established the local diagnostic reference levels (LDRLs) that can help set the national diagnostic reference levels (NDRLs). We believe our research is a significant step towards optimizing the protocols used in different hospitals in our country. [ABSTRACT FROM AUTHOR]