Similar Diseases Research Articles

First report of PURA syndrome in a Colombian patient with de novo missense variant c.692T>C (p.Phe231Ser)

We present the first documented case of PURA syndrome in Colombia. This rare neurological disease results from mutations in the PURA gene located on chromosome 5, leading to haploinsufficiency of the PUR-α protein. This protein is essential for early brain development and neuronal function. The patient, a seven-years-old boy, started showing dystonic hand movements at 14 days of age; at six, he had neurodevelopmental delay, generalized hypotonia, frequent episodes of apnea, and swallowing difficulties. Although other conditions were initially considered, such as Duchenne muscular dystrophy and neuronal ceroid lipofuscinosis, a whole exome sequencing revealed the pathogenic variant c.692T>C (p.Phe231Ser) in the exon 1 of the PURA gene, not previously reported in other patients. With this finding, we adopted a comprehensive management approach addressing the patient’s characteristics and alterations. Since the PURA syndrome is not on the list of orphan/rare diseases recognized by the Colombian Ministerio de Salud y Protección Social, we hope our report will contribute to its official recognition. The case shows the importance of considering rare diagnoses in patients with uncommon neurological symptoms, underlining the usefulness of genomic sequencing in diagnosis and the need for collaboration to optimize healthcare for patients with PURA syndrome and similar diseases.

Epigenetic modulation of fungal pathogens: a focus on Magnaporthe oryzae.

Epigenetics has emerged as a potent field of study for understanding the factors influencing the effectiveness of human disease treatments and for identifying alternations induced by pathogens in host plants. However, there has been a paucity of research on the epigenetic control of the proliferation and pathogenicity of fungal plant pathogens. Fungal plant pathogens such as Magnaporthe oryzae, a significant threat to global rice production, provide an important model for exploring how epigenetic mechanisms govern fungal proliferation and virulence. In M. oryzae, epigenetic alterations, such as DNA methylation, histone modification, and non-coding RNAs, regulate gene expression patterns that influence the pathogen's ability to infect its host. These modifications can enhance fungal adaptability, allowing the pathogen to survive in diverse environments and evade host immune responses. Our primary objective is to provide a comprehensive review of the existing epigenetic research on M. oryzae and shed light on how these changes influence the pathogen's lifecycle, its ability to invade host tissues, and the overall severity of the disease. We begin by examining the epigenetic alterations occurring in M. oryzae and their contributions to the virulence and proliferation of the fungus. To advance our understanding of epigenetic mechanisms in M. oryzae and similar plant diseases, we emphasize the need to address unanswered questions and explore future research directions. This information is crucial for developing new antifungal treatments that target epigenetic pathways, which could lead to improved disease management.

Determinants of COVID-19 vaccination acceptance based on the novel Omale INDEPT FORCIS Framework and recommendations for subsequent pandemics: a qualitative study among community members in Ebonyi state, Nigeria

BackgroundThe unprecedented COVID-19 pandemic has become an endemic disease of global public health importance. Mass COVID-19 vaccination has been an essential global control strategy amidst challenges of limited acceptance. Because of globalization, COVID-19/similar diseases vaccination acceptance and the determinants in any particular setting are important global public health issues. Using a novel and pragmatic framework, this study explored determinants of COVID-19 vaccination acceptance among community members during the pandemic in Ebonyi state, Nigeria, and made policy-relevant recommendations on how to increase vaccination acceptance in subsequent outbreaks/pandemics.MethodsThis qualitative study was based on the novel and pragmatic Individual Experiences and Perceptions and Complacency, Confidence, Convenience, and Compulsion (Four ‘Cis’) Determinants of Vaccination Acceptance Conceptual Framework – Omale INDEPT FORCIS Framework. On April 26 and 27, 2022, 20 semi-structured face-to-face focus group discussions were conducted in local language and pidgin English with 100 purposively selected consenting/assenting community members aged 15 years and above who had resided in the community for at least one year. Data was analysed using deductive (with some inductive) thematic analytic approach.ResultsThe many, diverse, and significant determinants of COVID-19 vaccination acceptance found were factors that were individual-related (individual experiences and perceptions and knowledge about COVID-19, COVID-19 vaccine/vaccination, and the vaccination process/system, sociodemographic, individual’s condition (e.g. pregnancy)); COVID-19-related (factuality, transmissibility, frequency, severity, fatality); COVID-19 vaccine/vaccination-related (safety/side-effects, effectiveness, speedy production); COVID-19 vaccination process/system-related (real availability/accessibility); family, group, and other individual-related (experiences and perceptions and actions); and broader local, national, international, and global (LONING) context-related (socio-political, economic, historic, health system factors). The broader LONING contextual factors included the unprecedented disinformation/conspiracy theories, non-sustained COVID-19 risk/behaviour change communication, enforcement and non-enforcement or termination of peculiar control policies/measures (lockdowns, social/physical distancing, use of face mask etc.), mandatory COVID-19 vaccination policies, provision of incentives, past experiences regarding the Ebola viral disease outbreak, (un)trustworthiness of the Nigerian health system and her international/global partners, and the (un)trustworthiness of the governments in Nigeria and bad/good governance, inclusive of the failure of the Ebonyi state government to distribute the COVID-19 palliatives to the people during the lockdowns.ConclusionThe evidence illuminates complex and interrelated, specific underlying, and peculiar policy-relevant LONING determinants of COVID-19 vaccination acceptance and emphasizes the need for concerted and comprehensive LONING strategies (involving all the relevant LONING stakeholders/policy makers) in addressing these determinants to increase vaccination acceptance among community members in subsequent outbreaks/pandemics in Ebonyi state/Nigeria and similar settings.

Efficiency of multivariate tests in trials in progressive supranuclear palsy

Measuring disease progression in clinical trials for testing novel treatments for multifaceted diseases as progressive supranuclear palsy (PSP), remains challenging. In this study we assess a range of statistical approaches to compare outcomes as measured by the items of the progressive supranuclear palsy rating scale (PSPRS). We consider several statistical approaches, including sum scores, a modified PSPRS rating scale that had been recommended by FDA in a pre-IND meeting, multivariate tests, and analysis approaches based on multiple comparisons of the individual items. In addition, we propose two novel approaches which measure disease status based on Item Response Theory models. We assess the performance of these tests under various scenarios in an extensive simulation study and illustrate their use with a re-analysis of the ABBV-8E12 clinical trial. Furthermore, we discuss the impact of the FDA-recommended scoring of item scores on the power of the statistical tests. We find that classical approaches as the PSPRS sum score demonstrate moderate to high power when treatment effects are consistent across the individual items. The tests based on Item Response Theory (IRT) models yield the highest power when the simulated data are generated from an IRT model. The multiple testing based approaches have a higher power in settings where the treatment effect is limited to certain domains or items. The study demonstrates that there is no one-size-fits-all testing procedure for evaluating treatment effects using PSPRS items; the optimal method varies based on the specific effect size patterns. The efficiency of the PSPRS sum score, while generally robust and straightforward to apply, varies depending on the specific patterns of effect sizes encountered and more powerful alternatives are available in specific settings. These findings can have important implications for the design of future clinical trials in PSP and similar multifaceted diseases.

CystNet: An AI driven model for PCOS detection using multilevel thresholding of ultrasound images

Polycystic Ovary Syndrome (PCOS) is a widespread endocrinological dysfunction impacting women of reproductive age, categorized by excess androgens and a variety of associated syndromes, consisting of acne, alopecia, and hirsutism. It involves the presence of multiple immature follicles in the ovaries, which can disrupt normal ovulation and lead to hormonal imbalances and associated health complications. Routine diagnostic methods rely on manual interpretation of ultrasound (US) images and clinical assessments, which are time-consuming and prone to errors. Therefore, implementing an automated system is essential for streamlining the diagnostic process and enhancing accuracy. By automatically analyzing follicle characteristics and other relevant features, this research aims to facilitate timely intervention and reduce the burden on healthcare professionals. The present study proposes an advanced automated system for detecting and classifying PCOS from ultrasound images. Leveraging Artificial Intelligence (AI) based techniques, the system examines affected and unaffected cases to enhance diagnostic accuracy. The pre-processing of input images incorporates techniques such as image resizing, normalization, augmentation, Watershed technique, multilevel thresholding, etc. approaches for precise image segmentation. Feature extraction is facilitated by the proposed CystNet technique, followed by PCOS classification utilizing both fully connected layers with 5-fold cross-validation and traditional machine learning classifiers. The performance of the model is rigorously evaluated using a comprehensive range of metrics, incorporating AUC score, accuracy, specificity, precision, F1-score, recall, and loss, along with a detailed confusion matrix analysis. The model demonstrated a commendable accuracy of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$96.54\\%$$\\end{document} when utilizing a fully connected classification layer, as determined by a thorough 5-fold cross-validation process. Additionally, it has achieved an accuracy of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$97.75\\%$$\\end{document} when employing an ensemble ML classifier. This proposed approach could be suggested for predicting PCOS or similar diseases using datasets that exhibit multimodal characteristics.

Exosomes Derived from Bone Marrow Dendritic Cells Exhibit Protective and Therapeutic Potential Against Chemically Induced Chronic Pancreatitis in Rats.

Chronic pancreatitis (CP) is a specific clinical disorder that develops from pancreatic fibrosis and immune cell dysregulation. It has been proposed that bone marrow dendritic cells (BMDCs) exosomes have significant effects on immune regulation. Thus, the current study acquainted the prophylactic and therapeutic effects of exosomes derived from BMDCs on a rat model of CP. BMDCs were prepared and identified, and then the exosomes were isolated by differential ultracentrifugation. Prophylactic and therapeutic effects of exosomes were investigated on L-arginine induced CP model. Administration of two tail vein injections of exosomes (200μg/kg/dose suspended in 0.2ml PBS) markedly improved the pancreatic function and histology compared to CP group. Moreover, exosomes prominently mitigated the increase in amylase, lipase, tumor necrosis factor-α (TNF-α), transforming growth factor-β (TGF-β) and elevated antioxidant enzymes; catalase, superoxide dismutase (SOD) and glutathione peroxidase (GPx). BMDCs exosomes can be considered as a promising candidate, with a high efficacy and stability compared with its parent cell, for management of CP and similar inflammatory diseases.

An Integrated Approach for Development of a Decision Support System for Early Surveillance of Japanese Encephalitis: a Remote Sensing-based study in Conjunction with Epidemiological Scenario in Assam, India

Abstract. Japanese Encephalitis (JE) is one of the serious mosquito borne viral diseases mostly prevalent in India and many of the South Asian countries. A comprehensive study was conducted in Dibrugarh, a JE endemic district of Assam taking into the account of the environmental and social factors that determine the outbreak of the disease. Effect of environmental and climatic factors on mosquito vector densities was investigated using geospatial tools and techniques. Wetlands were found to be the most preferred habitat type for most of the JE-transmitting mosquitoes followed by paddy-growing areas and ponds. The villages abundant with these habitats or located nearby were found to be more vulnerable to JE risk. Based on the spatial distribution of habitat types, villages in the district were divided into three categories of vector abundance and hence the risk of JE. It is found that rainfall (intensity as well as duration) is the most significant determinant in the modulation of mosquito density followed by temperature. It is also observed that the derived models could explain about 61% to 73% variation in mosquito density due to change in weather variables as indicated by R2 values of the Models. Among socio-economic determinants, pig rearing habit of the villagers was found to be most significant. JE risk map was prepared by integrating vector abundance map with the map of host abundance in GIS domain. Attempts were also made to forecast the intensity of JE cases based on a time series analysis of historical morbidity pattern, whereas forecast for onset of the disease was based on the modulation of weather variables in the study area. A web-based decision support system (DSS) developed by integrating the forecasts of JE onset, intensity and JE prone villages has been provided to the concerned health authorities to take timely intervention measures. The JE early warning system developed for Dibrugarh district was extended to two more JE prone districts viz, Sibsagar and Tinsukia as validation sites and observed that the accuracy of forecast was within the range of 69% to 77%. DSS on JE has opened up new opportunities to study similar vector-borne diseases of the JE serogroup viz., West Nile.

A classification and recognition model for multiple fruit tree leaf diseases

Abstract Fruit tree leaf diseases affect both fruit survival rate and orchard revenue. Rapid and accurate identification of tree leaf diseases not only prevents the spread of diseases but also ensures healthy tree growth and improves fruit quality. Existing models mostly focus on individual types of tree leaf diseases, with limited applications in the identification of multiple tree leaf diseases. In response to the insensitivity of current classification models to disease region features and the issue of increased error rates due to the presence of similar diseases, this study proposes a residual network model based on the ECA channel attention mechanism and meta-Acon adaptive activation function.The model employs ResNet34 as the backbone network and incorporates the ECA module after each residual block to focus on relevant information. Additionally, a new activation function called meta-Acon is introduced to enhance the model's generalization ability through its dynamic learning capability. Finally, the model's recognition performance is improved by fusing bottom-level features with features from other layers using the Feature Pyramid Network (FPN).Experimental results on a dataset augmented with Mosaic processing show that the FPEM-ResNet34 model achieves a classification accuracy of 98.46%. Compared to other common models such as VGG-16, Inception-V1,AlexNet, and ResNet50, the proposed method mentioned in this paper demonstrates more effective improvement in the accuracy of tree leaf classification, making it highly valuable for practical applications..

An Effective DNA Methylation Biomarker Screening Mechanism for Amyotrophic Lateral Sclerosis (ALS) Based on Comorbidities and Gene Function Analysis.

This study used epigenomic methylation differential expression analysis to identify primary biomarkers in patients with amyotrophic lateral sclerosis (ALS). We combined electronic medical record datasets from MIMIC-IV (United States) and NHIRD (Taiwan) to explore ALS comorbidities in depth and discover any comorbidity-related biomarkers. We also applied word2vec to these two clinical diagnostic medical databases to measure similarities between ALS and other similar diseases and evaluated the statistical assessment of the odds ratio to discover significant comorbidities for ALS subjects. Important and representative DNA methylation biomarker candidates could be effectively selected by cross-comparing similar diseases to ALS, comorbidity-related genes, and differentially expressed methylation loci for ALS subjects. The screened epigenomic and comorbidity-related biomarkers were clustered based on their genetic functions. The candidate DNA methylation biomarkers associated with ALS were comprehensively discovered. Gene ontology annotations were then applied to analyze and cluster the candidate biomarkers into three different groups based on gene function annotations. The results showed that a potential testing kit for ALS detection can be composed of SOD3, CACNA1H, and ERBB4 for effective early screening of ALS using blood samples. By developing an effective DNA methylation biomarker screening mechanism, early detection and prophylactic treatment of high-risk ALS patients can be achieved.

Highly Sensitive Molecular Diagnostic Platform for Scrub Typhus Diagnosis Using O. tsutsugamushi Enrichment and Nucleic Acid Extraction.

Scrub typhus is caused by the Gram-negative obligate intracellular bacterium Orientia tsutsugamushi, and this tick-borne disease is difficult to distinguish from other acute febrile illnesses as it typically presents with symptoms such as rash, crusting at the bite site, headache, myalgia, lymphadenopathy, and elevated liver transaminases. It can often be diagnosed clinically, but not all patients present with characteristic symptoms, so serological diagnosis and molecular techniques may be required. However, existing diagnostic tests often have low sensitivity and specificity, making early detection difficult. This study presents a nucleic acid extraction method using large volumes of plasma and buffy coat to increase sensitivity, as well as an improved detection method using two target genes. Using the I-PULL device, nucleic acids can be extracted from up to 4 mL of sample in 30 min, avoiding contamination. The extracted DNA detects two genes of O. tsutsugamushi, increasing sensitivity compared to single-gene detection. Clinical validation in 38 patient samples showed 100% specificity and 95.24% sensitivity for the single target gene, with specificity and sensitivity rising to 100% when both genes are analyzed. This molecular diagnostic platform can be useful for distinguishing scrub typhus from similar diseases.

Single-Atom Catalysts with Isolated Cu1-N4 Sites for Atopic Dermatitis Cascade Catalytic Therapy via Activating PPAR Signaling.

Atopic dermatitis (AD) is one of the most common allergic skin disorders affecting over 230 million people worldwide, while safe and efficient therapeutic options for AD are currently rarely available. Reactive oxygen species (ROS) accumulation plays a key role in AD's disease progression. Therefore, a novel single-atom catalyst is designed with isolated Cu1-N4 sites anchored on carbon support (Cu1-N4 ISAC), featuring triple antioxidant enzyme-mimicking activities, for efficient AD cascade catalytic therapy (CCT). The excellent superoxide dismutase (SOD)-, glutathione peroxidase (GPx)-, and ascorbate peroxidase (APx)-like activities of Cu1-N4 ISACs enable the sequential conversion of O2•- to H2O2 and then to harmless H2O, thereby protecting keratinocytes from oxidative stress damage. Notably, two novel experimental methods are developed to directly prove the SOD-GPx and SOD-APx cascade catalytic activities for the first time. In vivo experiments show that Cu1-N4 ISACs are more potent than a recommended typical medicine (halcinonide solution). Additionally, RNA sequencing and bioinformatic analysis reveal that Cu1-N4 ISACs reduce inflammation and inhibit ROS production by activating PPAR signaling, which is aberrantly reduced in AD. Therefore, the synthesized catalytic medicine offers an alternative to alleviate AD and has the potential to serve as PPAR agonists for treating similar diseases.

A real-time eye movement-based computer interface for people with disabilities

It is costly to develop systems that enable individuals exposed to Amyotrophic Lateral Sclerosis and similar diseases that directly affect the neuromotor ability to communicate with the outside world. In this study, a budget friendly, high-accuracy, software-based, gaze-controlled, real-time virtual keyboard approach that can enable these people to communicate effectively is proposed. The proposed application requires only a computer and a webcam and has a user-friendly interface that meets the basic daily needs of individuals with disabilities. Since the proposed system does not require an extra action such as blinking, it makes it possible to use computers in advanced stage patients who cannot blink their eyes. The application which uses a deep learning-based facial landmark detector, determines the letters the user focuses on the screen and converts thoughts into text. The part of the screen that the user focuses on is determined with a new selection approach inspired by the K-Nearest Neighbors algorithm. This approach, which does not require blinking, offers high speed and accuracy. In the tests, a typing speed of 23.33 characters per minute is achieved with an accuracy rate of 95.12%. It is anticipated that the study will increase computer accessibility for disabled individuals with limited mobility and contribute to the development of real-time eye tracking systems.

Ethnobotanical study of medicinal and aromatic plants commonly used in the daily life of the populations of the Moroccan Middle Atlas: results of pharmacy surveys

Abstract This in-depth ethnobotanical study was conducted specifically in the Middle Atlas region of Morocco to document and analyze the use of medicinal and aromatic plants (MAPs) among local populations suffering from various chronic conditions. This survey involved 99 participants with gastrointestinal, cardiovascular, endocrine, urinary, gynecological, or similar diseases. The main objective was to draw up a complete inventory of the MAPs used, detailing their therapeutic indications and identifying any adverse effects associated with their use, whether through misapplication, overdosing, or interaction with other drugs taken by the patients. In the present, twenty MAPs species belonging to twelve distinct botanical families were identified. Among these plants, oregano (Origanum compactum) from the Lamiaceae family is the most widely used species, with a frequency of over 30%. Leaves were the most commonly used plant organ in traditional recipes (60.6%), whereas decoction was the most predominant preparation method (51.5%). This survey was conducted using questionnaires distributed in some pharmacies, and provided valuable data on MAP use practices, highlighting the predominance of certain plant species, their main therapeutic indications, and the preparation methods preferred by the Middle Atlas inhabitants to treat their ailments.

A Novel Phytocolorant, Neoxanthin, as a Potent Chemopreventive: Current Progress and Future Prospects.

Cancer is a general term for a group of similar diseases. It is a combined process that results from an accumulation of abnormalities at different biological levels, which involves changes at both genetic and biochemical levels in the cells. Several modifiable risk factors for each type of cancer include heredity, age, and institutional screening guidelines, including colonoscopy, mammograms, prostate-specific antigen testing, etc., which an individual cannot modify. Although a wide range of resources is available for cancer drugs and developmental studies, the cases are supposed to increase by about 70% in the next two decades due to environmental factors commonly driven by the way of living. The drugs used in cancer prevention are not entirely safe, have potential side effects and are generally unsuitable owing to substantial monetary costs. Interventions during the initiation and progression of cancer can prevent, diminish, or stop the transformation of healthy cells on the way to malignancy. Diet modifications are one of the most promising lifestyle changes that can decrease the threat of cancer development by nearly 40%. Neoxanthin is a xanthophyll pigment found in many microalgae and macroalgae, having significant anti-cancer, antioxidant and chemo-preventive activity. In this review, we have focused on the anti-cancer activity of neoxanthin on different cell lines and its cancer-preventive activity concerning obesity and oxidative stress. In addition to this, the preclinical studies and future perspectives are also discussed in this review.

Sandfly Virus Infection in Adana, Türkiye.

We described the clinical and laboratory characteristics of the patients infected by sandfly viruses between August and September 2008 in Adana, Türkiye. The immunofluorescence antibody test (EUROIMMUN Medizinische Labordiagnostika AG, Lübeck, Germany) was used for diagnosis. Antibodies of immunoglobulin M (IgM) type against Cyprus and Sicily species of phleboviruses were detected in 15 patients. In one patient, IgM antibodies against Naples and Tuscany species were found to be positive. The mean age of the patients was 28, and 47% was female. The most common symptoms were myalgia, fatigue, fever, and arthralgia. The mean duration of fever was four days, and the mean duration of the symptoms was 7-10 days. The median platelet count was 137,000/mL, the median white blood cell count was 3040/mL, the median aspartate transaminase (AST) was 57 (22-1251) IU/L, the median alanine transaminase (ALT) was 71 (17-1137) IU/L, the median creatinine kinase was 314 (33-4638) IU/L, and the median lactate dehydrogenase (LDH) was 193 (111-490) IU/L. The sandfly virus infections should be differentiated from similar viral infectious diseases such as Crimean-Congo hemorrhagic fever.

A multi-task learning model for clinically interpretable sesamoiditis grading

Sesamoiditis is a common equine disease with varying severity, leading to increased injury risks and performance degradation in horses. Accurate grading of sesamoiditis is crucial for effective treatment. Although deep learning-based approaches for grading sesamoiditis show promise, they remain underexplored and often lack clinical interpretability. To address this issue, we propose a novel, clinically interpretable multi-task learning model that integrates clinical knowledge with machine learning. The proposed model employs a dual-branch decoder to simultaneously perform sesamoiditis grading and vascular channel segmentation. Feature fusion is utilized to transfer knowledge between these tasks, enabling the identification of subtle radiographic variations. Additionally, our model generates a diagnostic report that, along with the vascular channel mask, serves as an explanation of the model’s grading decisions, thereby increasing the transparency of the decision-making process. We validate our model on two datasets, demonstrating its superior performance compared to state-of-the-art models in terms of accuracy and generalization. This study provides a foundational framework for the interpretable grading of similar diseases.

Characterization and Management of Epithelioid Hemangioma on the Lip Mucosa: A Case Report

Epithelioid Hemangioma (EH) is a rare benign vascular tumor characterized by well-formed blood vessels and epithelioid endothelial cells. Known as Angiolymphoid Hyperplasia with Eosinophilia (ALHE), the World Health Organization does not recommend the use of this term since 2020. Despite its common occurrence in subcutaneous soft tissues of head and neck, cases involving oral mucosa and other extra cutaneous areas are infrequent. This article presents a case of EH in an unusual location and discusses the differential diagnosis with other entities, including Kimura´s Disease (KD). We report the clinical case of a 26 year old man with no significant medical history who presented with a painless increase in volume on the right upper lip, subsequently diagnosed as EH through biopsy and histopathology. The lesion exhibited typical clinical features and a complex histological pattern including epithelioid endothelial cells and an inflammatory infiltrate rich in eosinophils. Diagnosis of EH is challenging due to morphological variability and the need to differentiate it from similar conditions such as KD. Differential diagnosis is crucial, especially in atypical locations. This case shows the importance of histopathology and complementary examinations in diagnosis and the need to consider KD in the differential diagnosis due to its therapeutic and prognostic implications. Therefore, it is concluded that EH, although benign, requires a rigorous diagnostic approach to differentiate it from similar diseases and manage it appropriately. Continuous control is essential due to the potential for recurrence. This case shows the importance of careful differential diagnosis, especially in atypical locations, to optimize management and improve patient outcomes.

Explainable Deep Learning Approach for Mpox Skin Lesion Detection with Grad-CAM

Mpox is a viral zoonotic disease that presents with skin lesions similar to other conditions like chickenpox, measles, and hand-foot-mouth disease, making accurate diagnosis challenging. Early and precise detection of mpox is critical for effective treatment and outbreak control, particularly in resource-limited settings where traditional diagnostic methods are often unavailable. While deep learning models have been applied successfully in medical imaging, their use in mpox detection remains underexplored. To address this gap, we developed a deep learning-based approach using the ResNet50v2 model to classify mpox lesions alongside five other skin conditions. We also incorporated Grad-CAM (Gradient-weighted Class Activation Mapping) to enhance model interpretability. The results show that the ResNet50v2 model achieved an accuracy of 99.33%, precision of 99.34%, sensitivity of 99.33%, and an F1-score of 99.32% on a dataset of 1,594 images. Grad-CAM visualizations confirmed that the model focused on relevant lesion areas for its predictions. While the model performed exceptionally well overall, it struggled with misclassifications between visually similar diseases, such as chickenpox and mpox. These results demonstrate that AI-based diagnostic tools can provide reliable, interpretable support for clinicians, particularly in settings with limited access to specialized diagnostics. However, future work should focus on expanding datasets and improving the model's capacity to distinguish between similar conditions.

Understanding hospital antimicrobial prescribing decisions and determinants of uptake of new local antimicrobial prescribing guidelines in Laos.

Antimicrobial use in Laos is among the highest in Southeast Asia. The first Lao comprehensive antimicrobial prescribing guidelines have been available since 2021. This study explored the determinants of antibiotic prescribing decisions and how the new prescribing guidelines were being used. In August 2022, in-depth interviews were conducted with 16 Lao prescribers from two hospitals. Participants were questioned about their prescribing behaviours, attitudes to guidelines, how they learned about the guidelines and factors influencing their uptake. The interviews were audio-recorded, transcribed, and translated into English. Thematic analysis of the transcripts was conducted. Lao prescribers considered multiple factors before deciding to prescribe antibiotics to their patients. The most common factor was based on the clinical judgement of the prescribers. Lack of certain antibiotics and turnaround times of laboratory results were the main challenges to prescribing antibiotics appropriately. The majority of participants were satisfied with the guidelines, regarding them as comprehensive, simple and convenient. However, most participants admitted that they did not access the guidelines very often. The main reason was that they could remember the treatment recommendations because they treat similar diseases on a daily basis. Improving antibiotic knowledge was the most common recommendation in order to improve the appropriate use of antibiotics. Raising awareness of the guidelines and promoting their use should also be considered. In addition, heads of the wards, and policy and implementation leaders, should support, monitor and feedback their use to encourage all prescribers to follow the guidelines. Several factors contribute to enhancing appropriate antibiotic prescription. Key factors for improving antibiotic prescription include enhancing prescribers' clinical knowledge, ensuring access to essential antibiotics, and updating guidelines regularly. Health leaders must get involved to promote their use.

Cardiovascular adverse effects of antiviral therapies for COVID-19: Evidence and plausible mechanisms.

Antiviral therapeutics have made a critical contribution in mitigating the symptoms and clinical outcomes of the coronavirus disease of 2019 (COVID-19), in which a single-stranded RNA viral pathogen, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causes multi-organ injuries. Several antivirals were widely prescribed to treat COVID-19, either through the emergency use authorization (EUA) by the governmental regulatory agencies (i.e., remdesivir, paxlovid, molnupiravir, and the SARS-CoV-2-targeted monoclonal antibodies - tixagevimab and cilgavimab), as well as the repurposed use of the existing antiviral or antimalarial drugs (e.g., hydroxychloroquine, chloroquine, and ivermectin). Despite their efficacy in ameliorating COVID-19 symptoms, some adverse side-effects of the antivirals were also reported during the COVID-19 pandemic. Our current review has aimed to gather and extrapolate the recently published information concerning cardiovascular adverse effects caused by each of the antivirals. We also provide further discussion on the potential cellular mechanisms underlying the cardiovascular adverse effects of the selected antiviral drugs, which should be carefully considered when evaluating risk factors in managing patients with COVID-19 or similar infectious diseases. It is foreseeable that future antiviral drug development assisted with the newest artificial intelligence platform may improve the accuracy to predict the structures of biomolecules of antivirals and therefore to mitigate their associated cardiovascular adversities.