Seroepidemiological Study of Dengue Virus Infection in Nepal
Phytochemical Profiling and Invitro Anthelmintic Evaluation of Bryophyllum Pinnatum Leaf Extracts
Classification of Pancreatic Cancer Classification using Deep Learning
Heart Disease Prediction
Risk Management for Medical Devices: An Examination of Industry-Based Best Practices
Raising Concern of Substance Abuse among Adolescents in India: A Narrative Review
Aquagenic Urticaria: When Water Becomes Lethal
Executing Quality Management Systems in Laboratory Testing and Biomedical Product Control
Transferosomes an Emerging Versatile Transformation in Research: An Advanced Review
Rubinstein - Taybi Syndrome: A Rare Genetic Disorder
Classification of Pancreatic Cancer Classification using Deep Learning
Comparative Analysis of Ventilators and Oxygen Concentrator: Application, Advantages and Challenges
Raising Concern of Substance Abuse among Adolescents in India: A Narrative Review
Phytochemical Profiling and Invitro Anthelmintic Evaluation of Bryophyllum Pinnatum Leaf Extracts
Optimizing Patient Safety and Dose Reduction Strategies in Abdominal, Chest, and Skull CT Imaging: A Comprehensive Analysis of Effective Dose Quantification
Dengue fever (DF) is an emerging mosquito-borne viral disease and an important public health problem in the lowlands of the Terai region, which is also expanding to the hilly region of Nepal. The study aims to shed light on the clinical, epidemiological, and serological aspects associated with dengue virus infections (DVI) and its implications for future diagnosis, management, prevention, and control of the disease in Nepal. Two hundred sixty-one serum samples were collected from patients suspected of dengue virus infection visiting hospitals in Parsha districts during August–November 2023 and tested by the IgM Capture Enzyme-Linked Immunosorbent Assay (Standard Diagnostic Inc., Korea) and the Dengue IgM/IgG Rapid immunochromatographic test kit (Panbio, Australia). The anti-Dengue IgM positivity was found to be 18.8% and 15.3% by IgM capture enzyme-linked immunosorbent assay and rapid immunochromatographic test, respectively. Compared to ELISA, the sensitivity and specificity of RDT were 73.46% and 98.1%, respectively. RDT performed poorly (kappa value-0.77) and should not be used as a sole diagnostic method for diagnosis of dengue virus infection. In 49 anti-Dengue IgM-positive cases, 67.4% were male and 32.6% were female (male-to-female ratio=2.06:1). The highest number of cases (81.6%) was observed in the age group of 15-50 years. Students were the most commonly affected, with the highest number of positive cases (32.7%). Patients with joint pain, retro-orbital pain, and skin rash as clinical symptoms were more likely to be diagnosed as anti-Dengue IgM positive. Hemorrhagic manifestation was seen in 12.2% of cases. The highest number of cases, 199 (79.6%), have a fever duration of more than 5 days. Anti-Dengue IgM was not found to be detected significantly in cases with a duration of fever of 5 days or more (p=0.686). Knowledge of dengue was found in 65.9%, of which 11.6% was found to be anti-Dengue IgM positive. Waterlogging (10.7%) and travel to endemic areas (37%) were found as the more likely risk factors in anti-Dengue IgM- positive cases. Flowerpots were found as the most likely breeding place with the highest number of positive cases, 36.55%. Use of nets (87.3%) and changing stored water (85.8%) were the most likely used preventive measures, respectively.
The present study focuses on the phytochemical screening and in vitro anthelmintic activity of Bryophyllum pinnatum leaves, a traditionally used medicinal plant from the Crassulaceae family. Standardization of crude plant extracts is critical for guaranteeing the quality and therapeutic efficacy of herbal formulations. In this study, aqueous, methanolic, and chloroform extracts of Bryophyllum pinnatum leaves were prepared and screened for phytochemical constituents. The in vitro anthelmintic activity was evaluated using adult Indian earthworms (Pheretima posthuma), which exhibit anatomical and physiological similarities with human intestinal helminths. Different concentrations (25, 50, and 100 mg/mL) of each extract were tested, and parameters such as the time to paralysis and death were recorded. The methanolic extract at 100 mg/mL demonstrated significant anthelmintic activity, comparable to that of the standard drug piperazine citrate. Distilled water was used as a control. The results suggest that Bryophyllum pinnatum leaves, particularly in methanolic form, possess notable anthelmintic potential and can serve as a promising alternative to conventional therapies.
The prevalence of pancreatic cancer has prompted the development of AI-driven diagnostic systems that can improve early detection and classification. This study presents a novel approach for pancreatic cancer classification using a decision tree model trained on genomic biomarkers derived from patient blood and urine samples. Contrary to traditional image-based methods, which typically result in limited sensitivity and specificity, this research leverages structured biological data for improved accuracy. The dataset, Pancrease_clean_data.csv, was sourced from Kaggle and contains clinically relevant features such as age, sex, CA 19-9, CEA, creatinine, LYVE1, REG1B, and TFF1. Data preprocessing included missing value imputation, normalization, and label encoding. The dataset was balanced using SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance. The class distribution post-balancing was 50:50 between cancer-positive and negative cases. Stratified sampling ensured consistent class proportions across training (80%) and test (20%) sets. The proposed decision tree model achieved an accuracy of 95.3%, outperforming traditional models in both recall and F1-score. This model demonstrates potential for clinical application, particularly in resource-constrained environments where imaging-based diagnostics may be impractical.
The article focuses on building a heart disease prediction system utilizing Federated Learning (FL) combined with Explainable AI (XAI) techniques. FL allows us to train models on decentralized healthcare datasets, ensuring data privacy by keeping patient data local to each institution. This method is especially suited for collaborative research where sensitive patient data cannot be centralized. To ensure transparency and trust in the model's predictions, we integrate XAI techniques such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations). These methods provide detailed explanations of how the model arrives at its conclusions, highlighting the features most important in predicting heart disease risk. For implementation, we use Python-based frameworks such as PySyft for Federated Learning and Scikit-learn for model building, with Random Forests and Logistic Regression serving as the base models. Our dataset is sourced from the UCI Heart Disease Dataset, with feature engineering and normalization applied before model training. Performance evaluation will focus on metrics such as accuracy, F1-score, and AUC-ROC, alongside interpretability using SHAP and LIME. The result is a privacy-preserving, interpretable model capable of accurately predicting heart disease. This system empowers healthcare providers by offering not only predictions but also clear insights into how those predictions are made. The major advantage of combining FL and XAI is the ability to maintain high accuracy and data privacy, while providing actionable and understandable insights for medical professionals.
The term "risk" originates from the Italian word "risicare," meaning "to dare." In the context of medical devices, various stakeholders, including designers, manufacturers, patients, and surgeons, contribute to different types of risks. Several standards and guidelines address medical device safety, such as ISO 14971, IEC 60601-1, IEC 62304, IEC 62366, and ISO 10993-1. Among these, ISO 14971 stands out as the central standard for medical device risk management, recognized in both the European Union and the United States. Consequently, implementing risk management processes in line with ISO 14971 is a legal requirement in many countries, essential for obtaining approval to market medical devices. The present study focuses on the ISO 14971 standard, aiming to familiarize future researchers with its fundamental terminology and guide them in effectively preparing risk management plans and reports specific to medical devices.
