Journal of Health and Biomedical Informatics

• During the COVID-19, e-learning and virtual exams became key solution to continuing university education.
• Issues like infrastructure, internet, security, and technology acceptance required usability evaluation of such systems.
• The "Faradid" exam system’s usability was assessed via QUIS on learning and interface.
• Users found ease of use positive but criticized message placement and page loading speed.

Research highlights

• Hepatitis C virus infection and hepatocellular carcinoma show significant prevalence in Iran.
• Patients with hepatitis C infection can also be assessed for their risk of hepatocellular carcinoma.
• Bioinformatics can evaluate gene expression profiles to analyze differential expression between groups.
• This technique uses computational data analysis to help select more precise genes and biomolecules.
• Selected proteins and microRNAs play crucial roles in HCV-related hepatocellular carcinoma development.

• Digital Twin (DT) tech combines IoT, AI, and electronic health records for healthcare use.
• It enables personalized care, scenario simulation, and continuous patient monitoring.
• DT improves diagnosis, hospital efficiency, lowers costs, and boosts patient satisfaction.
• Challenges include data quality, privacy, high costs, complexity, and tech acceptance.
• Scalable, flexible architecture is key to adapting DT tech to changing care and tech needs.

• Identifying a drug compound targeting the COVID-19 virus directly is crucial in combating the pandemic. 
• The Mpro protease is a key focus for molecular targeting due to its vital role in viral replication. 
• Antiviral screening and molecular docking found optimal compounds with strong binding energy to Mpro.
• The combination of Cyanidin-3, 5-diglucoside was identified as a promising candidate for targeting Mpro. 
• This research represents the early steps in the complex journey to discover effective treatments for COVID-19. 

• Accurate prediction of 30-day mortality was achieved in heart failure patients.
• Reducing features from 34 to 4 improved prediction accuracy and clinical utility.
• Key factors identified were age, lactate level, diabetes, and confusion.
• The GBM algorithm showed better performance with selected important features.
• Findings support better clinical decisions and higher survival rates for patients.

• Novel’s foresight approach identifies emerging trends and predicts trends in breast cancer diagnosis research. 
• Key algorithms have been recognized to direct future diagnostic efforts effectively.  
• Deep learning is expected to continue as the predominant method in breast cancer research. 
• This research informs the application of AI tools to enhance the accuracy of breast cancer detection. 
• The study highlights AI's importance in improving healthcare outcomes. 

• The study identifies factors affecting workflow, patient interaction, and drug safety.
• A total of 28 factors affecting the performance of pharmacies were identified.
• These factors enable pharmacies to maintain high standards of patient care and safety.
• Addressing these factors can enhance operational effectiveness in pharmacies.
• Findings can guide health policymakers in improving electronic prescription systems.

• The use of mobile health has a positive impact on shared decision-making.
• The mobile-based application had the most usage in shared decision-making.
• Shared decision-making was more commonly performed for musculoskeletal, cancer, and cardiovascular diseases.
• The USA has conducted more research on mobile health interventions for shared decision-making.
• Mobile health interventions to facilitate shared decision-making have received more attention in recent years.