ABSTRACT: Japan's healthcare system recognized for its achievements in universal healthcare coverage, for one of the highest life expectancies, and for low infant mortality rate. These impressive results achieved after decades of thoughtful policy making process and financial investments into the equitable access. In addition to that Japan has a long history of strong public health traditions and sophisticated insurance model. On the other hand, despite these achievements, the healthcare system recently faces growing pressure which may impact its sustainability and fairness.  One of the challenges is rapid aging of population.  In combination with persistently low fertility, these challenges are reshaping the demand for healthcare and long-term care services. Moreover, financial pressure in increasing with health-related and social security expenditures consume a growing share of the national budget. A shrinking number of workforces, unequal healthcare provider distribution, and fragmentation across nearly 3,000 health private insurers create additional inefficiencies and threaten equitable access of the population to healthcare services, especially in the rural areas. In addition, the healthcare system is under strain from rising number of multimorbidity and increasing mental health issues among young population. Advance in technological progress creates opportunities but at the same time requires substantial adaptation.

ABSTRACT: Adverse drug reactions (ADRs) remain a major global challenge, contributing substantially to patient morbidity, mortality, and healthcare costs. Traditional pharmacovigilance approaches—spontaneous reporting and post-marketing surveillance—are hampered by underreporting, delays, and limited contextual data. The growing availability of electronic health records (EHRs), which capture longitudinal structured and unstructured patient information, presents an unprecedented opportunity to advance ADR prediction. This narrative review synthesizes recent progress in developing and validating predictive models that leverage EHRs, highlighting methodological approaches, challenges, and future directions. Predictive strategies range from traditional regression models to advanced machine learning and deep learning architectures, with multimodal frameworks increasingly integrating structured fields (demographics, labs, prescriptions) and unstructured clinical text through natural language processing. While ensemble and deep learning methods demonstrate superior performance, issues of data quality, missingness, bias, and interpretability persist. Robust validation frameworks—spanning internal cross-validation to multi-center external testing—are critical to ensure generalizability and clinical trustworthiness. Ethical considerations, including fairness, privacy, and transparency, remain central to safe deployment. Looking forward, promising avenues include federated learning across institutions, integration of multi-omics and pharmacogenomic data, explainable AI tailored for clinical use, and real-time monitoring through digital twin frameworks. These trajectories, combined with robust governance and clinician–data scientist collaboration, have the potential to transform ADR detection from a reactive process to proactive, personalized prevention. By synthesizing the existing evidence, this review provides insights into the development of more effective predictive models for ADRs and informs strategies for improving pharmacovigilance. This study will contribute to the ongoing efforts to leverage EHRs and predictive models for improving patient outcomes and reducing the burden of ADRs.

ABSTRACT: Background. There is growing interest in the association between antiepileptic drugs (AEDs) exposure and subsequent Parkinson’s disease (PD).
Methods. We conducted a literature search in the PubMed, SCOPUS, and Web of Science databases. We identified studies using an observational design and performed a meta-analysis to evaluate the association between AEDs exposure and incident PD. We assessed the quality of the studies and identified the pooled odds ratio (OR) for those exposed to AEDs compared to those who were not.
Results. Of the 1,775 unique studies identified, 55 were selected for full-text review. Five studies (n = 127,324) were included. Quality assessment revealed moderate-to-high methodological quality in the studies included. The overall OR for a PD was 1.82 (95% CI: 1.35-2.45) in AEDs recipients. When considering each drug individually, the magnitude of association was highest for valproate (OR 3.94, 95% CI: 3.15-4.92) and lowest for carbamazepine (OR 1.32, 95% CI: 1.16-1.49). Further interaction tests revealed higher odds for lamotrigine than for carbamazepine and valproate than for carbamazepine and lamotrigine.
Conclusion. This study revealed potential associations between AEDs and incident PD. However, existing evidence remains insufficient, making it premature to draw inferences on this matter.

ABSTRACT: Artificial intelligence (AI) is increasingly shaping modern healthcare by enabling data-driven decision-making, improving diagnostic accuracy, and optimizing resource use. In transfusion medicine, AI offers substantial opportunities to enhance donor management, automate blood typing and compatibility testing, strengthen inventory forecasting, and support early detection of transfusion-related complications. This review summarizes current applications of AI technologies—including machine learning, deep learning, natural language processing, computer vision, and predictive analytics—and evaluates their impact across laboratory, clinical, and operational domains. Emerging innovations such as precision transfusion, patient digital twins, multi-omics integration, and federated learning highlight AI’s potential to advance personalized and interconnected transfusion practices. However, successful implementation requires addressing challenges related to data heterogeneity, algorithmic bias, privacy and ethical considerations, and evolving regulatory requirements. Establishing rigorous validation standards and promoting interdisciplinary collaboration will be essential to ensure that AI improves the safety, efficiency, and sustainability of transfusion medicine.

ABSTRACT: Diabetic foot ulcers (DFUs) are among the most severe complications of  diabetes mellitus, contributing to infection, limb loss, and premature mortality. In Africa, the rising prevalence of diabetes, combined with limited laboratory capacity and frequent empirical antibiotic use, has intensified the problem of multidrug-resistant (MDR) infections. Understanding the microbial spectrum and associated outcomes is critical for guiding evidence-based management. This review systematically synthesizes data on microbial etiologies, antimicrobial-resistance patterns, and clinical outcomes of DFUs in African populations. Methods: Following PRISMA 2020 guidelines, PubMed, Scopus, Embase, Web of Science, African Journals Online, and Google Scholar were searched for studies published between 2000 and 2025. Eligible studies included adults with DFUs in African settings that reported bacterial isolates, resistance profiles, or clinical outcomes. Two reviewers independently screened and extracted data, and study quality was appraised using the Joanna Briggs Institute checklist. Data were synthesized narratively and summarized using descriptive statistics. Sixteen verified studies from ten African countries, encompassing approximately 2,700 participants, were included. Staphylococcus aureus and Pseudomonas aeruginosa were the predominant isolates, followed by Escherichia coli, Klebsiella pneumoniae, and Proteus mirabilis. MDR prevalence was high, with methicillin-resistant S. aureus (MRSA) detected in 25–45% of isolates and extended-spectrum β-lactamase (ESBL)–producing Enterobacterales in 30–50%. Among studies reporting outcomes, amputation rates ranged from 15% to 38% and mortality from 7% to 16%, with poorer outcomes in MDR infections. Considerable heterogeneity existed in sampling and testing methods across studies. Saureus remains the dominant pathogen in African DFUs, but AMR is pervasive across bacterial species. Strengthening diagnostic laboratory systems, infection-control practices, and antimicrobial stewardship (alongside integrated diabetic foot care) is essential to reduce preventable amputations, mortality, and the continent’s growing burden of drug-resistant infections.
Keywords: Diabetic