Precision Medicine

Leptospirosis or commonly referred to as Weil’s disease is an emerging neglected zoonotic bacterial infection that poses great challenges globally due to its complicated epidemiology, varied presentations, and increased prevalence linked with climate change, urbanization, and environmental pollution. Despite advancements in the diagnostic procedures and treatment options with antibiotics, delayed diagnosis and inadequate surveillance systems still hinder disease control efforts. This review provides current information about the disease with regard to the causative agents, pathogenesis, epidemiology, bio-screening techniques, and therapies of leptospirosis together with new developments in artificial intelligence (AI), machine learning (ML), modeling, geospatial analysis, three-dimensional (3D) fingerprinting, digital pathology, and precision medicine. Some of the emerging technologies such as explainable AI, deep learning (DL), Geographic Information System (GIS), Bayesian, and Long Short-Term Memory (LSTM) model, biomarker-based diagnostics, and AI -based drug discovery have demonstrated significant potential in the context of disease surveillance, prediction of outbreaks, prognosis, and personalization of clinical interventions. This review further addresses aspects like vaccine development, digital health, and the difficulties of transitioning these technologies into practice. The integration of intelligent computing techniques and molecular diagnostics in conjunction with One Health surveillance may offer a promising approach to achieve early detection, precise treatment, and preparedness in leptospirosis.

Artificial intelligence (AI) has emerged as one of the most transformative technologies in pharmaceutical research by accelerating drug discovery and medicinal chemistry through machine learning, deep learning, and advanced computational approaches. This review examines the available literature from human clinical studies, computational drug discovery research, systematic reviews, meta-analyses, and clinical investigations, highlighting the applications of AI in target identification, virtual screening, lead optimization, drug repurposing, ADMET prediction, and precision medicine. The review also explores interdisciplinary approaches integrating medicinal chemistry, bioinformatics, structural biology, cheminformatics, and digital healthcare to improve molecular design, reduce research costs, and enhance drug development efficiency. Current evidence indicates that AI significantly improves the accuracy and speed of discovering novel therapeutic compounds while supporting personalized treatment strategies and optimizing clinical trials across diverse therapeutic areas. Despite these advancements, challenges remain regarding data quality, model interpretability, algorithmic bias, regulatory acceptance, and prospective clinical validation. Addressing these limitations through interdisciplinary collaboration and standardized validation frameworks will further strengthen the role of artificial intelligence in advancing drug discovery and medicinal chemistry.

VEXAS syndrome (Vacuoles, E1 enzyme, X-linked, Autoinflammatory, Somatic syndrome) is a more recently described, acquired somatic mutation in the UBA1 gene, and is an autoinflammatory disorder of adults. Its clinical features include systemic inflammation, clonal hematopoiesis, abnormalities of the bone marrow and various hematological, rheumatological and dermatologic changes. This review discusses the molecular pathogenesis of VEXAS syndrome with emphasis on the UBA1 mutations, immune dysfunction and disease pathogenesis. In addition, it emphasizes the importance of artificial intelligence, next generation sequencing (NGS), integration of multi-omics and three-dimensional (3D) genomic fingerprinting in the improvement of diagnosis and molecular characterization. The current therapeutic options such as the use of corticosteroids, JAK inhibitors, azacitidine and biologic agents, as well as novel stem cell-based therapies are reviewed. Special focus is on the most promising potentially curative therapy, namely, allogeneic hematopoietic stem cell transplantation (HSCT). Although genomic medicine and precision diagnostics has come a long way, there are still issues to be addressed, particularly with respect to diagnosis at an early stage and longer-term management. The synergy of genomics, artificial intelligence (AI), and regenerative medicine could have a profound impact on the care of VEXAS syndrome patients in the future.

Syphilis is a chronic and multi-stage infectious disease caused by Treponema pallidum, which has a rapid spread, resistance to immune responses, and chronic infection. This review is a synthesis of animal evidence to study the pathogenesis, clinical synergies, diagnostic plans, treatment plans, and epidemiological implications of the disease. The use of animal models, especially rabbits, has been critical in understanding the interaction of the host and pathogen, development of lesions, and immunological reactions. This research indicates the relative performance of the traditional and reverse screening algorithm, which shows that reverse screening has a better sensitivity during both early and latent periods, whereas the traditional approach is useful in monitoring active infection. The development of molecular diagnostics, particularly PCR and immunoassays, has improved early diagnosis and evaluation of the disease, whereas penicillin remains the most effective treatment despite the emerging resistance issues in other treatments. Additionally, experimental epidemiological research adds to the knowledge on the dynamics and persistence of transmission. Nevertheless, animal model limitations and issues with vaccine development because of immune evasion remain a major problem. The review highlights the necessity of a better experimental model, combined diagnostic, and novel treatment and vaccine options to improve the management of the disease and future research outcomes.

Poor water solubility is still a big problem in medication development since it typically makes oral formulations less bioavailable and less effective at treating diseases. The goal of this project was to create and test solid lipid nanoparticles (SLNs) as a new way to deliver medications that don't dissolve well. Four SLN formulations were made and tested using a hot homogenization followed by ultrasonication method. The tests looked at the size of the particles, the polydispersity index (PDI), the zeta potential, the entrapment efficiency, the drug loading, and the in vitro drug release. The results showed that higher concentrations of surfactants and lipids made the particles smaller, trapped more drugs, and released them over a longer period of time. Formulation F4 had the best performance, with a particle size of 130 nm, an entrapment efficiency of 88%, and a drug release rate of 85% at 24 hours. Statistical analysis showed that there were big differences across the formulations (p