Anubha Dhuriya

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.

These rare facial diseases, which include hemifacial microsomia, Parry–Romberg syndrome, Moebius syndrome, Treacher Collins syndrome, Apert syndrome, and Crouzon syndrome, among others, pose considerable difficulties in diagnosis and treatment due to their clinical variability, rare occurrence, and complex genetic etiology. This review highlights the increasing potential of artificial intelligence (AI) and machine learning (ML) technologies in the better diagnosis, phenotyping, genotyping, and clinical management of these rare disorders. In this review, the applications of AI to facial phenotyping, three-dimensional (3D) imaging, radiomics, multimodal learning, and explainable AI have been highlighted. It also points out some latest developments in the field of bioinformatics, genome editing, RNA therapies, patient-derived models, and digital twin technology for precision medicine and translational research. Presently, there is sufficient evidence indicating the benefits of AI in increasing the accuracy in diagnosis, objective craniofacial evaluation, and customized treatment plans, whereas computational therapy is still experimental. However, various barriers such as data limitations, variability in phenotype, bias in the algorithms, external validation, ethical issues, and regulation prevent its widespread adoption in clinical practice. Future studies need to concentrate on multicenter data sharing, multimodal explainable AI, precision genomics, and translational framework to speed up the adoption of AI in rare craniofacial medicine.

Rare appendix malignancies, such as mucinous adenocarcinoma, goblet cell adenocarcinoma, signet-ring cell carcinoma, and appendiceal neuroendocrine tumors, are rare gastrointestinal cancers with diagnostic and treatment challenges due to their histological and molecular diversity. This study presents an explainable Artificial Intelligence (AI)-based approach that integrates digital histological biopsy images, molecular markers, and patient data to enhance disease classification and prediction of response to treatment. A quantitative methodology was used, based on an anonymized dataset of 200 patients with rare appendix malignancies. The performance of machine learning algorithms, such as Logistic Regression, Random Forest, XGBoost, and LightGBM, was compared using Accuracy, Precision, Recall, F1 score, and ROC-AUC metrics, while SHAP and Grad-CAM methods increased model interpretability. LightGBM outperformed all other methods and achieved the highest accuracy (96.2%), F1-score (95.5%), and ROC-AUC (0.987). KRAS, Ki-67, and TP53 were determined to be the most significant predictors from the biomarker analysis and the overall treatment response prediction accuracy of the combined model was found to be 95.1%. These results show that combining digital histopathology, biomarkers, and explainable AI (XAI) can enhance diagnosis accuracy, treatment response predictions, and clinical decision-making in rare appendiceal malignancies.

The process of aging is a complicated biological phenomenon that is marked by a loss of functionalities and high susceptibility to illness. Recent developments in geroscience have narrowed down the classical paradigm of aging processes into the so-called Hallmarks of Aging 2.0 where molecular pathways including genomic instability, epigenetic changes, mitochondrial dysfunction, chronic inflammation, and cellular senescence are connected. The buildup of senescent cells also known as the zombie cells has come out as one of the major causes of tissue degeneration during the aging process. These cells are metabolically active and irreversibly differentiate but continue to secrete pro-inflammatory factors referred to as senescence-associated secretory phenotype (SASP). There are two key therapeutic approaches that have been optimized to combat cellular senescence; senolytics, which are specific to eliminate senescent cells, and senomorphics, which inhibit the pathogenic secretory phenotype of senescent cells, but do not kill them. The current review presents an overview of the current results of animal-based experimental research on the molecular hallmark of aging and compares the relative efficacy of senolytic and senomorphic treatment in regulating aging pathways. The article identifies the most important experimental models, mechanisms of action, therapeutic potential and limitations of the existing approaches. The knowledge of these strategies offers an insightful critical view of longevity science and can lead to new possibilities in creating anti-aging interventions to work on basic biological processes.