Machine Learning

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.

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.

The human aging process can be characterized by progressive cellular degeneration, mitochondria malfunctioning, inflammatory responses, epigenetics modifications, and loss of tissue regenerative ability. The recent progress made in the field of longevity has revealed several promising treatment opportunities for prolonging a healthy lifespan in humans through epigenetics regulation, senolytics application, NAD+ precursors' intake, telomerase activation, and regenerative treatments. This review considers evidence from human studies about the impact of DNA methylation, cell senescence elimination, mitochondria recovery, enhanced immunity, tissue renewal, and cognitive reserve increase in human aging biology. According to findings based on human research, interventions involving senolytic compounds, Nicotinamide Riboside (NR), Nicotinamide Mononucleotide (NMN), and telomerase-linked regenerative treatment have the ability to contribute to improved metabolism, vascular functions, immunological resilience, and cognitive efficiency while reducing inflammatory processes and decreasing the number of senescent cells in a human body. In addition, comprehensive longevity approaches consisting of the mentioned interventions seem to possess combined benefits in terms of human longevity improvement. However, there are certain drawbacks that must be addressed when applying these interventions into clinical practice; namely, small sample sizes used in studies, lack of long-term safety testing, ethical issues, and inadequate biomarkers. Future directions in the research are discussed.

Brain-Computer Interfaces (BCIs) have been proposed as assistive technologies for Locked-In Syndrome (LIS) patients that can facilitate communication based on decoding of neural signals. Traditional BCI systems based on recurrent neural network (RNN) models exhibit certain constraints in terms of decoding accuracy, communication speed, and response latency. The current study aims to assess the effectiveness of transformer-based frameworks in optimizing the efficiency of both invasive and non-invasive BCI systems as compared to classical RNN models. A computational-clinical study design was used which involved participation of 48 LIS or severely paralysed participants. Subjects were grouped in accordance with their involvement in invasive or non-invasive BCI groups, and assessments were conducted during a period of eight weeks of intervention. Neural activity data processing was done with the help of two different approaches, including transformer-based model application and RNN application, assessing communication speed, decoding accuracy, latency, and error rates of both systems. Results suggest that transformer-based neural decoding frameworks proved to be superior to RNNs in terms of all evaluated criteria. Invasive transformer-based BCI demonstrated the best results concerning communication speed, decoding accuracy, lowest latency, and lowest error rates. Non-invasive transformer BCIs also yielded better results than RNN-based BCIs.

Critical Congenital Heart Disease (CHD) is one of the most important causes of neonatal morbidity and mortality and early prenatal diagnosis is essential for effective treatment and better clinical outcomes. In this study, ECHOFOCUS-CHD, an AI-inspired autonomous framework for the diagnosis of Critical Congenital Heart Disease from ultrasound images of fetuses is proposed. A quantitative experimental research design was used and 200 prenatal ultrasound scans acquired from hospitals and fetal echocardiography databases are used. A Convolutional Neural Network (CNN) was created to classify fetal cardiac conditions into normal, mild, moderate and critical CHD categories. The models are enhanced by using image preprocessing methods like noise reduction, normalization, contrast enhancement, and data augmentation. An overall accuracy of 94.5%, sensitivity of 92.8%, specificity of 95.6% with an AUC value of 0.96% was obtained, which shows an excellent diagnostic capability in the proposed system. The reliability of the framework was also verified using confusion matrix and ROC curve analyses. The system was also able to automatically classify the heart defects of foals using images from their prenatal scans, aiding clinical decisions during pregnancy and early treatment planning. The results show that AI-powered prenatal ultrasound analysis has the potential to greatly improve the effectiveness of early detection of CHD and prenatal screening.

Aim and Objectives: This study aims to evaluate the role of physiotherapy in pain modulation and to provide mechanistic insights into non-pharmacological interventions that complement or substitute pharmacologic therapies. The objectives include reviewing underlying neurophysiological mechanisms, therapeutic techniques, and clinical evidence supporting physiotherapy in chronic pain management. Methodology:A literature review was conducted using PubMed, Scopus, and Web of Science, focusing on studies published between 2005 and 2025. Randomized controlled trials, systematic reviews, and mechanistic studies examining physiotherapy modalities—such as exercise therapy, manual therapy, transcutaneous electrical nerve stimulation (TENS), and therapeutic ultrasound—were analyzed. Results: Evidence suggests that physiotherapy alleviates pain through multiple mechanisms including modulation of nociceptive signaling, enhancement of endogenous opioid release, reduction of central sensitization, and improvement of musculoskeletal function. Interventions such as exercise therapy and TENS demonstrated significant reductions in pain intensity, improved mobility, and decreased reliance on pharmacological agents across diverse patient populations. Conclusion: Physiotherapy offers a safe, effective, and mechanistically validated non-pharmacological strategy for chronic pain management. By targeting neural, muscular, and psychosocial components of pain, physiotherapy serves as a vital adjunct or alternative to pharmacological treatments, promoting long-term functional recovery and enhanced quality of life.