Bulletin of Interdisciplinary Research and Reviews

MACHINE LEARNING-DRIVEN COGNITIVE ASSESSMENT IN PSYCHIATRIC DISORDERS: CURRENT ADVANCES AND FUTURE PERSPECTIVES-A REVIEW

2026-05-24T10:27:36-04:00

Psychiatric disorders are among the leading causes of disability worldwide and are frequently associated with substantial cognitive dysfunction that significantly impairs daily functioning, social interaction, occupational productivity, and overall quality of life. Cognitive deficits in psychiatric illnesses, including schizophrenia, major depressive disorder, bipolar disorder, anxiety disorders, attention-deficit/hyperactivity disorder, and autism spectrum disorder, often remain underdiagnosed or inadequately managed due to the subjective nature of conventional assessment techniques. Recent developments in artificial intelligence, particularly machine learning (ML), have introduced transformative opportunities for objective, scalable, and personalized cognitive assessment in psychiatric care. Machine learning-driven cognitive assessment integrates clinical, neuroimaging, electrophysiological, behavioral, and digital phenotyping data to identify subtle cognitive impairments, predict disease progression, and support clinical decision-making. Advanced computational approaches such as supervised learning, unsupervised learning, deep learning, and natural language processing have demonstrated considerable utility in classifying psychiatric conditions, predicting treatment responses, and enhancing diagnostic precision. Furthermore, neuroimaging modalities, electroencephalography, smartphone-based behavioral analytics, and wearable technologies increasingly contribute to precision psychiatry by enabling continuous monitoring of cognitive performance. Despite promising advancements, several challenges remain, including data heterogeneity, algorithmic bias, limited interpretability, privacy concerns, inadequate clinical validation, and ethical issues surrounding patient confidentiality. Therefore, integrating explainable artificial intelligence and standardized datasets is essential for achieving clinical translation. This review comprehensively explores the current landscape of machine learning-driven cognitive assessment in psychiatric disorders, highlighting technological advances, clinical applications, limitations, ethical implications, and future directions toward personalized psychiatric care and precision mental health interventions.

ADVANCES IN CLINICAL PHARMACY PRACTICE: OPTIMIZING MEDICATION THERAPY MANAGEMENT AND IMPROVING PATIENT SAFETY OUTCOMES

2026-05-24T12:26:44-04:00

Abstract: Clinical pharmacy practice has evolved into a highly specialized and patient-centered discipline focused on optimizing medication therapy and improving patient safety outcomes. The increasing complexity of pharmacotherapy, rising burden of chronic diseases, polypharmacy in aging populations, and frequent occurrence of drug-related problems (DRPs) have necessitated structured pharmaceutical care services. Medication Therapy Management (MTM) has emerged as a core clinical pharmacy intervention aimed at ensuring rational drug use, improving adherence, reducing adverse drug reactions (ADRs), and enhancing therapeutic outcomes. Clinical pharmacists now function as integral members of multidisciplinary healthcare teams, actively participating in medication reconciliation, dose individualization, therapeutic drug monitoring, and pharmacovigilance activities. Evidence suggests that structured clinical pharmacy services significantly reduce medication errors, hospital readmissions, and healthcare costs while improving quality of life and patient satisfaction. The integration of clinical decision support systems (CDSS), electronic health records (EHR), and artificial intelligence (AI)-based tools has further strengthened clinical decision-making and safety surveillance. Despite these advancements, challenges such as workforce shortages, lack of standardized implementation models, and limited awareness among healthcare professionals persist. This review critically examines recent advances in clinical pharmacy practice with emphasis on MTM, DRP management, pharmacovigilance, and patient safety optimization. It also highlights emerging trends such as precision medicine, pharmacogenomics, and digital health integration. The article concludes that clinical pharmacy is a cornerstone of modern healthcare systems and plays a pivotal role in ensuring safe, effective, and individualized pharmacotherapy across diverse patient populations.

RECENT ADVANCES IN PRECISION PHARMACOLOGY: NOVEL DRUG TARGETING STRATEGIES AND THEIR IMPACT ON PERSONALIZED THERAPY

2026-05-24T12:59:37-04:00

Precision pharmacology has emerged as a transformative approach in modern therapeutics, enabling individualized treatment based on genetic, molecular, environmental, and phenotypic characteristics of patients. Traditional pharmacotherapy often follows a generalized treatment paradigm that may lead to variable therapeutic outcomes, adverse drug reactions, and treatment resistance among individuals. The growing understanding of molecular biology, pharmacogenomics, bioinformatics, and advanced drug delivery systems has accelerated the development of precision pharmacology, which aims to optimize therapeutic efficacy and minimize toxicity. Recent advancements in novel drug targeting strategies, including ligand-mediated targeting, receptor-specific drug delivery, nanotechnology-based therapeutics, antibody-drug conjugates, RNA-based therapeutics, and gene-editing technologies, have significantly improved disease management and patient outcomes. Artificial intelligence and machine learning technologies are further enhancing drug discovery, biomarker identification, and personalized therapeutic decision-making. Precision pharmacology has shown substantial clinical utility in oncology, psychiatry, cardiovascular medicine, neurology, and infectious diseases by facilitating individualized therapeutic regimens tailored to genetic and molecular profiles. Despite promising advancements, challenges such as high treatment costs, limited accessibility, ethical concerns, regulatory complexities, and insufficient genomic infrastructure continue to restrict widespread implementation. Future developments in precision pharmacology are expected to integrate advanced computational technologies, real-world clinical evidence, and molecular diagnostics to establish more effective patient-centered healthcare systems. This review comprehensively discusses recent advances in precision pharmacology, novel drug targeting strategies, clinical applications, challenges, and future implications in personalized therapy while emphasizing the transformative role of precision medicine in improving therapeutic outcomes and healthcare sustainability.

RECENT ADVANCES IN AI-DRIVEN DRUG DESIGN AND GREEN SYNTHESIS STRATEGIES: AN INNOVATIVE REVIEW IN MODERN PHARMACEUTICAL CHEMISTRY

2026-05-24T13:03:17-04:00

Artificial intelligence (AI) and green synthesis strategies are transforming modern pharmaceutical chemistry by improving drug discovery efficiency and reducing environmental burden. Conventional drug development is costly, time-consuming, and associated with high attrition rates. AI-driven approaches, including machine learning (ML), deep learning (DL), artificial neural networks (ANNs), and generative algorithms, have accelerated target identification, lead optimization, molecular docking, predictive toxicology, and pharmacokinetic assessment. These computational tools facilitate rapid screening of chemical compounds and improve precision in identifying promising therapeutic candidates. Simultaneously, green chemistry has emerged as an environmentally sustainable approach in pharmaceutical manufacturing by minimizing hazardous chemicals, waste generation, and energy consumption. Techniques such as microwave-assisted synthesis, ultrasound-assisted reactions, biocatalysis, solvent-free synthesis, and continuous flow chemistry have gained substantial attention in reducing ecological impact while maintaining product efficiency. The integration of AI with green synthesis strategies further enhances pharmaceutical innovation through optimized reaction pathways, predictive process modeling, and sustainable molecular design. AI-supported green chemistry enables safer chemical production and resource-efficient pharmaceutical manufacturing. Applications in oncology, infectious diseases, neurological disorders, and precision medicine demonstrate the growing impact of these technologies. Despite remarkable advancements, challenges including algorithm transparency, data quality, regulatory concerns, computational costs, and scalability remain barriers to widespread implementation. Future pharmaceutical research is expected to increasingly adopt AI-assisted sustainable synthesis models for rapid and eco-friendly therapeutic development. This review summarizes recent developments in AI-driven drug design and green synthesis approaches, emphasizing their applications, advantages, limitations, and future perspectives in modern pharmaceutical chemistry.

THE FUTURE OF ANIMAL SCIENCE: MERGING BIOLOGICAL SIGNALS, ENVIRONMENTAL DATA, AND MACHINE INTERPRETATION

2026-05-24T14:03:27-04:00

Animal science is undergoing a substantial transformation through the convergence of biological signal monitoring, environmental intelligence, and computational machine interpretation. Traditional livestock and animal management systems largely relied on observational approaches, manual diagnostics, and generalized husbandry practices. However, advances in sensor technologies, precision livestock farming, artificial intelligence (AI), machine learning (ML), big data analytics, and Internet of Things (IoT)-enabled monitoring systems are revolutionizing animal production, welfare, health surveillance, and sustainability. Biological signals such as body temperature, heart rate, respiratory rhythm, hormonal fluctuations, rumination behavior, locomotion, feeding habits, and genomic indicators provide real-time insights into physiological and pathological states. Simultaneously, environmental variables including humidity, temperature, ventilation, air quality, noise, and climatic stressors significantly influence animal productivity, disease occurrence, and welfare. The integration of these datasets with machine interpretation technologies facilitates predictive analytics, automated disease detection, precision nutrition, reproductive optimization, and behavioral assessment. Emerging computational models enable early warning systems, reduce economic losses, and support evidence-based decision-making in livestock industries. Furthermore, smart farming technologies contribute toward sustainable production by minimizing environmental impacts, improving feed efficiency, and enhancing welfare standards. Despite notable advancements, challenges related to data interoperability, sensor accuracy, infrastructure costs, ethical concerns, and algorithmic transparency continue to hinder widespread implementation. Future animal science is expected to increasingly depend on multidisciplinary integration involving biology, veterinary sciences, engineering, computer science, and environmental analytics. This review explores the transformative role of biological signals, environmental data integration, and machine interpretation in shaping next-generation animal science while highlighting opportunities, limitations, and future prospects for intelligent livestock systems.