Background: Depression is a globally prevalent psychiatric disorder, and while conventional pharmacotherapies have transformed care, they are often limited by delayed onset, suboptimal efficacy, and side effects. Emerging evidence links chronic inflammation, oxidative stress, HPA axis dysregulation, neurotrophic deficits, and gut-brain axis disturbances to Major Depressive Disorder (MDD) pathophysiology. In this context, phytochemicals bioactive plant compounds show promise as adjuncts or alternatives. This review explores major phytochemical classes (flavonoids, alkaloids, terpenoids, saponins, lignans, glycosides), highlighting their antidepressant mechanisms, including monoaminergic modulation, anti-inflammation, antioxidant effects, neuroplasticity enhancement (via BDNF), HPA axis regulation, and gut microbiota influence. Notable agents like curcumin, resveratrol, berberine, ashwagandha, and Rhodiola rosea have demonstrated efficacy in preclinical and clinical settings. Traditional medical systems also utilize synergistic multi-herb formulations. Despite their potential, challenges such as poor bioavailability, lack of standardization, potential herb-drug interactions, and regulatory inconsistencies impede clinical translation. Future directions involve personalized phytomedicine, bioavailability-enhancing technologies, systems biology, and large-scale randomized controlled trials to establish efficacy and safety. Overall, phytochemicals offer a scientifically grounded and patient-friendly avenue for advancing depression therapeutics.

Drug repurposing has emerged as a powerful, cost-effective strategy for therapeutic innovation amid increasing global disease burdens and the limitations of traditional drug development, which is time-consuming, costly, and marked by high failure rates. This systematic review synthesizes the current landscape of drug repurposing, drawing from literature published between 2000 and 2025 across major biomedical databases. The review highlights mechanistic rationales, successful examples, computational and experimental approaches, and regulatory and ethical considerations. Repurposed drugs such as thalidomide, sildenafil, metformin, and minoxidil demonstrate the clinical potential of repositioning strategies guided by mechanistic insight, phenotypic screening, and real-world observations. The applications span oncology, infectious diseases, neurology, and cardiovascular medicine, where existing drugs have shown efficacy beyond their original indications. Emerging tools including artificial intelligence, network pharmacology, high-throughput screening, multi-omics integration, and real-world evidence from electronic health records are transforming how candidates are identified and validated. Despite these advances, challenges remain particularly intellectual property constraints, regulatory barriers, and limited commercial incentives for off-patent drugs. A coordinated approach involving regulatory innovation, ethical oversight, and public-private collaboration is essential to fully leverage repurposing as a path to safer, faster, and more affordable treatments for both common and neglected diseases.

Background: Non-plant natural products have emerged as powerful contributors to modern pharmacotherapy, offering structurally diverse and mechanistically novel compounds for the treatment of complex diseases. This systematic review examines bioactive agents derived from microorganisms, fungi, marine organisms, animal venoms, and minerals, highlighting their therapeutic applications and mechanisms of action across major disease areas, including infections, cancer, neurological disorders, cardiovascular diseases, and immune-mediated conditions. Using PRISMA guidelines, literature published between 2000 and 2025 was screened across PubMed, Scopus, Web of Science, and ClinicalTrials.gov, yielding 48 eligible studies. Notable compounds include penicillin, lovastatin, cyclosporin A, ziconotide, and eribulin, each exemplifying the clinical relevance of non-plant sources. These agents act through diverse mechanisms—such as calcium channel blockade, DNA crosslinking, microtubule inhibition, and immunosuppression—often with greater specificity and potency than plant-derived or synthetic alternatives. Key advantages include modular biosynthesis via PKS and NRPS enzymes, high biological selectivity, and scalable production via microbial fermentation or synthetic biology. However, challenges remain, including toxicity, complex synthesis, and limited natural abundance. Emerging technologies such as genomic mining, nanocarrier delivery systems, and AI-assisted drug discovery offer promising solutions to these barriers. By mapping the landscape of non-plant-derived natural products, this review underscores their value in addressing unmet clinical needs and expanding the drug discovery pipeline beyond traditional botanical sources.

The catastrophic impact of Hurricane Helene proved that standard FEMA flood maps are often inadequate for assessing risk in complex mountainous terrain. Using Buncombe County, North Carolina, as a case study, this research introduces a replicable framework for siting emergency shelters based on a multi-dimensional Flood Risk Index. By synthesizing HAND-derived inundation data, land-use intensity, and a machine learning-based Socio-Economic Vulnerability Index (SEVI), we mapped the intersection of hazard and vulnerability. Our analysis reveals a significant misalignment—a large portion of the current shelter network sits in high-risk zones, while safer upland corridors in the north and west remain underutilized. This study delivers a data-driven roadmap for disaster preparedness, ensuring that future shelter placement is not only safe from terrain-driven floods but also strategically and equitably located.

The current clinical pathologic diagnostic process using histological slide evaluation demonstrates inconsistent accuracy in medical diagnosis. Progress in digital pathology and other modern technologies now enables the utilization of AI for medical image diagnostics, along with genomic technology for profile assessment and molecular image functionality. This systematic review examines the impact of artificial intelligence technology combined with genomic analysis and molecular imaging systems on present-day pathological medicine advancement. The review includes research published between 2014 and 2025, obtained from the top five databases, to demonstrate how each technology improves diagnosis separately and collaborates for precise medicine advancement. The analysis evaluated ten studies that matched all the established criteria for inclusion. Medical diagnostics benefit from combined system platforms, which also strengthen patient classification systems and treatment selection. However, these platforms require improvements in data standards and workflow connections, as well as computational resources and moral framework requirements. The study defines the necessary criteria for government-connected data platforms and interpretive artificial intelligence models, and then creates regulatory mechanisms in collaboration with interdisciplinary partnerships to develop safe and equitable healthcare applications. A single organized system triggers an essential transformation that shifts pathology from traditional morphological practices toward complex multivariate modern data methods. The research delivers strategic recommendations to enhance future practice and policy development, which will enable these technologies to be widely used in clinical settings.