Search Results (251)

Pharmacogenomics AI offers significant potential for individualized drug therapy; however, its clinical benefits remain unevenly distributed. Models trained predominantly on European-ancestry data consistently underperform in non-European populations, with polygenic risk scores (PRS) showing an estimated 39–73% reduction in predictive accuracy in African-ancestry cohorts across complex traits. These disparities have driven increased interest in moving beyond single-layer genomic approaches. Multi-omics frameworks integrating genomic, transcriptomic, proteomic, and metabolomic data have emerged as a promising strategy to improve prediction across heterogeneous clinical populations, as each molecular layer provides distinct and complementary biological information. Among these layers, metabolomics may represent a particularly transferable component across populations. Metabolite profiles capture the downstream functional output of biological systems influenced by genetic, environmental, dietary, and microbiome-related factors, and may therefore be less reliant on ancestry-stratified allele frequency structures that underlie performance disparities in genomic models. This review synthesizes evidence regarding the mechanistic basis of genomic bias in pharmacogenomics AI, the emerging role of multi-omics integration, especially metabolomics, in improving predictive performance, and the current landscape of computational strategies for bias mitigation, including federated learning, transfer learning, domain adaptation, and synthetic data generation. Collectively, current evidence supports metabolomics-inclusive multi-omics frameworks as a biologically plausible, hypothesis-generating strategy to reduce reliance on ancestry-linked genomic features. However, direct evidence that such frameworks reduce ancestry-related bias in clinical AI outputs remains limited, underscoring the need for globally diverse datasets and prospective multi-population validation. Full article

Chemoradiotherapy plays an important role in the management of locally advanced head and neck squamous cell carcinoma. Unfortunately, a substantial fraction of patients experience treatment failure, while others suffer from significant treatment-related toxicity caused by intensive chemoradiotherapy regimens. This underscores the need for new biomarkers that can accurately capture the biological tumor heterogeneity and guide personalized therapy. Functional imaging combined with AI-based approaches such as radiomics and deep learning may offer a promising strategy for treatment stratification. However, a substantial number of challenges remain before clinical implementation can be achieved. Therefore, this review proposes a biology-driven framework for AI analysis of functional imaging in head and neck squamous cell carcinoma. In addition, it emphasizes the need for clinically oriented validation strategies to facilitate the translation of stratification models into clinical management. Full article

Obesity and type 2 diabetes mellitus (T2DM) represent intertwined global epidemics driven by gut dysbiosis, chronic inflammation, and impaired SCFA production, identifying the microbiome as a therapeutic target. This review synthesizes mechanistic insights and clinical evidence on the role of probiotics as microbiome modulators in the management of metabolic disease. A comprehensive literature search across PubMed, Scopus, Web of Science, and Google Scholar up to May 2026 identified ~230 records using keywords such as probiotics, SCFAs, obesity, and T2DM; a narrative synthesis integrated preclinical, RCT, and meta-analytic data without formal pooling due to heterogeneity. Probiotics restore eubiosis via strain-specific mechanisms, Lacticaseibacillus rhamnosus GG enhances tight junctions (ZO-1), Bifidobacterium breve BBr60 boosts butyrate cross-feeding, and pasteurized Akkermansia muciniphila remodels bile acids (FXR/FGF19), activating G-Protein Coupled Receptor 41 (GPR41)/43-GLP-1 signaling, Treg expansion, and NF-κB suppression. Beyond immunometabolic effects, probiotics mitigate obesity- and T2DM-related oxidative stress by upregulating endogenous antioxidant enzymes (e.g., SOD, catalase, GPx), modulating Nrf2/Keap1 signaling, and reducing lipid peroxidation and other oxidative stress markers in experimental and clinical settings. Meta-analyses of RCTs reveal modest benefits: BMI reductions (~0.3 kg m⁻²), waist circumference (WC) reductions (1–2 cm), HbA1c reductions (0.3–0.4%), and improvements in homeostatic model assessment of insulin resistance (HOMA-IR), especially with multi-strain (>10⁹ CFU day⁻¹, ≥12 weeks) synbiotics. Innovative strategies—synbiotics, postbiotics, AI-tailored consortia, and fermented dairy—address engraftment and response variability. Current guidelines recommend 10⁹–10¹¹ CFU day⁻¹ using multi-strain formulations for 12–24 weeks alongside lifestyle measures, with regimen selection tailored to the dysbiosis phenotype (e.g., NAFLD). Future longitudinal RCTs integrating multi-omics endpoints with AI-driven strain selection should refine—and ultimately individualize—precision probiotic strategies for metabolic therapy. Full article

Breast cancer is a heterogeneous disease characterized by substantial molecular diversity and variable treatment outcomes across patients. Despite advances in targeted and systemic therapies, anticipating individual benefit remains a major clinical challenge. In this context, Artificial Intelligence (AI) can support precision oncology by integrating high-dimensional molecular profiles with clinical and pharmacological information. Here, we present B.R.E.A.S.T. (Breast canceR Enhanced AI-Supported Therapy), an interpretable machine learning framework designed to predict therapy outcome from tumor proteomic profiles integrated with clinical and treatment annotations. Proteomic data from The Cancer Genome Atlas (TCGA) and The Cancer Proteome Atlas (TCPA) were harmonized with outcome and therapy information, and thirteen supervised classifiers were systematically evaluated using stratified 5-fold cross-validation. Therapeutic outcome labels were operationally defined by integrating available treatment response annotations with complementary clinical outcome information. Across both cohorts, ensemble-based models consistently achieved the most stable and highest discriminative performance, supported by learning-curve analyses and consistent behavior across independent datasets. To enhance interpretability, we implemented a two-step feature selection strategy combining model-specific importance measures with a global consensus ranking, enabling the identification of a compact set of robust proteomic biomarkers associated with therapeutic outcome. Top-ranked features mapped to molecular programs relevant to breast cancer progression and treatment sensitivity, including regulators of cell survival, DNA damage response, PI3K/AKT/mTOR signaling, and invasion-related processes. Re-evaluation using only the top 30 globally ranked features preserved high predictive performance across both independent breast cancer cohorts, indicating that a parsimonious proteomic signature captures core molecular determinants of outcome. Overall, B.R.E.A.S.T. provides a robust and generalizable proteomics-driven framework for modeling outcome-associated therapeutic response patterns and supporting biologically informed biomarker discovery in breast cancer. Full article

Healthcare professionals must acquire and maintain both declarative knowledge and fine psychomotor skills across a wide range of clinical procedures. Human working memory is physiologically limited, and the high cognitive demands of clinical environments frequently contribute to medical errors and adverse events. Intra-individual performance variability—driven by fatigue, stress, and motivation—represents a further challenge that conventional medical safety education has not adequately addressed. According to the World Health Organization, patient harm ranks fourteenth in the global burden of disease, with approximately 10% of hospitalised patients in high-income countries experiencing harm within healthcare facilities. This study reports the design, theoretical rationale, and preliminary outcomes of an augmented reality (AR) glasses system for hands-free, self-directed medical procedural training, developed from a human factors and ergonomics (HFE) perspective. The system integrates a see-through head-mounted display (HMD; Epson Moverio BT-40S), bone-conduction earphones (Shokz OpenComm), and an industrial-grade voice recognition application (NEC Solution Innovators), achieving fully hands-free operation compatible with aseptic technique. Content design is grounded in cognitive load theory (CLT) and the cognitive theory of multimedia learning (CTML), extended by neuroscientific evidence on multisensory integration and memory consolidation. More than 40 procedure-specific modules have been developed in-house at Tokyo University of Technology, spanning airway management, vascular access, respiratory therapy, dialysis, and cardiac support. In a four-year longitudinal survey (virtual reality (VR) simulator; n = 286), major satisfaction items consistently exceeded the scale midpoint. In an AR endotracheal suctioning cohort (n = 38/22), procedural flow understanding was rated 3.95/5.0. A peer-reviewed randomised controlled trial (Clinical Simulation in Nursing, n = 36) demonstrated significantly superior skill improvement (p < 0.001) and learning motivation (p = 0.001) in the AR group versus textbook self-practice. Principal ergonomic limitations of current HMD hardware—excessive weight, narrow field of view, and absence of medical-grade certification—are documented, and AI-based real-time procedural assessment is identified as a priority for the next research phase. Full article

Background: Gynecological cancers include collections of cancers with diverse cellular and molecular characteristics that often develop drug resistance, making them treatment-resistant. Biomolecule–drug conjugates (BDCs), especially antibody–drug conjugates (ADCs), have revolutionized the targeted therapy of cancer; however, the creation of these entities has so far been achieved by empirical, resource-intensive design methods. Objective: The aim of this review is to critically analyze how AI can be used for the rational design and optimization of high-affinity BDCs for gynecological cancer treatment. Methods and discussion: Recent advances in machine learning (ML)- and deep learning (DL)-based methods to predict biomolecule-target binding affinity, structural compatibility, linker stability, payload selection, trafficking in the cell, and biomolecule resistance mechanisms are summarized. The review also explores the possibilities for incorporation of structural, chemical, biological, and multi-omics data to enhance specificity, efficacy, and safety of conjugates. Besides antibody-based systems, AI-assisted design approaches with peptides, aptamers, and hybrid biomolecular systems are also included. This review also highlights parameters and experimental/numerical validation restrictions related to data quality, interpretability of models, regulatory aspects, etc. Conclusions: AI-based conjugate engineering is increasingly moving BDC development from a largely ‘trial and error’ approach to a more predictive and data-driven approach. While there are still challenges to be addressed in terms of translations and validations, the potential of AI approaches in the field of precision oncology and the development of more personalized treatment is promising in the context of gynecological cancers. Full article

Background/Objectives: The rapid growth of the global aging population, projected to reach 2.1 billion older adults by 2050, presents major challenges for pharmacotherapy and drug delivery. Age-related physiological changes affecting pharmacokinetics and pharmacodynamics, widespread polypharmacy, and functional impairments such as dysphagia, cognitive decline, and sensory or motor limitations reduce the effectiveness and safety of conventional “one-size-fits-all” medication approaches. This review aimed to evaluate the major barriers to effective drug delivery in older adults and to assess emerging patient-centered and technology-driven drug delivery systems designed to improve medication adherence, safety, and therapeutic outcomes in geriatric populations. Methods: A comprehensive narrative review of current literature was conducted focusing on geriatric pharmacotherapy, age-related barriers to medication administration, and advanced drug delivery technologies. The review analyzed evidence regarding modified oral formulations, transdermal systems, long-acting injectables, implantable devices, nanotechnology-based platforms, digital health integrations, pharmacogenomics, biomarker-guided therapy, and deprescribing strategies including STOPP/START criteria and Beers Criteria. Studies addressing polypharmacy, medication adherence, and personalized medicine in older adults were also evaluated. Results: Evidence indicates that older adults experience significant medication-related challenges due to multimorbidity, polypharmacy, and functional decline. Dysphagia affects more than half of nursing home residents, while polypharmacy prevalence reaches up to 86.6% in some populations. Emerging drug delivery technologies demonstrated potential to improve adherence, dosing precision, and patient convenience. Personalized approaches incorporating pharmacogenomics, biomarker-guided treatment, and AI-assisted dosing showed promise for optimizing therapy. However, major limitations remain, including underrepresentation of older adults in clinical trials, limited high-quality evidence supporting many polypharmacy interventions, and insufficient implementation of advanced drug delivery systems in routine clinical practice. Conclusions: Current evidence supports a transition from standardized medication approaches toward flexible, individualized, and patient-centered drug delivery strategies for older adults. Advanced delivery technologies and personalized pharmacotherapy may improve medication safety, adherence, and quality of life in aging populations, although stronger clinical evidence and broader implementation are still needed. Future progress will require interdisciplinary care models, improved geriatric representation in clinical research, and regulatory reforms supporting the integration of innovative drug delivery systems into routine healthcare practice. Full article

Cancer research has undergone a fundamental transformation in recent decades due to the integration of artificial intelligence (AI) models into the study of tumor biology. However, tumor evolution, driven by genetic and phenotypic alterations leading to heterogeneity, resistance and metastasis, remains a major challenge in oncology. To understand these processes is crucial for developing effective therapeutic strategies and improving patient outcomes. Conventional methods often fail to capture the complexity and dynamics of these processes. In contrast, AI tools have the ability to integrate and analyze large-scale multi-omics, imaging and clinical data, offering the capability to decode tumor complexity. AI-driven methods facilitate multi-modal data integration, enabling the recognition of patterns that connect molecular alterations with phenotypic outcomes. In functional genomics, AI tools predict the effects of genetic variants, identify regulatory elements and map dysregulated pathways, thus clarifying mechanisms underlying tumor development and resistance. In the imaging field, deep learning techniques improve tumor segmentation, characterization and longitudinal monitoring, providing more accurate insights into tumor progression and treatment response. Predictive modeling could allow the anticipation of tumor evolution and drug response, supporting adaptive therapeutic plans and real-time treatment adjustments. Moreover, AI supports biomarker discovery, patient stratification and decision support systems that can improve clinical trial design and accelerate the development of personalized therapies. However, these advances raise important ethical challenges, including data privacy, algorithmic bias and the preservation of patient autonomy. Addressing these concerns is essential to ensure the responsible deployment of AI in oncology. Full article

Background/Objectives: Psoriasis is a chronic immune-mediated inflammatory disease increasingly recognized as a systemic disorder associated with significant metabolic and cardiovascular comorbidities. Among these, obesity (defined as BMI > 30 kg/m²) plays a pivotal role, acting both as a risk factor for psoriasis development and as a modifier of disease severity, clinical phenotype, and therapeutic response. The relationship between psoriasis and obesity is bidirectional and sustained by shared inflammatory and metabolic pathways. This review aims to provide a comprehensive and updated synthesis of the epidemiological association between psoriasis and obesity, to elucidate the underlying pathophysiological mechanisms, and to discuss the clinical and therapeutic implications of excess body weight in psoriasis management. Methods: A narrative review of the literature was conducted, including epidemiological studies, mechanistic research, clinical trials, and real-world evidence addressing the interplay between psoriasis and obesity. Relevant data were identified from peer-reviewed publications focusing on inflammatory pathways, metabolic dysfunction, cardiovascular risk, and treatment outcomes in obese patients with psoriasis. The graphical figures included in this manuscript were created with the assistance of a large language model–based image-generation tool, ChatGPT-5 by OpenAI, using author-defined prompts. The prompts requested schematic medical illustrations summarizing the pathophysiological links between obesity and psoriasis, including adipose tissue dysfunction, adipokine imbalance, systemic inflammation, and activation of the IL-23/Th17 axis. For the therapeutic algorithm, the prompt requested a stepwise clinical flowchart for obese patients with psoriasis, including BMI assessment, comorbidity screening, universal weight-management measures, psoriasis severity stratification, obesity-adapted biologic selection, and management of suboptimal response. The generated images were subsequently reviewed, edited, and approved by the authors to ensure scientific accuracy, clarity, and consistency with the manuscript content. Results: Epidemiological evidence consistently demonstrates a higher prevalence of obesity among patients with psoriasis, with obesity independently associated with increased disease severity. Shared mechanisms include adipose tissue–driven cytokine production, dysregulated adipokine secretion, insulin resistance, endothelial dysfunction, and activation of the IL-23/Th17 axis, collectively contributing to systemic inflammation and accelerated atherogenesis. Obesity negatively impacts the efficacy, pharmacokinetics, and long-term drug survival of conventional systemic agents and biologic therapies, leading to suboptimal clinical outcomes. Conclusions: Obesity is a key determinant of psoriasis burden, influencing disease expression, comorbidities, and therapeutic response. Integrating weight reduction strategies into personalized psoriasis management may improve both dermatological outcomes and overall cardiometabolic health, supporting a holistic approach to patient care. Full article

Immune checkpoint receptors (ICRs) play a pivotal role in modulating antitumor immunity and have become central targets in the immunotherapy of genitourinary (GU) malignancies. This review provides a comprehensive overview of the fundamental mechanisms of ICR signaling, the expression and pathophysiological roles of these receptors in GU cancers (kidney, bladder, prostate, testicular, and penile), and the evolving therapeutic landscape. Key ICRs, including PD-1, CTLA-4, LAG-3, TIM-3, and TIGIT, orchestrate complex signaling cascades that can lead to T-cell exhaustion and tumor immune evasion. Their expression varies significantly across GU cancer types, histological subtypes, and tumor stages, influencing prognosis and therapeutic response. Immune checkpoint inhibitors (ICIs) reinvigorate antitumor immunity by disrupting these inhibitory pathways and remodeling the tumor microenvironment (TME); however, resistance mechanisms (primary, adaptive, and acquired) and immune-related adverse events (irAEs) pose significant clinical challenges. Established biomarkers such as PD-L1 expression, tumor mutational burden (TMB), and microsatellite instability (MSI)/deficient mismatch repair (dMMR) status guide ICI use, but their predictive power has limitations. Consequently, emerging tissue-based (e.g., immune cell signatures, multiplex IHC/IF, spatial transcriptomics), liquid biopsy-based (e.g., ctDNA, CTCs, exosomes), and imaging-based (radiomics, AI-driven analysis) biomarkers are under active investigation to refine patient selection and monitor treatment efficacy. The therapeutic armamentarium is rapidly expanding with novel ICIs targeting new receptors, bispecific antibodies, and innovative combination strategies involving ICIs with chemotherapy, targeted therapies, radiotherapy, and other immunotherapies. Furthermore, ICIs are increasingly explored in neoadjuvant, adjuvant, and maintenance settings. This review highlights the dynamic progress in understanding ICR biology and its clinical translation, emphasizing the ongoing efforts to develop more personalized and effective immunotherapeutic strategies for patients with genitourinary tumors. Full article

Stroke is one of the leading causes of disability and mortality worldwide, and approximately 87% of cases are acute ischemic stroke (AIS). For patients with AIS, rapid administration of reperfusion therapy within the therapeutic time window remains the most effective treatment strategy. Over the past decade, numerous high-quality clinical trials have driven rapid advances in treatment strategies. Meanwhile, increasing attention has been directed toward cytoprotective therapies aimed at mitigating ischemic and reperfusion-related brain injury, which may act synergistically with reperfusion strategies. Although many related clinical trials have failed to demonstrate clear clinical benefit, they have provided valuable insights for the development of future cytoprotective agents. This review focuses on recent advances and remaining challenges in reperfusion therapy and cytoprotection for AIS. Full article

The process of drug discovery is one of the most expensive, time-consuming, and high-risk endeavors in modern science. Translating initial scientific insights into safe and effective therapies, supported by genomics, structural biology, and computational chemistry, typically requires more than a decade and substantial financial investment. Machine learning (ML) has emerged as a powerful tool for improving efficiency across the drug discovery pipeline. By enabling the analysis of large and complex datasets, ML supports target identification, lead discovery, optimization, and prediction of preclinical and clinical outcomes. Its integration with experimental validation and automation is illustrated by recent advances such as protein structure prediction, AI-driven antifibrotic compound discovery, and antibiotic identification. Despite these advances, significant challenges remain. Model generalizability is limited by data scarcity, heterogeneity, and hidden biases. In addition, the translation of in silico predictions into clinically validated outcomes remains a major bottleneck, and regulatory acceptance is constrained by limited model interpretability. Ethical considerations, including data privacy, equitable representation, and the potential misuse of generative models, further complicate adoption. This review examines the applications of ML across the drug discovery pipeline, with a focus on translational and regulatory considerations. It also discusses emerging directions, including hybrid physics–AI approaches, multimodal foundation models, federated learning, and explainable AI. The effective integration of ML will depend on rigorous validation, interdisciplinary collaboration, responsible data governance, and alignment with regulatory frameworks. Full article

Polymer-based nanoparticle systems have emerged as a versatile platform for advancing precision medicine by enabling controlled, targeted, and multifunctional drug delivery. This narrative review synthesizes recent progress in the design, functionalization, and clinical translation of polymer-based nanoparticles, with a focused scope on drug delivery, diagnostics, theranostics, nanosponges, and regenerative medicine. Specifically, it highlights three key insights: (i) surface engineering strategies, including ligand conjugation and stealth coatings, substantially enhance targeting specificity and reduce off-target toxicity; (ii) stimulus-responsive polymers enable spatiotemporally controlled drug release, improving therapeutic outcomes in complex disease microenvironments; and (iii) integration with artificial intelligence (AI) supports the rational design of personalized nanomedicines based on patient-specific molecular profiles. The innovative nature of this review lies in its comprehensive approach, which combines material design parameters with clinical outcomes and the barriers to implementation. Despite significant progress, serious challenges remain, including scalable and reproducible manufacturing, regulatory harmonization, and comprehensive long-term biosafety assessment. In the future, the priority should be to develop reliable manufacturing processes, a harmonized regulatory framework, and data-driven, clinically validated design methodologies. Overall, polymer-based nanoparticles are poised to redefine targeted therapy, but their clinical impact will depend on bridging the gap between laboratory innovation and scalable, safe, and personalized medical applications. Full article

Obesity is a chronic metabolic disorder associated with multiple serious complications and has become a major global public health problem. Accumulating evidence indicates that members of the cathepsin (Cath) family play an important role in the development of obesity pathogenesis, thereby emerging as promising therapeutic targets for intervention. This study summarizes the multiple regulatory mechanisms of Caths involved in obesity and discusses their regulation of adipocyte differentiation, cell death, metabolism, and adipose tissue inflammation. Building on these mechanisms, we further elaborate on three novel strategies targeting Caths for obesity intervention, including selective small-molecule inhibitor development, targeted delivery systems via nanocarriers, and gene modulation approaches targeting specific Cath subtypes. Despite robust preclinical data demonstrating the efficacy of Cath-targeted interventions in ameliorating obesity and associated metabolic disorders, several critical challenges impede their clinical translation, notably: functional redundancy among Cath family members, off-target effects and unpredictable long-term safety profiles, limited subtype selectivity of existing inhibitors and immunogenicity risks associated with nanodelivery systems. To promote strategies for the clinical translation of Cath-targeted anti-obesity therapies, future research priorities should encompass artificial intelligence (AI)-driven high-throughput screening and rational design of highly selective Cath inhibitors, validation of specific Cath subtypes as clinically actionable diagnostic and prognostic biomarkers for obesity and metabolic risk stratification, and the development of personalized precision medicine strategies tailored to individual metabolic phenotypes and Cath expression profiles. Full article

Protein–protein interactions (PPIs) are critical for cellular signaling, apoptosis regulation, and immune function in the body, and dysregulation is a hallmark of cancer. The large, dynamic, and shallow nature of PPI interfaces rendered them “undruggable” by conventional small molecules in the past. Recent advances in structural biology, chemical innovation, and artificial intelligence have revolutionized the landscape of PPI-directed drug discovery. This review summarizes the mechanistic roles of PPIs in oncogenesis, critically discusses novel therapeutic interventions, such as small molecules, peptidomimetics, stapled peptides, proteolysis-targeting chimeras (PROTACs), molecular glues, and AI-based drug optimization strategies, altering the druggable proteome in oncology. Therapeutics with clinically well-validated action, including Venetoclax and AMG 510, and next-generation candidates demonstrate the translational applications of these approaches. Some of the key challenges, such as interface complexity, specificity, bioavailability, and resistance, are addressed together with countermeasures like rational design, combination therapies, enhanced delivery systems, and biomarker-based patient selection. To this end, the incorporation of multi-omics data and artificial-intelligence (AI)-driven modeling technologies is revolutionizing the personalized cancer therapeutics development space. Collectively, these advances mark a paradigm shift: PPIs, once considered inaccessible, are now at the forefront of precision oncology, offering new hope for patients with previously intractable malignancies. Full article