Search Results (234)

Optimizing immunosuppressant dosing presents significant challenges in kidney transplantation due to narrow therapeutic ranges and considerable inter-patient pharmacokinetic differences. Emerging strategies for precision dosing, encompassing Bayesian population pharmacokinetic models, pharmacogenomic integration, and artificial intelligence algorithms, aim to enhance drug monitoring by moving beyond traditional trough-based approaches. This review critically assesses available evidence for predictive dosing models targeting immunosuppressants, including calcineurin inhibitors, antimetabolites, and mTOR inhibitors in kidney transplant patients. Available observational and simulation studies demonstrate substantial methodological diversity, with Bayesian PopPK-guided strategies showing 15–35% better target exposure achievement compared to trough-based monitoring. The absence of pooled estimates precludes a precise summary effect size, and evidence from randomized controlled trials remains limited. Machine learning models, particularly for tacrolimus, frequently reduced prediction error relative to traditional regression approaches, but substantial heterogeneity in study design, outcome definitions, and external validation limits quantitative synthesis. Hybrid Bayesian–AI frameworks and explainable AI tools show conceptual promise but are largely supported by proof-of-concept studies rather than reproducible clinical implementations. Overall, Bayesian pharmacokinetic modelling represents the most mature and clinically interpretable approach for precision dosing in transplantation, whereas AI-driven and hybrid systems remain investigational. Key gaps include the need for standardized reporting, rigorous risk-of-bias assessment, prospective validation, and clearer regulatory and implementation pathways to support safe and equitable clinical adoption. Full article

The discovery and development of new drugs is a lengthy, complex, and costly process, often requiring 10–20 years to progress from initial concept to market approval, with clinical trials representing the most resource-intensive stage. In recent years, Artificial Intelligence (AI) has emerged as a transformative technology capable of reshaping the entire pharmaceutical research and development (R&D) pipeline. The purpose of this narrative review is to examine the role of AI in drug discovery and development, highlighting its contributions, challenges, and future implications for pharmaceutical sciences and global public health. A comprehensive review of the scientific literature was conducted, focusing on published studies, reviews, and reports addressing the application of AI across the stages of drug discovery, preclinical development, clinical trials, and post-marketing surveillance. Key themes were identified, including AI-driven target identification, molecular screening, de novo drug design, predictive toxicity modelling, and clinical monitoring. The reviewed evidence indicates that AI has significantly accelerated drug discovery and development by reducing timeframes, costs, and failure rates. AI-based approaches have enhanced the efficiency of target identification, optimized lead compound selection, improved safety predictions, and supported adaptive clinical trial designs. Collectively, these advances position AI as a catalyst for innovation, particularly in promoting accessible, efficient, and sustainable healthcare solutions. However, substantial challenges remain, including reliance on high-quality and representative biomedical data, limited algorithmic transparency, high implementation costs, regulatory uncertainty, and ethical and legal concerns related to data privacy, bias, and equitable access. In conclusion, AI represents a paradigm shift in pharmaceutical research and drug development, offering unprecedented opportunities to improve efficiency and innovation. Addressing its technical, ethical, and regulatory limitations will be essential to fully realize its potential as a sustainable and globally impactful tool for therapeutic innovation. Full article

Three-dimensional (3D) printing has transformed the development of microneedle arrays (MNAs) by enabling exceptional control over their geometry, distribution, materials, and functionality in a single-step, customizable process. This review represents a design-centric framework that organizes recent advancements in four interconnected levers: (i) individual microneedle (MN) geometry and size; (ii) patch-level MN distribution and multi-array architectures; (iii) computer-aided design (CAD), finite element analysis (FEA), computational fluid dynamics (CFD), and artificial intelligence/machine learning (AI/ML)-driven optimization; and (iv) manufacturing constraints and emerging solutions for scalability and reproducibility. Outcomes show that small changes in the radius of the MN’s tip, the MN’s aspect ratio, the MN’s internal lattice architecture, and the spacing of the array can dramatically influence their insertion force, mechanical reliability, payload capacity, and therapeutic coverage. Now, digital tools can bridge the design and experimental outcomes, while novel morphologies, hybrid materials, and theranostic integrations are expanding the clinical potential of MNs. The remaining challenges, resolution-versus-throughput trade-offs, biocompatibility, batch-to-batch consistency, and lack of testing standardization are examined alongside promising directions in high-throughput 3D printing, stimuli-responsive materials, and closed-loop systems. Finally, rational, model-guided design strategies are positioning 3D-printed MNAs as versatile platforms for painless, patient-specific drug delivery, diagnostics, and personalized medicine. Full article

Antimicrobial resistance (AMR) is escalating worldwide, posing a serious threat to global public health by driving infections that are no longer treatable with conventional antibiotics. CRISPR–Cas technology offers a programmable and highly specific therapeutic alternative by directly targeting the genetic determinants responsible for resistance. Various CRISPR systems can restore antibiotic susceptibility and induce selective bactericidal effects by eliminating resistance genes, disrupting biofilm formation, and inhibiting virulence pathways. Moreover, CRISPR can suppress horizontal gene transfer (HGT) by removing mobile genetic elements such as plasmids, thereby limiting the ecological spread of AMR across humans, animals, and the environment. Advances in delivery platforms—including conjugative plasmids, phagemids, and nanoparticle-based carriers—are expanding the translational potential of CRISPR-based antimicrobial strategies. Concurrent progress in Cas protein engineering, spatiotemporal activity regulation, and AI-driven optimization is expected to overcome current technical barriers. Collectively, these developments position CRISPR-based antimicrobials as next-generation precision therapeutics capable of treating refractory bacterial infections while simultaneously suppressing the dissemination of antibiotic resistance. Full article

The pursuit of fine chemicals from natural sources is advancing rapidly, driven by a growing demand for safe, sustainable, and high-performance ingredients in cosmetic and pharmaceutical formulations. Emerging extraction and biotransformation technologies, including enzyme-assisted procedures, precision fermentation, and green solvent systems, are enabling the selective recovery of complex molecules with enhanced purity and stability. Simultaneously, AI-guided approaches to the discovery of bioactive compounds are accelerating the identification of multifunctional molecules exhibiting, for example, anti-inflammatory, antioxidant or microbiome-modulating activities. These developments not only expand the chemical diversity accessible to the cosmetic and pharmaceutical sectors but also promote the adoption of circular bioeconomy frameworks. Together, they define a new generation of natural fine chemicals with strong potential for targeted therapeutic and cosmetic applications. Accordingly, this commentary focuses on emerging trends and key technological advances in the use of renewable, natural sources for the production of fine chemicals relevant to cosmetic and pharmaceutical industries. It further highlights the critical roles of biotechnology, green chemistry, and digital innovation in shaping a more sustainable future for cosmetic and pharmaceutical chemistry. Full article

Background: Artificial intelligence (AI) is transforming medicine by supporting data-driven diagnosis, prognosis, and personalized care. In rheumatology, AI applications are rapidly expanding in imaging, disease monitoring, and therapeutic decision support. This review aimed to summarize current evidence on AI in osteoporosis and chronic inflammatory rheumatic diseases, with a focus on methodological robustness and clinical applicability. Methods: A narrative review was conducted following SANRA criteria. PubMed and the Cochrane Library were systematically searched for studies published between January 2015 and July 2025 using MeSH terms and free-text keywords related to AI, osteoporosis, and inflammatory rheumatic diseases. A total of 323 articles were included. Results: Machine learning and deep learning models show strong performance in osteoporosis for predicting bone mineral density (BMD), bone loss, and fractures. In chronic inflammatory rheumatic diseases, AI improves imaging interpretation, particularly for sacroiliitis. AI tools also demonstrate potential for predicting disease risk and activity, diagnostic support and treatment response. Hybrid models combining imaging, clinical, and biological data appear particularly promising. However, most studies rely on retrospective single-center datasets, with limited external validation, suboptimal explainability, and scarce evidence of real-world implementation. Conclusions: AI holds significant promise for advancing diagnosis and personalized management in osteoporosis and rheumatic diseases. However, major challenges persist, including heterogeneous data quality, inconsistent methodological reporting, limited clinical validation, and barriers to integration into routine practice. Bridging the gap between algorithmic performance and clinical impact will require prospective studies, robust validation frameworks, and strategies to build trust among clinicians and patients. Full article

Bladder cancer (BC) represents a major global health issue with high recurrence and significant mortality rates in cases of advanced disease. Currently, the development of molecular profiling, liquid biopsy technologies, and artificial intelligence (AI) software has resulted in unprecedented opportunities to improve diagnosis, prognostic assessment, and treatment selection. Recent multicenter studies have identified emerging metabolomic, proteomic, and genomic biomarkers with high sensitivity and specificity that may help replace or complement invasive approaches. AI-driven models that combine multi-omics datasets with radiomics and clinical parameters have demonstrated improved accuracy for predicting both therapeutic response and long-term outcomes, compared to standard approaches for risk stratification. Additionally, the incremental clinical usefulness of liquid biopsy platforms has been demonstrated for the monitoring of non-muscle-invasive bladder cancer and minimal disease detection. As these innovations converge, they herald the advent of a new era of personalized management of urothelial carcinoma; however, broad-based clinical implementation will require large-scale validation, standardization, regulatory harmonization, and economic analyses. Background: Bladder cancer continues to be a global health problem, particularly in the advanced disease setting where treatment options are limited, and mortality remains high. The exciting advances in precision medicine, including breakthrough molecular profiling techniques, liquid biopsy, and opportunities to apply AI to interpret these molecular data, hold unprecedented promise in improving the accuracy of diagnosis, prognostic stratification, and therapeutic decision-making. Full article

Cancer remains one of the main global public health challenges, with rising incidence and mortality rates demanding more effective diagnostic and therapeutic approaches. Recent advances in artificial intelligence (AI) have positioned it as a transformative force in oncology, offering the ability to process vast and complex datasets that extend beyond human analytic capabilities. By integrating radiological, histopathological, genomic, and clinical data, AI enables more precise tumor characterization, including refined molecular classification, thereby improving risk stratification and facilitating individualized therapeutic decisions. In diagnostics, AI-driven image analysis platforms have demonstrated excellent performance, particularly in radiology and pathology. Prognostic algorithms are increasingly applied to predict survival, recurrence, and treatment response, while reinforcement learning models are being explored for dynamic radiotherapy and optimization of complex treatment regimens. Beyond direct patient care, AI is accelerating drug discovery and clinical trial design, reducing costs and timelines associated with translating novel therapies into clinical practice. Clinical decision support systems are gradually being integrated into practice, assisting physicians in managing the growing complexity of cancer care. Despite this progress, challenges such as data quality, interoperability, algorithmic bias, and the opacity of complex models limit widespread integration. Additionally, ethical and regulatory hurdles must be addressed to ensure that AI applications are safe, equitable, and clinically effective. Nevertheless, the trajectory of current research suggests that AI will play an increasingly important role in the evolution of precision oncology, complementing human expertise and improving patient outcomes. Full article

Background/Objectives: Cardiovascular (CV) diseases remain the leading cause of morbidity and mortality across Europe. Despite substantial progress in prevention, diagnostics, and therapeutics, outpatient cardiology care continues to face systemic challenges, including limited consultation time, workforce constraints, and incomplete clinical information at the point of care. The primary objective of this study is threefold. First, to evaluate whether AI-enhanced telemedicine improves clinical control of hypertension, dyslipidemia, and heart failure compared to standard ambulatory care. Second, to assess the impact on physician workflow efficiency and documentation burden through AI-assisted clinical documentation. Third, to determine patient satisfaction and safety profiles of integrated telemedicine–AI systems. Clinical control will be measured by a composite endpoint of disease-specific targets assessed at the 12-month follow-up visit. Methods: The TeleAI-CVD Concept Study aims to evaluate the integration of telemedicine and artificial intelligence (AI) to enhance the efficiency, quality, and individualization of cardiovascular disease management in the ambulatory setting. Within this framework, AI-driven tools will be employed to collect structured clinical histories and current symptomatology from patients prior to outpatient visits using digital questionnaires and conversational interfaces. Results: Obtained data, combined with telemonitoring metrics, laboratory parameters, and existing clinical records, will be synthesized to support clinical decision-making. Conclusions: This approach is expected to streamline consultations, increase diagnostic accuracy, and enable personalized, data-driven care through continuous evaluation of patient trajectories. The anticipated outcomes of the TeleAI-CVD study include the development of optimized, AI-assisted management protocols for cardiology patients, a reduction in unnecessary in-person visits through effective telemedicine-based follow-up, and accelerated attainment of therapeutic targets. Ultimately, this concept seeks to redefine the paradigm of outpatient cardiovascular care by embedding advanced digital technologies within routine clinical workflows. Full article

Beyond their traditional role as short-lived antimicrobial cells, neutrophils are increasingly recognized as key regulators of adaptive immunity and tumor progression. This AI-assisted integrative review investigated the neutrophil–T-cell axis, particularly the role of Galectin-9 (Gal-9), across adult T-cell leukemia/lymphoma (ATL), Sézary syndrome (SS), coronavirus disease 2019 (COVID-19), and psoriasis. Leveraging AI tools (GPT-5 and Adobe Acrobat AI Assistant) for literature synthesis (2000–2025) and expert validation, we aimed to identify common immunological mechanisms. Across all conditions, neutrophils displayed persistent activation, elevated Gal-9 expression, and modulated T-cell interactions. In ATL and SS, neutrophilia correlated with poor survival and TCR signaling dysregulation, suggesting Gal-9-mediated immune modulation. In COVID-19 and psoriasis, neutrophil-derived Gal-9-linked innate hyperactivation to T-cell exhaustion and IL-17-driven inflammation. These findings define a recurring neutrophil–Gal-9 regulatory module connecting innate and adaptive immune responses. This study underscores the feasibility of combining AI-driven literature synthesis with expert review to identify unifying immunological mechanisms and therapeutic targets across malignancy and inflammation. Full article

Wearable healthcare is shifting from passive tracking to active, closed-loop care by integrating polymeric three-dimensional (3D)-printed microneedle arrays (MNAs) with soft electronics and wireless modules. This review surveys the design, materials, and the manufacturing routes that enable skin-conformal MNA wearables for minimally invasive access to the interstitial fluid and precise but localized drug delivery. Looking ahead, the converging advances in multimaterial printing, nano/biofunctional coatings, and artificial intelligence (AI)-driven control are promising “wearable clinics” that can personalize monitoring and therapy in real time, thus accelerating the translation of MNA-integrated wearables from laboratory prototypes to clinically robust, patient-centric systems. Overall, this review identifies a clear transition from proof-of-concept MNA devices toward integrated, wearable, and closed-loop therapeutic platforms. Key challenges remain in scalable manufacturing, drug dose limitations, long-term stability, and regulatory translation. Addressing these gaps through advances in hollow MNA architectures, system integration, and standardized evaluation protocols is expected to accelerate clinical adoption. However, the realization of closed-loop wearable MNA-based systems remains constrained by challenges related to power consumption, real-time data latency, and the need for robust clinical validation. Full article

Background: Children with autism spectrum disorder (ASD) commonly experience persistent difficulties in social communication, emotional regulation, and social engagement. In recent years, artificial intelligence (AI)-based technologies, particularly socially assistive robots and intelligent sensing systems, have been explored as complementary tools to support psychosocial interventions in this population. Objective: This systematic review aimed to critically evaluate recent evidence on the effectiveness of AI-based interventions in improving social, emotional, and cognitive functioning in children with ASD. Methods: A systematic literature search was conducted in PubMed following PRISMA guidelines, targeting English-language studies published between 2020 and 2025. Eligible studies involved children with ASD and implemented AI-driven tools within therapeutic or educational settings. Eight studies met inclusion criteria and were analyzed using the PICO framework. Results: The reviewed interventions included humanoid and non-humanoid robots, gaze-tracking systems, and theory of mind-oriented applications. Across studies, AI-based interventions were associated with improvements in joint attention, social communication and reciprocity, emotion recognition and regulation, theory of mind, and task engagement. Outcomes were assessed using standardized behavioral measures, observational coding, parent or therapist reports, and physiological or sensor-based indices. However, the studies were characterized by small and heterogeneous samples, short intervention durations, and variability in outcome measures. Conclusions: Current evidence suggests that AI-based systems may serve as valuable adjuncts to conventional interventions for children with ASD, particularly for supporting structured social and emotional skill development. Nonetheless, methodological limitations and limited long-term data underscore the need for larger, multi-site trials with standardized protocols to better establish efficacy, generalizability, and ethical integration into clinical practice. Full article

This research focuses on ontology-driven conversational agents (CAs) that harness large language models (LLMs) and their mediating role in performing collective tasks and facilitating knowledge-sharing capabilities among multiple healthcare stakeholders. The research addresses how CAs can promote a therapeutic working alliance and foster trustful human–AI collaboration between emergency department (ED) stakeholders, thereby supporting collaborative tasks with healthcare professionals (HPs). The research contributes to developing a service-oriented human–AI collaborative framework (SHAICF) to promote co-creation and collaborative learning among patients, CAs, and HPs, and improve information flow procedures within the ED. The research incorporates agile heavy-weight ontology engineering methodology (OEM) rooted in the design science research method (DSRM) to construct an ontological metadata model (PEDology), which underpins the development of semantic artifacts. A customized OEM is used to address the issues mentioned earlier. The shared ontological model framework helps developers to build AI-based information systems (ISs) integrated with LLMs’ capabilities to comprehend, interpret, and respond to complex healthcare queries by leveraging the structured knowledge embedded within ontologies such as PEDology. As a result, LLMs facilitate on-demand health-related services regarding patients and HPs and assist in improving information provision, quality care, and patient workflows within the ED. Full article

Precision medicine has reshaped the clinical management of prostate cancer by integrating comprehensive genomic profiling, biomarker-driven patient stratification, and the development of molecularly targeted therapeutics. Advances in next-generation sequencing have uncovered diverse genomic alterations—including homologous recombination repair defects, MSI-H/MMRd, PTEN loss, BRCA1/BRCA2 mutations, ATM alterations, SPOP mutations, and molecular hallmarks of neuroendocrine differentiation—that now inform individualized treatment decisions. This review synthesizes established clinical evidence with emerging translational insights to provide an updated and forward-looking overview of precision oncology in prostate cancer. Landmark trials of PARP inhibitors and PSMA-targeted radioligand therapy have redefined treatment standards for biomarker-selected patients. Concurrently, efforts to optimize immune checkpoint inhibition, AKT pathway targeting, and rational combinations with androgen receptor pathway inhibitors continue to expand therapeutic possibilities. Rapidly evolving investigational strategies—including bipolar androgen therapy (BAT), immunotherapeutic approaches for CDK12-altered tumors, targeted interventions for SPOP-mutated cancers, and epigenetic modulation such as EZH2 inhibition for neuroendocrine prostate cancer—further illuminate mechanisms of tumor evolution, lineage plasticity, and treatment resistance. Integrating multi-omics technologies, liquid biopsy platforms, and AI-assisted imaging offers new opportunities for dynamic disease monitoring and biology-driven treatment selection. By consolidating current clinical practices with emerging experimental directions, this review provides clinicians and researchers with a comprehensive perspective on the evolving landscape of precision medicine in prostate cancer and highlights future opportunities to improve patient outcomes. Full article

Serum Amyloid A (SAA) is an acute-phase apolipoprotein that acts as both a sensitive biomarker of systemic inflammation and an active modulator of lipid metabolism and vascular homeostasis. This review summarises current insights into the interaction between SAA and high-density lipoproteins (HDL), with particular emphasis on its role in inflammation-driven cardiovascular disease (CVD). The incorporation of SAA into HDL markedly alters its composition and function. The displacement of apolipoprotein A-I impairs cholesterol efflux capacity, reduces antioxidative activity, and promotes a pro-inflammatory phenotype, transforming protective HDL into a dysfunctional particle. These changes contribute to endothelial dysfunction, foam cell formation, and atherogenesis. Elevated SAA levels are also associated with adverse cardiovascular and metabolic outcomes, including coronary artery disease, type 2 diabetes, and chronic kidney disease. Isoform-specific variations in SAA–HDL interactions are emerging as key modulators of these effects. This review also discusses emerging therapeutic and nutritional strategies to modulate the SAA–HDL axis, including anti-inflammatory therapies, HDL mimetics, and diet-based interventions. Future research should prioritise the standardisation of SAA measurement, characterisation of isoform-specific functions, and translational studies integrating SAA into cardiovascular risk stratification and therapy. Full article