The Special Programme for Research and Training in Tropical Diseases (TDR), co-sponsored by WorldHealthOrganization, United Nations Children's Fund, United Nations Development Programme, and the World Bank, launched the Clinical Research and Development Fellowship (CRDF) in 1999 to strengthen clinical research capacity in low- and middle-income countries (LMICs). Despite sustained investment, evidence on the longer-term scientific outcomes of such programmes remains limited. This study evaluated the scientific outcomes of the CRDF programme (1999-2021) using bibliometric analysis of fellows' peer-reviewed publications before and after their fellowship. A retrospective bibliometric analysis was conducted on peer-reviewed publications authored by CRDF fellows from 1999 through 2021. Publication data were retrieved from Open Researcher and Contributor ID profiles and verified using PubMed, Google search, and Google scholar. A retrospective bibliometric analysis was conducted using descriptive and comparative statistical methods to evaluate publication productivity, authorship position, journal quality metrics, collaboration patterns, disease focus, and research themes before and after fellowship participation. Analyses were conducted using R statistical software. Scientific outcome of the fellowship was judged based on the evolution of collaboration network, journal metrics, and seniority in authorship. Among 128 fellows, 76 had verifiable publications, contributing a total of 1821 peer-reviewed articles. Publications were predominantly produced after fellowship participation (1309; 71.9%), compared with 512 pre-fellowship publications. Median publications per fellow increased from 5 [interquartile range (IQR): 2-10] before the fellowship to 14 (IQR: 8-28). The proportion of first-author publications declined (30.7-19.2%), while last-author publications increased (8.0-11.0%), suggesting progression toward senior research roles. Journal quality improved, with publications in Q1 journals increasing from 66.6 to 71.3%, and the median impact factor rising from 2.8 (IQR: 1.9-3.9) to 3.4 (IQR: 2.6-4.9). North-South collaborations increased substantially after the fellowship, while South-South collaborations grew modestly. Participation in the CRDF programme was associated with a visible increase in research productivity, publication quality, and international collaboration among fellows. Research output aligned closely with infectious diseases of poverty and evolving global health priorities. While the collaboration network expanded, leadership as gauged by the evolution of authorship position remained constrained, highlighting the need for continued investment in research leadership development. Bibliometric analysis offers a useful approach for examining longer-term scientific outcomes of capacity-strengthening initiatives, while recognizing its limitations in capturing broader policy and societal impacts.

Beliefs about relationships between symptoms and experiences in daily life: Evidence from experience sampling and self-report in individuals with or without major depression or social phobia.

The study explored community perspectives on ethical considerations in clinical research, focusing on the ESRIFAL trial conducted in Cameroon. The hermeneutic phenomenological approach was used to analyze and interpret the data. Participants were selected by systematic sampling. In-depth interviews were used to gather data from the participants. Some findings revealed that while ethical principles such as informed consent are highly recommended in clinical trials, participants did not understand and interpret these documents with as much importance as expected. Participants had other priorities and objectives for engaging in the trial; getting rid of the disease onchocerciasis was one of them. Signing the forms was nothing more than a formality for them. In conclusion, the study emphasizes the importance of strengthening ethical oversight mechanisms, but more importantly, the fact that ethical principles need to be contextualized to fit into the social and cultural realities of the societies in which trials are implemented.

Neuromyelitis optica spectrum disorders (NMOSDs) comprise rare autoimmune diseases of the CNS in which disabilities accrue with relapses. The ability to predict and prevent relapses could dramatically improve clinical outcomes, potentially reducing morbidity and quality-of-life declines. This proteomic study aimed to identify individual and composite candidate serum biomarkers predictive of NMOSD relapse. Patients with NMOSD previously enrolled in the Collaborative International Research in Clinical and Longitudinal Experience Study (CIRCLES) cohort were selected based on documented relapses simultaneous with retrievable banked cryopreserved serum. Longitudinal serum proteomic profiles were characterized using high-resolution mass spectrometry. We used linear models with logistic regression, Cox proportional hazards models with fixed-time intervals, and time-dependent Cox proportional hazards models to analyze individual proteins and proteomic profiles for their association with future relapses factoring demographics, clinical phenotype/course, and treatments. We characterized a total of 305 longitudinally collected serum samples (N = 126), using high-resolution mass spectrometry, and identified 265 proteins overall. There was a 10-protein signature with the highest average association coefficient consistently across at least 4 of the 6 modeling analyses, including factor XI, surfactant protein B, C1RL, filamin A, cholesteryl ester transfer protein, cathelicidin antimicrobial peptide, C4A, transferrin receptor, for consistency immunoglobulin kappa constant, and serum amyloid A2 protein. This signature could significantly stratify patients with higher vs lower risk of subsequent relapse. These proteins differed in their increasing or decreasing abundance trajectories in advance of relapse. Most belong to pathways plausibly related to the immunopathology of NMOSD. Collectively, these findings provide a basis for novel biomarker development to predict NMOSD relapses sufficiently in advance to enable preventive treatment.

Current methods and criteria for defining foot symmetry in biomechanical, clinical, and anthropometric assessments: A systematic review.

The International Glossary on Infertility and Fertility Care, 2025†.

Do somatic symptoms bias depression screening? Reliability and equivalence of PHQ-8 in those with and without chronic pain: A nationally representative study of U.S. adults.

Structural and construct validity assessment of the schizophrenia cognition rating scale (SCoRS) using data from three international phase 3 randomized controlled trials (CONNEX programme).

Current pharmacometrics (PMX) model development is a manual process with iterative model building, fitting, and evaluation, which can be resource-intensive and time-consuming. Existing automated model development approaches utilize algorithms that still rely on iterative processes and perform model selection based on goodness-of-fit criteria. Recent advances in machine learning and artificial intelligence, particularly neural ordinary differential equations (NODEs), have demonstrated strong potential for characterizing complex pharmacokinetic (PK) and pharmacodynamic (PD) dynamics directly from data. However, NODEs are inherently black-box models, which limits their interpretability and their ability to provide mechanistic insights, both of which are essential in PMX. We recently presented a promising concept that proposes interpretable ODE-based structural models from NODEs but relied on manually identifying functional relationships, which can be challenging and prevents full automation. In this work, we present an automated model development approach that combines NODEs with least absolute shrinkage and selection operator (LASSO) regression to automatically propose structural models based on the dynamics learned by NODEs. The approach leverages LASSO's feature selection capability, thereby linking data-driven modeling with interpretable mechanistic structures. We demonstrate the applicability of this automated NODE-LASSO model development approach in three different scenarios: neonatal weight development, bi-exponential PK data, and warfarin PK/PD data. The results indicate that our automated NODE-LASSO model development approach can recover meaningful, mechanism-based structures while reducing the need for extensive iterative and manual model development. This highlights its potential as a resource-efficient and interpretable modeling strategy for PMX and its applications in model-informed drug development and clinical research.

From Oxidative Stress to Pain Modulation With Melatonin in Neonatal and Pediatric Care.

Recent advances in polymeric nanoparticle-mediated drug delivery system across the blood-brain barrier in Alzheimer's disease.

Recent advances in drug repurposing for dentin repair.

Including diverse populations in clinical research is essential for generalizable findings. Prior studies show lower consent rates among vulnerable populations in higher-risk studies, but whether these trends apply to minimal-risk studies has not been explored. This study investigated factors influencing consent to a musculoskeletal ultrasound study for extremity injuries in the emergency department. Demographic characteristics, injury severity, interpreter use, and researcher-patient racial concordance were compared between those who consented and declined. Patients were more likely to decline if they were admitted to the hospital (p = 0.002) or had a displaced fracture (displaced-nondisplaced: p = 0.032; displaced-no fracture: p = 0.009). Younger patients (<45 years) most often declined due to pain, while older patients (>45 years) primarily cited lack of interest (p = 0.04). Although not statistically significant, Black and Hispanic patients most often declined for lack of interest. Older adults' lower interest highlights the need to increase research participation in this underrepresented population.

In recent decades, clinical practice has been founded on the principles of evidence-based medicine, where therapeutic decisions arise from the integration of clinical expertise, patient preferences, and scientific evidence derived from controlled studies and meta-analyses. The advent of artificial intelligence (AI) in health care, however, is driving a significant evolution in clinical research, owing to its ability to analyze large volumes of heterogeneous data and overcome the limitations of traditional statistical approaches. The availability of large-scale datasets, increasing computational capability, and reduced storage costs have supported the transition towards a "data-intensive" research model, progressively integrated with conventional methods. Within cardiology, arrhythmology represents one of the fields in which AI finds extensive application. The analysis of complex electrophysiological signals, data from implantable devices, advanced cardiac imaging, and clinical parameters enables the development of algorithms capable of identifying patterns not detectable by human interpretation. These tools have already demonstrated practical utility in the early diagnosis of arrhythmias, risk stratification, procedural planning and guidance for catheter ablation, prediction of response to cardiac stimulation therapies, and optimization of remote device monitoring. Among the key emerging benefits, AI promises increasingly personalized care, enabling more targeted interventions while reducing overtreatment. Furthermore, the development of "digital twins" opens the possibility of simulating patient-specific therapeutic scenarios to support complex clinical decision-making. This manuscript provides an overview of current evidence, emerging applications, and remaining challenges related to the integration of AI in arrhythmology, highlighting its potential to drive a transition towards predictive, preventive, and personalized cardiovascular medicine.

BackgroundClinical trials are vital for advancing medical knowledge, yet engaging diverse populations-those under-represented due to race, age, income, education, geography, sexual orientation, or gender identity-remains a challenge, limiting research generalizability.MethodsThis study used a literature review and semi-structured interviews with participants from diverse backgrounds recruited on LinkedIn. A demographic survey was conducted via Qualtrics, yielding 21 responses and 15 study participants. The research identified barriers to participation: inadequate community engagement, trial awareness, mistrust, and unaddressed social determinants of health, with data analyzed for key themes.ResultsFour themes emerged: (1) Community engagement builds trust; (2) Addressing social determinants alleviates barriers; (3) Peer education empowers participants; (4) A representative workforce enhances recruitment.DiscussionFindings emphasize the necessity of inclusive practices in clinical research, highlighting that targeted strategies can improve diversity in clinical trials, leading to more equitable healthcare outcomes. Future research should refine these strategies in specific therapeutic areas.

BoneMesh: An open-source 3D slicer framework for automated mesh generation and material mapping from CT to finite elements.

1H-qNMR analysis of human urine: Validation of an external standard approach for absolute metabolite quantitation.

Clinical trials are the backbone of evidence-based medicine. Numerous regulations and oversight mechanisms have been established to ensure ethical conduct and scientific rigor. Clinical trials often fail to represent the full spectrum of global patient populations. Many developing nations lack the infrastructure, funding, and regulatory resources needed to conduct robust clinical research, leaving their most prevalent diseases understudied. Global participation in clinical trials remains highly unequal. Despite this unequal burden of disease, global funding does not reflect this reality, as most funding is allocated to developed nations. This underrepresentation is a significant global disparity, as research-driven solutions in these regions often have the potential to have a higher public health impact. Several barriers have been identified, including stringent sponsor requirements, insufficient regulatory infrastructure, limited clinical research training, and other cultural and systemic factors. Strategies to address global trial disparities have been proposed, such as understanding cultural and socioeconomic barriers and streamlined guidelines to reduce operational obstacles without compromising the scientific quality of the trials. Ideally, governments, in partnership with industry, should develop long-term plans for the progressive development of research infrastructure, including educational programs and dedicated in-house monitoring teams.

This study aimed to systematically characterize the global research landscape, collaboration patterns, knowledge structure, and emerging hotspots of glucagon-like peptide-1 receptor agonists in neurodegenerative diseases using bibliometric methods. Publications related to glucagon-like peptide-1 receptor agonists and neurodegenerative diseases were retrieved from the Web of Science Core Collection from 2006 to 2025. Only English-language articles and reviews were included. Bibliometric analyses were performed using Bibliometrix, VOSviewer, and CiteSpace to evaluate annual publication trends, country and institutional contributions, author collaborations, journal distribution, citation structures, keyword co-occurrence, thematic evolution, and citation bursts. A Scopus-based sensitivity analysis was conducted to assess the robustness of the main bibliometric findings. A total of 1,202 publications were included, with annual output increasing from 2 in 2006 to 241 in 2025, particularly after 2020. China, the USA, and England were the leading contributors and major collaboration hubs. Shanxi Medical University, Lancaster University, and the National Institute on Aging were among the most productive institutions, while major journals included International Journal of Molecular Sciences, Neuropharmacology, European Journal of Pharmacology, Frontiers in Endocrinology, Frontiers in Pharmacology, and Journal of Alzheimer's Disease. Keyword and citation analyses indicated a thematic shift from exendin-4, Alzheimer's disease, Parkinson's disease, and neuroprotection toward semaglutide, neuroinflammation, cognitive impairment, clinical efficacy, evidence synthesis, and combination therapy. Research on glucagon-like peptide-1 receptor agonists in neurodegenerative diseases has expanded rapidly over the past two decades. Current bibliometric evidence suggests that this field has evolved from preclinical exploration toward broader translational and clinical research, with increasing attention to neuroinflammation, metabolic dysfunction, cognitive outcomes, and newer incretin-based therapies. However, the therapeutic implications of glucagon-like peptide-1 receptor agonists for neurodegenerative diseases remain to be further validated by high-quality mechanistic studies and well-designed clinical trials.

A Scoping Review of Toolkits Addressing Ethical Issues in Health Registry and Clinical Research Enrollment Among African Americans With CKD.