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The mission of translational science is to bring predictivity and efficiency to the development and dissemination of interventions that improve human health. Ten years ago this year, the National Center for Advancing Translational Sciences was founded to embody, conduct, and support this new discipline. The Center’s first decade has brought substantial progress across a broad range of translational areas, from diagnostic and drug development to clinical trials to implementation science to education. The origins of the translational science and advances to this point are reviewed here and allow the establishment of an ambitious future research agenda for the field.

Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)–based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of AI-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing AI-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and AI should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by AI-powered analysis tools.

The main histopathology of Alzheimer’s disease (AD) is featured by the extracellular accumulation of amyloid-β (Aβ) plaques and intracellular tau neurofibrillary tangles (NFT) in the brain, which is likely to result from co-pathogenic interactions among multiple factors, e.g., aging or genes. The link between defective autophagy/mitophagy and AD pathologies is still under investigation and not fully established. In this review, we consider how AD is associated with impaired autophagy and mitophagy, and how these impact pathological hallmarks as well as the potential mechanisms. This complicated interplay between autophagy or mitophagy and histopathology in AD suggests that targeting autophagy or mitophagy probably is a promising anti-AD drug candidate. Finally, we review the implications of some new insights for induction of autophagy or mitophagy as the new therapeutic way that targets processes upstream of both NFT and Aβ plaques, and hence stops the neurodegenerative course in AD.

Clinical and translational science needs to address roadblocks to the translational processes. We conducted a survey at two institutions, a private medical school and a large public university, to understand the frequency and distribution of barriers and roadblocks to research. We reviewed the literature to compile a pool of barriers and roadblocks and convened a panel of relevant stakeholders to develop a 20‐item questionnaire. Survey respondents were asked to select and prioritize the five leading clinical and translational roadblocks, provide information regarding their academic degrees and rank/position, complete open‐ended items regarding their areas of research, and optionally add additional remarks in a comment box. The survey was disseminated in August 2022 to faculty and staff with active research protocols at Baylor College of Medicine and the University of Houston. In total, 227 respondents completed the survey. Their disciplines were basic science (29.5%), translational research (52.9%), clinical research (55.5%), community‐engaged research (9.7%), and educational research (9.7%). Respondents identified (1) lack of access to trained research coordinators, (2) lack of understanding about different resources that facilitate research, (3) complex regulatory environment and delays, (4) fragmented infrastructure for administrative and fiscal processes, and (5) inadequate funding for pilot projects to foster new research. Other roadblocks included lack of established community stakeholder partnerships, inadequate access to medical record data, and limited biostatistical support. We identified leading roadblocks to research from the perspectives of scientists and staff conducting clinical and translational research. Operational innovations addressing these roadblocks can accelerate the pace of translation.

Background Clinical practice guidelines (CPGs) are essential for enhancing healthcare quality and informing evidence-based clinical practices. Despite the availability of strategies, their implementation remains challenging due to the complexities of managing translation CPGs into practice, such as barriers to change, resource limitations and high costs. This study examines management mechanisms that offer valuable insights into how healthcare organizations can manage CPG implementation at the organizational level to optimise high-quality care. Methods This qualitative study examines the management of CPG implementation using interview data ( n  = 33) from healthcare managers and clinicians in Finnish public primary care. The data were collected through seven focus group interviews across nine public primary care organizations. The interview transcripts were analysed using thematic analysis with a multidisciplinary approach. Results CPGs are considered important tools for improving care quality and promoting shared evidence-based practices. The obstacles to managing implementation included dissemination difficulties, motivation challenges and information overload. Managers and clinicians had contrary views on their roles and responsibilities in CPG implementation. To lead the knowledge translation processes, managers emphasised unit managers’ support, dissemination and communication channels, whereas clinicians viewed CPG implementation as a grassroots effort and the responsibility of each individual. The results illustrate the need for enhancing shared views on CPGs and managing social implementation activities within organizations. Conclusions Successful CPG implementation requires active managerial efforts and clinician dialogue to transform new evidence into locally viable practices. To inform more effective knowledge translation, the five identified management mechanisms included instructions; accountability structures; motivation, goal setting and feedback; communication strategies and participatory practices. In managing CPG implementation, a focus on interaction processes, motivation and feedback is essential for ensuring collective knowledge formation. This study improves the understanding of critical organizational knowledge translation processes by drawing attention to the previously underrepresented managerial aspects in CPG implementation studies. Future researchers, guideline developers, and policymakers should integrate managerial activities and clinician engagement in CPG implementation to ensure effective practices and healthcare quality.

Surgeon-directed knowledge translation (KT) interventions for rectal cancer surgery are designed to improve patient measures, such as rates of permanent colostomy and in-hospital mortality, and to improve survival. To evaluate the association of sustained, iterative, integrated KT rectal cancer surgery interventions directed at all surgeons with process and outcome measures among patients undergoing rectal cancer surgery in a geographic region. This quality improvement study used administrative data from patients who underwent rectal cancer surgery from April 1, 2004, to March 31, 2015, in 14 health regions in Ontario, Canada. Follow-up was completed on March 31, 2020. Surgeons in 2 regions were offered intensive KT interventions, including annual workshops, audit and feedback sessions, and, in 1 of the 2 regions, operative demonstrations, from 2006 to 2012 (high-intensity KT group). Surgeons in the remaining 12 regions did not receive these interventions (low-intensity KT group). Among patients undergoing rectal cancer surgery, proportions of preoperative pelvic magnetic resonance imaging (MRI), preoperative radiotherapy, and type of surgery were evaluated, as were in-hospital mortality and overall survival. Logistic regression models with an interaction term between group and year were used to assess whether process measures and in-hospital mortality differed between groups over time. A total of 15 683 patients were included in the analysis (10 052 [64.1%] male; mean [SD] age, 65.9 [12.1] years), of whom 3762 (24.0%) were in the high-intensity group (2459 [65.4%] male; mean [SD] age, 66.4 [12.0] years) and 11 921 (76.0%) were in the low-intensity KT group (7593 [63.7%] male; mean [SD] age, 65.7 [12.1] years). A total of 1624 patients (43.2%) in the high-intensity group and 4774 (40.0%) in the low-intensity KT group underwent preoperative MRI (P < .001); 1321 (35.1%) and 4424 (37.1%), respectively, received preoperative radiotherapy (P = .03); and 967 (25.7%) and 2365 (19.8%), respectively, received permanent stoma (P < .001). In-hospital mortality was 1.6% (59 deaths) in the high-intensity KT group and 2.2% (258 deaths) in the low-intensity KT group (P = .02). Differences remained significant in multivariable models only for permanent stoma (odds ratio [OR], 1.67; 95% CI, 1.24-2.24; P < .001) and in-hospital mortality (OR, 0.67; 95% CI, 0.51-0.87; P = .003). In both groups over time, significant increases in the proportion of patients undergoing preoperative MRI (from 6.3% to 67.1%) and preoperative radiotherapy (from 16.5% to 44.7%) occurred, but there were no significant changes for permanent stoma (25.4% to 25.3% in the high-intensity group and 20.0% to 18.3% in the low-intensity group) and in-hospital mortality (0.8% to 0.8% in the high-intensity group and 2.2% to 1.8% in the low-intensity group). Time trends were similar between groups for measures that did or did not change over time. Patient overall survival was similar between groups (hazard ratio, 1.00; 95% CI, 0.90-1.11; P = .99). In this quality improvement study, between-group differences were found in only 2 measures (permanent stoma and in-hospital mortality), but these differences were stable over time. High-intensity KT group interventions were not associated with improved patient measures and outcomes. Proper evaluation of KT or quality improvement interventions may help avoid opportunity costs associated with ineffective strategies.

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called ‘Strengthening The Organization and Reporting of Microbiome Studies’ (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results. The STORMS tool provides guidance for concise and complete reporting of microbiome studies to facilitate manuscript preparation, peer review, reader comprehension of publications, and comparative analysis of published results.

Background Advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the medical field and transformed translational medicine. These technologies enable more accurate disease trajectory models while enhancing patient-centered care. However, challenges such as heterogeneous datasets, class imbalance, and scalability remain barriers to achieving optimal predictive performance. Methods This study proposes a novel AI-based framework that integrates Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) to address these challenges. The framework was evaluated using two distinct datasets: MIMIC-IV, a critical care database containing clinical data of critically ill patients, and the UK Biobank, which comprises genetic, clinical, and lifestyle data from 500,000 participants. Key performance metrics, including Accuracy, Precision, Recall, F1-Score, and AUROC, were used to assess the framework against traditional and advanced ML models. Results The proposed framework demonstrated superior performance compared to classical models such as Logistic Regression, Random Forest, Support Vector Machines (SVM), and Neural Networks. For example, on the UK Biobank dataset, the model achieved an AUROC of 0.96, significantly outperforming Neural Networks (0.92). The framework was also efficient, requiring only 32.4 s for training on MIMIC-IV, with low prediction latency, making it suitable for real-time applications. Conclusions The proposed AI-based framework effectively addresses critical challenges in translational medicine, offering superior predictive accuracy and efficiency. Its robust performance across diverse datasets highlights its potential for integration into real-time clinical decision support systems, facilitating personalized medicine and improving patient outcomes. Future research will focus on enhancing scalability and interpretability for broader clinical applications.

Several studies have indicated that a neutrophil extracellular trap (NET) formation, apart from its role in host defense, can contribute to or drive pathogenesis in a wide range of inflammatory and thrombotic disorders. Therefore, NETs may serve as a therapeutic target or/and a diagnostic tool. Here, we compare the most commonly used techniques for the assessment of NET formation. Furthermore, we review recent data from the literature on the application of basic laboratory tools for detecting NET release and discuss the challenges and the advantages of these strategies in NET evaluation. Taken together, we provide some important insights into the qualitative and quantitative molecular analysis of NETs in translational medicine today.