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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.

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.

Alzheimer’s disease (AD) is a cognitive disease with high morbidity and mortality. In AD patients, the diversity of the gut microbiota is altered, which influences pathology through the gut–brain axis. Probiotic therapy alleviates pathological and psychological consequences by restoring the diversity of the gut microbial flora. This study addresses the role of altered gut microbiota in the progression of neuroinflammation, which is a major hallmark of AD. This process begins with the activation of glial cells, leading to the release of proinflammatory cytokines and the modulation of cholinergic anti-inflammatory pathways. Short-chain fatty acids, which are bacterial metabolites, provide neuroprotective effects and maintain blood‒brain barrier integrity. Furthermore, the gut microbiota stimulates oxidative stress and mitochondrial dysfunction, which promote AD progression. The signaling pathways involved in gut dysbiosis-mediated neuroinflammation-mediated promotion of AD include cGAS-STING, C/EBPβ/AEP, RAGE, TLR4 Myd88, and the NLRP3 inflammasome. Preclinical studies have shown that natural extracts such as Ganmaidazao extract, isoorentin, camelia oil, Sparassis crispa-1 , and xanthocerasides improve gut health and can delay the worsening of AD. Clinical studies using probiotics such as Bifidobacterium spp., yeast beta-glucan, and drugs such as sodium oligomannate and rifaximine have shown improvements in gut health, resulting in the amelioration of AD symptoms. This study incorporates the most current research on the pathophysiology of AD involving the gut microbiota and highlights the knowledge gaps that need to be filled to develop potent therapeutics against AD.

In this review article, we provide a comprehensive overview of current practices and challenges associated with research synthesis in preclinical biomedical research. We identify critical barriers and roadblocks that impede effective identification, utilisation, and integration of research findings to inform decision making in research translation. We examine practices at each stage of the research lifecycle, including study design, conduct, and publishing, that can be optimised to facilitate the conduct of timely, accurate, and comprehensive evidence synthesis. These practices are anchored in open science and engaging with the broader research community to ensure evidence is accessible and useful to all stakeholders. We underscore the need for collective action from researchers, synthesis specialists, institutions, publishers and journals, funders, infrastructure providers, and policymakers, who all play a key role in fostering an open, robust and synthesis-ready research environment, for an accelerated trajectory towards integrated biomedical research and translation.

Innovation in biomedical research has increased markedly over the last few decades. However, clinical, therapeutic, and public health advances have often not yielded expected improvements in health outcomes nor reduced disparities. Translational science was developed to improve social benefits related to research and development. We propose a practical model for socially responsible translational science that aims to better align research with its expected social benefits. Scientists and community members from the Houston-Galveston region participated in 12 focus groups and a one-day Deliberative Dialogue Summit to examine the expected social benefits of science, establish the factors and practices of social responsibility, and design an empirical model for socially responsible translational science. Researchers and community members discussed three distinct fields of research – HIV, maternal health, and mental health and substance use disorders. We conducted deductive qualitative data analysis based on theoretical social responsibility criteria of translational science, namely: relevance, usability, and sustainability. We then developed inductive codes to capture the factors and practices identified during discussions as necessary for the translation of research to increase social benefit. First, participants explored ways to broaden the scope of biomedical research beyond a narrow emphasis on scientific impact to also consider social impacts and determinants of health; this heightens the relevance of research and underscores its responsibility to address social needs and reduce inequities. Second, to improve usability of translational research, participants suggested increasing access to research products, processes, and participation. They also recommended modifying the research infrastructure to incorporate other systems that can assist with translation including the system of care and the broader community-based systems. Third and finally, for the long-term sustainability of research practices, co-development and co-funding of research was promoted to include local community needs, cultures, knowledges and preferences from project commencement to completion.

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.

The field of translational bioinformatics is rapidly evolving, driving the convergence of molecular sciences and computational methods with their applications in industrial and clinical practice [...]

Fenestrated/branched endovascular aortic repair emerges as the primary therapeutic modality for intricate aortic pathologies encompassing the paravisceral and thoracoabdominal segments, where bridging stent grafts (BSGs) play a vital role in linking the primary aortic endograft with target vessels. Bridging stent grafts can be categorized mainly into self‐expanding stent grafts (SESGs) and balloon‐expandable stent grafts (BESGs). Physiological factors significantly influence post‐complex endovascular aortic repair BSG behaviour, impacting clinical outcomes of SESGs and BESGs in different but overlapping ways. Crucial prerequisites for BSGs encompass not only flexibility but also resilience against mechanical stress and compliance mismatch, especially when bridging the rigid aortic main body with dynamic target vessels. The significance of considering these physiological factors in clinical decision‐making is underscored by recognizing the interplay between SESG and BESG characteristics, vessel physiology and patient haemorheology. Such factors include the anatomy and tortuosity of the vessel, diameter of the vessel and BSG, deployment and durability, extrinsic stenosis and respiratory motion. Haemorheological factors, such as anti‐thrombotic therapy and hydration status, need to be considered. This narrative review examines both in vitro and in vivo evidence regarding the impact of physiological factors on the behaviour of BSGs and assesses the consequences for clinical outcomes following complex endovascular aortic repair. What is the topic of this review? The narrative review aims to examine both in vitro and in vivo evidence regarding the impact of physiological factors on the behaviour of bridging stent grafts (BSGs) and to assess the consequences for clinical outcomes following fenestrated/branched endovascular aortic repair (F/BEVAR). What advances does it highlight? Bridging stent grafts play a major role in F/BEVAR. Physiological factors significantly influence post‐F/BEVAR BSG behaviour, impacting clinical outcomes. The significance of considering these physiological factors in clinical decision‐making is underscored by recognizing the interplay between BSG characteristics, vessel physiology and patient haemorheology.

Artificial intelligence (AI) is driving innovation in clinical pharmacology and translational science with tools to advance drug development, clinical trials, and patient care. This review summarizes the key takeaways from the AI preconference at the American Society for Clinical Pharmacology and Therapeutics (ASCPT) 2024 Annual Meeting in Colorado Springs, where experts from academia, industry, and regulatory bodies discussed how AI is streamlining drug discovery, dosing strategies, outcome assessment, and patient care. The theme of the preconference was centered around how AI can empower clinical pharmacologists and translational researchers to make informed decisions and translate research findings into practice. The preconference also looked at the impact of large language models in biomedical research and how these tools are democratizing data analysis and empowering researchers. The application of explainable AI in predicting drug efficacy and safety, and the ethical considerations that should be applied when integrating AI into clinical and biomedical research were also touched upon. By sharing these diverse perspectives and real‐world examples, this review shows how AI can be used in clinical pharmacology and translational science to bring efficiency and accelerate drug discovery and development to address patients' unmet clinical needs.

Artificial intelligence (AI) is making a significant impact across various industries, including healthcare, where it is driving innovation and increasing efficiency. In the fields of Quantitative Clinical Pharmacology (QCP) and Translational Sciences (TS), AI offers the potential to transform traditional practices through the use of agentic workflows—systems with different levels of autonomy where specialized AI agents work together to perform complex tasks, while keeping “human in the loop.” These workflows can simplify processes, such as data collection, analysis, modeling, and simulation, leading to greater efficiency and consistency. This review explores how these AI‐powered agentic workflows can help in addressing some of the current challenges in QCP and TS by streamlining pharmacokinetic and pharmacodynamic analyses, optimizing clinical trial designs, and advancing precision medicine. By integrating domain‐specific tools while maintaining data privacy and regulatory standards, well‐designed agentic workflows empower scientists to automate routine tasks and make more informed decisions. Herein, we showcase practical examples of AI agents in existing platforms that support QCP and biomedical research and offer recommendations for overcoming potential challenges involved in implementing these innovative workflows. Looking ahead, fostering collaborative efforts, embracing open‐source initiatives, and establishing robust regulatory frameworks will be key to unlocking the full potential of agentic workflows in advancing QCP and TS. These efforts hold the promise of speeding up research outcomes and improving the efficiency of drug development and patient care.