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The global burden of cardiovascular diseases continues to rise, making their prevention, diagnosis and treatment increasingly critical. With advancements and breakthroughs in omics technologies such as high-throughput sequencing, multi-omics approaches can offer a closer reflection of the complex physiological and pathological changes in the body from a molecular perspective, providing new microscopic insights into cardiovascular diseases research. However, due to the vast volume and complexity of data, accurately describing, utilising, and translating these biomedical data demands substantial effort. Researchers and clinicians are actively developing artificial intelligence (AI) methods for data-driven knowledge discovery and causal inference using various omics data. These AI approaches, integrated with multi-omics research, have shown promising outcomes in cardiovascular studies. In this review, we outline the methods for integrating machine learning, one of the most successful applications of AI, with omics data and summarise representative AI models developed that leverage various omics data to facilitate the exploration of cardiovascular diseases from underlying mechanisms to clinical practice. Particular emphasis is placed on the effectiveness of using AI to extract potential molecular information to address current knowledge gaps. We discuss the challenges and opportunities of integrating omics with AI into routine diagnostic and therapeutic practices and anticipate the future development of novel AI models for wider application in the field of cardiovascular diseases.

Background The Target Product Profile (TPP) is a tool used in industry to guide development strategies by addressing user needs and fostering effective communication among stakeholders. However, they are not frequently used in academic research, where they may be equally useful. This systematic review aims to extract the features of accessible TPPs, to identify commonalities and facilitate their integration in academic research methodology. Methods We searched peer-reviewed papers published in English developing TPPs for different products and health conditions in four biomedical databases. Interrater agreement, computed on random abstract and paper sets (Cohen’s Kappa; percentage agreement with zero tolerance) was > 0.91. We interviewed experts from industry contexts to gain insight on the process of TPP development, and extracted general and specific features on TPP use and structure. Results 138 papers were eligible for data extraction. Of them, 92% ( n  = 128) developed a new TPP, with 41.3% ( n  = 57) focusing on therapeutics. The addressed disease categories were diverse; the largest (47.1%, n  = 65) was infectious diseases. Only one TPP was identified for several fields, including global priorities like dementia. Our analyses found that 56.5% of papers ( n  = 78) was authored by academics, and 57.8% of TPPs ( n  = 80) featured one threshold level of product performance. The number of TPP features varied widely across and within product types ( n  = 3–44). Common features included purpose/context of use, shelf life for drug stability and validation aspects. Most papers did not describe the methods used to develop the TPP. We identified aspects to be taken into account to build and report TPPs, as a starting point for more focused initiatives guiding use by academics. Discussion TPPs are used in academic research mostly for infectious diseases and have heterogeneous features. Our extraction of key features and common structures helps to understand the tool and widen its use in academia. This is of particular relevance for areas of notable unmet needs, like dementia. Collaboration between stakeholders is key for innovation. Tools to streamline communication such as TPPs would support the development of products and services in academia as well as industry.

Background Biospecimen collection from study participants is essential for translational research, but operational challenges in study setup and conduct often impede successful delivery. This study uses a comparative approach to explore key logistical and staffing factors influencing setup duration, recruitment efficiency, sample acquisition, and data completeness across three investigator-led microbiome-wide association studies (MWAS) conducted at cancer centres in Ireland. Methods Three academic observational MWAS enrolling participants with cancers of the breast, gastrointestinal tract, lung, biliary system, kidney, and skin were compared. Data from three cancer centres were analysed. Key variables included study team composition, administrative infrastructure, and full-time equivalent (FTE) research staffing. Metrics assessed included setup duration, recruitment rates, sample acquisition, and data completeness. Descriptive statistics, correlation analyses, and regression models were used to examine relationships between staffing and study performance. Results Setup duration ranged from 30 days (Site B, with a pre-established trials unit) to 390 days (Site A, with no dedicated setup personnel). At Site C, the addition of an Academic Clinical Trials Coordinator reduced the remaining setup timeline from 274 to 185 days. Recruitment rates ranged from 1.1 to 1.3 participants/month, with the highest rates at sites with dedicated research nurses (RN +). Sample acquisition was 100% at RN + sites and 70.5% at the RN− site. Site C achieved full data completeness, defined as comprehensive documentation of screening, exclusions, and follow-up outcomes. Statistical modelling suggested that dedicated staffing (both administrative and clinical) was associated with improvements across all metrics, although small sample size limited statistical significance. Conclusions Dedicated administrative and clinical trial personnel significantly may enhance study efficiency, participant recruitment, and biospecimen collection in academic translational research. This study provides practical insights for improving study design and infrastructure planning in future observational studies. To our knowledge, this is the first multi-site comparative evaluation of operational determinants in academic MWAS.

Background The expansion of clinical research beyond academic hospitals into private hospitals is reshaping the way new therapies are tested and implemented. Traditionally, university and public hospitals have been the primary drivers of clinical research, yet private hospitals are increasingly positioned to contribute meaningfully to this landscape. Discussion This perspective explores the opportunities and complexities of establishing a clinical trial unit within a private setting, highlighting strategies to conduct innovative studies and deliver high-quality, patient-centered research. While private hospitals may face initial challenges related to infrastructure, regulatory compliance, and quality assurance, they offer important advantages, including more rapid decision-making, streamlined administrative pathways, and efficiency in initiating and conducting studies, all while adhering to the same regulatory requirements. Conclusion By positioning themselves as complementary partners to academic institutions, private hospitals can provide efficient and fast paced environments for industry-sponsored trials, ultimately enriching the broader research ecosystem. Most importantly, these developments enable the realization of personalized medicine, where cutting-edge, individualized therapies, particularly in oncology, can be directly tailored and delivered to patients, transforming the promise of precision medicine into real clinical outcomes.

Background Over the last two decades, substantial investments have been directed towards supporting fundamental and applied research in Alzheimer’s disease (AD), breast cancer (BC), and prostate cancer (PC), which continue to pose significant health challenges. Recently, the Joint Research Centre (JRC) of the European Commission (EC) conducted a retrospective analysis to examine the major scientific advancements resulting from EU-funded research in these disease areas and their impact on society. Methods Building upon this analysis, our subsequent investigation delves into the methodological approaches—both animal and non-animal models and methods—employed in AD, BC, and PC research funded under past EU framework programs (FP5, FP6, FP7, and H2020), and explored the notable research outputs associated with these approaches. Results Our findings indicate a prevalent use of animal-based methodologies in AD research, particularly evident in projects funded under H2020. Notably, projects focused on drug development, testing, or repurposing heavily relied on animal models. Conversely, research aimed at clinical trial design, patient stratification, diagnosis and diagnostic tool development, lifestyle interventions, and prevention—outputs with potential societal impact—more frequently utilised non-animal methods. Advanced investigations leveraging imaging, computational tools, biomarker discovery and organ/tissue chip technologies predominantly favoured non-animal strategies. Conclusions These insights highlight a correlation between methodological choices and the translational potential of research outcomes, suggesting the need for a reconsideration of research strategy planning in future framework programs.

Medical Laboratory Data (MLD) models, which combine artificial intelligence with big medical data, have great potential in disease screening, diagnosis, personalized medicine, and health management. This study thoroughly examines the opportunities, challenges, and solutions in this field. The use of large-scale MLD improves diagnostic accuracy and allows for real-time disease monitoring. Additionally, integrating social and environmental data enables the analysis of disease mechanisms and trends. Despite these benefits, challenges such as data quality, model optimization, computational requirements, and limited interpretability remain, along with concerns about data privacy, fairness, and security. Proposed solutions include establishing standardized data formats, utilizing deep learning frameworks, employing distributed computing, improving interpretability, and implementing techniques like federated learning and algorithm optimization to address bias and safeguard privacy. Future directions will focus on enhancing performance in specific scenarios, expanding applications across different domains, increasing transparency, enabling real-time processing, and building a supportive ecosystem. It is essential to strengthen policy oversight and promote collaboration among governments, medical institutions, and academia to ensure that technological advancements align with societal progress.

Background Biobanks are essential for advancing biomedical research, yet sustainable and systematic biobanking remains a challenge in low- and middle-income countries with high infectious disease burdens like the Philippines. Acute respiratory infections have been a major public health concern in the country since 2003, substantiating the need for a dedicated biobank to enhance surveillance, research, and response efforts. This prospective cross-sectional study aimed to identify common respiratory viruses in Metro Manila, streamline biobanking protocols, and establish a physical biobank of respiratory swab specimens and culture isolates of respiratory viruses. Methods Nasopharyngeal (NPS) and oropharyngeal swabs (OPS) from 114 pediatric patients with influenza-like illness from three tertiary hospitals in Metro Manila were screened for respiratory pathogens using the BioFire Respiratory Panel Test (BioFire) and confirmed by quantitative real-time PCR (qPCR). Samples were inoculated in appropriate cell lines for virus propagation and isolation. NPS and OPS samples, infected culture fluids (ICFs), extracted nucleic acids, and respiratory virus isolates were banked in −80 °C ultra-low freezer at the Department of Science and Technology – Industrial Technology Development Institute following international biobanking standards. A Laboratory Information Management System utilizing R and offline Microsoft Excel was developed for sample tracking and data security. Results Sample analysis through BioFire revealed that the most frequently detected viruses were rhinovirus (29%), influenza A (18%), human metapneumovirus (13%), and respiratory syncytial virus (12%), with co-infections in 19% of cases. Discrepancies were observed in 25% of qPCR-positive samples compared with BioFire results. Biobanking protocols, including pre- and post-sample collection procedures were streamlined to serve not only as a guide for future biobanking initiatives in similar settings, but also to implement a sustainable and efficient system of inventory, storage, and retrieval of biological resources. Conclusion This study successfully established a functional respiratory virus biobank that can provide a foundation for the future conduct of human health research and development on diagnostics, therapeutics, and vaccines, thereby enhancing public health preparedness for respiratory infections in the Philippines.

The prevention and control of human immunodeficiency virus and sexually transmitted infections (HIV/STI) face challenges worldwide, especially in China. Prediction tools, which analyze medical data and information to make future predictions, were once mainly used in HIV/STI research to help make diagnostic or prognostic decisions, has have now extended to the public as a freely accessible tool. This article provides an overview of the different roles of prediction tools in preventing and controlling HIV/STI from the perspectives of individuals, healthcare providers, and policymakers. For individuals, prediction tools serve as a risk assessment solution that assess their risk and consciously improve risk reception or change risky behaviors. For researchers, prediction tools are powerful for assisting in identifying risk factors and predicting patients’ infection risk, which can inform timely and accurate intervention planning in the future. In order to achieve the best performance, current research increasingly underscores the necessity of considering multiple levels of information, such as socio-behavioral data, in developing a robust prediction tool. In addition, it is also crucial to conduct trials in clinical settings to validate the effectiveness of prediction tools. Many studies only use theoretical parameters such as model accuracy to estimate its predictive. If these improvements are made, the application of prediction tools could be a potentially inspiring solution in the prevention and control of HIV/STI, and an opportunity for achieving the World Health Organization’s agenda to end the HIV/STI epidemic by 2030.