Duke-NUS Medical School was established through a landmark collaboration between two world-class institutions: Duke University and the National University of Singapore (NUS), with the objective of providing innovative education and impactful research that enhance the practice of medicine in Singapore and beyond.As a graduate-entry medical school, Duke-NUS attracts the brightest minds from a range of backgrounds, who bring with them significant academic and life experiences. Our MD curriculum is designed to provide an opportunity for our students to become outstanding clinicians and curious, critical thinkers who may also contribute to medicine as researchers, educators, leaders, entrepreneurs or policy makers. This is the basis of our “Clinician First, Clinician Plus” curriculum.Along with our innovative educational programmes, we are a research powerhouse and our distinguished scientists are transforming the way we understand, diagnose and treat diseases. Our world-class signature research programmes are aligned to critical areas of public health needs in Singapore. These disease-focused, multi-disciplinary programmes have facilitated research discoveries that will improve the lives of patients in Singapore and around the world.Our School benefits greatly from our strategic partnership with Singapore Health Services (SingHealth), which includes Singapore General Hospital, widely recognised as one of the top hospitals in the world. Through our SingHealth Duke-NUS Academic Medical Centre, we are able to leverage our joint capabilities and infrastructure to develop outstanding clinical education programmes and cutting-edge research collaborations that translate fundamental science into better health.
Thousands of short open reading frames (sORFs) are translated outside of annotated coding sequences. Recent studies have pioneered searching for sORF-encoded microproteins in mass spectrometry (MS)-based proteomics and peptidomics datasets. Here, we assessed literature-reported MS-based identifications of unannotated human proteins. We find that studies vary by three orders of magnitude in the number of unannotated proteins they report. Of nearly 10,000 reported sORF-encoded peptides, 96% were unique to a single study, and 12% mapped to annotated proteins or proteoforms. Manual curation of a benchmark dataset of 406 manually evaluated spectra from 204 sORF-encoded proteins revealed large variation in peptide-spectrum match (PSM) quality between studies, with immunopeptidomics studies generally reporting higher quality PSMs than conventional enzymatic digests of whole cell lysates. We estimate that 65% of predicted sORF-encoded protein detections in immunopeptidomics studies were supported by high-quality PSMs versus 7.8% in non-immunopeptidomics datasets. Our work stresses the need for standardized protocols and analysis workflows to guide future advancements in microprotein detection by MS towards uncovering how many human microproteins exist.
With the advent of spatial multi-omics, mosaic integration of diverse datasets with partially overlapping modalities enables the construction of comprehensive multimodal spatial atlases from heterogeneous sources. Here we present SpaMosaic, a tool that uses contrastive learning and graph neural networks to build a modality-agnostic, batch-corrected latent space for spatial domain identification and missing-modality imputation. We systematically benchmarked SpaMosaic against existing integration methods using simulated data and experimentally acquired datasets spanning RNA and protein abundance, chromatin accessibility and histone modifications from brain, embryo, tonsil and lymph node tissues. SpaMosaic consistently outperformed other methods in identifying coherent spatial domains by reducing noise and mitigating batch effects. We further challenged SpaMosaic with heterogeneous real-world datasets spanning different technologies, developmental stages, resolutions and modality compositions, where it consistently resolved fine anatomical structures and enabled comprehensive mouse embryo atlasing. Beyond integration, SpaMosaic enables accurate imputation of missing modalities. In a mosaic mouse brain dataset, the imputed histone modifications not only recapitulated expected transcriptome-epigenome correlations but also uncovered more region-specific regulatory links compared to the measured chromatin accessibility data, demonstrating the ability to infer relationships across modalities without coprofiling. Computationally, SpaMosaic is highly scalable, capable of integrating over 100 sections and processing a single section with more than 800,000 spots. In summary, SpaMosaic provides a versatile framework for unifying the rapidly accumulating heterogeneous spatial omics data into comprehensive biological atlases.
Access to trustworthy artificial intelligence (AI) for clinical applications is uneven, especially in low-resource settings with limited and inconsistent data. Models from high-resource settings often fail to generalize. Transfer learning (TL) can adapt established models to new settings. Using neurological outcome prediction for out-of-hospital cardiac arrest (OHCA) as a proof of concept, we adapted a model trained on a large cohort to Vietnam (243 patients) and Singapore (15,916 patients) using the Pan-Asian Resuscitation Outcomes Study registry. The external model performed poorly on the Vietnam cohort, with an area under the receiver operating characteristic curve (AUROC) of 0.467 (95
Background Social prescribing (SP) connects individuals to non-clinical community resources to address social determinants of health (SDH). This review aimed to map and categorise SP models to inform adaptation and implementation across the Western Pacific Region (WPR). Methods A scoping review was conducted using peer-reviewed databases (MEDLINE, CINAHL, Web of Science, Scopus, Embase) and the WHO Institutional Repository for Information Sharing (IRIS). Grey literature was identified through expert consultations with key stakeholders in WPR countries, informed by the World Health Organization and the International Social Prescribing Collaborative. Data were extracted and categorised by SDH focus, intervention type, delivery mechanism, funding, target population, and implementation challenges. Findings Fifty-five sources were included (42 peer-reviewed, 13 grey literature), with most studies from Australia (62%), Singapore (10%), and New Zealand (7%). SP models varied in structure, funding, and population reach. Common interventions addressed chronic disease, mental health, and social isolation. Key challenges included limited resources, fragmented referral systems, and a lack of standardised evaluation. Interpretation Many WPR countries implement SP-like activities, though not always recognised as such. A structured yet flexible SP framework is needed to support scale-up, tailored to local systems and community strengths.
Background Dementia-the seventh leading cause of death globally-is most prevalent in Asia, home to over half of those affected. Yet, palliative approaches to dementia, endorsed by the World Health Organization (WHO) Global Action Plan on the Public Health Response to Dementia, and focused on improving quality of life through a holistic and person-centered approach, are largely absent in the region.Methods We reviewed the available literature related to end-of-life experiences with advanced dementia from countries in the WHO South-east Asian and Western Pacific regions. We used the Consolidated Framework for Implementation Research to synthesize barriers and facilitators to implementing a palliative approach to dementia and propose a research agenda.Results Broad barriers identified in the outer setting of the implementation framework include sociocultural values and a lack of supportive policies, guidelines, and financing. Within the inner setting of the healthcare system, challenges stem from underdeveloped long-term care infrastructure, limited professional training, and gaps in equity and person-centeredness. At the individual level, barriers include low dementia literacy and limited uptake of advance care planning. Potential facilitators were growing digital fluency and established community norms around caring for older adults at home.Conclusions Based on our review, we propose a research agenda prioritizing partnering with individuals with dementia and their caregivers, especially in low- and middle-income countries, de-implementing low-value interventions and implementing community-level palliative care models, leveraging technological innovations, and developing core evaluation metrics to advance WHO's action plan and foster culturally relevant and effective interventions tailored to the region's unique needs.
