Duke-NUS Medical School

To assess the outcomes of surgical management of aqueous misdirection (AM) by endoscopic pars plana vitrectomy with hyaloido-zonulo-iridectomy. In this prospective, longitudinal, noncomparative interventional study, 53 eyes from 46 patients with AM refractory to medical and laser therapy after intraocular surgery were enrolled. All eyes underwent lens removal (if phakic), endoscopic pars plana vitrectomy, and hyaloido-zonulo-iridectomy. Primary outcomes included intraocular pressure (IOP), best-corrected visual acuity (BCVA), anterior chamber depth (ACD), postoperative complications, relapse rate, and composite surgical success. Surgical success was defined as the combination of IOP control and anterior chamber reformation, using two alternative IOP thresholds ( ≥ 6 and ≤18 mmHg, or ≥6 and ≤21 mmHg). Continuous variables were analysed using linear mixed models and expressed as estimated means (est)± standard error. Mean age was 59.7 ± 12.2 years. The est. IOP decreased from 34.36 ± 0.82 mmHg to 17.47 ± 0.82 mmHg at 12 months (p < 0.001). The est. BCVA improved from 1.50 ± 0.07 to 0.70 ± 0.07 logMAR at 12 months (p < 0.001). The est. ACD increased from 0.70 ± 0.06 to 3.34 ± 0.06 mm (p < 0.001), with complete anterior chamber reformation in all eyes. Postoperative complications occurred in 22 eyes (41.8%), mostly transient and resolved with medical or YAG laser treatment; only one required glaucoma surgery. At 12 months, overall success was 98.1% using the ≤21 mmHg criterion and 75.5% using the ≤18 mmHg criterion. No relapses were observed. Surgical management using lens removal, endoscopic anterior vitrectomy, and hyaloido-zonulo-iridectomy is a significantly effective and safe treatment for AM.

Older people mount poorer adaptive immune responses to mRNA vaccines, leaving them more vulnerable to infection with SARS-CoV-2. To design better mRNA vaccines for older people, we need to understand how aging alters mechanisms of adjuvancy that shape immunogenicity. To first define age-related changes in immunogenicity, we vaccinated young (< 5 months old) and aged (> 18 months old) C57BL/6 mice with an mRNA vaccine encoding the SARS-CoV-2 spike protein. Tcell responses were markedly reduced in aged mice at peak and memory timepoints, using intracellular cytokine staining or activation-induced marker assays. Spike and receptor-binding domain binding and neutralizing antibody titers were also markedly reduced in aged mice, consistent with deficits seen in older humans. To define age-related changes in adjuvancy mechanisms, we vaccinated young and aged mice with mRNA vaccines loaded with DiD lipid dye or mScarlet mRNA, then tracked dendritic cell (DC) numbers, phenotype, vaccine uptake, antigen expression, and activation, as well as local and systemic cytokine production. DC numbers in the draining lymph nodes (dLN) were dramatically reduced before and early after vaccination in aged compared to young mice, with delayed recruitment of DCs to the dLN. Vaccine uptake was not impacted by age, but the frequency of DCs expressing antigen increased with age and DC activation decreased with age. Aging accelerated the expression of some cytokines (IL-1α, IL-6), while delaying others (IFNγ, MCP-1) in dLNs and sera. This illustrates that aging impairs multiple adjuvancy mechanisms but mRNA vaccine strategies that address these age-related deficits could improve responses in older people.

Multi-omics in combination with advanced computational methodologies synthesizes diverse omics data to provide deeper insights into molecular interactions and offers transformative potential for unravelling phenomenon behind disease complexities, improving diagnostics, disease prevention, and personalized treatments. This integrative strategy enables our understanding of gene-environment relationships, chronic disease progression, and the intricate molecular pathways involved in health. Effective multi-omics analyses require robust data sharing, accessibility, interoperability, and governance, which are critical for linking genomic elements to phenotypic traits. The Global Alliance for Genomics and Health advocates for responsible data-sharing practices, by promoting key principles such as transparency and equity. By emphasizing a collaborative approach to data utilization, our proposed framework seeks to advance improved disease prevention and treatment strategies. Multi-disciplinary collaboration, encompassing researchers, clinicians, policy makers, and patient representatives, is pivotal for driving innovation and addressing rare disease diagnostics. The success of multi-omics applications hinges on the establishment of comprehensive datasets, understanding the functional implications of multi-omic variation, adherence to findable, accessible, interoperable, reusable (FAIR) and Collective Benefit, Authority to Control, Responsibility, and Ethics (CARE) principles, and the strengthening of global genomic commons, benefiting scientific research, drug development, and broader health initiatives. Our review highlights essential components of multi-omics integration, underscoring its potential to transform the landscape of precision medicine and improved patient outcomes worldwide.

Abstract Cell-type–specific regulation of gene expression plays a central role in complex disease etiology, yet most transcriptome-wide association studies (TWAS) rely on bulk tissue models. Recently, a couple methods leverage single-cell transcriptomics to perform TWAS at the cell-type resolution, but they are limited by scarce matched genotype– single-cell cohorts and restricted to peripheral blood, with minimal coverage of less accessible, disease-relevant tissues. In this study, we developed S-MiXcan, a summary-statistics–based TWAS framework that enables cell-type–aware association analysis using bulk transcriptomic data across K ≥ 2 cell types without requiring individual-level data. As a major advancement over our prior tool MiXcan, S-MiXcan jointly models genetically regulated expression (GReX) across K cell types, accounts for cross–cell-type correlations, identifies disease-associated genes, and provides probabilistic interpretations for distinguishing cell-type–specific from shared associations. In real data analyses, compared with using individual-level genotype-based implementation, S-MiXcan achieved highly concordant results (Pearson’s r ≈ 1) in cell-type-aware TWAS. Applied to large-scale multi-cohort Genome-Wide Association Study (GWAS) meta-analyses from the Breast Cancer Association Consortium, S-MiXcan maintained well-controlled type I error (genomic inflation λ = 1.057), identified key breast cancer risk associated genes that function in a cell-type specific manner, and revealed relevant cell types through probabilistic inference. These results demonstrate that S-MiXcan, publicly available at https://github.com/songxiaoyu/SMiXcan , provides a scalable and interpretable frame-work for cell-type–aware TWAS.

IntroductionAs the population rapidly ages, older adults are faced with a multitude of physical, psychological, and social challenges that limit their ability to age well. Intergenerational programs have gained traction as a potential solution to address social isolation and loneliness among older adults, while also improving intergenerational solidarity among young adults. The study aimed to explore and compare the experiences, facilitators, and barriers to intergenerational relationships and participation in intergenerational programs, from the perspectives of young and older adults in Singapore.MethodsA qualitative descriptive approach with semi-structured interviews was conducted with 14 young adults and 15 older adults via purposive sampling. Data were analyzed thematically using inductive coding.ResultsThree themes emerged: (1) intergenerational perceptions and attitudes towards intergenerational interactions; (2) language, interpersonal traits, and responsibility to bridge the intergenerational gap as facilitators and barriers towards intergenerational relationships; and (3) the role and impact of intergenerational programs on intergenerational relationships.ConclusionThis study highlighted the complex interplay of interpersonal, structural, and cultural factors that shape intergenerational relationships and the effectiveness of intergenerational programs in Singapore. To foster authentic and reciprocal intergenerational relationships, intergenerational programs must be intentionally designed to promote equal status, cooperation, and culturally sensitive practices that reflect the lived realities of both young adults and older adults.

The integration of generative AI (GenAI) and large language models (LLMs) in healthcare presents both unprecedented opportunities and challenges, necessitating innovative regulatory approaches. In this perspective, we discuss the risks of GenAI and LLM-based medical devices, the limitations of current medical device regulation frameworks when applied to GenAI or LLMs, and advocate for global collaboration in regulatory science research through engaging multidisciplinary expertise and focusing on the needs of diverse populations.

Advances in artificial intelligence (AI) systems suggest that they can be used to improve healthcare outcomes via diagnosis, prognostication, patient management, risk assessment, etc. AI systems could be particularly useful in emergency healthcare (EHC) by synthesizing data to generate accurate conclusions rapidly. But the use of AI in EHC raises ethical, legal, and social concerns. The present study undertakes a scoping review to collate, map, and synthesize existing literature on the ethical, legal and social issues (ELSIs) associated with AI in EHC. The aim was to assess which ELSI issues were recognized and analyzed in the current literature and which were under-explored. Online databases were used to identify papers published on the identified topic. An initial search strategy of IEEE, Pubmed, and Scopus yielded 156 unique records; 40 records underwent textual review, after which another 7 were excluded due to scope. The final 33 were analysed for content. Overall, the literature was mostly positive towards AI applications on EHC, with key themes aligning with the general AI ethics literature: transparency, bias, benefit/harm, justice, accountability, privacy and trust. Analyses of these issues, however, were mostly superficial and did not substantially engage with some of the distinctive features of EHC like urgency and high-stakes decision-making. In particular, urgency and stakes were under-recognised or under-explored in the EHC AI literature. Arguably, urgency in some emergency scenarios could justify more flexible ethical/regulatory standards, while conversely high-stakes contexts might require more stringent standards. Lack of discussion of these contextual nuances suggests a significant gap in the literature of deeper research into the unique ethical, legal and social issues arising from AI use in EHC. This paper extends current knowledge by highlighting the need for deeper and more contextualized investigation of AI ethics in EHC. Not applicable.