Literacy and attitude of Asian youths on dengue and its prevention in an endemic developed community.

Background: Over the past few decades, the incidence of dengue fever has considerably increased. Effective vector control strategies and specific protection using dengue vaccine are thought to be the key elements to combat dengue. The dengue incidence among the Singapore youths (15-24 years) was second only to that of adults (25-44 years).

Extending the diabetic retinopathy screening intervals in Singapore: methodology and preliminary findings of a cohort study.

Background: The Diabetic Retinopathy Extended Screening Study (DRESS) aims to develop and validate a new DR/diabetic macular edema (DME) risk stratification model in patients with Type 2 diabetes (DM) to identify low-risk groups who can be safely assigned to biennial or triennial screening intervals. We describe the study methodology, participants' baseline characteristics, and preliminary DR progression rates at the first annual follow-up.

Methods: DRESS is a 3-year ongoing longitudinal study of patients with T2DM and no or mild non-proliferative DR (NPDR, non-referable) who underwent teleophthalmic screening under the Singapore integrated Diabetic Retinopathy Programme (SiDRP) at four SingHealth Polyclinics.

External validation and diagnostic value of the Elderly Functional Index version 2.0 for assessing functional status and frailty in older Danish patients with gastrointestinal cancer receiving chemotherapy: A prospective, clinical study.

Introduction: Patient perspectives on functioning are often overlooked in oncology practice. This study externally validates the ELderly Functional Index (ELFI), a patient-reported measure for assessing multidimensional functioning, in older patients with gastrointestinal cancer receiving chemotherapy. The study compares ELFI scoring methods, evaluates its diagnostic value with geriatric oncology tools, and proposes a cut-off point for clinical use.

Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies.

Training recurrent neural networks (RNNs) has become a go-to approach for generating and evaluating mechanistic neural hypotheses for cognition. The ease and efficiency of training RNNs with backpropagation through time and the availability of robustly supported deep learning libraries has made RNN modeling more approachable and accessible to neuroscience. Yet, a major technical hindrance remains.