Some New Constructions of -ary Codes for Correcting a Burst of at Most Deletions.

In this paper, we construct -ary codes for correcting a burst of at most deletions, where t,q≥2 are arbitrarily fixed positive integers. We consider two scenarios of error correction: the classical error correcting codes, which recover each codeword from one read (channel output), and the reconstruction codes, which allow to recover each codeword from multiple channel reads. For the first scenario, our construction has redundancy logn+8loglogn+o(loglogn) bits, encoding complexity O(q7tn(logn)3) and decoding complexity O(nlogn).

Visit-to-visit HbA1c variability and risk of potentially avoidable hospitalisations in adults with type 2 diabetes receiving outpatient care at a tertiary hospital.

Aims: This study aims to investigate the relationship between long-term visit-to-visit within-person HbA1c variability and hospitalisation outcomes in adults with type 2 diabetes (T2D).

Methods: We conducted a cohort study at a tertiary hospital in Singapore involving people aged 21 to 101 years with T2D who had ≥3 HbA1c tests over 2 years. HbA1c variability was assessed using coefficient of variation (CV), variability independent of the mean (VIM) and HbA1c variability score (HVS).

Non-Islet-Cell Tumor Hypoglycemia Secondary to Malignant Phyllodes Tumor of the Breast.

Background/objective: Non-islet cell tumor hypoglycemia (NICTH) is an uncommon condition, of which only a few cases caused by malignant phyllodes tumor of the breast have been reported. We describe a case of NICTH secondary to malignant phyllodes tumor with good response to glucocorticoid therapy.

Case Report: A 62-year-old woman with a rapidly enlarging left breast mass presented with drowsiness and a capillary blood glucose level of 32.

Comparative Analysis of Vision Transformers and Conventional Convolutional Neural Networks in Detecting Referable Diabetic Retinopathy.

Objective: Vision transformers (ViTs) have shown promising performance in various classification tasks previously dominated by convolutional neural networks (CNNs). However, the performance of ViTs in referable diabetic retinopathy (DR) detection is relatively underexplored. In this study, using retinal photographs, we evaluated the comparative performances of ViTs and CNNs on detection of referable DR.