Assessing glycaemic impact of FreeStyle libre monitoring in patients with insulin-treated type 2 diabetes: a retrospective real-world analysis.

Objectives: FreeStyle Libre (FSL) monitoring is available for all patients in Wales with insulin-treated diabetes. English guidance permits FSL in patients with type 1 diabetes mellitus (T1D) and type 2 diabetes mellitus (T2D) requiring multiple daily insulin doses (MDI) (National Institute for Health and Care Excellence 2023). The literature suggests benefits from using FSL, specifically improved glycaemic control and reduced hypoglycaemia.

Single-cell multiome and spatial profiling reveals pancreas cell type-specific gene regulatory programs driving type 1 diabetes progression.

Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here we performed single nucleus multiomics and spatial transcriptomics in up to 32 non-diabetic (ND), autoantibody-positive (AAB+), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine sub-types.

Search for Magnetic Monopole Pair Production in Ultraperipheral Pb+Pb Collisions at sqrt[s_{NN}]=5.36 TeV with the ATLAS Detector at the LHC.

This Letter presents a search for highly ionizing magnetic monopoles in 262  μb^{-1} of ultraperipheral Pb+Pb collision data at sqrt[s_{NN}]=5.36  TeV collected by the ATLAS detector at the LHC. A new methodology that exploits the properties of clusters of hits reconstructed in the innermost silicon detector layers is introduced to study highly ionizing particles in heavy-ion data.

Machine learning models predict long COVID outcomes based on baseline clinical and immunologic factors.

The post-acute sequelae of SARS-CoV-2 (PASC), also known as long COVID, remain a significant health issue that is incompletely understood. Predicting which acutely infected individuals will go on to develop long COVID is challenging due to the lack of established biomarkers, clear disease mechanisms, or well-defined sub-phenotypes. Machine learning (ML) models offer the potential to address this by leveraging clinical data to enhance diagnostic precision.

Identification of a multi-omics factor predictive of long COVID in the IMPACC study.

Following SARS-CoV-2 infection, ~10-35% of COVID-19 patients experience long COVID (LC), in which often debilitating symptoms persist for at least three months. Elucidating the biologic underpinnings of LC could identify therapeutic opportunities. We utilized machine learning methods on biologic analytes and patient reported outcome surveys provided over 12 months after hospital discharge from >500 hospitalized COVID-19 patients in the IMPACC cohort to identify a multi-omics "recovery factor".