Machine learning for early dynamic prediction of functional outcome after stroke.

Background: Prediction of outcome after stroke is critical for treatment planning and resource allocation but is complicated by fluctuations during the first days after onset. We propose a machine learning model that can provide hourly predictions based on the integration of continuous variables acquired within 72 h of hospital admission.

Methods: We analyzed 2492 admissions for ischemic stroke in the Geneva University Hospital from 01.

Bone consolidation under septic condition and hardware retention: about 69 patients.

Introduction: Management of infection on internal fixation hardware is particularly complex. The main aim of the present study was to assess bone consolidation rates under septic conditions in patients treated for bone and joint infection (BJI) with hardware retention. Secondary objectives were to determine a time limit beyond which it is unreasonable to retain hardware, and to assess risk factors for non-consolidation and functional results.

Single-cell atlas of ABCA7 loss-of-function reveals impaired neuronal respiration via choline-dependent lipid imbalances.

Loss-of-function (LoF) variants in the lipid transporter ABCA7 significantly increase the risk of Alzheimer's disease (odds ratio ∼2), yet the pathogenic mechanisms and the neural cell types affected by these variants remain largely unknown. Here, we performed single-nuclear RNA sequencing of 36 human samples from the prefrontal cortex of 12 ABCA7 LoF carriers and 24 matched non-carrier control individuals. ABCA7 LoF was associated with gene expression changes in all major cell types.