Validation Against Polysomnography of a Transthoracic Impedance Sensor for Screening of Sleep Apnea in Heart Failure Patients: A Pooled Analysis of AIRLESS and UPGRADE.

Cardiac implantable electronic devices and their integrated thoracic impedance sensors have been used to detect sleep apnea for over a decade now. Despite their usage in daily clinical practice, there are only limited data on their diagnostic accuracy. AIRLESS and UPGRADE were prospective investigator-driven trials meant to validate the AP scan (Boston Scientific, Marlborough, MA, USA) in heart failure cohorts.

Detection and quantification of introgression using Bayesian inference based on conjugate priors.

Summary: Introgression (the flow of genes between species) is a major force structuring the evolution of genomes, potentially providing raw material for adaptation. Here, we present a versatile Bayesian model selection approach for detecting and quantifying introgression, df-BF, that builds upon the recently published distance-based df statistic. Unlike df, df-BF accounts for the number of variant sites within a genomic region.

Unlocking nature's brilliance: using Antarctic extremophile Shewanella baltica to biosynthesize lanthanide-containing nanoparticles with optical up-conversion.

Both lanthanide-containing and fluorine-containing nanomaterials present challenging targets for microbial biosynthesis because these elements are toxic to most bacteria. Here, we overcome these challenges by using an Antarctic Shewanella baltica strain that tolerates these elements and report the first biosynthesis of lanthanide-doped fluoride nanoparticles (NPs) from them. NaYF NPs doped with Er/Yb are prototypical lanthanide-based upconverting nanoparticles (UCNPs) with upconverted luminescence at visible wavelengths under infrared excitation.

Collaborative weighting in federated graph neural networks for disease classification with the human-in-the-loop.

The authors introduce a novel framework that integrates federated learning with Graph Neural Networks (GNNs) to classify diseases, incorporating Human-in-the-Loop methodologies. This advanced framework innovatively employs collaborative voting mechanisms on subgraphs within a Protein-Protein Interaction (PPI) network, situated in a federated ensemble-based deep learning context. This methodological approach marks a significant stride in the development of explainable and privacy-aware Artificial Intelligence, significantly contributing to the progression of personalized digital medicine in a responsible and transparent manner.

Microbial green synthesis of luminescent terbium sulfide nanoparticles using E. Coli: a rare earth element detoxification mechanism.

Background: Rare-earth sulfide nanoparticles (NPs) could harness the optical and magnetic features of rare-earth ions for applications in nanotechnology. However, reports of their synthesis are scarce and typically require high temperatures and long synthesis times.

Results: Here we present a biosynthesis of terbium sulfide (TbS) NPs using microorganisms, identifying conditions that allow Escherichia coli to extracellularly produce TbS NPs in aqueous media at 37 °C by controlling cellular sulfur metabolism to produce a high concentration of sulfide ions.