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http://dx.doi.org/10.1136/heart.83.3.256DOI Listing

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Toward subtask-decomposition-based learning and benchmarking for predicting genetic perturbation outcomes and beyond.

Nat Comput Sci

October 2024

State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.

Article Synopsis
  • Understanding how cells respond to changes in genes is crucial for many medical fields, but there are challenges in predicting outcomes from single or multiple genetic changes across different cell types.
  • The research introduces an AI method called STAMP, which breaks down the prediction of genetic outcomes into three manageable tasks: finding genes that change after perturbation, determining how they change, and measuring the extent of those changes.
  • STAMP shows significant improvements over previous methods, including the ability to identify important regulatory genes and pathways, even with small sample sizes, and to uncover detailed interactions among genes.
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Article Synopsis
  • - The study focused on developing and preliminarily validating the Scale for Objective Diagnostic Components of Paroxysmal Events (STAMP), which categorizes seizure-like events, including epileptic seizures, functional seizures, and syncope.
  • - Researchers analyzed data from 200 patients referred to a Dutch epilepsy center, noting that 131 had epileptic seizures, 17 had functional seizures, and findings indicated significant diagnostic improvements through various event recordings during follow-up.
  • - The findings suggest that regular monitoring through STAMP can enhance diagnostic accuracy, although further validation of the scale is needed to fully establish its effectiveness.
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The energy demand of interventional imaging systems has historically been estimated using manufacturer-provided specifications rather than directly measured. The purpose of this study was to investigate the energy consumption of interventional imaging systems and estimate potential savings in the carbon emissions and electricity costs of such systems through hypothetical operational adjustments. An interventional radiology suite, neurointerventional suite, radiology fluoroscopy unit, two cardiology laboratories, and two urology fluoroscopy units were equipped with power sensors.

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Purpose: The Oldenburg Sentence Test (OLSA) is a German matrix test designed to determine speech recognition thresholds (SRT). It is widely used for hearing-aids and cochlear implant fitting, but an age-adjusted standard is still lacking. In addition, knowing that the ability to concentrate is an important factor in OLSA performance, we hypothesized that OLSA performance would depend on the time of day it was administered.

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