The exposure prediction component of the Control of Substances Hazardous to Health (COSHH) Essentials model (paper version) was evaluated using field measurements from National Institute of Occupational Safety and Health (NIOSH) Health Hazard Evaluation (HHE) reports. Overall 757 measured exposures for 94 similar exposure groups (SEGs) were compared with the COSHH Essentials predicted exposure range (PER). The SEGs were stratified based on the magnitude of measured exposures (high, medium, or low) and physical state of the substance (vapor or particulate). The majority of measured exposures observed involved low-level exposure to vapors; thus, overall findings from the current study are limited to low-level vapor exposure scenarios. Overall, the exposure prediction component of COSHH Essentials vastly overestimated low-level exposures to vapors. This study went beyond the scope of previous studies and investigated which model components led to the overestimation. It was concluded that COSHH Essential's tendency to overestimate was due to multiple complex interactions among model components. Overall, the magnitude of overestimation seems to increase exponentially as values for predictor variables increase. This is likely due to the log-based scale used by the model to allocate concentration ranges. In addition, the current banding scheme used to allocate volatility appears to play a role in the overestimation of low-level exposures to vapors.
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http://dx.doi.org/10.1080/15459624.2020.1717501 | DOI Listing |
STAR Protoc
January 2025
Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA. Electronic address:
Host response to environmental exposures such as pathogens and chemicals can include modifications to the epigenome and transcriptome. Improved signature discovery, including the identification of the agent and timing of exposure, has been enabled by advancements in assaying techniques to detect RNA expression, DNA base modifications, histone modifications, and chromatin accessibility. The interrogation of the epigenome and transcriptome cascade requires analyzing disparate datasets from multiple assay types, often at single-cell resolution, derived from the same biospecimen.
View Article and Find Full Text PDFInt J Surg
January 2025
Department of Cardiovascular Surgery, Xijing Hospital, Xi'an, Shaanxi, China.
Background: The impact of aortic arch (AA) morphology on the management of the procedural details and the clinical outcomes of the transfemoral artery (TF)-transcatheter aortic valve replacement (TAVR) has not been evaluated. The goal of this study was to evaluate the AA morphology of patients who had TF-TAVR using an artificial intelligence algorithm and then to evaluate its predictive value for clinical outcomes.
Materials And Methods: A total of 1480 consecutive patients undergoing TF-TAVR using a new-generation transcatheter heart valve at 12 institutes were included in this retrospective study.
JAMA Neurol
January 2025
Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore.
Importance: Biomarkers would greatly assist decision-making in the diagnosis, prevention, and treatment of chronic pain.
Objective: To undertake analytical validation of a sensorimotor cortical biomarker signature for pain consisting of 2 measures: sensorimotor peak alpha frequency (PAF) and corticomotor excitability (CME).
Design, Setting, And Participants: This cohort study at a single center (Neuroscience Research Australia) recruited participants from November 2020 to October 2022 through notices placed online and at universities across Australia.
Organisms continually tune their perceptual systems to the features they encounter in their environment . We have studied how ongoing experience reorganizes the synaptic connectivity of neurons in the olfactory (piriform) cortex of the mouse. We developed an approach to measure synaptic connectivity , training a deep convolutional network to reliably identify monosynaptic connections from the spike-time cross-correlograms of 4.
View Article and Find Full Text PDFBackground: Several modifiable risk factors for dementia and related neurodegenerative diseases have been identified including education level, socio-economic status, and environmental exposures - however, how these population-level risks relate to individual risk remains elusive. To address this, we assess over 450 potential risk factors in one deeply clinically and demographically phenotyped cohort using random forest classifiers to determine predictive markers of poor cognitive function. This study aims to understand early risk factors for dementia by identifying predictors of poor cognitive performance amongst a comprehensive battery of imaging, blood, atmospheric pollutant and socio-economic measures.
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