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21 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Transcutaneous and End-Tidal CO 2 Measurements in Hypoxia and Hyperoxia

BACKGROUND: Transcutaneous measurement of carbon dioxide (CO 2 ) has been proposed for physiological monitoring of tactical jet aircrew because in some clinical settings it mirrors arterial CO 2 partial pressure (P a co 2 ). End-tidal monitoring in laboratory settings is known to give high-fidelity estimates of P a co 2 . METHODS: The correspondence between end-tidal (P ET co 2 ) and transcutaneous Pco 2 (tcPco 2 ) was examined in healthy volunteers under laboratory conditions of hyperoxia and hypoxia. Rest and exercise, skin heating and cooling, hyperventilation, and induced CO 2 retention were employed. RESULTS: Neither measure followed all known changes in P a co 2 and tcPco 2 changed when the skin temperature near the probe changed. Bland-Altman analysis showed significant nonzero slopes under most conditions. Regression analysis indicated that oxygen partial pressure (Po 2 ) in tissue measured as transcutaneous Po 2 (tcPo 2 ) is an important explanatory variable for tcPco 2 in addition to P ET co 2 , and that local skin temperature also has an effect. Additionally, absorption atelectasis from breathing 100% O 2 may cause P ET co 2 to deviate from P a co 2 . DISCUSSION: Even as a trend indicator for P a co 2 , tcPco 2 is not useful under conditions that resemble those in the highly dynamic tactical jet aircraft environment. P ET co 2 is also not a good indicator of CO 2 status in pilots who breathe nearly 100% O 2 . Shykoff BE, Lee LR, Gallo M, Griswold CA. Transcutaneous and end-tidal CO 2 measurements in hypoxia and hyperoxia . Aerosp Med Hum Perform. 2021; 92(11):864–872 .

Biophysics↗
DOE OSTI2025

Feed status and skin injury modulate immunopathology, global gene expression, and survival in channel catfish during virulent Aeromonas hydrophila infection

Introduction VirulentAeromonas hydrophilais a major pathogen in channel catfish (Ictalurus punctatus), that causes motileAeromonassepticemia and significant economic losses. We investigated the effect of feeding status and skin integrity on the host immune response, disease survival, and gastrointestinal pathology following a vAh challenge. Methods Using a bath immersion model, channel catfish were divided into four treatment groups: fin clipped and fed (FCF), fin clipped but not fed (FCN), not fin clipped but fed (NCF), and not fin clipped nor fed (NCN) alongside non-challenged control groups The FCF and NCF groups were fed 2 h prior to the challenge, but the FCN and NCN groups were not. Survival analysis, histopathological assessment, and RNA sequencing were conducted across groups at different time intervals throughout the vAh challenge. Results Survival rates were lowest in the FCF and FCN groups (30% and 23% survival, respectively), suggesting that both feeding and skin damage contributed to disease severity. Histopathological analyses revealed more severe intestinal and gastric lesions in fed groups, characterized by epithelial necrosis, hemorrhage, and edema. Transcriptomic analysis among the groups identified significant differentially expressed genes associated with inflammation, apoptosis, and metabolic stress, with notable upregulation of interleukin 1-beta (il-1β), and complement C3 (c3). Gene ontology enrichment highlighted distinct immune activation patterns between fed and unfed groups, with enhanced pathogen recognition and pro-inflammatory responses in unfed fish. Discussion These findings suggest feeding prior to infection may exacerbate disease pathology, potentially by creating a physiological state conducive to facilitate pathogen proliferation and dampened early immune responses, whereas short-term fasting appears to promote early immune activation. This study provides novel insights into the complex interplay between feed status, physical injury, and immune response to vAh infection.

Immunology↗
DOE OSTI2022

Feature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia

Feature selection plays a crucial role in the development of machine learning algorithms. Understanding the impact of the features on a model, and their physiological relevance can improve the performance. This is particularly helpful in the healthcare domain wherein disease states need to be identified with relatively small quantities of data. Autonomic Dysreflexia (AD) is one such example, wherein mismanagement of this neurological condition could lead to severe consequences for individuals with spinal cord injuries. We explore different methods of feature selection needed to improve the performance of a machine learning model in the detection of the onset of AD. We present different techniques used as well as the ideal metrics using a dataset of thirty-six features extracted from electrocardiograms, skin nerve activity, blood pressure and temperature. The best performing algorithm was a 5-layer neural network with five relevant features, which resulted in 93.4% accuracy in the detection of AD. The techniques in this paper can be applied to a myriad of healthcare datasets allowing forays into deeper exploration and improved machine learning model development. Through critical feature selection, it is possible to design better machine learning algorithms for detection of niche disease states using smaller datasets.

electrocardiography↗