Multiple-breath inert gas washout (MBW) is ideally suited for early detection and monitoring of serious lung disease, such as cystic fibrosis, in infants and young children. Validated commercial options for the MBW technique are limited, and suitability of nitrogen (N)-based MBW is of concern given the detrimental effect of exposure to pure O on infant breathing pattern. We propose novel methodology using commercially available N MBW equipment to facilitate 4% sulfur hexafluoride (SF) multiple-breath inert gas wash-in and washout suitable for the infant age range. CO, O, and sidestream molar mass sensor signals were used to accurately calculate SF concentrations. An improved dynamic method for synchronization of gas and respiratory flow was developed to take into account variations in sidestream sample flow during MBW measurement. In vitro validation of triplicate functional residual capacity (FRC) assessments was undertaken under dry ambient conditions using lung models ranging from 90 to 267 ml, with tidal volumes of 28-79 ml, and respiratory rates 20-60 per minute. The relative mean (SD, 95% confidence interval) error of triplicate FRC determinations by washout was -0.26 (1.84, -3.86 to +3.35)% and by wash-in was 0.57 (2.66, -4.66 to +5.79)%. The standard deviations [mean (SD)] of percentage error among FRC triplicates were 1.40 (1.14) and 1.38 (1.32) for washout and wash-in, respectively. The novel methodology presented achieved FRC accuracy as outlined by current MBW consensus recommendations (95% of measurements within 5% accuracy). Further clinical evaluation is required, but this new technique, using existing commercially available equipment, has exciting potential for research and clinical use.
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http://dx.doi.org/10.1152/japplphysiol.00115.2016 | DOI Listing |
Background: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and validate an explainable machine learning model to evaluate overall health status in patients with comorbid CHD and depression.
View Article and Find Full Text PDFAnal Chem
January 2025
Environment Research Institute, Shandong University, Qingdao 266237, China.
Globally, drug-impaired driving fatalities now exceed those from drunk driving, urging the need for on-site and roadside detection methods. In this study, a photothermal desorption and reagent-assisted low-temperature plasma ionization miniature ion trap mass spectrometer (PDRA-LTP-ITMS) was developed for on-site detection of drug-impaired driving. The pseudomultiple reaction monitoring (MRM) in PDRA-LTP-ITMS enables continuous ion selection during ion introduction and improved sensitivity to nearly 3-fold compared with the conventional full scan mode.
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Psychiatry Department, Weill Cornell Medicine, New York, NY, United States.
Background: Mental illness is one of the top causes of preventable pregnancy-related deaths in the United States. There are many barriers that interfere with the ability of perinatal individuals to access traditional mental health care. Digital health interventions, including app-based programs, have the potential to increase access to useful tools for these individuals.
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Background: Digital nerve injuries significantly affect hand function and quality of life, necessitating effective reconstruction strategies. Autologous nerve grafting remains the gold standard due to its superior biocompatibility, despite recent advancements in nerve conduits and allogenic grafts. This study aims to propose a novel zone-based strategy for donor nerve selection to improve outcomes in digital nerve reconstruction.
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Geneis (Beijing) Co. Ltd., Beijing 100102, China.
Identification of potential drug-target interactions (DTIs) is a crucial step in drug discovery and repurposing. Although deep learning effectively deciphers DTIs, most deep learning-based methods represent drug features from only a single perspective. Moreover, the fusion method of drug and protein features needs further refinement.
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