Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractStudy on lipolysis-oxidation and volatile flavour compounds of dry-cured goose with different curing salt content during production    Next AbstractVegetation shift from deciduous to evergreen dwarf shrubs in response to selective herbivory offsets carbon losses: evidence from 19 years of warming and simulated herbivory in the subarctic tundra »

Molecules


Title:pyAIR-A New Software Tool for Breathomics Applications-Searching for Markers in TD-GC-HRMS Analysis
Author(s):Yishai Aviram L; Marder D; Prihed H; Tartakovsky K; Shem-Tov D; Sinelnikov R; Dagan S; Tzanani N;
Address:"Department of Analytical Chemistry, Israel Institute for Biological Research1, P.O. Box 19, Ness Ziona 7410001, Israel. Scent Medical Technologies, Rehovot 7670107, Israel"
Journal Title:Molecules
Year:2022
Volume:20220323
Issue:7
Page Number: -
DOI: 10.3390/molecules27072063
ISSN/ISBN:1420-3049 (Electronic) 1420-3049 (Linking)
Abstract:"Volatile metabolites in exhaled air have promising potential as diagnostic biomarkers. However, the combination of low mass, similar chemical composition, and low concentrations introduces the challenge of sorting the data to identify markers of value. In this paper, we report the development of pyAIR, a software tool for searching for volatile organic compounds (VOCs) markers in multi-group datasets, tailored for Thermal-Desorption Gas-Chromatography High Resolution Mass-Spectrometry (TD-GC-HRMS) output. pyAIR aligns the compounds between samples by spectral similarity coupled with retention times (RT), and statistically compares the groups for compounds that differ by intensity. This workflow was successfully tested and evaluated on gaseous samples spiked with 27 model VOCs at six concentrations, divided into three groups, down to 0.3 nL/L. All analytes were correctly detected and aligned. More than 80% were found to be significant markers with a p-value < 0.05; several were classified as possibly significant markers (p-value < 0.1), while a few were removed due to background level. In all group comparisons, low rates of false markers were found. These results showed the potential of pyAIR in the field of trace-level breathomics, with the capability to differentially examine several groups, such as stages of illness"
Keywords:Biomarkers/analysis *Breath Tests/methods Gas Chromatography-Mass Spectrometry/methods Software *Volatile Organic Compounds/analysis GC-MS (Orbitrap) Ms-dial Td-gc-hrms VOCs breath analysis pyAIR;
Notes:"MedlineYishai Aviram, Lilach Marder, Dana Prihed, Hagit Tartakovsky, Konstantin Shem-Tov, Daniel Sinelnikov, Regina Dagan, Shai Tzanani, Nitzan eng Switzerland 2022/04/13 Molecules. 2022 Mar 23; 27(7):2063. doi: 10.3390/molecules27072063"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 16-11-2024