A Collecting Device for Exhaled Breath Detection by Nanostructure-initiator Mass Spectrometry DOI
Jing Ji, Yang Gao, Jianmin Wu

et al.

Published: Oct. 27, 2023

The metabolites in exhaled breath (EB) dynamically reflect the metabolic status of human body and can serve as biomarkers for health management disease diagnosis. However, there is currently a lack clinical diagnostic methods rapid metabolite detection EB samples metabolomic data processing. In this project, we develop method that uses collecting device to efficiently collect on perfluoroethylene propylene (FEP) modified silicon nanowires (SiNWs). sample chips are directly detected by nanostructure-initiator mass spectrometry (NIMS), (MS) results collected dataset analyzed machine learning techniques. FEP@SiNWs improved trapping efficiency laser desorption/ionization during collection detection, enabling analysis with good stability repeatability. Through technology, MS information patients pulmonary nodules healthy people was processed, characteristic peaks were selected, prediction accuracy (>95.4 %) sensitivity (92.6 – 98.5 model tested. Based work, have potential be used home devices, database respiratory diseases via established future research, which applied diagnosis intelligent healthcare.

Language: Английский

Effect of Food Intake on Exhaled Volatile Organic Compounds Profile Analyzed by an Electronic Nose DOI Creative Commons
Silvano Dragonieri, Vitaliano Nicola Quaranta, Andrea Portacci

et al.

Molecules, Journal Year: 2023, Volume and Issue: 28(15), P. 5755 - 5755

Published: July 30, 2023

Exhaled breath analysis using an e-nose is a groundbreaking tool for exhaled volatile organic compound (VOC) analysis, which has already shown its applicability in several respiratory and systemic diseases. It still unclear whether food intake can be considered confounder when analyzing the VOC-profile. We aimed to assess discriminate before after predefined at different time periods. enrolled 28 healthy non-smoking adults collected their as follows: (a) intake, (b) within 5 min consumption, (c) 1 h eating, (d) 2 eating. was by formerly validated method analyzed (Cyranose 320). By principal component significant variations VOC-profile were (capturing 63.4% of total variance) comparing baseline vs. (both p < 0.05). No significance comparison between intake. Therefore, seems influenced very recent Interestingly, two hours might sufficient avoid induced alterations VOC-spectrum sampling research protocols.

Language: Английский

Citations

9
Clinical Applications of Nasal Nitric Oxide in Allergic Rhinitis: A Review of the Literature DOI Open Access
Giuseppina Marcuccio, Pasquale Ambrosino, Claudia Merola

et al.

Journal of Clinical Medicine, Journal Year: 2023, Volume and Issue: 12(15), P. 5081 - 5081

Published: Aug. 2, 2023

Allergic rhinitis, a common allergic disease affecting significant number of individuals worldwide, is observed in 25% children and 40% adults, with its highest occurrence between the ages 20 40. Its pathogenesis, like other diseases, involves innate adaptive immune responses, characterized by immunologic hypersensitivity to environmental substances. This response mediated type 2 immunity. Within certain molecules have been identified as clinical biomarkers that contribute diagnosis, prognosis, therapy monitoring. Among these biomarkers, nitric oxide has shown play key role various physiological pathological processes, including neurotransmission, immunity, inflammation, regulation mucus cilia, inhibition microorganisms, tumor cell growth. Therefore, measurement nasal proposed an objective method for monitoring airway obstruction inflammation different settings (community, hospital, rehabilitation) conditions, upper airways diseases nose paranasal sinuses. The purpose this review analyze potential mechanisms contributing production rhinitis related health issues. Additionally, aims identify implications future research, treatment strategies, long-term management symptoms.

Language: Английский

Citations

5
Electronic nose based analysis of exhaled volatile organic compounds spectrum reveals asthmatic shifts and consistency in controls post-exercise and spirometry DOI Creative Commons
Silvano Dragonieri,

Marcin Di Marco,

Madiha Ahroud

et al.

Journal of Breath Research, Journal Year: 2024, Volume and Issue: 18(3), P. 036006 - 036006

Published: June 14, 2024

Abstract Analyzing exhaled volatile organic compounds (VOCs) with an electronic nose (e-nose) is emerging in medical diagnostics as a non-invasive, quick, and sensitive method for disease detection monitoring. This study investigates if activities like spirometry or physical exercise affect VOCs measurements asthmatics healthy individuals, crucial step e-nose technology’s validation clinical use. The analyzed using 27 individuals patients stable asthma, before after performing climbing five flights of stairs. Breath samples were collected validated technique Cyranose 320 e-nose. In controls, the spectrum remained unchanged both lung function test exercise. asthmatics, principal component analysis subsequent discriminant revealed significant differences post-spirometry (vs. baseline 66.7% cross accuracy [CVA], p < 0.05) 70.4% CVA, 0.05). E-nose are consistent, unaffected by However, asthma patients, changes detected post-activities, indicating airway responses likely due to constriction inflammation, underscoring e-nose’s potential respiratory condition diagnosis

Language: Английский

Citations

1
Association between triglyceride-glucose index and fractional exhaled nitric oxide in adults with asthma from NHANES 2007–2012 DOI Creative Commons
Yao Pan, Lizhen Wu,

S.‐P. Yao

et al.

Lipids in Health and Disease, Journal Year: 2024, Volume and Issue: 23(1)

Published: Nov. 3, 2024

Several studies have shown a potential relationship between triglyceride-glucose index (TGI) and asthma. However, limited research has been conducted on the TGI fractional exhaled nitric oxide (FeNO).

Language: Английский

Citations

1
Breathe out the Secret of the Lung: Video Classification of Exhaled Flows from Normal and Asthmatic Lung Models Using CNN-Long Short-Term Memory Networks DOI Creative Commons
Mohamed Talaat, Xiuhua Si, Jinxiang Xi

et al.

Journal of Respiration, Journal Year: 2023, Volume and Issue: 3(4), P. 237 - 257

Published: Dec. 14, 2023

In this study, we present a novel approach to differentiate normal and diseased lungs based on exhaled flows from 3D-printed lung models simulating asthmatic conditions. By leveraging the sequential learning capacity of Long Short-Term Memory (LSTM) network automatic feature extraction convolutional neural networks (CNN), evaluated feasibility detection staging airway constrictions. Two (D1, D2) with increasing levels severity were generated by decreasing bronchiolar calibers in right upper lobe (D0). Expiratory recorded mid-sagittal plane using high-speed camera at 1500 fps. addition baseline flow rate (20 L/min) which trained verified, two additional rates (15 L/min 10 considered evaluate network’s robustness deviations. Distinct patterns vortex dynamics observed among three disease states (D0, D1, across rates. The AlexNet-LSTM proved be robust, maintaining perfect performance three-class classification when deviated recommendation 25%, still performed reasonably (72.8% accuracy) despite 50% deviation. GoogleNet-LSTM also showed satisfactory (91.5% 25% deviation but exhibited low (57.7% was 50%. Considering effects task, video classifications only slightly outperformed those images (i.e., 3–6%). occlusion sensitivity analyses distinct heat maps specific state.

Language: Английский

Citations

3
Qualitative and Quantitative Analysis of Volatile Molecular Biomarkers in Breath Using THz-IR Spectroscopy and Machine Learning DOI Creative Commons
A. K. Tretyakov, Denis A. Vrazhnov, A. P. Shkurinov

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(24), P. 11521 - 11521

Published: Dec. 11, 2024

Exhaled air contains volatile molecular compounds of endogenous origin, being products current metabolic pathways. It can be used for medical express diagnostics through control these in the patient’s breath using absorption spectroscopy. The fundamental problem this field is that composition exhaled or other gas mixtures natural origin unknown, and content analysis such spectra by conventional iterative methods unpredictable. Machine learning enable establishment latent dependencies spectral data conducting their qualitative quantitative analysis. This review devoted to most effective machine sample focus on interpretable methods, which are important reliable diagnosis. Also, steps additional standard pipeline decision support discussed.

Language: Английский

Citations

0
Multi-modal breath measurements for biomarker discovery DOI Open Access
Phillip J. Tomezsko,

Jordan Wynn,

Alla Ostrinskaya

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 13, 2023

Abstract Breath contains numerous classes of compounds and biomolecules that could potentially be used as biomarkers for infectious disease well a range other respiratory conditions or states. A testbed simultaneous, multi-modal measurements was developed. Seventeen healthy subjects provided breath samples at baseline repiratory rate particle size, lipid composition bacterial nucleic acid analysis. The majority the particles participants exhaled were smaller than 5 μm, consistent with previous literature. particulate contained lipids found in lung surfactant, indicating origin lung. Although DNA not significantly higher environmental background, metagenome distinct from environment, oral cavity nasal passages participants. low abundance microbiome limited has promise discovery reference different are currently being used.

Language: Английский

Citations

0
A Collecting Device for Exhaled Breath Detection by Nanostructure-initiator Mass Spectrometry DOI
Jing Ji, Yang Gao, Jianmin Wu

et al.

Published: Oct. 27, 2023

The metabolites in exhaled breath (EB) dynamically reflect the metabolic status of human body and can serve as biomarkers for health management disease diagnosis. However, there is currently a lack clinical diagnostic methods rapid metabolite detection EB samples metabolomic data processing. In this project, we develop method that uses collecting device to efficiently collect on perfluoroethylene propylene (FEP) modified silicon nanowires (SiNWs). sample chips are directly detected by nanostructure-initiator mass spectrometry (NIMS), (MS) results collected dataset analyzed machine learning techniques. FEP@SiNWs improved trapping efficiency laser desorption/ionization during collection detection, enabling analysis with good stability repeatability. Through technology, MS information patients pulmonary nodules healthy people was processed, characteristic peaks were selected, prediction accuracy (>95.4 %) sensitivity (92.6 – 98.5 model tested. Based work, have potential be used home devices, database respiratory diseases via established future research, which applied diagnosis intelligent healthcare.

Language: Английский

Citations

0