A cross-sectional study: a breathomics based pulmonary tuberculosis detection method

Key messages What is already known on this topic—Breath VOC analysis is a potential technology for PTB detection. However, it is still desirable for a real-time, robust, accurate, and simple breath analysis platform for clinical application. What this study adds—An online breath detection for PTB wa...
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Autor*in:	Liang Fu [verfasserIn] Lei Wang [verfasserIn] Haibo Wang [verfasserIn] Min Yang [verfasserIn] Qianting Yang [verfasserIn] Yi Lin [verfasserIn] Shanyi Guan [verfasserIn] Yongcong Deng [verfasserIn] Lei Liu [verfasserIn] Qingyun Li [verfasserIn] Mengqi He [verfasserIn] Peize Zhang [verfasserIn] Haibin Chen [verfasserIn] Guofang Deng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Pulmonary tuberculosis Machine learning Volatile organic compounds Breathomics

Übergeordnetes Werk:	In: BMC Infectious Diseases - BMC, 2003, 23(2023), 1, Seite 11
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:1 ; pages:11

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12879-023-08112-3

Katalog-ID:	DOAJ08777027X

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allfields	10.1186/s12879-023-08112-3 doi (DE-627)DOAJ08777027X (DE-599)DOAJ19bd7f1429574e9ab6ea7addea9c0028 DE-627 ger DE-627 rakwb eng RC109-216 Liang Fu verfasserin aut A cross-sectional study: a breathomics based pulmonary tuberculosis detection method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Key messages What is already known on this topic—Breath VOC analysis is a potential technology for PTB detection. However, it is still desirable for a real-time, robust, accurate, and simple breath analysis platform for clinical application. What this study adds—An online breath detection for PTB was proposed and demonstrated with high sensitivity and specificity in a large clinical cohort. How this study might affect research, practice, or policy—This study may promote the application of breath detection in clinical TB detection and related biomarker studies. Pulmonary tuberculosis Machine learning Volatile organic compounds Breathomics Infectious and parasitic diseases Lei Wang verfasserin aut Haibo Wang verfasserin aut Min Yang verfasserin aut Qianting Yang verfasserin aut Yi Lin verfasserin aut Shanyi Guan verfasserin aut Yongcong Deng verfasserin aut Lei Liu verfasserin aut Qingyun Li verfasserin aut Mengqi He verfasserin aut Peize Zhang verfasserin aut Haibin Chen verfasserin aut Guofang Deng verfasserin aut In BMC Infectious Diseases BMC, 2003 23(2023), 1, Seite 11 (DE-627)326645381 (DE-600)2041550-3 14712334 nnns volume:23 year:2023 number:1 pages:11 https://doi.org/10.1186/s12879-023-08112-3 kostenfrei https://doaj.org/article/19bd7f1429574e9ab6ea7addea9c0028 kostenfrei https://doi.org/10.1186/s12879-023-08112-3 kostenfrei https://doaj.org/toc/1471-2334 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 11
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allfieldsGer	10.1186/s12879-023-08112-3 doi (DE-627)DOAJ08777027X (DE-599)DOAJ19bd7f1429574e9ab6ea7addea9c0028 DE-627 ger DE-627 rakwb eng RC109-216 Liang Fu verfasserin aut A cross-sectional study: a breathomics based pulmonary tuberculosis detection method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Key messages What is already known on this topic—Breath VOC analysis is a potential technology for PTB detection. However, it is still desirable for a real-time, robust, accurate, and simple breath analysis platform for clinical application. What this study adds—An online breath detection for PTB was proposed and demonstrated with high sensitivity and specificity in a large clinical cohort. How this study might affect research, practice, or policy—This study may promote the application of breath detection in clinical TB detection and related biomarker studies. Pulmonary tuberculosis Machine learning Volatile organic compounds Breathomics Infectious and parasitic diseases Lei Wang verfasserin aut Haibo Wang verfasserin aut Min Yang verfasserin aut Qianting Yang verfasserin aut Yi Lin verfasserin aut Shanyi Guan verfasserin aut Yongcong Deng verfasserin aut Lei Liu verfasserin aut Qingyun Li verfasserin aut Mengqi He verfasserin aut Peize Zhang verfasserin aut Haibin Chen verfasserin aut Guofang Deng verfasserin aut In BMC Infectious Diseases BMC, 2003 23(2023), 1, Seite 11 (DE-627)326645381 (DE-600)2041550-3 14712334 nnns volume:23 year:2023 number:1 pages:11 https://doi.org/10.1186/s12879-023-08112-3 kostenfrei https://doaj.org/article/19bd7f1429574e9ab6ea7addea9c0028 kostenfrei https://doi.org/10.1186/s12879-023-08112-3 kostenfrei https://doaj.org/toc/1471-2334 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 11
allfieldsSound	10.1186/s12879-023-08112-3 doi (DE-627)DOAJ08777027X (DE-599)DOAJ19bd7f1429574e9ab6ea7addea9c0028 DE-627 ger DE-627 rakwb eng RC109-216 Liang Fu verfasserin aut A cross-sectional study: a breathomics based pulmonary tuberculosis detection method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Key messages What is already known on this topic—Breath VOC analysis is a potential technology for PTB detection. However, it is still desirable for a real-time, robust, accurate, and simple breath analysis platform for clinical application. What this study adds—An online breath detection for PTB was proposed and demonstrated with high sensitivity and specificity in a large clinical cohort. How this study might affect research, practice, or policy—This study may promote the application of breath detection in clinical TB detection and related biomarker studies. Pulmonary tuberculosis Machine learning Volatile organic compounds Breathomics Infectious and parasitic diseases Lei Wang verfasserin aut Haibo Wang verfasserin aut Min Yang verfasserin aut Qianting Yang verfasserin aut Yi Lin verfasserin aut Shanyi Guan verfasserin aut Yongcong Deng verfasserin aut Lei Liu verfasserin aut Qingyun Li verfasserin aut Mengqi He verfasserin aut Peize Zhang verfasserin aut Haibin Chen verfasserin aut Guofang Deng verfasserin aut In BMC Infectious Diseases BMC, 2003 23(2023), 1, Seite 11 (DE-627)326645381 (DE-600)2041550-3 14712334 nnns volume:23 year:2023 number:1 pages:11 https://doi.org/10.1186/s12879-023-08112-3 kostenfrei https://doaj.org/article/19bd7f1429574e9ab6ea7addea9c0028 kostenfrei https://doi.org/10.1186/s12879-023-08112-3 kostenfrei https://doaj.org/toc/1471-2334 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 11
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authorswithroles_txt_mv	Liang Fu @@aut@@ Lei Wang @@aut@@ Haibo Wang @@aut@@ Min Yang @@aut@@ Qianting Yang @@aut@@ Yi Lin @@aut@@ Shanyi Guan @@aut@@ Yongcong Deng @@aut@@ Lei Liu @@aut@@ Qingyun Li @@aut@@ Mengqi He @@aut@@ Peize Zhang @@aut@@ Haibin Chen @@aut@@ Guofang Deng @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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callnumber-first	R - Medicine
author	Liang Fu
spellingShingle	Liang Fu misc RC109-216 misc Pulmonary tuberculosis misc Machine learning misc Volatile organic compounds misc Breathomics misc Infectious and parasitic diseases A cross-sectional study: a breathomics based pulmonary tuberculosis detection method
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topic_title	RC109-216 A cross-sectional study: a breathomics based pulmonary tuberculosis detection method Pulmonary tuberculosis Machine learning Volatile organic compounds Breathomics
topic	misc RC109-216 misc Pulmonary tuberculosis misc Machine learning misc Volatile organic compounds misc Breathomics misc Infectious and parasitic diseases
topic_unstemmed	misc RC109-216 misc Pulmonary tuberculosis misc Machine learning misc Volatile organic compounds misc Breathomics misc Infectious and parasitic diseases
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title	A cross-sectional study: a breathomics based pulmonary tuberculosis detection method
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title_full	A cross-sectional study: a breathomics based pulmonary tuberculosis detection method
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author_browse	Liang Fu Lei Wang Haibo Wang Min Yang Qianting Yang Yi Lin Shanyi Guan Yongcong Deng Lei Liu Qingyun Li Mengqi He Peize Zhang Haibin Chen Guofang Deng
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title_sort	cross-sectional study: a breathomics based pulmonary tuberculosis detection method
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title_auth	A cross-sectional study: a breathomics based pulmonary tuberculosis detection method
abstract	Key messages What is already known on this topic—Breath VOC analysis is a potential technology for PTB detection. However, it is still desirable for a real-time, robust, accurate, and simple breath analysis platform for clinical application. What this study adds—An online breath detection for PTB was proposed and demonstrated with high sensitivity and specificity in a large clinical cohort. How this study might affect research, practice, or policy—This study may promote the application of breath detection in clinical TB detection and related biomarker studies.
abstractGer	Key messages What is already known on this topic—Breath VOC analysis is a potential technology for PTB detection. However, it is still desirable for a real-time, robust, accurate, and simple breath analysis platform for clinical application. What this study adds—An online breath detection for PTB was proposed and demonstrated with high sensitivity and specificity in a large clinical cohort. How this study might affect research, practice, or policy—This study may promote the application of breath detection in clinical TB detection and related biomarker studies.
abstract_unstemmed	Key messages What is already known on this topic—Breath VOC analysis is a potential technology for PTB detection. However, it is still desirable for a real-time, robust, accurate, and simple breath analysis platform for clinical application. What this study adds—An online breath detection for PTB was proposed and demonstrated with high sensitivity and specificity in a large clinical cohort. How this study might affect research, practice, or policy—This study may promote the application of breath detection in clinical TB detection and related biomarker studies.
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title_short	A cross-sectional study: a breathomics based pulmonary tuberculosis detection method
url	https://doi.org/10.1186/s12879-023-08112-3 https://doaj.org/article/19bd7f1429574e9ab6ea7addea9c0028 https://doaj.org/toc/1471-2334
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up_date	2024-07-03T13:51:51.657Z
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A cross-sectional study: a breathomics based pulmonary tuberculosis detection method

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