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Fu L, Wang L, Wang H, Yang M, Yang Q, Lin Y, Guan S, Deng Y, Liu L, Li Q, He M, Zhang P, Chen H, Deng G. A cross-sectional study: a breathomics based pulmonary tuberculosis detection method. BMC Infect Dis 2023; 23:148. [PMID: 36899314 PMCID: PMC9999612 DOI: 10.1186/s12879-023-08112-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 02/22/2023] [Indexed: 03/12/2023] Open
Abstract
BACKGROUND Diagnostics for pulmonary tuberculosis (PTB) are usually inaccurate, expensive, or complicated. The breathomics-based method may be an attractive option for fast and noninvasive PTB detection. METHOD Exhaled breath samples were collected from 518 PTB patients and 887 controls and tested on the real-time high-pressure photon ionization time-of-flight mass spectrometer. Machine learning algorithms were employed for breathomics analysis and PTB detection mode, whose performance was evaluated in 430 blinded clinical patients. RESULTS The breathomics-based PTB detection model achieved an accuracy of 92.6%, a sensitivity of 91.7%, a specificity of 93.0%, and an AUC of 0.975 in the blinded test set (n = 430). Age, sex, and anti-tuberculosis treatment does not significantly impact PTB detection performance. In distinguishing PTB from other pulmonary diseases (n = 182), the VOC modes also achieve good performance with an accuracy of 91.2%, a sensitivity of 91.7%, a specificity of 88.0%, and an AUC of 0.961. CONCLUSIONS The simple and noninvasive breathomics-based PTB detection method was demonstrated with high sensitivity and specificity, potentially valuable for clinical PTB screening and diagnosis.
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Affiliation(s)
- Liang Fu
- Division Two of the Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China
| | - Lei Wang
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100074, China
| | - Haibo Wang
- Peking University Clinical Research Institute, Peking University First Hospital, Beijing, 100000, China
| | - Min Yang
- Division Two of the Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China
| | - Qianting Yang
- Institute for Hepatology, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China
| | - Yi Lin
- Division Two of the Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China
| | - Shanyi Guan
- Medical Examination Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China
| | - Yongcong Deng
- Pulmonary Diseases Out-Patient Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China
| | - Lei Liu
- Division Two of the Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China
| | - Qingyun Li
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100074, China
| | - Mengqi He
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100074, China
| | - Peize Zhang
- Division Two of the Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China.
| | - Haibin Chen
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100074, China.
| | - Guofang Deng
- Division Two of the Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National Clinical Research Center for Infectious Disease, Southern University of Science and Technology, Shenzhen, 518112, China.
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Determination of tuberculosis-related volatile organic biomarker methyl nicotinate in vapor using fluorescent assay based on quantum dots and cobalt-containing porphyrin nanosheets. Mikrochim Acta 2022; 189:108. [PMID: 35171382 DOI: 10.1007/s00604-022-05212-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 01/31/2022] [Indexed: 10/19/2022]
Abstract
Methyl nicotinate (MN) is a representative and typical volatile organic marker of Mycobacterium tuberculosis, and the specific detection of MN in human breath facilitates non-invasive, rapid, and accurate epidemic screening of tuberculosis infection. Herein, we constructed a fluorescent assay consisted of CdTe quantum dots (QD) and cobalt-metalized tetrakis(4-carboxyphenyl) porphyrin (CoTCPP) nanosheets to determine methyl nicotinate (MN) in vapor samples. Red-emission QD (λex=370 nm, λem=658 nm) acts as signal switches whose fluorescence signals can be effectively quenched by CoTCPP nanosheets but restored in the presence of MN. The strategy relied on the distinct binding affinity of cobalt ion and MN. MN restored the fluorescence of QD quenched by CoTCPP in a concentration-dependent manner, which exhibited a well-linear relationship in the range 1-100 μM, and a limit of detection of 0.59 μM. The proposed platform showed sensitivity and selectivity to detect MN in vapor samples with satisfactory RSD below 3.33%. The method is cheap, simple, and relatively rapid (detected within 4 min), which suggests a potential in tuberculosis diagnosis in resource- and professional-lacked areas.
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Bezdekova J, Vodova M, Dolezelikova K, Zitka J, Smerkova K, Zitka O, Adam V, Vaculovicova M. Detection of microbial contamination based on uracil-selective synthetic receptors. Talanta 2021; 224:121813. [PMID: 33379038 DOI: 10.1016/j.talanta.2020.121813] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/21/2020] [Accepted: 10/23/2020] [Indexed: 12/22/2022]
Abstract
The here presented work is focused on the development of a method for detection of microbial contamination of food based on uracil-selective synthetic receptors. Because uracil may serve as an indicator of bacterial contamination, its selective and on-site detection may prevent spreading of foodborne diseases. The synthetic receptors were created by molecular imprinting. Molecularly imprinted polymers for selective uracil isolation were prepared by a non-covalent imprinting method using dopamine as a functional monomer. Detection of isolated uracil was performed by capillary electrophoresis with absorption detection (λ - 260 nm). The conditions of preparation of molecularly imprinted polymers, their binding properties, adsorption kinetics and selectivity were investigated in detail. Furthermore, the prepared polymer materials were used for selective isolation and detection of uracil from complex samples as tomato products by miniaturized electrophoretic system suggesting the potential of in situ analysis of real samples.
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Affiliation(s)
- Jaroslava Bezdekova
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00, Brno, Czech Republic; Central European Institute of Technology, Brno University of Technology, Purkynova 123, CZ-612 00, Brno, Czech Republic
| | - Milada Vodova
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00, Brno, Czech Republic
| | - Kristyna Dolezelikova
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00, Brno, Czech Republic; Central European Institute of Technology, Brno University of Technology, Purkynova 123, CZ-612 00, Brno, Czech Republic
| | - Jan Zitka
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00, Brno, Czech Republic; Central European Institute of Technology, Brno University of Technology, Purkynova 123, CZ-612 00, Brno, Czech Republic
| | - Kristyna Smerkova
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00, Brno, Czech Republic; Central European Institute of Technology, Brno University of Technology, Purkynova 123, CZ-612 00, Brno, Czech Republic
| | - Ondrej Zitka
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00, Brno, Czech Republic; Central European Institute of Technology, Brno University of Technology, Purkynova 123, CZ-612 00, Brno, Czech Republic
| | - Vojtech Adam
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00, Brno, Czech Republic; Central European Institute of Technology, Brno University of Technology, Purkynova 123, CZ-612 00, Brno, Czech Republic
| | - Marketa Vaculovicova
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00, Brno, Czech Republic; Central European Institute of Technology, Brno University of Technology, Purkynova 123, CZ-612 00, Brno, Czech Republic.
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Lundahl M, Lynch DM, Barnes D, McSweeney L, Gorman A, Lebre F, Gordon SV, Lavelle EC, Scanlan EM. Mycobacterial para-Hydroxybenzoic Acid-Derivatives ( pHBADs) and Related Structures Induce Macrophage Innate Memory. ACS Chem Biol 2020; 15:2415-2421. [PMID: 32786261 DOI: 10.1021/acschembio.0c00378] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Macrophages are key immune cells for combatting Mycobacterium tuberculosis. However, M. tuberculosis possesses means to evade macrophage bactericidal responses by, for instance, secretion of the immunomodulatory para-hydroxybenzoic acid derivatives (pHBADs). While these molecules have been implicated in inhibiting macrophage responses in an acute context, little is known about their ability to reprogram macrophages via induction of long-term innate memory. Since innate memory has been highlighted as a promising strategy to augment bactericidal immune responses against M. tuberculosis, investigating corresponding immune evasion mechanisms is highly relevant. Our results reveal for the first time that pHBAD I and related molecules (unmethylated pHBAD I and the hexose l-rhamnose) reduce macrophage bactericidal mechanisms in both the short- and the long-term. Moreover, we demonstrate how methyl-p-anisate hinders bactericidal responses soon after exposure yet results in enhanced pro-inflammatory responses in the long-term. This work highlights new roles for these compounds in M. tuberculosis pathogenesis.
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Affiliation(s)
- Mimmi Lundahl
- School of Chemistry and Trinity Biomedical Sciences Institute, Trinity College, Pearse St, D02 R590 Dublin 2, Ireland
- Adjuvant Research Group, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, D02 R590 Dublin 2, Ireland
| | - Dylan M. Lynch
- School of Chemistry and Trinity Biomedical Sciences Institute, Trinity College, Pearse St, D02 R590 Dublin 2, Ireland
| | - Danielle Barnes
- School of Chemistry and Trinity Biomedical Sciences Institute, Trinity College, Pearse St, D02 R590 Dublin 2, Ireland
| | - Lauren McSweeney
- School of Chemistry and Trinity Biomedical Sciences Institute, Trinity College, Pearse St, D02 R590 Dublin 2, Ireland
| | - Aoife Gorman
- Adjuvant Research Group, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, D02 R590 Dublin 2, Ireland
| | - Filipa Lebre
- Adjuvant Research Group, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, D02 R590 Dublin 2, Ireland
| | - Stephen V. Gordon
- UCD School of Veterinary Medicine, University College Dublin, D02 R590 Dublin, Ireland
| | - Ed C. Lavelle
- Adjuvant Research Group, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, D02 R590 Dublin 2, Ireland
| | - Eoin M. Scanlan
- School of Chemistry and Trinity Biomedical Sciences Institute, Trinity College, Pearse St, D02 R590 Dublin 2, Ireland
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Sankar A, Ranu S, Raman K. Predicting novel metabolic pathways through subgraph mining. Bioinformatics 2017; 33:3955-3963. [DOI: 10.1093/bioinformatics/btx481] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 07/26/2017] [Indexed: 11/13/2022] Open
Affiliation(s)
- Aravind Sankar
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
| | - Sayan Ranu
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
- Initiative for Biological Systems Engineering (IBSE), Interdisciplinary Laboratory for Data Sciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
| | - Karthik Raman
- Initiative for Biological Systems Engineering (IBSE), Interdisciplinary Laboratory for Data Sciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
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