Development of a novel sustainable, portable, fast, and non-invasive platform based on ATR-FTIR technology coupled with machine learning algorithms for Helicobacter pylori detection in human saliva
Helicobacter pylori (H. pylori) infection can increase the risk of peptic ulcers and gastric neoplasms. H. pylori detection in gastric epithelial tissue collected by esophagogastroduodenoscopy (EGD) is an invasive, costly, and stands as an invasive and examiner-dependent procedure necessitating suit...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Talanta Open |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666831924000973 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850245202274418688 |
|---|---|
| author | Ghabriel Honório-Silva Marco Guevara-Vega Nagela Bernadelli Sousa Silva Marcelo Augusto Garcia-Júnior Deborah Cristina Teixeira Alves Luiz Ricardo Goulart Mario Machado Martins André Luiz Oliveira Rui Miguel Pinheiro Vitorino Thulio Marquez Cunha Carlos Henrique Gomes Martins Murillo Guimarães Carneiro Robinson Sabino-Silva |
| author_facet | Ghabriel Honório-Silva Marco Guevara-Vega Nagela Bernadelli Sousa Silva Marcelo Augusto Garcia-Júnior Deborah Cristina Teixeira Alves Luiz Ricardo Goulart Mario Machado Martins André Luiz Oliveira Rui Miguel Pinheiro Vitorino Thulio Marquez Cunha Carlos Henrique Gomes Martins Murillo Guimarães Carneiro Robinson Sabino-Silva |
| author_sort | Ghabriel Honório-Silva |
| collection | DOAJ |
| description | Helicobacter pylori (H. pylori) infection can increase the risk of peptic ulcers and gastric neoplasms. H. pylori detection in gastric epithelial tissue collected by esophagogastroduodenoscopy (EGD) is an invasive, costly, and stands as an invasive and examiner-dependent procedure necessitating suitable sedation. complex execution procedure, reducing access for isolated populations. H. pylori detection by Urea Breath Test (UBT) presents high outlay cost with limited access in low- and middle-income countries. In this context, it is critical to develop novel alternative non-invasive platforms for the portable, fast, accessible through self-collection and reagent-free detection of H. pylori. Here, we used attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) supported by Machine Learning algorithms to identify infrared vibrational modes of H. pylori diluted in human saliva. To perform it, saliva was diluted in 4 different concentrations (108 CFU/mL, 107 CFU/mL, 106 CFU/mL, and 105 CFU/mL) of H. pylori. Then, diluted saliva with or without H. pylori were applied to ATR-FTIR spectroscopy to perform a reagent-free, fast, and sustainable analysis of spectral signatures to identify unique vibrational modes to identify this pathogen. The obtained spectra were applied to Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) algorithms to perform the H. pylori detection. The results indicate that the method was highly accurate between 108 - 105 CFU/mL, achieving an accuracy of 89 % for 108 CFU/mL, 93 % for 107 CFU/mL, 94 % for 106 CFU/mL, and 85 % for 105 CFU/mL with SVM algorithm. This proof-of-concept study demonstrates the significant potential of a biophotonic platform supported by artificial intelligence for the non-invasive detection of H. pylori in human saliva samples obtained by self-collection, without the use of reagents. The data reveal that this proof-of-concept study has significant potential for the non-invasive detection of H. pylori using a biophotonic platform supported by artificial intelligence without the use of reagents with human saliva samples obtained by self-collection. |
| format | Article |
| id | doaj-art-dba6fa1f64f7494c9f6cabb1a89a291c |
| institution | OA Journals |
| issn | 2666-8319 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Talanta Open |
| spelling | doaj-art-dba6fa1f64f7494c9f6cabb1a89a291c2025-08-20T01:59:31ZengElsevierTalanta Open2666-83192024-12-011010038310.1016/j.talo.2024.100383Development of a novel sustainable, portable, fast, and non-invasive platform based on ATR-FTIR technology coupled with machine learning algorithms for Helicobacter pylori detection in human salivaGhabriel Honório-Silva0Marco Guevara-Vega1Nagela Bernadelli Sousa Silva2Marcelo Augusto Garcia-Júnior3Deborah Cristina Teixeira Alves4Luiz Ricardo Goulart5Mario Machado Martins6André Luiz Oliveira7Rui Miguel Pinheiro Vitorino8Thulio Marquez Cunha9Carlos Henrique Gomes Martins10Murillo Guimarães Carneiro11Robinson Sabino-Silva12Innovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Minas Gerais, BrazilInnovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Minas Gerais, BrazilAntimicrobial Testing Laboratory, Institute of Biomedical Sciences, Federal University of Uberlandia, Minas Gerais, BrazilInnovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Minas Gerais, BrazilInnovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Minas Gerais, BrazilInstitute of Biotechnology, Federal University of Uberlandia, Minas Gerais, BrazilInstitute of Biotechnology, Federal University of Uberlandia, Minas Gerais, BrazilSchool of Medicine, Federal University of Uberlandia, Minas Gerais, BrazilDepartment of Medical Sciences, University of Aveiro, PortugalSchool of Medicine, Federal University of Uberlandia, Minas Gerais, BrazilAntimicrobial Testing Laboratory, Institute of Biomedical Sciences, Federal University of Uberlandia, Minas Gerais, BrazilFaculty of Computing, Federal University of Uberlandia, Minas Gerais, BrazilInnovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Minas Gerais, Brazil; Corresponding author at: Federal University of Uberlandia (UFU), Institute of Biomedical Sciences (ICBIM), ARFIS, Av. Pará, 1720, Campus Umuarama, CEP 38400-902, Uberlandia, Minas Gerais, Brazil.Helicobacter pylori (H. pylori) infection can increase the risk of peptic ulcers and gastric neoplasms. H. pylori detection in gastric epithelial tissue collected by esophagogastroduodenoscopy (EGD) is an invasive, costly, and stands as an invasive and examiner-dependent procedure necessitating suitable sedation. complex execution procedure, reducing access for isolated populations. H. pylori detection by Urea Breath Test (UBT) presents high outlay cost with limited access in low- and middle-income countries. In this context, it is critical to develop novel alternative non-invasive platforms for the portable, fast, accessible through self-collection and reagent-free detection of H. pylori. Here, we used attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) supported by Machine Learning algorithms to identify infrared vibrational modes of H. pylori diluted in human saliva. To perform it, saliva was diluted in 4 different concentrations (108 CFU/mL, 107 CFU/mL, 106 CFU/mL, and 105 CFU/mL) of H. pylori. Then, diluted saliva with or without H. pylori were applied to ATR-FTIR spectroscopy to perform a reagent-free, fast, and sustainable analysis of spectral signatures to identify unique vibrational modes to identify this pathogen. The obtained spectra were applied to Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) algorithms to perform the H. pylori detection. The results indicate that the method was highly accurate between 108 - 105 CFU/mL, achieving an accuracy of 89 % for 108 CFU/mL, 93 % for 107 CFU/mL, 94 % for 106 CFU/mL, and 85 % for 105 CFU/mL with SVM algorithm. This proof-of-concept study demonstrates the significant potential of a biophotonic platform supported by artificial intelligence for the non-invasive detection of H. pylori in human saliva samples obtained by self-collection, without the use of reagents. The data reveal that this proof-of-concept study has significant potential for the non-invasive detection of H. pylori using a biophotonic platform supported by artificial intelligence without the use of reagents with human saliva samples obtained by self-collection.http://www.sciencedirect.com/science/article/pii/S2666831924000973ATR-FTIRHelicobacter pyloriGastrointestinal infectionScreening testSalivary detectionSaliva, non-invasive test |
| spellingShingle | Ghabriel Honório-Silva Marco Guevara-Vega Nagela Bernadelli Sousa Silva Marcelo Augusto Garcia-Júnior Deborah Cristina Teixeira Alves Luiz Ricardo Goulart Mario Machado Martins André Luiz Oliveira Rui Miguel Pinheiro Vitorino Thulio Marquez Cunha Carlos Henrique Gomes Martins Murillo Guimarães Carneiro Robinson Sabino-Silva Development of a novel sustainable, portable, fast, and non-invasive platform based on ATR-FTIR technology coupled with machine learning algorithms for Helicobacter pylori detection in human saliva Talanta Open ATR-FTIR Helicobacter pylori Gastrointestinal infection Screening test Salivary detection Saliva, non-invasive test |
| title | Development of a novel sustainable, portable, fast, and non-invasive platform based on ATR-FTIR technology coupled with machine learning algorithms for Helicobacter pylori detection in human saliva |
| title_full | Development of a novel sustainable, portable, fast, and non-invasive platform based on ATR-FTIR technology coupled with machine learning algorithms for Helicobacter pylori detection in human saliva |
| title_fullStr | Development of a novel sustainable, portable, fast, and non-invasive platform based on ATR-FTIR technology coupled with machine learning algorithms for Helicobacter pylori detection in human saliva |
| title_full_unstemmed | Development of a novel sustainable, portable, fast, and non-invasive platform based on ATR-FTIR technology coupled with machine learning algorithms for Helicobacter pylori detection in human saliva |
| title_short | Development of a novel sustainable, portable, fast, and non-invasive platform based on ATR-FTIR technology coupled with machine learning algorithms for Helicobacter pylori detection in human saliva |
| title_sort | development of a novel sustainable portable fast and non invasive platform based on atr ftir technology coupled with machine learning algorithms for helicobacter pylori detection in human saliva |
| topic | ATR-FTIR Helicobacter pylori Gastrointestinal infection Screening test Salivary detection Saliva, non-invasive test |
| url | http://www.sciencedirect.com/science/article/pii/S2666831924000973 |
| work_keys_str_mv | AT ghabrielhonoriosilva developmentofanovelsustainableportablefastandnoninvasiveplatformbasedonatrftirtechnologycoupledwithmachinelearningalgorithmsforhelicobacterpyloridetectioninhumansaliva AT marcoguevaravega developmentofanovelsustainableportablefastandnoninvasiveplatformbasedonatrftirtechnologycoupledwithmachinelearningalgorithmsforhelicobacterpyloridetectioninhumansaliva AT nagelabernadellisousasilva developmentofanovelsustainableportablefastandnoninvasiveplatformbasedonatrftirtechnologycoupledwithmachinelearningalgorithmsforhelicobacterpyloridetectioninhumansaliva AT marceloaugustogarciajunior developmentofanovelsustainableportablefastandnoninvasiveplatformbasedonatrftirtechnologycoupledwithmachinelearningalgorithmsforhelicobacterpyloridetectioninhumansaliva AT deborahcristinateixeiraalves developmentofanovelsustainableportablefastandnoninvasiveplatformbasedonatrftirtechnologycoupledwithmachinelearningalgorithmsforhelicobacterpyloridetectioninhumansaliva AT luizricardogoulart developmentofanovelsustainableportablefastandnoninvasiveplatformbasedonatrftirtechnologycoupledwithmachinelearningalgorithmsforhelicobacterpyloridetectioninhumansaliva AT mariomachadomartins developmentofanovelsustainableportablefastandnoninvasiveplatformbasedonatrftirtechnologycoupledwithmachinelearningalgorithmsforhelicobacterpyloridetectioninhumansaliva AT andreluizoliveira developmentofanovelsustainableportablefastandnoninvasiveplatformbasedonatrftirtechnologycoupledwithmachinelearningalgorithmsforhelicobacterpyloridetectioninhumansaliva AT ruimiguelpinheirovitorino developmentofanovelsustainableportablefastandnoninvasiveplatformbasedonatrftirtechnologycoupledwithmachinelearningalgorithmsforhelicobacterpyloridetectioninhumansaliva AT thuliomarquezcunha developmentofanovelsustainableportablefastandnoninvasiveplatformbasedonatrftirtechnologycoupledwithmachinelearningalgorithmsforhelicobacterpyloridetectioninhumansaliva AT carloshenriquegomesmartins developmentofanovelsustainableportablefastandnoninvasiveplatformbasedonatrftirtechnologycoupledwithmachinelearningalgorithmsforhelicobacterpyloridetectioninhumansaliva AT murilloguimaraescarneiro developmentofanovelsustainableportablefastandnoninvasiveplatformbasedonatrftirtechnologycoupledwithmachinelearningalgorithmsforhelicobacterpyloridetectioninhumansaliva AT robinsonsabinosilva developmentofanovelsustainableportablefastandnoninvasiveplatformbasedonatrftirtechnologycoupledwithmachinelearningalgorithmsforhelicobacterpyloridetectioninhumansaliva |