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Abu-Aqil G, Suleiman M, Lapidot I, Huleihel M, Salman A. Infrared spectroscopy-based machine learning algorithms for rapid detection of Klebsiella pneumoniae isolated directly from patients' urine and determining its susceptibility to antibiotics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 314:124141. [PMID: 38513317 DOI: 10.1016/j.saa.2024.124141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/15/2024] [Accepted: 03/08/2024] [Indexed: 03/23/2024]
Abstract
Among the most prevalent and detrimental bacteria causing urinary tract infections (UTIs) is Klebsiella (K.) pneumoniae. A rapid determination of its antibiotic susceptibility can enhance patient treatment and mitigate the spread of resistant strains. In this study, we assessed the viability of using infrared spectroscopy-based machine learning as a rapid and precise approach for detecting K. pneumoniae bacteria and determining its susceptibility to various antibiotics directly from a patient's urine sample. In this study, 2333 bacterial samples, including 636 K. pneumoniae were investigated using infrared micro-spectroscopy. The obtained spectra (27996spectra) were analyzed with XGBoost classifier, achieving a success rate exceeding 95 % for identifying K. pneumoniae. Moreover, this method allows for the simultaneous determination of K. pneumoniae susceptibility to various antibiotics with sensitivities ranging between 74 % and 81 % within approximately 40 min after receiving the patient's urine sample.
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Affiliation(s)
- George Abu-Aqil
- Department of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Manal Suleiman
- Department of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Itshak Lapidot
- Department of Electrical and Electronics Engineering, ACLP-Afeka Center for Language Processing, Afeka Tel-Aviv Academic College of Engineering, Tel-Aviv 69107, Israel
| | - Mahmoud Huleihel
- Department of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.
| | - Ahmad Salman
- Department of Physics, SCE - Shamoon College of Engineering, Beer-Sheva 84100, Israel.
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Gunčar G, Kukar M, Smole T, Moškon S, Vovko T, Podnar S, Černelč P, Brvar M, Notar M, Köster M, Jelenc MT, Osterc Ž, Notar M. Differentiating viral and bacterial infections: A machine learning model based on routine blood test values. Heliyon 2024; 10:e29372. [PMID: 38644832 PMCID: PMC11033127 DOI: 10.1016/j.heliyon.2024.e29372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 04/02/2024] [Accepted: 04/07/2024] [Indexed: 04/23/2024] Open
Abstract
The growing threat of antibiotic resistance necessitates accurate differentiation between bacterial and viral infections for proper antibiotic administration. In this study, a Virus vs. Bacteria machine learning model was developed to distinguish between these infection types using 16 routine blood test results, C-reactive protein concentration (CRP), biological sex, and age. With a dataset of 44,120 cases from a single medical center, the model achieved an accuracy of 82.2 %, a sensitivity of 79.7 %, a specificity of 84.5 %, a Brier score of 0.129, and an area under the ROC curve (AUC) of 0.905, outperforming a CRP-based decision rule. Notably, the machine learning model enhanced accuracy within the CRP range of 10-40 mg/L, a range where CRP alone is less informative. These results highlight the advantage of integrating multiple blood parameters in diagnostics. The "Virus vs. Bacteria" model paves the way for advanced diagnostic tools, leveraging machine learning to optimize infection management.
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Affiliation(s)
- Gregor Gunčar
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
- Faculty of Chemistry and Chemical Technology, University of Ljubljana, Slovenia
| | - Matjaž Kukar
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
- Faculty of Computer and Information Science, University of Ljubljana, Slovenia
| | - Tim Smole
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
| | - Sašo Moškon
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
| | - Tomaž Vovko
- Department of Infectious Diseases, University Medical Centre Ljubljana, Slovenia
| | - Simon Podnar
- Division of Neurology, University Medical Centre Ljubljana, Slovenia
| | - Peter Černelč
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
| | - Miran Brvar
- Centre for Clinical Toxicology and Pharmacology, University Medical Centre Ljubljana, Slovenia
| | - Mateja Notar
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
| | - Manca Köster
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
| | | | - Žiga Osterc
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
| | - Marko Notar
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
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Eshel YD, Sharaha U, Beck G, Cohen-Logasi G, Lapidot I, Huleihel M, Mordechai S, Kapelushnik J, Salman A. Monitoring the efficacy of antibiotic therapy in febrile pediatric oncology patients with bacteremia using infrared spectroscopy of white blood cells-based machine learning. Talanta 2024; 270:125619. [PMID: 38199122 DOI: 10.1016/j.talanta.2023.125619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 01/12/2024]
Abstract
Bacteremia refers to the presence of bacteria in the bloodstream, which can lead to a serious and potentially life-threatening condition. In oncology patients, individuals undergoing cancer treatment have a higher risk of developing bacteremia due to a weakened immune system resulting from the disease itself or the treatments they receive. Prompt and accurate detection of bacterial infections and monitoring the effectiveness of antibiotic therapy are essential for enhancing patient outcomes and preventing the development and dissemination of multidrug-resistant bacteria. Traditional methods of infection monitoring, such as blood cultures and clinical observations, are time-consuming, labor-intensive, and often subject to limitations. This manuscript presents an innovative application of infrared spectroscopy of leucocytes of pediatric oncology patients with bacteremia combined with machine learning to diagnose the etiology of infection as bacterial and simultaneously monitor the efficacy of the antibiotic therapy in febrile pediatric oncology patients with bacteremia infections. Through the implementation of effective monitoring, it becomes possible to promptly identify any indications of treatment failure. This, in turn, indirectly serves to limit the progression of antibiotic resistance. The logistic regression (LR) classifier was able to differentiate the samples as bacterial or control within an hour, after receiving the blood samples with a success rate of over 95 %. Additionally, initial findings indicate that employing infrared spectroscopy of white blood cells (WBCs) along with machine learning is viable for monitoring the success of antibiotic therapy. Our follow up results demonstrate an accuracy of 87.5 % in assessing the effectiveness of the antibiotic treatment.
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Affiliation(s)
- Yotam D Eshel
- Department of Hematology and Oncology, Saban Pediatric Medical Center Soroka University Medical Center and Faculty of Health Sciences, Beer-Sheva, 84105, Israel
| | - Uraib Sharaha
- Department of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel; Department of Biology, Science and Technology College, Hebron University, Hebron, P760, Palestine
| | - Guy Beck
- Department of Hematology and Oncology, Saban Pediatric Medical Center Soroka University Medical Center and Faculty of Health Sciences, Beer-Sheva, 84105, Israel
| | - Gal Cohen-Logasi
- Department of Green Engineering, SCE-Sami Shamoon College of Engineering, Beer-Sheva, 84100, Israel
| | - Itshak Lapidot
- Department of Electrical and Electronics Engineering, ACLP-Afeka Center for Language Processing, Afeka Tel-Aviv Academic College of Engineering, Tel-Aviv, 69107, Israel; LIA Avignon Université, 339 Chemin des Meinajaries, Avignon, 84000, France
| | - Mahmoud Huleihel
- Department of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel
| | - Shaul Mordechai
- Department of Physics, Ben-Gurion University, Beer-Sheva, 84105, Israel
| | - Joseph Kapelushnik
- Department of Hematology and Oncology, Saban Pediatric Medical Center Soroka University Medical Center and Faculty of Health Sciences, Beer-Sheva, 84105, Israel
| | - Ahmad Salman
- Department of Physics, SCE-Sami Shamoon College of Engineering, Beer-Sheva, 84100, Israel.
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Barroso TG, Queirós C, Monteiro-Silva F, Santos F, Gregório AH, Martins RC. Reagentless Vis-NIR Spectroscopy Point-of-Care for Feline Total White Blood Cell Counts. BIOSENSORS 2024; 14:53. [PMID: 38275306 PMCID: PMC11154536 DOI: 10.3390/bios14010053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/16/2024] [Accepted: 01/18/2024] [Indexed: 01/27/2024]
Abstract
Spectral point-of-care technology is reagentless with minimal sampling (<10 μL) and can be performed in real-time. White blood cells are non-dominant in blood and in spectral information, suffering significant interferences from dominant constituents such as red blood cells, hemoglobin and billirubin. White blood cells of a bigger size can account for 0.5% to 22.5% of blood spectra information. Knowledge expansion was performed using data augmentation through the hybridization of 94 real-world blood samples into 300 synthetic data samples. Synthetic data samples are representative of real-world data, expanding the detailed spectral information through sample hybridization, allowing us to unscramble the spectral white blood cell information from spectra, with correlations of 0.7975 to 0.8397 and a mean absolute error of 32.25% to 34.13%; furthermore, we achieved a diagnostic efficiency between 83% and 100% inside the reference interval (5.5 to 19.5 × 109 cell/L), and 85.11% for cases with extreme high white blood cell counts. At the covariance mode level, white blood cells are quantified using orthogonal information on red blood cells, maximizing sensitivity and specificity towards white blood cells, and avoiding the use of non-specific natural correlations present in the dataset; thus, the specifity of white blood cells spectral information is increased. The presented research is a step towards high-specificity, reagentless, miniaturized spectral point-of-care hematology technology for Veterinary Medicine.
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Affiliation(s)
- Teresa Guerra Barroso
- 1H-TOXRUN—One Health Toxicology Research Unit, University Institute of Health Sciences (IUCS), CESPU, CRL, 4585-116 Gandra, Portugal;
| | - Carla Queirós
- LAQV-REQUIMTE, Faculty of Sciences, University of Porto, R. Campo Alegre, 4169-007 Porto, Portugal;
| | - Filipe Monteiro-Silva
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science—Campus da FEUP, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal; (F.M.-S.); (F.S.)
| | - Filipe Santos
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science—Campus da FEUP, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal; (F.M.-S.); (F.S.)
| | - António Hugo Gregório
- Anicura CHV—Veterinary Hospital Center, R. Manuel Pinto de Azevedo 118, 4100-320 Porto, Portugal;
| | - Rui Costa Martins
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science—Campus da FEUP, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal; (F.M.-S.); (F.S.)
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Monaghan JF, Cullen D, Wynne C, Lyng FM, Meade AD. Effect of pre-analytical variables on Raman and FTIR spectral content of lymphocytes. Analyst 2023; 148:5422-5434. [PMID: 37750362 DOI: 10.1039/d3an00686g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
The use of Fourier transform infrared (FTIR) and Raman spectroscopy (RS) for the analysis of lymphocytes in clinical applications is increasing in the field of biomedicine. The pre-analytical phase, which is the most vulnerable stage of the testing process, is where most errors and sample variance occur; however, it is unclear how pre-analytical variables affect the FTIR and Raman spectra of lymphocytes. In this study, we evaluated how pre-analytical procedures undertaken before spectroscopic analysis influence the spectral integrity of lymphocytes purified from the peripheral blood of male volunteers (n = 3). Pre-analytical variables investigated were associated with (i) sample preparation, (blood collection systems, anticoagulant, needle gauges), (ii) sample storage (fresh or frozen), and (iii) sample processing (inter-operator variability, time to lymphocyte isolation). Although many of these procedural pre-analytical variables did not alter the spectral signature of the lymphocytes, evidence of spectral effects due to the freeze-thaw cycle, in vitro culture inter-operator variability and the time to lymphocyte isolation was observed. Although FTIR and RS possess clinical potential, their translation into a clinical environment is impeded by a lack of standardisation and harmonisation of protocols related to the preparation, storage, and processing of samples, which hinders uniform, accurate, and reproducible analysis. Therefore, further development of protocols is required to successfully integrate these techniques into current clinical workflows.
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Affiliation(s)
- Jade F Monaghan
- School of Physics, Clinical and Optometric Sciences, Technological University Dublin, Central Quad, City Campus, Grangegorman, D07 XT95, Ireland.
- Radiation and Environmental Science Centre, Focas Research Institute, Technological University Dublin, Aungier Street, D02 HW71, Ireland
| | - Daniel Cullen
- School of Physics, Clinical and Optometric Sciences, Technological University Dublin, Central Quad, City Campus, Grangegorman, D07 XT95, Ireland.
- Radiation and Environmental Science Centre, Focas Research Institute, Technological University Dublin, Aungier Street, D02 HW71, Ireland
| | - Claire Wynne
- School of Biological, Health and Sports Sciences, Technological University Dublin, Central Quad, City Campus, Grangegorman, D07 XT95, Ireland
| | - Fiona M Lyng
- School of Physics, Clinical and Optometric Sciences, Technological University Dublin, Central Quad, City Campus, Grangegorman, D07 XT95, Ireland.
- Radiation and Environmental Science Centre, Focas Research Institute, Technological University Dublin, Aungier Street, D02 HW71, Ireland
| | - Aidan D Meade
- School of Physics, Clinical and Optometric Sciences, Technological University Dublin, Central Quad, City Campus, Grangegorman, D07 XT95, Ireland.
- Radiation and Environmental Science Centre, Focas Research Institute, Technological University Dublin, Aungier Street, D02 HW71, Ireland
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Martin FL. Translating Biospectroscopy Techniques to Clinical Settings: A New Paradigm in Point-of-Care Screening and/or Diagnostics. J Pers Med 2023; 13:1511. [PMID: 37888122 PMCID: PMC10608143 DOI: 10.3390/jpm13101511] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/11/2023] [Accepted: 10/18/2023] [Indexed: 10/28/2023] Open
Abstract
As healthcare tools increasingly move towards a more digital and computational format, there is an increasing need for sensor-based technologies that allow for rapid screening and/or diagnostics [...].
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Affiliation(s)
- Francis L Martin
- Department of Cellular Pathology, Blackpool Teaching Hospitals NHS Foundation Trust, Whinney Heys Road, Blackpool FY3 8NR, UK
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Fadlelmoula A, Catarino SO, Minas G, Carvalho V. A Review of Machine Learning Methods Recently Applied to FTIR Spectroscopy Data for the Analysis of Human Blood Cells. MICROMACHINES 2023; 14:1145. [PMID: 37374730 DOI: 10.3390/mi14061145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/23/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023]
Abstract
Machine learning (ML) is a broad term encompassing several methods that allow us to learn from data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision-making. This paper presents a review of articles that discuss the use of Fourier transform infrared (FTIR) spectroscopy and ML for human blood analysis between the years 2019-2023. The literature review was conducted to identify published research of employed ML linked with FTIR for distinction between pathological and healthy human blood cells. The articles' search strategy was implemented and studies meeting the eligibility criteria were evaluated. Relevant data related to the study design, statistical methods, and strengths and limitations were identified. A total of 39 publications in the last 5 years (2019-2023) were identified and evaluated for this review. Diverse methods, statistical packages, and approaches were used across the identified studies. The most common methods included support vector machine (SVM) and principal component analysis (PCA) approaches. Most studies applied internal validation and employed more than one algorithm, while only four studies applied one ML algorithm to the data. A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of ML methods. There is a need to ensure that multiple ML approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that the discrimination of human blood cells is being made with the highest efficient evidence.
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Affiliation(s)
- Ahmed Fadlelmoula
- Center for Microelectromechanical Systems (CMEMS-UMinho), Campus de Azurém, University of Minho, 4800-058 Guimarães, Portugal
- LABBELS-Associate Laboratory, 4800-058 Guimarães, Portugal
| | - Susana O Catarino
- Center for Microelectromechanical Systems (CMEMS-UMinho), Campus de Azurém, University of Minho, 4800-058 Guimarães, Portugal
- LABBELS-Associate Laboratory, 4800-058 Guimarães, Portugal
| | - Graça Minas
- Center for Microelectromechanical Systems (CMEMS-UMinho), Campus de Azurém, University of Minho, 4800-058 Guimarães, Portugal
- LABBELS-Associate Laboratory, 4800-058 Guimarães, Portugal
| | - Vítor Carvalho
- 2Ai, School of Technology, IPCA, 4750-810 Barcelos, Portugal
- Algoritmi Research Center/LASI, University of Minho, 4800-058 Guimarães, Portugal
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Ha Y, Du Z, Tian J. Fine-grained interactive attention learning for semi-supervised white blood cell classification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103611] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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