Quick Detection of <i>Proteus</i> and <i>Pseudomonas</i> in Patients’ Urine and Assessing Their Antibiotic Susceptibility Using Infrared Spectroscopy and Machine Learning
Bacterial resistance to antibiotics is a primary global healthcare concern as it hampers the effectiveness of commonly used antibiotics used to treat infectious diseases. The development of bacterial resistance continues to escalate over time. Rapid identification of the infecting bacterium and dete...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/19/8132 |
| _version_ | 1797575155782778880 |
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| author | George Abu-Aqil Itshak Lapidot Ahmad Salman Mahmoud Huleihel |
| author_facet | George Abu-Aqil Itshak Lapidot Ahmad Salman Mahmoud Huleihel |
| author_sort | George Abu-Aqil |
| collection | DOAJ |
| description | Bacterial resistance to antibiotics is a primary global healthcare concern as it hampers the effectiveness of commonly used antibiotics used to treat infectious diseases. The development of bacterial resistance continues to escalate over time. Rapid identification of the infecting bacterium and determination of its antibiotic susceptibility are crucial for optimal treatment and can save lives in many cases. Classical methods for determining bacterial susceptibility take at least 48 h, leading physicians to resort to empirical antibiotic treatment based on their experience. This random and excessive use of antibiotics is one of the most significant drivers of the development of multidrug-resistant (MDR) bacteria, posing a severe threat to global healthcare. To address these challenges, considerable efforts are underway to reduce the testing time of taxonomic classification of the infecting bacterium at the species level and its antibiotic susceptibility determination. Infrared spectroscopy is considered a rapid and reliable method for detecting minor molecular changes in cells. Thus, the main goal of this study was the use of infrared spectroscopy to shorten the identification and the susceptibility testing time of <i>Proteus mirabilis</i> and <i>Pseudomonas aeruginosa</i> from 48 h to approximately 40 min, directly from patients’ urine samples. It was possible to identify the <i>Proteus mirabilis</i> and <i>Pseudomonas aeruginosa</i> species with 99% accuracy and, simultaneously, to determine their susceptibility to different antibiotics with an accuracy exceeding 80%. |
| first_indexed | 2024-03-10T21:35:43Z |
| format | Article |
| id | doaj.art-71e961c539f8458cadc2c3d0d6672cee |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T21:35:43Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-71e961c539f8458cadc2c3d0d6672cee2023-11-19T15:03:11ZengMDPI AGSensors1424-82202023-09-012319813210.3390/s23198132Quick Detection of <i>Proteus</i> and <i>Pseudomonas</i> in Patients’ Urine and Assessing Their Antibiotic Susceptibility Using Infrared Spectroscopy and Machine LearningGeorge Abu-Aqil0Itshak Lapidot1Ahmad Salman2Mahmoud Huleihel3Department of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, IsraelDepartment of Electrical Engineering, ACLP-Afeka Center for Language Processing, Afeka Tel-Aviv Academic College of Engineering, Tel-Aviv 69107, IsraelDepartment of Physics, SCE-Shamoon College of Engineering, Beer-Sheva 84100, IsraelDepartment of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, IsraelBacterial resistance to antibiotics is a primary global healthcare concern as it hampers the effectiveness of commonly used antibiotics used to treat infectious diseases. The development of bacterial resistance continues to escalate over time. Rapid identification of the infecting bacterium and determination of its antibiotic susceptibility are crucial for optimal treatment and can save lives in many cases. Classical methods for determining bacterial susceptibility take at least 48 h, leading physicians to resort to empirical antibiotic treatment based on their experience. This random and excessive use of antibiotics is one of the most significant drivers of the development of multidrug-resistant (MDR) bacteria, posing a severe threat to global healthcare. To address these challenges, considerable efforts are underway to reduce the testing time of taxonomic classification of the infecting bacterium at the species level and its antibiotic susceptibility determination. Infrared spectroscopy is considered a rapid and reliable method for detecting minor molecular changes in cells. Thus, the main goal of this study was the use of infrared spectroscopy to shorten the identification and the susceptibility testing time of <i>Proteus mirabilis</i> and <i>Pseudomonas aeruginosa</i> from 48 h to approximately 40 min, directly from patients’ urine samples. It was possible to identify the <i>Proteus mirabilis</i> and <i>Pseudomonas aeruginosa</i> species with 99% accuracy and, simultaneously, to determine their susceptibility to different antibiotics with an accuracy exceeding 80%.https://www.mdpi.com/1424-8220/23/19/8132urinary tract infection (UTI)<i>Proteus mirabilis</i><i>Pseudomonas aeruginosa</i>bacterial resistanceinfrared spectroscopymachine learning |
| spellingShingle | George Abu-Aqil Itshak Lapidot Ahmad Salman Mahmoud Huleihel Quick Detection of <i>Proteus</i> and <i>Pseudomonas</i> in Patients’ Urine and Assessing Their Antibiotic Susceptibility Using Infrared Spectroscopy and Machine Learning Sensors urinary tract infection (UTI) <i>Proteus mirabilis</i> <i>Pseudomonas aeruginosa</i> bacterial resistance infrared spectroscopy machine learning |
| title | Quick Detection of <i>Proteus</i> and <i>Pseudomonas</i> in Patients’ Urine and Assessing Their Antibiotic Susceptibility Using Infrared Spectroscopy and Machine Learning |
| title_full | Quick Detection of <i>Proteus</i> and <i>Pseudomonas</i> in Patients’ Urine and Assessing Their Antibiotic Susceptibility Using Infrared Spectroscopy and Machine Learning |
| title_fullStr | Quick Detection of <i>Proteus</i> and <i>Pseudomonas</i> in Patients’ Urine and Assessing Their Antibiotic Susceptibility Using Infrared Spectroscopy and Machine Learning |
| title_full_unstemmed | Quick Detection of <i>Proteus</i> and <i>Pseudomonas</i> in Patients’ Urine and Assessing Their Antibiotic Susceptibility Using Infrared Spectroscopy and Machine Learning |
| title_short | Quick Detection of <i>Proteus</i> and <i>Pseudomonas</i> in Patients’ Urine and Assessing Their Antibiotic Susceptibility Using Infrared Spectroscopy and Machine Learning |
| title_sort | quick detection of i proteus i and i pseudomonas i in patients urine and assessing their antibiotic susceptibility using infrared spectroscopy and machine learning |
| topic | urinary tract infection (UTI) <i>Proteus mirabilis</i> <i>Pseudomonas aeruginosa</i> bacterial resistance infrared spectroscopy machine learning |
| url | https://www.mdpi.com/1424-8220/23/19/8132 |
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