Artificial Intelligence, Microbiology, and Raman Technologies

Abstract

Artificial intelligence which became important in the laboratory is used in medical microbiology in infectious disease testing to support decision-making, identification and antimicrobial susceptibility testing with Raman technologies, image analysis, and MALDI-TOF-MS. Antimicrobial resistance is a worldwide risk for human health. Treatment of infections requires fast and correct identification and antimicrobial susceptibility testing. Current microbiology laboratory procedures give broad information in identification and antimicrobial susceptibility testing, however, they are complex and time-consuming. Thus, new methods are required such as Raman technologies. Vibrational spectroscopy method Raman spectroscopy is one of the useful and new tools that is used in different fields of medicine. Recently, fast and accurate Raman technologies used identification, differentiation of resistant and sensitive strains, and antimicrobial susceptibility testing became important in microbiology. Raman technologies include various kinds of methods. Raman spectroscopy can implement identification, and antibiotic susceptibility together with increased accuracy. It is a cheap, label-free, and effective method that differentiates bacterial infections. Besides bacteria, it is also used in rapid and sensitive virus detection such as COVID-19 by using saliva. When PCR is used in COVID-19 detection, as the variants increase sensitivity decreases. Raman technology overcomes this problem. This review summarizes the applications, challenges, and future of Raman technologies in microbiology to improve the treatment of infectious diseases and improve human health.

Keywords

• artificial intelligence
• identification
• Raman technologies
• antimicrobial susceptibility testing

References

1. [1] Agrebia, S., Larbib, A. (2020). Use of artificial intelligence in infectious diseases, Artificial Intelligence in Precision Health. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7153335/415–438, doi: 10.1016/B978-0-12-817133-2.00018-5
2. [2] Smith, P., Wang, H., Durant, T., Mathison, B.A., Sharp, S., Kirby, J.E., Long, S.W., Rhoads, D.D., Israel, B. (2020). Applications of Artificial Intelligence in Clinical Microbiology Diagnostic Testing., CMN. Vol. 42, No. 8.
3. [3] Behera, B., Anil Vishnu, G.K., Chatterjee, S., Sitaramgupta, V.V.S.N., Sreekumar, N., Nagabhushan, A., Rajendran, N., Prathik, B.H., Pandya, H.J. (2019). Emerging technologies for antibiotic susceptibility testing. Biosens. Bioelectron., 142, 111552
4. [4] Florio, W., Morici, P., Ghelardi, E., Barnini, S., Lupetti, A. (2018). Recent advances in the microbiological diagnosis of bloodstream infections. Crit. Rev. Microbiol., 44, 351–370.
5. [5] Van Belkum, A., Burnham, C.A.D., Rossen, J.W.A., Mallard, F., Rochas, O., Dunne, W.M. (2020). Innovative and rapid antimicrobial susceptibility testing systems. Nat. Rev. Microbiol., 18, 299–311.
6. [6] Van Belkum, A., Bachmann, T.T., Lüdke, G., Lisby, J.G., Kahlmeter, G., Mohess, A., Becker, K., Hays, J.P., Woodford, N., Mitsakakis, K., et al. (2019). Developmental roadmap for antimicrobial susceptibility testing systems. Nat. Rev. Microbiol., 17, 51–62.
7. [7] Dietvorst, J., Vilaplana, L., Uria, N., Marco, M.-P., Muñoz-Berbel, X. (2020). Current and near-future technologies for antibiotic susceptibility testing and resistant bacteria detection. TrAC Trends Anal. Chem., 127, 115891.
8. [8] Syal, K., Mo, M., Yu, H., Iriya, R., Jing, W., Guodong, S., Wang, S., Grys, T.E., Haydel, S.E., Tao, N. (2017). Current and emerging techniques for antibiotic susceptibility tests. Theranostics 7, 1795–1805.

9. [9] Li, Y., Yang, X., Zhao, W. (2017). Emerging Microtechnologies and Automated Systems for Rapid Bacterial Identification and Antibiotic Susceptibility Testing. Transl. Life Sci. Innov., 22, 585–608.
10. [10] Jayan, H., Pu, H., Sun, D.-W. (2021). Recent developments in Raman spectral analysis of microbial single cells: Techniques and applications. Crit. Rev. Food Sci. Nutr., 61, 2623–2639.
11. [11] Lee, K.S., Landry, Z., Pereira, F.C., Wagner, M., Berry, D., Huang, W.E., Taylor, G.T., Kneipp, J., Popp, J., Zhang, M. et al. (2021). Raman microspectroscopy for microbiology. Nat. Rev. Methods Primers, 1, 80.
12. [12] Ivleva, N.P., Kubryk, P., Niessner, R. (2017). Raman microspectroscopy, surface-enhanced Raman scattering microspectroscopy, and stable-isotope Raman microspectroscopy for biofilm characterization. Anal. Bioanal. Chem., 409, 4353–4375.
13. [13] Ho, C.-S., Jean, N., Hogan, C.A., Blackmon, L., Jeffrey, S.S., Holodniy, M., Banaei, N., Saleh, A.A.E., Ermon, S., Dionne, J. (2019). Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning. Nat. Commun., 10, 4927.
14. [14] Li, J., Wang, C., Shi, L., Shao, L., Fu, P., Wang, K., Xiao, R., Wang, S., Gu, B. (2019). Rapid identification and antibiotic susceptibility test of pathogens in blood-based on magnetic separation and surface-enhanced Raman scattering. Microchim. Acta, 186, 475.
15. [15] Dina, N.E., Zhou, H., Colni¸tă, A., Leopold, N., Szoke-Nagy, T., Coman, C., Haisch, C. (2017). Rapid single-cell detection and identification of pathogens by using surface-enhanced Raman spectroscopy. Analyst, 142, 1782–1789.
16. [16] Wang, Y., Huang, W.E., Cui, L., Wagner, M. (2016). Single-cell stable isotope probing in microbiology using Raman microspectroscopy. Curr. Opin. Biotechnol., 41, 34–42.
17. [17] Liu, Y., Xu, J., Tao, Y., Fang, T., Du, W., Ye, A. (2020). Rapid and accurate identification of marine microbes with single-cell Raman spectroscopy. Analyst, 145, 3297–3305. [CrossRef]
18. [18] Hong, W., Karanja, C.W., Abutaleb, N.S., Younis, W., Zhang, X., Seleem, M.N., Cheng, J.-X. (2018). Antibiotic Susceptibility Determination within One Cell Cycle at Single-Bacterium Level by Stimulated Raman Metabolic Imaging. Anal. Chem., 90, 3737–3743.
19. [19] Tao, Y., Wang, Y., Huang, S., Zhu, P, Huang, W.E., Ling, J., Xu, J. (2017). Metabolic-Activity-Based Assessment of Antimicrobial Effects by D2O-Labeled Single-Cell Raman Microspectroscopy. Anal. Chem., 89, 4108–4115.
20. [20] Zhang, M., Hong, W., Abutaleb, N.S., Li, J., Dong, P.-T., Zong, C., Wang, P., Seleem, M.N., Cheng, J.-X. (2020). Rapid Determination of Antimicrobial Susceptibility by Stimulated Raman Scattering Imaging of D2O Metabolic Incorporation in a Single Bacterium. Adv. Sci., 7, 2001452.
21. [21] Michael, R.J., Jordan, D.C., Daniel, D.R. (2021). Recent advances in rapid antimicrobial susceptibility testing systems. Expert Rev. Mol. Diagn., 21, 563–578.
22. [22] Kasas, S., Malovichko, A.,Villalba, M.I., Vela, M.E., Yantorno, O., Willaert, R.G. (2021). Nanomotion Detection-Based Rapid Antibiotic Susceptibility Testing. Antibiotics, 10, 287.
23. [23] Chen, C., Hong, W. (2021). Recent Development of Rapid Antimicrobial Susceptibility Testing Methods through Metabolic Profiling of Bacteria. Antibiotics, 10, 311.
24. [24] Winstanley, T., Courvalin, P. (2011). Expert systems in clinical microbiology. Clin Microbiol Rev., 24:515-56
25. [25] Garcia, E., Kundu, I., Ali, A., Soles, R. (2018). The American Society for Clinical Pathology’s 2016-2017 Vacancy Survey of Medical Laboratories in the United States. Am J Clin Pathol., 149:387-400.
26. [26] Smith, K.P., Kang, A.D., Kirby, J.E. (2018). Automated interpretation of blood culture gram stains by use of a deep convolutional neural network. J Clin Microbiol., 56.
27. [27] Glasson, J., Hill, R., Summerford, M., Olden, D., Papadopoulos, F., Young, S., et al. (2017). Multicenter evaluation of an image analysis device (APAS): Comparison between digital image and traditional plate reading using urine cultures. Ann Lab Med., 37:499-504.
28. [28] Faron, M.L., Buchan, B.W., Samra, H., Ledeboer, N.A. (2019). Evaluation of the WASPLab software to automatically read CHROMID CPS Elite Agar for reporting of urine cultures. J Clin Microbiol.
29. [29] Van, T.T., Mata, K., Dien Bard, J. (2019). Automated Detection of Streptococcus pyogenes Pharyngitis by use of colorex Strep A CHROMagar and WASPLab artificial intelligence chromogenic detection module software. J Clin Microbiol., 57.
30. [30] Croxatto, A., Marcelpoil, R., Orny, C., Morel, D., Prod’hom, G., Greub, G. (2017). Towards automated detection, semi-quantification and identification of microbial growth in clinical bacteriology: A proof of concept. Biomed J., 40:317-28.
31. [31] Faron, M.L., Buchan, B.W., Relich, R.F., Clark, J., Ledeboer, N.A. (2020). Evaluation of the WASPLab segregation software to automatically analyze urine cultures using routine blood and MacConkey agars. J Clin Microbiol.
32. [32] Florio, W., Tavanti, A., Barnini, S., Ghelardi, E., Lupetti, A. (2018). Recent advances and ongoing challenges in the diagnosis of microbial infections by MALDI-TOF mass spectrometry. Front Microbiol.,9:1097.
33. [33] Datta, S. (2013). Chapter.10: Feature selection and machine learning with mass spectrometry data. In: Matthiesen, ed. Mass Spectrometry Data Analysis in Proteomics, 2nd ed: Springer;
34. [34] Wang, H.Y., Chen, C.H., Lee, T.Y., Horng, J.T., Liu, T.P., Tseng, Y.J., et al. (2018). Rapid detection of heterogeneous vancomycin-intermediate Staphylococcus aureus based on matrix-assisted laser desorption ionization time-of-flight: Using a machine learning approach and unbiased validation. Front Microbiol.,9:2393.
35. [35] Mather, C.A., Werth, B.J., Sivagnanam S, SenGupta DJ, Butler-Wu SM. Rapid detection of vancomycin-intermediate Staphylococcus aureus by matrix-assisted laser desorption ionization-time of flight mass spectrometry. J Clin Microbiol 2016;54:883-90.
36. [36] Lasch, P., Fleige, C., Stammler, M., Layer, F., Nubel, U., Witte, W., et al. (2014). Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates. J Microbiol Methods, 100:58-69.
37. [37] Lau, A.F., Walchak, R.C., Miller, H.B., Slechta, E.S., Kamboj, K., Riebe, K. et al. (2019). Multicenter study demonstrates standardization requirements for mold identification by MALDI-TOF MS. Front Microbiol., 10:2098.
38. [38] Long, S.W., Olsen, R.J., Eagar, T.N., Beres, S.B., Zhao P, Davis JJ, et al. (2017). Population genomic analysis of 1,777 extended-spectrum beta-lactamase-producing Klebsiella pneumoniae isolates, Houston, Texas: Unexpected abundance of clonal group 307. MBio.,8
39. [39] Nguyen, M., Long, S.W., McDermott, P.F., Olsen, R.J., Olson, R., Stevens, R.L. et al. (2019). Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella. J Clin Microbiol 2019;57:e01260-18.
40. [40] Zhang, W., He, S., Hong, W., and Wang, P. (2022). A Review of Raman-Based Technologies for Bacterial Identification and Antimicrobial Susceptibility Testing. Photonics, 9(3), 133.
41. [41] Ivleva, N.P., Kubryk, P., Niessner, R. (2017). Raman microspectroscopy, surface-enhanced Raman scattering microspectroscopy, and stable-isotope Raman microspectroscopy for biofilm characterization. Anal. Bioanal. Chem., 409, 4353–4375.
42. [42] Ho, C.-S., Jean, N., Hogan, C.A., Blackmon, L., Jeffrey, S.S., Holodniy, M, Banaei, N., Saleh, A.A.E., Ermon, S., Dionne, J. (2019). Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning. Nat. Commun., 10, 4927.
43. [43] Jayan, H., Pu, H., Sun, D.-W. (2021). Recent developments in Raman spectral analysis of microbial single cells: Techniques and applications. Crit. Rev. Food Sci. Nutr., 61, 2623–2639.
44. [44] Awad, F., Wichmann, C., Rösch, P., Popp, J. (2018). Raman spectroscopy for the characterization of antimicrobial photodynamic therapy against Staphylococcus epidermidis. J. Raman Spectrosc., 49, 1907–1910.
45. [45] Rebrošová, K., Bernatová, S., Šiler, M., Uhlirova, M., Samek, O., Ježek, J., Holá, V., R ˚užiˇcka, F., Zemanek, P. (2021). Raman spectroscopy— A tool for rapid differentiation among microbes causing urinary tract infections. Anal. Chim. Acta, 1191, 339292.
46. [46] Weng, S., Hu, X., Wang, J., Tang, L., Li, P., Zheng, S., Zheng, L., Huang, L., Xin, Z. (2021). Advanced Application of Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy in Plant Disease Diagnostics: A Review. J. Agric. Food Chem., 69, 2950–2964.
47. [47] Kloß, S., Kampe, B., Sachse, S., Rösch, P., Straube, E., Pfister, W., Kiehntopf, M., Popp, J. (2013). Culture Independent Raman Spectroscopic Identification of Urinary Tract Infection Pathogens: A Proof of Principle Study. Anal. Chem., 85, 9610–9616.
48. [48] Rebrošová, K., Šiler, M., Samek, O., R ˚užiˇcka, F., Bernatová, S., Holá, V., Ježek, J., Zemánek, P., Sokolová, J., Petráš, P. (2017). Rapid identification of staphylococci by Raman spectroscopy. Sci. Rep., 7, 14846. [CrossRef]
49. [49] Yan, S., Wang, S., Qiu, J., Li, M., Li, D., Xu, D., Li, D., Liu, Q. (2021). Raman spectroscopy combined with machine learning for rapid detection of food-borne pathogens at the single-cell level. Talanta, 226, 122195.
50. [50] Galvan, D.D., Yu, Q. (2018). Surface-Enhanced Raman Scattering for Rapid Detection and Characterization of Antibiotic-Resistant Bacteria. Adv. Healthc. Mater., 7, 1701335.
51. [51] Dina, N.E., Zhou, H., Colni¸tă, A., Leopold, N., Szoke-Nagy, T., Coman, C., Haisch, C. (2017). Rapid single-cell detection and identification of pathogens by using surface-enhanced Raman spectroscopy. Analyst, 142, 1782–1789.
52. [52] Zhao, X., Li, M., Xu, Z. (2018). Detection of Foodborne Pathogens by Surface Enhanced Raman Spectroscopy. Front. Microbiol., 9, 1236.
53. [53] Jin, L., Wang, S., Shao, Q.İ., Cheng, Y. (2022). A rapid and facile analytical approach to detecting Salmonella Enteritidis with aptamer-based surface-enhanced Raman spectroscopy. Spectrochim. Acta Part A Mol. Biomol. Spectrosc., 267, 120625.
54. [54] Bashir, S., Nawaz, H., Irfan Majeed, M., Mohsin, M., Nawaz, A., Rashid, N., Batool, F., Akbar, S., Abubakar, M., Ahmad, S. et al. (2021). Surface-enhanced Raman spectroscopy for the identification of tigecycline-resistant E. coli strains. Spectrochim. Acta Part A Mol. Biomol. Spectrosc., 258, 119831.
55. [55] Liu, S., Hu, Q., Li, C., Zhang, F., Gu, H., Wang, X., Li, S., Xue, L., Madl, T., Zhang, Y. et al. (2021). Wide-Range, Rapid, and Specific Identification of Pathogenic Bacteria by Surface-Enhanced Raman Spectroscopy. ACS Sensors, 6, 2911–2919. [CrossRef] [PubMed]
56. [56] Dina, E.N., Colnita, A., Marconi, D. and Gherman, A.M.R. (2020). Microfluidic Portable Device for Pathogens Rapid SERS Detection. Proceedings. 60, 2.
57. [57] Fang, T., Shang, W., Liu, C., Xu, J., Zhao, D., Liu, Y., Ye, A. (2019). Nondestructive Identification and Accurate Isolation of Single Cells through a Chip with Raman Optical Tweezers. Anal. Chem., 91, 9932–9939.
58. [58] Lee, K.S., Palatinszky, M., Pereira, F.C., Nguyen, J., Fernandez, V.I., Mueller, A.J., Menolascina, F., Daims, H., Berry, D., Wagner, M. et al. (2019). An automated Raman-based platform for the sorting of live cells by functional properties. Nat. Microbiol., 4, 1035–1048.
59. [59] Xie, C., Mace, J., Dinno, M.A., Li, Y.Q., Tang, W., Newton, R.J., Gemperline, P.J. (2005). Identification of Single Bacterial Cells in Aqueous Solution Using Confocal Laser Tweezers Raman Spectroscopy. Anal. Chem., 77, 4390–4397.
60. [60] Lu, W., Chen, X., Wang, L., Li, H., Fu, Y.V. (2020). Combination of an Artificial Intelligence Approach and Laser Tweezers Raman Spectroscopy for Microbial Identification. Anal. Chem., 92, 6288–6296.
61. [61] Zhang, C., Zhang, D., Cheng, J.X. (2015). Coherent Raman Scattering Microscopy in Biology and Medicine. Annu. Rev. Biomed. Eng., 17, 415–445.
62. [62] Hong, W., Liao, C.-S., Zhao, H., Younis, W., Zhang, Y., Seleem, M.N., Cheng, J.-X. (2016). In situ Detection of a Single Bacterium in Complex Environment by Hyperspectral CARS Imaging. ChemistrySelect, 1, 513–517.
63. [63] Arora, R., Petrov, G.I., Yakovlev, V.V., Scully, M.O. (2012). Detecting anthrax in the mail by coherent Raman microspectroscopy. Proc. Natl. Acad. Sci. USA, 109, 1151.
64. [64] Zhang, C., Aldana-Mendoza, J.A. (2021). Coherent Raman scattering microscopy for chemical imaging of biological systems. J. Phys. Photonics, 3, 032002.
65. [65] Cheng, S., Tu, Z., Zheng, S., Cheng, X., Han, H., Wang, C., Xiao, R., Gu, B. (2021). An efficient SERS platform for the ultrasensitive detection of Staphylococcus aureus and Listeria monocytogenes via wheat germ agglutinin-modified magnetic SERS substrate and streptavidin/aptamer co-functionalized SERS tags. Anal. Chim. Acta, 1187, 339155.
66. [66] Karanja, C.W., Hong, W., Younis, W., Eldesouky, H.E., Seleem, M.N., Cheng, J.-X. (2017). Stimulated Raman Imaging Reveals Aberrant Lipogenesis as a Metabolic Marker for Azole-Resistant Candida albicans. Anal. Chem., 89, 9822–9829.
67. [67] Yang, K., Li, H.-Z., Zhu, X., Su, J.-Q.,Ren, B., Zhu, Y.-G., Cui, L. (2019). Rapid Antibiotic Susceptibility Testing of Pathogenic Bacteria Using Heavy-Water-Labeled Single-Cell Raman Spectroscopy in Clinical Samples. Anal. Chem., 91, 6296–6303.
68. [68] Han, Y.-Y., Lin, Y.-C., Cheng, W.-C., Lin, Y.-T., Teng, L.-J., Wang, J.-K., Wang, Y.-L. (2020). Rapid antibiotic susceptibility testing of bacteria from patients’ blood via assaying bacterial metabolic response with surface-enhanced Raman spectroscopy. Sci. Rep., 10, 12538.
69. [69] Novelli-Rousseau, A., Espagnon, I., Filiputti, D., Gal, O., Douet, A., Mallard, F., Josso, Q. (2018). Culture-free Antibiotic-susceptibility Determination From Single-bacterium Raman Spectra. Sci. Rep., 8, 3957.
70. [70] Germond, A., Ichimura, T., Horinouchi, T., Fujita, H., Furusawa, C., Watanabe, T.M. (2018). Raman spectral signature reflects transcriptomic features of antibiotic resistance in Escherichia coli. Commun. Biol., 1, 85.
71. [71] Rousseau, A.N., Faure, N., Rol, F., Sedaghat, Z., Le Galudec, J., Mallard, F., Josso, Q. (2021). Fast Antibiotic Susceptibility Testing via Raman Microspectrometry on Single Bacteria: An MRSA Case Study. ACS Omega, 6, 16273–16279.
72. [72] Moritz, T.J., Polage, C.R., Taylor, D.S., Krol, D.M., Lane, S.M., Chan, J.W. (2010). Evaluation of Escherichia coli cell response to antibiotic treatment by use of Raman spectroscopy with laser tweezers. J. Clin. Microbiol., 48, 4287–4290.
73. [73] Wang, Y., Xu, J., Kong, L., Liu, T., Yi, L., Wang, H., Huang, W.E., Zheng, C. (2020). Raman-deuterium isotope probing to study metabolic activities of single bacterial cells in human intestinal microbiota. Microb. Biotechnol., 13, 572–583.
74. [74] Yi, X.; Song, Y., Xu, X., Peng, D., Wang, J., Qie, X., Lin, K., Yu, M., Ge, M., Wang, Y. et al. (2021). Development of a Fast Raman-Assisted Antibiotic Susceptibility Test (FRAST) for the Antibiotic Resistance Analysis of Clinical Urine and Blood Samples. Anal. Chem., 93, 5098–5106.
75. [75] Zhou, X., Hu, Z., Yang, D., Xie, S., Jiang, Z., Niessner, R., Haisch, C., Zhou, H., Sun, P. (2020). Bacteria Detection: From Powerful SERS to Its Advanced Compatible Techniques. Adv. Sci., 7, 2001739. [CrossRef]
76. [76] Kim, H., Kim, Y., Han, B., Jang, J.-Y., Kim, Y. (2019). Clinically Applicable Deep Learning Algorithm Using Quantitative Proteomic Data. J. Proteome Res., 18, 3195–3202. [77] Fu, S., Wang, X., Wang, T., Li, Z., Han, D., Yu, C., Yang, C., Qu, H., Chi, H., Wang, Y. et al. (2020). A sensitive and rapid bacterial antibiotic susceptibility test method by surface enhanced Raman spectroscopy. Braz. J. Microbiol., 51, 875–881.
77. [78] Thrift, W.J., Ronaghi, S., Samad, M., Wei, H., Nguyen, D.G., Cabuslay, A.S., Groome, C.E., Santiago, P.J., Baldi, P., Hochbaum, A.I., et al. (2020). Deep Learning Analysis of Vibrational Spectra of Bacterial Lysate for Rapid Antimicrobial Susceptibility Testing. ACS Nano, 14, 15336–15348.
78. [79] Xia, L., Li, G. (2021). Recent progress of microfluidics in surface-enhanced Raman spectroscopic analysis. J. Sep. Sci., 44, 1752–1768.
79. [80] Yan, S., Qiu, J., Guo, L., Li, D., Xu, D., Liu, Q. (2021). Development overview of Raman-activated cell sorting devoted to bacterial detection at single-cell level. Appl. Microbiol. Biotechnol., 105, 1315–1331.
80. [81] Chang, K.-W., Cheng, H.-W., Shiue, J., Wang, J.-K., Wang, Y.-L., Huang, N.-T. (2019) Antibiotic Susceptibility Test with Surface-Enhanced Raman Scattering in a Microfluidic System. Anal. Chem., 91, 10988–10995.
81. [82] Cheng, J.X., Xie, X.S. (2015). Vibrational spectroscopic imaging of living systems: An emerging platform for biology and medicine. Science, 350, aaa8870.
82. [83] Wang, P., Liu, B., Zhang, D., Belew, M.Y., Tissenbaum, H.A., Cheng, J.X. (2014). Imaging lipid metabolism in live Caenorhabditis elegans using fingerprint vibrations. Angew. Chem., 126, 11981–11986.
83. [84] Sun, B., Kang, X., Yue, S., Lan, L., Li, R., Chen, C., Zhang, W., He, S., Zhang, C., Fan, Y. (2022) et al. A rapid procedure for bacterial identification and antimicrobial susceptibility testing directly from positive blood cultures. Analyst, 147, 147–154. [CrossRef]
84. [85] Ember, K., Daoust,F., Mahfoud,M., Dallaire F., Ahmad, E. et al. (2022). Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning. Journal of Biomedical Optics 025002-1 February • Vol. 27(2).
85. [86] Zhang, Z., Jiang, S., Wang, X., Dong, T., Wang, Y., Li, D., Gao, X., Qu, Z.; Li, Y. (2022) A novel enhanced substrate for label-free detection of SARS-CoV-2 based on surface-enhanced Raman scattering Sensors and Actuators: B. Chemical 359 131568
86. [87] Stöckel, S., Kirchhoff, J., Neugebauer, U.,Röscha P,b and Poppa J. (2016). The application of Raman spectroscopy for the detection and identification of microorganisms J. Raman Spectrosc. 47, 89–109,
87. [88] Rebrosova, K. (2022). Raman Spectroscopy—A Novel Method for Identification and Characterization of Microbes on a Single-Cell Level in Clinical Settings. Front. Cell. Infect. Microbiol.