1
|
de Araujo WR, Lukas H, Torres MDT, Gao W, de la Fuente-Nunez C. Low-Cost Biosensor Technologies for Rapid Detection of COVID-19 and Future Pandemics. ACS Nano 2024; 18:1757-1777. [PMID: 38189684 DOI: 10.1021/acsnano.3c01629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Many systems have been designed for the detection of SARS-CoV-2, which is the virus that causes COVID-19. SARS-CoV-2 is readily transmitted, resulting in the rapid spread of disease in human populations. Frequent testing at the point of care (POC) is a key aspect for controlling outbreaks caused by SARS-CoV-2 and other emerging pathogens, as the early identification of infected individuals can then be followed by appropriate measures of isolation or treatment, maximizing the chances of recovery and preventing infectious spread. Diagnostic tools used for high-frequency testing should be inexpensive, provide a rapid diagnostic response without sophisticated equipment, and be amenable to manufacturing on a large scale. The application of these devices should enable large-scale data collection, help control viral transmission, and prevent disease propagation. Here we review functional nanomaterial-based optical and electrochemical biosensors for accessible POC testing for COVID-19. These biosensors incorporate nanomaterials coupled with paper-based analytical devices and other inexpensive substrates, traditional lateral flow technology (antigen and antibody immunoassays), and innovative biosensing methods. We critically discuss the advantages and disadvantages of nanobiosensor-based approaches compared to widely used technologies such as PCR, ELISA, and LAMP. Moreover, we delineate the main technological, (bio)chemical, translational, and regulatory challenges associated with developing functional and reliable biosensors, which have prevented their translation into the clinic. Finally, we highlight how nanobiosensors, given their unique advantages over existing diagnostic tests, may help in future pandemics.
Collapse
Affiliation(s)
- William Reis de Araujo
- Portable Chemical Sensors Lab, Department of Analytical Chemistry, Institute of Chemistry, State University of Campinas - UNICAMP, Campinas, SP 13083-970, Brazil
| | - Heather Lukas
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California 91125, United States
| | - Marcelo D T Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Wei Gao
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California 91125, United States
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| |
Collapse
|
2
|
Mshani IH, Siria DJ, Mwanga EP, Sow BB, Sanou R, Opiyo M, Sikulu-Lord MT, Ferguson HM, Diabate A, Wynne K, González-Jiménez M, Baldini F, Babayan SA, Okumu F. Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis. Malar J 2023; 22:346. [PMID: 37950315 PMCID: PMC10638832 DOI: 10.1186/s12936-023-04780-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 11/01/2023] [Indexed: 11/12/2023] Open
Abstract
Studies on the applications of infrared (IR) spectroscopy and machine learning (ML) in public health have increased greatly in recent years. These technologies show enormous potential for measuring key parameters of malaria, a disease that still causes about 250 million cases and 620,000 deaths, annually. Multiple studies have demonstrated that the combination of IR spectroscopy and machine learning (ML) can yield accurate predictions of epidemiologically relevant parameters of malaria in both laboratory and field surveys. Proven applications now include determining the age, species, and blood-feeding histories of mosquito vectors as well as detecting malaria parasite infections in both humans and mosquitoes. As the World Health Organization encourages malaria-endemic countries to improve their surveillance-response strategies, it is crucial to consider whether IR and ML techniques are likely to meet the relevant feasibility and cost-effectiveness requirements-and how best they can be deployed. This paper reviews current applications of IR spectroscopy and ML approaches for investigating malaria indicators in both field surveys and laboratory settings, and identifies key research gaps relevant to these applications. Additionally, the article suggests initial target product profiles (TPPs) that should be considered when developing or testing these technologies for use in low-income settings.
Collapse
Affiliation(s)
- Issa H Mshani
- Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania.
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.
| | - Doreen J Siria
- Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Emmanuel P Mwanga
- Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Bazoumana Bd Sow
- Department of Medical Biology and Public Health, Institut de Recherche en Sciences de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Roger Sanou
- Department of Medical Biology and Public Health, Institut de Recherche en Sciences de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Mercy Opiyo
- Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique
- Malaria Elimination Initiative (MEI), Institute for Global Health Sciences, University of California, San Francisco, USA
| | - Maggy T Sikulu-Lord
- Faculty of Science, School of the Environment, The University of Queensland, Brisbane, QLD, Australia
| | - Heather M Ferguson
- Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Abdoulaye Diabate
- Department of Medical Biology and Public Health, Institut de Recherche en Sciences de la Santé (IRSS), Bobo-Dioulasso, Burkina Faso
| | - Klaas Wynne
- School of Chemistry, The University of Glasgow, Glasgow, G12 8QQ, UK
| | - Mario González-Jiménez
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
- School of Chemistry, The University of Glasgow, Glasgow, G12 8QQ, UK
| | - Francesco Baldini
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Simon A Babayan
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.
| | - Fredros Okumu
- Ifakara Health Institute, Environmental Health, and Ecological Sciences Department, Morogoro, United Republic of Tanzania.
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.
- School of Life Sciences and Biotechnology, Nelson Mandela African Institution of Science and Technology, Arusha, United Republic of Tanzania.
- School of Public Health, The University of the Witwatersrand, Park Town, South Africa.
| |
Collapse
|
3
|
Kariyawasam TN, Ciocchetta S, Visendi P, Soares Magalhães RJ, Smith ME, Giacomin PR, Sikulu-Lord MT. Near-infrared spectroscopy and machine learning algorithms for rapid and non-invasive detection of Trichuris. PLoS Negl Trop Dis 2023; 17:e0011695. [PMID: 37956181 PMCID: PMC10681298 DOI: 10.1371/journal.pntd.0011695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 11/27/2023] [Accepted: 10/02/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Trichuris trichiura (whipworm) is one of the most prevalent soil transmitted helminths (STH) affecting 604-795 million people worldwide. Diagnostic tools that are affordable and rapid are required for detecting STH. Here, we assessed the performance of the near-infrared spectroscopy (NIRS) technique coupled with machine learning algorithms to detect Trichuris muris in faecal, blood, serum samples and non-invasively through the skin of mice. METHODOLOGY We orally infected 10 mice with 30 T. muris eggs (low dose group), 10 mice with 200 eggs (high dose group) and 10 mice were used as the control group. Using the NIRS technique, we scanned faecal, serum, whole blood samples and mice non-invasively through their skin over a period of 6 weeks post infection. Using artificial neural networks (ANN) and spectra of faecal, serum, blood and non-invasive scans from one experiment, we developed 4 algorithms to differentiate infected from uninfected mice. These models were validated on mice from a second independent experiment. PRINCIPAL FINDINGS NIRS and ANN differentiated mice into the three groups as early as 2 weeks post infection regardless of the sample used. These results correlated with those from concomitant serological and parasitological investigations. SIGNIFICANCE To our knowledge, this is the first study to demonstrate the potential of NIRS as a diagnostic tool for human STH infections. The technique could be further developed for large scale surveillance of soil transmitted helminths in human populations.
Collapse
Affiliation(s)
- Tharanga N. Kariyawasam
- School of the Environment, Faculty of Science, The University of Queensland, Brisbane, Queensland, Australia
| | - Silvia Ciocchetta
- School of Veterinary Science, Faculty of Science, The University of Queensland, Gatton, Queensland, Australia
| | - Paul Visendi
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Ricardo J. Soares Magalhães
- School of Veterinary Science, Faculty of Science, The University of Queensland, Gatton, Queensland, Australia
- Children’s Health and Environment Program, UQ Children’s Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Maxine E. Smith
- Australian Institute of Tropical Health & Medicine, James Cook University, Cairns, Queensland, Australia
| | - Paul R. Giacomin
- Australian Institute of Tropical Health & Medicine, James Cook University, Cairns, Queensland, Australia
| | - Maggy T. Sikulu-Lord
- School of the Environment, Faculty of Science, The University of Queensland, Brisbane, Queensland, Australia
| |
Collapse
|
4
|
Theodosiou AA, Read RC. Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician. J Infect 2023; 87:287-294. [PMID: 37468046 DOI: 10.1016/j.jinf.2023.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 07/12/2023] [Indexed: 07/21/2023]
Abstract
BACKGROUND Artificial intelligence (AI), machine learning and deep learning (including generative AI) are increasingly being investigated in the context of research and management of human infection. OBJECTIVES We summarise recent and potential future applications of AI and its relevance to clinical infection practice. METHODS 1617 PubMed results were screened, with priority given to clinical trials, systematic reviews and meta-analyses. This narrative review focusses on studies using prospectively collected real-world data with clinical validation, and on research with translational potential, such as novel drug discovery and microbiome-based interventions. RESULTS There is some evidence of clinical utility of AI applied to laboratory diagnostics (e.g. digital culture plate reading, malaria diagnosis, antimicrobial resistance profiling), clinical imaging analysis (e.g. pulmonary tuberculosis diagnosis), clinical decision support tools (e.g. sepsis prediction, antimicrobial prescribing) and public health outbreak management (e.g. COVID-19). Most studies to date lack any real-world validation or clinical utility metrics. Significant heterogeneity in study design and reporting limits comparability. Many practical and ethical issues exist, including algorithm transparency and risk of bias. CONCLUSIONS Interest in and development of AI-based tools for infection research and management are undoubtedly gaining pace, although the real-world clinical utility to date appears much more modest.
Collapse
Affiliation(s)
- Anastasia A Theodosiou
- Clinical and Experimental Sciences and NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Tremona Road, SO166YD Southampton, United Kingdom.
| | - Robert C Read
- Clinical and Experimental Sciences and NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Tremona Road, SO166YD Southampton, United Kingdom
| |
Collapse
|
5
|
Gao Z, Harrington LC, Zhu W, Barrientos LM, Alfonso-Parra C, Avila FW, Clark JM, He L. Accurate age-grading of field-aged mosquitoes reared under ambient conditions using surface-enhanced Raman spectroscopy and artificial neural networks. J Med Entomol 2023; 60:917-923. [PMID: 37364175 DOI: 10.1093/jme/tjad067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/27/2023] [Accepted: 06/08/2023] [Indexed: 06/28/2023]
Abstract
Age-grading mosquitoes are significant because only older mosquitoes are competent to transmit pathogens to humans. However, we lack effective tools to do so, especially at the critical point where mosquitoes become a risk to humans. In this study, we demonstrated the capability of using surface-enhanced Raman spectroscopy and artificial neural networks to accurately age-grade field-aged low-generation (F2) female Aedes aegypti mosquitoes held under ambient conditions (error was 1.9 chronological days, in the range 0-22 days). When degree days were used for model calibration, the accuracy was further improved to 20.8 degree days (approximately equal to 1.4 chronological days), which indicates the impact of temperature fluctuation on prediction accuracy. This performance is a significant advancement over binary classification. The great accuracy of this method outperforms traditional age-grading methods and will facilitate effective epidemiological studies, risk assessment, vector intervention monitoring, and evaluation.
Collapse
Affiliation(s)
- Zili Gao
- Department of Food Science, University of Massachusetts, Amherst, MA 01003, USA
- Raman, IR and XRF Core Facility, University of Massachusetts, Amherst, MA 01003, USA
| | - Laura C Harrington
- Department of Entomology, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA
| | - Wei Zhu
- Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA 01003, USA
| | - Luisa M Barrientos
- Max Planck Tandem Group in Mosquito Reproductive Biology, Universidad de Antioquia, Medellin, Colombia
| | - Catalina Alfonso-Parra
- Max Planck Tandem Group in Mosquito Reproductive Biology, Universidad de Antioquia, Medellin, Colombia
- Instituto Colombiano de Medicina Tropical, Universidad CES, Sabaneta, Colombia
| | - Frank W Avila
- Max Planck Tandem Group in Mosquito Reproductive Biology, Universidad de Antioquia, Medellin, Colombia
| | - John M Clark
- Department of Veterinary and Animal Sciences, University of Massachusetts, Amherst, MA 01003, USA
| | - Lili He
- Department of Food Science, University of Massachusetts, Amherst, MA 01003, USA
- Raman, IR and XRF Core Facility, University of Massachusetts, Amherst, MA 01003, USA
- Department of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
| |
Collapse
|
6
|
Alkhuder K. Fourier-transform infrared spectroscopy: a universal optical sensing technique with auspicious application prospects in the diagnosis and management of autoimmune diseases. Photodiagnosis Photodyn Ther 2023; 42:103606. [PMID: 37187270 DOI: 10.1016/j.pdpdt.2023.103606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 04/27/2023] [Accepted: 05/09/2023] [Indexed: 05/17/2023]
Abstract
Autoimmune diseases (AIDs) are poorly understood clinical syndromes due to breakdown of immune tolerance towards specific types of self-antigens. They are generally associated with an inflammatory response mediated by lymphocytes T, autoantibodies or both. Ultimately, chronic inflammation culminates in tissue damages and clinical manifestations. AIDs affect 5% of the world population, and they represent the main cause of fatality in young to middle-aged females. In addition, the chronic nature of AIDs has a devastating impact on the patient's quality of life. It also places a heavy burden on the health care system. Establishing a rapid and accurate diagnosis is considered vital for an ideal medical management of these autoimmune disorders. However, for some AIDs, this task might be challenging. Vibrational spectroscopies, and more particularly Fourier-transform infrared (FTIR) spectroscopy, have emerged as universal analytical techniques with promising applications in the diagnosis of various types of malignancies and metabolic and infectious diseases. The high sensitivity of these optical sensing techniques and their minimal requirements for test reagents qualify them to be ideal analytical techniques. The aim of the current review is to explore the potential applications of FTIR spectroscopy in the diagnosis and management of most common AIDs. It also aims to demonstrate how this technique has contributed to deciphering the biochemical and physiopathological aspects of these chronic inflammatory diseases. The advantages that can be offered by this optical sensing technique over the traditional and gold standard methods used in the diagnosis of these autoimmune disorders have also been extensively discussed.
Collapse
|
7
|
Garcia GA, Lord AR, Santos LMB, Kariyawasam TN, David MR, Couto-Lima D, Tátila-Ferreira A, Pavan MG, Sikulu-Lord MT, Maciel-de-Freitas R. Rapid and Non-Invasive Detection of Aedes aegypti Co-Infected with Zika and Dengue Viruses Using Near Infrared Spectroscopy. Viruses 2022; 15:11. [PMID: 36680052 PMCID: PMC9863061 DOI: 10.3390/v15010011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/03/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
The transmission of dengue (DENV) and Zika (ZIKV) has been continuously increasing worldwide. An efficient arbovirus surveillance system is critical to designing early-warning systems to increase preparedness of future outbreaks in endemic countries. The Near Infrared Spectroscopy (NIRS) is a promising high throughput technique to detect arbovirus infection in Ae. aegypti with remarkable advantages such as cost and time effectiveness, reagent-free, and non-invasive nature over existing molecular tools for similar purposes, enabling timely decision making through rapid detection of potential disease. Our aim was to determine whether NIRS can differentiate Ae. aegypti females infected with either ZIKV or DENV single infection, and those coinfected with ZIKV/DENV from uninfected ones. Using 200 Ae. aegypti females reared and infected in laboratory conditions, the training model differentiated mosquitoes into the four treatments with 100% accuracy. DENV-, ZIKV-, and ZIKV/DENV-coinfected mosquitoes that were used to validate the model could be correctly classified into their actual infection group with a predictive accuracy of 100%, 84%, and 80%, respectively. When compared with mosquitoes from the uninfected group, the three infected groups were predicted as belonging to the infected group with 100%, 97%, and 100% accuracy for DENV-infected, ZIKV-infected, and the co-infected group, respectively. Preliminary lab-based results are encouraging and indicate that NIRS should be tested in field settings to evaluate its potential role to monitor natural infection in field-caught mosquitoes.
Collapse
Affiliation(s)
- Gabriela A. Garcia
- Laboratório de Mosquitos Transmissores de Hematozoários, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Rio de Janeiro, Brazil
| | - Anton R. Lord
- School of Biological Sciences, University of Queensland, Brisbane, QLD 4072, Australia
- Spectroscopy and Data Consultants Pty Ltd., Brisbane, QLD 4035, Australia
| | - Lilha M. B. Santos
- Laboratório de Mosquitos Transmissores de Hematozoários, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Rio de Janeiro, Brazil
| | | | - Mariana R. David
- Laboratório de Mosquitos Transmissores de Hematozoários, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Rio de Janeiro, Brazil
| | - Dinair Couto-Lima
- Laboratório de Mosquitos Transmissores de Hematozoários, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Rio de Janeiro, Brazil
| | - Aline Tátila-Ferreira
- Laboratório de Mosquitos Transmissores de Hematozoários, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Rio de Janeiro, Brazil
| | - Márcio G. Pavan
- Laboratório de Mosquitos Transmissores de Hematozoários, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Rio de Janeiro, Brazil
| | - Maggy T. Sikulu-Lord
- School of Biological Sciences, University of Queensland, Brisbane, QLD 4072, Australia
| | - Rafael Maciel-de-Freitas
- Laboratório de Mosquitos Transmissores de Hematozoários, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Rio de Janeiro, Brazil
- Department of Arbovirology, Bernhard Nocht Institute of Tropical Medicine, 20359 Hamburg, Germany
| |
Collapse
|
8
|
Barik AK, M SP, Lukose J, Upadhya R, Pai MV, Kartha VB, Chidangil S. In vivo spectroscopy: optical fiber probes for clinical applications. Expert Rev Med Devices 2022; 19:657-675. [PMID: 36175393 DOI: 10.1080/17434440.2022.2130046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Fiber optic probe based in-vivo spectroscopy techniques are fast and highly objective methods for intraoperative diagnoses and minimally invasive surgical interventions for all procedures where endoscopic observations are carried out for cancers of different types. The Raman spectral features provide molecular fingerprint-type information and can reveal the subjects' pathological state in label-free manner, making endoscopy multiplexed fiber optic probe-based devices with the potential for translation from bench to bedside for routine applications. AREAS COVERED This review provides a general overview of different fiber-optic probes for in-vivo measurements with emphasis on Raman spectroscopy for biomedical application. Various aspects such as fiber-optic probe, radiation source, detector, and spectrometer for extracting optimum spectral features have also been discussed. EXPERT OPINION : Optical spectroscopy-based fiber probe systems with "Chip-on-Tip" technology, combined with machine learning, can in the near future, become a complimentary diagnostic tool to magnetic resonance imaging (MRI), computed tomography (CT) scan, ultrasound, etc. Hyperspectral imaging and fluorescence-based devices are in the advanced stage of technology readiness level (TRL), and with advances in lasers and miniature spectroscopy systems, probe-based Raman devices are also coming up.
Collapse
Affiliation(s)
- Ajaya Kumar Barik
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education -576104, Manipal, India
| | - Sanoop Pavithran M
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education -576104, Manipal, India
| | - Jijo Lukose
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education -576104, Manipal, India
| | - Rekha Upadhya
- Department of Obstetrics and Gynaecology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education -576104, Manipal, India
| | - Muralidhar V Pai
- Department of Obstetrics and Gynaecology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education -576104, Manipal, India
| | - V B Kartha
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education -576104, Manipal, India
| | - Santhosh Chidangil
- Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education -576104, Manipal, India
| |
Collapse
|
9
|
Mgaya JN, Siria DJ, Makala FE, Mgando JP, Vianney JM, Mwanga EP, Okumu FO. Effects of sample preservation methods and duration of storage on the performance of mid-infrared spectroscopy for predicting the age of malaria vectors. Parasit Vectors 2022; 15:281. [PMID: 35933384 PMCID: PMC9356448 DOI: 10.1186/s13071-022-05396-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 07/09/2022] [Indexed: 11/27/2022] Open
Abstract
Background Monitoring the biological attributes of mosquitoes is critical for understanding pathogen transmission and estimating the impacts of vector control interventions on the survival of vector species. Infrared spectroscopy and machine learning techniques are increasingly being tested for this purpose and have been proven to accurately predict the age, species, blood-meal sources, and pathogen infections in Anopheles and Aedes mosquitoes. However, as these techniques are still in early-stage implementation, there are no standardized procedures for handling samples prior to the infrared scanning. This study investigated the effects of different preservation methods and storage duration on the performance of mid-infrared spectroscopy for age-grading females of the malaria vector, Anopheles arabiensis. Methods Laboratory-reared An. arabiensis (N = 3681) were collected at 5 and 17 days post-emergence, killed with ethanol, and then preserved using silica desiccant at 5 °C, freezing at − 20 °C, or absolute ethanol at room temperature. For each preservation method, the mosquitoes were divided into three groups, stored for 1, 4, or 8 weeks, and then scanned using a mid-infrared spectrometer. Supervised machine learning classifiers were trained with the infrared spectra, and the support vector machine (SVM) emerged as the best model for predicting the mosquito ages. Results The model trained using silica-preserved mosquitoes achieved 95% accuracy when predicting the ages of other silica-preserved mosquitoes, but declined to 72% and 66% when age-classifying mosquitoes preserved using ethanol and freezing, respectively. Prediction accuracies of models trained on samples preserved in ethanol and freezing also reduced when these models were applied to samples preserved by other methods. Similarly, models trained on 1-week stored samples had declining accuracies of 97%, 83%, and 72% when predicting the ages of mosquitoes stored for 1, 4, or 8 weeks respectively. Conclusions When using mid-infrared spectroscopy and supervised machine learning to age-grade mosquitoes, the highest accuracies are achieved when the training and test samples are preserved in the same way and stored for similar durations. However, when the test and training samples were handled differently, the classification accuracies declined significantly. Protocols for infrared-based entomological studies should therefore emphasize standardized sample-handling procedures and possibly additional statistical procedures such as transfer learning for greater accuracy. Graphical Abstract ![]()
Collapse
Affiliation(s)
- Jacqueline N Mgaya
- Environmental Health and Ecological Science Department, Ifakara Health Institute, P.O. Box 53, Ifakara, Tanzania. .,School of Life Science and Bioengineering, The Nelson Mandela African Institute of Science and Technology, P.O. Box 447, Arusha, Tanzania.
| | - Doreen J Siria
- Environmental Health and Ecological Science Department, Ifakara Health Institute, P.O. Box 53, Ifakara, Tanzania
| | - Faraja E Makala
- Environmental Health and Ecological Science Department, Ifakara Health Institute, P.O. Box 53, Ifakara, Tanzania
| | - Joseph P Mgando
- Environmental Health and Ecological Science Department, Ifakara Health Institute, P.O. Box 53, Ifakara, Tanzania
| | - John-Mary Vianney
- School of Life Science and Bioengineering, The Nelson Mandela African Institute of Science and Technology, P.O. Box 447, Arusha, Tanzania
| | - Emmanuel P Mwanga
- Environmental Health and Ecological Science Department, Ifakara Health Institute, P.O. Box 53, Ifakara, Tanzania. .,Institute of Biodiversity, Animal Health, and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK.
| | - Fredros O Okumu
- Environmental Health and Ecological Science Department, Ifakara Health Institute, P.O. Box 53, Ifakara, Tanzania. .,School of Life Science and Bioengineering, The Nelson Mandela African Institute of Science and Technology, P.O. Box 447, Arusha, Tanzania. .,School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. .,Institute of Biodiversity, Animal Health, and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK.
| |
Collapse
|
10
|
Lyimo BM, Popkin-Hall ZR, Giesbrecht DJ, Mandara CI, Madebe RA, Bakari C, Pereus D, Seth MD, Ngamba RM, Mbwambo RB, MacInnis B, Mbwambo D, Garimo I, Chacky F, Aaron S, Lusasi A, Molteni F, Njau R, Cunningham JA, Lazaro S, Mohamed A, Juliano JJ, Bailey J, Ishengoma DS. Potential Opportunities and Challenges of Deploying Next Generation Sequencing and CRISPR-Cas Systems to Support Diagnostics and Surveillance Towards Malaria Control and Elimination in Africa. Front Cell Infect Microbiol 2022; 12:757844. [PMID: 35909968 PMCID: PMC9326448 DOI: 10.3389/fcimb.2022.757844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 03/17/2022] [Indexed: 12/02/2022] Open
Abstract
Recent developments in molecular biology and genomics have revolutionized biology and medicine mainly in the developed world. The application of next generation sequencing (NGS) and CRISPR-Cas tools is now poised to support endemic countries in the detection, monitoring and control of endemic diseases and future epidemics, as well as with emerging and re-emerging pathogens. Most low and middle income countries (LMICs) with the highest burden of infectious diseases still largely lack the capacity to generate and perform bioinformatic analysis of genomic data. These countries have also not deployed tools based on CRISPR-Cas technologies. For LMICs including Tanzania, it is critical to focus not only on the process of generation and analysis of data generated using such tools, but also on the utilization of the findings for policy and decision making. Here we discuss the promise and challenges of NGS and CRISPR-Cas in the context of malaria as Africa moves towards malaria elimination. These innovative tools are urgently needed to strengthen the current diagnostic and surveillance systems. We discuss ongoing efforts to deploy these tools for malaria detection and molecular surveillance highlighting potential opportunities presented by these innovative technologies as well as challenges in adopting them. Their deployment will also offer an opportunity to broadly build in-country capacity in pathogen genomics and bioinformatics, and to effectively engage with multiple stakeholders as well as policy makers, overcoming current workforce and infrastructure challenges. Overall, these ongoing initiatives will build the malaria molecular surveillance capacity of African researchers and their institutions, and allow them to generate genomics data and perform bioinformatics analysis in-country in order to provide critical information that will be used for real-time policy and decision-making to support malaria elimination on the continent.
Collapse
Affiliation(s)
- Beatus M. Lyimo
- National Institute for Medical Research, Dar es Salaam, Tanzania
- School of Life Sciences and Bio-Engineering, Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania
| | | | - David J. Giesbrecht
- Pathology and Laboratory Medicine, Center for International Health Research, Brown University, Providence, RI, United States
| | | | - Rashid A. Madebe
- National Institute for Medical Research, Dar es Salaam, Tanzania
| | - Catherine Bakari
- National Institute for Medical Research, Dar es Salaam, Tanzania
| | - Dativa Pereus
- National Institute for Medical Research, Dar es Salaam, Tanzania
| | - Misago D. Seth
- National Institute for Medical Research, Dar es Salaam, Tanzania
| | | | - Ruth B. Mbwambo
- National Institute for Medical Research, Dar es Salaam, Tanzania
| | - Bronwyn MacInnis
- Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Infectious Disease and Microbiome Program, Broad Institute, Boston, MA, United States
| | | | - Issa Garimo
- National Malaria Control Programme, Dodoma, Tanzania
| | - Frank Chacky
- National Malaria Control Programme, Dodoma, Tanzania
| | | | | | | | - Ritha Njau
- World Health Organization, Country Office, Dar es Salaam, Tanzania
| | - Jane A. Cunningham
- Global Malaria Programme, World Health Organization, Headquarters, Geneva, Switzerland
| | - Samwel Lazaro
- National Malaria Control Programme, Dodoma, Tanzania
| | - Ally Mohamed
- National Malaria Control Programme, Dodoma, Tanzania
| | - Jonathan J. Juliano
- School of Medicine, University of North Carolina, Chapel Hill, NC, United States
| | - Jeffrey A. Bailey
- Pathology and Laboratory Medicine, Center for International Health Research, Brown University, Providence, RI, United States
| | - Deus S. Ishengoma
- National Institute for Medical Research, Dar es Salaam, Tanzania
- Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Faculty of Pharmaceutical Sciences, Monash University, Melbourne, VIC, Australia
| |
Collapse
|
11
|
Kongklad G, Chitaree R, Taechalertpaisarn T, Panvisavas N, Nuntawong N. Discriminant Analysis PCA-LDA Assisted Surface-Enhanced Raman Spectroscopy for Direct Identification of Malaria-Infected Red Blood Cells. Methods Protoc 2022; 5:mps5030049. [PMID: 35736550 PMCID: PMC9231316 DOI: 10.3390/mps5030049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 05/31/2022] [Accepted: 06/07/2022] [Indexed: 12/13/2022] Open
Abstract
Various methods for detecting malaria have been developed in recent years, each with its own set of advantages. These methods include microscopic, antigen-based, and molecular-based analysis of blood samples. This study aimed to develop a new, alternative procedure for clinical use by using a large data set of surface-enhanced Raman spectra to distinguish normal and infected red blood cells. PCA-LDA algorithms were used to produce models for separating P. falciparum (3D7)-infected red blood cells and normal red blood cells based on their Raman spectra. Both average normalized spectra and spectral imaging were considered. However, these initial spectra could hardly differentiate normal cells from the infected cells. Then, discrimination analysis was applied to assist in the classification and visualization of the different spectral data sets. The results showed a clear separation in the PCA-LDA coordinate. A blind test was also carried out to evaluate the efficiency of the PCA-LDA separation model and achieved a prediction accuracy of up to 80%. Considering that the PCA-LDA separation accuracy will improve when a larger set of training data is incorporated into the existing database, the proposed method could be highly effective for the identification of malaria-infected red blood cells.
Collapse
Affiliation(s)
- Gunganist Kongklad
- Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand;
| | - Ratchapak Chitaree
- Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand;
- Correspondence:
| | - Tana Taechalertpaisarn
- Department of Microbiology, Faculty of Science, Mahidol University, Bangkok 10400, Thailand;
| | - Nathinee Panvisavas
- Department of Plant, Faculty of Science, Mahidol University, Bangkok 10400, Thailand;
| | - Noppadon Nuntawong
- National Electronics and Computer Technology Center (NECTEC), 112 Thailand Science Park, Pathum Thani 12120, Thailand;
| |
Collapse
|
12
|
Joy T, Chen M, Arnbrister J, Williamson D, Li S, Nair S, Brophy M, Garcia VM, Walker K, Ernst K, Gouge DH, Carrière Y, Riehle MA. Assessing Near-Infrared Spectroscopy (NIRS) for Evaluation of Aedes aegypti Population Age Structure. Insects 2022; 13:insects13040360. [PMID: 35447802 PMCID: PMC9029691 DOI: 10.3390/insects13040360] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/03/2022] [Accepted: 04/05/2022] [Indexed: 02/04/2023]
Abstract
Given that older Aedes aegypti (L.) mosquitoes typically pose the greatest risk of pathogen transmission, the capacity to age grade wild Ae. aegypti mosquito populations would be a valuable tool in monitoring the potential risk of arboviral transmission. Here, we compared the effectiveness of near-infrared spectroscopy (NIRS) to age grade field-collected Ae. aegypti with two alternative techniques—parity analysis and transcript abundance of the age-associated gene SCP1. Using lab-reared mosquitoes of known ages from three distinct populations maintained as adults under laboratory or semi-field conditions, we developed and validated four NIRS models for predicting the age of field-collected Ae. aegypti. To assess the accuracy of these models, female Ae. aegypti mosquitoes were collected from Maricopa County, AZ, during the 2017 and 2018 monsoon season, and a subset were age graded using the three different age-grading techniques. For both years, each of the four NIRS models consistently graded parous mosquitoes as significantly older than nulliparous mosquitoes. Furthermore, a significant positive linear association occurred between SCP1 and NIRS age predictions in seven of the eight year/model combinations, although considerable variation in the predicted age of individual mosquitoes was observed. Our results suggest that although the NIRS models were not adequate in determining the age of individual field-collected mosquitoes, they have the potential to quickly and cost effectively track changes in the age structure of Ae. aegypti populations across locations and over time.
Collapse
Affiliation(s)
- Teresa Joy
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Minhao Chen
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Joshua Arnbrister
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Daniel Williamson
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Shujuan Li
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Shakunthala Nair
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Maureen Brophy
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Valerie Madera Garcia
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85724, USA; (V.M.G.); (K.E.)
| | - Kathleen Walker
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Kacey Ernst
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85724, USA; (V.M.G.); (K.E.)
| | - Dawn H. Gouge
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Yves Carrière
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
| | - Michael A. Riehle
- Department of Entomology, University of Arizona, Tucson, AZ 85721, USA; (T.J.); (M.C.); (J.A.); (D.W.); (S.L.); (S.N.); (M.B.); (K.W.); (D.H.G.); (Y.C.)
- Correspondence: ; Tel.: +1-520-626-8500
| |
Collapse
|
13
|
Tátila-Ferreira A, Garcia GA, Dos Santos LMB, Pavan MG, de C Moreira CJ, Victoriano JC, da Silva-Junior R, Dos Santos-Mallet JR, Verly T, Britto C, Sikulu-Lord MT, Maciel-de-Freitas R. Near infrared spectroscopy accurately detects Trypanosoma cruzi non-destructively in midguts, rectum and excreta samples of Triatoma infestans. Sci Rep 2021; 11:23884. [PMID: 34903840 PMCID: PMC8668913 DOI: 10.1038/s41598-021-03465-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/29/2021] [Indexed: 11/09/2022] Open
Abstract
Chagas disease is a neglected tropical disease caused by Trypanosoma cruzi parasite with an estimated 70 million people at risk. Traditionally, parasite presence in triatomine vectors is detected through optical microscopy which can be low in sensitivity or molecular techniques which can be costly in endemic countries. The aim of this study was to evaluate the ability of a reagent-free technique, the Near Infrared Spectroscopy (NIRS) for rapid and non-invasive detection of T. cruzi in Triatoma infestans body parts and in wet/dry excreta samples of the insect. NIRS was 100% accurate for predicting the presence of T. cruzi infection Dm28c strain (TcI) in either the midgut or the rectum and models developed from either body part could predict infection in the other part. Models developed to predict infection in excreta samples were 100% accurate for predicting infection in both wet and dry samples. However, models developed using dry excreta could not predict infection in wet samples and vice versa. This is the first study to report on the potential application of NIRS for rapid and non-invasive detection of T. cruzi infection in T. infestans in the laboratory. Future work should demonstrate the capacity of NIRS to detect T. cruzi in triatomines originating from the field.
Collapse
Affiliation(s)
- Aline Tátila-Ferreira
- Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Gabriela A Garcia
- Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Lilha M B Dos Santos
- Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Márcio G Pavan
- Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Carlos José de C Moreira
- Laboratório de Doenças Parasitárias, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Juliana C Victoriano
- Laboratório Interdisciplinar de Vigilância Entomológica de Diptera E Hemiptera, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Renato da Silva-Junior
- Laboratório Interdisciplinar de Vigilância Entomológica de Diptera E Hemiptera, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- Universidade Iguaçu - UNIG, Rio de Janeiro, Brazil
| | - Jacenir R Dos Santos-Mallet
- Laboratório Interdisciplinar de Vigilância Entomológica de Diptera E Hemiptera, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- Universidade Iguaçu - UNIG, Rio de Janeiro, Brazil
| | - Thaiane Verly
- Laboratório de Biologia Molecular e Doenças Endêmicas, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Constança Britto
- Laboratório de Biologia Molecular e Doenças Endêmicas, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Maggy T Sikulu-Lord
- The School of Public Health, The University of Queensland, Herston, QLD, 4006, Australia
| | - Rafael Maciel-de-Freitas
- Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
- Instituto Nacional de Ciência e Tecnologia em Entomologia Molecular, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
| |
Collapse
|
14
|
Wang L, Liu W, Tang JW, Wang JJ, Liu QH, Wen PB, Wang MM, Pan YC, Gu B, Zhang X. Applications of Raman Spectroscopy in Bacterial Infections: Principles, Advantages, and Shortcomings. Front Microbiol 2021; 12:683580. [PMID: 34349740 PMCID: PMC8327204 DOI: 10.3389/fmicb.2021.683580] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 06/17/2021] [Indexed: 12/13/2022] Open
Abstract
Infectious diseases caused by bacterial pathogens are important public issues. In addition, due to the overuse of antibiotics, many multidrug-resistant bacterial pathogens have been widely encountered in clinical settings. Thus, the fast identification of bacteria pathogens and profiling of antibiotic resistance could greatly facilitate the precise treatment strategy of infectious diseases. So far, many conventional and molecular methods, both manual or automatized, have been developed for in vitro diagnostics, which have been proven to be accurate, reliable, and time efficient. Although Raman spectroscopy (RS) is an established technique in various fields such as geochemistry and material science, it is still considered as an emerging tool in research and diagnosis of infectious diseases. Based on current studies, it is too early to claim that RS may provide practical guidelines for microbiologists and clinicians because there is still a gap between basic research and clinical implementation. However, due to the promising prospects of label-free detection and noninvasive identification of bacterial infections and antibiotic resistance in several single steps, it is necessary to have an overview of the technique in terms of its strong points and shortcomings. Thus, in this review, we went through recent studies of RS in the field of infectious diseases, highlighting the application potentials of the technique and also current challenges that prevent its real-world applications.
Collapse
Affiliation(s)
- Liang Wang
- Institute Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai, China
| | - Wei Liu
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Jia-Wei Tang
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Jun-Jiao Wang
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Qing-Hua Liu
- State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, China
| | - Peng-Bo Wen
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Meng-Meng Wang
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Ya-Cheng Pan
- School of Life Sciences, Xuzhou Medical University, Xuzhou, China
| | - Bing Gu
- Laboratory Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xiao Zhang
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China
| |
Collapse
|