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| Schematic illustration of the basic principles of Raman effects and the brief architecture of Raman spectroscopy. (A) Raman spectrum energy level diagram, which shows the transition process of infrared light irradiation, Stokes rays, anti-Stokes rays, Rayleigh scattering, and Raman scattering. hv k , initial irradiation energy; E 0 , ground state; E 1 , vibration excited state; E 0 +hv 0 and E 1 +hv 0 , excited virtual state. (B) Schematic diagram of Raman spectroscopy. After the incident light is irradiated, the molecules reach an excited state. The light of different frequencies during the scattering process is Raman scattering, which is reflected on the grating and captured by the detector.
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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...
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... for the application, RS produces a series of spectral signal lines when measuring a particular sample, in which Raman shift is the frequency difference between the Raman scattered light and the aforementioned Rayleigh scattered light (Cialla-May et al., 2019). Some specific molecules in biological samples will have characteristic peaks, and the concentration or amount of a certain molecule in the sample will affect the intensity of the molecule (Figure 1). ...
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... addition, Yin et al. (2021) analyzed 177 serum samples (63 confirmed COVID-19 patients, 59 suspected cases, and 55 healthy individuals) via RS, together with 20 independent individuals for external validation. According to the study, accuracy between the COVID-19 and the healthy controls is 0.90, which also indicated that RS held the promise of being a safe and efficient technique for COVID-19 screening (Yin et al., 2021). For a schematic illustration of the workflow of Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS), please refer to Figure 2. ...
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... the typical procedure normally takes 3-4 days or even longer for fastidious bacteria on average to obtain the final AST results ( Han et al., 2020), with MALDI-TOF MS-based approaches, i.e., for positive blood culture bottles, a result can be obtained after <24 h, in some cases also the same day ( Verroken et al., 2014). Due to its simple operations, RS, especially SERS, has been used for testing antibiotic resistance phenotypes in many bacterial species, such as E. coli (Chang et al., 2019), S. aureus (Uysal Ciloglu et al., 2020), and Pseudomonas aeruginosa ( Li et al., 2019). A variety of signatures have been observed in terms of bacterial antibiotic resistance and susceptibility, which could be used for rapidly identifying resistance to sublethal concentrations of antibiotics ( Galvan and Yu, 2018;Han et al., 2020). ...
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... (TD202001) and Jiang-Su Qing-Lan Project (2020). BG thanks the financial support of National Natural Science Foundation of China (81871734, 81471994), Key R&D Program of Jiangsu Province (BE2020646), Jiangsu Provincial Medical Talent (ZDRCA2016053), Six Talent Peaks Project of Jiangsu Province (WSN-135), Advanced Health Talent of Six-one Project of Jiangsu Province (LGY2016042), and Research Foundation for Advanced Talents of Guangdong Provincial People's Hospital (KJ012021097). ...
Citations
... Raman spectroscopy has emerged as a new tool for field-based phenotyping-particularly for early detection of plant stress [3][4][5][6][7][8][9][10][11][12][13][14][15] . This involves detecting bacterial infections 12,16 , insect infestations 17 , fungal infections 7,18 , nutrient deficiencies 15,19,20 , and various other pathogens 17,21 . ...
... Raman spectroscopy has emerged as a new tool for field-based phenotyping-particularly for early detection of plant stress [3][4][5][6][7][8][9][10][11][12][13][14][15] . This involves detecting bacterial infections 12,16 , insect infestations 17 , fungal infections 7,18 , nutrient deficiencies 15,19,20 , and various other pathogens 17,21 . These various stresses manifest through changes in the various metabolites observed in leaf Raman spectra: these include carotenoids, pectin, lignin, carbohydrates such as starch, amino acids, and nitrate. ...
Here, we explore the application of Raman spectroscopy for the assessment of plant biodiversity. Raman spectra from 11 vascular plant species commonly found in forest ecosystems, specifically angiosperms (both monocots and eudicots) and pteridophytes (ferns), were acquired in vivo and in situ using a Raman leaf-clip. We achieved an overall accuracy of 91% for correct classification of a species within a plant group and identified lignin Raman spectral features as a useful discriminator for classification. The results demonstrate the potential of Raman spectroscopy in contributing to plant biodiversity assessment.
... Clinically significant bacterial pathogens such as Mycobacterium tuberculosis and Klebsiella pneumoniae can cause many severe infectious diseases, resulting in approximately 550 million infection cases and 5.2 million deaths annually, which makes them a serious global health threat [4,5]. Therefore, rapid diagnosis of bacterial pathogens and precise treatment of infectious diseases are critical [6][7][8]. Conventional approaches such as cell culture, biochemical assays, immunological tests, and genetic analysis are generally time-consuming and laborintensive, with bacterial identification and eradication limitations [9]. However, delays in results continue to be responsible for an infinite number of deaths and health problems, which requires the development of more rapid, inexpensive, and potentially decentralized procedures for detecting and eradicating bacterial pathogens. ...
Infectious diseases caused by bacterial pathogens are currently a significant problem for global public health. Rapid diagnosis and effective treatment of clinically significant bacterial pathogens can prevent, control, and inhibit infectious diseases. Therefore, there is an urgent need to develop selective and accurate diagnostic methods for bacterial pathogens and clinically effective treatment strategies for infectious diseases. In recent years, developing novel nanoparticles has dramatically facilitated the rapid and accurate diagnosis of bacterial pathogens and the precise treatment of contagious diseases. In this review, we systematically investigated a variety of nanoparticles currently applied in the diagnosis and treatment of bacterial pathogens, from synthesis procedures to structural characterization and then to biological functions. In particular, we first discussed the current progress in applying representative nanoparticles for bacterial pathogen diagnostics. The potential nanoparticle-based treatment for the control of bacterial infections was then carefully explored. We also discussed nanoparticles as a drug delivery method for reducing antibiotic global adverse effects and eradicating bacterial biofilm formation. Furthermore, we studied the highly effective nanoparticles for therapeutic applications in terms of safety issues. Finally, a concise and insightful discussion of nanoparticles’ limitations, challenges, and perspectives for diagnosing and eradicating bacterial pathogens in clinical settings was conducted to provide a direction for future development.
... Raman spectroscopy has emerged as a new tool for eld-based phenotyping -particularly for early detection of plant stress [3][4][5][6][7][8][9][10][11][12][13][14][15] . This involves detecting bacterial infections 12,16 , insect infestations 17 , fungal infections 7,18 , nutrient de ciencies 15,19,20 , and various other pathogens 17,21 . ...
... Raman spectroscopy has emerged as a new tool for eld-based phenotyping -particularly for early detection of plant stress [3][4][5][6][7][8][9][10][11][12][13][14][15] . This involves detecting bacterial infections 12,16 , insect infestations 17 , fungal infections 7,18 , nutrient de ciencies 15,19,20 , and various other pathogens 17,21 . These various stresses manifest through changes in the various metabolites observed in leaf Raman spectra: these include carotenoids, pectin, lignin, carbohydrates such as starch, amino acids, and nitrate. ...
Here, we explore the application of Raman spectroscopy for the assessment of plant biodiversity. Raman spectra from 11 vascular plant species commonly found in forest ecosystems, specifically angiosperms (both monocots and eudicots) and pteridophytes (ferns), were acquired in vivo and in situ using a Raman leaf-clip. We achieved an overall accuracy of 91% for correct classification of a species within a plant group and identified lignin Raman spectral features as a useful discriminator for classification. The results demonstrate the potential of Raman spectroscopy in contributing to plant biodiversity assessment.
... 22 Even though Raman spectroscopy is a promising technique for reliable and fast identification of bacterial species, there are some potential limitations of this method; the requirement of a Raman database is crucial for routine pathogen detection. 27 However, recommendations for the preparation of samples and data processing guidelines should be introduced, which will significantly promote the use of Raman spectroscopy and convert it into a routine diagnostic method in clinical laboratories, 43 and trained personnel and possible automatization is necessary for routine diagnosis and future work. 34 Hutsebaut et al. ...
The intensive labour and time required for conventional methods to identify bacterial fish pathogens have revealed the need to develop alternative methods. Raman spectroscopy has been used in the rapid optical identification of bacterial pathogens in recent years as an alternative method in microbiology. Strains of bacterial fish pathogens (Vibrio anguillarum, Lactococcus garvieae and Yersinia ruckeri) that often cause infectious diseases in fish were here identified and analyzed in terms of their biochemical structures in different media and at different incubation times, and the data were specified by using Raman spectroscopy. The results demonstrated that Raman spectroscopy presents species-specific Raman spectra of each disease-causing bacteria and that it would be more appropriate to choose general microbiological media over selective media for routine studies. Additionally, it was found that species-specific band regions did not differ in 24- and 48-hour cultures, but there could be a difference in peak intensity which may lead to difficult characterization of spectrum. The current study, conducted for the first time with bacterial fish pathogens under different incubation conditions, is believed to provide a basis for the routine use of Raman spectroscopy for quick pathogen identification and the precise determination of the methodology for further research.
... RS is a powerful technique broadly investigated in science, and also currently used in product quality analysis in the pharmaceutical, materials, and food industries, and in clinics for the recognition of infection factors [12,[53][54][55]. The costs depend on sample preparation (e.g., the usage of antibodies conjugated with markers, and dedicated platforms that might contain silver nanoparticles), equipment, the time needed to measure by trained personnel, and programs for analysis. ...
The incidence of thyroid nodules (TNs) is estimated at 36.5% and 23% in females and males, respectively. A single thyroid nodule is usually detected during ultrasound assessment in patients with symptoms of thyroid dysfunction or neck mass. TNs are classified as benign tumours (non-malignant hyperplasia), benign neoplasms (e.g., adenoma, a non-invasive follicular tumour with papillary nuclear features) or malignant carcinomas (follicular cell-derived or C-cell derived). The differential diagnosis is based on fine-needle aspiration biopsies and cytological assessment (which is burdened with the bias of subjectivity). Raman spectroscopy (RS) is a laser-based, semiquantitative technique which shows for oscillations of many chemical groups in one label-free measurement. RS, through the assessment of chemical content, gives insight into tissue state which, in turn, allows for the differentiation of disease on the basis of spectral characteristics. The purpose of this study was to report if RS could be useful in the differential diagnosis of TN. The Web of Science, PubMed, and Scopus were searched from the beginning of the databases up to the end of June 2023. Two investigators independently screened key data using the terms “Raman spectroscopy” and “thyroid”. From the 4046 records found initially, we identified 19 studies addressing the differential diagnosis of TNs applying the RS technique. The lasers used included 532, 633, 785, 830, and 1064 nm lines. The thyroid RS investigations were performed at the cellular and/or tissue level, as well as in serum samples. The accuracy of papillary thyroid carcinoma detection is approx. 90%. Furthermore, medullary, and follicular thyroid carcinoma can be detected with up to 100% accuracy. These results might be biased with low numbers of cases in some research and overfitting of models as well as the reference method. The main biochemical changes one can observe in malignancies are as follows: increase of protein, amino acids (like phenylalanine, tyrosine, and tryptophan), and nucleic acid content in comparison with non-malignant TNs. Herein, we present a review of the literature on the application of RS in the differential diagnosis of TNs. This technique seems to have powerful application potential in thyroid tumour diagnosis.
... Raman spec-troscopy is defined as a powerful molecular fingerprinting technique that analyzes with the help of a laser beam, the interactions of molecules of matter. Although this technique has been widely used for the characterization of different materials, it has been rapidly found in many biological applications to provide optical detection of microorganisms [42,82]. ...
Microbial contaminants are responsible for several infectious diseases, and they have been introduced as important potential food- and water-borne risk factors. They become a global burden due to their health and safety threats. In addition, their tendency to undergo mutations that result in antimicrobial resistance makes them difficult to treat. In this respect, rapid and reliable detection of microbial contaminants carries great significance, and this research area is explored as a rich subject within a dynamic state. Optical sensing serving as analytical devices enables simple usage, low-cost, rapid, and sensitive detection with the advantage of their miniaturization. From the point of view of microbial contaminants, on-site detection plays a crucial role, and portable, easy-applicable, and effective point-of-care (POC) devices offer high specificity and sensitivity. They serve as advanced on-site detection tools and are pioneers in next-generation sensing platforms. In this review, recent trends and advances in optical sensing to detect microbial contaminants were mainly discussed. The most innovative and popular optical sensing approaches were highlighted, and different optical sensing methodologies were explained by emphasizing their advantages and limitations. Consequently, the challenges and future perspectives were considered.
... From 5 h of incubation, the band at 1006 cm −1 from phenylalanine increased [47,48]. It was reported the phenylalanine bands could be used as the signature peak for biofilm formation [49]. ...
The prevalence of antimicrobial-resistant bacteria has become a major challenge worldwide. Methicillin-resistant Staphylococcus aureus (MRSA)-a leading cause of infections-forms biofilms on polymeric medical devices and implants, increasing their resistance to antibiotics. Antibiotic administration before biofilm formation is crucial. Raman spectroscopy was used to assess MRSA biofilm development on solid culture media from 0 to 48 h. Biofilm formation was monitored by measuring DNA/RNA-associated Raman peaks and protein/lipid-associated peaks. The search for an antimicrobial agent against MRSA biofilm revealed that Eugenol was a promising candidate as it showed significant potential for breaking down biofilm. Eugenol was applied at different times to test the optimal time for inhibiting MRSA biofilms, and the Raman spectrum showed that the first 5 h of biofilm formation was the most antibiotic-sensitive time. This study investigated the performance of Raman spectroscopy coupled with principal component analysis (PCA) to identify planktonic bacteria from biofilm conglomerates. Raman analysis, microscopic observation, and quantification of the biofilm growth curve indicated early adhesion from 5 to 10 h of the incubation time. Therefore, Raman spectroscopy can help in monitoring biofilm formation on a solid culture medium and performing rapid antibiofilm assessments with new antibiotics during the early stages of the procedure.
... An area of topical interest is the frontier of Raman spectroscopy, chemometrics and artificial intelligence (AI), with its promise of more autonomous, flexible and data-driven RS analytics [18][19][20]. There has been a recent surge in the adoption of AI methods in Raman-based research [4], with applications to RS now spanning domains as broad as the identification of pathogens and other microbes [21][22][23][24]; the characterisation of chemicals, including minerals [25], pesticides [26] and other analytes [27,28]; the development of novel diagnostic platforms [29][30][31][32]; as well as the application of techniques from computer vision for denoising and super-resolution in Raman imaging [33]. ...
Raman spectroscopy is a non-destructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardisation, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of ready-to-use tools for spectroscopic analysis, which streamlines day-to-day tasks, integrative analyses, as well as novel research and algorithmic development. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis and machine learning in Python.
... An area of topical interest is the frontier of Raman spectroscopy, chemometrics and artificial intelligence (AI), with its promise of more autonomous, flexible and data-driven RS analytics [18][19][20]. There has been a recent surge in the adoption of AI methods in Raman-based research [4], with applications to RS now spanning domains as broad as the identification of pathogens and other microbes [21][22][23][24]; the characterisation of chemicals, including minerals [25], pesticides [26] and other analytes [27,28]; the development of novel diagnostic platforms [29][30][31][32]; as well as the application of techniques from computer vision for denoising and super-resolution in Raman imaging [33]. ...
Raman spectroscopy is a non-destructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardisation, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of ready-to-use tools for spectroscopic analysis, which streamlines day-to-day tasks, integrative analyses, as well as novel research and algorithmic development. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis and machine learning in Python.
... From 5 h of incubation, the band at 1006 cm −1 from phenylalanine increased [46,47]. It was reported the phenylalanine bands could be used as the signature peak for biofilm formation [48]. ...
Prevalence of antimicrobial-resistant bacteria has become a major challenge worldwide. Methicillin-resistant Staphylococcus aureus (MRSA)—a leading cause of infections—forms biofilms on polymeric medical devices and implants, increasing their resistance to antibiotics. Antibiotic administration before biofilm formation is crucial. Raman spectroscopy was used to assess MRSA biofilm development on solid culture media from 0 to 48 h. Biofilm formation was monitored by measuring DNA/RNA-associated Raman peaks and protein/lipid-associated peaks. The search for an antimicrobial agent against MRSA biofilm revealed that Eugenol was a promising candidate as it showed significant potential for breaking down the biofilm. Eugenol was applied at different times to test the optimal time for inhibiting MRSA biofilms, and the Raman spectrum showed that the first 5 h of biofilm formation was the most antibiotic-sensitive time. This study investigated the performance of Raman spectroscopy coupled with Principal Component Analysis (PCA) to identify planktonic bacteria from biofilm conglomerates. Raman analysis, microscopic observation, and quantification of the biofilm growth curve indicated early adhesion from 5 to 10 h of incubation time. Therefore, Raman spectroscopy can help in monitoring biofilm formation on a solid culture medium and performing rapid antibiofilm assessments with new antibiotics during the early stages of the procedure.
