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Umar Hussain M, Kainat K, Nawaz H, Irfan Majeed M, Akhtar N, Alshammari A, Albekairi NA, Fatima R, Amber A, Bano A, Shabbir I, Tahira M, Pallares RM. SERS characterization of biochemical changes associated with biodesulfurization of dibenzothiophene using Gordonia sp. HS126-4N. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 320:124534. [PMID: 38878718 DOI: 10.1016/j.saa.2024.124534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 05/08/2024] [Accepted: 05/24/2024] [Indexed: 07/08/2024]
Abstract
In this study, Gordonia sp. HS126-4N was employed for dibenzothiophene (DBT) biodesulfurization, tracked over 9 days using SERS. During the initial lag phase, no significant spectral changes were observed, but after 48 h, elevated metabolic activity was evident. At 72 h, maximal bacterial population correlated with peak spectrum variance, followed by stable spectral patterns. Despite 2-hydroxybiphenyl (2-HBP) induced enzyme suppression, DBT biodesulfurization persisted. PCA and PLS-DA analysis of the SERS spectra revealed distinctive features linked to both bacteria and DBT, showcasing successful desulfurization and bacterial growth stimulation. PLS-DA achieved a specificity of 95.5 %, sensitivity of 94.3 %, and AUC of 74 %, indicating excellent classification of bacteria exposed to DBT. SERS effectively tracked DBT biodesulfurization and bacterial metabolic changes, offering insights into biodesulfurization mechanisms and bacterial development phases. This study highlights SERS' utility in biodesulfurization research, including its use in promising advancements in the field.
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Affiliation(s)
- Muhammad Umar Hussain
- Industrial Biotechnology Division, National Institute for Biotechnology and Genetic Engineering College, Pakistan Institute of Engineering and Applied Sciences (NIBGE-C, PIEAS), Faisalabad 38000, Pakistan
| | - Kiran Kainat
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Nasrin Akhtar
- Industrial Biotechnology Division, National Institute for Biotechnology and Genetic Engineering College, Pakistan Institute of Engineering and Applied Sciences (NIBGE-C, PIEAS), Faisalabad 38000, Pakistan.
| | - Abdulrahman Alshammari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Post Box 2455, Riyadh 11451, Saudi Arabia
| | - Norah A Albekairi
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Post Box 2455, Riyadh 11451, Saudi Arabia
| | - Rida Fatima
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Arooj Amber
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Aqsa Bano
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Ifra Shabbir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Maryam Tahira
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Roger M Pallares
- Institute for Experimental Molecular Imaging, RWTH Aachen University Hospital, Aachen 52074, Germany
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2
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Fernández-Manteca MG, Ocampo-Sosa AA, Vecilla DF, Ruiz MS, Roiz MP, Madrazo F, Rodríguez-Grande J, Calvo-Montes J, Rodríguez-Cobo L, López-Higuera JM, Fariñas MC, Cobo A. Identification of hypermucoviscous Klebsiella pneumoniae K1, K2, K54 and K57 capsular serotypes by Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 319:124533. [PMID: 38820814 DOI: 10.1016/j.saa.2024.124533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 05/17/2024] [Accepted: 05/24/2024] [Indexed: 06/02/2024]
Abstract
Antimicrobial resistance poses a significant challenge in modern medicine, affecting public health. Klebsiella pneumoniae infections compound this issue due to their broad range of infections and the emergence of multiple antibiotic resistance mechanisms. Efficient detection of its capsular serotypes is crucial for immediate patient treatment, epidemiological tracking and outbreak containment. Current methods have limitations that can delay interventions and increase the risk of morbidity and mortality. Raman spectroscopy is a promising alternative to identify capsular serotypes in hypermucoviscous K. pneumoniae isolates. It provides rapid and in situ measurements with minimal sample preparation. Moreover, its combination with machine learning tools demonstrates high accuracy and reproducibility. This study analyzed the viability of combining Raman spectroscopy with one-dimensional convolutional neural networks (1-D CNN) to classify four capsular serotypes of hypermucoviscous K. pneumoniae: K1, K2, K54 and K57. Our approach involved identifying the most relevant Raman features for classification to prevent overfitting in the training models. Simplifying the dataset to essential information maintains accuracy and reduces computational costs and training time. Capsular serotypes were classified with 96 % accuracy using less than 30 Raman features out of 2400 contained in each spectrum. To validate our methodology, we expanded the dataset to include both hypermucoviscous and non-mucoid isolates and distinguished between them. This resulted in an accuracy rate of 94 %. The results obtained have significant potential for practical healthcare applications, especially for enabling the prompt prescription of the appropriate antibiotic treatment against infections.
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Affiliation(s)
- María Gabriela Fernández-Manteca
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Photonics Engineering Group, Universidad de Cantabria, Santander, Spain.
| | - Alain A Ocampo-Sosa
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Servicio de Microbiología, Hospital Universitario Marqués de Valdecilla, Santander, Spain; CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain
| | - Domingo Fernandez Vecilla
- Clinical Microbiology and Parasitology Department, Basurto University Hospital, Bilbao, Vizcaya, Spain; Biocruces Bizkaia Health Research Institute, Barakaldo, Vizcaya, Spain
| | - María Siller Ruiz
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Servicio de Microbiología, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - María Pía Roiz
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Servicio de Microbiología, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - Fidel Madrazo
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain
| | - Jorge Rodríguez-Grande
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Servicio de Microbiología, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - Jorge Calvo-Montes
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Servicio de Microbiología, Hospital Universitario Marqués de Valdecilla, Santander, Spain; CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain
| | - Luis Rodríguez-Cobo
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Photonics Engineering Group, Universidad de Cantabria, Santander, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - José Miguel López-Higuera
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Photonics Engineering Group, Universidad de Cantabria, Santander, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - María Carmen Fariñas
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain; Servicio de Enfermedades Infecciosas, Hospital Universitario Marqués de Valdecilla, Santander, Spain; Departamento de Medicina y Psiquiatría, Universidad de Cantabria, Santander, Spain
| | - Adolfo Cobo
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Photonics Engineering Group, Universidad de Cantabria, Santander, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain.
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Si YT, Xiong XS, Wang JT, Yuan Q, Li YT, Tang JW, Li YN, Zhang XY, Li ZK, Lai JX, Umar Z, Yang WX, Li F, Wang L, Gu B. Identification of chronic non-atrophic gastritis and intestinal metaplasia stages in the Correa's cascade through machine learning analyses of SERS spectral signature of non-invasively-collected human gastric fluid samples. Biosens Bioelectron 2024; 262:116530. [PMID: 38943854 DOI: 10.1016/j.bios.2024.116530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 06/13/2024] [Accepted: 06/15/2024] [Indexed: 07/01/2024]
Abstract
The progression of gastric cancer involves a complex multi-stage process, with gastroscopy and biopsy being the standard procedures for diagnosing gastric diseases. This study introduces an innovative non-invasive approach to differentiate gastric disease stage using gastric fluid samples through machine-learning-assisted surface-enhanced Raman spectroscopy (SERS). This method effectively identifies different stages of gastric lesions. The XGBoost algorithm demonstrates the highest accuracy of 96.88% and 91.67%, respectively, in distinguishing chronic non-atrophic gastritis from intestinal metaplasia and different subtypes of gastritis (mild, moderate, and severe). Through blinded testing validation, the model can achieve more than 80% accuracy. These findings offer new possibilities for rapid, cost-effective, and minimally invasive diagnosis of gastric diseases.
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Affiliation(s)
- Yu-Ting Si
- Medical Technology School, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Xue-Song Xiong
- Huai'an Hospital Affiliated to Yangzhou University (The Fifth People's Hospital of Huai'an), Huai'an, Jiangsu Province, China
| | - Jin-Ting Wang
- Huai'an Hospital Affiliated to Yangzhou University (The Fifth People's Hospital of Huai'an), Huai'an, Jiangsu Province, China
| | - Quan Yuan
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China; Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Yu-Ting Li
- Huai'an Hospital Affiliated to Yangzhou University (The Fifth People's Hospital of Huai'an), Huai'an, Jiangsu Province, China
| | - Jia-Wei Tang
- Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China; Division of Microbiology and Immunology, School of Biomedical Sciences, The University of Western Australia, Crawley, Western Australia, Australia
| | - Yong-Nian Li
- Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Xin-Yu Zhang
- Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Zheng-Kang Li
- Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Jin-Xin Lai
- Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Zeeshan Umar
- Marshall Laboratory of Biomedical Engineering, School of Medicine, Shenzhen University, Guangdong Province, China
| | - Wei-Xuan Yang
- Huai'an Hospital Affiliated to Yangzhou University (The Fifth People's Hospital of Huai'an), Huai'an, Jiangsu Province, China
| | - Fen Li
- Huai'an Hospital Affiliated to Yangzhou University (The Fifth People's Hospital of Huai'an), Huai'an, Jiangsu Province, China.
| | - Liang Wang
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China; Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China; Division of Microbiology and Immunology, School of Biomedical Sciences, The University of Western Australia, Crawley, Western Australia, Australia; The Center for Precision Health, School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, 6027, Australia.
| | - Bing Gu
- Medical Technology School, Xuzhou Medical University, Xuzhou, Jiangsu Province, China; Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China.
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Contessa CR, Moreira EC, Moraes CC, de Medeiros Burkert JF. Production and SERS characterization of bacteriocin-like inhibitory substances by latilactobacillus sakei in whey permeate powder: exploring natural antibacterial potential. Bioprocess Biosyst Eng 2024; 47:1723-1734. [PMID: 39014172 DOI: 10.1007/s00449-024-03065-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 07/10/2024] [Indexed: 07/18/2024]
Abstract
Bacteriocins are antimicrobial compounds that have awakened interest across several industries due to their effectiveness. However, their large-scale production often becomes unfeasible on an industrial scale, primarily because of high process costs. Addressing this challenge, this work analyzes the potential of using low-cost whey permeate powder, without any supplementation, to produce bacteriocin-like inhibitory substances (BLIS) through the fermentation of Latilactobacillus sakei. For this purpose, different concentrations of whey permeate powder (55.15 gL-1, 41.3 gL-1 and 27.5 gL-1) were used. The ability of L. sakei to produce BLIS was evaluated, as well as the potential of crude cell-free supernatant to act as a preservative. Raman spectroscopy and surface-enhanced Raman scattering (SERS) provided detailed insights into the composition and changes occurring during fermentation. SERS, in particular, enhanced peak definition significantly, allowing for the identification of key components, such as lactose, proteins, and phenylalanine, which are crucial in understanding the fermentation process and BLIS characteristics. The results revealed that the concentration of 55.15 gL-1 of whey permeate powder, in flasks without agitation and a culture temperature of 32.5 °C, presented the highest biological activity of BLIS, reaching 99% of inhibition of Escherichia coli and Staphylococcus aureus with minimum inhibitory concentration of 36-45%, respectively. BLIS production began within 60 h of cultivation and was associated with class II bacteriocins. The results demonstrate a promising approach for producing BLIS in an economical and environmentally sustainable manner, with potential implications for various industries.
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Affiliation(s)
- Camila Ramão Contessa
- Engineering and Science of Food Graduate Program, College of Chemistry and Food Engineering, Laboratory Bioprocess Engineering, Federal University of Rio Grande, PO Box 474, Rio Grande, RS, 96203-900, Brazil.
| | - Eduardo Ceretta Moreira
- Science and Engineering of Materials Graduate Program, Spectroscopy Laboratory, Federal University of Pampa, PO Box 1650, Bagé, RS, 96413170, Brazil
| | - Caroline Costa Moraes
- Science and Engineering of Materials Graduate Program, Laboratory of Microbiology and Food Toxicology, Federal University of Pampa, PO Box 1650, Bagé, RS, 96413170, Brazil
| | - Janaína Fernandes de Medeiros Burkert
- Engineering and Science of Food Graduate Program, College of Chemistry and Food Engineering, Laboratory Bioprocess Engineering, Federal University of Rio Grande, PO Box 474, Rio Grande, RS, 96203-900, Brazil
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Peng S, Zhang Z, Xin M, Liu D. SERS-based Ag NCs@PDMS flexible substrate combined with chemometrics for rapid detection of foodborne pathogens on egg surface. Mikrochim Acta 2024; 191:612. [PMID: 39305299 DOI: 10.1007/s00604-024-06669-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Accepted: 08/26/2024] [Indexed: 09/25/2024]
Abstract
An innovative method is introduced based on the combination of label-free surface-enhanced Raman scattering with advanced multivariate analysis. This technique allows both quantitative and qualitative assessment of Salmonella typhimurium and Escherichia coli on eggshells. Using silver nanocubes embedded in polydimethylsiloxane, we consistently achieved Raman spectra of bacteria. The stability of the Ag NCs@PDMS substrate is confirmed using rhodamine 6G over 30 days under standard conditions. Principal component analysis (PCA) effectively distinguishes between S. typhimurium and E. coli spectra. Partial least squares regression (PLS) models were developed for quantitative determination of bacteria on egg surfaces, yielding accurate results with minimal error. The S. typhimurium model achieves Rc2 = 0.9563 and RMSEC = 0.601 in calibration, and Rv2 = 0.9113 and RMSEV = 0.907 in validation. Similarly, the E. coli model achieves Rc2 = 0.9877 and RMSEC = 0.322 in calibration, and Rv2 = 0.9606 and RMSEV = 0.579 in validation. Recoveries validate PLS predictions by inoculating egg surfaces with varying bacterial amounts. Our study demonstrates the feasibility of SERS-PLS for quantitative determination of S. typhimurium and E. coli on eggshells, promising enhanced food safety protocols.
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Affiliation(s)
- Sasa Peng
- College of Food Science and Technology, Northwest University, 229 Taibai North Road, Xi'an, 710069, People's Republic of China
| | - Zhilong Zhang
- College of Food Science and Technology, Northwest University, 229 Taibai North Road, Xi'an, 710069, People's Republic of China
| | - Mingwei Xin
- College of Food Science and Technology, Northwest University, 229 Taibai North Road, Xi'an, 710069, People's Republic of China
| | - Dongli Liu
- College of Food Science and Technology, Northwest University, 229 Taibai North Road, Xi'an, 710069, People's Republic of China.
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Kanwal N, Rashid N, Irfan Majeed M, Nawaz H, Amber A, Zohaib M, Bano A, Albekairi NA, Alshammari A, Shahzadi A, Yaseen S, Bashir A. Surface-enhanced Raman spectroscopy for the characterization of xylanases enzyme. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 325:125065. [PMID: 39217950 DOI: 10.1016/j.saa.2024.125065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 08/20/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024]
Abstract
Xylanases are essential hydrolytic enzymes which break down the plant cell wall polysaccharide, xylan composed of D-xylose monomers. Surface-enhanced Raman Spectroscopy (SERS) was utilized for the characterization of interaction of xylanases with xylan at varying concentrations. The study focuses on the application of SERS for the characterization of enzymatic activity of xylanases causing hydrolysis of Xylan substrate with increase in its concentration which is substrate for this enzyme in the range of 0.2% to 1.0%. SERS differentiating features are identified which can be associated with xylanases treated with different concentrations of xylan. SERS measurements were performed using silver nanoparticles as SERS substrate to amplify Raman signal intensity for the characterization of xylan treated with xylanases. Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) were applied to analyze the spectral data to analyze differentiation between the SERS spectra of different samples. Mean SERS spectra revealed significant differences in spectral features particularly related to carbohydrate skeletal mode and O-C-O and C-C-C ring deformations. PCA scatter plot effectively differentiates data sets, demonstrating SERS ability to distinguish treated xylanases samples and the PC-loadings plot highlights the variables responsible for differentiation. PLS-DA was employed as a quantitative classification model for treated xylanase enzymes with increasing concentrations of xylan. The values of sensitivity, specificity, and accuracy were found to be 0.98%, 0.99%, and 100% respectively. Moreover, the AUC value was found to be 0.9947 which signifies the excellent performance of PLS-DA model. SERS combined with multivariate techniques, effectively characterized and differentiated xylanase samples as a result of interaction with different concentrations of the Xylan substrate. The identified SERS features can help to characterize xylanases treated with various concentrations of xylan with promising applications in the bio-processing and biotechnology industries.
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Affiliation(s)
- Naeema Kanwal
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Nosheen Rashid
- Department of Chemistry, University of Education, Faisalabad Campus, Faisalabad 38000, Pakistan
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan; School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan, PR China.
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Arooj Amber
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Zohaib
- Department of Zoology, Wildlife and Fisheries, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Aqsa Bano
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Norah A Albekairi
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Post Box 2455, Riyadh 11451, Saudi Arabia
| | - Abdulrahman Alshammari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Post Box 2455, Riyadh 11451, Saudi Arabia
| | - Aleena Shahzadi
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Sonia Yaseen
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Arslan Bashir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
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Moreno CN, Gomez JN, Taranto MP, Ledesma AE, Bustos AY. Molecular Insight into the Response of Lactic Acid Bacteria to Bile Acids. BIOTECH 2024; 13:29. [PMID: 39189208 PMCID: PMC11348023 DOI: 10.3390/biotech13030029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2024] [Revised: 07/25/2024] [Accepted: 07/30/2024] [Indexed: 08/28/2024] Open
Abstract
Bile acids (BAs) are the main endogenous modulators of the composition and metabolic activity of the intestinal microbiota. In the present work, the effect of conjugated (glycodeoxycholic, glycocholic, taurodeoxycholic, taurocholic acids) and free BAs [cholic acid (CA) and deoxycholic acid (DCA)] on the survival, biological molecules, and structural and surface properties of two potential probiotic lactic acid bacteria (LAB) was evaluated. For this, viability assays, Raman spectroscopy, scanning electron microscopy (SEM), and zeta potential (ZP) measurements were employed. Our results evidenced that free BAs were more toxic than conjugates, with CA being significantly more harmful than deoxycholic acid (DCA). RAMAN studies show that BAs modify the bands corresponding to proteins, lipids, carbohydrates, and DNA. SEM showed that BAs cause surface distortions with depressions and fold formation, as well as incomplete cell division. DCA was the one that least altered the ZP of bacteria when compared to CA and taurodeoxycholic acid, with gradual changes towards more positive values. In general, the magnitude of these effects was different according to the BA and its concentration, being more evident in the presence of CA, even at low concentrations, which would explain its greater inhibitory effect. This work provides solid evidence on the effects of BAs on LAB that will allow for the development of strategies by which to modulate the composition of the microbiota positively.
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Affiliation(s)
- Caren N. Moreno
- Centro de Investigación en Biofísica Aplicada y Alimentos (CIBAAL-UNSE-CONICET), RN 9, Km 1125, Santiago del Estero 4206, Argentina; (C.N.M.); (J.N.G.); (A.E.L.)
| | - Jorge N. Gomez
- Centro de Investigación en Biofísica Aplicada y Alimentos (CIBAAL-UNSE-CONICET), RN 9, Km 1125, Santiago del Estero 4206, Argentina; (C.N.M.); (J.N.G.); (A.E.L.)
| | - María P. Taranto
- Centro de Referencia de Lactobacilos (CERELA-CONICET), Chacabuco 145, San Miguel de Tucumán 4000, Argentina;
| | - Ana E. Ledesma
- Centro de Investigación en Biofísica Aplicada y Alimentos (CIBAAL-UNSE-CONICET), RN 9, Km 1125, Santiago del Estero 4206, Argentina; (C.N.M.); (J.N.G.); (A.E.L.)
- Departamento Académico de Química, Facultad de Ciencias Exactas y Tecnologías, Universidad Nacional de Santiago del Estero, Av. Belgrano Sur 1912, Santiago del Estero 4200, Argentina
| | - Ana Y. Bustos
- Centro de Investigación en Biofísica Aplicada y Alimentos (CIBAAL-UNSE-CONICET), RN 9, Km 1125, Santiago del Estero 4206, Argentina; (C.N.M.); (J.N.G.); (A.E.L.)
- Facultad de Agronomía y Agroindustrias, Universidad Nacional de Santiago del Estero. Av. Belgrano Sur 1912, Santiago del Estero 4200, Argentina
- Facultad de Humanidades, Ciencias Sociales y de la Salud, Universidad Nacional de Santiago del Estero. Av. Belgrano Sur 1912, Santiago del Estero 4200, Argentina
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Wang Z, Li Y, Zhai J, Yang S, Sun B, Liang P. Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach. Talanta 2024; 275:126138. [PMID: 38677164 DOI: 10.1016/j.talanta.2024.126138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 04/29/2024]
Abstract
Raman spectroscopy is a general and non-destructive detection technique that can obtain detailed information of the chemical structure of materials. In the past, when using chemometric algorithms to analyze the Raman spectra of mixtures, the challenges of complex spectral overlap and noise often limited the accurate identification of components. The emergence of deep learning has introduced a novel approach to qualitative analysis of mixed Raman spectra. In this paper, we propose a deep learning-based Raman spectroscopy qualitative analysis algorithm (RST) by borrowing the ideas of convolutional neural network and Transformer. By transforming the Raman spectrum into 64 word vectors, the contribution weights of each word vector to the components are obtained. For the 75 spectral data used for validation, the positive identification rate can reach 100.00 %, the recall rate can reach 99.3 %, the average identification score can reach 9.51, and it is applicable to the fields of Raman and surface-enhanced Raman spectroscopy. Furthermore, compared with traditional CNN models, RST has excellent accuracy and robustness in identifying components in complex mixtures. The model's interpretability has been enhanced, aiding in a deeper understanding of spectroscopic learning patterns for future analysis of more complex mixtures.
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Affiliation(s)
- Zilong Wang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China; Xiamen Palantier Technology Co., Ltd., Xiamen, 361000, China
| | - Yunfeng Li
- College of Information Engineering, China Jiliang University, Hangzhou, 310018, China
| | - Jinglei Zhai
- School of Electrical and Information Engineering, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, 300072, China
| | - Siwei Yang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China
| | - Biao Sun
- School of Electrical and Information Engineering, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, 300072, China
| | - Pei Liang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China; Xiamen Palantier Technology Co., Ltd., Xiamen, 361000, China.
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Ali A, Nawaz H, Irfan Majeed M, Ghamkhar M. Quantitative analysis of solid dosage forms of Atenolol by Raman spectroscopy. Drug Dev Ind Pharm 2024; 50:619-627. [PMID: 38980706 DOI: 10.1080/03639045.2024.2377331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 06/26/2024] [Accepted: 07/02/2024] [Indexed: 07/10/2024]
Abstract
OBJECTIVE To develop a Raman spectroscopy-based analytical model for quantification of solid dosage forms of active pharmaceutical ingredient (API) of Atenolol.Significance: For the quantitative analysis of pharmaceutical drugs, Raman Spectroscopy is a reliable and fast detection method. As part of this study, Raman Spectroscopy is explored for the quantitative analysis of different concentrations of Atenolol. METHODS Various solid-dosage forms of Atenolol were prepared by mixing API with excipients to form different solid-dosage formulations of Atenolol. Multivariate data analysis techniques, such as Principal Component Analysis (PCA) and Partial least square regression (PLSR) were used for the qualitative and quantitative analysis, respectively. RESULTS As the concentration of the drug increased in formulation, the peak intensities of the distinctive Raman spectral characteristics associated with the API (Atenolol) gradually increased. Raman spectral data sets were classified using PCA due to their distinctive spectral characteristics. Additionally, a prediction model was built using PLSR analysis to assess the quantitative relationship between various API (Atenolol) concentrations and spectral features. With a goodness of fit value of 0.99, the root mean square errors of calibration (RMSEC) and prediction (RMSEP) were determined to be 1.0036 and 2.83 mg, respectively. The API content in the blind/unknown Atenolol formulation was determined as well using the PLSR model. CONCLUSIONS Based on these results, Raman spectroscopy may be used to quickly and accurately analyze pharmaceutical samples and for their quantitative determination.
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Affiliation(s)
- Arslan Ali
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | | | - Madiha Ghamkhar
- Department of Mathematics and Statistics, University of Agriculture Faisalabad, Faisalabad, Pakistan
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10
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Anwer A, Shahzadi A, Nawaz H, Majeed MI, Alshammari A, Albekairi NA, Hussain MU, Amin I, Bano A, Ashraf A, Rehman N, Pallares RM, Akhtar N. Differentiation of different dibenzothiophene (DBT) desulfurizing bacteria via surface-enhanced Raman spectroscopy (SERS). RSC Adv 2024; 14:20290-20299. [PMID: 38932985 PMCID: PMC11200166 DOI: 10.1039/d4ra01735h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 05/01/2024] [Indexed: 06/28/2024] Open
Abstract
Fossil fuels are considered vital natural energy resources on the Earth, and sulfur is a natural component present in them. The combustion of fossil fuels releases a large amount of sulfur in the form of SO x in the atmosphere. SO x is the major cause of environmental problems, mainly air pollution. The demand for fuels with ultra-low sulfur is growing rapidly. In this aspect, microorganisms are proven extremely effective in removing sulfur through a process known as biodesulfurization. A major part of sulfur in fossil fuels (coal and oil) is present in thiophenic structures such as dibenzothiophene (DBT) and substituted DBTs. In this study, the identification and characterization of DBT desulfurizing bacteria (Chryseobacterium sp. IS, Gordonia sp. 4N, Mycolicibacterium sp. J2, and Rhodococcus sp. J16) based on their specific biochemical constituents were conducted using surface-enhanced Raman spectroscopy (SERS). By differentiating DBT desulfurizing bacteria, researchers can gain insights into their unique characteristics, thus leading to improved biodesulfurization strategies. SERS was used to differentiate all these species based on their biochemical differences and different SERS vibrational bands, thus emerging as a potential technique. Moreover, multivariate data analysis techniques such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to differentiate these DBT desulfurizing bacteria on the basis of their characteristic SERS spectral signals. For all these isolates, the accuracy, sensitivity, and specificity are above 90%, and an AUC (area under the curve) value of close to 1 was achieved for all PLS-DA models.
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Affiliation(s)
- Ayesha Anwer
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | - Aqsa Shahzadi
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | - Abdulrahman Alshammari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University Post Box 2455 Riyadh 11451 Saudi Arabia
| | - Norah A Albekairi
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University Post Box 2455 Riyadh 11451 Saudi Arabia
| | - Muhammad Umar Hussain
- Industrial Biotechnology Division, National Institute for Biotechnology and Genetic Engineering College, Pakistan Institute of Engineering and Applied Sciences (NIBGE-C, PIEAS) Faisalabad 38000 Pakistan
| | - Itfa Amin
- Industrial Biotechnology Division, National Institute for Biotechnology and Genetic Engineering College, Pakistan Institute of Engineering and Applied Sciences (NIBGE-C, PIEAS) Faisalabad 38000 Pakistan
| | - Aqsa Bano
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | - Ayesha Ashraf
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | - Nimra Rehman
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | - Roger M Pallares
- Institute for Experimental Molecular Imaging, RWTH Aachen University Hospital Aachen 52074 Germany
| | - Nasrin Akhtar
- Industrial Biotechnology Division, National Institute for Biotechnology and Genetic Engineering College, Pakistan Institute of Engineering and Applied Sciences (NIBGE-C, PIEAS) Faisalabad 38000 Pakistan
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11
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Liu X, Jiang S, Zhang T, Xu Z, Liu L, Zhang Z, Pan S, Li Y. "Magnet" Based on Activated Silver Nanoparticles Adsorbed Bacteria to Predict Refractory Apical Periodontitis Via Surface-Enhanced Raman Scattering. ACS APPLIED MATERIALS & INTERFACES 2024; 16:8499-8508. [PMID: 38335515 DOI: 10.1021/acsami.3c16677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2024]
Abstract
Refractory apical periodontitis (RAP) is an endodontic apical inflammatory disease caused by Enterococcus faecalis (E. faecalis). Bacterial detection using surface-enhanced Raman scattering (SERS) technology is a hot research topic, but the specific and direct detection of oral bacteria is a challenge, especially in real clinical samples. In this paper, we develop a novel SERS-based green platform for label-free detection of oral bacteria. The platform was built on silver nanoparticles with a two-step enhancement way using NaBH4 and sodium (Na+) to form "hot spots," which resulted in an enhanced SERS fingerprint of E. faecalis with fast, quantitative, lower-limit, reproducibility, and stability. In combination with machine learning, four different oral bacteria (E. faecalis, Porphyromonas gingivalis, Streptococcus mutans, and Escherichia coli) could be intelligently distinguished. The unlabeled detection method emphasized the specificity of E. faecalis in simulated saliva, serum, and even real samples from patients with clinical root periapical disease. In addition, the assay has been shown to be environmentally friendly and without secondary contamination through antimicrobial assays. The proposed label-free, rapid, safe, and green SERS detection strategy for oral bacteria provided an innovative solution for the early diagnosis and prevention of RAP and other perioral diseases.
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Affiliation(s)
- Xin Liu
- The First Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Department of Endodontics, School of Stomatology, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
| | - Shen Jiang
- College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
| | - Ting Zhang
- College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Department of Inorganic Chemistry and Physical Chemistry, College of Pharmacy, Harbin Medical University, Heilongjiang 150081, P. R. China
| | - Ziming Xu
- The First Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Department of Endodontics, School of Stomatology, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
| | - Ling Liu
- College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
| | - Zhe Zhang
- College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- College of Public Health, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
| | - Shuang Pan
- The First Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
- Department of Endodontics, School of Stomatology, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
| | - Yang Li
- College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine University of Oulu, 2125B, Aapistie 5A, Oulu 90220, Finland
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12
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Tabussam T, Shehnaz H, Majeed MI, Nawaz H, Alghamdi AA, Iqbal MA, Shahid M, Shahid U, Umer R, Rehman MT, Farooq U, Hassan A, Imran M. Surface-enhanced Raman spectroscopy for studying the interaction of organometallic compound bis(1,3-dihexylimidazole-2-yl) silver(i) hexafluorophosphate (v) with the biofilm of Escherichia coli. RSC Adv 2024; 14:7112-7123. [PMID: 38419676 PMCID: PMC10899858 DOI: 10.1039/d3ra08667d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 01/23/2024] [Indexed: 03/02/2024] Open
Abstract
Escherichia coli biofilms are a major cause of gastrointestinal tract diseases, such as esophageal, stomach and intestinal diseases. Nowadays, these are the most commonly occurring diseases caused by consuming contaminated food. In this study, we evaluated the efficacy of probiotics in controlling multidrug-resistant E. coli and reducing its ability to form biofilms. Our results substantiate the effective use of probiotics as antimicrobial alternatives and to eradicate biofilms formed by multidrug-resistant E. coli. In this research, surface enhanced Raman spectroscopy (SERS) was utilized to identify and evaluate Escherichia coli biofilms and their response to the varying concentrations of the organometallic compound bis(1,3-dihexylimidazole-2-yl) silver(i) hexafluorophosphate (v). Given the escalating challenge of antibiotic resistance in bacteria that form biofilms, understanding the impact of potential antibiotic agents is crucial for the healthcare sector. The combination of SERS with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) enabled the detection and characterization of the biofilm, providing insights into the biochemical changes induced by the antibiotic candidate. The identified SERS spectral features served as indicators for elucidating the mode of action of the potential drug on the biofilm. Through PCA and PLS-DA, metabolic variations allowing the differentiation and classification of unexposed biofilms and biofilms exposed to different concentrations of the synthesized antibiotic were successfully identified, with 95% specificity, 96% sensitivity, and a 0.75 area under the curve (AUC). This research underscores the efficiency of surface enhanced Raman spectroscopy in differentiating the impact of potential antibiotic agents on E. coli biofilms.
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Affiliation(s)
- Tania Tabussam
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | - Hina Shehnaz
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | - Abeer Ahmed Alghamdi
- Department of Physics, College of Science, Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia
| | - Muhammad Adnan Iqbal
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | - Muhammad Shahid
- Department of Biochemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | - Urwa Shahid
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | - Rabiea Umer
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | | | - Umer Farooq
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | - Ahmad Hassan
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad 38000 Pakistan
| | - Muhammad Imran
- Department of Chemistry, Faculty of Science, King Khalid University P.O. Box 9004 Abha 61413 Saudi Arabia
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13
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Tahir F, Kamran A, Majeed MI, Alghamdi AA, Javed MR, Nawaz H, Iqbal MA, Tahir M, Tariq A, Rashid N, Shahid U, Hassan A, Shoukat US. Surface-Enhanced Raman Scattering (SERS) in Combination with PCA and PLS-DA for the Evaluation of Antibacterial Activity of 1-Isopentyl-3-pentyl-1 H-imidazole-3-ium Bromide against Bacillus subtilis. ACS OMEGA 2024; 9:6861-6872. [PMID: 38371792 PMCID: PMC10870359 DOI: 10.1021/acsomega.3c08196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/05/2024] [Accepted: 01/16/2024] [Indexed: 02/20/2024]
Abstract
In the current study, surface-enhanced Raman scattering (SERS) was performed to evaluate the antibacterial activity of lab-synthesized drug (1-isopentyl-3-pentyl-1H-imidazole-3-ium bromide salt) and commercial drug tinidazole againstBacillus subtilis. The changes in SERS spectral features were studied for unexposed bacillus and exposed one with various dosages of drug synthesized in the lab (1-isopentyl-3-pentyl-1H-imidazole-3-ium bromide salt), and SERS bands were assigned associated with the drug-induced biochemical alterations in bacteria. Multivariate data analysis tools including principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) have been utilized to analyze the antibacterial activity of the imidazole derivative (lab drug). PCA was employed in differentiating all the SERS spectral data sets associated with the various doses of the lab-synthesized drug. There is clear discrimination among the spectral data sets of a bacterial strain treated with different concentrations of the drug, which are analyzed by PLS-DA with 86% area under the curve in receiver operating curve (ROC), 99% sensitivity, 100% accuracy, and 98% specificity. Various dominant spectral features are observed with a gradual increase in the different concentrations of the applied drug including 715, 850, 1002, 1132, 1237, 1396, 1416, and 1453 cm-1, which indicate the possible biochemical changes caused in bacteria during the antibacterial activity of the lab-synthesized drug. Overall, the findings show that imidazole and imidazolium compounds generated from tinidazole with various alkyl lengths in the amide substitution can be effective antibacterial agents with low cytotoxicity in humans, and these results indicate the efficiency of SERS in pharmaceuticals and biomedical applications.
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Affiliation(s)
- Fatima Tahir
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
| | - Ali Kamran
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Irfan Majeed
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
| | - Abeer Ahmed Alghamdi
- Department
of Physics, College of Science, Princess
Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Muhammad Rizwan Javed
- Department
of Bioinformatics and Biotechnology, Government
College University Faisalabad (GCUF), Faisalabad 38000, Pakistan
| | - Haq Nawaz
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Adnan Iqbal
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Tahir
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
| | - Anam Tariq
- Department
of Biochemistry, Government College University
Faisalabad (GCUF), Faisalabad 38000, Pakistan
| | - Nosheen Rashid
- Department
of Chemistry, University of Education, Faisalabad
Campus, Faisalabad 38000, Pakistan
| | - Urwa Shahid
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
| | - Ahmad Hassan
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
| | - Umar Sohail Shoukat
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
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14
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Tang JW, Li F, Liu X, Wang JT, Xiong XS, Lu XY, Zhang XY, Si YT, Umar Z, Tay ACY, Marshall BJ, Yang WX, Gu B, Wang L. Detection of Helicobacter pylori Infection in Human Gastric Fluid Through Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms. J Transl Med 2024; 104:100310. [PMID: 38135155 DOI: 10.1016/j.labinv.2023.100310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/30/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023] Open
Abstract
Diagnostic methods for Helicobacter pylori infection include, but are not limited to, urea breath test, serum antibody test, fecal antigen test, and rapid urease test. However, these methods suffer drawbacks such as low accuracy, high false-positive rate, complex operations, invasiveness, etc. Therefore, there is a need to develop simple, rapid, and noninvasive detection methods for H. pylori diagnosis. In this study, we propose a novel technique for accurately detecting H. pylori infection through machine learning analysis of surface-enhanced Raman scattering (SERS) spectra of gastric fluid samples that were noninvasively collected from human stomachs via the string test. One hundred participants were recruited to collect gastric fluid samples noninvasively. Therefore, 12,000 SERS spectra (n = 120 spectra/participant) were generated for building machine learning models evaluated by standard metrics in model performance assessment. According to the results, the Light Gradient Boosting Machine algorithm exhibited the best prediction capacity and time efficiency (accuracy = 99.54% and time = 2.61 seconds). Moreover, the Light Gradient Boosting Machine model was blindly tested on 2,000 SERS spectra collected from 100 participants with unknown H. pylori infection status, achieving a prediction accuracy of 82.15% compared with qPCR results. This novel technique is simple and rapid in diagnosing H. pylori infection, potentially complementing current H. pylori diagnostic methods.
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Affiliation(s)
- Jia-Wei Tang
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China; School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Fen Li
- Department of Laboratory Medicine, The Affiliated Huaian Hospital of Yangzhou University, The Fifth People's Hospital of Huaian, Huaian, Jiangsu Province, China
| | - Xin Liu
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Jin-Ting Wang
- Department of Gastroenterology, The Affiliated Huaian Hospital of Yangzhou University, The Fifth People's Hospital of Huaian, Huaian, Jiangsu Province, China
| | - Xue-Song Xiong
- Department of Laboratory Medicine, The Affiliated Huaian Hospital of Yangzhou University, The Fifth People's Hospital of Huaian, Huaian, Jiangsu Province, China
| | - Xiang-Yu Lu
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Xin-Yu Zhang
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Yu-Ting Si
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Zeeshan Umar
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China; Marshall Laboratory of Biomedical Engineering, International Cancer Center, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
| | - Alfred Chin Yen Tay
- Marshall Laboratory of Biomedical Engineering, International Cancer Center, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China; Marshall Medical Research Center, Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Marshall International Digestive Diseases Hospital, Zhengzhou University, Zhengzhou, China; The Marshall Centre for Infectious Diseases Research and Training, University of Western Australia, Perth, Western Australia, Australia
| | - Barry J Marshall
- Marshall Laboratory of Biomedical Engineering, International Cancer Center, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China; Marshall Medical Research Center, Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Marshall International Digestive Diseases Hospital, Zhengzhou University, Zhengzhou, China; The Marshall Centre for Infectious Diseases Research and Training, University of Western Australia, Perth, Western Australia, Australia
| | - Wei-Xuan Yang
- Department of Gastroenterology, The Affiliated Huaian Hospital of Yangzhou University, The Fifth People's Hospital of Huaian, Huaian, Jiangsu Province, China.
| | - Bing Gu
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China.
| | - Liang Wang
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China; Division of Microbiology and Immunology, School of Biomedical Sciences, University of Western Australia, Perth, Western Australia, Australia.
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15
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Naman A, Tahseen H, Nawaz H, Majeed MI, Ali A, Haque A, Akbar MU, Mehmood N, Nosheen R, Nadeem S, Shahzadi A, Imran M. Surface-enhanced Raman spectroscopy for characterization of supernatant samples of biofilm forming bacterial strains. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 305:123414. [PMID: 37852119 DOI: 10.1016/j.saa.2023.123414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 09/07/2023] [Accepted: 09/13/2023] [Indexed: 10/20/2023]
Abstract
Staphylococcus epidermidis is considered major cause of nosocomial infections. Its pathogenicity is mainly due to the ability to form biofilms on different surfaces, particularly indwelling medical devices. This bacterium consists of different strains consisting of non, medium and strong biofilm forming ones. Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical technique that can be used to detect and analyze biochemical composition of the supernatant samples of different strains of bacteria including non, medium and strong biofilm forming bacterial strains. SERS is a powerful technique for the robust, reliable, rapid detection and discrimination of bacteria in the form of characteristic SERS spectral features which can be used for detection and classification. SERS is used to differentiate three classes of bacteria with respect to their biofilm forming ability. Silver nanoparticles (Ag NPs) are used as SERS substrate and synthesized with chemical reduction method. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are used to discriminate SERS spectral data sets of non, medium and strong biofilm forming bacteria. PLS-DA analysis is a multivariate statistical technique that can be used to analyze data from bacterial sets.
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Affiliation(s)
- Abdul Naman
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Hira Tahseen
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Aamir Ali
- National Institute for Biotechnology and Genetic Engineering College, Pakistan Institute of Engineering and Applied Sciences (NIBGE-C, PIEAS), Jhang Road, Faisalabad 38000, Pakistan
| | - Asma Haque
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad 38000, Pakistan
| | - Muhammad Umair Akbar
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad 38000, Pakistan
| | - Nasir Mehmood
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Rashid Nosheen
- Department of Chemistry, University of Education, Faisalabad Campus, Faisalabad 38000, Pakistan.
| | - Sana Nadeem
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Aqsa Shahzadi
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Imran
- Department of Chemistry, Faculty of Science, King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
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16
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Ma Y, Chen R, Zhang R, Liang J, Ren S, Gao Z. Application of DNA-fueled molecular machines in food safety testing. Compr Rev Food Sci Food Saf 2024; 23:1-22. [PMID: 38284608 DOI: 10.1111/1541-4337.13299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 12/15/2023] [Accepted: 12/21/2023] [Indexed: 01/30/2024]
Abstract
Food is consumed by humans, which is indispensable to human life. Therefore, considerable attention of the whole society has been paid to food safety. Over the last few years, dramatic social development has brought new challenges to food safety, making developing new and quick methods for on-site food safety testing an important necessity. As a result, DNA-fueled molecular machines, characterized by high efficiency, accuracy, and sensitivity in testing, have come into the spotlight, based on which sensors can be constructed to detect toxic and harmful substances in food products. This study reviewed recent research on several DNA-fueled molecular machines, including DNA tweezers, DNA walkers, and DNA origami, for rapidly detecting toxic and harmful substances. Based on the above studies, the sensitivity and timeliness of several DNA molecular machines were summarized and compared, and the development prospect of DNA fuel molecular machines in the field of food safety detection was prospected.
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Affiliation(s)
- Yujing Ma
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, China
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science & Technology, Tianjin, China
| | - Ruipeng Chen
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, China
| | - Rui Zhang
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, China
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science & Technology, Tianjin, China
| | - Jun Liang
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science & Technology, Tianjin, China
| | - Shuyue Ren
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, China
| | - Zhixian Gao
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, China
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Du Y, Hu L, Wu G, Tang Y, Cai X, Yin L. Diagnoses in multiple types of cancer based on serum Raman spectroscopy combined with a convolutional neural network: Gastric cancer, colon cancer, rectal cancer, lung cancer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 298:122743. [PMID: 37119637 DOI: 10.1016/j.saa.2023.122743] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 04/02/2023] [Accepted: 04/11/2023] [Indexed: 05/26/2023]
Abstract
Cancer is one of the major diseases that seriously threaten human health. Timely screening is beneficial to the cure of cancer. There are some shortcomings in current diagnosis methods, so it is very important to find a low-cost, fast, and nondestructive cancer screening technology. In this study, we demonstrated that serum Raman spectroscopy combined with a convolutional neural network model can be used for the diagnosis of four types of cancer including gastric cancer, colon cancer, rectal cancer, and lung cancer. Raman spectra database containing four types of cancer and healthy controls was established and a one-dimensional convolutional neural network (1D-CNN) was constructed. The classification accuracy of the Raman spectra combined with the 1D-CNN model was 94.5%. A convolutional neural network (CNN) is regarded as a black box, and the learning mechanism of the model is not clear. Therefore, we tried to visualize the CNN features of each convolutional layer in the diagnosis of rectal cancer. Overall, Raman spectroscopy combined with the CNN model is an effective tool that can be used to distinguish different cancer from healthy controls.
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Affiliation(s)
- Yu Du
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Lin Hu
- Department of Laboratory Medicine, The First Affiliated Hospital of Chongqing Medical University, 400016 Chongqing, China
| | - Guohua Wu
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
| | - Yishu Tang
- Department of Laboratory Medicine, The First Affiliated Hospital of Chongqing Medical University, 400016 Chongqing, China.
| | - Xiongwei Cai
- Department of Gynecology, Chongqing Health Center for Women and Children, Women and Children's Hospital of Chongqing Medical University, 400016 Chongqing, China
| | - Longfei Yin
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
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18
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Dos Santos DP, Sena MM, Almeida MR, Mazali IO, Olivieri AC, Villa JEL. Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends. Anal Bioanal Chem 2023; 415:3945-3966. [PMID: 36864313 PMCID: PMC9981450 DOI: 10.1007/s00216-023-04620-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/02/2023] [Accepted: 02/20/2023] [Indexed: 03/04/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.
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Affiliation(s)
- Diego P Dos Santos
- Instituto de Química, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, 13083-970, Brazil
| | - Marcelo M Sena
- Departamento de Química, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG, 31270-901, Brazil
- Instituto Nacional de Ciência e Tecnologia em Bioanalítica (INCT Bio), Campinas, SP, 13083-970, Brazil
| | - Mariana R Almeida
- Departamento de Química, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG, 31270-901, Brazil
| | - Italo O Mazali
- Instituto de Química, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, 13083-970, Brazil
| | - Alejandro C Olivieri
- Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Instituto de Química Rosario (IQUIR-CONICET), Suipacha 531, 2000, Rosario, Argentina
| | - Javier E L Villa
- Instituto de Química, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, 13083-970, Brazil.
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Fernández-Manteca MG, Ocampo-Sosa AA, Ruiz de Alegría-Puig C, Pía Roiz M, Rodríguez-Grande J, Madrazo F, Calvo J, Rodríguez-Cobo L, López-Higuera JM, Fariñas MC, Cobo A. Automatic classification of Candida species using Raman spectroscopy and machine learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 290:122270. [PMID: 36580749 DOI: 10.1016/j.saa.2022.122270] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 11/29/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
One of the problems that most affect hospitals is infections by pathogenic microorganisms. Rapid identification and adequate, timely treatment can avoid fatal consequences and the development of antibiotic resistance, so it is crucial to use fast, reliable, and not too laborious techniques to obtain quick results. Raman spectroscopy has proven to be a powerful tool for molecular analysis, meeting these requirements better than traditional techniques. In this work, we have used Raman spectroscopy combined with machine learning algorithms to explore the automatic identification of eleven species of the genus Candida, the most common cause of fungal infections worldwide. The Raman spectra were obtained from more than 220 different measurements of dried drops from pure cultures of each Candida species using a Raman Confocal Microscope with a 532 nm laser excitation source. After developing a spectral preprocessing methodology, a study of the quality and variability of the measured spectra at the isolate and species level, and the spectral features contributing to inter-class variations, showed the potential to discriminate between those pathogenic yeasts. Several machine learning and deep learning algorithms were trained using hyperparameter optimization techniques to find the best possible classifier for this spectral data, in terms of accuracy and lowest possible overfitting. We found that a one-dimensional Convolutional Neural Network (1-D CNN) could achieve above 80 % overall accuracy for the eleven classes spectral dataset, with good generalization capabilities.
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Affiliation(s)
| | - Alain A Ocampo-Sosa
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Servicio de Microbiología, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - Carlos Ruiz de Alegría-Puig
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Servicio de Microbiología, Hospital Universitario Marqués de Valdecilla, Santander, Spain; CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain
| | - María Pía Roiz
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Servicio de Microbiología, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - Jorge Rodríguez-Grande
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Servicio de Microbiología, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - Fidel Madrazo
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain
| | - Jorge Calvo
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Servicio de Microbiología, Hospital Universitario Marqués de Valdecilla, Santander, Spain; CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain
| | - Luis Rodríguez-Cobo
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Photonics Engineering Group, Universidad de Cantabria, Santander, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - José Miguel López-Higuera
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Photonics Engineering Group, Universidad de Cantabria, Santander, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - María Carmen Fariñas
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain; Servicio de Enfermedades Infecciosas, Hospital Universitario Marqués de Valdecilla, Santander, Spain; Departamento de Medicina y Psiquiatría, Universidad de Cantabria, Santander, Spain
| | - Adolfo Cobo
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Photonics Engineering Group, Universidad de Cantabria, Santander, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain.
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20
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Hu W, Li Z, Ou H, Wang X, Wang Q, Tao Z, Huang S, Huang Y, Wang G, Pan X. Novosphingobium album sp. nov., Novosphingobium organovorum sp. nov. and Novosphingobium mangrovi sp. nov. with the organophosphorus pesticides degrading ability isolated from mangrove sediments. Int J Syst Evol Microbiol 2023; 73. [PMID: 37115596 DOI: 10.1099/ijsem.0.005843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023] Open
Abstract
Members of the genus Novosphingobium were frequently isolated from polluted environments and possess great bioremediation potential. Here, three species, designated B2637T, B2580T and B1949T, were isolated from mangrove sediments and might represent novel species in the genus Novosphingobium based on a polyphasic taxonomy study. Phylogenomic analysis revealed that strains B2580T, B1949T and B2637T clustered with Novosphingobium naphthalenivorans NBRC 102051T, 'N. profundi' F72 and N. decolorationis 502str22T, respectively. The average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) values between isolates and their closely related species were less than 94 and 54 %, respectively, all below the threshold of species discrimination. The sizes of the genomes of isolates B2580T, B2637T and B1949T ranged from 4.4 to 4.6 Mb, containing 63.3-66.4 % G+C content. Analysis of their genomic sequences identified genes related to pesticide degradation, heavy-metal resistance, nitrogen fixation, antibiotic resistance and sulphur metabolism, revealing the biotechnology potential of these isolates. Except for B2637T, B1949T and B2580T were able to grow in the presence of quinalphos. Results from these polyphasic taxonomic analyses support the affiliation of these strains to three novel species within the genus Novosphingobium, for which we propose the name Novosphingobium album sp. nov. B2580T (=KCTC 72967T=MCCC 1K04555T), Novosphingobium organovorum sp. nov. B1949T (=KCTC 92158T=MCCC 1K03763T) and Novosphingobium mangrovi sp. nov. B2637T (KCTC 72969T=MCCC 1K04460T).
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Affiliation(s)
- Wenjin Hu
- Guangxi Key Laboratory of Marine Natural Products and Combinatorial Biosynthesis Chemistry, Institute of Eco-Environmental Research, Guangxi Academy of Sciences, Nanning, 530007, PR China
- National Engineering Research Center for Non-Food Biorefinery, State Key Laboratory of Non-Food Biomass and Enzyme Technology, Guangxi Key Laboratory of Bio-refinery, Guangxi Biomass Engineering Technology Research Center, Guangxi Academy of Sciences, Nanning, 530007, PR China
| | - Zhe Li
- Guangxi Key Laboratory of Marine Natural Products and Combinatorial Biosynthesis Chemistry, Institute of Eco-Environmental Research, Guangxi Academy of Sciences, Nanning, 530007, PR China
| | - Haisheng Ou
- Guangxi Normal University School of Physical Science and Technology, Guilin, 541004, PR China
| | - Xiaochun Wang
- Guangxi Key Laboratory of Marine Natural Products and Combinatorial Biosynthesis Chemistry, Institute of Eco-Environmental Research, Guangxi Academy of Sciences, Nanning, 530007, PR China
| | - Qiaozhen Wang
- Guangxi Key Laboratory of Marine Natural Products and Combinatorial Biosynthesis Chemistry, Institute of Eco-Environmental Research, Guangxi Academy of Sciences, Nanning, 530007, PR China
| | - Zhanhua Tao
- Guangxi Key Laboratory of Marine Natural Products and Combinatorial Biosynthesis Chemistry, Institute of Eco-Environmental Research, Guangxi Academy of Sciences, Nanning, 530007, PR China
| | - Shushi Huang
- Guangxi Key Laboratory of Marine Natural Products and Combinatorial Biosynthesis Chemistry, Institute of Eco-Environmental Research, Guangxi Academy of Sciences, Nanning, 530007, PR China
| | - Yuanlin Huang
- Guangxi Key Laboratory of Marine Natural Products and Combinatorial Biosynthesis Chemistry, Institute of Eco-Environmental Research, Guangxi Academy of Sciences, Nanning, 530007, PR China
| | - Guiwen Wang
- Guangxi Key Laboratory of Marine Natural Products and Combinatorial Biosynthesis Chemistry, Institute of Eco-Environmental Research, Guangxi Academy of Sciences, Nanning, 530007, PR China
| | - Xinli Pan
- Guangxi Key Laboratory of Marine Natural Products and Combinatorial Biosynthesis Chemistry, Institute of Eco-Environmental Research, Guangxi Academy of Sciences, Nanning, 530007, PR China
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21
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Nawaz MZ, Nawaz H, Majeed MI, Rashid N, Javed MR, Naz S, Ali MZ, Sabir A, Sadaf N, Rafiq N, Shakeel M, Ali Z, Amin I. Comparison of surface-enhanced Raman spectral data sets of filtrate portions of serum samples of hepatitis B and Hepatitis C infected patients obtained by centrifugal filtration. Photodiagnosis Photodyn Ther 2023; 42:103532. [PMID: 36963645 DOI: 10.1016/j.pdpdt.2023.103532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 03/13/2023] [Accepted: 03/21/2023] [Indexed: 03/26/2023]
Abstract
BACKGROUND Surface-enhanced Raman spectroscopy (SERS) is an efficient technique which has been used for the analysis of filtrate portions of serum samples of Hepatitis B (HBV) and Hepatitis C (HCV) virus. OBJECTIVES The main reason for this study is to differentiate and compare HBV and HCV serum samples for disease diagnosis through SERS. Hepatitis B and hepatitis C disease biomarkers are more predictable in their centrifuged form as compared in their uncentrifuged form. For differentiation of SERS spectral data sets of hepatitis B, hepatitis C and healthy person principal component analysis (PCA) proved to be a helpful. Centrifugally filtered serum samples of hepatitis B and hepatitis C are clearly differentiated from centrifugally filtered serum samples of healthy individuals by using partial least square discriminant analysis (PLS-DA). METHODOLOGY Serum sample of HBV, HCV and healthy patients were centrifugally filtered to separate filtrate portion for studying biochemical changes in serum sample. The SERS of these samples is performed using silver nanoparticles as substrates to identify specific spectral features of both viral diseases which can be used for the diagnosis and differentiation of these diseases. The purpose of centrifugal filtration of the serum samples of HBV and HCV positive and control samples by using filter membranes of 50 KDa size is to eliminate the proteins bigger than 50 KDa so that their contribution in the SERS spectrum is removed and disease related smaller proteins may be observed. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are statistical tools which were used for the further validation of SERS. RESULTS HBV and HCV centrifugally filtered serum sample were compared and biomarkers including (uracil, phenylalanine, methionine, adenine, phosphodiester, proline, tyrosine, tryptophan, amino acid, thymine, fatty acid, nucleic acid, triglyceride, guanine and hydroxyproline) were identified through PCA and PLS-DA. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were used as a multivariate data analysis tool for the diagnosis of the characteristic SERS spectral features associated with both types of viral diseases. For the classification and differentiation of centrifugally filtered HBV, HCV, and control serum samples, Principal component analysis is found helpful. Moreover, PLS-DA can classify these two distinct sets of SERS spectral data with 0.90 percent specificity, 0.85 percent precision, and 0.83 percent accuracy. CONCLUSIONS Surface enhanced Raman spectroscopy along with chemometric analysis like PCA and PLS-DA have been successfully differentiated HBV and HCV and healthy individuals' serum samples.
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Affiliation(s)
- Muhammad Zaman Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan.
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan.
| | - Nosheen Rashid
- Department of Chemistry, University of Education, Faisalabad Campus, Faisalabad (38000), Pakistan.
| | - Muhammad Rizwan Javed
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad (38000), Pakistan
| | - Saima Naz
- Department of Chemistry, University of Education, Faisalabad Campus, Faisalabad (38000), Pakistan
| | - Muhammad Zeeshan Ali
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan
| | - Amina Sabir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan
| | - Nimra Sadaf
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan
| | - Nighat Rafiq
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan
| | - Muhammad Shakeel
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan
| | - Zain Ali
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan
| | - Imran Amin
- PCR Laboratory, PINUM Hospital, Faisalabad (38000), Pakistan
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22
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Raza A, Parveen S, Majeed MI, Nawaz H, Javed MR, Iqbal MA, Rashid N, Haider MZ, Ali MZ, Sabir A, Mahmood Ul Hasan H, Majeed B. Surface-enhanced Raman spectral characterization of antifungal activity of selenium and zinc based organometallic compounds. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121903. [PMID: 36209714 DOI: 10.1016/j.saa.2022.121903] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 06/16/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) is used to identify the biochemical changes associated with the antifungal activities of selenium and zinc organometallic complexes against Aspergillus niger fungus. These biochemical changes identified in the form of SERS peaks can help to understand the mechanism of action of these antifungal agents which is important for development of new antifungal drugs. The SERS spectral changes indicate the denaturation and conformational changes of proteins and fungal cell wall decomposition in complex exposed fungal samples. The SERS spectra of these organometallic complexes exposed fungi are analyzed by using statistical tools like principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). PCA is employed to differentiate the SERS spectra of fungal samples exposed to ligands and complexes. The PLS-DA discriminated different groups of spectra with 99.8% sensitivity, 100% specificity, 98% accuracy and 86 % area under receiver operating characteristic (AUROC) curve.
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Affiliation(s)
- Ali Raza
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Soneya Parveen
- Medicine and Allied, Faisalabad Medical University, Faisalabad 38000, Pakistan
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Muhammad Rizwan Javed
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad 38000, Pakistan
| | - Muhammad Adnan Iqbal
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Nosheen Rashid
- Department of Chemistry, University of Education, Faisalabad Campus, Faisalabad 38000, Pakistan
| | | | - Muhammad Zeeshan Ali
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Amina Sabir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Hafiz Mahmood Ul Hasan
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Beenish Majeed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
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23
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Saleem M, Nawaz H, Majeed MI, Rashid N, Anjum F, Tahir M, Shahzad R, Sehar A, Sabir A, Rafiq N, Ishtiaq S, Shahid M. Surface-enhanced Raman spectroscopy (SERS) for the characterization of supernatants of bacterial cultures of bacterial strains causing sinusitis. Photodiagnosis Photodyn Ther 2023; 41:103278. [PMID: 36627069 DOI: 10.1016/j.pdpdt.2023.103278] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/29/2022] [Accepted: 01/04/2023] [Indexed: 01/08/2023]
Abstract
BACKGROUND Sinusitis is defined as inflammation of the paranasal sinus mucous membrane lining caused by bacteria which usually invade the sinus by upper respiratory tract viral infections (UTI). OBJECTIVES In the present study, Surface-enhanced Raman spectroscopy (SERS) has been applied to differentiate and characterize supernatant samples, in triplicate, of three different types of bacteria which are considered leading cause of sinusitis disease. METHODS For this purpose, supernatant samples of three different strains of bacteria namely Staphylococcus aureus, Klebsiella pneumoniae and Enterococcus faecalis. The SERS has identified significant changes as a result of secretions of biomolecules by these bacteria in their supernatants which can be helpful to explore the potential of this technique for the identification and characterization of different strains of bacteria causing same disease. RESULTS These differentiating characteristic SERS spectral features including 552 cm-1 (C-S-S-C bonds), 951 cm-1 (CN stretching), 1008 cm-1 (Phenylalanine), 1032 cm-1 (In plane CH bending mode Phenylalanine), 1280 cm-1, 1320 cm-1, 1329 cm-1 (Amide III band), 1368 cm-1, 1400 cm-1, 1420 cm-1 (COO-sym. stretching and CH bending), 1583 cm-1 (Tyrosine) correspond to Proteins and 1051 cm-1 (C-C, C-O, -C-OH def.) correspond to carbohydrates contents of these three different types of bacterial secretions in their respective supernatants. Furthermore, multivariate data analysis techniques like principal component analysis (PCA) and a supervised method partial least squares-discriminant analysis (PLS-DA) were found to be useful for the identification and characterization of different bacterial supernatants. CONCLUSIONS Surface-enhanced Raman spectroscopy is proven to be a helpful approach for the characterization and discrimination of three bacterial supernatants including S. aureus, K. pneumonia and E. faecalis.
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Affiliation(s)
- Mudassar Saleem
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Nosheen Rashid
- Department of Chemistry, University of Education, Faisalabad Campus, Faisalabad 38000, Pakistan
| | - Fozia Anjum
- Department of Chemistry, Government College University Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Tahir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Rida Shahzad
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Aafia Sehar
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Amina Sabir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Nighat Rafiq
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Shazra Ishtiaq
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Shahid
- Department of Biochemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
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24
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Shang L, Xu L, Wang Y, Liu K, Liang P, Zhou S, Chen F, Peng H, Zhou C, Lu Z, Li B. Rapid detection of beer spoilage bacteria based on label-free SERS technology. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:5056-5064. [PMID: 36448743 DOI: 10.1039/d2ay01221a] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Beer spoilage bacteria have been a headache for major breweries. In order to rapidly identify spoilage bacteria and improve the sensitivity and signal-to-noise ratio of bacterial SERS detection, the label-free SERS technique was used as a starting point, and we found eight bacteria species that led to beer spoilage. The impact of AgNP concentration and AgNP and bacterial binding time on the final results were thoroughly investigated. To maximize the increase in the SERS signal, an aluminized chip was created. We merged the t-SNE reduced dimensional analysis algorithm, and SVM, KNN, and LDA machine learning algorithms to further investigate the effect of the approach on the final identification rate. The results demonstrate that SERS spectra had an increased intensity and signal-to-noise ratio. The machine learning classification accuracy rates were all above 90%, indicating that the bacteria were correctly classified and identified.
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Affiliation(s)
- Lindong Shang
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, P. R. China.
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Lei Xu
- National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, 214122, P. R. China
| | - Yu Wang
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, P. R. China.
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Kunxiang Liu
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, P. R. China.
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Peng Liang
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, P. R. China.
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Shuangjun Zhou
- Department of Chemistry, Yanbian University, Park Road 977, Yanji 133002, Jilin Province, P. R. China
| | - Fuyuan Chen
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, P. R. China.
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Hao Peng
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, P. R. China.
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Chunyang Zhou
- College of Life Sciences and Technology, Changchun University of Science and Technology, Changchun, 130022, P. R. China
| | - Zhenming Lu
- National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, 214122, P. R. China
| | - Bei Li
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, P. R. China.
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- HOOKE Instruments Ltd, Changchun, 130031, P. R. China
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25
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Xin K, Tian K, Yu Q, Han L, Zang Z. Effects of altitude on meat quality difference and its relationship with HIF-1α during postmortem maturation of beef. J Food Biochem 2022; 46:e14470. [PMID: 36288466 DOI: 10.1111/jfbc.14470] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 09/22/2022] [Accepted: 09/26/2022] [Indexed: 01/14/2023]
Abstract
This study investigated the differences in meat quality during postmortem aging of yak meat from different altitudes as well as the relationship between the release of hypoxic factor HIF-1α and meat quality. The results showed that the HIF-1α increased with altitude but during aging process, there was an initial increase before a subsequent decrease (p < .05). Moreover, significant increases were showed in glycolytic potential, a* value, pH, HIF-1α mRNA expression, HIF-1α protein expression and shear force with altitude (p < .05). Additionally, the b* value, L* value, water holding power and MFI decreased significantly (p < .05). HIF-1α was shown, by PLS-DA method analysis, to be the main protein marker for differences in the quality during aging time of meat from three altitude groups. HIF-1α protein expression was high correlated with glycolytic potential, pH value, meat color, tenderness and water holding capacity during postmortem aging. The results demonstrated that HIF-1α is a novel marker protein that influences meat quality in yak from different altitudes and that HIF-1α-mediated glycolytic pathway was key to the meat quality during postmortem aging. PRACTICAL APPLICATIONS: Yak meat has the advantages of high protein, low fat, good amino acid and fatty acid composition, so the nutritional value of yak meat is in line with the current best-selling beef with less fat in domestic and foreign markets. But consumers often think that the meat tenderness of yak meat is worse than that of beef and improving the quality of yak meat was worthy of attention specifically. This study investigated the differences in meat quality during postmortem aging of yak meat at different altitudes and the relationship between hypoxic factor HIF-1α release and meat quality.
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Affiliation(s)
- Keqi Xin
- College of Food Science and Engineering, Gansu Agricultural University, Lanzhou, China
| | - Kai Tian
- College of Food Science and Engineering, Gansu Agricultural University, Lanzhou, China
| | - Qunli Yu
- College of Food Science and Engineering, Gansu Agricultural University, Lanzhou, China
| | - Ling Han
- College of Food Science and Engineering, Gansu Agricultural University, Lanzhou, China
| | - Zhixuan Zang
- College of Food Science and Engineering, Gansu Agricultural University, Lanzhou, China
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26
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Shakeel M, Majeed MI, Nawaz H, Rashid N, Ali A, Haque A, Akbar MU, Tahir M, Munir S, Ali Z, Shahbaz M, Saleem M. Surface-enhanced Raman spectroscopy for the characterization of pellets of biofilm forming bacterial strains of Staphylococcus epidermidis. Photodiagnosis Photodyn Ther 2022; 40:103145. [PMID: 36210039 DOI: 10.1016/j.pdpdt.2022.103145] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 09/20/2022] [Accepted: 10/04/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Surface-enhanced Raman spectroscopy (SERS) is an effective tool for identifying biofilm forming bacterial strains. Biofilm forming bacteria are considered a major issue in the health sector because they have strong resistance against antibiotics. Staphylococcus epidermidis is commonly present on intravascular devices and prosthetic joints, catheters and wounds. OBJECTIVES To identify and characterize biofilm forming and non-biofilm forming bacterial strains, surface- enhanced Raman spectroscopy with principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were used. METHODS Surface-enhanced Raman spectroscopy (SERS) with silver nanoparticles were employed for the analysis and characterization of biofilm forming bacterial strains. SERS is used to differentiate between non biofilm forming (five samples), medium biofilm forming (five samples) and strong biofilm forming (five samples) bacterial strains by applying silver nanoparticles (AgNPs) as SERS substrate. Principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA) were used to discriminate between non, medium and strong biofilm ability of bacterial strains. RESULTS Principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA) have been used to identify the biochemical differences in the form of SERS features which can be used to differentiate between biofilm forming and non-biofilm forming bacterial strains. PLS-DA provides successful differentiation and classification of these different strains with 94.5% specificity, 96% sensitivity and 89% area under the curve (AUC). CONCLUSIONS Surface-enhanced Raman spectroscopy can be utilized to differentiate between non, medium and strong biofilm forming bacterial strains.
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Affiliation(s)
- Muhammad Shakeel
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Nosheen Rashid
- Department of Chemistry, University of Education, Faisalabad Campus, Faisalabad 38000, Pakistan.
| | - Aamir Ali
- National Institute for Biotechnology and Genetic Engineering College, Pakistan Institute of Engineering and Applied Sciences (NIBGE-C, PIEAS), Jhang Road, Faisalabad 38000, Pakistan
| | - Asma Haque
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Umair Akbar
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Tahir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Saania Munir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Zain Ali
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Shahbaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Mudassar Saleem
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
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Berus SM, Adamczyk-Popławska M, Goździk K, Przedpełska G, Szymborski TR, Stepanenko Y, Kamińska A. SERS-PLSR Analysis of Vaginal Microflora: Towards the Spectral Library of Microorganisms. Int J Mol Sci 2022; 23:ijms232012576. [PMID: 36293436 PMCID: PMC9604117 DOI: 10.3390/ijms232012576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 10/14/2022] [Accepted: 10/17/2022] [Indexed: 11/16/2022] Open
Abstract
The accurate identification of microorganisms belonging to vaginal microflora is crucial for establishing which microorganisms are responsible for microbial shifting from beneficial symbiotic to pathogenic bacteria and understanding pathogenesis leading to vaginosis and vaginal infections. In this study, we involved the surface-enhanced Raman spectroscopy (SERS) technique to compile the spectral signatures of the most significant microorganisms being part of the natural vaginal microbiota and some vaginal pathogens. Obtained data will supply our still developing spectral SERS database of microorganisms. The SERS results were assisted by Partial Least Squares Regression (PLSR), which visually discloses some dependencies between spectral images and hence their biochemical compositions of the outer structure. In our work, we focused on the most common and typical of the reproductive system microorganisms (Lactobacillus spp. and Bifidobacterium spp.) and vaginal pathogens: bacteria (e.g., Gardnerella vaginalis, Prevotella bivia, Atopobium vaginae), fungi (e.g., Candida albicans, Candida glabrata), and protozoa (Trichomonas vaginalis). The obtained results proved that each microorganism has its unique spectral fingerprint that differentiates it from the rest. Moreover, the discrimination was obtained at a high level of explained information by subsequent factors, e.g., in the inter-species distinction of Candida spp. the first three factors explain 98% of the variance in block Y with 95% of data within the X matrix, while in differentiation between Lactobacillus spp. and Bifidobacterium spp. (natural flora) and pathogen (e.g., Candida glabrata) the information is explained at the level of 45% of the Y matrix with 94% of original data. PLSR gave us insight into discriminating variables based on which the marker bands representing specific compounds in the outer structure of microorganisms were found: for Lactobacillus spp. 1400 cm−1, for fungi 905 and 1209 cm−1, and for protozoa 805, 890, 1062, 1185, 1300, 1555, and 1610 cm−1. Then, they can be used as significant marker bands in the analysis of clinical subjects, e.g., vaginal swabs.
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Affiliation(s)
- Sylwia Magdalena Berus
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland
- Correspondence: (S.M.B.); (A.K.)
| | - Monika Adamczyk-Popławska
- Department of Molecular Virology, Faculty of Biology, University of Warsaw, Miecznikowa 1, 02-096 Warsaw, Poland
| | - Katarzyna Goździk
- Department of Parasitology, Faculty of Biology, University of Warsaw, Miecznikowa 1, 02-096 Warsaw, Poland
| | - Grażyna Przedpełska
- Department of Dermatology and Venerology, Infant Jesus Clinical Hospital, Koszykowa 82a, 02-008 Warsaw, Poland
| | - Tomasz R. Szymborski
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland
| | - Yuriy Stepanenko
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland
| | - Agnieszka Kamińska
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland
- Correspondence: (S.M.B.); (A.K.)
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Mushtaq A, Nawaz H, Irfan Majeed M, Rashid N, Tahir M, Zaman Nawaz M, Shahzad K, Dastgir G, Zaki Abdul Bari R, Ul Haq A, Saleem M, Akhtar F. Surface-enhanced Raman spectroscopy (SERS) for monitoring colistin-resistant and susceptible E. coli strains. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 278:121315. [PMID: 35576839 DOI: 10.1016/j.saa.2022.121315] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/21/2022] [Accepted: 04/24/2022] [Indexed: 06/15/2023]
Abstract
The emergence of drug-resistant bacteria is a precarious global health concern. In this study, surface-enhanced Raman spectroscopy (SERS) is used to characterize colistin-resistant and susceptible E. coli strains based on their distinguished SERS spectral features for the development of rapid and cost-effective detection and differentiation methods. For this purpose, three colistin-resistant and three colistin susceptible E. coli strains were analyzed by comparing their SERS spectral signatures. Moreover, multivariate data analysis techniques including Principal component analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were used to examine the SERS spectral data of colistin-resistant and susceptible strains. PCA technique was employed for differentiating colistin susceptible and resistant E.coli strains due to alteration in biochemical compositions of the bacterial cell. PLS-DA is employed on SERS spectral data sets for discrimination of these resistant and susceptible E. coli strains with 100% specificity, 100% accuracy, 99.8% sensitivity, and 86% area under receiver operating characteristics (AUROC) curve.
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Affiliation(s)
- Aqsa Mushtaq
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Nosheen Rashid
- Department of Chemistry, University of Education, Faisalabad Campus, Faisalabad 38000, Pakistan.
| | - Muhammad Tahir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Zaman Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Kashif Shahzad
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Ghulam Dastgir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Rana Zaki Abdul Bari
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Anwar Ul Haq
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Mudassar Saleem
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Farwa Akhtar
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
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29
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Recent Developments in Surface-Enhanced Raman Spectroscopy and Its Application in Food Analysis: Alcoholic Beverages as an Example. Foods 2022; 11:foods11142165. [PMID: 35885407 PMCID: PMC9316878 DOI: 10.3390/foods11142165] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 07/07/2022] [Accepted: 07/11/2022] [Indexed: 01/27/2023] Open
Abstract
Surface-enhanced Raman spectroscopy (SERS) is an emerging technology that combines Raman spectroscopy and nanotechnology with great potential. This technology can accurately characterize molecular adsorption behavior and molecular structure. Moreover, it can provide rapid and sensitive detection of molecules and trace substances. In practical application, SERS has the advantages of portability, no need for sample pretreatment, rapid analysis, high sensitivity, and ‘fingerprint’ recognition. Thus, it has great potential in food safety detection. Alcoholic beverages have a long history of production in the world. Currently, a variety of popular products have been developed. With the continuous development of the alcoholic beverage industry, simple, on-site, and sensitive detection methods are necessary. In this paper, the basic principle, development history, and research progress of SERS are summarized. In view of the chemical composition, the beneficial and toxic components of alcoholic beverages and the practical application of SERS in alcoholic beverage analysis are reviewed. The feasibility and future development of SERS are also summarized and prospected. This review provides data and reference for the future development of SERS technology and its application in food analysis.
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30
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Zhao H, Zheng D, Wang H, Lin T, Liu W, Wang X, Lu W, Liu M, Liu W, Zhang Y, Liu M, Zhang P. In Situ Collection and Rapid Detection of Pathogenic Bacteria Using a Flexible SERS Platform Combined with a Portable Raman Spectrometer. Int J Mol Sci 2022; 23:7340. [PMID: 35806345 PMCID: PMC9267095 DOI: 10.3390/ijms23137340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 06/25/2022] [Accepted: 06/27/2022] [Indexed: 11/16/2022] Open
Abstract
This study aims to develop a simple, sensitive, low-cost, environmentally friendly and flexible surface-enhanced Raman scattering (SERS) platform, combined with a portable Raman spectrometer, for the rapid and on-site SERS detection of bacteria. Commercial tobacco packaging paper (TPP) with little background interference was used as a loading medium that effectively adsorbed Au nanoparticles and provided sufficient "hot spots". This Au-tobacco packaging paper (Au-TPP) substrate used as a flexible SERS platform can maximize sample collection by wiping irregular surfaces, and was successfully applied to the on-site and rapid detection of pathogenic bacteria. Raman fingerprints of pathogenic bacteria can be obtained by SERS detection of spiked pork using wipeable Au-TPP, which verifies its value in practical applications. The results collected by SERS were further verified by polymerase chain reaction (PCR) results. It showed several advantages in on-site SERS detection, including accurate discrimination, simple preparation, easy operation, good sensitivity, accuracy and reproducibility. This study indicates that the established flexible SERS platform has good practical applications in pathogenic bacterial identification and other rapid detections.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Ping Zhang
- Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base of Antivirus Drug, Beijing University of Technology, Beijing 100124, China; (H.Z.); (D.Z.); (H.W.); (T.L.); (W.L.); (X.W.); (W.L.); (M.L.); (W.L.); (Y.Z.); (M.L.)
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31
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Liu S, Zhu Y, Li M, Liu W, Zhao L, Ma Y, Xu L, Wang N, Zhao G, Liang D, Yu Q. Rapid Identification of Different Pathogenic Spore-Forming Bacteria in Spice Powders Using Surface-Enhanced Raman Spectroscopy and Chemometrics. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02326-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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32
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Zhu A, Ali S, Xu Y, Ouyang Q, Wang Z, Chen Q. SERS-based Au@Ag NPs Solid-phase substrate combined with chemometrics for rapid discrimination of multiple foodborne pathogens. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 270:120814. [PMID: 34973615 DOI: 10.1016/j.saa.2021.120814] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 12/07/2021] [Accepted: 12/22/2021] [Indexed: 06/14/2023]
Abstract
In this study, a surface enhanced Raman scattering (SERS) sensor based on Au@Ag NPs solid-phase substrate combined with chemometrics was constructed for the discrimination of three pathogenic bacteria (Staphylococcus aureus, Escherichia coli and Listeria monocytogenes). The Au@Ag NPs were synthesized and self-assembled on filter paper using the dip-coating method. The good absorbency of the filter paper immobilized the bacteria on the substrate, increased the interaction between the bacteria and the substrate, and enhanced the SERS signal of the bacteria. The main peaks of the bacterial spectra were close to each other, but the relative intensities of the vibrational peaks were significantly different, and each strain exhibited unique Raman peaks. The combination of partial least squared discriminant analysis (PLS-DA) method with bacterial SERS allowed the effective identification of the three bacteria. Moreover, the method was applied for the quantitative detection of Staphylococcus aureus with a minimum detection concentration of 104 cfu/mL.
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Affiliation(s)
- Afang Zhu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, P.R. China
| | - Shujat Ali
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, PR China
| | - Yi Xu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, P.R. China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, P.R. China.
| | - Zhen Wang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, P.R. China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, P.R. China; College of Food and Biological Engineering, Jimei University, Ximen 361021, PR China.
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33
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Chen YF, Wang CH, Chang WR, Li JW, Hsu MF, Sun YS, Liu TY, Chiu CW. Hydrophilic-Hydrophobic Nanohybrids of AuNP-Immobilized Two-Dimensional Nanomica Platelets as Flexible Substrates for High-Efficiency and High-Selectivity Surface-Enhanced Raman Scattering Microbe Detection. ACS APPLIED BIO MATERIALS 2022; 5:1073-1083. [PMID: 35195391 DOI: 10.1021/acsabm.1c01151] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
A flexible hybrid substrate was developed by affixing gold nanoparticles (AuNPs) onto the surface of two-dimensional nanomica platelets (NMPs). The substrate was successfully used in biosensors with high efficiency and high selectivity through surface-enhanced Raman scattering (SERS). By controlling the amphiphilicity of the hybrid substrate, the flexible substrate was made highly selective toward biomolecules. Four different SERS substrate systems were constructed, including intercalated mica, exfoliated NMPs, hydrophilic exfoliated NMPs, and hydrophobic exfoliated NMPs. NMPs were only 1 nm thick. AuNPs adsorbed on both sides of NMPs and thus created excellent three-dimensional hot junction effects in the z-axis direction. For the detection of adenine in DNA, a satisfactory Raman enhancement factor (EF) of up to 8.9 × 106 was achieved with the detection limit as low as 10-8 M. Subsequently, the AuNP/NMP hybrids were adopted to rapidly detect hydrophilic Staphylococcus hominis and hydrophobic Escherichia coli. The AuNP/PIB-POE-PIB/NMP nanohybrid was concurrently hydrophilic and hydrophobic. This amphiphilic property greatly enhanced the detection selectivity and signal intensity for hydrophilic or hydrophobic bacteria. Overall, AuNPs/PIB-POE-PIB/NMPs developed as SERS substrates enable rapid, sensitive biodetection.
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Affiliation(s)
- Yan-Feng Chen
- Department of Materials Science and Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
| | - Chih-Hao Wang
- Department of Materials Science and Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
| | - Wen-Ru Chang
- Department of Materials Science and Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
| | - Jia-Wun Li
- Department of Materials Science and Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
| | - Mao-Feng Hsu
- Research & Development Division, Zhen Ding Technology Holding Limited, Taoyuan 33754, Taiwan
| | - Ya-Sen Sun
- Department of Chemical and Materials Engineering, National Central University, Taoyuan 32001, Taiwan
| | - Ting-Yu Liu
- Department of Materials Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan
| | - Chih-Wei Chiu
- Department of Materials Science and Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
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Vaitiekūnaitė D, Snitka V. Differentiation of Closely Related Oak-Associated Gram-Negative Bacteria by Label-Free Surface Enhanced Raman Spectroscopy (SERS). Microorganisms 2021; 9:1969. [PMID: 34576865 PMCID: PMC8466144 DOI: 10.3390/microorganisms9091969] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/08/2021] [Accepted: 09/14/2021] [Indexed: 12/02/2022] Open
Abstract
Due to the harmful effects of chemical fertilizers and pesticides, the need for an eco-friendly solution to improve soil fertility has become a necessity, thus microbial biofertilizer research is on the rise. Plant endophytic bacteria inhabiting internal tissues represent a novel niche for research into new biofertilizer strains. However, the number of species and strains that need to be differentiated and identified to facilitate faster screening in future plant-bacteria interaction studies, is enormous. Surface enhanced Raman spectroscopy (SERS) may provide a platform for bacterial discrimination and identification, which, compared with the traditional methods, is relatively rapid, uncomplicated and ensures high specificity. In this study, we attempted to differentiate 18 bacterial isolates from two oaks via morphological, physiological, biochemical tests and SERS spectra analysis. Previous 16S rRNA gene fragment sequencing showed that three isolates belong to Paenibacillus, 3-to Pantoea and 12-to Pseudomonas genera. Additional tests were not able to further sort these bacteria into strain-specific groups. However, the obtained label-free SERS bacterial spectra along with the high-accuracy principal component (PCA) and discriminant function analyses (DFA) demonstrated the possibility to differentiate these bacteria into variant strains. Furthermore, we collected information about the biochemical characteristics of selected isolates. The results of this study suggest a promising application of SERS in combination with PCA/DFA as a rapid, non-expensive and sensitive method for the detection and identification of plant-associated bacteria.
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Affiliation(s)
- Dorotėja Vaitiekūnaitė
- Laboratory of Forest Plant Biotechnology, Institute of Forestry, Lithuanian Research Centre for Agriculture and Forestry, Liepų Str. 1, Girionys, 53101 Kaunas, Lithuania
| | - Valentinas Snitka
- Research Center for Microsystems and Nanotechnology, Kaunas University of Technology, Studentu Str. 65, 51369 Kaunas, Lithuania;
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