scholarly journals Discrimination between ricin and sulphur mustard toxicity in vitro using Raman spectroscopy

2004 ◽  
Vol 1 (1) ◽  
pp. 79-90 ◽  
Author(s):  
Ioan Notingher ◽  
Chris Green ◽  
Chris Dyer ◽  
Elaine Perkins ◽  
Neil Hopkins ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Raman Spectra ◽  
Structural Changes ◽  
High Accuracy ◽  
Biochemical Properties ◽  
Prediction Errors ◽  
Toxic Agent ◽  
Linear Discriminant ◽  
Sulphur Mustard

A Raman spectroscopy cell-based biosensor has been proposed for rapid detection of toxic agents, identification of the type of toxin and prediction of the concentration used. This technology allows the monitoring of the biochemical properties of living cells over long periods of time by measuring the Raman spectra of the cells non-invasively, rapidly and without use of labels (Notingher et al. 2004 doi:10.1016/j.bios.2004.04.008). Here we show that this technology can be used to distinguish between changes induced in A549 lung cells by the toxin ricin and the chemical warfare agent sulphur mustard. A multivariate model based on principal component analysis (PCA) and linear discriminant analysis (LDA) was used for the analysis of the Raman spectra of the cells. The leave-one-out cross-validation of the PCA-LDA model showed that the damaged cells can be detected with high sensitivity (98.9%) and high specificity (87.7%). High accuracy in identifying the toxic agent was also found: 88.6% for sulphur mustard and 71.4% for ricin. The prediction errors were observed mostly for the ricin treated cells and the cells exposed to the lower concentration of sulphur mustard, as they induced similar biochemical changes, as indicated by cytotoxicity assays. The concentrations of sulphur mustard used were also identified with high accuracy: 93% for 200 μM and 500 μM, and 100% for 1000 μM. Thus, biological Raman microspectroscopy and PCA-LDA analysis not only distinguishes between viable and damaged cells, but can also discriminate between toxic challenges based on the cellular biochemical and structural changes induced by these agents and the eventual mode of cell death.

scholarly journals Formation of irregular rings in silica aerogel structure during the sintering process

2003 ◽  
Vol 35 (2) ◽  
pp. 67-73
Author(s):  
Ivana Hinic ◽  
Goran Stanisic ◽  
Zoran Popovic
Keyword(s):  
Raman Spectroscopy ◽  
Bulk Density ◽  
Raman Spectra ◽  
Silica Aerogel ◽  
Structural Changes ◽  
Low Density ◽  
Sintering Process ◽  
Time Intervals ◽  
Defect Modes

Samples of low-density, highly disordered silica aerogel with initial bulk density of 0.16 g/cm3, were sintered isothermally in different time intervals at 1000?C. Structural changes during the sintering process have been investigated by Raman spectroscopy. Defect modes of irregular three and four membered rings were observed in the Raman spectra of sintered samples.

Cysteine Esters Protect Cultured Rodent Lung Slices from Sulphur Mustard

1994 ◽  
Vol 13 (11) ◽  
pp. 743-748 ◽  
Author(s):  
Paul E. Wilde ◽  
David G. Upshall
Keyword(s):  
Structural Changes ◽  
Methyl Esters ◽  
Dimethyl Ester ◽  
Rat Lung ◽  
Sulphur Mustard ◽  
Lung Slice ◽  
Pretreatment Time ◽  
Acetyl Cysteine

1 In previous studies an in vitro rat lung slice system was used to investigate the metabolic and structural changes after exposure to known lung toxicants. 2 In this study, the same system was used to identify the ability of cysteine esters to protect against sulphur mustard toxicity. 3 The cyclopentyl (CCPE), cyclohexyl (CCHE), isopropyl (CIPE), methyl (CME) esters of cysteine, cystine dimethyl ester (CDME), cysteine (CySH) and N-acetyl cysteine (NAc) were all non-toxic to cultured rat lung slices at 5 mM (equivalent cysteine concentration) after a pretreatment time of 30 min. 4 Pretreatment with the isopropyl, cyclohexyl, cyclopentyl and methyl esters of cysteine at concentrations higher than 1 mM protected against an IC50 of sulphur mustard, however, neither cysteine nor N-acetylcysteine protected. 5 We propose that the extent of protection is directly related to increased levels of intracellular cysteine provided by the esters of cysteine.

scholarly journals Raman Spectroscopy in Colorectal Cancer Diagnostics: Comparison of PCA-LDA and PLS-DA Models

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Wenjing Liu ◽  
Zhaotian Sun ◽  
Jinyu Chen ◽  
Chuanbo Jing
Keyword(s):  
Colorectal Cancer ◽  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Diagnostic Accuracy ◽  
Raman Spectra ◽  
Principal Component ◽  
Cancer Diagnostics ◽  
Analysis Model ◽  
Tissue Samples ◽  
Linear Discriminant

Raman spectra of human colorectal tissue samples were employed to diagnose colorectal cancer. High-quality Raman spectra were acquired from normal and cancerous colorectal tissues from 81 patients. Subtle Raman variations, such as for peaks at 1134 cm−1 (protein, C-C/C-N stretching) and 1297 cm−1 (lipid, C-H2 twisting), were observed between normal and cancerous colorectal tissues. The average peak intensity at 1134 and 1297 cm−1 was increased from approximately 235 and 72 in the normal group, respectively, to 315 and 273 in the cancer group. The variations of Raman spectra reflected the changes of cell molecules during canceration. The multivariate statistical methods of principal component analysis-linear discriminant analysis (PCA-LDA) and partial least-squares-discriminant analysis (PLS-DA), together with leave-one-patient-out cross-validation, were employed to build the discrimination model. PCA-LDA was used to evaluate the capability of this approach for classifying colorectal cancer, resulting in a diagnostic accuracy of 79.2%. Further PLS-DA modeling yielded a diagnostic accuracy of 84.3% for colorectal cancer detection. Thus, the PLS-DA model is preferable between the two to discriminate cancerous from normal tissues. Our results demonstrate that Raman spectroscopy can be used with an optimized multivariate data analysis model as a sensitive diagnostic alternative to identify pathological changes in the colon at the molecular level.

Nanoindentation-Induced Phase Transformation in Silicon Thin Films

2013 ◽  
Vol 586 ◽  
pp. 112-115 ◽  
Author(s):  
Radim Ctvrtlik ◽  
Jan Tomastik ◽  
Vaclav Ranc
Keyword(s):  
Thin Films ◽  
Raman Spectroscopy ◽  
High Pressure ◽  
Phase Transformation ◽  
Raman Spectra ◽  
Structural Changes ◽  
Berkovich Indenter ◽  
Silicon Thin Films ◽  
Free Silicon ◽  
Amorphous Si

Nanoindentation-induced phase transformation of amorphous, annealed amorphous and microcrystalline hydrogen-free silicon thin films were studied. Series of nanoindentation experiments were performed with a sharp Berkovich indenter at various unloading rates. The structural changes in indentation deformed regions were examined using Raman spectroscopy. Analyses of indentation curves and Raman spectra suggest that high pressure phases appear more easily in annealed amorphous Si thin films than in microcrystalline ones.

scholarly journals Disinfection chemicals mode of action on the bacterial spore structure and their Raman spectra

2020 ◽  
Author(s):  
Dmitry Malyshev ◽  
Tobias Dahlberg ◽  
Krister Wiklund ◽  
Per Ola Andersson ◽  
Sara Henriksson ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Raman Spectra ◽  
Sodium Hypochlorite ◽  
Chlorine Dioxide ◽  
Peracetic Acid ◽  
Structural Changes ◽  
Detection Methods ◽  
Laser Tweezers ◽  
Rapid Diagnostics ◽  
Spore Forming Bacteria

AbstractContamination of toxic spore-forming bacteria is problematic since spores can survive a plethora of disinfection chemicals. It is also problematic to rapidly detect if the disinfection chemical was active, leaving spores dead. Robust decontamination strategies, as well as reliable detection methods to identify dead from viable spores, are thus critical. Vibrational detection methods such as Raman spectroscopy has been suggested for rapid diagnostics and differentiation of live and dead spores. We investigate in this work, using laser tweezers Raman spectroscopy, the changes in Raman spectra of Bacillus thuringiensis spores treated with sporicidal agents such as chlorine dioxide, peracetic acid, and sodium hypochlorite. We also imaged treated spores using SEM and TEM to verify if any changes to the spore structure can be correlated to the Raman spectra. We found that chlorine dioxide did not change the Raman spectrum or the spore structure; peracetic acid shows a time-dependent decrease in the characteristic DNA/DPA peaks and ∼20 % of the spores were degraded and collapsed; spores treated with sodium hypochlorite show an abrupt drop in DNA and DPA peaks within 20 minutes all though the spore structure was overall intact, however, the exosporium layer was reduced. Structural changes appeared over several minutes, compared to the inactivation time of the spores, which is less than a minute. We conclude that vibrational spectroscopy provides powerful means to detect changes in spores but it might be problematic to identify if spores are live or dead after a decontamination procedure.

scholarly journals Using Raman Spectroscopy to Investigate the Molecular Level Characteristics of Endometriosis-=SUP=-*-=/SUP=-

2020 ◽  
Vol 128 (8) ◽  
pp. 1223
Author(s):  
Sevim Akyuz ◽  
Sefa Celik ◽  
Abdullah Taner Usta ◽  
Aysen E. Ozel ◽  
Gozde Yi lmaz -=SUP=-4-=/SUP=- ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Statistical Analysis ◽  
Raman Spectra ◽  
Young Women ◽  
Molecular Level ◽  
Multivariate Statistical ◽  
Tissue Samples ◽  
Linear Discriminant ◽  
Keywords Endometriosis

Endometriosis is a benign gynecologic disorder. It is particularly common among young women and may make pregnancy difficult. In this study molecular level characterization of endometriosis tissues were performed using Raman spectroscopy in combination with multivariate statistical analysis. Three hundred sixty-six Raman spectra recorded from different points of seventy two tissue samples, taken from the cyst walls of twelve patients were examined. Principle Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) were performed on the Raman data, and the samples were then classified into three groups; severe, moderate, and weak endometriosis. In the severe endometriosis group, the relative band intensities of DNA were increased. Moreover, increase in pyrrole moieties and kynurenine were seen. The results show that endometriosis severity correlates to increase in DNA concentration, and degradation of tryptophan due to increased indoleamine-pyrrole 2, 3-dioxygenase (IDO) activity, and an increase in kynurenine concentration and pyrrole intermediate. It is concluded that Raman spectroscopy is capable of providing a quick diagnosis, ahead of the pathology result being reported. Keywords: Endometriosis; Raman spectra, PCA-LDA analysis.

scholarly journals Novel Quantitative Analysis Using Optical Imaging (VELscope) and Spectroscopy (Raman) Techniques for Oral Cancer Detection

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3364
Author(s):  
Ming-Jer Jeng ◽  
Mukta Sharma ◽  
Lokesh Sharma ◽  
Shiang-Fu Huang ◽  
Liann-Be Chang ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Quantitative Analysis ◽  
Discriminant Analysis ◽  
Oral Cancer ◽  
Raman Spectra ◽  
Cross Validation ◽  
Error Rates ◽  
Control Group ◽  
Optical Techniques ◽  
Linear Discriminant

In this study, we developed a novel quantitative analysis method to enhance the detection capability for oral cancer screening. We combined two different optical techniques, a light-based detection technique (visually enhanced lesion scope) and a vibrational spectroscopic technique (Raman spectroscopy). Materials and methods: Thirty-five oral cancer patients who went through surgery were enrolled. Thirty-five cancer lesions and thirty-five control samples with normal oral mucosa (adjacent to the cancer lesion) were analyzed. Thirty-five autofluorescence images and 70 Raman spectra were taken from 35 cancer and 35 control group cryopreserved samples. The normalized intensity and heterogeneity of the 70 regions of interest (ROIs) were calculated along with 70 averaged Raman spectra. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to differentiate the cancer and control groups (normal). The classifications rates were validated using two different validation methods, leave-one-out cross-validation (LOOCV) and k-fold cross-validation. Results: The cryopreserved normal and tumor tissues were differentiated using the PCA–LDA and PCA–QDA models. The PCA–LDA of Raman spectroscopy (RS) had 82.9% accuracy, 80% sensitivity, and 85.7% specificity, while ROIs on the autofluorescence images were differentiated with 90% accuracy, 100% sensitivity, and 80% specificity. The combination of two optical techniques differentiated cancer and normal group with 97.14% accuracy, 100% sensitivity, and 94.3% specificity. Conclusion: In this study, we combined the data of two different optical techniques. Furthermore, PCA–LDA and PCA–QDA quantitative analysis models were used to differentiate tumor and normal groups, creating a complementary pathway for efficient tumor diagnosis. The error rates of RS and VELcope analysis were 17.10% and 10%, respectively, which was reduced to 3% when the two optical techniques were combined.

Deep learning-based component identification for the Raman spectra of mixtures

The Analyst ◽  
2019 ◽  
Vol 144 (5) ◽  
pp. 1789-1798 ◽  
Author(s):  
Xiaqiong Fan ◽  
Wen Ming ◽  
Huitao Zeng ◽  
Zhimin Zhang ◽  
Hongmei Lu
Keyword(s):  
Raman Spectroscopy ◽  
Deep Learning ◽  
Raman Spectra ◽  
High Accuracy ◽  
False Positives ◽  
Component Identification

DeepCID can achieve high accuracy, excellent sensitivity and few false positives for component identification in mixtures based on Raman spectroscopy and deep learning.

scholarly journals Biochemical Analysis of Leukocytes after In Vitro and In Vivo Activation with Bacterial and Fungal Pathogens Using Raman Spectroscopy

2021 ◽  
Vol 22 (19) ◽  
pp. 10481
Author(s):  
Aikaterini Pistiki ◽  
Anuradha Ramoji ◽  
Oleg Ryabchykov ◽  
Daniel Thomas-Rüddel ◽  
Adrian T. Press ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Single Cell ◽  
Raman Spectra ◽  
Fungal Pathogens ◽  
Infection Model ◽  
Label Free ◽  
Analytical Technique ◽  
Monocytes And Lymphocytes

Biochemical information from activated leukocytes provide valuable diagnostic information. In this study, Raman spectroscopy was applied as a label-free analytical technique to characterize the activation pattern of leukocyte subpopulations in an in vitro infection model. Neutrophils, monocytes, and lymphocytes were isolated from healthy volunteers and stimulated with heat-inactivated clinical isolates of Candida albicans, Staphylococcus aureus, and Klebsiella pneumoniae. Binary classification models could identify the presence of infection for monocytes and lymphocytes, classify the type of infection as bacterial or fungal for neutrophils, monocytes, and lymphocytes and distinguish the cause of infection as Gram-negative or Gram-positive bacteria in the monocyte subpopulation. Changes in single-cell Raman spectra, upon leukocyte stimulation, can be explained with biochemical changes due to the leukocyte’s specific reaction to each type of pathogen. Raman spectra of leukocytes from the in vitro infection model were compared with spectra from leukocytes of patients with infection (DRKS-ID: DRKS00006265) with the same pathogen groups, and a good agreement was revealed. Our study elucidates the potential of Raman spectroscopy-based single-cell analysis for the differentiation of circulating leukocyte subtypes and identification of the infection by probing the molecular phenotype of those cells.

scholarly journals Raman spectroscopy to differentiate between fresh tissue samples of glioma and normal brain: a comparison with 5-ALA–induced fluorescence-guided surgery

2020 ◽  
pp. 1-11
Author(s):  
Laurent J. Livermore ◽  
Martin Isabelle ◽  
Ian M. Bell ◽  
Oliver Edgar ◽  
Natalie L. Voets ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Predictive Model ◽  
Raman Spectra ◽  
Aminolevulinic Acid ◽  
Temporal Lobectomy ◽  
Normal Brain ◽  
Fresh Tissue ◽  
Tissue Samples ◽  
Linear Discriminant ◽  
Resection Cavity

OBJECTIVERaman spectroscopy is a biophotonic tool that can be used to differentiate between different tissue types. It is nondestructive and no sample preparation is required. The aim of this study was to evaluate the ability of Raman spectroscopy to differentiate between glioma and normal brain when using fresh biopsy samples and, in the case of glioblastomas, to compare the performance of Raman spectroscopy to predict the presence or absence of tumor with that of 5-aminolevulinic acid (5-ALA)–induced fluorescence.METHODSA principal component analysis (PCA)–fed linear discriminant analysis (LDA) machine learning predictive model was built using Raman spectra, acquired ex vivo, from fresh tissue samples of 62 patients with glioma and 11 glioma-free brain samples from individuals undergoing temporal lobectomy for epilepsy. This model was then used to classify Raman spectra from fresh biopsies from resection cavities after functional guided, supramaximal glioma resection. In cases of glioblastoma, 5-ALA–induced fluorescence at the resection cavity biopsy site was recorded, and this was compared with the Raman spectral model prediction for the presence of tumor.RESULTSThe PCA-LDA predictive model demonstrated 0.96 sensitivity, 0.99 specificity, and 0.99 accuracy for differentiating tumor from normal brain. Twenty-three resection cavity biopsies were taken from 8 patients after supramaximal resection (6 glioblastomas, 2 oligodendrogliomas). Raman spectroscopy showed 1.00 sensitivity, 1.00 specificity, and 1.00 accuracy for predicting tumor versus normal brain in these samples. In the glioblastoma cases, where 5-ALA–induced fluorescence was used, the performance of Raman spectroscopy was significantly better than the predictive value of 5-ALA–induced fluorescence, which showed 0.07 sensitivity, 1.00 specificity, and 0.24 accuracy (p = 0.0009).CONCLUSIONSRaman spectroscopy can accurately classify fresh tissue samples into tumor versus normal brain and is superior to 5-ALA–induced fluorescence. Raman spectroscopy could become an important intraoperative tool used in conjunction with 5-ALA–induced fluorescence to guide extent of resection in glioma surgery.

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