Vibrational spectroscopy for cervical cancer pathology, from biochemical analysis to diagnostic tool

Non-invasive diagnostic test for lung cancer using biospectroscopy and variable selection techniques in saliva samples

Lung cancer is one of the most commonly occurring malignant tumours worldwide. Although some reference methods such as X-ray, computed tomography or bronchoscope are widely used for clinical diagnosis of lung cancer, there is still a need to develop new methods for early detection of lung cancer. Especially needed are approaches that might be non-invasive and fast with high analytical precision and statistically reliable. Herein, we developed a swab "dip" test in saliva whereby swabs were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy harnessed to principal component analysis-quadratic discriminant analysis (QDA) and variable selection techniques employing successive projections algorithm (SPA) and genetic algorithm (GA) for feature selection/extraction combined with QDA. A total of 1944 saliva samples (56 designated as lung-cancer positive and 1888 designed as controls) were obtained in a lung cancer-screening programme being undertaken in North-West England. GA-QDA models achieved, for the test set, sensitivity and specificity values of 100.0% and 99.1%, respectively. Three wavenumbers (1422 cm-1, 1546 cm-1 and 1578 cm-1) were identified using the GA-QDA model to distinguish between lung cancer and controls, including ring C-C stretching, CN adenine, Amide II [δ(NH), ν(CN)] and νs(COO-) (polysaccharides, pectin). These findings highlight the potential of using biospectroscopy associated with multivariate classification algorithms to discriminate between benign saliva samples and those with underlying lung cancer.

Surface-enhanced Raman spectroscopy for the characterization of bacterial pellets of Staphylococcus aureus infected by bacteriophage

Drug-resistant pathogenic bacteria are a major cause of infectious diseases in the world and they have become a major threat through the reduced efficacy of developed antibiotics. This issue can be addressed by using bacteriophages, which can kill lethal bacteria and prevent them from causing infections. Surface-enhanced Raman spectroscopy (SERS) is a promising technique for studying the degradation of infectious bacteria by the interaction of bacteriophages to break the vicious cycle of drug-resistant bacteria and help to develop chemotherapy-independent remedial strategies. The phage (viruses)-sensitive Staphylococcus aureus (S. aureus) bacteria are exposed to bacteriophages (Siphoviridae family) in the time frame from 0 min (control) to 50 minutes with intervals of 5 minutes and characterized by SERS using silver nanoparticles as SERS substrate. This allows us to explore the effects of the bacteriophages against lethal bacteria (S. aureus) at different time intervals. The differentiating SERS bands are observed at 575 (C-C skeletal mode), 620 (phenylalanine), 649 (tyrosine, guanine (ring breathing)), 657 (guanine (COO deformation)), 728-735 (adenine, glycosidic ring mode), 796 (tyrosine (C-N stretching)), 957 (C-N stretching (amide lipopolysaccharides)), 1096 (PO2 (nucleic acid)), 1113 (phenylalanine), 1249 (CH2 of amide III, N-H bending and C-O stretching (amide III)), 1273 (CH2, N-H, C-N, amide III), 1331 (C-N stretching mode of adenine), 1373 (in nucleic acids (ring breathing modes of the DNA/RNA bases)) and 1454 cm-1 (CH2 deformation of saturated lipids), indicating the degradation of bacteria and replication of bacteriophages. Multivariate data analysis was performed by employing principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to study the biochemical differences in the S. aureus bacteria infected by the bacteriophage. The SERS spectral data sets were successfully differentiated by PLS-DA with 94.47% sensitivity, 98.61% specificity, 94.44% precision, 98.88% accuracy and 81.06% area under the curve (AUC), which shows that at 50 min interval S. aureus bacteria is degraded by the replicating bacteriophages.

Tandem SERS and MS/MS Profiling of Plasma Extracellular Vesicles for Early Ovarian Cancer Biomarker Discovery

Extracellular vesicles (EVs) are vectors of biomolecular cargo that play essential roles in intercellular communication across a range of cells. Protein, lipid, and nucleic acid cargo harbored within EVs may serve as biomarkers at all stages of disease; however, the choice of methodology may challenge the specificity and reproducibility of discovery. To address these challenges, the integration of rigorous EV purification methods, cutting-edge spectroscopic technologies, and data analysis are critical to uncover diagnostic signatures of disease. Herein, we demonstrate an EV isolation and analysis pipeline using surface-enhanced Raman spectroscopy (SERS) and mass spectrometry (MS) techniques on plasma samples obtained from umbilical cord blood, healthy donor (HD) plasma, and plasma from women with early stage high-grade serous carcinoma (HGSC). Plasma EVs were purified by size exclusion chromatography and analyzed by surface-enhanced Raman spectroscopy (SERS), mass spectrometry (MS), and atomic force microscopy. After determining the fraction of highest EV purity, SERS and MS were used to characterize EVs from HDs, pooled donors with noncancerous gynecological ailments (n = 6), and donors with early stage [FIGO (I/II)] with HGSC. SERS spectra were subjected to different machine learning algorithms such as PCA, logistic regression, support vector machine, naïve Bayes, random forest, neural network, and k nearest neighbors to differentiate healthy, benign, and HGSC EVs. Collectively, we demonstrate a reproducible workflow with the potential to serve as a diagnostic platform for HGSC.

For cervical cancer diagnosis: Tissue Raman spectroscopy and multi-level feature fusion with SENet attention mechanism

Cervical cancer ranks among the most prevalent forms of gynecological malignancies. Timely identification of cervical lesions and prompt intervention can effectively prevent the development of cervical cancer or enhance patients' chances of survival. In this study, we propose an innovative method based on Raman spectroscopy, i.e., a multi-level SENet attention mechanism feature fusion architecture (MAFA) for rapid diagnosis of cervical cancer and precancerous lesions. The convolution process of this architecture can extract features from shallow to deep layers, and the attention mechanism is added to achieve the fusion of features from different layers. The added attention mechanism can automatically determine the importance of each layer feature channel and assign weight values to that layer according to the importance of each layer to achieve the purpose of focusing the model on certain waveform features and improve the targeting of model learning. We collected Raman spectra of 212 cervical tissues containing cervical cancer and its precancerous lesions.The experimental results show that MAFA can effectively improve the diagnostic accuracy of VGGNet, GoogLeNet and ResNet models in the validation of Raman spectral data of cervical tissue. Among them, ResNet performed the best, with the highest average accuracy, precision, recall and F1-Score of 82.36%, 84.00%, 82.35% and 82.26%, respectively, when no feature fusion was performed. The evaluation metrics improved by 4.91%, 3.97%, 4.97%, and 5.06%, respectively, after using the MAFA; they also improved by 4.16%, 2.90%, 4.17%, and 4.32%, respectively, compared with the model that directly performs feature fusion without using the attention mechanism. Therefore, the MAFA proposed in this study is better than that of the neural network that directly fuses the features of each convolutional layer. The experimental results show that the performance of the MAFA proposed in this paper is significantly higher than that of traditional deep learning algorithms, indicating that the present architecture can effectively improve the diagnostic accuracy of deep learning networks for cervical cancer.

The Biochemical Characteristics in Experimental Keratomycosis

PURPOSE

To investigate the biochemical characteristics in experimental keratomycosis.

METHODS

Experimental mice were injected with Fusarium solanum solution Controls mice received liposomes containing phosphate-buffered saline (PBS-LIP). Raman spectroscopy was used to analyze the biochemical characteristics. The infiltration of inflammatory cells was analyzed by histopathology. Cytokine mRNA levels were detected by real-time polymerase chain reaction.

RESULTS

Raman Spectroscopy: In the experimental group, collagen, lipids, amide I and III were decreased, amide II, hyper proline amino acids, and arginine were increased, and proline and phenylalanine were significantly increased on day 3. Histopathology: more severe keratomycosis developed in the experimental group than in the control group. Statistically significant mRNA expression of Collagen4\MMP2\MMP9\TIMP1.MMP9 was negatively correlated with the secretion of Collagen4.

CONCLUSIONS

Matrix metalloproteinases are involved in biochemical changes in keratomycosis.

Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning

INTRODUCTION

In the UK alone, the incidence of cervical cancer is increasing, hence an urgent need for early and rapid detection of cancer before it develops. Spectroscopy in conjunction with machine learning offers a disruptive technology that promises to be pick up cancer early as compared to the current diagnostic techniques used.

AREAS COVERED

This review article explores the different spectroscopy techniques that have been used for the analysis of cervical cancer. Along with the extensive description of spectroscopic techniques, the various machine learning techniques are also described as well as the applications that have been explored in the diagnosis of cervical cancer. This review delimits the literature specifically associated with cervical cancer studies performed solely with the use of a spectroscopy technique, and machine learning.

EXPERT OPINION

Although there are several methods and techniques to detect cervical cancer, the clinical sector requires to introduce new diagnostic technologies that help improving the quality of life of patient. These innovative technologies involve spectroscopy as a qualitative method and machine learning as a quantitative method. In this article, both the techniques and methodologies that allow and promise to be a new screening tool for the detection of cervical cancer is covered.

Novel exploration of Raman microscopy and non-linear optical imaging in adenomyosis

Background

Adenomyosis is a common gynecological disease in women. A relevant literature search found that approximately 82% of patients with adenomyosis chose to undergo hysterectomy. However, women of childbearing age are more likely to undergo surgery to preserve the uterus. Because it is difficult to determine the extent of adenomyosis, it is almost impossible to resect adenomyotic tissue and retain the uterus at the same time.

Materials and methods

Following ethics approval and patient consent, tissue samples were resected and prepared to create frozen slices for analysis. One slice was subjected to H&E staining while the remaining slices were photographed with Coherent Anti-Stokes Raman Scattering (CARS), Second-Harmonic Generation (SHG) microscopy, and Raman spectroscopy. Comparative observations and analyses at the same positions were carried out to explore the diagnostic ability of CARS, SHG, and Raman spectroscopy for adenomyosis.

Results

In adenomyotic tissue, we found two characteristic peaks at 1,155 and 1,519 cm^–1 in the Raman spectrum, which were significantly different from normal tissue. The substances shown in the CARS spectrum were represented by peaks of 1,519 cm^–1. SHG microscopy showed a distribution of collagen at the focus of the adenomyosis.

Conclusion

This study represents a novel analysis of Raman microscopy, CARS, and SHG in the analysis of adenomyotic lesions. We found the diffraction spectrum useful in determining the focal boundary and the diagnosis of adenomyosis in the tested samples.

Deep autoencoder based hybrid dimensionality reduction approach for classification of SERS for melanoma cancer diagnostics

Melanoma, a kind of fatal skin cancer, originates in melanin secreting cells of the dermis. Disease identification in the early stages assures a high survival rate for the patient. Most of the existing techniques retard the cancer detection phase. Surface-Enhanced Raman Spectroscopy (SERS) can capture fine details from the specimens that machine learning models can utilize to discriminate between healthy and diseased individuals rapidly. Our research work proposes a deep autoencoder based hybrid dimensionality reduction approach with a machine learning model on SERS spectrums of human skin fibroblast for melanoma cancer diagnostics. SERS measurements of 307 samples in total, belonging to two different classes, such as normal (157 samples) and malignant melanoma (150 samples), are used in this study. The SERS spectra measurements for both the samples lie between 100cm - 1 and 4278cm - 1. The variations in the intensity of Raman bands between both classes are intrinsically subtle. Neighborhood Component Analysis (NCA) technique has been exerted to transform 2090 dimensional spectral features into 2090 dimensional vectors and then the Deep Autoencoder (DAE) model is used to handle the nonlinearity in the data and produce the latent space, while Linear Discriminant Analysis (LDA) classifier have been employed for discriminating the normal and cancer cells. The k-fold cross-validation technique with a k value of 10 is implemented to assess the metrics of the model. The stated hybrid (NCA and DAE) model with 10-dimension latent space achieves an accuracy of 98%, the sensitivity of 99% and specificity of 97%, respectively. Due to the high-intensity nature of the SERS spectrum, the existing linear dimensionality reduction based discriminating model fails if the class label (Normal or Cancer) gets distributed on the low variance side. The proposed methodology captures both linear and nonlinear underlying structures present in the spectrums, resulting in better classification compared to the standard dimensionality reduction techniques.

A micro-Raman spectroscopy study of inflammatory condition of human cervix: Probing of Tissues and blood plasma samples

The present study explores the application of the micro-Raman spectroscopy technique to discriminate normal and cervicitis condition from cervical malignancy by analyzing the Raman signatures of tissues and plasma samples of the same subjects. The Raman peaks from tissue samples at 1026 cm^-1,1298 cm^-1 and 1243 cm^-1 are attributed to glycogen, fatty acids and collagen and are found to be reliable signatures capable of identifying cervicitis and normal condition from cervical cancer. The Raman signatures from plasma samples belonging to carbohydrates (578 cm^-1), lipids (1059 cm^-1) and nucleic acids (1077 cm^-1,1341 cm^-1 and 1357 cm^-1) are quite useful to classify various stages of cervix at par with tissue based diagnosis. The PCA-SVM based classification of the spectral data indicates the potential of Raman spectroscopy based liquid biopsy to rule out false diagnosis of cervicitis as cervical malignancy.

Raman spectroscopy and multivariate analysis as potential tool to follow Alzheimer's disease progression

Raman spectroscopy is an emerging tool in the research and diagnosis of different diseases, including neurodegenerative disorders. In this work, blood serum samples collected from healthy controls and dementia patients were analysed by Raman spectroscopy to develop a classification model for the diagnosis of dementia of Alzheimer’s type (DAT). Raman spectra were processed by means of multivariate tools for multivariate analysis. Lower concentration levels of carotenoids were detected in blood serum from patients, which allowed for a good discrimination with respect to controls, such as 93% of correct predictions on the test set with random forest. We also hypothesize that carotenoid levels might be informative about the severity and progression of the disease, since the intensity of carotenoid signals decreased from the early stage to more severe patients. These encouraging results suggest the possibility to use Raman spectroscopy for the analysis of alternative biofluids (e.g. saliva) and the unobtrusive diagnosis of other neurodegenerative disorders.

Efficacy of Raman Spectroscopy in the Diagnosis of Uterine Cervical Neoplasms: A Meta-Analysis

Background

Uterine cervical neoplasms is widely concerned due to its high incidence rate. Early diagnosis is extremely important for prognosis. The purpose of this article is evaluating the efficacy of Raman spectroscopy in the diagnosis of suspected uterine cervical neoplasms.

Methods

We searched PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of science up to September 1, 2021. By analyzing the true positive (TP), false positive (FP), true negative (TN) and false negative (FN) of six included study, we evaluated the pooled and grouping sensitivity, specificity, positive, and negative likelihood ratios (LR), and diagnostic odds ratio (DOR), with 95% confidence intervals (CI), based on random effects models. The overall diagnostic accuracy of Raman spectrum was evaluated by SROC curve analysis and AUC.

Results

After screening with inclusion and exclusion criteria, a total of six study were included in the study. The pooled sensitivity and specificity was 0.98 (95% Cl, 0.93–0.99) and 0.95 (95% Cl, 0.89–0.98). The total PLR and NLR were 21.05 (95% CI, 8.23–53.86) and 0.03 (95% CI, 0.01–0.07), respectively. And the AUC of the SROC curve which show the overall diagnostic accuracy was 0.99 (0.98–1.00).

Conclusion

Through analysis, we confirmed the role of Raman spectroscopy (RS) in the diagnosis of suspected uterine cervical tumors.

Systematic Review Registration

[https://www.crd.york.ac.uk/prospero/], identifier [CRD42021284966].

Feature fusion of Raman chemical imaging and digital histopathology using machine learning for prostate cancer detection

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP-RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and mean and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.

Imaging and SERS Study of the Au Nanoparticles Interaction with HPV and Carcinogenic Cervical Tissues

In this work, gold NPs were prepared by the Turkevich method, and their interaction with HPV and cancerous cervical tissues were studied by scanning electron microscopy, energy-dispersive x-ray spectroscopy, confocal and multiphoton microscopy and SERS. The SEM images confirmed the presence and localization of the gold NPs inside of the two kinds of tissues. The light absorption of the gold NPs was at 520 nm. However, it was possible to obtain two-photon imaging (red emission region) of the gold NPs inside of the tissue, exciting the samples at 900 nm, observing the morphology of the tissues. The infrared absorption was probably due to the aggregation of gold NPs inside the tissues. Therefore, through the interaction of gold nanoparticles with the HPV and cancerous cervical tissues, a surface enhanced Raman spectroscopy (SERS) was obtained. As preliminary studies, having an average of 1000 Raman spectra per tissue, SERS signals showed changes between the HPV-infected and the carcinogenic tissues; these spectral signatures occurred mainly in the DNA bands, potentially offering a tool for the rapid screening of cancer.

Discrimination of malignant from benign thyroid lesions through neural networks using FTIR signals obtained from tissues

The current gold standard in cancer diagnosis-the microscopic examination of hematoxylin and eosin (H&E)-stained biopsies-is prone to bias since it greatly relies on visual examination. Hence, there is a need to develop a more sensitive and specific method for diagnosing cancer. Here, Fourier transform infrared (FTIR) spectroscopy of thyroid tumors (n = 164; 76 malignant, 88 benign) was performed and five (5) neural network (NN) models were designed to discriminate the obtained spectral data. PCA-LDA was used as classical benchmark for comparison. Each NN model was evaluated using a stratified 10-fold cross-validation method to avoid overfitting, and the performance metrics-accuracy, area under the curve (AUC), positive predictive value (PPV), negative predictive value (NPV), specificity rate (SR), and recall rate (RR)-were averaged for comparison. All NN models were able to perform excellently as classifiers, and all were able to surpass the LDA model in terms of accuracy. Among the NN models, the RNN model performed best, having an AUC of 95.29% ± 6.08%, an accuracy of 98.06% ± 2.87%, a PPV of 98.57% ± 4.52%, a NPV of 93.18% ± 7.93%, a SR value of 98.89% ± 3.51%, and a RR value of 91.25% ± 10.29%. The RNN model outperformed the LDA model for all metrics except for the AUC, NPV, and RR. In conclusion, NN-based tools were able to predict thyroid cancer based on infrared spectroscopy of tissues with a high level of diagnostic performance in comparison to the gold standard.

Raman spectroscopy of human skin for kidney failure detection

The object of this paper is in vivo study of skin spectral-characteristics in patients with kidney failure by conventional Raman spectroscopy in near infrared region. The experimental dataset was subjected to discriminant analysis with the projection on latent structures (PLS-DA). Application of Raman spectroscopy to investigate the forearm skin in 85 adult patients with kidney failure (90 spectra) and 40 healthy adult volunteers (80 spectra) has yielded the accuracy of 0.96, sensitivity of 0.94 and specificity of 0.99 in terms of identifying the target subjects with kidney failure. The autofluorescence analysis in the near infrared region identified the patients with kidney failure among healthy volunteers of the same age group with specificity, sensitivity, and accuracy of 0.91, 0.84, and 0.88, respectively. When classifying subjects by the presence of kidney failure using the PLS-DA method, the most informative Raman spectral bands are 1315 to 1330, 1450 to 1460, 1700 to 1800 cm^-1 . In general, the performed study demonstrates that for in vivo skin analysis, the conventional Raman spectroscopy can provide the basis for cost-effective and accurate detection of kidney failure and associated metabolic changes in the skin.

Understanding synergistic mechanisms of ferrous iron activated sulfite oxidation and organic polymer flocculation for enhancing wastewater sludge dewaterability

The bound water in waste activated sludge (WAS) is trapped in extracellular polymeric substances (EPS) in the form of gel-like structure, leading to a great challenge in the sludge deep dewatering. Traditional flocculation conditioning is unable to destroy EPS and ineffective to remove the bound water in WAS. In this study, we employed integration of Fe(II)-sulfite oxidation and polyacrylamide flocculation (F/S-PAM) treatment for removing the bound water and improving sludge dewaterability under aerobic conditions. Meanwhile, the floc microstructure and EPS properties were examined to understand the mechanisms of F/S-PAM conditioning. F/S produced SO₃^·- radicals which could decompose the EPS in sludge, releasing bound water into free water. In addition, the formed Fe(III) from F/S led to re-coagulation of decomposed EPS, and C=O groups of tryptophan played the leading role in Fe-EPS association binding, causing transformation of the secondary structure of proteins (especially β-sheets and α-helices). Then, the introduction of PAM caused re-flocculation of disintegrated sludge flocs, enhancing the sludge filterability. This work provides a novel and cost-effective method for efficient removal of bound water in sludge, and subsequence improvement in sludge dewaterability.

Surface enhanced Raman spectroscopy of RNA samples extracted from blood of hepatitis C patients for quantification of viral loads

BACKGROUND

Raman spectroscopy is a promising technique to analyze the body fluids for the purpose of non-invasive disease diagnosis.

OBJECTIVES

To develop a surface-enhanced Raman spectroscopy (SERS) based method for qualitative and quantitative analysis of HCV from blood samples.

METHODS

SERS was employed to characterize the Hepatitis C viral RNA extracted from different blood samples of hepatitis C virus (HCV) infected patients with predetermined viral loads in comparison with total RNA of healthy individuals. The SERS measurements were performed on 27 extracted RNA samples including low viral loads, medium viral loads, high viral loads and healthy/negative viral load samples. For this purpose, silver nanoparticles (Ag NPs) were used as SERS substrates. Furthermore, multivariate data analysis technique, Principal Component Analysis (PCA) and Partial Least Square Regression (PLSR) were also performed on SERS spectral data.

RESULTS

The SERS spectral features due to biochemical changes in the extracted RNA samples associated with the increasing viral loads were established which could be employed for HCV diagnostic purpose. PCA was found helpful for the differentiation between Raman spectral data of RNA extracted from hepatitis infected and healthy blood samples. PLSR model is established for the determination of viral loads in HCV positive RNA samples with 99 % accuracy.

CONCLUSION

SERS can be employed for qualitative and quantitative analysis of HCV from blood samples.

Rapid identification of cervical adenocarcinoma and cervical squamous cell carcinoma tissue based on Raman spectroscopy combined with multiple machine learning algorithms

Cervical cancer has a long latency, and early screening greatly reduces mortality. In this study, cervical adenocarcinoma and cervical squamous cell carcinoma tissue data were collected by Raman spectroscopy, and then, the adaptive iteratively reweighted penalized least squares (airPLS) algorithm and Vancouver Raman algorithm (VRA) were used to subtract the background of the collected data. The following five feature extraction algorithms were applied: partial least squares (PLS), principal component analysis (PCA), kernel principal component analysis (KPCA), isometric feature mapping (isomap) and locally linear embedding (LLE). The k-nearest neighbour (KNN), extreme learning machine (ELM), decision tree (DT), backpropagation neural network (BP), genetic optimization backpropagation neural network (GA-BP) and linear discriminant analysis (LDA) classification models were then established through the features extracted by different feature extraction algorithms. In total, 30 types of classification models were established in this experiment. This research includes eight good models, airPLS-PLS-KNN, airPLS-PLS-ELM, airPLS-PLS-GA-BP, airPLS-PLS-BP, airPLS-PLS-LDA, airPLS-PCA-KNN, airPLS-PCA-LDA, and VRA-PLS-KNN, whose diagnostic accuracy was 96.3 %, 95.56 %, 95.06 %, 94.07 %, 92.59 %, 85.19 %, 85.19 % and 85.19 %, respectively. The experimental results showed that the model established in this article is simple to operate and highly accurate and has a good reference value for the rapid screening of cervical cancer.

Raman spectral characterization of silver metal-based complexes of different benzimidazolium ligands

In this study, Raman spectroscopy has been employed for the characterization of two structurally different monodentate N-heterocyclic carbene ligands (ligand-1 and ligand-2) and their respective complexes (complex-1 and complex-2). The Raman spectral features are found helpful for the confirmation of formation of complexes. The significant Raman spectral features are identified for benzimidazole ring with higher intensities in carbene complexes having more polarizability as compared to their ligands, providing the evidence for the formation of coordinate covalent bond. The successful complexation is further supported by using multivariate data analysis technique, Principal Component Analysis (PCA), which is found very helpful to highlight the variability of Raman spectral data of both ligands and their respective metal complexes from each other. Moreover, the coordination of carbene with Ag(I) is confirmed from the dominant spectral markers of higher intensities at 359 cm^-1 in complex-1 and 338 cm^-1 in complex-2. The effective and reliable characterization and confirmation of metal complexes indicates the potential of Raman spectroscopy for its use for the characterization of the organometallic complexes and other chemical products.

Can ethanol affect the cell structure? A dynamic molecular and Raman spectroscopy study

The role that tobacco consumption plays in the etiology of oral cancer carcinogenesis, and of alcohol consumption acting as a co-factor, have been well established. However, in recent years, the contribution of alcohol consumption alone to oral cancer has been proposed. In fact, a high percentage of patients who develop oral cancer have both habits (tobacco and alcohol consumption), and other small patient groups only consume alcohol or do not have any other identifiable bad habits. In the present study we demonstrate, using a combination of dynamic molecular modelling and Raman spectroscopy, that ethanol has a significant effect on oral cells in vitro, mainly interacting with the lipids of the cell membrane, changing their conformation. Thus, it is possible to conclude that ethanol can affect the cell permeability, and by consequence serve as a possible trigger in oral carcinogenesis.

Implementation of a classification strategy of Raman data collected in different clinical conditions: application to the diagnosis of chronic lymphocytic leukemia

The literature is rich in proof of concept studies demonstrating the potential of Raman spectroscopy for disease diagnosis. However, few studies are conducted in a clinical context to demonstrate its applicability in current clinical practice and workflow. Indeed, this translational research remains far from the patient's bedside for several reasons. First, samples are often cultured cell lines. Second, they are prepared on non-standard substrates for clinical routine. Third, a unique supervised classification model is usually constructed using inadequate cross-validation strategy. Finally, the implemented models maximize classification accuracy without taking into account the clinician's needs. In this paper, we address these issues through a diagnosis problem in real clinical conditions, i.e., the diagnosis of chronic lymphocytic leukemia from fresh unstained blood smears spread on glass slides. From Raman data acquired in different experimental conditions, a repeated double cross-validation strategy was combined with different cross-validation approaches, a consensus label strategy and adaptive thresholds able to adapt to the clinician's needs. Combined with validation at the patient level, classification results were improved compared to traditional strategies.

In vivo, ex vivo and in vitro assessment of buccal permeation of drugs from delivery systems

Introduction: Buccal mucosa has been described as an attractive site for local and systemic drug delivery, owing its accessibility, safety, and excellent blood supply. The absorption of drugs through buccal mucosa has been assessed by in vivo, ex vivo and in vitro permeability studies, using animal and cell-based models with close resemblance to the human buccal mucosa.Areas covered: This paper focuses on the current in vivo, ex vivo and in vitro permeability studies to analyze the absorption of compounds of interest through buccal mucosa, as well as their advantages and limitations in the preclinical studies of the drugs absorption profiles. The techniques for preparation and preservation of the animal buccal tissue are also discussed to evaluate their interference in the integrity and permeability of the tissues.Expert opinion: Overall, the permeability studies have been useful to evaluate the drugs absorption and to clarify the mechanism of transport of drugs across human buccal mucosa, as well as to explain the enhancement of permeability provided by certain dosage forms. Currently, several researchers have demonstrated particular interest in ex vivo permeability studies, due to their effectiveness in the evaluation of drug absorption and low costs in the acquisition of buccal mucosa samples.

Diagnosis of cervical squamous cell carcinoma and cervical adenocarcinoma based on Raman spectroscopy and support vector machine

In this report, we collected the Raman spectrum of cervical adenocarcinoma and cervical squamous cell carcinoma tissues by a micro-Raman spectroscopy system. We analysed, compared and summarized the characteristics and differences of the normalized mean Raman spectra of the two tissues and pointed out the major differences in the biochemical composition between the two tissues. The PCA-SVM model that was used to distinguish the two types of cervical cancer tissues was established. The accuracy of the model in differentiating cervical adenocarcinoma from cervical squamous cell carcinoma was 93.125%. The results of this study indicate that Raman spectroscopy of cervical adenocarcinoma and cervical squamous cell carcinoma tissue in combination with SVM (support vector analysis) and PCA (principal component analysis) can be useful for the classification of cervical adenocarcinoma and cervical squamous cell carcinoma tissues and for the exploration of the differences in biochemical compositions between the two types of cervical tissue. This study lays a foundation to further study Raman spectroscopy as a clinical diagnostic method for cervical cancer.

Predicting Ewing Sarcoma Treatment Outcome Using Infrared Spectroscopy and Machine Learning

Background: Improved outcome prediction is vital for the delivery of risk-adjusted, appropriate and effective care to paediatric patients with Ewing sarcoma—the second most common paediatric malignant bone tumour. Fourier transform infrared (FTIR) spectroscopy of tissues allows the bulk biochemical content of a biological sample to be probed and makes possible the study and diagnosis of disease. Methods: In this retrospective study, FTIR spectra of sections of biopsy-obtained bone tissue were recorded. Twenty-seven patients (between 5 and 20 years of age) with newly diagnosed Ewing sarcoma of bone were included in this study. The prognostic value of FTIR spectra obtained from Ewing sarcoma (ES) tumours before and after neoadjuvant chemotherapy were analysed in combination with various data-reduction and machine learning approaches. Results: Random forest and linear discriminant analysis supervised learning models were able to correctly predict patient mortality in 92% of cases using leave-one-out cross-validation. The best performing model for predicting patient relapse was a linear Support Vector Machine trained on the observed spectral changes as a result of chemotherapy treatment, which achieved 92% accuracy. Conclusion: FTIR spectra of tumour biopsy samples may predict treatment outcome in paediatric Ewing sarcoma patients with greater than 92% accuracy.

Study on the pathological and biomedical characteristics of spinal cord injury by confocal Raman microspectral imaging

Confocal Raman microspectral imaging (CRMI) in combination with multivariate analysis was used to study pathological progression after spinal cord injury (SCI). By establishing moderate contusion in rat models, ex vivo longitudinal spinal cord tissue sections were prepared for microspectroscopic analysis. Comparative studies were then performed to determine the pathological distinctions among before injury (BI), one day post-injury (1 DPI), seven days post-injury (7 DPI), and 14 days post-injury (14 DPI) groups. Multivariate analysis algorithms, including K-mean cluster analysis (KCA) and principal component analysis (PCA), were conducted to highlight biochemical and structural variations after tissue damage. It is confirmed that typical spectral features and profiles can illustrate some fundamental and significant pathological processes post-injury, such as neuron apoptosis, hemorrhage, demyelination, and chondroitin sulfate proteoglycans (CSPGs) upregulation. Further, by establishing spectra-structure correlations, the reconstructed spectral images revealed some minute and important morphological characteristics following tissue injury, such as glial scar formation surrounding the cavity structure. The observed spectral phenomena also provide a detailed view on relevant pathobiological factors, which are involved in the spread of secondary damage after traumatic spinal cord injury. Our findings not only provide a spectral perspective to the well-known cellular mechanisms underlying SCI, but further provide a sound basis for developing real-time Raman methodologies to evaluate the prognostic factors and therapeutic results of SCI.

Raman spectroscopic detection of high-grade cervical cytology: Using morphologically normal appearing cells

This study aims to detect high grade squamous intraepithelial cells (HSIL) by investigating HSIL associated biochemical changes in morphologically normal appearing intermediate and superficial cells using Raman spectroscopy. Raman spectra (n = 755) were measured from intermediate and superficial cells from negative cytology ThinPrep specimens (n = 18) and from morphologically normal appearing intermediate and superficial cells from HSIL cytology ThinPrep specimens (n = 17). The Raman data was subjected to multivariate algorithms including the standard principal component analysis (PCA)-linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) together with random subsets cross-validation for discriminating negative cytology from HSIL. The PCA-LDA method yielded sensitivities of 74.9%, 72.8%, and 75.6% and specificities of 89.9%, 81.9%, and 84.5%, for HSIL diagnosis based on the dataset obtained from intermediate, superficial and mixed intermediate/superficial cells, respectively. The PLS-DA method provided improved sensitivities of 95.5%, 95.2% and 96.1% and specificities of 92.7%, 94.7% and 93.5% compared to the PCA-LDA method. The results demonstrate that the biochemical signatures of morphologically normal appearing cells can be used to discriminate between negative and HSIL cytology. In addition, it was found that mixed intermediate and superficial cells could be used for HSIL diagnosis as the biochemical differences between negative and HSIL cytology were greater than the biochemical differences between intermediate and superficial cell types.

Properties of phase transition of ice binding protein from Arctic yeast (LeIBP) utilizing differential scanning calorimetry (DSC) and Raman spectroscopy

Ice binding proteins (IBPs) have been attracting significant interest on account of their characteristic of inhibiting ice growth and recrystallization. Owing to their unique characteristics, IBPs have been studied for applications in food, pharmaceuticals, and medicine, as well as from a general scientific point of view. In this study, we have used differential scanning calorimetry (DSC) and Raman spectroscopy as tools to understand the ice binding activity of the Arctic-yeast-originating extracellular ice binding glycoprotein (LeIBP) isolated from Leucosporidium sp. AY30. From the DSC results, an increase in the specific heat capacity was confirmed for 1 mg/mL LeIBP, which suggested that additional heat flow was required for the change in temperature. In addition, the temperature corresponding to the phase change of the solution was measured, and Raman spectroscopy was carried out on the frozen and molten phases, respectively. From the results of Raman analysis, we confirmed that the helical vibrations related to the ice binding sites on LeIBP were dramatically suppressed when the LeIBP solution was frozen. Furthermore, principal component analysis (PCA) of the Raman spectra yielded the contrast factor between the freezing and melting states. Both DSC and Raman spectroscopy are widely used to study the ice binding activity and the structural changes associated with molecular vibrations in cryobiology.

Prediction of Ewing Sarcoma treatment outcome using attenuated tissue reflection FTIR tissue spectroscopy

Ewing sarcoma is the second most common type of primary bone cancer and predominantly affects children and young people. Improved outcome prediction is key to delivering risk-adjusted, appropriate and effective care to cancer patients. Advances in the Fourier Transform Infrared (FTIR) spectroscopy of tissues enable it to be a non-invasive method to obtain information about the biochemical content of any biological sample. In this retrospective study, attenuated tissue reflection FTIR spectroscopy of biopsy samples from paediatric patients reveals spectral features that are diagnostic for Ewing Sarcoma. Furthermore, our results suggest that spectral features such as these may be of value for the prediction of treatment outcome independent to well-known, routinely used risk factors.

Raman spectral analysis for rapid screening of dengue infection

Infection with the dengue virus is currently clinically detected according to different biomarkers in human blood plasma, commonly measured by enzyme linked immunosorbent assays, including non-structural proteins (Ns1), immunoglobulin M (IgM) and immunoglobulin G (IgG). However, there is little or no mutual correlation between the biomarkers, as demonstrated in this study by a comparison of their levels in samples from 17 patients. As an alternative, the label free, rapid screening technique, Raman spectroscopy has been used for the characterisation/diagnosis of healthy and dengue infected human blood plasma samples. In dengue positive samples, changes in specific Raman spectral bands associated with lipidic and amino acid/protein content are observed and assigned based on literature and these features can be considered as markers associated with dengue development. Based on the spectroscopic analysis of the current, albeit limited, cohort of samples, Principal Components Analysis (PCA) coupled Factorial Discriminant Analysis, yielded values of 97.95% sensitivity and 95.40% specificity for identification of dengue infection. Furthermore, in a comparison of the normal samples to the patient samples which scored low for only one of the biomarker tests, but high or medium for either or both of the other two, PCA-FDA demonstrated a sensitivity of 97.38% and specificity of 86.18%, thus providing an unambiguous screening technology.

Aluminium foil as an alternative substrate for the spectroscopic interrogation of endometrial cancer

Biospectroscopy has the potential to investigate and characterize biological samples and could, therefore, be utilized to diagnose various diseases in a clinical environment. An important consideration in spectrochemical studies is the cost-effectiveness of the substrate used to support the sample, as high expense would limit their translation into clinic. In this paper, the performance of low-cost aluminium (Al) foil substrates was compared with the commonly used low-emissivity (low-E) slides. Attenuated total reflection-Fourier transform infrared spectroscopy was used to analyse blood plasma and serum samples from women with endometrial cancer and healthy controls. The 2 populations were differentiated using principal component analysis with support vector machines with 100% sensitivity in plasma samples (endometrial cancer = 70; healthy controls = 15) using both Al foil and low-E slides as substrates. The same sensitivity results (100%) were achieved for serum samples (endometrial cancer = 60; healthy controls = 15). Specificity was found higher using Al foil (90%) in comparison to low-E slides (85%) and lower using Al foil (70%) in comparison to low-E slides in serum samples. The establishment of Al foil as low-cost and highly performing substrate would pave the way for large-scale, multicentre studies and potentially for routine clinical use.

In vivo Raman spectroscopy for biochemical monitoring of the human cervix throughout pregnancy

BACKGROUND

The cervix must undergo significant biochemical remodeling to allow for successful parturition. This process is not fully understood, especially in instances of spontaneous preterm birth. In vivo Raman spectroscopy is an optical technique that can be used to investigate the biochemical composition of tissue longitudinally and noninvasively in human beings, and has been utilized to measure physiology and disease states in a variety of medical applications.

OBJECTIVE

The purpose of this study is to measure in vivo Raman spectra of the cervix throughout pregnancy in women, and to identify biochemical markers that change with the preparation for delivery and postpartum repair.

STUDY DESIGN

In all, 68 healthy pregnant women were recruited. Raman spectra were measured from the cervix of each patient monthly in the first and second trimesters, weekly in the third trimester, and at the 6-week postpartum visit. Raman spectra were measured using an in vivo Raman system with an optical fiber probe to excite the tissue with 785 nm light. A spectral model was developed to highlight spectral regions that undergo the most changes throughout pregnancy, which were subsequently used for identifying Raman peaks for further analysis. These peaks were analyzed longitudinally to determine if they underwent significant changes over the course of pregnancy (P < .05). Finally, 6 individual components that comprise key biochemical constituents of the human cervix were measured to extract their contributions in spectral changes throughout pregnancy using a linear combination method. Patient factors including body mass index and parity were included as variables in these analyses.

RESULTS

Raman peaks indicative of extracellular matrix proteins (1248 and 1254 cm^-1) significantly decreased (P < .05), while peaks corresponding to blood (1233 and 1563 cm^-1) significantly increased (P < .0005) in a linear manner throughout pregnancy. In the postpartum cervix, significant increases in peaks corresponding to actin (1003, 1339, and 1657 cm^-1) and cholesterol (1447 cm^-1) were observed when compared to late gestation, while signatures from blood significantly decreased. Postpartum actin signals were significantly higher than early pregnancy, whereas extracellular matrix proteins and water signals were significantly lower than early weeks of gestation. Parity had a significant effect on blood and extracellular matrix protein signals, with nulliparous patients having significant increases in blood signals throughout pregnancy, and higher extracellular matrix protein signals in early pregnancy compared to patients with prior pregnancies. Body mass index significantly affected actin signal contribution, with low body mass index patients showing decreasing actin contribution throughout pregnancy and high body mass index patients demonstrating increasing actin signals.

CONCLUSION

Raman spectroscopy was successfully used to biochemically monitor cervical remodeling in pregnant women during prenatal visits. This foundational study has demonstrated sensitivity to known biochemical dynamics that occur during cervical remodeling, and identified patient variables that have significant effects on Raman spectra throughout pregnancy. Raman spectroscopy has the potential to improve our understanding of cervical maturation, and be used as a noninvasive preterm birth risk assessment tool to reduce the incidence, morbidity, and mortality caused by preterm birth.

Raman spectroscopy for cytopathology of exfoliated cervical cells

Cervical cancer is the fourth most common cancer affecting women worldwide but mortality can be decreased by early detection of pre-malignant lesions. The Pap smear test is the most commonly used method in cervical cancer screening programmes. Although specificity is high for this test, it is widely acknowledged that sensitivity can be poor mainly due to the subjective nature of the test. There is a need for new objective tests for the early detection of pre-malignant cervical lesions. Over the past two decades, Raman spectroscopy has emerged as a promising new technology for cancer screening and diagnosis. The aim of this study was to evaluate the potential of Raman spectroscopy for cervical cancer screening using both Cervical Intraepithelial Neoplasia (CIN) and Squamous Intraepithelial Lesion (SIL) classification terminology. ThinPrep® Pap samples were recruited from a cervical screening population. Raman spectra were recorded from single cell nuclei and subjected to multivariate statistical analysis. Normal and abnormal ThinPrep® samples were discriminated based on the biochemical fingerprint of the cells using Principal Component Analysis (PCA). Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA) was employed to build classification models based on either CIN or SIL terminology. This study has shown that Raman spectroscopy can be successfully applied to the study of routine cervical cytology samples from a cervical screening programme and that the use of CIN terminology resulted in improved sensitivity for high grade cases.

Near-infrared Raman spectroscopy for estimating biochemical changes associated with different pathological conditions of cervix

The molecular level changes associated with oncogenesis precede the morphological changes in cells and tissues. Hence molecular level diagnosis would promote early diagnosis of the disease. Raman spectroscopy is capable of providing specific spectral signature of various biomolecules present in the cells and tissues under various pathological conditions. The aim of this work is to develop a non-linear multi-class statistical methodology for discrimination of normal, neoplastic and malignant cells/tissues. The tissues were classified as normal, pre-malignant and malignant by employing Principal Component Analysis followed by Artificial Neural Network (PC-ANN). The overall accuracy achieved was 99%. Further, to get an insight into the quantitative biochemical composition of the normal, neoplastic and malignant tissues, a linear combination of the major biochemicals by non-negative least squares technique was fit to the measured Raman spectra of the tissues. This technique confirms the changes in the major biomolecules such as lipids, nucleic acids, actin, glycogen and collagen associated with the different pathological conditions. To study the efficacy of this technique in comparison with histopathology, we have utilized Principal Component followed by Linear Discriminant Analysis (PC-LDA) to discriminate the well differentiated, moderately differentiated and poorly differentiated squamous cell carcinoma with an accuracy of 94.0%. And the results demonstrated that Raman spectroscopy has the potential to complement the good old technique of histopathology.

Nature and Value of Freely Dissolved EPS Ecosystem Services: Insight into Molecular Coupling Mechanisms for Regulating Metal Toxicity

Extracellular polymeric substances (EPSs) dispersed in natural waters play a significant role in relieving impacts to microbial survival associated with heavy metal release, yet little is known about the association of freely dissolved EPS ecosystem services with metal transformation in natural waters. Here, we demonstrate that dispersive EPSs mitigate the metal toxicity to microbial cells through an associative coordination reaction. Microtitrimetry coupled with fluorescence spectroscopy ascribes the combination of freely dissolved EPSs from Escherichia coli (E. coli) with Cu²⁺/Cd²⁺ to a coordination reaction associated with chemical static quenching. Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and computational chemistry confirm that carboxyl residues in protein-like substances of the EPSs are responsible for the coordination. Frontier molecular orbitals (MOs) of a deprotonated carboxyl integrate with the occupied d orbitals of Cu²⁺ and/or d, s orbitals of Cd²⁺ to form metal-EPS complexes. Microcosmic systems show that because the metal-EPS complexes decrease cellular absorbability of metals, E. coli survivals increase by 4.3 times for Cu²⁺ and 1.6 times for Cd²⁺, respectively. Based on bonding energies for six metals-EPS coordination, an associative toxic effect further confirms that increased bonding energies facilitate retardation of metals in the EPS matrix, protecting against E. coli apoptosis.

Tuberculosis determination using SERS and chemometric methods

Nanostructures have been multiplying the advantages of Raman spectroscopy and further amplify the advantages of Raman spectroscopy is a continuous effort focused on the appropriate design of nanostructures. Herein, we designed different shapes of plasmonic nanostructures such as Vertical, Zig Zag, Slant nanorods and Spherical nanoparticles employing the DC magnetron sputtering system as SERS-active substrates for ultrasensitive detection of target molecules. The fabricated plasmonic nanostructures sensitivity and uniformity were exploited by reference dye analyte. These nanostructures were utilized in the label free detection of infectious disease, Tuberculosis (TB). For the first time, TB detection from serum samples using SERS has been demonstrated. Various multivariate statistical methods such as principal component analysis, support vector machine, decision tree and random forest were developed and tested their ability to discriminate the healthy and active TB samples. The results demonstrate the performance of the SERS spectra, chemometric methods and potential of the method in clinical diagnosis.

Raman spectral signatures of cervical exfoliated cells from liquid-based cytology samples

It is widely accepted that cervical screening has significantly reduced the incidence of cervical cancer worldwide. The primary screening test for cervical cancer is the Papanicolaou (Pap) test, which has extremely variable specificity and sensitivity. There is an unmet clinical need for methods to aid clinicians in the early detection of cervical precancer. Raman spectroscopy is a label-free objective method that can provide a biochemical fingerprint of a given sample. Compared with studies on infrared spectroscopy, relatively few Raman spectroscopy studies have been carried out to date on cervical cytology. The aim of this study was to define the Raman spectral signatures of cervical exfoliated cells present in liquid-based cytology Pap test specimens and to compare the signature of high-grade dysplastic cells to each of the normal cell types. Raman spectra were recorded from single exfoliated cells and subjected to multivariate statistical analysis. The study demonstrated that Raman spectroscopy can identify biochemical signatures associated with the most common cell types seen in liquid-based cytology samples; superficial, intermediate, and parabasal cells. In addition, biochemical changes associated with high-grade dysplasia could be identified suggesting that Raman spectroscopy could be used to aid current cervical screening tests.

Evaluating oral epithelial dysplasia classification system by near-infrared Raman spectroscopy

Until now, the classification system of oral epithelial dysplasia is still based on the architectural and cytological changes, which relies on the observation of pathologists and is relatively subjective. The purpose of present research was to discriminate the oral dysplasia by the near-infrared Raman spectroscope, in order to evaluate the classification system. We collected Raman spectra of normal mucosa, oral squamous cell carcinoma (OSCC) and dysplasia by near-infrared Raman spectroscope. The biochemical variations between different stages were analyzed by the characteristic peaks in the subtracted mean spectra. Gaussian radial basis function support vector machines (SVM) were used to establish the diagnostic models. At the same time, principal component analysis (PCA) and linear discriminant analysis (LDA) were used to verify the results of SVM. Raman spectral differences were observed in the range between 730~1913 cm-1. Compared with normal mucosa, high contents of protein and DNA in oral dysplasia and OSCC were observed. There were no significant or gradual variation of Raman peaks among different dysplastic grades. The accuracies of comparison between mild, moderate, severe dysplasia with OSCC were 100%, 44.44%, 71.15%, which elucidated the low modeling ability of support vector machines, especially for the moderate dysplasia. The analysis by PCA-LDA could not discriminate the stages, either. Combined with support vector machines, near-infrared Raman spectroscopy could detect the biochemical variations in oral normal, OSCC and dysplastic tissues, but could not establish diagnostic model accurately. The classification system needs further improvements.

In vivo Raman spectral analysis of impaired cervical remodeling in a mouse model of delayed parturition

Monitoring cervical structure and composition during pregnancy has high potential for prediction of preterm birth (PTB), a problem affecting 15 million newborns annually. We use in vivo Raman spectroscopy, a label-free, light-based method that provides a molecular fingerprint to non-invasively investigate normal and impaired cervical remodeling. Prostaglandins stimulate uterine contractions and are clinically used for cervical ripening during pregnancy. Deletion of cyclooxygenase-1 (Cox-1), an enzyme involved in production of these prostaglandins, results in delayed parturition in mice. Contrary to expectation, Cox-1 null mice displayed normal uterine contractility; therefore, this study sought to determine whether cervical changes could explain the parturition differences in Cox-1 null mice and gestation-matched wild type (WT) controls. Raman spectral changes related to extracellular matrix proteins, lipids, and nucleic acids were tracked over pregnancy and found to be significantly delayed in Cox-1 null mice at term. A cervical basis for the parturition delay was confirmed by other ex vivo tests including decreased tissue distensibility, hydration, and elevated progesterone levels in the Cox-1 null mice at term. In conclusion, in vivo Raman spectroscopy non-invasively detected abnormal remodeling in the Cox-1 null mouse, and clearly demonstrated that the cervix plays a key role in their delayed parturition.

Raman spectroscopy and regenerative medicine: a review

The field of regenerative medicine spans a wide area of the biomedical landscape-from single cell culture in laboratories to human whole-organ transplantation. To ensure that research is transferrable from bench to bedside, it is critical that we are able to assess regenerative processes in cells, tissues, organs and patients at a biochemical level. Regeneration relies on a large number of biological factors, which can be perturbed using conventional bioanalytical techniques. A versatile, non-invasive, non-destructive technique for biochemical analysis would be invaluable for the study of regeneration; and Raman spectroscopy is a potential solution. Raman spectroscopy is an analytical method by which chemical data are obtained through the inelastic scattering of light. Since its discovery in the 1920s, physicists and chemists have used Raman scattering to investigate the chemical composition of a vast range of both liquid and solid materials. However, only in the last two decades has this form of spectroscopy been employed in biomedical research. Particularly relevant to regenerative medicine are recent studies illustrating its ability to characterise and discriminate between healthy and disease states in cells, tissue biopsies and in patients. This review will briefly outline the principles behind Raman spectroscopy and its variants, describe key examples of its applications to biomedicine, and consider areas of regenerative medicine that would benefit from this non-invasive bioanalytical tool.

Diagnosing clean margins through Raman spectroscopy in human and animal mammary tumour surgery: a short review

Breast cancer frequency in human and other mammal female populations has worryingly increased lately. The acute necessity for taxonomy of the aetiological factors along with seeking for new diagnostic tools and therapy procedures aimed at reducing mortality have yielded in an intense research effort worldwide. Surgery is a regular method to counteract extensive development of breast cancer and prevent metastases provided that negative surgical margins are achieved. This highly technical challenge requires fast, extremely sensitive and selective discrimination between malignant and benign tissues even down to molecular level. The particular advantages of Raman spectroscopy, such as high chemical specificity, and the ability to measure raw samples and optical responses in the visible or near-infrared spectral range, have recently recommended it as a means with elevated potential in precise diagnostic in oncology surgery. This review spans mainly the latter 10 years of exceptional efforts of scientists implementing Raman spectroscopy as a nearly real-time diagnostic tool for clean margins assessment in mastectomy and lumpectomy. Although greatly contributing to medical discoveries for the wealth of humanity, animals as patients have benefitted less from advances in surgery diagnostic using Raman spectroscopy. This work also dedicates a few lines to applications of surface enhanced Raman spectroscopy in veterinary oncological surgery.

Different Phases of Breast Cancer Cells: Raman Study of Immortalized, Transformed, and Invasive Cells

Breast cancer is the most prevalent cause of cancer-associated death in women the world over, but if detected early it can be treated successfully. Therefore, it is important to diagnose this disease at an early stage and to understand the biochemical changes associated with cellular transformation and cancer progression. Deregulated lipid metabolism has been shown to contribute to cell transformation as well as cancer progression. In this study, we monitored the biomolecular changes associated with the transformation of a normal cell into an invasive cell associated with breast cancer using Raman microspectroscopy. We have utilized primary normal breast cells, and immortalized, transformed, non-invasive, and invasive breast cancer cells. The Raman spectra were acquired from all these cell lines under physiological conditions. The higher wavenumber (2800–3000 cm⁻¹) and lower wavenumber (700–1800 cm⁻¹) range of the Raman spectrum were analyzed and we observed increased lipid levels for invasive cells. The Raman spectral data were analyzed by principal component–linear discriminant analysis (PC-LDA), which resulted in the formation of distinct clusters for different cell types with a high degree of sensitivity. The subsequent testing of the PC-LDA analysis via the leave-one-out cross validation approach (LOOCV) yielded relatively high identification sensitivity. Additionally, the Raman spectroscopic results were confirmed through fluorescence staining tests with BODIPY and Nile Red biochemical assays. Furthermore, Raman maps from the above mentioned cells under fixed conditions were also acquired to visualize the distribution of biomolecules throughout the cell. The present study shows the suitability of Raman spectroscopy as a non-invasive, label-free, microspectroscopic technique, having the potential of probing changes in the biomolecular composition of living cells as well as fixed cells.

Optical diagnosis of actinic cheilitis by infrared spectroscopy

Actinic cheilitis (AC) is considered a potentially malignant disorder of the lip. Biomolecular markers study is important to understand malignant transformation into squamous cell carcinoma. Fourier transform infra red (FT-IR) spectroscopy was used to analyze AC in this study.

OBJECTIVES

The aim of the study was to evaluate if FT-IR spectral regions of nucleic acids and collagen can help in early diagnosis of malignant transformation.

METHODS

Tissues biopsies of 14 patients diagnosed with AC and 14 normal tissues were obtained. FT-IR spectra were measured at five different points resulting in 70 spectra of each. Analysis of Principal components analysis (PCA) and linear discrimination analysis (LDA) model were also used. In order to verify the statistical difference in the spectra, Mann-Whitney U test was performed in each variable (wavenumber) with p-value <0.05.

RESULTS

After the Mann-Whitney U test the vibrational modes of CO (Collagen 1), PO2 (Nucleic Acids) and CO asymmetric (Triglycerides/Lipids) were observed as a possible spectral biomarker. These bands were chosen because they represent the vibrational modes related to collagen and DNA, which are supposed to be changed in AC samples. Based on the PCA-LDA results, the predictive model corresponding to the area under the curve was 0.91 for the fingerprint region and 0.83 for the high wavenumber region, showing the greater accuracy of the test.

CONCLUSIONS

FT-IR changes in collagen and nucleic acids could be used as molecular biomarkers for malignant transformation.