Raman technologies in cancer diagnostics

Subtype-Specific Detection in Stage Ia Breast Cancer: Integrating Raman Spectroscopy, Machine Learning, and Liquid Biopsy for Personalised Diagnostics

This study explores the integration of Raman spectroscopy (RS) with machine learning for the early detection and subtyping of breast cancer using blood plasma samples. We performed detailed spectral analyses, identifying significant spectral patterns associated with cancer biomarkers. Our findings demonstrate the potential for classifying the four major subtypes of breast cancer at stage Ia with an average sensitivity and specificity of 90% and 95%, respectively, and a cross-validated macro-averaged area under the curve (AUC) of 0.98. This research highlights efforts to integrate vibrational spectroscopy with machine learning, enhancing cancer diagnostics through a non-invasive, personalised approach for early detection and monitoring disease progression. This study is the first of its kind to utilise RS and machine learning to classify the four major breast cancer subtypes at stage Ia.

Single color digital H&E staining with In-and-Out Net

Digital staining streamlines traditional staining procedures by digitally generating stained images from unstained or differently stained images. While conventional staining methods involve time-consuming chemical processes, digital staining offers an efficient and low-infrastructure alternative. Researchers can expedite tissue analysis without physical sectioning by leveraging microscopy-based techniques, such as confocal microscopy. However, interpreting grayscale or pseudo-color microscopic images remains challenging for pathologists and surgeons accustomed to traditional histologically stained images. To fill this gap, various studies explore digitally simulating staining to mimic targeted histological stains. This paper introduces a novel network, In-and-Out Net, designed explicitly for digital staining tasks. Based on Generative Adversarial Networks (GAN), our model efficiently transforms Reflectance Confocal Microscopy (RCM) images into Hematoxylin and Eosin (H&E) stained images. Using aluminum chloride preprocessing for skin tissue, we enhance nuclei contrast in RCM images. We trained the model with digital H&E labels featuring two fluorescence channels, eliminating the need for image registration and providing pixel-level ground truth. Our contributions include proposing an optimal training strategy, conducting a comparative analysis demonstrating state-of-the-art performance, validating the model through an ablation study, and collecting perfectly matched input and ground truth images without registration. In-and-Out Net showcases promising results, offering a valuable tool for digital staining tasks and advancing the field of histological image analysis.

Cervical cancer biomarker screening based on Raman spectroscopy and multivariate statistical analysis

Cervical cancer (CC) stands as one of the most prevalent malignancies among females, and the examination of serum tumor markers(TMs) assumes paramount significance in both its diagnosis and treatment. This research delves into the potential of combining Surface-Enhanced Raman Spectroscopy (SERS) with Multivariate Statistical Analysis (MSA) to diagnose cervical cancer, coupled with the identification of prospective serum biomarkers. Serum samples were collected from 95 CC patients and 81 healthy subjects, with subsequent MSA employed to analyze the spectral data. The outcomes underscore the superior efficacy of Partial Least Squares Discriminant Analysis (PLS-DA) within the MSA framework, achieving predictive accuracy of 97.73 %, and exhibiting sensitivities and specificities of 100 % and 95.83 % respectively. Additionally, the PLS-DA model yields a Variable Importance in Projection (VIP) list, which, when coupled with the biochemical information of characteristic peaks, can be utilized for the screening of biomarkers. Here, the Random Forest (RF) model is introduced to aid in biomarker screening. The two findings demonstrate that the principal contributing features distinguishing cervical cancer Raman spectra from those of healthy individuals are located at 482, 623, 722, 956, 1093, and 1656 cm-1, primarily linked to serum components such as DNA, tyrosine, adenine, valine, D-mannose, and amide I. Predictive models are constructed for individual biomolecules, generating ROC curves. Remarkably, D-mannose of V (C-N) exhibited the highest performance, boasting an AUC value of 0.979. This suggests its potential as a serum biomarker for distinguishing cervical cancer from healthy subjects.

Non-invasive screening and subtyping for breast cancer by serum SERS combined with LGB-DNN algorithms

Early detection of breast cancer and its molecular subtyping is crucial for guiding clinical treatment and improving survival rate. Current diagnostic methods for breast cancer are invasive, time consuming and complicated. In this work, an optical detection method integrating surface-enhanced Raman spectroscopy (SERS) technology with feature selection and deep learning algorithm was developed for identifying serum components and building diagnostic model, with the aim of efficient and accurate noninvasive screening of breast cancer. First, the high quality of serum SERS spectra from breast cancer (BC), breast benign disease (BBD) patients and healthy controls (HC) were obtained. Chi-square tests were conducted to exclude confounding factors, enhancing the reliability of the study. Then, LightGBM (LGB) algorithm was used as the base model to retain useful features to significantly improve classification performance. The DNN algorithm was trained through backpropagation, adjusting the weights and biases between neurons to improve the network's predictive ability. In comparison to traditional machine learning algorithms, this method provided more accurate information for breast cancer classification, with classification accuracies of 91.38 % for BC and BBD, and 96.40 % for BC, BBD, and HC. Furthermore, the accuracies of 90.11 % for HR+/HR- and 88.89 % for HER2+/HER2- can be reached when evaluating BC patients' molecular subtypes. These results demonstrate that serum SERS combined with powerful LGB-DNN algorithm would provide a supplementary method for clinical breast cancer screening.

Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation

Background

Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy.

Patients and methods

Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN.

Results

The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test).

Conclusions

Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.

A Molecular Typing Method for Invasive Breast Cancer by Serum Raman Spectroscopy

BACKGROUND

The incidence of breast cancer ranks highest among cancers and is exceedingly heterogeneous. Immunohistochemical staining is commonly used clinically to identify the molecular subtype for subsequent treatment and prognosis.

PURPOSE

Raman spectroscopy and support vector machine (SVM) learning algorithm were utilized to identify blood samples from breast cancer patients in order to investigate a novel molecular typing approach.

METHOD

Tumor tissue coarse needle aspiration biopsy samples, and peripheral venous blood samples were gathered from 459 invasive breast cancer patients admitted to the breast department of Sichuan Cancer Hospital between June 2021 and September 2022. Immunohistochemical staining and in situ hybridization were performed on the coarse needle aspiration biopsy tissues to obtain their molecular typing pathological labels, including: 70 cases of Luminal A, 167 cases of Luminal B (HER2-positive), 57 cases of Luminal B (HER2-negative), 84 cases of HER2-positive, and 81 cases of triple-negative. Blood samples were processed to obtained Raman spectra taken for SVM classification models establishment with machine algorithms (using 80% of the sample data as the training set), and then the performance of the SVM classification models was evaluated by the independent validation set (20% of the sample data).

RESULTS

The AUC values of SVM classification models remained above 0.85, demonstrating outstanding model performance and excellent subtype discrimination of breast cancer molecular subtypes.

CONCLUSION

Raman spectroscopy of serum samples can promptly and precisely detect the molecular subtype of invasive breast cancer, which has the potential for clinical value.

An outlier removal method based on PCA-DBSCAN for blood-SERS data analysis

Surface-enhanced Raman spectroscopy (SERS) has shown promising potential in cancer screening. In practical applications, Raman spectra are often affected by deviations from the spectrometer, changes in measurement environments, and anomalies in spectrum characteristic peak intensities due to improper sample storage. Previous research has overlooked the presence of outliers in categorical data, leading to significant impacts on model learning outcomes. In this study, we propose a novel method, called Principal Component Analysis and Density Based Spatial Clustering of Applications with Noise (PCA-DBSCAN) to effectively remove outliers. This method employs dimensionality reduction and spectral data clustering to identify and remove outliers. The PCA-DBSCAN method introduces adjustable parameters (Eps and MinPts) to control the clustering effect. The effectiveness of the proposed PCA-DBSCAN method is verified through modeling on outlier-removed datasets. Further refinement of the machine learning model and PCA-DBSCAN parameters resulted in the best cancer screening model, achieving 97.41% macro-average recall and 97.74% macro-average F1-score. This paper introduces a new outlier removal method that significantly improves the performance of the SERS cancer screening model. Moreover, the proposed method serves as inspiration for outlier detection in other fields, such as biomedical research, environmental monitoring, manufacturing, quality control, and hazard prediction.

Distinguishing brain tumors by Label-free confocal micro-Raman spectroscopy

BACKGROUND

Brain tumors have serious adverse effects on public health and social economy. Accurate detection of brain tumor types is critical for effective and proactive treatment, and thus improve the survival of patients.

METHODS

Four types of brain tumor tissue sections were detected by Raman spectroscopy. Principal component analysis (PCA) has been used to reduce the dimensionality of the Raman spectra data. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were utilized to discriminate different types of brain tumors.

RESULTS

Raman spectra were collected from 40 brain tumors. Variations in intensity and shift were observed in the Raman spectra positioned at 721, 854, 1004, 1032, 1128, 1248, 1449 cm-1 for different brain tumor tissues. The PCA results indicated that glioma, pituitary adenoma, and meningioma are difficult to differentiate from each other, whereas acoustic neuroma is clearly distinguished from the other three tumors. Multivariate analysis including QDA and LDA methods showed the classification accuracy rate of the QDA model was 99.47 %, better than the rate of LDA model was 95.07 %.

CONCLUSIONS

Raman spectroscopy could be used to extract valuable fingerprint-type molecular and chemical information of biological samples. The demonstrated technique has the potential to be developed to a rapid, label-free, and intelligent approach to distinguish brain tumor types with high accuracy.

Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy

The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spectra were analyzed using a range of classifiers in which random forest (RF) achieved the best results, e.g., for saliva, the precision and recall equals 94.0% and 88.9%, respectively. The results demonstrate that even with a relatively small number of clinical samples, the combination of SERS and shallow machine learning can be used to identify SARS-CoV-2 virus in clinical practice.

Antiepileptic drug concentration detection based on Raman spectroscopy and an improved snake optimization-convolutional neural network algorithm

A method for measurement of antiepileptic drug concentrations based on Raman spectroscopy and an optimization algorithm for mathematical models are proposed and investigated. This study uses Raman spectroscopy to measure mixed antiepileptic drugs, and an Improved Snake Optimization (ISO)-Convolutional Neural Network (CNN) algorithm is proposed. Raman spectroscopy is widely used in the identification of pharmaceutical ingredients due to its sharp peaks, no pre-treatment of samples and non-destructive detection. To analyze the spectral data precisely, a machine learning method is used in this paper. The ISO algorithm is an improved intelligent swarm algorithm in which the method of generating random solutions is improved, which can ensure that a comprehensive local search of the model is performed, the global search capability is maintained at a later stage, and the convergence speed is accelerated. In this study, 360 groups of oxcarbazepine, carbamazepine, and lamotrigine drug mixtures are measured using Raman spectroscopy, and the raw spectral data after pre-processing are trained and evaluated using ISO-CNN algorithms, and the results are compared and analyzed with those obtained from other algorithms such as the Northern Goshawk Optimization algorithm, Chameleon Swarm Algorithm, and White Shark Optimizer algorithm. The results show that the best ISO-CNN algorithm training is achieved for oxcarbazepine, with a determination coefficient and root mean square error of 0.99378 and 0.0295 for the validation set, and 0.99627 and 0.0278 for the test set. The overall results suggest that Raman spectroscopy combined with machine learning algorithms can be a potential tool for drug concentration prediction.

Sub-Cellular Dynamic Analysis of BGC823 Cells after Treatment with the Multi-Component Drug CKI Using Raman Spectroscopy

Multi-component drugs (MCDs) can induce various cellular changes covering multiple levels, from molecular and subcellular structure to cell morphology. A "non-invasive" method for comprehensively detecting the dynamic changes of cellular fine structure and chemical components on the subcellular level is highly desirable for MCD studies. In this study, the subcellular dynamic processes of gastric cancer BGC823 cells after treatment with a multi-component drug, Compound Kushen Injection (CKI), were investigated using a homemade, high-resolution, confocal Raman spectroscopy (RS) device combined with bright-field imaging. The Raman spectra of the nucleus, cytoplasm and intracellular vesicles (0.4-1 μm) were collected simultaneously for each cell treated with CKI at different times and doses. The RS measurements showed that CKI decreased the DNA signatures, which the drug is known to inhibit. Meanwhile, the CKI-induced subcellular dynamic changes in the appearance of numerous intracellular vesicles and the deconstruction of cytoplasm components were observed and discussed. The results demonstrated that high-resolution subcellular micro-Raman spectroscopy has potential for detecting fine cellular dynamic variation induced by drugs and the screening of MCDs in cancer therapy.

Multi-scale sequential feature selection for disease classification using Raman spectroscopy data

Raman spectroscopy (RS) optical technology promises non-destructive and fast application in medical disease diagnosis in a single step. However, achieving clinically relevant performance levels remains challenging due to the inability to search for significant Raman signals at different scales. Here we propose a multi-scale sequential feature selection method that can capture global sequential features and local peak features for disease classification using RS data. Specifically, we utilize the Long short-term memory network (LSTM) module to extract global sequential features in the Raman spectra, as it can capture long-term dependencies present in the Raman spectral sequences. Meanwhile, the attention mechanism is employed to select local peak features that were ignored before and are the key to distinguishing different diseases. Experimental results on three public and in-house datasets demonstrate the superiority of our model compared with state-of-the-art methods for RS classification. In particular, our model achieves an accuracy of 97.9 ± 0.2% on the COVID-19 dataset, 76.3 ± 0.4% on the H-IV dataset, and 96.8 ± 1.9% on the H-V dataset.

SERS Signature of SARS-CoV-2 in Saliva and Nasopharyngeal Swabs: Towards Perspective COVID-19 Point-of-Care Diagnostics

In this study, the intrinsic surface-enhanced Raman spectroscopy (SERS)-based approach coupled with chemometric analysis was adopted to establish the biochemical fingerprint of SARS-CoV-2 infected human fluids: saliva and nasopharyngeal swabs. The numerical methods, partial least squares discriminant analysis (PLS-DA) and support vector machine classification (SVMC), facilitated the spectroscopic identification of the viral-specific molecules, molecular changes, and distinct physiological signatures of pathetically altered fluids. Next, we developed the reliable classification model for fast identification and differentiation of negative CoV(-) and positive CoV(+) groups. The PLS-DA calibration model was described by a great statistical value-RMSEC and RMSECV below 0.3 and R²_cal at the level of ~0.7 for both type of body fluids. The calculated diagnostic parameters for SVMC and PLS-DA at the stage of preparation of calibration model and classification of external samples simulating real diagnostic conditions evinced high accuracy, sensitivity, and specificity for saliva specimens. Here, we outlined the significant role of neopterin as the biomarker in the prediction of COVID-19 infection from nasopharyngeal swab. We also observed the increased content of nucleic acids of DNA/RNA and proteins such as ferritin as well as specific immunoglobulins. The developed SERS for SARS-CoV-2 approach allows: (i) fast, simple and non-invasive collection of analyzed specimens; (ii) fast response with the time of analysis below 15 min, and (iii) sensitive and reliable SERS-based screening of COVID-19 disease.

Toward next-generation endoscopes integrating biomimetic video systems, nonlinear optical microscopy, and deep learning

According to the World Health Organization, the proportion of the world's population over 60 years will approximately double by 2050. This progressive increase in the elderly population will lead to a dramatic growth of age-related diseases, resulting in tremendous pressure on the sustainability of healthcare systems globally. In this context, finding more efficient ways to address cancers, a set of diseases whose incidence is correlated with age, is of utmost importance. Prevention of cancers to decrease morbidity relies on the identification of precursor lesions before the onset of the disease, or at least diagnosis at an early stage. In this article, after briefly discussing some of the most prominent endoscopic approaches for gastric cancer diagnostics, we review relevant progress in three emerging technologies that have significant potential to play pivotal roles in next-generation endoscopy systems: biomimetic vision (with special focus on compound eye cameras), non-linear optical microscopies, and Deep Learning. Such systems are urgently needed to enhance the three major steps required for the successful diagnostics of gastrointestinal cancers: detection, characterization, and confirmation of suspicious lesions. In the final part, we discuss challenges that lie en route to translating these technologies to next-generation endoscopes that could enhance gastrointestinal imaging, and depict a possible configuration of a system capable of (i) biomimetic endoscopic vision enabling easier detection of lesions, (ii) label-free in vivo tissue characterization, and (iii) intelligently automated gastrointestinal cancer diagnostic.

Monitoring of microbial proteome dynamics using Raman stable isotope probing

Abnormal protein kinetics could be a cause of several diseases associated with essential life processes. An accurate understanding of protein dynamics and turnover is essential for developing diagnostic or therapeutic tools to monitor these changes. Raman spectroscopy in combination with stable isotope probes (SIP) such as carbon-13, and deuterium has been a breakthrough in the qualitative and quantitative study of various metabolites. In this work, we are reporting the utility of Raman-SIP for monitoring dynamic changes in the proteome at the community level. We have used ¹³ C-labeled glucose as the only carbon source in the medium and verified its incorporation in the microbial biomass in a time-dependent manner. A visible redshift in the Raman spectral vibrations of major biomolecules such as nucleic acids, phenylalanine, tyrosine, amide I, and amide III were observed. Temporal changes in the intensity of these bands demonstrating the feasibility of protein turnover monitoring were also verified. Kanamycin, a protein synthesis inhibitor was used to assess the feasibility of identifying effects on protein turnover in the cells. Successful application of this work can provide an alternate/adjunct tool for monitoring proteome-level changes in an objective and nondestructive manner.

Monitoring dexamethasone skin biodistribution with ex vivo MALDI-TOF mass spectrometry imaging and confocal Raman microscopy

Two of the most promising techniques in terms of ex vivo skin imaging and quantifying are confocal Raman microscopy and MALDI-TOF mass spectrometry imaging (MALDI-TOF MSI). Both techniques were set up, and the semiquantitative skin biodistribution of previously developed dexamethasone (DEX) loaded lipomers was compared using Benzalkonium chloride (BAK) as a tracer of the nanoparticles. In MALDI-TOF MSI, DEX was derivatised with GirT (DEX-GirT) and the semiquantitative biodistribution of both DEX-GirT and BAK was successfully obtained. The amount of DEX measured by confocal Raman microscopy was higher than that measured by MALDI-TOF MSI, but MALDI-TOF MSI proved to be a more suitable technique for tracing BAK. An absorption-promoting tendency of DEX loaded in lipomers versus a free-DEX solution was observed in confocal Raman microscopy. The higher spatial resolution of confocal Raman microscopy (350 nm) with respect to MALDI-TOF MSI (50 μm) allowed to observe specific skin structures like hair follicles. Nevertheless, the faster sampling rate of MALDI-TOF-MSI, permitted the analysis of larger tissue regions. In conclusion, both techniques allowed to simultaneously analyze semiquantitative data together with qualitative images of biodistribution, which is a very helpful tool when designing nanoparticles that accumulate in specific anatomical regions.

Open-sourced Raman spectroscopy data processing package implementing a baseline removal algorithm validated from multiple datasets acquired in human tissue and biofluids

SIGNIFICANCE

Standardized data processing approaches are required in the field of bio-Raman spectroscopy to ensure information associated with spectral data acquired by different research groups, and with different systems, can be compared on an equal footing.

AIM

An open-sourced data processing software package was developed, implementing algorithms associated with all steps required to isolate the inelastic scattering component from signals acquired using Raman spectroscopy devices. The package includes a novel morphological baseline removal technique (BubbleFill) that provides increased adaptability to complex baseline shapes compared to current gold standard techniques. Also incorporated in the package is a versatile tool simulating spectroscopic data with varying levels of Raman signal-to-background ratios, baselines with different morphologies, and varying levels of stochastic noise.

RESULTS

Application of the BubbleFill technique to simulated data demonstrated superior baseline removal performance compared to standard algorithms, including iModPoly and MorphBR. The data processing workflow of the open-sourced package was validated in four independent in-human datasets, demonstrating it leads to inter-systems data compatibility.

CONCLUSIONS

A new open-sourced spectroscopic data pre-processing package was validated on simulated and real-world in-human data and is now available to researchers and clinicians for the development of new clinical applications using Raman spectroscopy.

A methodology to evaluate different histological preparations of soft tissues: Intervertebral disc tissues study

A tissue preparation method will inevitably alter the tissue content. This study aims to evaluate how different common sample preparation methods will affect the tissue morphology, biomechanical properties, and chemical composition of samples. The study focuses on intervertebral disc (IVD) tissue; however, it can be applied to other soft tissues. Raman spectroscopy synchronized with nanoindentation instrumentation was employed to investigate the compositional changes of IVD, specifically, nucleus pulposus (NP) and annulus fibrosus (AF), together with their biomechanical properties of IVD. These properties were examined through the following histological specimen types: fresh cryosection (control), fixed cryosection, and paraffin-embedded. The IVD tissue could be located using an optical microscope under three different preparation methods. Paraffin-embedded samples showed the most explicit details where the lamellae structure of AF could be identified. In terms of biomechanical properties, there was no significant difference between the fresh and fixed cryosection (p > 0.05). In contrast, the fresh cryosection and paraffin-embedded samples showed a significant difference (p < 0.05). It was also found that the tissue preparations affected the chemical content of the tissues and structure of the tissue, which are expected to contribute to biomechanical properties changes. Fresh cryosection and fixed cryosection samples are more promising to work with for biomechanical assessment in histological tissues. The findings fill essential gaps in the literature by providing valuable insight into the characteristics of IVD at the microscale. This study can also become a reference for a better approach to assessing the mechanical properties and chemical content of soft tissues at the microscale.

Raman spectroscopy and supervised learning as a potential tool to identify high-dose-rate-brachytherapy induced biochemical profiles of prostate cancer

High-dose-rate-brachytherapy (HDR-BT) is an increasingly attractive alternative to external beam radiation-therapy for patients with intermediate risk prostate cancer. Despite this, no bio-marker based method currently exists to monitor treatment response, and the changes which take place at the biochemical level in hypo-fractionated HDR-BT remain poorly understood. The aim of this pilot study is to assess the capability of Raman spectroscopy (RS) combined with principal component analysis (PCA) and random-forest classification (RF) to identify radiation response profiles after a single dose of 13.5 Gy in a cohort of nine patients. We here demonstrate, as a proof-of-concept, how RS-PCA-RF could be utilised as an effective tool in radiation response monitoring, specifically assessing the importance of low variance PCs in complex sample sets. As RS provides information on the biochemical composition of tissue samples, this technique could provide insight into the changes which take place on the biochemical level, as result of HDR-BT treatment.

Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study

This work combines Raman spectroscopy (RS) with supervised learning methods-group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)-to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D'Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring.

Prediction of the postoperative prognosis in patients with non-muscle-invasive bladder cancer based on preoperative serum surface-enhanced Raman spectroscopy

Non-muscle-invasive bladder cancer (NMIBC) is a common urinary tumor and has a high recurrence rate due to improper or inadequate conservative treatment. The early and accurate prediction of its recurrence can be helpful to implement timely and rational treatment. In this study, we explored a preoperative serum surface-enhanced Raman spectroscopy based prognostic protocol to predict the postoperative prognosis for NMIBC patients at the time even before treatment. The biochemical analysis results suggested that biomolecules related to DNA/RNA, protein substances, trehalose and collagen are expected to be potential prognostic markers, which further compared with several routine clinically used immunohistochemistry expressions with prognostic values. In addition, high prognostic accuracies of 87.01% and 89.47% were achieved by using the proposed prognostic models to predict the future postoperative recurrence and recurrent type, respectively. Therefore, we believe that the proposed method has great potential in the early and accurate prediction of postoperative prognosis in patients with NMIBC, which is with important clinical significance to guide the treatment and further improve the recurrence rate and survival time.

In Situ Identification of Unknown Crystals in Acute Kidney Injury Using Raman Spectroscopy

Raman spectroscopy is a well-established and powerful tool for in situ biomolecular evaluation. Type 2 crystal nephropathies are characterized by the deposition of crystalline materials in the tubular lumen, resulting in rapid onset of acute kidney injury without specific symptoms. Timely crystal identification is essential for its diagnosis, mechanism exploration and therapy, but remains challenging. This study aims to develop a Raman spectroscopy-based method to assist pathological diagnosis of type 2 crystal nephropathies. Unknown crystals in renal tissue slides from a victim suffered extensive burn injury were detected by Raman spectroscopy, and the inclusion of crystals was determined by comparing Raman data with established database. Multiple crystals were scanned to verify the reproducibility of crystal in situ. Raman data of 20 random crystals were obtained, and the distribution and uniformity of substances in crystals were investigated by Raman imaging. A mouse model was established to mimic the crystal nephropathy to verify the availability of Raman spectroscopy in frozen biopsy. All crystals on the human slides were identified to be calcium oxalate dihydrate, and the distribution and content of calcium oxalate dihydrate on a single crystal were uneven. Raman spectroscopy was further validated to be available in identification of calcium oxalate dihydrate crystals in the biopsy specimens. Here, a Raman spectroscopy-based method for in situ identification of unknown crystals in both paraffin-embedded tissues and biopsy specimens was established, providing an effective and promising method to analyze unknown crystals in tissues and assist the precise pathological diagnosis in both clinical and forensic medicine.

Towards Raman imaging of centimeter scale tissue areas for real-time opto-molecular visualization of tissue boundaries for clinical applications

Raman spectroscopy combined with augmented reality and mixed reality to reconstruct molecular information of tissue surface.

Use of Raman Spectroscopy, Scanning Electron Microscopy and Energy Dispersive X-ray Spectroscopy in a Multi-Technique Approach for Physical Characterization of Purple Urine Bag Syndrome

Purple urine bag syndrome (PUBS) is a rare condition characterized by purple discoloration of urine and urine bags. Although it is benign, it represents an alarming symptom to the patients and their relatives because of purple discoloration. We have physically characterized urine and urine bags belonging to a patient suffering from PUBS using an approach that combines Raman spectroscopy (RS) and scanning electron microscopy (SEM) coupled with energy dispersive X-ray (EDX). Five “blue” discolored bags and one sterile urine bag, representing the control, were cut into 1 cm2 square samples and analyzed by using RS and SEM + EDX technique. RS enabled us to identify the presence of indigo, a metabolite of tryptophan, while SEM analysis showed the biofilm deposit, probably due to the presence of microorganisms, and the EDX measurements exhibited the elemental composition of the bags. In particular, urine bags before and after the presence of PUBS urine showed an increase of ~32% of Cl, ~33% of O, ~667% of Ca, ~65% of Al and Mg, while C decreased by about 41%. Our results, to be taken as a proof-of-principle study, are promising for the aim to characterizing the urine bags in a flexible, inexpensive, and comprehensive manner.

Real-time molecular imaging of near-surface tissue using Raman spectroscopy

The steady progress in medical diagnosis and treatment of diseases largely hinges on the steady development and improvement of modern imaging modalities. Raman spectroscopy has attracted increasing attention for clinical applications as it is label-free, non-invasive, and delivers molecular fingerprinting information of a sample. In combination with fiber optic probes, it also allows easy access to different body parts of a patient. However, image acquisition with fiber optic probes is currently not possible. Here, we introduce a fiber optic probe-based Raman imaging system for the real-time molecular virtual reality data visualization of chemical boundaries on a computer screen and the physical world. The approach is developed around a computer vision-based positional tracking system in conjunction with photometric stereo and augmented and mixed chemical reality, enabling molecular imaging and direct visualization of molecular boundaries of three-dimensional surfaces. The proposed approach achieves a spatial resolution of 0.5 mm in the transverse plane and a topology resolution of 0.6 mm, with a spectral sampling frequency of 10 Hz, and can be used to image large tissue areas in a few minutes, making it highly suitable for clinical tissue-boundary demarcation. A variety of applications on biological samples, i.e., distribution of pharmaceutical compounds, brain-tumor phantom, and various types of sarcoma have been characterized, showing that the system enables rapid and intuitive assessment of molecular boundaries.

Raman Spectroscopy: A Personalized Decision-Making Tool on Clinicians' Hands for In Situ Cancer Diagnosis and Surgery Guidance

Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic "molecular finger-print" of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.

Rapid and Label-Free Prenatal Detection of Down's Syndrome Using Body Fluid Surface Enhanced Raman Spectroscopy

Down's syndrome (DS) is the leading genetic cause of intellectual disability. In this work, the surface enhanced Raman spectroscopy (SERS) was used for the detection of amniotic fluid and plasma from pregnant women with DS fetus for the first time. High-quality and characteristic spectral features of amniotic fluid and plasma samples from DS groups can be obtained in comparison to normal group. Moreover, principal component analysis with linear discriminant analysis was applied to generate the efficient diagnostic model, achieving accuracies of 94.3% and 88.5% for the DS detection with amniotic fluid and plasma samples, respectively. This preliminary study would provide a novel, convenient and accurate prenatal test based on blood SERS technology for clinical DS screening.

A Novel and Rapid Serum Detection Technology for Non-Invasive Screening of Gastric Cancer Based on Raman Spectroscopy Combined With Different Machine Learning Methods

Gastric cancer (GC) is the fifth most common cancer in the world and a serious threat to human health. Due to its high morbidity and mortality, a simple, rapid and accurate early screening method for GC is urgently needed. In this study, the potential of Raman spectroscopy combined with different machine learning methods was explored to distinguish serum samples from GC patients and healthy controls. Serum Raman spectra were collected from 109 patients with GC (including 35 in stage I, 14 in stage II, 35 in stage III, and 25 in stage IV) and 104 healthy volunteers matched for age, presenting for a routine physical examination. We analyzed the difference in serum metabolism between GC patients and healthy people through a comparative study of the average Raman spectra of the two groups. Four machine learning methods, one-dimensional convolutional neural network, random forest, support vector machine, and K-nearest neighbor were used to explore identifying two sets of Raman spectral data. The classification model was established by using 70% of the data as a training set and 30% as a test set. Using unseen data to test the model, the RF model yielded an accuracy of 92.8%, and the sensitivity and specificity were 94.7% and 90.8%. The performance of the RF model was further confirmed by the receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 0.9199. This exploratory work shows that serum Raman spectroscopy combined with RF has great potential in the machine-assisted classification of GC, and is expected to provide a non-destructive and convenient technology for the screening of GC patients.

Roadmap on Universal Photonic Biosensors for Real-Time Detection of Emerging Pathogens

The COVID-19 pandemic has made it abundantly clear that the state-of-the-art biosensors may not be adequate for providing a tool for rapid mass testing and population screening in response to newly emerging pathogens. The main limitations of the conventional techniques are their dependency on virus-specific receptors and reagents that need to be custom-developed for each recently-emerged pathogen, the time required for this development as well as for sample preparation and detection, the need for biological amplification, which can increase false positive outcomes, and the cost and size of the necessary equipment. Thus, new platform technologies that can be readily modified as soon as new pathogens are detected, sequenced, and characterized are needed to enable rapid deployment and mass distribution of biosensors. This need can be addressed by the development of adaptive, multiplexed, and affordable sensing technologies that can avoid the conventional biological amplification step, make use of the optical and/or electrical signal amplification, and shorten both the preliminary development and the point-of-care testing time frames. We provide a comparative review of the existing and emergent photonic biosensing techniques by matching them to the above criteria and capabilities of preventing the spread of the next global pandemic.

Ultrasensitive and Selective Impedimetric Determination of Prostate Specific Membrane Antigen Based on Di-Succinimide Functionalized Polythiophene Covered Cost-Effective Indium Tin Oxide

A new and ultrasensitive impedimetric biosensor fabricated by using conjugated di-succinimide substituted polythiophene (P(ThidiSuc)) polymer modified indium tin oxide electrode is developed for the first time to detect the prostate specific membrane antigen (PSMA). The polymer P(Thi-diSuc) is synthesized by using a simple way and used in the fabrication of the proposed biosensor. The synthesized polymer contains di-succinimide groups, which offers covalent immobilization of PSMA specific antibodies. The developed strategy shortens the biosensor fabrication steps, because these active groups bind covalently to the amino ends of PSMA specific antibodies and this reaction does not require any crosslinking agent. Various characterization studies like impedimetric and voltammetric measurements, and morphological analyses are utilized to confirm the successful development of the biosensor. Under optimum conditions, the biosensing ability of the PSMA determination has a wide linear determination range from 0.015 to 14.4 pg mL^-1 , as well as a low limit of detection of 6.4 fg mL^-1 and a high sensitivity of 1.36 kohm pg^-1 mL cm^-2 . Furthermore, the proposed biosensor is able to measure the PSMA antigen in real human serums, which offers that it is a simple, low-cost, and sensitive tool with excellent potential for application in the quantification of PSMA.

Suppression of β-catenin signaling in colon carcinoma cells by a bacterial protein

Colorectal cancer is one of the leading causes of cancer-related death worldwide. The adenomatous polyposis coli (APC) gene is mutated in hereditary colorectal tumors and in more than 80% of sporadic colorectal tumors. APC mutations impair β-catenin degradation, leading to its permanent stabilization and increased transcription of cancer-driving target genes. In colon cancer, impairment of β-catenin degradation leads to its cytoplasmic accumulation, nuclear translocation, and subsequent activation of tumor cell proliferation. Suppressing β-catenin signaling in cancer cells therefore appears to be a promising strategy for new anticancer strategies. Recently, we discovered a novel Vibrio cholerae cytotoxin, motility-associated killing factor A (MakA), that affects both invertebrate and vertebrate hosts. It promotes bacterial survival and proliferation in invertebrate predators but has unknown biological role(s) in mammalian hosts. Here, we report that MakA can cause lethality of tumor cells via induction of apoptosis. Interestingly, MakA exhibited potent cytotoxic activity, in particular against several tested cancer cell lines, while appearing less toxic toward nontransformed cells. MakA bound to the tumor cell surface became internalized into the endolysosomal compartment and induced leakage of endolysosomal membranes, causing cytosolic release of cathepsins and activation of proapoptotic proteins. In addition, MakA altered β-catenin integrity in colon cancer cells, partly through a caspase- and proteasome-dependent mechanism. Importantly, MakA inhibited β-catenin-mediated tumor cell proliferation. Remarkably, intratumor injection of MakA significantly reduced tumor development in a colon cancer murine solid tumor model. These data identify MakA as a novel candidate to be considered in new strategies for development of therapeutic agents against colon cancer.

Classifying breast cancer tissue by Raman spectroscopy with one-dimensional convolutional neural network

As the most common cancer in women, breast cancer is becoming lethal worldwide. However, the current breast diagnosis technologies are not enough to meet the requirements in clinic due to some shortages of early-stage insensitiveness, time consumption and relying on the doctor's experience, etc. It's necessary to develop a creative method for the automatical diagnosis of breast cancer. Therefore, Raman spectroscopy and one-dimensional convolutional neural network (1D-CNN) algorithm were combined together for the first time to classify the healthy and cancerous breast tissues in this study. First, a number of Raman spectra were collected from breast samples of 20 patients for spectral analysis. Then, a 1D-CNN model was developed and trained for classification. In addition, the Fisher Discrimination Analysis (FDA) and Support Vector Machine (SVM) classifiers were trained and tested with the same spectral data for comparison. The best classification performance, namely the overall diagnostic accuracy of 92%, the sensitivity of 98% and the specificity of 86%, has been achieved by using 1D-CNN model. This study proves that 1D-CNN combined with Raman spectroscopy can classify breast tissues effectively and automatically and lay the foundation for automated cancer diagnosis in the future.

High spatial resolution dosimetry with uncertainty analysis using Raman micro-spectroscopy readout of radiochromic films

PURPOSE

The purpose of this work is to develop a new approach for high spatial resolution dosimetry based on Raman micro-spectroscopy scanning of radiochromic film (RCF). The goal is to generate dose calibration curves over an extended dose range from 0 to 50 Gy and with improved sensitivity to low (<2 Gy) doses, in addition to evaluating the uncertainties in dose estimation associated with the calibration curves.

METHODS

Samples of RCF (EBT3) were irradiated at a broad dose range of 0.03-50 Gy using an Elekta Synergy clinical linear accelerator. Raman spectra were acquired with a custom-built Raman micro-spectroscopy setup involving a 500 mW, multimode 785 nm laser focused to a lateral spot diameter of 30 µm on the RCF. The depth of focus of 34 µm enabled the concurrent collection of Raman spectra from the RCF active layer and the polyester laminate. The preprocessed Raman spectra were normalized to the intensity of the 1614 cm^-1 Raman peak from the polyester laminate that was unaltered by radiation. The mean intensities and the corresponding standard deviation of the active layer Raman peaks at 696, 1445, and 2060 cm^-1 were determined for the 150 × 100 µm² scan area per dose value. This was used to generate three calibration curves that enabled the conversion of the measured Raman intensity to dose values. The experimental, fitting, and total dose uncertainty was determined across the entire dose range for the dosimetry system of Raman micro-spectroscopy and RCF.

RESULTS

In contrast to previous work that investigated the Raman response of RCFs using different methods, high resolution in the dose response of the RCF, even down to 0.03 Gy, was obtained in this study. The dynamic range of the calibration curves based on all three Raman peaks in the RCF extended up to 50 Gy with no saturation. At a spatial resolution of 30 × 30 µm² , the total uncertainty in estimating dose in the 0.5-50 Gy dose range was [6-9]% for all three Raman calibration curves. This consisted of the experimental uncertainty of [5-8]%, and the fitting uncertainty of [2.5-4.5]%. The main contribution to the experimental uncertainty was determined to be from the scan area inhomogeneity which can be readily reduced in future experiments. The fitting uncertainty could be reduced by performing Raman measurements on RCF samples at further intermediate dose values in the high and low dose range.

CONCLUSIONS

The high spatial resolution experimental dosimetry technique based on Raman micro-spectroscopy and RCF presented here, could become potentially useful for applications in microdosimetry to produce meaningful dose estimates in cellular targets, as well as for applications based on small field dosimetry that involve high dose gradients.

Long non-coding RNAs in Epstein-Barr virus-related cancer

Epstein Barr-virus (EBV) is related to several cancers. Long non-coding RNAs (lncRNAs) act by regulating target genes and are involved in tumourigenesis. However, the role of lncRNAs in EBV-associated cancers is rarely reported. Understanding the role and mechanism of lncRNAs in EBV-associated cancers may contribute to diagnosis, prognosis and clinical therapy in the future. EBV encodes not only miRNAs, but also BART lncRNAs during latency and the BHLF1 lncRNA during both the latent and lytic phases. These lncRNAs can be targeted regulate inflammation, invasion, and migration and thus tumourigenesis. The products of EBV also directly and indirectly regulate host lncRNAs, including LINC00312, NORAD CYTOR, SHNG8, SHNG5, MINCR, lncRNA-BC200, LINC00672, MALATI1, LINC00982, LINC02067, IGFBP7-AS1, LOC100505716, LOC100128494, NAG7 and RP4-794H19.1, to facilitate tumourigenesis using different mechanisms. Additionally, lncRNAs have been previously validated to interact with microRNAs (miRNAs), and lncRNAs and miRNAs mutually suppress each other. The EBV-miR-BART6-3p/LOC553103/STMN1 axis inhibits EBV-associated tumour cell proliferation. Additionally, H. pylori-EBV co-infection promotes inflammatory lesions and results in EMT. HPV-EBV co-infection inhibits the transition from latency to lytic replication. KSHV-EBV co-infection aggravates tumourigenesis in huNSG mice. COVID-19-EBV co-infection may activate the immune system to destroy a tumour, although this situation is rare and the mechanism requires further confirmation. Hopefully, this information will shed some light on tumour therapy strategies tumourigenesis. Additionally, this strategy benefits for infected patients by preventing latency to lytic replication. Understanding the role and expression of lnRNAs in these two phases of EBV is critical to control the transition from latency to the lytic replication phase. This review presents differential expressed lncRNAs in EBV-associated cancers and provides resources to aid in developing superior strategies for clinical therapy.

Cigarette smoke-induced changes in the murine vocal folds: a Raman spectroscopic observation

Raman spectroscopic methods are being projected as novel tools to study the early invisible molecular level changes in a label-free manner. In the present study, we have used Raman spectroscopy to explore the earliest biochemical changes in murine vocal folds in response to time-bound cigarette smoke exposure. Mice were exposed to cigarette smoke for 2 or 4-weeks through a customized smoke inhalation system. The larynx was collected and initial evaluations using standard methods of analysis such as histopathology and immunofluorescence was performed. Concurrent unstained sections were used for Raman imaging. Two common pathological features of vocal fold disorders including alterations in collagen content and epithelial hypercellularity, or hyperplasia, were observed. The mean spectra, principal component analysis, and Raman mapping also revealed differences in the collagen content and hypercellularity in the smoke exposed tissues. The differences in 2-week exposed tissues were found to be more prominent as compared to 4-week. This was attributed to adaptive responses and the already reported biphasic effects, which suggest that collagen synthesis is significantly reduced at higher cigarette smoke concentrations. Overall findings of the study are supportive of the prospective application of Raman imaging in monitoring changes due to cigarette smoke in the vocal folds.

Detection of acquired radioresistance in breast cancer cell lines using Raman spectroscopy and machine learning

Radioresistance-a living cell's response to, and development of resistance to ionising radiation-can lead to radiotherapy failure and/or tumour recurrence. We used Raman spectroscopy and machine learning to characterise biochemical changes that occur in acquired radioresistance for breast cancer cells. We were able to distinguish between wild-type and acquired radioresistant cells by changes in chemical composition using Raman spectroscopy and machine learning with 100% accuracy. In studying both hormone receptor positive and negative cells, we found similar changes in chemical composition that occur with the development of acquired radioresistance; these radioresistant cells contained less lipids and proteins compared to their parental counterparts. As well as characterising acquired radioresistance in vitro, this approach has the potential to be translated into a clinical setting, to look for Raman signals of radioresistance in tumours or biopsies; that would lead to tailored clinical treatments.

Unveiling Cancer Metabolism through Spontaneous and Coherent Raman Spectroscopy and Stable Isotope Probing

Metabolic reprogramming is a common hallmark in cancer. The high complexity and heterogeneity in cancer render it challenging for scientists to study cancer metabolism. Despite the recent advances in single-cell metabolomics based on mass spectrometry, the analysis of metabolites is still a destructive process, thus limiting in vivo investigations. Being label-free and nonperturbative, Raman spectroscopy offers intrinsic information for elucidating active biochemical processes at subcellular level. This review summarizes recent applications of Raman-based techniques, including spontaneous Raman spectroscopy and imaging, coherent Raman imaging, and Raman-stable isotope probing, in contribution to the molecular understanding of the complex biological processes in the disease. In addition, this review discusses possible future directions of Raman-based technologies in cancer research.

Comparison of surface enhanced Raman spectroscopy and Raman spectroscopy for the detection of breast cancer based on serum samples

In this study, surface enhanced Raman spectroscopy (SERS) and Raman spectroscopy (RS), are employed for the classification of different stages of breast cancer using clinically diagnosed serum samples from breast cancer patients and healthy individuals. These serum samples are compared for their spectral features acquired by SERS and RS to establish spectral features that can be considered as spectral markers of breast cancer diagnosis and classification. SERS features related to DNA, proteins and lipids were observed which are solely observed in the serum samples of patients at different stages of breast cancer as compared to healthy samples. In order to explore the capability of SERS and RS and their comparison as an analytical tool for the efficient understanding of the progression of breast cancer, Principal Component Analysis (PCA) is done for the SERS and RS spectra of control, stage 2, stage 3 and stage 4. Furthermore, the Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to compare the diagnostic performance of SERS and Raman spectroscopy for the classification of disease positive samples and healthy ones. The sensitivity and specificity and area under receiver operating characteristic (AUROC) curve values for SERS data were 90%, 98.4%, and 94% respectively which were higher as compared to Raman spectral data for which these values were found to be 88.2%, 97.7%, and 83.4% respectively.

Raman Imaging: An Impending Approach Towards Cancer Diagnosis

In accordance with the recent studies, Raman spectroscopy is well experimented as a highly sensitive analytical and imaging technique in biomedical research, mainly for various disease diagnosis including cancer. In comparison with other imaging modalities, Raman spectroscopy facilitate numerous assistances owing to its low background signal, immense spatial resolution, high chemical specificity, multiplexing capability, excellent photo stability and non-invasive detection capability. In cancer diagnosis Raman imaging intervened as a promising investigative tool to provide molecular level information to differentiate the cancerous vs non-cancerous cells, tissues and even in body fluids. Anciently, spontaneous Raman scattering is very feeble due to its low signal intensity and long acquisition time but new advanced techniques like coherent Raman scattering (CRS) and surface enhanced Raman scattering (SERS) gradually superseded these issues. So, the present review focuses on the recent developments and applications of Raman spectroscopy-based imaging techniques for cancer diagnosis.

In situ Raman investigation of the phase transition of NaVO2F2 under variable temperature conditions

In this study, the phase transition of NaVO₂F₂ was measured at different temperatures via in situ Raman spectroscopy. The NaVO₂F₂ compounds were synthesized by a hydrothermal method and were identified to be monoclinic with the P2₁/c space group at room temperature by XRD. Accordingly, the variations of Raman shifts and intensities of the characteristic peaks for NaVO₂F₂ associated with temperature were obtained and investigated. It was confirmed that NaVO₂F₂ had three types of phase transitions, which occurred in the temperature region from 78 K to 573 K. Further, the results indicate that transition from a low-temperature phase (I) to another low-temperature phase (II), low-temperature phase (II) to P2₁/c phase and P2₁/c phase to P2₁/m phase occurred near the three temperature points of 93 K, 233 K, and 453 K, respectively, during the heating process. Therefore, a novel characterization method was provided for further research on the phase transition theory and performance of vanadate compounds.

In this study, the phase transition of NaVO₂F₂ was measured at different temperatures via in situ Raman spectroscopy.

Screening for Alzheimer's Disease Using Saliva: A New Approach Based on Machine Learning and Raman Hyperspectroscopy

BACKGROUND

Alzheimer's disease and related dementias (ADRDs) are being diagnosed at epidemic rates, with incidence to triple from 35 to 115 million cases worldwide. Most ADRDs are characterized by progressive neurodegeneration, and Alzheimer's disease (AD) is the sixth leading cause of death in the United States. The ideal moment for diagnosing ADRDs is during the earliest stages of its progression; however, current diagnostic methods are inefficient, expensive, and unsuccessful at making diagnoses during the earliest stages of the disease.

OBJECTIVE

The aim of this project was to utilize Raman hyperspectroscopy in combination with machine learning to develop a novel method for the diagnosis of AD based on the analysis of saliva.

METHODS

Raman hyperspectroscopy was used to analyze saliva samples collected from normative, AD, and mild cognitive impairment (MCI) individuals. Genetic Algorithm and Artificial Neural Networks machine learning techniques were applied to the spectral dataset to build a diagnostic algorithm.

RESULTS

Internal cross-validation showed 99% accuracy for differentiating the three classes; blind external validation was conducted using an independent dataset to further verify the results, achieving 100% accuracy.

CONCLUSION

Raman hyperspectroscopic analysis of saliva has a remarkable potential for use as a non-invasive, efficient, and accurate method for diagnosing AD.

Experimental validation of a spectroscopic Monte Carlo light transport simulation technique and Raman scattering depth sensing analysis in biological tissue

SIGNIFICANCE

Raman spectroscopy (RS) applied to surgical guidance is attracting attention among scientists in biomedical optics. Offering a computational platform for studying depth-resolved RS and probing molecular specificity of different tissue layers is of crucial importance to increase the precision of these techniques and facilitate their clinical adoption.

AIM

The aim of this work was to present a rigorous analysis of inelastic scattering depth sampling and elucidate the relationship between sensing depth of the Raman effect and optical properties of the tissue under interrogation.

APPROACH

A new Monte Carlo (MC) package was developed to simulate absorption, fluorescence, elastic, and inelastic scattering of light in tissue. The validity of the MC algorithm was demonstrated by comparison with experimental Raman spectra in phantoms of known optical properties using nylon and polydimethylsiloxane as Raman-active compounds. A series of MC simulations were performed to study the effects of optical properties on Raman sensing depth for an imaging geometry consistent with single-point detection using a handheld fiber optics probe system.

RESULTS

The MC code was used to estimate the Raman sensing depth of a handheld fiber optics system. For absorption and reduced scattering coefficients of 0.001 and 1  mm  -  1, the sensing depth varied from 105 to 225  μm for a range of Raman probabilities from 10  -  6 to 10  -  3. Further, for a realistic Raman probability of 10  -  6, the sensing depth ranged between 10 and 600  μm for the range of absorption coefficients 0.001 to 1.4  mm  -  1 and reduced scattering coefficients of 0.5 to 30  mm  -  1.

CONCLUSIONS

A spectroscopic MC light transport simulation platform was developed and validated against experimental measurements in tissue phantoms and used to predict depth sensing in tissue. It is hoped that the current package and reported results provide the research community with an effective simulating tool to improve the development of clinical applications of RS.

Discrimination of Benign and Malignant Lesions in Canine Mammary Tissue Samples Using Raman Spectroscopy: A Pilot Study

Breast cancer is a health problem that affects individual life quality and the family system. It is the most frequent type of cancer in women, but men are also affected. As an integrative approach, comparative oncology offers an opportunity to learn more about natural cancers in different species. Methods based on Raman spectroscopy have shown significant potential in the study of the human breast through the fingerprinting of biological tissue, which provides valuable information that can be used to identify, characterize and discriminate structures in breast tissue, in both healthy and carcinogenic environments. One of the most important applications of Raman spectroscopy in medical diagnosis is the characterization of microcalcifications, which are highly important diagnostic indicators of breast tissue diseases. Raman spectroscopy has been used to analyze the chemical composition of microcalcifications. These occur in benign and malignant lesions in the human breast, and Raman helps to discriminate microcalcifications as type I and type II according to their composition. This paper demonstrates the recent progress in understanding how this vibrational technique can discriminate through the fingerprint regions of lesions in unstained histology sections from canine mammary glands.

Multiplex micro-SERS imaging of cancer-related markers in cells and tissues using poly(allylamine)-coated Au@Ag nanoprobes

Surface-enhanced Raman scattering (SERS) nanoprobes based on Au@Ag core@shell nanoparticles coated with poly(allylamine) were functionalized with small targeting molecules to evaluate simultaneously the level of expression of two cancer-related markers, both in cells and in tissues. The Au@Ag nanoparticles provide a high SERS signal enhancement in the visible range when combined with resonant Raman-active molecules. The poly(allylamine) coating plays a dual key role in (i) protecting the metal surface against the complex biological medium, leading to a stable signal of the Raman-active molecules, and (ii) enabling specific biofunctionalization through its amine functions. Using small targeting molecules linked to the polymer coating, two different nanoprobes (duplex approach) were designed. Each was able to specifically target a particular cancer-related marker: folate receptors (FRs) and sialic acid (SA). We demonstrate that the level of expression of these targeted markers can be evaluated following the SERS signal of the probes incubated on cells or tissues. The potential overexpression of folate receptors and of sialic acid was evaluated and measured in breast and ovarian cancerous tissue sections. In addition, FR and/or SA overexpression in the tumor region can be visualized with high contrast with respect to the healthy region and with high spatial accuracy consistent with histology by SERS imaging of the nanoprobe signal. Owing to the unique spectral signature of the designed nanoprobes, this approach offers an efficient tool for the spatially resolved, in situ measurement of the expression level of several cancer-related markers in tumors at the same time.Graphical abstract.

Raman spectroscopy-based biomarker screening by studying the fingerprint characteristics of chronic lymphocytic leukemia and diffuse large B-cell lymphoma

Raman spectroscopy (RS) can provide fingerprint-type information on biochemical molecules. RS-based blood plasma analysis of solid tumors has been reported in recent years; however, there are no studies on the use of this analysis for detecting blood diseases. We studied the features of blood plasma in patients with diffuse large B-cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL) by RS with the aim of developing a simple blood test for noninvasive DLBCL and CLL detection. We analyzed blood plasma from 33 DLBCL patients, 39 CLL patients and 30 healthy volunteers. Orthogonal partial least squares discriminant analysis (OPLS-DA) could build two clusters with almost no overlap between DLBCL/CLL and the controls. We used the prediction set to test the model built by OPLS-DA. For the CLL model, the sensitivity was 92.86%, and the specificity was 100%, whereas for the DLBCL model, the sensitivity was 80% and the specificity was 92.31%. We found Raman bands specific to both DLBCL and CLL patients in comparison with the healthy volunteers. Most importantly, we found that the combination of the 1445 cm^-1 and 1655 cm^-1 Raman shifts could discriminate DLBCL from CLL and even the other solid tumors reported to date. Further analysis of the assignments of 1655 cm^-1 also gave us a clue to find potential important variables hemoglobin and serum albumin related with the CLL prognosis. Our exploratory study primarily demonstrated the great potential of developing RS blood plasma analysis as a novel clinical tool for the noninvasive detection of DLBCL and CLL.