Screening and staging for non-small cell lung cancer by serum laser Raman spectroscopy

Utility of Raman Spectroscopy in Pulmonary Medicine

The Raman effect, or as per its original description, "modified scattering", is an observation that the number of scattered light waves shifts after photons make nonelastic contact with a molecule. This effect allows Raman spectroscopy to be very useful in various fields. Although it is well known that Raman spectroscopy could be very beneficial in medicine as a diagnostic tool, there are not many applications of Raman spectroscopy in pulmonary medicine. Mostly tumor tissue, sputum and saliva have been used as material for analysis in respiratory medicine. Raman spectroscopy has shown promising results in malignancy recognition and even tumor staging. Saliva is a biological fluid that could be used as a reliable biomarker of the physiological state of the human body, and is easily acquired. Saliva analysis using Raman spectroscopy has the potential to be a relatively inexpensive and quick tool that could be used for diagnostic, screening and phenotyping purposes. Chronic obstructive pulmonary disease (COPD) is a growing cause of disability and death, and its phenotyping using saliva analysis via Raman spectroscopy has a great potential to be a dependable tool to, among other things, help reduce hospitalizations and disease burden. Although existing methods are effective and generally available, Raman spectroscopy has the benefit of being quick and noninvasive, potentially reducing healthcare costs and workload.

Advances in Raman spectroscopy for characterising oral cancer and oral potentially malignant disorders

Oral cancer survival rates have seen little improvement over the past few decades. This is mainly due to late detection and a lack of reliable markers to predict disease progression in oral potentially malignant disorders (OPMDs). There is a need for highly specific and sensitive screening tools to enable early detection of malignant transformation. Biochemical alterations to tissues occur as an early response to pathological processes; manifesting as modifications to molecular structure, concentration or conformation. Raman spectroscopy is a powerful analytical technique that can probe these biochemical changes and can be exploited for the generation of novel disease-specific biomarkers. Therefore, Raman spectroscopy has the potential as an adjunct tool that can assist in the early diagnosis of oral cancer and the detection of disease progression in OPMDs. This review describes the use of Raman spectroscopy for the diagnosis of oral cancer and OPMDs based on ex vivo and liquid biopsies as well as in vivo applications that show the potential of this powerful tool to progress from benchtop to chairside.

Raman Spectroscopy of Blood Serum for Essential Thrombocythemia Diagnosis: Correlation with Genetic Mutations and Optimization of Laser Wavelengths

Essential thrombocythemia (ET) is a type of myeloproliferative neoplasm that increases the risk of thrombosis. To diagnose this disease, the analysis of mutations in the Janus Kinase 2 (JAK2), thrombopoietin receptor (MPL), or calreticulin (CALR) gene is recommended. Disease poses diagnostic challenges due to overlapping mutations with other neoplasms and the presence of triple-negative cases. This study explores the potential of Raman spectroscopy combined with machine learning for ET diagnosis. We assessed two laser wavelengths (785, 1064 nm) to differentiate between ET patients and healthy controls. The PCR results indicate that approximately 50% of patients in our group have a mutation in the JAK2 gene, while only 5% of patients harbor a mutation in the ASXL1 gene. Additionally, only one patient had a mutation in the IDH1 and one had a mutation in IDH2 gene. Consequently, patients having no mutations were also observed in our group, making diagnosis challenging. Raman spectra at 1064 nm showed lower amide, polysaccharide, and lipid vibrations in ET patients, while 785 nm spectra indicated significant decreases in amide II and C-H lipid vibrations. Principal Component Analysis (PCA) confirmed that both wavelengths could distinguish ET from healthy subjects. Support Vector Machine (SVM) analysis revealed that the 800-1800 cm-1 range provided the highest diagnostic accuracy, with 89% for 785 nm and 72% for 1064 nm. These findings suggest that FT-Raman spectroscopy, paired with multivariate and machine learning analyses, offers a promising method for diagnosing ET with high accuracy by detecting specific molecular changes in serum. Principal Component Analysis (PCA) confirmed that both wavelengths could distinguish ET from healthy subjects. Support Vector Machine (SVM) analysis revealed that the 800-1800 cm-1 range provided the highest diagnostic accuracy, with 89% for 785 nm and 72% for 1064 nm. These findings suggest that FT-Raman spectroscopy, paired with multivariate and machine learning analyses, offers a promising method for diagnosing ET with high accuracy by detecting specific molecular changes in serum.

Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study

BACKGROUND

Early screening and detection of lung cancer is essential for the diagnosis and prognosis of the disease. In this paper, we investigated the feasibility of serum Raman spectroscopy for rapid lung cancer screening.

METHODS

Raman spectra were collected from 45 patients with lung cancer, 45 with benign lung lesions, and 45 healthy volunteers. And then the support vector machine (SVM) algorithm was applied to build a diagnostic model for lung cancer. Furthermore, 15 independent individuals were sampled for external validation, including 5 lung cancer patients, 5 benign lung lesion patients, and 5 healthy controls.

RESULTS

The diagnostic sensitivity, specificity, and accuracy were 91.67%, 92.22%, 90.56% (lung cancer vs. healthy control), 92.22%,95.56%,93.33% (benign lung lesion vs. healthy) and 80.00%, 83.33%, 80.83% (lung cancer vs. benign lung lesion), repectively. In the independent validation cohort, our model showed that all the samples were classified correctly.

CONCLUSION

Therefore, this study demonstrates that the serum Raman spectroscopy analysis technique combined with the SVM algorithm has great potential for the noninvasive detection of lung cancer.

Detection of lung cancer through SERS analysis of serum

Early detection and postoperative assessment are crucial for improving overall survival among lung cancer patients. Here, we report a non-invasive technique that integrates Raman spectroscopy with machine learning for the detection of lung cancer. The study encompassed 88 postoperative lung cancer patients, 73 non-surgical lung cancer patients, and 68 healthy subjects. The primary aim was to explore variations in serum metabolism across these cohorts. Comparative analysis of average Raman spectra was conducted, while principal component analysis was employed for data visualization. Subsequently, the augmented dataset was used to train convolutional neural networks (CNN) and Resnet models, leading to the development of a diagnostic framework. The CNN model exhibited superior performance, as verified by the receiver operating characteristic curve. Notably, postoperative patients demonstrated an increased likelihood of recurrence, emphasizing the crucial need for continuous postoperative monitoring. In summary, the integration of Raman spectroscopy with CNN-based classification shows potential for early detection and postoperative assessment of lung cancer.

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.

Serum laser Raman spectroscopy as a potential diagnostic tool to discriminate the benignancy or malignancy of pulmonary nodules

It has been proved that Raman spectral intensities could be used to diagnose lung cancer patients. However, the application of Raman spectroscopy in identifying the patients with pulmonary nodules was barely studied. In this study, we revealed that Raman spectra of serum samples from healthy participants and patients with benign and malignant pulmonary nodules were significantly different. A support vector machine (SVM) model was developed for the classification of Raman spectra with wave points, according to ANOVA test results. It got a good performance with a median area under the curve (AUC) of 0.89, when the SVM model was applied in discriminating benign from malignant individuals. Compared with three common clinical models, the SVM model showed a better discriminative ability and added more net benefits to participants, which were also excellent in the small-size nodules. Thus, the Raman spectroscopy could be a less-invasive and low-costly liquid biopsy.

Raman microspectroscopy based TNM staging and grading of breast cancer

The tumor-node-metastasis (TNM) system is the most common way that doctors determine the anatomical extent of cancer on the basis of clinical and pathological criteria. In this study, a spectral histopathological study has been carried out to bridge Raman micro spectroscopy with the breast cancer TNM system. A total of seventy breast tissue samples, including healthy tissue, early, middle, and advanced cancer, were investigated to provide detailed insights into compositional and structural variations that accompany breast malignant evolution. After evaluating the main spectral variations in all tissue types, the generalized discriminant analysis (GDA) pathological diagnostic model was established to discriminate the TNM staging and grading information. Moreover, micro-Raman images were reconstructed by K-means clustering analysis (KCA) for visualizing the lobular acinar in healthy tissue and ductal structures in all early, middle and advanced breast cancer tissue groups. While, univariate imaging techniques were adapted to describe the distribution differences of biochemical components such as tryptophan, β-carotene, proteins, and lipids in the scanned regions. The achieved spectral histopathological results not only established a spectra-structure correlations via tissue biochemical profiles but also provided important data and discriminative model references for in vivo Raman-based breast cancer diagnosis.

Raman spectroscopy and FTIR spectroscopy fusion technology combined with deep learning: A novel cancer prediction method

According to the limited molecular information reflected by single spectroscopy, and the complementarity of FTIR spectroscopy and Raman spectroscopy, we propose a novel diagnostic technology combining multispectral fusion and deep learning. We used serum samples from 45 healthy controls, 44 non-small cell lung cancer (NSCLC), 38 glioma and 37 esophageal cancer patients, and the Raman spectra and FTIR spectra were collected respectively. Then we performed low-level fusion and feature fusion on the spectral, and used SVM, Convolutional Neural Network-Long-Short Term Memory (CNN-LSTM) and the multi-scale convolutional fusion neural network (MFCNN). The accuracy of low-level fusion and feature fusion models are improved by about 10% compared with single spectral models.

Micro-Raman spectroscopy study of blood samples from myocardial infarction patients

Acute myocardial infarction (MI) is found to be a major causative factor for global mortality and morbidity. This situation demands necessity of developing efficient and rapid diagnostic tools to detect acute MI. Raman spectroscopy is a non-destructive optical diagnostic technique, which has high potential in probing biochemical changes in clinical samples during initiation and progress of diseases. In this work, blood was taken as the sample to examine inflammation in acute MI patients using Raman spectroscopy. Ratio of Raman peak intensities that corresponds to phenylalanine (1000 cm^-1) and tyrosine (825 cm^-1) can facilitate indirect information about tetrahydrobiopterin (BH4) availability, which can indicate inflammatory status in patients. This ratio obtained was higher for MI patients in comparison with control subjects. The decrease in phenylalanine and tyrosine ratio (Phe-Tyr ratio) is attributed to the prognosis of standard of care (medications like antiplatelets including aspirin, statin and revascularisation) leading to inflammation reduction. Phe-Tyr ratio estimated from the Raman spectra of blood can be exploited as a reliable method to probe inflammation due to MI. The method is highly objective, require only microliters of sample and minimal sample preparation, signifying its clinical utility.

Diagnosis and staging of diffuse large B-cell lymphoma using label-free surface-enhanced Raman spectroscopy

Non-invasive diagnosis and staging of diffuse large B-cell lymphoma (DLBCL) were achieved using label-free surface-enhanced Raman spectroscopy (SERS). SERS spectra were measured for serum samples of DLBCL patients at different progressive stages and healthy controls (HCs), using colloidal silver nano-particles (AgNPs) as the substrate. Differences in the spectral intensities of Raman peaks were observed between the DLBCL and HC groups, and a close correlation between the spectral intensities of Raman peaks with the progressive stages of the cancer was obtained, demonstrating the possibility of diagnosis and staging of the disease using the serum SERS spectra. Multivariate analysis methods, including principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM) classifier, and k-nearest neighbors (kNN) classifier, were used to build the diagnosis and staging models for DLBCL. Leave-one-out cross-validation was used to evaluate the performances of the models. The kNN model achieved the best performances for both diagnosis and staging of DLBCL: for the diagnosis analysis, the accuracy, sensitivity, and specificity were 87.3%, 0.921, and 0.809, respectively; for the staging analysis between the early (Stage I & II) and the late (Stage III & IV) stages, the accuracy was 90.6%, and the sensitivity values for the early and the late stages were 0.947 and 0.800, respectively. The label-free serum SERS in combination with multivariate analysis could serve as a potential technique for non-invasive diagnosis and staging of DLBCL.

Highly accurate diagnosis of lung adenocarcinoma and squamous cell carcinoma tissues by deep learning

Intraoperative detection of the marginal tissues is the last and most important step to complete the resection of adenocarcinoma and squamous cell carcinoma. However, the current intraoperative diagnosis is time-consuming and requires numerous steps including staining. In this paper, we present the use of Raman spectroscopy with deep learning to achieve accurate diagnosis with stain-free process. To make the spectrum more suitable for deep learning, we utilize an unusual way of thinking which regards Raman spectral signal as a sequence and then converts it into two-dimensional Raman spectrogram by short-time Fourier transform as input. The normal-adenocarcinoma deep learning model and normal-squamous carcinoma deep learning model both achieve more than 96% accuracy, 95% sensitivity and 98% specificity when test, which higher than the conventional principal components analysis-linear discriminant analysis method with normal-adenocarcinoma model (0.896 accuracy, 0.867 sensitivity, 0.926 specificity) and normal-squamous carcinoma model (0.821 accuracy, 0.776 sensitivity, 1.000 specificity). The high performance of deep learning models provides a reliable way for intraoperative detection of marginal tissue, and is expected to reduce the detection time and save human lives.

Vibrational Spectroscopy: A Valuable Screening and Diagnostic Tool for Obstetric Disorders?

Preeclampsia (PE) is a common obstetric disorder typically affecting 2–8% of all pregnancies and can lead to several adverse obstetric outcomes for both mother and fetus with the greatest burden of severe outcomes in low middle-income countries (LMICs), therefore, screening for PE is vital. Globally, screening is based on maternal characteristics and medical history which are nonspecific for the disorder. In 2004, the World Health Organization acknowledged that no clinically useful test was able to predict the onset of PE, which prompted a universal search for alternative means of screening. Over the past decade or so, emphasis has been placed on the use of maternal characteristics in conjunction with biomarkers of disease combined into predictive algorithms, however these are yet to transition into the clinic and are cost prohibitive in LMICs. As a result, the screening paradigm for PE remains unchanged. It is evident that novel approaches are needed. Vibrational spectroscopy, specifically Raman spectroscopy and Fourier-transform infrared spectroscopy (FTIR), could provide better alternatives suited for implementation in low resource settings as no specialized reagents are required for conventional approaches and there is a drive to portable platforms usable in both urban and rual community settings. These techniques are based on light scattering and absorption, respectively, allowing detailed molecular analysis of samples to produce a unique molecular fingerprint of diseased states. The specificity of vibrational spectroscopy might well make it suited for application in other obstetric disorders such as gestational diabetes mellitus and obstetric cholestasis. In this review, we summarize current approaches sought as alternatives to current screening methodologies and introduce how vibrational spectroscopy could offer superior screening and diagnostic paradigms in obstetric care. Additionally, we propose a real benefit of such tools in LMICs where limited resources battle the higher prevalence of obstetric disorders.

Nanoparticle surface-enhanced Raman spectroscopy as a noninvasive, label-free tool to monitor hematological malignancy

Aim: Monitoring minimal residual disease remains a challenge to the effective medical management of hematological malignancies; yet surface-enhanced Raman spectroscopy (SERS) has emerged as a potential clinical tool to do so. Materials & methods: We developed a cell-free, label-free SERS approach using gold nanoparticles (nanoSERS) to classify hematological malignancies referenced against two control cohorts: healthy and noncancer cardiovascular disease. A predictive model was built using machine-learning algorithms to incorporate disease burden scores for patients under standard treatment upon. Results: Linear- and quadratic-discriminant analysis distinguished three cohorts with 69.8 and 71.4% accuracies, respectively. A predictive nanoSERS model correlated (MSE = 1.6) with established clinical parameters. Conclusion: This study offers a proof-of-concept for the noninvasive monitoring of disease progression, highlighting the potential to incorporate nanoSERS into translational medicine.

Blood plasma resonance Raman spectroscopy combined with multivariate analysis for esophageal cancer detection

We herein report a novel, reliable and inexpensive method for detecting esophageal cancer using blood plasma resonance Raman spectroscopy combined with multivariate analysis methods. The blood plasma samples were divided into late stage cancer group (n = 164), early stage cancer group (n = 35) and normal group (n = 135) based on clinical pathological diagnosis. Using a specially designed quartz capillary tube as sample holder, we obtained higher quality resonance Raman spectra of blood plasma than existing method. The study demonstrated that the carotenoids levels in blood plasma were reduced in esophageal cancer patients. The area under the receiver operating characteristic curve (and 95% confidence interval) calculated by wavenumber selection and principal component analysis combined with linear discriminant analysis (PC-LDA) algorithm were 0.894 (0.858-0.929), 0.901 (0.841-0.960) and 0.871 (0.799-0.942) for differentiating late cancer from normal, late cancer from early cancer, and early cancer from normal respectively. The contribution from the two carotenoids wavenumber regions of 1155 and 1515 cm^-1 were more than 84.2%. The results show that the plasma carotenoids could be a potential biomarker for screening esophageal cancer using resonance Raman spectroscopy combined with wavenumber selection and PC-LDA algorithms.

The efficacy of Raman spectroscopy in lung cancer diagnosis: the first diagnostic meta-analysis

In recent years, many researches have explored the diagnostic value of Raman spectroscopy in multiple types of tumors. However, as an emerging clinical examination method, the diagnostic performance of Raman spectroscopy in lung cancer remains unclear. Relevant diagnostic studies published before 1 June 2020 were retrieved from the Cochrane Library, PubMed, EMBASE, China National Knowledge Internet (CNKI), and WanFang databases. After the literature was screened, two authors extracted the data from eligible studies according to the inclusion and exclusion criteria. Obtained data were pooled and analyzed using Stata 16.0, Meta-DiSc 1.4, and RevMan 5.3 software. Fourteen diagnostic studies were eligible for the pooled analysis which includes 779 patients. Total pooled sensitivity and specificity of Raman spectroscopy in diagnosing lung cancer were 0.92 (95% CI 0.87-0.95) and 0.94 (95% CI 0.88-0.97), respectively. The positive likelihood ratio was 15.2 (95% CI 7.5-30.9), the negative likelihood ratio was 0.09 (95% CI 0.05-0.14), and the area under the curve was 0.97 (95 % CI 0.95-0.98). Subgroup analysis suggested that the sensitivity and specificity of RS when analyzing human tissue, serum, and saliva samples were 0.95 (95% CI 0.88-0.98), 0.97 (95% CI 0.89-0.99), 0.88 (95% CI 0.80-0.93), 0.87 (95% CI 0.78-0.92), 0.91 (95% CI 0.80-0.96), and 0.95 (95% CI 0.73-0.99), respectively. No publication bias or threshold effects were detected in this meta-analysis. This initial meta-analysis indicated that Raman spectroscopy is a highly specific and sensitive diagnostic technology for detecting lung cancer. Further investigations are also needed to focus on real-time detection using Raman spectroscopy under bronchoscopy in vivo. Moreover, large-scale diagnostic studies should be conducted to confirm this conclusion.

Combining derivative Raman with autofluorescence to improve the diagnosis performance of echinococcosis

Echinococcosis is a zoonotic parasitic disease transmitted by animals and distributed all over the world. There is no standardized and widely accepted treatment method, and early and accurate diagnosis is crucial for the prevention and cure of echinococcosis. Here, we explored the feasibility of using derivative Raman in combination with autofluorescence (AF) to improve the diagnosis performance of echinococcosis. The spectra of serum samples from patients with echinococcosis, as well as healthy volunteers, were recorded at 633 nm excitation. The normalized mean Raman spectra showed that there is a decrease in the relative amounts of β carotene and phenylalanine and an increase in the percentage of tryptophan, tyrosine, and glutamic acid contents in the serum of echinococcosis patients as compared to that of healthy subjects. Then, principal components analysis (PCA), combined with linear discriminant analysis (LDA), were adopted to distinguish echinococcosis patients from healthy volunteers. Based on the area under the ROC curve (AUC) value, the derivative Raman + AF spectral data set achieved the optimal results. The AUC value was improved by 0.08 for derivative Raman + AF (AUC = 0.98), compared to Raman alone. The results demonstrated that the fusion of derivative Raman and AF could effectively improve the performance of the diagnostic model, and this technique has great application potential in the clinical screening of echinococcosis.

Label-free detection of echinococcosis and liver cirrhosis based on serum Raman spectroscopy combined with multivariate analysis

In this paper, we investigated the feasibility of using serum Raman spectroscopy and multivariate analysis method to discriminate echinococcosis and liver cirrhosis from healthy volunteers. Raman spectra of serum samples from echinococcosis, liver cirrhosis, and healthy volunteers were recorded under 532 nm excitation. The normalized mean Raman spectra revealed specific biomolecular differences associated with the disease, mainly manifested as the contents of β carotene in the serum of patients with echinococcosis and liver cirrhosis were lower than those of healthy people. Furthermore, principal components analysis (PCA), combined with linear discriminant analysis (LDA), was adopted to distinguish patients with echinococcosis, liver cirrhosis, and healthy volunteers. The overall diagnostic accuracy based on the PCA-LDA algorithm was 87.7 %. The diagnostic sensitivities to healthy volunteers, patients with echinococcosis, and liver cirrhosis were 92.5 %, 81.5 %, and 89.1 %, and the specificities were 93.2 %, 96.1 %, and 92.4 %, respectively. This exploratory work demonstrated that serum Raman spectroscopy technology combined with PCA-LDA diagnostic algorithm has great potential for the non-invasive identification of echinococcosis and liver cirrhosis.

Raman imaging diagnosis of the early stage differentiation of mouse embryonic stem cell (mESC)

Raman microspectroscopy as a non-invasive and label-free technique was applied to diagnose the early stage differentiation of mouse embryonic stem cells. The differentiated and undifferentiated embryonic bodies (EBs) were cultured using handing drop method by the control of Leukemia Inhibitory Factor (LIF). Raman spectra of the periphery cells of differentiated EBs (PrE cells) and those of the interior of undifferentiated EBs (ES cells) were obtained to diagnose the stem cells of different differentiation. It was found from the spectra that the protein content increased as the cells differentiated. Principal component analysis (PCA) was carried out to further analyze the differences between ES cells and PrE cells. The first three principle components contained 98.19% from the total variance. Characteristic bands of ES and PrE cells were chosen to acquire Raman images of two cells according to the results of PCA. In the Raman images, PrE cells had a clear and bright outline in the peripheral areas while ES cells were difficult to identify, this could be a distinct characteristic to discriminate them. The result of the Raman images was consistent with the biological agreement that the differentiated cells were distributed around the periphery.

Towards Raman spectroscopy of urine as screening tool

For the screening purposes urine is an especially attractive biofluid, since it offers easy and noninvasive sample collection and provides a snapshot of the whole metabolic status of the organism, which may change under different pathological conditions. Raman spectroscopy (RS) has the potential to monitor these changes and utilize them for disease diagnostics. The current study utilizes mouse models aiming to compare the feasibility of the urine based RS combined with chemometrics for diagnosing kidney diseases directly influencing urine composition and respiratory tract diseases having no direct connection to urine formation. The diagnostic models for included diseases were built using principal component analysis with linear discriminant analysis and validated with a leave-one-mouse-out cross-validation approach. Considering kidney disorders, the accuracy of 100% was obtained in discrimination between sick and healthy mice, as well as between two different kidney diseases. For asthma and invasive pulmonary aspergillosis achieved accuracies were noticeably lower, being, respectively, 77.27% and 78.57%. In conclusion, our results suggest that RS of urine samples not only provides a solution for a rapid, sensitive and noninvasive diagnosis of kidney disorders, but also holds some promises for the screening of nonurinary tract diseases.

Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning

This review summarizes recent progress made using Raman spectroscopy and machine learning for potential universal medical diagnostic applications.

Human red blood cell behaviour in hydroxyethyl starch: probed by single cell spectroscopy

Hydroxyethyl starch (HES) is a commonly used intravenous fluid in hospital settings. The merits and demerits of its application is still a debatable topic. Investigating the interaction of external agents like intravenous fluids with blood cells is of great significance in clinical environments. Micro-Raman spectroscopy combined with an optical tweezers technique has been utilized for conducting systematic investigations of single live red blood cells (RBCs) under the influence of external stress agents. The present work deals with a detailed biophysical study on the response of human live red blood cells in hydroxyethyl starch using optical techniques. Morphological changes in red blood cells were monitored using quantitate phase imaging techniques. Micro-Raman studies suggest that there is a significant reduction in the oxy-haemoglobin level in red blood cells suspended in HES. The spectra recorded by using different probe laser powers has shown that the cells are more vulnerable in HES under the influence of externally induced stress than in blood plasma. In addition, the spectral results support the possibility of heme aggregation and membrane damage for red blood cells in HES under externally induced stress. Principle component analysis performed on the Raman spectra were able to effectively discriminate between red blood cells in HES and in blood plasma. The use of Raman tweezers can be highly beneficial in elucidating biochemical alterations happening in live, human red blood cell.

Hydroxyethyl starch (HES) is a commonly used intravenous fluid in hospital settings.

Advancing cancer diagnostics with artificial intelligence and spectroscopy: identifying chemical changes associated with breast cancer

Background: Artificial intelligence (AI) and machine learning (ML) approaches in combination with Raman spectroscopy (RS) to obtain accurate medical diagnosis and decision-making is a way forward for understanding not only the chemical pathway to the progression of disease, but also for tailor-made personalized medicine. These processes remove unwanted affects in the spectra such as noise, fluorescence and normalization, and help in the optimization of spectral data by employing chemometrics. Methods: In this study, breast cancer tissues have been analyzed by RS in conjunction with principal component (PCA) and linear discriminate (LDA) analyses. Tissue microarray (TMA) breast biopsies were investigated using RS and chemometric methods and classified breast biopsies into luminal A, luminal B, HER2, and triple negative subtypes. Results: Supervised and unsupervised algorithms were applied on biopsy data to explore intra and inter data set biochemical changes associated with lipids, collagen, and nucleic acid content. LDA predicted specificity accuracy of luminal A, luminal B, HER2, and triple negative subtypes were 70%, 100%, 90%, and 96.7%, respectively. Conclusion: It is envisaged that a combination of RS with AI and ML may create a precise and accurate real-time methodology for cancer diagnosis and monitoring.

Application of Raman spectroscopy in the detection of hepatitis B virus infection

OBJECTIVE

Detection of hepatitis B virus (HBV) using Raman spectroscopy.

METHODS

Raman spectroscopy was used to examine the serum samples of 500 patients with HBV and 500 non-HBV persons. First, the adaptive iterative weighted penalty least squares method (airPLS) was used to deduct the fluorescence background in Raman spectra. Then, a principal component analysis (PCA) was used to extract the processed Raman spectra, and a support vector machine (SVM) was used for modeling and prediction. The particle swarm optimization (PSO) algorithm was selected to optimize the parameters of the SVM instead of a traditional grid search. Finally, 600 serum samples were detected by Raman spectroscopy, and the results wereverified using a double-blind method.

RESULTS

In the Raman spectra, the non-HBV human Raman peaks at 509, 957, 1002, 1153, 1260, 1512, 1648 and 2305 cm^-1 were different from those of patients with HBV. The reported accuracy, sensitivity and specificity of the HBV serum model established using airPLS-PCA-PSO-SVM was 93.1%, 100% and 88%, respectively. The two groups were verified by a double-blind method. In the first group sensitivity was 87%, specificity was 92%, and the KAPPA value was 0.79; in the second group sensitivity was 80%, specificity was 79%, and the KAPPA value was 0.59.

CONCLUSION

This preliminary study shows that serum Raman spectroscopy combined with the airPLS-PCA-PSO-SVM model can be used for hepatitis B virus detection.

Raman spectroscopy and chemometrics: A potential universal method for diagnosing cancer

Cancer is the second-leading cause of death worldwide. It affects an unfathomable number of people, with almost 16 million Americans currently living with it. While many cancers can be detected, current diagnostic efforts exhibit definite room for improvement. It is imperative that a person be diagnosed with cancer as early on in its progression as possible. An earlier diagnosis allows for the best treatment and intervention options available to be presented. Unfortunately, existing methods for diagnosing cancer can be expensive, invasive, inconclusive or inaccurate, and are not always made during initial stages of the disease. As such, there is a crucial unmet need to develop a singular universal method that is reliable, cost-effective, and non-invasive and can diagnose all forms of cancer early-on. Raman spectroscopy in combination with advanced statistical analysis is offered here as a potential solution for this need. This review covers recently published research in which Raman spectroscopy was used for the purpose of diagnosing cancer. The benefits and the risks of the methodology are presented; however, there is overwhelming evidence that suggests Raman spectroscopy is highly suitable for becoming the first universal method to be used for diagnosing cancer.

SERS-based differential diagnosis between multiple solid malignancies: breast, colorectal, lung, ovarian and oral cancer

PURPOSE

Surface-enhanced Raman scattering (SERS) spectroscopy on serum and other biofluids for cancer diagnosis represents an emerging field, which has shown promising preliminary results in several types of malignancies. The purpose of this study was to demonstrate that SERS spectroscopy on serum can be employed for the differential diagnosis between five of the leading malignancies, ie, breast, colorectal, lung, ovarian and oral cancer.

PATIENTS AND METHODS

Serum samples were acquired from healthy volunteers (n=39) and from patients diagnosed with breast (n=42), colorectal (n=109), lung (n=33), oral (n=17), and ovarian cancer (n=13), comprising n=253 samples in total. SERS spectra were acquired using a 532 nm laser line as excitation source, while the SERS substrates were represented by Ag nanoparticles synthesized by reduction with hydroxylamine. The classification accuracy yielded by SERS was assessed by principal component analysis-linear discriminant analysis (PCA-LDA).

RESULTS

The sensitivity and specificity in discriminating between cancer patients and controls was 98% and 91%, respectively. Cancer samples were correctly assigned to their corresponding cancer types with an accuracy of 88% for oral cancer, 86% for colorectal cancer, 80% for ovarian cancer, 76% for breast cancer and 59% for lung cancer.

CONCLUSION

SERS on serum represents a promising strategy of diagnosing cancer which can discriminate between cancer patients and controls, as well as between cancer types such as breast, colorectal, lung ovarian and oral cancer.