Raman spectroscopic techniques to detect ovarian cancer biomarkers in blood plasma

Exploratory Raman Spectroscopic Studies of Canine Oral Tumour Types

Canine cancers are becoming increasingly significant due to their natural occurrence, similar to the spontaneous cancers in humans as well as their histological and biological similarities to human cancers. Several oral cancer types have been witnessed in dogs, based on the cell type from which the tumour originates. The type of oral tumour dictates severity of the disease, treatment options and prognoses. The current tissue-based Raman Spectroscopy (RS) study explores stratification of canine cancers. Raman spectra of histopathologically confirmed normal and oral tumour types namely Epulis, Spindle cell sarcoma (SCS), and Squamous cell carcinoma (SCC) were acquired using a Raman confocal microscope with 532 nm laser, pre-processed and multivariate analyses performed. PC-LDA achieved overall classification accuracy of > 70%. Thus, the present study evaluates potential of RS to identify the tumour type based on the identification of characteristic spectral features. Findings warrant large scale in vivo RS explorations in canine cancer subjects.

Clinical study of the diagnosis of thyroid tumours using Raman spectroscopy

OBJECTIVE

The feasibility of the RS for the clinical diagnosis of thyroid tumours was explored.

METHODS

The tumour specimens from 30 benign patients and 30 malignant patients were collected. The collected specimens were subjected to RS and histopathological analysis. The Raman peak intensities of all the specimens were calculated, and the data were analysed using discriminant analysis.

RESULTS

(1) The prevalence rate of malignant tumours in females was as high as 76.7%. Central lymph node metastasis of malignant thyroid tumours accounted for 33.3% of cases, and lateral cervical lymph node metastasis accounted for only 6.7%. (2) The spectral intensity of malignant thyroid tumours was significantly greater than benign thyroid tumours at 1309 cm-1, which should be the characteristic peak of thyroid cancer. The accuracy, sensitivity, and specificity of the RS for differentiating benign from malignant thyroid tumours were 95%, 83.3% and 89.2%.

CONCLUSION

RS is feasible for the diagnosis of thyroid tumours. This study provides experimental and clinical support for the wider application of RS in the evaluation of thyroid tissue.

LEVELS OF EVIDENCE

Levels 4.

Raman Spectral Feature Enhancement Framework for Complex Multiclassification Tasks

Raman spectroscopy enables label-free clinical diagnosis in a single step. However, identifying an individual carrying a specific disease from people with a multi-disease background is challenging. To address this, we developed a Raman spectral implicit feature augmentation with a Raman Intersection, Union, and Subtraction augmentation strategy (RIUS). RIUS expands the data set without requiring additional labeled data by leveraging set operations at the feature level, significantly enhancing model performance across various applications. On a challenging 30-class bacterial classification task, RIUS demonstrated a substantial improvement, increasing the accuracy of ResNet by 2.1% and that of SE-ResNet by 1.4%, achieving accuracies of 85.7% and 87.1%, respectively, on the Bacteria-ID-4 Data set, where RIUS improved ResNet and SE-ResNet accuracies by 13.6% and 14.5%, respectively, with only ten samples per category. When the sample size was reduced, accuracy gains increased to 31.7% and 38.3%, demonstrating the method's robustness across different sample volumes. Compared to basic augmentation, our method exhibited superior performance across various sample volumes and demonstrated exceptional adaptability to different levels of complexity. RIUS exhibited superior performance, particularly in complex settings. Moreover, cluster analysis validated the effectiveness of the implicit feature augmentation module and the consistency between theoretical design and experimental results. We further validated our approach using clinical serum samples from 70 breast cancer patients and 70 controls, achieving an AUC of 0.94 and a sensitivity of 92.9%. Our approach enhances the potential for precisely identifying diseases in complex settings and offers plug-and-play enhancement for existing classification models.

Advanced biomolecular spectroscopic profiling of cardiovascular disease macromolecular markers: SIL-6, IL-9, LpA, ApoB, PCSK9 and NT-ProBNP for rapid in-situ detection and monitoring

Cardiovascular disease (CVD) remains a major global health concern and a leading cause of morbidity and mortality worldwide. Early-diagnosis and prompt medical attention are crucial in managing and reducing overall impact on health-and-wellbeing, necessitating the development of innovative diagnostics, which transcend traditional methodologies. Raman spectroscopy uniquely provides molecular fingerprinting and structural information, offering insights into biochemical composition. Integration of Raman spectroscopy with advanced machine learning is established as a powerful clinical adjunct for point-of-care detection of CVDs. A non-invasive, label-free spectroscopic platform coupled with neural network algorithm, 'SKiNET' has been developed to accurately detect the biomolecular changes within plasma of CVD versus healthy cohorts, enabling rapid diagnosis and longer-term monitoring, where the real-time capabilities provide dynamic assessment of progression, aligning treatment strategies with evolving states. CVD has been detected and classified via SKiNET with 88.6 %-accuracy, 92.9 %-specificity and 85.1 %-sensitivity and with 83.8 %-accuracy. The hybrid RS-SKiNET bio-molecularly specific detection signposted a comprehensive panel of CVD-indicative biomarkers, including SIL-6, IL-9, LpA, ApoB, PCSK9 and NT-ProBNP, offering important insights into disease mechanisms and risk-stratification. This multidimensional technique holds potential for improved patient-and-healthcare management for CVDs, laying the platform toward high-throughput biomolecular profiling of CVD-indicative macromolecular biomarkers, particularly vital for widespread point-of-care diagnostics and monitoring.

Label-free detection of the cytotoxicity effect of cisplatin in human leukemic cells using Raman spectroscopy in conjunction with multivariate analysis

The single-cell Raman spectra of human leukemic Jurkat cells can be obtained by confocal microscopy Raman spectroscopy, including cell groups treated with different doses of cisplatin (0, 3.5, 7, 10.5 and 14 μmol L-1) for 24 hours and those treated with 10.5 μmol L-1 cisplatin for different times (0, 6, 12, 24 and 36 hours). The structure and amount of protein, nucleic acid and other major molecules from different cell groups show special changes in the percentage of biochemical constituents. Compared with the control group, the two protein Raman bands (1449 and 1659 cm-1) and two DNA bands (1303 and 1338 cm-1) in the treatment groups decrease in intensity with the increase of the drug dose and treatment time of cisplatin. Partial least squares combined with support vector machines was used to develop diagnostic algorithms for distinguishing between control and treatment groups. The support vector machines for classification between the control group (0 μmol L-1) and cell groups treated with 10.5 and 14 μmol L-1 cisplatin for 24 hours have achieved good diagnostic results with a high sensitivity of 100%, specificity of 100% and accuracy of 100%, respectively, indicating that 10.5 μmol L-1 can be used as an appropriate therapeutic dose. Using the same method, the diagnostic sensitivity, specificity and accuracy between the control group (0 hours) and cell groups treated with 10.5 μmol L-1 cisplatin for 24 and 36 hours are all 100%, showing that 24 hours can be used as an appropriate therapeutic time. These results showed that Raman spectroscopy in conjunction with multivariate statistical analysis could be a useful tool for evaluating the cytotoxicity induced by cisplatin in human leukemic cells.

Spontaneous-stimulated Raman co-localization dual-modal analysis approach for efficient identification of tumor cells

The study of highly heterogeneous tumor cells, especially acute myeloid leukemia (AML) cells, usually relies on invasive analytical methods such as morphology, immunology, cytogenetics, and molecular biology classification, which are complex and time-consuming to perform. Mortality is high if patients are not diagnosed in a timely manner, so rapid label-free analysis of gene expression and metabolites within single-cell substructures is extremely important for clinical diagnosis and treatment. As a label-free and non-destructive vibrational detection technique, spontaneous Raman scattering provides molecular information across the full spectrum of the cell but lacks rapid imaging localization capabilities. In contrast, stimulated Raman scattering (SRS) provides a high-speed, high-resolution imaging view that can offer real-time subcellular localization assistance for spontaneous Raman spectroscopic detection. In this paper, we combined multi-color SRS microscopy with spontaneous Raman to develop a co-localized Raman imaging and spectral detection system (CRIS) for high-speed chemical imaging and quantitative spectral analysis of subcellular structures. Combined with multivariate statistical analysis methods, CRIS efficiently differentiated AML from normal leukocytes with an accuracy of 98.1 % and revealed the differences in the composition of nuclei and cytoplasm of AML relative to normal leukocytes. Compared to conventional Raman spectroscopy blind sampling without imaging localization, CRIS increased the efficiency of single-cell detection by at least three times. In addition, using the same approach for further identification of AML subtypes M2 and M3, we demonstrated that intracytoplasmic differential expression of proteins is a marker for their rapid and accurate classifying. CRIS analysis methods are expected to pave the way for clinical translation of rapid tumor cell identification.

Polymer-Based Terbium Complex as a Fluorescent Probe for Cancer Antigen 125 Detection: A Promising Tool for Early Diagnosis of Ovarian Cancer

A novel photoprobe, Tb-acetylacetone (Tb-ACAC) doped within a modified epoxy cellulose polymer immobilized with CA-125 monoclonal antibody, offers an accurate and highly selective method for early ovarian cancer (OC) diagnosis by detecting cancer antigen 125 (CA-125) in serum samples. This approach leverages quenching of the Tb-ACAC luminescence upon binding to CA-125. Characterization of the photoprobe film through UV-vis and fluorescence measurements confirmed the presence of Tb-ACAC within the polymer matrix. In aqueous solution (pH 6.8, λex = 365 nm), the characteristic emission band of Tb-ACAC at λem = 546.2 nm exhibited significant quenching upon CA-125 binding. This quenching effect enabled the sensitive and specific detection of CA-125 in diverse serum samples from OC patients, demonstrating the applicability, simplicity, and effectiveness of this novel approach.

Raman spectroscopy analysis of plasma of diabetes patients with and without retinopathy, nephropathy, and neuropathy

Diabetes is now one of the major public health challenges, globally. Prolonged diabetes leads to various diabetic microvascular complications (DMCs) like retinopathy, nephropathy, and neuropathy. Multiple factors are likely to be involved in predisposing diabetic individuals to complications. Early detection or diagnosis is essential in developing strategies to reduce the risk factors and management costs of these diabetic complications. In this study, we employed Raman Spectroscopy (RS) to analyse the plasma samples of diabetes patients without and with DMCs along with the plasma samples of healthy subjects. Spectral comparisons revealed decrease in protein content in Diabetes group and further subsequent decrease in proteins in DMC groups when compared with control group, which corroborates with the fact that there exists increased secretion of proteins in urine and corresponding decreased protein content in their blood in case of diabetic individuals. Among all study groups, it was noted that 75% of control spectra show correct classification, while spectral misclassification is high amongst the subjects with Diabetes and DMCs. Interestingly, very few Diabetes and DMC plasma spectra are misclassified as control spectra. Findings demonstrate that 70% of the Diabetes subjects without complications can be correctly identified from diabetes with complications. Further, investigations could also attempt to explore the use of serum instead of plasma to reduce the spectral misclassifications as one of the abundant constituents namely clotting factors could be avoided. The outcome of RS study may be imminent for the early detection or diagnosis of DMCs.

Comparative study of Raman spectroscopy techniques in blood plasma-based clinical diagnostics: A demonstration on Alzheimer's disease

Nowadays, there are still many diseases with limited or no reliable methods of early diagnosis. A popular approach in clinical diagnostic research is Raman spectroscopy, as a relatively simple, cost-effective, and high-throughput method for searching for disease-specific alterations in the composition of blood plasma. However, the high variability of the experimental designs, targeted diseases, or statistical processing in the individual studies makes it challenging to compare and compile the results to critically assess the applicability of Raman spectroscopy in real clinical practice. This study aimed to compare data from a single series of blood plasma samples of patients with Alzheimer's disease and non-demented elderly controls obtained by four different techniques/experimental setups - Raman spectroscopy with excitation at 532 and 785 nm, Raman optical activity, and surface-enhanced Raman scattering spectroscopy. The obtained results showed that the spectra from each Raman spectroscopy technique contain different information about biomolecules of blood plasma or their conformation and may, therefore, offer diverse points of view on underlying biochemical processes of the disease. The classification models based on the datasets generated by the three non-chiroptical variants of Raman spectroscopy exhibited comparable diagnostic performance, all reaching an accuracy close to or equal to 80%. Raman optical activity achieved only 60% classification accuracy, suggesting its limited applicability in the specific case of Alzheimer's disease diagnostics. The described differences in the outputs of the four utilized techniques/setups of Raman spectroscopy imply that their choice may crucially affect the acquired results and thus should be approached carefully concerning the specific purpose.

Impacts of polypropylene microplastics on lipid profiles of mouse liver uncovered by lipidomics analysis and Raman spectroscopy

Microplastics (MPs) are emerging contaminants, and there are only limited studies reporting the impacts of some MPs on liver lipid metabolism in animals. In this study, we investigated the accumulation of polypropylene-MPs in mouse liver and unraveled the change in lipid metabolic profiles by both lipidomics and Raman spectroscopy. Polypropylene-MP exposure did not cause obvious health symptoms, but hematoxylin-eosin staining showed pathological changes that polypropylene-MPs induced lipid droplet accumulation in liver. Lipidomics results showed a significant change in lipid metabolic profiles and the most influenced categories were triglycerides, fatty acids, free fatty acids and lysophosphatidylcholine, implying the effects of polypropylene-MPs on the hemostasis of lipid droplet biogenesis and catabolism. Most altered lipids contained unsaturated bonds and polyunsaturated phospholipids, possibly affecting the fluidity and curvature of membrane surfaces. Raman spectroscopy confirmed that the major spectral alterations of liver tissues were related to lipids, evidencing the altered lipid metabolism and cell membrane components in the presence of polypropylene-MPs. Our findings firstly disclosed the impacts of polypropylene-MPs on lipid metabolisms in mouse liver and hinted at their detrimental disturbance on membrane properties, cellular lipid storage and oxidation regulation, helping our deeper understanding on the toxicities and corresponding risks of polypropylene-MPs to mammals.

SERS Immunosensors for Cancer Markers Detection

Early diagnosis and monitoring are essential for the effective treatment and survival of patients with different types of malignancy. To this end, the accurate and sensitive determination of substances in human biological fluids related to cancer diagnosis and/or prognosis, i.e., cancer biomarkers, is of ultimate importance. Advancements in the field of immunodetection and nanomaterials have enabled the application of new transduction approaches for the sensitive detection of single or multiple cancer biomarkers in biological fluids. Immunosensors based on surface-enhanced Raman spectroscopy (SERS) are examples where the special properties of nanostructured materials and immunoreagents are combined to develop analytical tools that hold promise for point-of-care applications. In this frame, the subject of this review article is to present the advancements made so far regarding the immunochemical determination of cancer biomarkers by SERS. Thus, after a short introduction about the principles of both immunoassays and SERS, an extended presentation of up-to-date works regarding both single and multi-analyte determination of cancer biomarkers is presented. Finally, future perspectives on the field of SERS immunosensors for cancer markers detection are briefly discussed.

Application of Various Optical and Electrochemical Nanobiosensors for Detecting Cancer Antigen 125 (CA-125): A Review

Nowadays, diagnosing early-stage cancers can be vital for saving patients and dramatically decreases mortality rates. Therefore, specificity and sensitivity in the detection of cancer antigens should be elaborately ensured. Some early-stage cancers can be diagnosed via detecting the cancer antigen CA-125, such as ovarian cancer, and required treatments can be applied more efficiently. Thus, detection of CA-125 by employing various optical or electrochemical biosensors is a preliminary and crucial step to treating cancers. In this review, a diverse range of optical and electrochemical means of detecting CA-125 are reviewed. Furthermore, an applicable comparison of their performance and sensitivity is provided, several commercial detection kits are investigated, and their applications are compared and discussed to determine whether they are applicable and accurate enough.

Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer

We report a novel method with higher than 90% accuracy in diagnosing buccal mucosa cancer. We use Fourier transform infrared spectroscopic analysis of human serum by suppressing confounding high molecular weight signals, thus relatively enhancing the biomarkers’ signals. A narrower range molecular weight window of the serum was also investigated that yielded even higher accuracy on diagnosis. The most accurate results were produced in the serum’s 10–30 kDa molecular weight region to distinguish between the two hardest to discern classes, i.e., premalignant and cancer patients. This work promises an avenue for earlier diagnosis with high accuracy as well as greater insight into the molecular origins of these signals by identifying a key molecular weight region to focus on.

Vibrational Biospectroscopy: An Alternative Approach to Endometrial Cancer Diagnosis and Screening

Endometrial cancer (EC) is the sixth most common cancer and the fourth leading cause of death among women worldwide. Early detection and treatment are associated with a favourable prognosis and reduction in mortality. Unlike other common cancers, however, screening strategies lack the required sensitivity, specificity and accuracy to be successfully implemented in clinical practice and current diagnostic approaches are invasive, costly and time consuming. Such limitations highlight the unmet need to develop diagnostic and screening alternatives for EC, which should be accurate, rapid, minimally invasive and cost-effective. Vibrational spectroscopic techniques, Mid-Infrared Absorption Spectroscopy and Raman, exploit the atomic vibrational absorption induced by interaction of light and a biological sample, to generate a unique spectral response: a “biochemical fingerprint”. These are non-destructive techniques and, combined with multivariate statistical analysis, have been shown over the last decade to provide discrimination between cancerous and healthy samples, demonstrating a promising role in both cancer screening and diagnosis. The aim of this review is to collate available evidence, in order to provide insight into the present status of the application of vibrational biospectroscopy in endometrial cancer diagnosis and screening, and to assess future prospects.

Raman, Infrared and Brillouin Spectroscopies of Biofluids for Medical Diagnostics and for Detection of Biomarkers

This review surveys Infrared, Raman/SERS and Brillouin spectroscopies for medical diagnostics and detection of biomarkers in biofluids, that include urine, blood, saliva and other biofluids. These optical sensing techniques are non-contact, noninvasive and relatively rapid, accurate, label-free and affordable. However, those techniques still have to overcome some challenges to be widely adopted in routine clinical diagnostics. This review summarizes and provides insights on recent advancements in research within the field of vibrational spectroscopy for medical diagnostics and its use in detection of many health conditions such as kidney injury, cancers, cardiovascular and infectious diseases. The six comprehensive tables in the review and four tables in supplementary information summarize a few dozen experimental papers in terms of such analytical parameters as limit of detection, range, diagnostic sensitivity and specificity, and other figures of merits. Critical comparison between SERS and FTIR methods of analysis reveals that on average the reported sensitivity for biomarkers in biofluids for SERS vs FTIR is about 103 to 105 times higher, since LOD SERS are lower than LOD FTIR by about this factor. High sensitivity gives SERS an edge in detection of many biomarkers present in biofluids at low concentration (nM and sub nM), which can be particularly advantageous for example in early diagnostics of cancer or viral infections.HighlightsRaman, Infrared spectroscopies use low volume of biofluidic samples, little sample preparation, fast time of analysis and relatively inexpensive instrumentation.Applications of SERS may be a bit more complicated than applications of FTIR (e.g., limited shelf life for nanoparticles and substrates, etc.), but this can be generously compensated by much higher (by several order of magnitude) sensitivity in comparison to FTIR.High sensitivity makes SERS a noninvasive analytical method of choice for detection, quantification and diagnostics of many health conditions, metabolites, and drugs, particularly in diagnostics of cancer, including diagnostics of its early stages.FTIR, particularly ATR-FTIR can be a method of choice for efficient sensing of many biomarkers, present in urine, blood and other biofluids at sufficiently high concentrations (mM and even a few µM)Brillouin scattering spectroscopy detecting visco-elastic properties of probed liquid medium, may also find application in clinical analysis of some biofluids, such as cerebrospinal fluid and urine.

Screening ovarian cancers with Raman spectroscopy of blood plasma coupled with machine learning data processing

The mortality of ovarian cancer is closely related to its poor rate of early detection. In the search of an efficient diagnosis method, Raman spectroscopy of blood features as a promising technique allowing simple, rapid, minimally-invasive and cost-effective detection of cancers, in particular ovarian cancer. Although Raman spectroscopy has been demonstrated to be effective to detect ovarian cancers with respect to normal controls, a binary classification remains idealized with respect to the real clinical practice. This work considered a population of 95 woman patients initially suspected of an ovarian cancer and finally fixed with a cancer or a cyst. Additionally, 79 normal controls completed the ensemble of samples. Such sample collection proposed us a study case where a ternary classification should be realized with Raman spectroscopy of the collected blood samples coupled with suitable spectroscopic data treatment algorithms. In the medical as well as data points of view, the appearance of the cyst case considerably reduces the distances among the different populations and makes their distinction much more difficult, since the intermediate cyst case can share the specific features of the both cancer and normal cases. After a proper spectrum pretreatment, we first demonstrated the evidence of different behaviors among the Raman spectra of the 3 types of samples. Such difference was further visualized in a high dimensional space, where the data points of the cancer and the normal cases are separately clustered, whereas the data of the cyst case were scattered into the areas respectively occupied by the cancer and normal cases. We finally developed and tested an ensemble of models for a ternary classification with 2 consequent steps of binary classifications, based on machine learning algorithms, allowing identification with sensitivity and specificity of 81.0% and 97.3% for cancer samples, 63.6% and 91.5% for cyst samples, 100% and 90.6% for normal samples.

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.

From Raman to SESORRS: moving deeper into cancer detection and treatment monitoring

Raman spectroscopy is a non-invasive technique that allows specific chemical information to be obtained from various types of sample. The detailed molecular information that is present in Raman spectra permits monitoring of biochemical changes that occur in diseases, such as cancer, and can be used for the early detection and diagnosis of the disease, for monitoring treatment, and to distinguish between cancerous and non-cancerous biological samples. Several techniques have been developed to enhance the capabilities of Raman spectroscopy by improving detection sensitivity, reducing imaging times and increasing the potential applicability for in vivo analysis. The different Raman techniques each have their own advantages that can accommodate the alternative detection formats, allowing the techniques to be applied in several ways for the detection and diagnosis of cancer. This feature article discusses the various forms of Raman spectroscopy, how they have been applied for cancer detection, and the adaptation of the techniques towards their use for in vivo cancer detection and in clinical diagnostics. Despite the advances in Raman spectroscopy, the clinical application of the technique is still limited and certain challenges must be overcome to enable clinical translation. We provide an outlook on the future of the techniques in this area and what we believe is required to allow the potential of Raman spectroscopy to be achieved for clinical cancer diagnostics.

Raman spectroscopy of blood and urine liquid biopsies for ovarian cancer diagnosis: identification of chemotherapy effects

Blood plasma and serum Raman spectroscopy for ovarian cancer diagnosis has been applied in pilot studies, with promising results. Herein, a comparative analysis of these biofluids, with a novel assessment of urine, was conducted by Raman spectroscopy application in a large patient cohort. Spectra were obtained through samples measurements from 116 ovarian cancer patients and 307 controls. Principal component analysis identified significant spectral differences between cancers without previous treatment (n = 71) and following neo-adjuvant chemotherapy (NACT), (n = 45). Application of five classification algorithms achieved up to 73% sensitivity for plasma, high specificities and accuracies for both blood biofluids, and lower performance for urine. A drop in sensitivities for the NACT group in plasma and serum, with an opposite trend in urine, suggest that Raman spectroscopy could identify chemotherapy-related changes. This study confirms that biofluids' Raman spectroscopy can contribute in ovarian cancer's diagnostic work-up and demonstrates its potential in monitoring treatment response.

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.

Raman spectroscopy on blood serum samples of patients with end-stage liver disease

Raman spectroscopy has shown to be a promising method for the examination of biomedical samples. However, until now, its efficacy has not been established in clinical diagnostics. In this study, Raman spectroscopy’s potential application in medical laboratories is evaluated for a large variety (38) of biomarkers. Given 234 serum samples from a cohort of patients with different stages of liver disease, we performed Raman spectroscopy at 780nm excitation wavelength. The Raman spectra were analyzed in combination with the results of routine diagnostics using specifically developed complex mathematical algorithms, including fluorescence filtering, frequency subset selection and several overfitting circumventing strategies, such as independent validation. With the results of this cohort, which were validated in 328 independent samples, a significant proof-of-concept study was completed. This study highlights the need to prevent overfitting and to use independent data for validation. The results reveal that Raman spectroscopy has high potential for use in medical laboratory diagnostics to simultaneously quantify multiple biomarkers.

Non-invasive diagnosis of colorectal cancer by Raman spectroscopy: Recent developments in liquid biopsy and endoscopy approaches

Colorectal cancer (CRC) is the third most common cancer diagnosed globally and is also one of the leading causes of cancer deaths in both men and women. The progression of CRC is slow and is often contained in colon but the risk increases with age. Based on the high certainty that the net benefit of screening in an age group is substantial, screening for CRC is recommended beginning at the age of 50. Currently, most of the incidence is concentrated in developed countries but the rate is increasing rapidly in developing geographies. Detecting CRC at an early stage is critical to reduce morbidity and mortality. Colonoscopy is the most preferred screening method but not very widely implemented due to practical considerations such as cost involved, lack of personnel and facility. To address these concerns, Raman spectroscopy (RS) has been suggested as a viable alternative due to its potential as a rapid non-invasive diagnostic tool. Recently, several studies have been reported but many variations of RS applications in CRC exists and are not well understood by non-specialists. This review focuses particularly on developments of Raman based liquid biopsy and endoscopic studies in order to throw light on each of their significance and limitations. Necessary developments in the future to translate RS into a clinical tool for screening and diagnosis of CRC are also briefly presented.

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.

Novel Optical Biosensor Based on a Nano-Gold Coated by Schiff Base Doped in Sol/Gel Matrix for Sensitive Screening of Oncomarker CA-125

The urge for sensitive, facile, minimally invasive, and fast detection method of CA-125, a significant and crucial biomarker in ovarian malignancy, is currently substantial. This paper describes the detailed construction and characterization of a newly designed optical nano-biosensor to detect CA-125 accurately and sensitively. The fabricated sensor consists of a nano-gold thin film doped into a matrix of sol-gel, exhibiting a centered fluorescence band at 423 nm when excited at 340 nm. The quantification of CA-125 relies on its quenching ability of this fluorescence signal. The sensor was challenged to evaluate its sensitivity and specificity in detecting CA-125 present in samples collected from ovarian cancer diagnosed patients and compared to samples from healthy women as a control. Our findings revealed that the developed biosensor had a sensitivity of 97.35% and a specificity of 94.29%. Additionally, a wide linearity range over 2.0-127.0 U mL-1 for CA-125 was achieved with a detection limit of 1.45 U mL-1. Furthermore, the sensor could successfully discriminate samples between healthy and diseased people, which demonstrates its suitability in CA-125 assessment.

The application of feature engineering in establishing a rapid and robust model for identifying patients with glioma

The aim of the study is to evaluate the efficacy of the combination of Raman spectroscopy with feature engineering and machine learning algorithms for detecting glioma patients. In this study, we used Raman spectroscopy technology to collect serum spectra of glioma patients and healthy people and used feature engineering-based classification models for prediction. First, to reduce the dimensionality of the data, we used two feature extraction algorithms which are partial least squares (PLS) and principal component analysis (PCA). Then, the principal components were selected using the feature selection methods of four correlation indexes, namely, Relief-F (RF), the Pearson correlation coefficient (PCC), the F-score (FS) and term variance (TV). Finally, back-propagation neural network (BP), linear discriminant analysis (LDA) and support vector machine (SVM) classification models were established. To improve the reliability of the model, we used a fivefold cross validation to measure the prediction performance between different models. In this experiment, 33 classification models were established. Integrating 4 classification criteria, PLS-Relief-F-BP, PLS-F-Score-BP, PLS-LDA and PLS-Relief-F-SVM had better effects, and their accuracy rates reached 97.58%, 96.33%, 97.87% and 96.19%, respectively. The experimental results show that feature engineering can select more representative features, reduce computational time complexity and simplify the model. The classification model established in this experiment can not only increase the robustness of the model and shorten the discrimination time but also realize the rapid, stable and accurate diagnosis of glioma patients, which has high clinical application value.

Tear fluid biomarkers in major depressive disorder: Potential of spectral methods in biomarker discovery

Spectroscopic methods represent a group of analytical methods that demonstrate high potential in providing clinically relevant diagnostic information, such as biochemical, functional or structural changes of macromolecular complexes that might occur due to pathological processes or therapeutic intervention. Although application of these methods in the field of psychiatric research is still relatively recent, the preliminary results show that they have the capacity to detect subtle neurobiological abnormalities in major depressive disorder (MDD). Methods of mass spectrometry (MALDI-TOF MS), zymography, synchronous fluorescence spectroscopy (SFS), circular dichroism (CD) spectroscopy, Fourier-transform infrared (FTIR) spectroscopy and atomic force microscopy (AFM) were used to analyze the human tear fluid of subjects with MDD. Using MALDI-TOF MS, two diagnostically significant peaks (3747 and 16 411 m/z) were identified with an AUC value of 0.89 and 0.92 in tear fluid of subjects with MDD vs controls, respectively. We also identified various forms of matrix metalloproteinase 9 in subjects with MDD using zymography and synchronous fluorescence spectra (SFS) showed a significant increase in fluorescence intensity at 280 nm. CD spectra were redshifted in tear fluid of subjects with MDD vs healthy controls. FTIR spectroscopy showed changes in the positions of peaks for amide A, I, II in tear fluid of subjects with MDD vs controls. Moreover, atomic force microscopy (AFM) showed different pattern in the crystal structures of tear fluid components in subjects with MDD. SFS, CD, FTIR spectroscopy, AFM and MALDI-TOF MS confirmed, that the human tear fluid proteome could be helpful in discriminating between the group of subjects with MDD and healthy controls. These preliminary findings suggest that spectral methods could represent a useful tool in clinical psychiatry, especially in establishing differential diagnosis, monitoring illness progression and the effect of psychiatric treatment.

Machine learning-based LIBS spectrum analysis of human blood plasma allows ovarian cancer diagnosis

Early-stage screening and diagnosis of ovarian cancer represent an urgent need in medicine. Usual ultrasound imaging and cancer antigen CA-125 test when prescribed to a suspicious population still require reconfirmations. Spectroscopic analyses of blood, at the molecular and atomic levels, provide useful supplementary tests when coupled with effective information extraction methods. Laser-induced breakdown spectroscopy (LIBS) was employed in this work to record the elemental fingerprint of human blood plasma. A machine learning data treatment process was developed combining feature selection and regression with a back-propagation neural network, resulting in classification models for cancer detection among 176 blood plasma samples collected from patients, including also ovarian cyst and normal cases. Cancer diagnosis sensitivity and specificity of respectively 71.4% and 86.5% were obtained for randomly selected validation samples.

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.

Review of biomarker systems as an alternative for early diagnosis of ovarian carcinoma

Early diagnosis of ovarian carcinoma is bound to boost the long-term endurance rate of the patients. Most ovarian tumors happen post menopause when the ovaries have no vital operation and therefore irregular ovarian role causes no signs. According to Muinao T. et al. (Heliyon. 5(12):e02826, 2019), if we consider the frequency of ovarian carcinoma to be moderate, a screening technique must accomplish a base specificity of 99.6% and sensitivity of over 75%. The classification and approval of early diagnostic biomarkers explicit to ovarian carcinoma are essentially required. Prevailing methods for early diagnosis of ovarian carcinoma incorporate TVS, biological marker examination, or a blend of the two or other. In recent years, it has been revealed that a combination of at least two biomarkers has beaten single biomarkers in measures for early diagnosis of the illness. In the present document, we survey the ongoing exploration of innovative characteristic methodologies and possible panels of carcinoma biological markers for the early diagnosis of ovarian carcinoma and discuss biomarkers as the plausible apparatus for model improvement and other progressed approaches as an effective alternative to the prevailing methods for early diagnosis of this dreadful disease to evade bogus analysis and inordinate expense.

pyDRMetrics - A Python toolkit for dimensionality reduction quality assessment

High-dimensional data are pervasive in this bigdata era. To avoid the curse of the dimensionality problem, various dimensionality reduction (DR) algorithms have been proposed. To facilitate systematic DR quality comparison and assessment, this paper reviews related metrics and develops an open-source Python package pyDRMetrics. Supported metrics include reconstruction error, distance matrix, residual variance, ranking matrix, co-ranking matrix, trustworthiness, continuity, co-k-nearest neighbor size, LCMC (local continuity meta criterion), and rank-based local/global properties. pyDRMetrics provides a native Python class and a web-oriented API. A case study of mass spectra is conducted to demonstrate the package functions. A web GUI wrapper is also published to support user-friendly B/S applications.

A comparative analysis of different biofluids towards ovarian cancer diagnosis using Raman microspectroscopy

Biofluids, such as blood plasma or serum, are currently being evaluated for cancer detection using vibrational spectroscopy. These fluids contain information of key biomolecules, such as proteins, lipids, carbohydrates and nucleic acids, that comprise spectrochemical patterns to differentiate samples. Raman is a water-free and practically non-destructive vibrational spectroscopy technique, capable of recording spectrochemical fingerprints of biofluids with minimum or no sample preparation. Herein, we compare the performance of these two common biofluids (blood plasma and serum) together with ascitic fluid, towards ovarian cancer detection using Raman microspectroscopy. Samples from thirty-eight patients were analysed (n = 18 ovarian cancer patients, n = 20 benign controls) through different spectral pre-processing and discriminant analysis techniques. Ascitic fluid provided the best class separation in both unsupervised and supervised discrimination approaches, where classification accuracies, sensitivities and specificities above 80% were obtained, in comparison to 60–73% with plasma or serum. Ascitic fluid appears to be rich in collagen information responsible for distinguishing ovarian cancer samples, where collagen-signalling bands at 1004 cm⁻¹ (phenylalanine), 1334 cm⁻¹ (CH₃CH₂ wagging vibration), 1448 cm⁻¹ (CH₂ deformation) and 1657 cm⁻¹ (Amide I) exhibited high statistical significance for class differentiation (P < 0.001). The efficacy of vibrational spectroscopy, in particular Raman spectroscopy, combined with ascitic fluid analysis, suggests a potential diagnostic method for ovarian cancer.

Improving data splitting for classification applications in spectrochemical analyses employing a random-mutation Kennard-Stone algorithm approach

Motivation

Data splitting is a fundamental step for building classification models with spectral data, especially in biomedical applications. This approach is performed following pre-processing and prior to model construction, and consists of dividing the samples into at least training and test sets; herein, the training set is used for model construction and the test set for model validation. Some of the most-used methodologies for data splitting are the random selection (RS) and the Kennard-Stone (KS) algorithms; here, the former works based on a random splitting process and the latter is based on the calculation of the Euclidian distance between the samples. We propose an algorithm called the Morais-Lima-Martin (MLM) algorithm, as an alternative method to improve data splitting in classification models. MLM is a modification of KS algorithm by adding a random-mutation factor.

Results

RS, KS and MLM performance are compared in simulated and six real-world biospectroscopic applications using principal component analysis linear discriminant analysis (PCA-LDA). MLM generated a better predictive performance in comparison with RS and KS algorithms, in particular regarding sensitivity and specificity values. Classification is found to be more well-equilibrated using MLM. RS showed the poorest predictive response, followed by KS which showed good accuracy towards prediction, but relatively unbalanced sensitivities and specificities. These findings demonstrate the potential of this new MLM algorithm as a sample selection method for classification applications in comparison with other regular methods often applied in this type of data.

Availability and implementation

MLM algorithm is freely available for MATLAB at https://doi.org/10.6084/m9.figshare.7393517.v1.

Tutorial: multivariate classification for vibrational spectroscopy in biological samples

Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental.

Adaptive compressed sensing of Raman spectroscopic profiling data for discriminative tasks

Raman spectroscopy is widely used in discriminative tasks. It provides a wide-range physio-chemical fingerprint in a rapid and non-invasive way. The Raman spectrometry uses a sensor array to convert photon signals into digital spectroscopic data. This analog-to-digital process can benefit from the compressed sensing (CS) technique. The major benefits include less memory usage, shorter acquisition time, and more cost-efficient sensor. Traditional compressed sensing and reconstruction is a series of mathematical operations performed on the signal. Meanwhile, for discriminative tasks, both the signal and the categorical information are involved. For such scenarios, this paper proposes a method that uses both domain signal and categorical information to optimize CS hyper-parameters, including 1) the sampling ratio or the sensing matrix, 2) the basis matrix for the sparse transform, and 3) the regularization rate or shrinkage factor for L1-norm minimization. A case study of formula milk brand identification proves the proposed method can generate effective compressed sensing while preserving enough discriminative power in the reconstructed signal. Under the optimized hyper-parameters, a 100% classification accuracy is retained by only sampling 20% of the original signal.

Establishing spectrochemical changes in the natural history of oesophageal adenocarcinoma from tissue Raman mapping analysis

Raman spectroscopy is a fast and sensitive technique able to identify molecular changes in biological specimens. Herein, we report on three cases where Raman microspectroscopy was used to distinguish normal vs. oesophageal adenocarcinoma (OAC) (case 1) and Barrett’s oesophagus vs. OAC (cases 2 and 3) in a non-destructive and highly accurate fashion. Normal and OAC tissues were discriminated using principal component analysis plus linear discriminant analysis (PCA-LDA) with 97% accuracy (94% sensitivity and 100% specificity) (case 1); Barrett’s oesophagus vs. OAC tissues were discriminated with accuracies ranging from 98 to 100% (97–100% sensitivity and 100% specificity). Spectral markers responsible for class differentiation were obtained through the difference-between-mean spectrum for each group and the PCA loadings, where C–O–C skeletal mode in β-glucose (900 cm⁻¹), lipids (967 cm⁻¹), phosphodioxy (1296 cm⁻¹), deoxyribose (1456 cm⁻¹) and collagen (1445, 1665 cm⁻¹) were associated with normal and OAC tissue differences. Phenylalanine (1003 cm⁻¹), proline/collagen (1066, 1445 cm⁻¹), phospholipids (1130 cm⁻¹), CH₂ angular deformation (1295 cm⁻¹), disaccharides (1462 cm⁻¹) and proteins (amide I, 1672/5 cm⁻¹) were associated with Barrett’s oesophagus and OAC tissue differences. These findings show the potential of using Raman microspectroscopy imaging for fast and accurate diagnoses of oesophageal pathologies and establishing subtle molecular changes predisposing to adenocarcinoma in a clinical setting.

Graphical abstract

Graphical abstract demonstrating how oesophageal tissue is processed through Raman mapping analysis in order to detect spectral differences between stages of oesophageal transformation to adenocarcinoma

Collapse

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.

Multi-biomarker panel signature as the key to diagnosis of ovarian cancer

Early detection of ovarian cancer has been a challenge to manage the high mortality rate caused by this deadly disease. The trends in mortality have been reduced by the scientific contributions from the corners across the globe, however accounting for the fifth leading cause of gynecological mortality. The complexities in the clinical presentation, origin of tumor, and gene expression profiles had added to much difficulty in understanding and diagnosis of the disease. Stage 1 diagnosis of ovarian cancer improves the 5-year survival rate to around 92%. Cancer antigen-125 (CA-125) is the gold standard tumor marker found at abnormally high levels in the blood of many women in ovarian cancer. However, many non-cancerous conditions exhibit high levels of CA-125 and several women have normal CA-125 level in the early stage of ovarian cancer, suggesting CA-125 biomarker is not specific enough for the screening of early stage ovarian cancer. In addition, several other biomarkers, including HE4 have been added in the diagnostic field for higher sensitivity and specificity in the diagnosis and progression of ovarian cancer. HE4 is a prospective single serum biomarker which has been approved by the FDA to monitor the disease progression in epithelial ovarian cancer. However, owing to low sensitivity and specificity, combination of a panel of biomarkers has been proposed in the diagnosis of the disease. Based on extensive biomarkers research findings, here we discuss current trends in diagnostic approaches and updated potential several panels of cancer biomarkers for early detection of ovarian cancer. It has been recently reported that CA125 in combinations with two or more biomarkers have outperformed single biomarker assays for early detection of the disease. Moreover, CA-125 with CA 19–9, EGFR, G-CSF, Eotaxin, IL-2R, cVCAM, MIF improved the sensitivity with 98.2 % and specificity of 98.7% in early stage detection of ovarian cancer. Overall, this review demonstrates a panel of biomarkers signature as the potential tool for prototype development in future and other advanced approaches for early diagnosis of ovarian cancer to avoid false-diagnosis and excessive cost.

Ex vivo detection of cadmium-induced renal damage by using confocal Raman spectroscopy

Cadmium (Cd) is a toxic heavy metal which is harmful to environment and organisms. The reabsorption of Cd in kidney leads it to be the main damaged organ in animals under the Cd exposure. In this work, we applied confocal Raman spectroscopy to map the pathological changes in situ in normal and Cd-exposed mice kidney. The renal tissue from Cd-exposed group displayed a remarkable decreasing in the intensity of typical peaks related to mitochondria, DNA, proteins and lipids. On the contrary, the peaks of collagen in Cd-exposed group elevated significantly. The components in each tissue were identified and distinguished by principal component analysis. Furthermore, all the biological investigations in this study were consistent with the Raman spectrum detection, which revealed the progression and degree of lesion induced by Cd. The confocal Raman spectroscopy provides a new perspective for in situ monitoring of substances changes in tissues, which exhibits more comprehensive understanding of the pathogenic mechanisms of heavy metals in molecular toxicology.

Influence of drugs on the prospective diagnostic method for coronary heart disease with urine

The morbidity of coronary heart disease (CHD) with high risks has been rising in recent years. A novel and noninvasive method based on surface-enhanced Raman spectroscopy (SERS) was proposed by Yang et al. (Analyst 143: 2235, 2018) to prospectively diagnose the arterial blockage by detecting platelet-derived growth factor-BB (PDGF-BB) in urine. Clinically, anti-platelet drugs (such as aspirin, statins and clopidogrel) are often used for ordinary CHD patients or patients with percutaneous coronary intervention (PCI). Therefore, whether the previous developed method can be applied to the CHD patients on long-term medication (more than 6 months) or post-PCI patients was investigated here. Firstly, urine samples of 13 CHD patients on long-term medication (aspirin, rosuvastatin, clopidogrel bisulfate) and 13 post-PCI patients were measured by the proposed method. Clinical data of coronary angiography results provided by Xin Hua Hospital and Yangpu District Central Hospital Antu Branch revealed that these 26 patients were with serious arterial blockage, however, characteristic Raman peak at 1509 cm^-1 attributed to PDGF-BB was not observed in the SERS spectra of these 26 patients. In addition, an eight-day follow-up investigation was performed on a CHD patient with PCI three years ago and on long-term medication. It was found that the Raman peak at 1509 cm^-1 could be only observed in the third and fourth day after suspending the drugs. Furthermore, SERS spectra of mixed solutions of PDGF-BB and aspirin, rosuvastatin, mixed solutions of these two drugs and clopidogrel bisulfate were analyzed. The Raman peak at 1509 cm^-1 was not found in all these spectra, it indicated that all the three kinds of drugs could influence on the SERS signal of PDGF-BB. Therefore, the previous developed method is not suitable for CHD patients on long-term medication and post-PCI patients.