Diagnosis of pathological minor salivary glands in primary Sjogren's syndrome by using Raman spectroscopy

The construction of an inflammation classification model for HDPCs applied in guiding pulpotomy based on single-cell Raman spectroscopy

The immune defense and the repair function of the pulp tissue serve as the biological foundation of pulpotomy. The precise evaluation of the pulp inflammation extent and determining its reversibility are essential for the success of pulpotomy. The objective of this study was to classify the molecular-level of dental pulp cell physiology and inflammatory state based on the biochemical changes obtained by single-cell Raman spectroscopy. Firstly, we differentiated the growth of HDPCs (human dental pulp cells) under physiological states by employing Raman spectroscopy with multivariate statistical analysis. Raman spectroscopy reflected the biochemical changes at different growth phases, including the lag phase, log phase, and stationary phase. Secondly, we evaluated the optimal concentration and duration of Porphyromonas gingivalis lipopolysaccharide (P.g.LPS) stimulation to establish a six-level inflammation classification model of HDPCs. Thirdly, we performed label-free characterization of biological component changes in cells of different inflammation grades by Raman spectroscopy. As a result, the differences of peaks in the region 600-1800 cm-1 demonstrated the biochemical molecular alterations in the different inflammation grades of HDPCs. As inflammation progresses in steps, protein peaks increased first and then decreased, while lipid and nucleic acid peaks gradually decreased compared to unstimulated cells. However, when the inflammatory stimulation reached grade V, the changes in the biological properties were characterized by a recovery in protein and lipid content, and a decrease in nucleic acid content. We then established the diagnostic model using the Raman spectra of HDPCs in physiological and inflammatory states, which had a prediction accuracy of 100 % and 97.4 %, respectively. Finally, we determined the reversibility threshold of HDPCs at different grades of inflammation. We observed that the inflammation of grade I and II cells had potential reversibility and could be attempted to be retained. In conclusion, Raman spectroscopy combined with multivariate statistical analysis has potential possibility to effectively distinguish the degree of inflammation in the dental pulp, thus providing new tools and perspectives on pulpotomy in clinical practice.

Intraoperative rapid assessment of the deep muscle surgical margin of tongue squamous cell carcinoma via Raman spectroscopy

Purpose

An accurate assessment of the surgical margins of tongue squamous cell carcinoma (TSCC), especially the deep muscle tissue, can help completely remove the cancer cells and thus minimize the risk of recurrence. This study aimed to develop a classification model that classifies TSCC and normal tissues in order to aid in the rapid and accurate intraoperative assessment of TSCC surgical deep muscle tissue margins.

Materials and methods

The study obtained 240 Raman spectra from 60 sections (30 TSCC and 30 normal) from 15 patients diagnosed with TSCC. The classification model based on the analysis of Raman spectral data was developed, utilizing principal component analysis (PCA) and linear discriminant analysis (LDA) for the diagnosis and classification of TSCC. The leave-one-out cross-validation was employed to estimate and evaluate the prediction performance model.

Results

This approach effectively classified TSCC tissue and normal muscle tissue, achieving an accuracy of exceeding 90%. The Raman analysis showed that TSCC tissues contained significantly higher levels of proteins, lipids, and nucleic acids compared to the adjacent normal tissues. In addition, we have also explored the potential of Raman spectroscopy in classifying different histological grades of TSCC.

Conclusion

The PCA-LDA tissue classification model based on Raman spectroscopy exhibited good accuracy, which could aid in identifying tumor-free margins during surgical interventions and present a promising avenue for the development of rapid and accurate intraoperative techniques.

Raman micro-spectroscopy as a tool to study immunometabolism

In the past two decades, immunometabolism has emerged as a crucial field, unraveling the intricate molecular connections between cellular metabolism and immune function across various cell types, tissues, and diseases. This review explores the insights gained from studies using the emerging technology, Raman micro-spectroscopy, to investigate immunometabolism. Raman micro-spectroscopy provides an exciting opportunity to directly study metabolism at the single cell level where it can be combined with other Raman-based technologies and platforms such as single cell RNA sequencing. The review showcases applications of Raman micro-spectroscopy to study the immune system including cell identification, activation, and autoimmune disease diagnosis, offering a rapid, label-free, and minimally invasive analytical approach. The review spotlights three promising Raman technologies, Raman-activated cell sorting, Raman stable isotope probing, and Raman imaging. The synergy of Raman technologies with machine learning is poised to enhance the understanding of complex Raman phenotypes, enabling biomarker discovery and comprehensive investigations in immunometabolism. The review encourages further exploration of these evolving technologies in the rapidly advancing field of immunometabolism.

Application of serum SERS technology combined with deep learning algorithm in the rapid diagnosis of immune diseases and chronic kidney disease

Surface-enhanced Raman spectroscopy (SERS), as a rapid, non-invasive and reliable spectroscopic detection technique, has promising applications in disease screening and diagnosis. In this paper, an annealed silver nanoparticles/porous silicon Bragg reflector (AgNPs/PSB) composite SERS substrate with high sensitivity and strong stability was prepared by immersion plating and heat treatment using porous silicon Bragg reflector (PSB) as the substrate. The substrate combines the five deep learning algorithms of the improved AlexNet, ResNet, SqueezeNet, temporal convolutional network (TCN) and multiscale fusion convolutional neural network (MCNN). We constructed rapid screening models for patients with primary Sjögren's syndrome (pSS) and healthy controls (HC), diabetic nephropathy patients (DN) and healthy controls (HC), respectively. The results showed that the annealed AgNPs/PSB composite SERS substrates performed well in diagnosing. Among them, the MCNN model had the best classification effect in the two groups of experiments, with an accuracy rate of 94.7% and 92.0%, respectively. Previous studies have indicated that the AgNPs/PSB composite SERS substrate, combined with machine learning algorithms, has achieved promising classification results in disease diagnosis. This study shows that SERS technology based on annealed AgNPs/PSB composite substrate combined with deep learning algorithm has a greater developmental prospect and research value in the early identification and screening of immune diseases and chronic kidney disease, providing reference ideas for non-invasive and rapid clinical medical diagnosis of patients.

Raman spectroscopy combined with a support vector machine algorithm as a diagnostic technique for primary Sjögren's syndrome

The aim of this study was to explore the feasibility of Raman spectroscopy combined with computer algorithms in the diagnosis of primary Sjögren syndrome (pSS). In this study, Raman spectra of 60 serum samples were acquired from 30 patients with pSS and 30 healthy controls (HCs). The means and standard deviations of the raw spectra of patients with pSS and HCs were calculated. Spectral features were assigned based on the literature. Principal component analysis (PCA) was used to extract the spectral features. Then, a particle swarm optimization (PSO)-support vector machine (SVM) was selected as the method of parameter optimization to rapidly classify patients with pSS and HCs. In this study, the SVM algorithm was used as the classification model, and the radial basis kernel function was selected as the kernel function. In addition, the PSO algorithm was used to establish a model for the parameter optimization method. The training set and test set were randomly divided at a ratio of 7:3. After PCA dimension reduction, the specificity, sensitivity and accuracy of the PSO-SVM model were obtained, and the results were 88.89%, 100% and 94.44%, respectively. This study showed that the combination of Raman spectroscopy and a support vector machine algorithm could be used as an effective pSS diagnosis method with broad application value.

Targeted Therapy for Primary Sjögren's Syndrome: Where are We Now?

Primary Sjögren's syndrome (pSS) is an autoimmune exocrinopathy characterized by dryness symptoms. This review briefly describes recent advances in the targeted therapies for pSS. Biologics evaluated for pSS treatment mainly include B cell-depleting agents, inhibitors of B cell activation, and agents that target co-signaling molecules or proinflammatory cytokines. Small molecule inhibitors that target signaling pathways have also been evaluated. However, current evidence for the efficacy of targeted therapies in pSS is still sparse. Although ianalumab (an anti-B cell-activating factor [BAFF]-receptor antibody) and iscalimab (an anti-CD40 antibody) are promising biologics for pSS, their efficacy still needs to be evaluated in larger clinical trials. For other biologics, clinical trials have found no differences versus placebo in the change from baseline in European League Against Rheumatism Sjögren's Syndrome Disease Activity Index (ESSDAI) score and fatigue score. Possible causes of the disappointing outcomes mainly include the inefficacy of those evaluated biologics in treating pSS, the high heterogeneous nature of pSS, irreversible exocrine glandular failure at advanced disease stages, inappropriate recruitment strategy in clinical trials, and outcome measures. Early diagnosis and glandular function-centered outcome measures may help to improve the current situation in the systemic therapy of pSS.

Tracking of the biochemical changes upon pleomorphic adenoma progression using vibrational microspectroscopy

Head and neck tumors can be very challenging to treat because of the risk of problems or complications after surgery. Therefore, prompt and accurate diagnosis is extremely important to drive appropriate treatment decisions, which may reduce the chance of recurrence. This paper presents the original research exploring the feasibility of Fourier transform infrared (FT-IR) and Raman spectroscopy (RS) methods to investigate biochemical alterations upon the development of the pleomorphic adenoma. Principal component analysis (PCA) was used for a detailed assessment of the observed changes and to determine the spectroscopic basis for salivary gland neoplastic pathogenesis. It is implied that within the healthy margin, as opposed to the tumoral tissue, there are parts that differ significantly in lipid content. This observation shed new light on the crucial role of lipids in tissue physiology and tumorigenesis. Thus, a novel approach that eliminates the influence of lipids on the elucidation of biochemical changes is proposed. The performed analysis suggests that the highly heterogeneous healthy margin contains more unsaturated triacylglycerols, while the tumoral section is rich in proteins. The difference in protein content was also observed for these two tissue types, i.e. the healthy tissue possesses more proteins in the anti-parallel β-sheet conformation, whereas the tumoral tissue is dominated by proteins rich in unordered random coils. Furthermore, the pathogenic tissue shows a higher content of carbohydrates and reveals noticeable differences in nucleic acid content. Finally, FT-IR and Raman spectroscopy methods were proposed as very promising methods in the discrimination of tumoral and healthy tissues of the salivary gland.

Perspective of Future SERS Clinical Application Based on Current Status of Raman Spectroscopy Clinical Trials

Raman spectroscopy has emerged as a promising tool in biomedical analysis and clinical diagnosis. The development of surface-enhanced Raman scattering spectroscopy (SERS) improved the detection limit with ultrahigh sensitivity and simplicity. More and more Raman spectroscopy clinical trials (R-PCT) have been conducted recently. However, there is a lack of an up-to-date review summarizing the current status of Raman clinical trials performed until now. Hence, the clinical trials for Raman were retrieved from the International Clinical Trials Registration Platform. We summarized the clinical characteristics of 55 registered Raman spectroscopy clinical trials (R-RSCTs) and 44 published Raman spectroscopy clinical trials (P-RSCTs). This review could assist researchers and clinicians to understand the current status of Raman spectroscopy clinical research and perhaps could benefit the reasonable and accurate design of future SERS studies.

Quantitative evaluation of salivary gland scintigraphy in Sjögren's syndrome: comparison of diagnostic efficacy and relationship with pathological features of the salivary glands

OBJECTIVE

The value of salivary gland scintigraphy (SGS) in the evaluation of Sjögren's syndrome (SS) remains controversial. The aim of this study was to evaluate the diagnostic efficacy of quantitative SGS in patients with xerostomia and to assess the correlation between scintigraphic parameters and pathological features of salivary glands.

METHODS

Medical records of 165 patients with xerostomia who underwent [99mTc] pertechnetate SGS and labial biopsy were retrospectively reviewed. The maximum accumulation ratio (MAR), maximum secretion ratio (MSR), and time interval from stimulation to minimum count (T_min) of the parotid glands were calculated to quantify the glandular activity. Furthermore, pre-stimulatory oral activity index (PRI) and post-stimulatory oral activity index (POI) were calculated to quantify the oral activity.

RESULTS

All parameters except for T_min were significantly lower in patients with SS than in those without SS. Among the five SGS parameters, PRI showed the highest areas under the curve value (0.9005; p < 0.001), and PRI > 32.75 was associated with a sensitivity of 78.5% and specificity of 86.4% for the diagnosis of SS. A decrease in MAR, MSR, PRI, and POI and an increase in T_min correlate significantly with the histopathologic grade of labial gland biopsy and disease severity of SS. No significant differences in glandular parameters (MAR, MSR, and T_min) were found between the non-SS and early-stage SS groups.

CONCLUSION

Conventional scintigraphic parameters could be used as simple, reliable, and sensitive indicators for the early diagnosis of SS and determination of disease severity.

Surface-enhanced Raman scattering investigation of bovine serum albumin by Au nanoparticles with different sizes

BACKGROUND

Surface-enhanced Raman scattering (SERS) has become a useful spectroscopic tool for studying biomolecule structures. The main types of plasmonic substrates used in biological systems are Au nanoparticles (AuNPs), whose surface plasmon resonance depends on the nanoparticle size, morphology, particle interspace, and so on.

METHODS

In this study, AuNP colloids with different sizes were synthesized and used as the sensors to probe SERS signals of different biomarkers and biomolecules.

RESULTS

The results showed that an AuNP colloid of ~50 nm had excellent SERS effects in probing various molecules, and could be preserved for about 3 months with excellent repeatability and reproducibility (RSD <5%) in terms of the probed signal intensity (rhodamine 6G and crystal violet). Meanwhile, the fabricated AuNPs were applied to study the SERS signals and structural information of bovine serum albumin (BSA) in aqueous solution. It was found that SERS could rapidly provide the structural information and vibration characteristics of BSA.

CONCLUSION

It was concluded that biocompatible AuNP colloid may be a promising biosensor in the rapid and label-free detection of biological systems.

Surface-enhanced Raman spectroscopy of blood serum based on gold nanoparticles for tumor stages detection and histologic grades classification of oral squamous cell carcinoma

Background

Tumor stages detection and histologic grades classification are essential for the diagnosis and prognosis of oral squamous cell carcinoma (OSCC). In this research, we apply surface-enhanced Raman spectroscopy (SERS) of blood serum to detect the tumor stages and histologic classification of OSCC.

Methods

According to TNM classification and World Health Organization histologic grading system, the blood serum samples were collected from a total of 135 OSCC patients in the different tumor stages and histologic grades. Then the SERS spectra of serum samples from OSCC patients were diagnosed and classified into different groups using principal component analysis (PCA) and linear discriminant analysis (LDA) based on the tumor sizes, lymph node metastasis and histologic grades.

Results

The SERS spectra of blood serum samples have shown the distinct changes and differences compared with each other, which were assigned to the biomolecule alterations (nucleic acids, proteins, lipids, and so on) in blood serums. And all accuracies of detection and classification reached above 85%.

Conclusion

This study demonstrated that the SERS based on blood serum test had an enormous potential to carry out the preoperative assessment and prediction of the OSCC patients in different tumor stages and histologic classification.

Surface-enhanced Raman spectroscopy of blood serum based on gold nanoparticles for the diagnosis of the oral squamous cell carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) is becoming more common across the globe. The prognosis of OSCC is largely dependent on the early detection. But the routine oral cavity examination may delay the diagnosis because the early oral malignant lesions may be clinically indistinguishable from benign or inflammatory diseases. In this study, the new diagnostic method is developed by using the surface enhanced Raman spectroscopy (SERS) to detect the serum samples from the cancer patients.

METHOD

The blood serum samples were collected from the OSCC patients, MEC patients and the volunteers without OSCC or MEC. Gold nanoparticles(NPs) were then mixed in the serum samples to obtain the high quality SERS spectra. There were totally 135 spectra of OSCC, 90 spectra of mucoepidermoid carcinoma (MEC) and 145 spectra of normal control group, which were captured by SERS successfully. Compared with the normal control group, the Raman spectral differences exhibited in the spectra of OSCC and MEC groups, which were assigned to the nucleic acids, proteins and lipids. Based on these spectral differences and features, the algorithms of principal component analysis(PCA) and linear discriminant analysis (LDA) were employed to analyze and classify the Raman spectra of different groups.

RESULTS

Compared with the normal groups, the major increased peaks in the OSCC and MEC groups were assigned to the molecular structures of the nucleic acids and proteins. And these different major peaks between the OSCC and MEC groups were assigned to the special molecular structures of the carotenoids and lipids. The PCA-LDA results demonstrated that OSCC could be discriminated successfully from the normal control groups with a sensitivity of 80.7% and a specificity of 84.1%. The process of the cross validation proved the results analyzed by PCA-LDA were reliable.

CONCLUSION

The gold NPs were appropriate substances to capture the high-quality SERS spectra of the OSCC, MEC and normal serum samples. The results of this study confirm that SERS combined PCA-LDA had a giant capability to detect and diagnosis OSCC through the serum sample successfully.

Evaluation of the oral component of Sjögren's syndrome: An overview

Sjögren's syndrome is a chronic autoimmune disorder characterized by lymphocytic infiltration, and consequently hypofunction of lacrimal and salivary glands. The loss of salivary function induces oral dryness (xerostomia). This review focuses on methods for determining salivary gland function including clinical signs, salivary flow rate measurements (sialometry), analysis of salivary composition (sialochemistry), histopathological and radiologic examinations, and other recent advanced techniques.

Raman-based noninvasive metabolic profile evaluation of in vitro bovine embryos

The timing of the first embryonic cell divisions may predict the ability of an embryo to establish pregnancy. Similarly, metabolic profiles may be markers of embryonic viability. However, in bovine, data about the metabolomics profile of these embryos are still not available. In the present work, we describe Raman-based metabolomic profiles of culture media of bovine embryos with different developmental kinetics (fast x slow) throughout the in vitro culture. The principal component analysis enabled us to classify embryos with different developmental kinetics since they presented specific spectroscopic profiles for each evaluated time point. We noticed that bands at 1076  cm(−1) (lipids), 1300  cm(−1) (Amide III), and 2719  cm(−1) (DNA nitrogen bases) gave the most relevant spectral features, enabling the separation between fast and slow groups. Bands at 1001  cm(−1) (phenylalanine) and 2892  cm(−1) (methylene group of the polymethylene chain) presented specific patterns related to embryonic stage and can be considered as biomarkers of embryonic development by Raman spectroscopy. The culture media analysis by Raman spectroscopy proved to be a simple and sensitive technique that can be applied with high efficiency to characterize the profiles of in vitro produced bovine embryos with different development kinetics and different stages of development.

Label-free blood serum detection by using surface-enhanced Raman spectroscopy and support vector machine for the preoperative diagnosis of parotid gland tumors

BACKGROUND

It is difficult for the parotid gland neoplasms to make an accurate preoperative diagnosis due to the restriction of biopsy in the parotid gland neoplasms. The aim of this study is to apply the surface-enhanced Raman spectroscopy (SERS) method for the blood serum biochemical detection and use the support vector machine for the analysis in order to develop a simple but accurate blood serum detection for preoperative diagnosis of the parotid gland neoplasms.

METHODS

The blood serums were collected from four groups: the patients with pleomorphic adenoma, the patients with Warthin's tumor, the patients with mucoepidermoid carcinoma and the volunteers without parotid gland neoplasms. Au nanoparticles (Au NPs) were mixed with the blood serum as the SERS active nanosensor to enhance the Raman scattering signals produced by the various biochemical materials and high quality SERS spectrum were obtained by using the Raman microscope system. Then the support vector machine was utilized to analyze the differences of the SERS spectrum from the blood serum of different groups and established a diagnostic model to discriminate the different groups.

RESULTS

It was demonstrated that there were different intensities of SERS peaks assigned to various biochemical changes in the blood serum between the parotid gland tumor groups and normal control group. Compared with the SERS spectra of the normal serums, the intensities of peaks assigned to nucleic acids and proteins increased in the SERS spectra of the parotid gland tumor serums, which manifested the differences of the biochemical metabolites in the serum from the patients with parotid gland tumors. When the leave-one-sample-out method was used, support vector machine (SVM) played an outstanding performance in the classification of the SERS spectra with the high accuracy (84.1 % ~ 88.3 %), sensitivity (82.2 % ~ 97.4 %) and specificity (73.7 % ~ 86.7 %). Though the accuracy, sensitivity and specificity decreased in the leave-one-patient-out cross validation, the mucoepidermoid carcinoma was still easier to diagnose than other tumors.

DISCUSSION

The specific molecular differences of parotid gland tumors and normal serums were significantly demonstrated through the comparison between the various SERS spectra.But compared with the serum SERS spectra reported in the other studies, some differences exist between the spectra in this study and the ones reported in the lietratures. These differences may result from the various nano-particles, the different preparation of serum and equipment parameters, and we could need a further research to find an exact explanation.Based on the SERS spectra of the serum samples, SVM have shown a giant potential to diagnose the parotid gland tumors in our preliminary study. However, different cross validaiton methods could effect the accuracy and a further study involing a great number of samples should be needed.

CONCLUSIONS

This exploratory research demonstrated the great potential of SERS combined with SVM into a non-invasive clinical diagnostic method for preoperative diagnosis of parotid gland tumors. And the internal relation between the spectra and patients should be established in the further study.

Rapid and noninvasive technique to assess the metabolomics profile of bovine embryos produced in vitro by Raman spectroscopy

Morphological assessments are used to select embryos with the highest implantation potential, however it is still very limited. The development of new technologies, such as Raman spectroscopy have improved quantitative and qualitative analysis, and consequently led to a better characterization of embryos and improvements on the prediction of their potential. Therefore, we propose a method based on the conventional in vitro culture system of bovine embryos, and the subsequent analysis of the culture media drops by Raman spectroscopy. Our results obtained by PCA analysis clearly showed a separation of the spectral profiles from culture media drops with and without embryos.