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

Raman hyperspectroscopy of saliva and machine learning for Sjögren's disease diagnostics

Sjögren's disease is an autoimmune disorder affecting exocrine glands, causing dry eyes and mouth and other morbidities. Polypharmacy or a history of radiation to the head and neck can also lead to dry mouth. Sjogren's disease is often underdiagnosed due to its non-specific symptoms, limited awareness among healthcare professionals, and the complexity of diagnostic criteria, limiting the ability to provide therapy early. Current diagnostic methods suffer from limitations including the variation in individuals, the absence of a single diagnostic marker, and the low sensitivity and specificity, high cost, complexity, and invasiveness of current procedures. Here we utilized Raman hyperspectroscopy combined with machine learning to develop a novel screening test for Sjögren's disease. The method effectively distinguished Sjögren's disease patients from healthy controls and radiation patients. This technique shows potential for development of a single non-invasive, efficient, rapid, and inexpensive medical screening test for Sjögren's disease using a Raman hyper-spectral signature.

Unlocking Preclinical Alzheimer's: A Multi-Year Label-Free In Vitro Raman Spectroscopy Study Empowered by Chemometrics

Alzheimer's disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer's encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer's still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer's diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer's, which is particularly surprising because of Raman spectroscopy's high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer's disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods.

Applications of Raman spectroscopy in the diagnosis and monitoring of neurodegenerative diseases

Raman scattering is an inelastic light scattering that occurs in a manner reflective of the molecular vibrations of molecular structures and chemical conditions in a given sample of interest. Energy changes in the scattered light can be assessed to determine the vibration mode and associated molecular and chemical conditions within the sample, providing a molecular fingerprint suitable for sample identification and characterization. Raman spectroscopy represents a particularly promising approach to the molecular analysis of many diseases owing to clinical advantages including its instantaneous nature and associated high degree of stability, as well as its ability to yield signal outputs corresponding to a single molecule type without any interference from other molecules as a result of its narrow peak width. This technology is thus ideally suited to the simultaneous assessment of multiple analytes. Neurodegenerative diseases represent an increasingly significant threat to global public health owing to progressive population aging, imposing a severe physical and social burden on affected patients who tend to develop cognitive and/or motor deficits beginning between the ages of 50 and 70. Owing to a relatively limited understanding of the etiological basis for these diseases, treatments are lacking for the most common neurodegenerative diseases, which include Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. The present review was formulated with the goal of briefly explaining the principle of Raman spectroscopy and discussing its potential applications in the diagnosis and evaluation of neurodegenerative diseases, with a particular emphasis on the research prospects of this novel technological platform.

Image-free recognition of moderate ROP from mild with machine learning algorithm on plasma Raman spectrum

The retinopathy of prematurity (ROP) can cause serious clinical consequences and, fortunately, it is remediable while the time window for treatment is relatively narrow. Therefore, it is urgent to screen all premature infants and diagnose ROP degree timely, which has become a large workload for pediatric ophthalmologists. We developed a retinal image-free procedure using small amount of blood samples based on the plasma Raman spectrum with the machine learning model to automatically classify ROP cases before medical intervention was performed. Statistical differences in infrared Raman spectra of plasma samples were found among the control, mild (ZIIIS1), moderate (ZIIIS2 & ZIIS1), and advanced (ZIIS2) ROP groups. With the different wave points of Raman spectra as the inputs, the outputs of our support vector machine showed that the area under the curves in the receiver operating characteristic (AUC) were 0.763 for the pair comparisons of the control with the mild groups, 0.821 between moderate and advanced groups (ZIIS2), while more than 90% in comparisons of the other four pairs: control vs. moderate (0.981), control vs. advanced (0.963), mild vs. moderate (0.936), and mild vs. advanced (0.953), respectively. Our study could advance principally the ROP diagnosis in two dimensions: the moderate ROPs have been classified remarkably from the mild ones, which leaves more time for the medical treatments, and the procedure of Raman spectrum with a machine learning model based on blood samples can be conveniently promoted to those hospitals lacking of the pediatric ophthalmologists with experience in reading retinal images.

Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS): Current Applications and Future Prospects

Surface-enhanced Raman spectroscopy (SERS) has recently emerged as a potent analytical technique with significant potential in the field of brain research. This review explores the applications and innovations of SERS in understanding the pathophysiological basis and diagnosis of brain disorders. SERS holds significant advantages over conventional Raman spectroscopy, particularly in terms of sensitivity and stability. The integration of label-free SERS presents promising opportunities for the rapid, reliable, and non-invasive diagnosis of brain-associated diseases, particularly when combined with advanced computational methods such as machine learning. SERS has potential to deepen our understanding of brain diseases, enhancing diagnosis, monitoring, and therapeutic interventions. Such advancements could significantly enhance the accuracy of clinical diagnosis and further our understanding of brain-related processes and diseases. This review assesses the utility of SERS in diagnosing and understanding the pathophysiological basis of brain disorders such as Alzheimer's and Parkinson's diseases, stroke, and brain cancer. Recent technological advances in SERS instrumentation and techniques are discussed, including innovations in nanoparticle design, substrate materials, and imaging technologies. We also explore prospects and emerging trends, offering insights into new technologies, while also addressing various challenges and limitations associated with SERS in brain research.

Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context

This study reports on the successful use of a machine learning approach using attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy for the classification and prediction of a donor's sex from the fingernails of 63 individuals. A significant advantage of ATR FT-IR is its ability to provide a specific spectral signature for different samples based on their biochemical composition. The infrared spectrum reveals unique vibrational features of a sample based on the different absorption frequencies of the individual functional groups. This technique is fast, simple, non-destructive, and requires only small quantities of measured material with minimal-to-no sample preparation. However, advanced multivariate techniques are needed to elucidate multiplex spectral information and the small differences caused by donor characteristics. We developed an analytical method using ATR FT-IR spectroscopy advanced with machine learning (ML) based on 63 donors' fingernails (37 males, 26 females). The PLS-DA and ANN models were established, and their generalization abilities were compared. Here, the PLS scores from the PLS-DA model were used for an artificial neural network (ANN) to create a classification model. The proposed ANN model showed a greater potential for predictions, and it was validated against an independent dataset, which resulted in 92% correctly classified spectra. The results of the study are quite impressive, with 100% accuracy achieved in correctly classifying donors as either male or female at the donor level. Here, we underscore the potential of ML algorithms to leverage the selectivity of ATR FT-IR spectroscopy and produce predictions along with information about the level of certainty in a scientifically defensible manner. This proof-of-concept study demonstrates the value of ATR FT-IR spectroscopy as a forensic tool to discriminate between male and female donors, which is significant for forensic applications.

Hyperspectral Raman Imaging for Automated Recognition of Human Renal Amyloid

In the clinical setting, routine identification of the main types of tissue amyloid deposits, light-chain amyloid (AL) and serum amyloid A (AA), is based on histochemical staining; rarer types of amyloid require mass spectrometry analysis. Raman spectroscopic imaging is an analytical tool, which can be used to chemically map, and thus characterize, the molecular composition of fluid and solid tissue. In this proof-of-concept study, we tested the feasibility of applying Raman spectroscopy combined with artificial intelligence to detect and characterize amyloid deposits in unstained frozen tissue sections from kidney biopsies with pathologic diagnosis of AL and AA amyloidosis and control biopsies with no amyloidosis (NA). Raman hyperspectral images, mapped in a 2D grid-like fashion over the tissue sections, were obtained. Three machine learning-assisted analysis models of the hyperspectral images could accurately distinguish AL (types λ and κ), AA, and NA 93-100% of the time. Although very preliminary, these findings illustrate the potential of Raman spectroscopy as a technique to identify, and possibly, subtype renal amyloidosis.

Non-destructive diagnostic testing of cardiac myxoma by serum confocal Raman microspectroscopy combined with multivariate analysis

The symptoms of cardiac myxoma (CM) mainly occur when the tumor is growing, and the diagnosis is determined by clinical presentation. Unfortunately, there is no evidence that specific blood tests are useful in CM diagnosis. Raman spectroscopy (RS) has emerged as a promising auxiliary diagnostic tool because of its ability to simultaneously detect multiple molecular features without labelling. The objective of this study was to identify spectral markers for CM, one of the most common benign cardiac tumors with insidious onset and rapid progression. In this study, a preliminary analysis was conducted based on serum Raman spectra to obtain the spectral differences between CM patients (CM group) and healthy control subjects (normal group). Principal component analysis-linear discriminant analysis (PCA-LDA) was constructed to highlight the differences in the distribution of biochemical components among the groups according to the obtained spectral information. Principal component analysis was combined with a support vector machine model (PCA-SVM) based on three different kernel functions (linear, polynomial, and Gaussian radial basis function (RBF)) to resolve spectral variations between all study groups. The results showed that CM patients had lower serum levels of phenylalanine and carotenoid than those in the normal group, and increased levels of fatty acids. The resulting Raman data was used in a multivariate analysis to determine the Raman range that could be used for CM diagnosis. Also, the chemical interpretation of the spectral results obtained is further presented in the discussion section based on the multivariate curve resolution-alternating least squares (MCR-ALS) method. These results suggest that RS can be used as an adjunct and promising tool for CM diagnosis, and that vibrations in the fingerprint region can be used as spectral markers for the disease under study.

Exploration of novel biomarkers in Alzheimer's disease based on four diagnostic models

Background

Despite tremendous progress in diagnosis and prediction of Alzheimer's disease (AD), the absence of treatments implies the need for further research. In this study, we screened AD biomarkers by comparing expression profiles of AD and control tissue samples and used various models to identify potential biomarkers. We further explored immune cells associated with these biomarkers that are involved in the brain microenvironment.

Methods

By differential expression analysis, we identified differentially expressed genes (DEGs) of four datasets (GSE125583, GSE118553, GSE5281, GSE122063), and common expression direction of genes of four datasets were considered as intersecting DEGs, which were used to perform enrichment analysis. We then screened the intersecting pathways between the pathways identified by enrichment analysis. DEGs in intersecting pathways that had an area under the curve (AUC) > 0.7 constructed random forest, least absolute shrinkage and selection operator (LASSO), logistic regression, and gradient boosting machine models. Subsequently, using receiver operating characteristic curve (ROC) and decision curve analysis (DCA) to select an optimal diagnostic model, we obtained the feature genes. Feature genes that were regulated by differentially expressed miRNAs (AUC > 0.85) were explored further. Furthermore, using single-sample GSEA to calculate infiltration of immune cells in AD patients.

Results

Screened 1855 intersecting DEGs that were involved in RAS and AMPK signaling. The LASSO model performed best among the four models. Thus, it was used as the optimal diagnostic model for ROC and DCA analyses. This obtained eight feature genes, including ATP2B3, BDNF, DVL2, ITGA10, SLC6A12, SMAD4, SST, and TPI1. SLC6A12 is regulated by miR-3176. Finally, the results of ssGSEA indicated dendritic cells and plasmacytoid dendritic cells were highly infiltrated in AD patients.

Conclusion

The LASSO model is the optimal diagnostic model for identifying feature genes as potential AD biomarkers, which can supply new strategies for the treatment of patients with AD.

Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications

Brain disorders such as brain tumors and neurodegenerative diseases (NDs) are accompanied by chemical alterations in the tissues. Early diagnosis of these diseases will provide key benefits for patients and opportunities for preventive treatments. To detect these sophisticated diseases, various imaging modalities have been developed such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). However, they provide inadequate molecule-specific information. In comparison, Raman spectroscopy (RS) is an analytical tool that provides rich information about molecular fingerprints. It is also inexpensive and rapid compared to CT, MRI, and PET. While intrinsic RS suffers from low yield, in recent years, through the adoption of Raman enhancement technologies and advanced data analysis approaches, RS has undergone significant advancements in its ability to probe biological tissues, including the brain. This review discusses recent clinical and biomedical applications of RS and related techniques applicable to brain tumors and NDs.

Label-free laser spectroscopy for respiratory virus detection: A review

Infectious diseases are among the most severe threats to modern society. Current methods of virus infection detection based on genome tests need reagents and specialized laboratories. The desired characteristics of new virus detection methods are noninvasiveness, simplicity of implementation, real-time, low cost and label-free detection. There are two groups of methods for molecular biomarkers' detection and analysis: (i) a sample physical separation into individual molecular components and their identification, and (ii) sample content analysis by laser spectroscopy. Variations in the spectral data are typically minor. It requires the use of sophisticated analytical methods like machine learning. This review examines the current technological level of laser spectroscopy and machine learning methods in applications for virus infection detection.

A Novel Method for Detecting Duchenne Muscular Dystrophy in Blood Serum of mdx Mice

Duchenne muscular dystrophy (DMD) is the most common form of muscular dystrophy, typically affecting males in infancy. The disease causes progressive weakness and atrophy of skeletal muscles, with approximately 20,000 new cases diagnosed yearly. Currently, methods for diagnosing DMD are invasive, laborious, and unable to make accurate early detections. While there is no cure for DMD, there are limited treatments available for managing symptoms. As such, there is a crucial unmet need to develop a simple and non-invasive method for accurately detecting DMD as early as possible. Raman spectroscopy with chemometric analysis is shown to have the potential to fill this diagnostic need.

Novel aspects of Raman spectroscopy in skin research

The analytical technology of Raman spectroscopy has an almost 100‐year history. During this period, many modifications and developments happened in the method like discovery of laser, improvements in optical elements and sensitivity of spectrometer and also more advanced light detection systems. Many types of the innovative techniques appeared (e.g. Transmittance Raman spectroscopy, Coherent Raman Scattering microscopy, Surface‐Enhanced Raman scattering and Confocal Raman spectroscopy/microscopy). This review article gives a short description about these different Raman techniques and their possible applications. Then, a short statistical part is coming about the appearance of Raman spectroscopy in the scientific literature from the beginnings to these days. The third part of the paper shows the main application options of the technique (especially confocal Raman spectroscopy) in skin research, including skin composition analysis, drug penetration monitoring and analysis, diagnostic utilizations in dermatology and cosmeto‐scientific applications. At the end, the possible role of artificial intelligence in Raman data analysis and the regulatory aspect of these techniques in dermatology are briefly summarized. For the future of Raman Spectroscopy, increasing clinical relevance and in vivo applications can be predicted with spreading of non‐destructive methods and appearance with the most advanced instruments with rapid analysis time.

A Study on the Correlation Between Age-Related Macular Degeneration and Alzheimer's Disease Based on the Application of Artificial Neural Network

Age-related Macular Degeneration (AMD) is a kind of irreversible vision loss or disease caused by retinal pigment epithelial cells and neuroretinal degeneration, which has become the main cause of vision loss and blindness of the elderly over 65 years old in developed countries. The main clinical manifestations are cognitive decline, mental symptoms and behavioral disorders, and the gradual decline of daily living ability. In this paper, a feature extraction method of electroencephalogram (EEG) signal based on multi-spectral image fusion of multi-brain regions is proposed based on artificial neural network (ANN). In this method, the brain is divided into several different brain regions, and the EEG signals of different brain regions are transformed into several multispectral images by combining with the multispectral image transformation method. Using Alzheimer's disease (AD) classification algorithm, the depth residual network model pre-trained in ImageNet was transferred to sMRI data set for fine adjustment, instead of training a brand-new model from scratch. The results show that the proposed method solves the problem of few available medical image samples and shortens the training time of ANN model.

A Diagnostic Model for Alzheimer’s Disease Based on Blood Levels of Autophagy-Related Genes

Alzheimer’s disease (AD) is a common neurodegenerative disease. The major problems that exist in the diagnosis of AD include the costly examinations and the high-invasive sampling tissue. Therefore, it would be advantageous to develop blood biomarkers. Because AD’s pathological process is considered tightly related to autophagy; thus, a diagnostic model for AD based on ATGs may have more predictive accuracy than other models. We obtained GSE63060 dataset from the GEO database, ATGs from the HADb and screened 64 differentially expressed autophagy-related genes (DE-ATGs). We then applied them to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses as well as DisGeNET and PaGenBase enrichment analyses. By using the univariate analysis, least absolute shrinkage and selection operator (LASSO) regression method and the multivariable logistic regression, nine DE-ATGs were identified as biomarkers, which are ATG16L2, BAK1, CAPN10, CASP1, RAB24, RGS19, RPS6KB1, ULK2, and WDFY3. We combined them with sex and age to establish a nomogram model. To evaluate the model’s distinguishability, consistency, and clinical applicability, we applied the receiver operating characteristic (ROC) curve, C-index, calibration curve, and on the validation datasets GSE63061, GSE54536, GSE22255, and GSE151371 from GEO database. The results show that our model demonstrates good prediction performance. This AD diagnosis model may benefit both clinical work and mechanistic research.

Rapid Biomarker Screening of Alzheimer's Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy

The study of Alzheimer's disease (AD), the most common cause of dementia, faces challenges in terms of understanding the cause, monitoring the pathogenesis, and developing early diagnoses and effective treatments. Rapid and accurate identification of AD biomarkers in the brain is critical to providing key insights into AD and facilitating the development of early diagnosis methods. In this work, we developed a platform that enables a rapid screening of AD biomarkers by employing graphene-assisted Raman spectroscopy and machine learning interpretation in AD transgenic animal brains. Specifically, we collected Raman spectra on slices of mouse brains with and without AD and used machine learning to classify AD and non-AD spectra. By contacting monolayer graphene with the brain slices, the accuracy was increased from 77% to 98% in machine learning classification. Further, using a linear support vector machine (SVM), we identified a spectral feature importance map that reveals the importance of each Raman wavenumber in classifying AD and non-AD spectra. Based on this spectral feature importance map, we identified AD biomarkers including Aβ and tau proteins and other potential biomarkers, such as triolein, phosphatidylcholine, and actin, which have been confirmed by other biochemical studies. Our Raman-machine learning integrated method with interpretability will facilitate the study of AD and can be extended to other tissues and biofluids and for various other diseases.

Raman Spectroscopy and Its Modifications Applied to Biological and Medical Research

Nowadays, there is an interest in biomedical and nanobiotechnological studies, such as studies on carotenoids as antioxidants and studies on molecular markers for cardiovascular, endocrine, and oncological diseases. Moreover, interest in industrial production of microalgal biomass for biofuels and bioproducts has stimulated studies on microalgal physiology and mechanisms of synthesis and accumulation of valuable biomolecules in algal cells. Biomolecules such as neutral lipids and carotenoids are being actively explored by the biotechnology community. Raman spectroscopy (RS) has become an important tool for researchers to understand biological processes at the cellular level in medicine and biotechnology. This review provides a brief analysis of existing studies on the application of RS for investigation of biological, medical, analytical, photosynthetic, and algal research, particularly to understand how the technique can be used for lipids, carotenoids, and cellular research. First, the review article shows the main applications of the modified Raman spectroscopy in medicine and biotechnology. Research works in the field of medicine and biotechnology are analysed in terms of showing the common connections of some studies as caretenoids and lipids. Second, this article summarises some of the recent advances in Raman microspectroscopy applications in areas related to microalgal detection. Strategies based on Raman spectroscopy provide potential for biochemical-composition analysis and imaging of living microalgal cells, in situ and in vivo. Finally, current approaches used in the papers presented show the advantages, perspectives, and other essential specifics of the method applied to plants and other species/objects.

Advancing Raman spectroscopy from research to clinic: Translational potential and challenges

Raman spectroscopy has emerged as a non-invasive and versatile diagnostic technique due to its ability to provide molecule-specific information with ultrahigh sensitivity at near-physiological conditions. Despite exhibiting substantial potential, its translation from optical bench to clinical settings has been impacted by associated limitations. This perspective discusses recent clinical and biomedical applications of Raman spectroscopy and technological advancements that provide valuable insights and encouragement for resolving some of the most challenging hurdles.

Identification of the Raman Salivary Fingerprint of Parkinson's Disease Through the Spectroscopic- Computational Combinatory Approach

Despite the wide range of proposed biomarkers for Parkinson's disease (PD), there are no specific molecules or signals able to early and uniquely identify the pathology onset, progression and stratification. Saliva is a complex biofluid, containing a wide range of biological molecules shared with blood and cerebrospinal fluid. By means of an optimized Raman spectroscopy procedure, the salivary Raman signature of PD can be characterized and used to create a classification model. Raman analysis was applied to collect the global signal from the saliva of 23 PD patients and related pathological and healthy controls. The acquired spectra were computed using machine and deep learning approaches. The Raman database was used to create a classification model able to discriminate each spectrum to the correct belonging group, with accuracy, specificity, and sensitivity of more than 97% for the single spectra attribution. Similarly, each patient was correctly assigned with discriminatory power of more than 90%. Moreover, the extracted data were significantly correlated with clinical data used nowadays for the PD diagnosis and monitoring. The preliminary data reported highlight the potentialities of the proposed methodology that, once validated in larger cohorts and with multi-centered studies, could represent an innovative minimally invasive and accurate procedure to determine the PD onset, progression and to monitor therapies and rehabilitation efficacy.

Analysis of Saliva Lipids in Breast and Prostate Cancer by IR Spectroscopy

We have developed a method for studying the lipid profile of saliva, combining preliminary extraction and IR spectroscopic detection. The case-control study involved patients with a histologically verified diagnosis of breast and prostate cancer and healthy volunteers. The comparison group included patients with non-malignant pathologies of the breast (fibroadenomas) and prostate gland (prostatic intraepithelial neoplasia). Saliva was used as a material for biochemical studies. It has been shown that the lipid profile of saliva depends on gender, and for males it also depends on the age group. In cancer pathologies, the lipid profile changes significantly and also depends on gender and age characteristics. The ratio of 1458/1396 cm-1 for both breast and prostate cancer has a potential diagnostic value. In both cases, this ratio decreases compared to healthy controls. For prostate cancer, the ratio of 2923/2957 cm-1 is also potentially informative, which grows against the background of prostate pathologies. It is noted that, in all cases, changes in the proposed ratios are more pronounced in the early stages of diseases, which increases the relevance of their study in biomedical applications.

Towards development of a novel screening method for identifying Alzheimer's disease risk: Raman spectroscopy of blood serum and machine learning

There is an urgent clinical need for a fast and effective method for diagnosing Alzheimer's disease (AD). The identification of AD in its most initial stages, at which point treatment could provide maximum therapeutic benefits, is not only likely to slow down disease progression but to also potentially provide a cure. However, current clinical detection is complicated and requires a combination of several methods based on significant clinical manifestations due to widespread neurodegeneration. As such, Raman spectroscopy with machine learning is investigated as a novel alternative method for detecting AD in its earliest stages. Here, blood serum obtained from rats fed either a standard diet or a high-fat diet was analyzed. The high-fat diet has been shown to initiate a pre-AD state. Partial least squares discriminant analysis combined with receiver operating characteristic curve analysis was able to separate the two rat groups with 100% accuracy at the donor level during external validation. Although further work is necessary, this research suggests there is a potential for Raman spectroscopy to be used in the future as a successful method for identifying AD early on in its progression, which is essential for effective treatment of the disease.

An efficient primary screening of COVID-19 by serum Raman spectroscopy

The outbreak of COVID-19 coronavirus disease around the end of 2019 has become a pandemic. The preferred method for COVID-19 detection is the real-time polymerase chain reaction (RT-PCR)-based technique; however, it also has certain limitations, such as sample-dependent procedures with a relatively high false negative ratio. We propose a safe and efficient method for screening COVID-19 based on Raman spectroscopy. A total of 177 serum samples are collected from 63 confirmed COVID-19 patients, 59 suspected cases, and 55 healthy individuals as a control group. Raman spectroscopy is adopted to analyze these samples, and a machine learning support-vector machine (SVM) method is applied to the spectrum dataset to build a diagnostic algorithm. Furthermore, 20 independent individuals, including 5 asymptomatic COVID-19 patients and 5 symptomatic COVID-19 patients, 5 suspected patients, and 5 healthy patients, were sampled for external validation. In these three groups-confirmed COVID-19, suspected, and healthy individuals-the distribution of statistically significant points of difference showed highly consistency for intergroups after repeated sampling processes. The classification accuracy between the COVID-19 cases and the suspected cases is 0.87 (95% confidence interval [CI]: 0.85-0.88), and the accuracy between the COVID-19 and the healthy controls is 0.90 (95% CI: 0.89-0.91), while the accuracy between the suspected cases and the healthy control group is 0.68 (95% CI: 0.67-0.73). For the independent test dataset, we apply the obtained SVM model to the classification of the independent test dataset to have all the results correctly classified. Our model showed that the serum-level classification results were all correct for independent test dataset. Our results suggest that Raman spectroscopy could be a safe and efficient technique for COVID-19 screening.

Cognitive Dysfunction in a Mouse Model of Cerebral Ischemia Influences Salivary Metabolomics

Vascular dementia, caused by cerebrovascular disease, is associated with cognitive impairment and reduced hippocampal metabolite levels. Specifically, cognitive impairment can be induced by decreased hippocampal brain-derived neurotrophic factor (BDNF) expression. The development of low or non-invasive biomarkers to characterize these diseases is an urgent task. Disturbance of metabolic pathways has been frequently observed in cognitive impairment, and salivary molecules also showed the potentials to reflect cognitive impairment. Therefore, we evaluated salivary metabolic profiles associated with altered hippocampal BDNF expression levels in a cerebral ischemia mouse model using metabolomic analyses. The effect of tacrine (a cholinesterase inhibitor) administration was also examined. The arteries of ICR mice were occluded with aneurysm clips to generate the cerebral ischemia model. Learning and memory performance was assessed using the elevated plus maze (EPM) test. Hippocampal and blood BDNF levels were quantified using an enzyme-linked immunosorbent assay. Glutamate decarboxylase 1 (GAD1) mRNA expression, is associated with cognitive impairment, was quantified by a real-time polymerase chain reaction. The EPM test revealed impaired spatial working memory in the cerebral ischemia mouse model; tacrine administration ameliorated this memory impairment. Cerebral ischemia suppressed GAD1 expression by decreasing hippocampal BDNF expression. In total, seven salivary metabolites, such as trimethylamine N-oxide and putrescine, were changed by cognitive impairment and tacrine administration. Our data suggest that salivary metabolite patterns were associated with cognitive function.

Raman spectroscopy and machine learning for biomedical applications: Alzheimer's disease diagnosis based on the analysis of cerebrospinal fluid

Current Alzheimer's disease (AD) diagnostics is based on clinical assessments, imaging and neuropsychological tests that are efficient only at advanced stages of the disease. Early diagnosis of AD will provide decisive opportunities for preventive treatment and development of disease-modifying drugs. Cerebrospinal fluid (CSF) is in direct contact with the human brain, where the deadly pathological process of the disease occurs. As such, the CSF biochemical composition reflects specific changes associated with the disease and is therefore the most promising body fluid for AD diagnostic test development. Here, we describe a new method to diagnose AD based on CSF via near infrared (NIR) Raman spectroscopy in combination with machine learning analysis. Raman spectroscopy is capable of probing the entire biochemical composition of a biological fluid at once. It has great potential to detect small changes specific to AD, even at the earliest stages of pathogenesis. NIR Raman spectra were measured of CSF samples acquired from 21 patients diagnosed with AD and 16 healthy control (HC) subjects. Artificial neural networks (ANN) and support vector machine discriminant analysis (SVM-DA) statistical methods were used for differentiation purposes, with the most successful results allowing for the differentiation of AD and HC subjects with 84% sensitivity and specificity. Our classification models show high discriminative power, suggesting the method has a great potential for AD diagnostics. The reported Raman spectroscopic examination of CSF can complement current clinical tests, making early AD detection fast, accurate, and inexpensive. While this study shows promise using a small sample set, further method validation on a larger scale is required to indicate the true strength of the approach.

Salivary Aβ Secretion and Altered Oral Microbiome in Mouse Models of AD

BACKGROUND

Beta amyloid (Aβ) peptide containing plaque aggregations in the brain are a hallmark of Alzheimer's Disease (AD). However, Aβ is produced by cell types outside of the brain suggesting that the peptide may serve a broad physiologic purpose.

OBJECTIVE

Based upon our prior work documenting expression of amyloid β precursor protein (APP) in intestinal epithelium we hypothesized that salivary epithelium might also express APP and be a source of Aβ.

METHODS

To begin testing this idea, we compared human age-matched control and AD salivary glands to C57BL/6 wild type, App^NL-G-F , and APP/PS1 mice.

RESULTS

Both male and female AD, App^NL-G-F , and APP/PS1 glands demonstrated robust APP and Aβ immunoreactivity. Female App^NL-G-F mice had significantly higher levels of pilocarpine stimulated Aβ 1-42 compared to both wild type and APP/PS1 mice. No differences in male salivary Aβ levels were detected. No significant differences in total pilocarpine stimulated saliva volumes were observed in any group. Both male and female App^NL-G-F but not APP/PS1 mice demonstrated significant differences in oral microbiome phylum and genus abundance compared to wild type mice. Male, but not female, APP/PS1 and App^NL-G-F mice had significantly thinner molar enamel compared to their wild type counterparts.

CONCLUSION

These data support the idea that oral microbiome changes exist during AD in addition to changes in salivary Aβ and oral health.

Utilization of Time Series Tools in Life-sciences and Neuroscience

Time series tools are part and parcel of modern day research. Their usage in the biomedical field; specifically, in neuroscience, has not been previously quantified. A quantification of trends can tell about lacunae in the current uses and point towards future uses. We evaluated the principles and applications of few classical time series tools, such as Principal Component Analysis, Neural Networks, common Auto-regression Models, Markov Models, Hidden Markov Models, Fourier Analysis, Spectral Analysis, in addition to diverse work, generically lumped under time series category. We quantified the usage from two perspectives, one, information technology professionals', other, researchers utilizing these tools for biomedical and neuroscience research. For understanding trends from the information technology perspective, we evaluated two of the largest open source question and answer databases of Stack Overflow and Cross Validated. We quantified the trends in their application in the biomedical domain, and specifically neuroscience, by searching literature and application usage on PubMed. While the use of all the time series tools continues to gain popularity in general biomedical and life science research, and also neuroscience, and so have been the total number of questions asked on Stack overflow and Cross Validated, the total views to questions on these are on a decrease in recent years, indicating well established texts, algorithms, and libraries, resulting in engineers not looking for what used to be common questions a few years back. The use of these tools in neuroscience clearly leaves room for improvement.

Vibrational Spectroscopy Saliva Profiling as Biometric Tool for Disease Diagnostics: A Systematic Literature

Saliva is a biofluid that can be considered as a “mirror” reflecting our body’s health status. Vibrational spectroscopy, Raman and infrared, can provide a detailed salivary fingerprint that can be used for disease biomarker discovery. We propose a systematic literature review based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to evaluate the potential of vibrational spectroscopy to diagnose oral and general diseases using saliva as a biological specimen. Literature searches were recently conducted in May 2020 through MEDLINE-PubMed and Scopus databases, without date limitation. Finally, over a period of 10 years, 18 publications were included reporting on 10 diseases (three oral and seven general diseases), with very high diagnostic performance rates in terms of sensitivity, specificity, and accuracy. Thirteen articles were related to six different cancers of the following anatomical sites: mouth, nasopharynx, lung, esophagus, stomach, and breast. The other diseases investigated and included in this review were periodontitis, Sjögren’s syndrome, diabetes, and myocardial infarction. Moreover, most articles focused on Raman spectroscopy (n = 16/18) and more specifically surface-enhanced Raman spectroscopy (n = 12/18). Interestingly, vibrational spectroscopy appears promising as a rapid, label-free, and non-invasive diagnostic salivary biometric tool. Furthermore, it could be adapted to investigate subclinical diseases—even if developmental studies are required.

Surface-enhanced Raman spectroscopy of tears: toward a diagnostic tool for neurodegenerative disease identification

SIGNIFICANCE

A noninvasive method based on surface-enhanced Raman spectroscopy (SERS) of tears was proposed as a support for diagnosing neurodegenerative pathologies, including different forms of dementia and Alzheimer's disease (AD). In this field, timely and reliable discrimination and diagnosis are critical aspects for choosing a valid medical therapy, and new methods are highly required.

AIM

The aim is to evince spectral differences in SERS response of human tears from AD affected, mild cognitive impaired (MCI), and healthy control (Ctr) subjects.

APPROACH

Human tears were characterized by SERS coupled with multivariate data analysis. Thirty-one informed subjects (Ctr, MCI, and AD) were considered.

RESULTS

Average SERS spectra from Ctr, MCI, and AD subjects evidenced differences related to lactoferrin and lysozyme protein components. Quantitative changes were also observed by determining the intensity ratio between selected bands. We also constructed a classification model that discriminated among AD, MCI, and Ctr subjects. The model was built using the scores obtained by performing principal component analysis on specific spectral regions (i-PCA).

CONCLUSIONS

The results are very encouraging with interesting perspectives for medical applications as support of clinical diagnosis and discrimination of AD from other forms of dementia.

Diagnosis of a model of Duchenne muscular dystrophy in blood serum of mdx mice using Raman hyperspectroscopy

Duchenne muscular dystrophy (DMD) is the most common and severe form of muscular dystrophy and affects boys in infancy or early childhood. Current methods for diagnosing DMD are often laborious, expensive, invasive, and typically diagnose the disease late in its progression. In an effort to improve the accuracy and ease of diagnosis, this study focused on developing a novel method for diagnosing DMD which combines Raman hyperspectroscopic analysis of blood serum with advanced statistical analysis. Partial least squares discriminant analysis was applied to the spectral dataset acquired from blood serum of a mouse model of Duchenne muscular dystrophy (mdx) and control mice. Cross-validation showed 95.2% sensitivity and 94.6% specificity for identifying diseased spectra. These results were verified via external validation, which achieved 100% successful classification accuracy at the donor level. This proof-of-concept study presents Raman hyperspectroscopic analysis of blood serum as an easy, fast, non-expensive, and minimally invasive detection method for distinguishing control and mdx model mice, with a strong potential for clinical diagnosis of DMD.

Differentiating smokers and nonsmokers based on Raman spectroscopy of oral fluid and advanced statistics for forensic applications

Raman spectroscopy has proven to be a valuable tool for analyzing various types of forensic evidence such as traces of body fluids. In this work, Raman spectroscopy was employed as a nondestructive technique for the analysis of dry traces of oral fluid to differentiate between smoker and nonsmoker donors with the aid of advanced statistical tools. A total of 32 oral fluid samples were collected from donors of differing gender, age and race and were subjected to Raman spectroscopic analysis. A genetic algorithm was used to determine eight spectral regions that contribute the most to the differentiation of smokers and nonsmokers. Thereafter, a classification model was developed based on the artificial neural network that showed 100% accuracy after external validation. The developed approach demonstrates great potential for the differentiation of smokers and nonsmokers based on the analysis of dry traces of oral fluid.

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.