Raman Spectroscopic Characterisation of Non Stimulated and Stimulated Human Whole Saliva

Human saliva is a unique biofluid which can reflect the physiopathological state of an individual. The wide spectrum of molecules present in saliva, compounded by the close association of salivary composition to serum metabolites, can provide valuable information for clinical diagnostic applications through highly sensitive vibrational spectroscopic techniques such as Raman spectroscopy. However, the nature of saliva, in terms of collection and patient-related characteristics, can be considered factors which may strongly affect the Raman spectral profile of salivary samples and disrupt the search for specific salivary biomarkers in the detection of diseases. The main objective of this study was to highlight spectral features associated with the type of collection in an intra- and inter-patient approach. Saliva was collected using both stimulated and non-stimulated approaches from 20 donors, concentrated by centrifugal filtration and further analysed using Raman spectroscopy. The methodology adopted for liquid saliva showed consistency in the qualitative analysis of the groups, confirming the reproducibility of this Raman spectroscopic approach. Using principal component analysis (PCA) and partial least squares – discriminant analysis (PLSDA), non stimulated saliva could be differentiated from stimulated saliva in both intra- and inter-patient analysis, with a classification efficiency of 77 and 87%, respectively. The bicinchoninic acid (BCA) assay showed a similar trend in terms of total protein concentration, showing a slight increase in stimulated saliva samples. These results are valuable in the process of developing and establishing Raman spectroscopy as a novel diagnostic tool in the future as well as controlling variability, in order to determine specific spectroscopic markers related to a multifactorial disease for diagnostic or follow-up purposes

Optimisation of Raman Spectral Processing for Classification of Radiotherapeutic Toxicity

Severe radiation toxicity can continue years after the completion of radiotherapy for prostate cancer patients. Currently, it is impossible to predict before treatment which patients will experience these long-term side effects. New approaches based on vibrational spectroscopy have advantages over lymphocyte and genomic assays in terms of minimal sample preparation, speed and cost. A high throughput method has been developed to measure Raman spectra from liquid plasma in a cover glass bottomed 96 well plate. However, the Raman spectra can show contributions from glass and water. The current study aims to optimise pre-processing steps to improve classification performance

Raman Spectroscopic Analysis of Saliva for the Diagnosis of Oral Cancer: a Systematic Review

Abstract Oral squamous cell carcinoma (OSCC) is one of the most common malignancies worldwide, and new protocols for routine and early detection are required. Raman spectroscopy is an optical based method that can provide sensitive and non-invasive real time detailed information on the biochemical content of a sample like saliva, through the unique vibrations of its constituent molecules and this is sensitive to changes associated with disease. A comprehensive systematic review of the available scientific literature related to Raman spectroscopy of human saliva for diagnosis of OSCC was performed. The 785 nm laser line was most applied wavelength along with principal components analysis associated with linear discriminant analysis. The main salivary components possibly associated with the presence of OSCC were proteins and lipids. Measurement in the liquid physical state, and with no addition of nanoparticles for signal enhancement, seemed to best conserve the salivary integrity. However, in terms of sampling protocols, no differentiation was generally made between stimulated and non-stimulated saliva. Raman spectroscopy of saliva holds a promising future for clinical applications such as early detection of OSCC. However, more systematic analyses are still required for a better elucidation regarding sampling procedure, storage and degradation

Biomedical Applications of Vibrational Spectroscopy: Oral Cancer Diagnostics

Vibrational spectroscopy, based on either infrared absorption or Raman scattering, has attracted increasing attention for biomedical applications. Proof of concept explorations for diagnosis of oral potentially malignant disorders and cancer are reviewed, and recent advances critically appraised. Specific examples of applications of Raman microspectroscopy for analysis of histological, cytological and saliva samples are presented for illustrative purposes, and the future prospects, ultimately for routine, chairside in vivo screening are discussed

A Pilot Study for Early Detection of Oral Premalignant Diseases Using Oral Cytology and Raman Micro-Spectroscopy: Assessment of Confounding Factors

This study demonstrates the efficacy of Raman micro-spectroscopy of oral cytological samples for differentiating dysplastic, potentially malignant lesions from those of normal, healthy donors. Cells were collected using brush biopsy from healthy donors (n = 20) and patients attending a Dysplasia Clinic (n = 20). Donors were sampled at four different sites (buccal mucosa, tongue, alveolus, gingiva), to ensure matched normal sites for all lesions, while patient samples were taken from clinically evident, histologically verified dysplastic lesions. Spectra were acquired from the nucleus and cytoplasm of individual cells of all samples and subjected to partial least squares-discriminant analysis. Discriminative sensitivities of 94% and 86% and specificity of 85% were achieved for the cytoplasm and nucleus, respectively, largely based on lipidic contributions of dysplastic cells. Alveolar/gingival samples were differentiated from tongue/buccal samples, indicating that anatomical site is potentially a confounding factor, while age, gender, smoking and alcohol consumption were confirmed not to be

Raman microspectroscopic study for the detection of oral field cancerisation using brush biopsy samples

Field cancerisation (FC) is potentially an underlying cause of poor treatment outcomes of oral squamous cell carcinoma (OSCC). To explore the phenomenon using Raman microspectroscopy, brush biopsies from the buccal mucosa, tongue, gingiva and alveolus of healthy donors (n = 40) and from potentially malignant lesions (PML) of Dysplasia Clinic patients (n = 40) were examined. Contralateral normal samples (n = 38) were also collected from the patients. Raman spectra were acquired from the nucleus and cytoplasm of each cell, and subjected to partial least squares-discriminant analysis (PLS-DA). High discriminatory accuracy for donor and PML samples was achieved for both cytopalmic and nuclear data sets. Notably, contralateral normal (patient) samples were also accurately discriminated from donor samples and contralateral normal samples from patients with multiple lesions showed a similar spectral profile to PML samples, strongly indicating a FC effect. These findings support the potential of Raman microspectroscopy as a screening tool for PML using oral exfoliated cells

Development of Methodology for Raman Microspectroscopic Analysis of Oral Exfoliated Cells

Oral squamous cell carcinoma ranks as the 15th most common cancer worldwide. The present study was undertaken to standardise a protocol for the analysis of oral exfoliated cells using Raman microspectroscopy. For this purpose, samples were obtained from two different sites, based on prevalence of disease (ventral side of the tongue and buccal mucosa). Different oral rinsing agents were employed and it was concluded that non-alcoholic mouthwash adequately removes food debris. Samples were collected using various collection tools and compared. It was observed that endo-cervical brushes yielded cells from deeper layers of the epithelium. Furthermore, monolayer formation of cells was carried out adopting cytospin and ThinPrep techniques and only the ThinPrep method provided flat and separated cells on the glass slide. Raman spectra were acquired from the nuclear and cytoplasmic regions of the cell using an XploRA confocal Raman instrument (HORIBA Jobin Yvon) with a 532 nm laser as the source. Glass spectral contamination was removed using non negatively constrained least squares (NNLS) algorithms. Corrected spectra were subjected to principal components analysis (PCA) which was able to differentiate the nucleus and cytoplasm regions of the cell; based on nucleic acid and protein features, respectively. However, no classification of the two anatomically different sites was observed according to PCA or PCA–LDA (linear discriminant analysis) using either the nuclear or cytoplasmic spectra. Nevertheless, the study has developed a standardised protocol for sample collection, sample preparation, spectral acquisition and data processing for future studies of oral exfoliated cells based on Raman microspectroscopy

Gamifying the gig: transitioning the dark side to bright side of online engagement

Gig work has transformed the work culture, globally. It’s sprawl, and popularity has also attracted excellent talent to join the gig workforce, most of which are online. While it has unfolded new avenues to showcase talent, its management irregularities have resulted in more significant dropouts. The study addresses a key research gap investigating the dropouts of gig workers on digital earning platforms by the moderating impact of gamified interventions on online platforms. We have based our arguments and derived our hypotheses based on social exchange theory and self-determination theory. A total of 367 responses were collected from white-collar gig workers who have completed tasks on one or more gig platforms in the past two years. We test our hypotheses using partial least square structural equation modelling (PLS-SEM). Results confirm that gamifying the online platform would enhance job satisfaction and productivity of gig employees, reducing the chances of quitting gig work. It is further observed that in the case of gig workers, high-performance work systems have a non-significant effect on the intentions to quit. The results contribute to the redesigning of online gig platforms with a layer of gamified artifacts to increase gig workers' retention

The role of organizational culture and voluntariness in the adoption of artificial intelligence for disaster relief operations

Purpose: The study explores the readiness of government agencies to adopt artificial intelligence (AI) to improve the efficiency of disaster relief operations (DRO). For understanding the behavior of state-level and national-level government agencies involved in DRO, this study grounds its theoretical arguments on the civic voluntarism model (CVM) and the unified theory of acceptance and use of technology (UTAUT). Design/methodology/approach: We collected the primary data for this study from government agencies involved in DRO in India. To test the proposed theoretical model, we administered an online survey questionnaire to 184 government agency employees. To test the hypotheses, we employed partial least squares structural equation modeling (PLS-SEM). Findings: Our findings confirm that resources (time, money and skills) significantly influence the behavioral intentions related to the adoption of AI tools for DRO. Additionally, we identified that the behavioral intentions positively translate into the actual adoption of AI tools. Research limitations/implications: Our study provides a unique viewpoint suited to understand the context of the adoption of AI in a governmental context. Companies often strive to invest in state-of-the-art technologies, but it is important to understand how government bodies involved in DRO strategize to adopt AI to improve efficiency. Originality/value: Our study offers a fresh perspective in understanding how the organizational culture and perspectives of government officials influence their inclinations to adopt AI for DRO. Additionally, it offers a multidimensional perspective by integrating the theoretical frameworks of CVM and UTAUT for a greater understanding of the adoption and deployment of AI tools with organizational culture and voluntariness as critical moderators