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Drug resistance plays a vital role in both cancer treatment and prognosis. Especially, early insights into such drug‐induced resistance in acute myeloid leukemia (AML) can help to improve treatment plans, reduce costs, and bring overall positive outcomes for patients. Raman spectroscopy provides precise biomolecular information and can provide all these necessities effectively. In this study, we employed machine learning (ML) discrimination of Raman micro‐spectroscopic data of myelocytic leukemia cell line HL‐60 from its drug‐resistant counterpart HL‐60/MX2. Principal component analysis (PCA), linear discriminant analysis (LDA), and logistic regression (LR) methods were evaluated for their ability to identify and discriminate drug resistance in AML cells. Our study demonstrates the power of ML to classify drug‐induced resistance in AML cells utilizing subtle variations in biomolecular information contained in molecular spectroscopic data by obtaining 94.11% and 97.05% classification accuracies by LDA and LR models, respectively. We also showed that the ML methods are discernable. Our findings depict the importance of automation and its optimal usage in cancer study and diagnosis. The results of our study are expected to take ML‐assisted Raman spectroscopy one step closer to making it a generalized tool in medical diagnosis in the future. [ABSTRACT FROM AUTHOR]

Saliva is a non-invasive sample for oral cancer detection due to the presence of cancer biomarkers. In this reported study, we investigated the potential use of SERS (Surface-Enhanced Raman Spectroscopy) for the non-invasive identification of oral cavity cancer by analysing the SERS spectra of saliva. We developed a SERS substrate by depositing Ag nanoparticles on glass ripples. SERS analysis was conducted on saliva samples collected from 10 oral cancer patients and 8 healthy volunteers having tobacco habits. The average spectra of cancer patient saliva and healthy volunteers’ saliva show differences. Principal component analysis (PCA) and Linear discriminant analysis (LDA)-based multivariate statistical analysis were used for classifying the collected spectra. The PCA-LDA-based classification shows 70% sensitivity and 62.5% specificity. This study suggests that SERS analysis of saliva incorporated with PCA-LDA-based multivariate analysis is a potential tool for the non-invasive detection of cancer. [ABSTRACT FROM AUTHOR]

This article discusses a pilot study that used Raman spectroscopy (RS) to characterize chemoresistant cell lines in cancer therapy. The study found that the Raman spectra of resistant cells were different from those of parental cells, indicating that RS could be used to predict chemoresistance. The study achieved good accuracy in classifying the cell lines using multivariate analyses. The authors suggest that RS has potential for better patient stratification in clinics, but further validation is needed with a larger sample size. [Extracted from the article]

This article discusses the use of Raman spectroscopy in diagnosing oral cancers and predicting their recurrence. The study found that serum Raman spectroscopy was effective in identifying individuals at risk of oral cancer recurrence, with an efficiency of approximately 80%. Additionally, both salivary and serum Raman spectroscopy successfully distinguished individuals with oral premalignant diseases, such as leukoplakia and oral submucous fibrosis, with an efficiency of over 75%. The study also investigated the potential of Raman spectroscopy in urine samples for detecting oral cancers. Overall, the findings suggest that Raman spectroscopy of various biological fluids shows promise as a means for early detection, recurrence prediction, and classification of oral potentially malignant disorders. [Extracted from the article]

Several serum Raman spectroscopy (RS) studies have demonstrated its potential as an oral cancer screening tool. This study investigates influence of low tumour load (LTL) and high tumour load (HTL) on serum RS using hamster buccal pouch model of experimental oral carcinogenesis. Sera of untreated control, LTL, and HTL groups at week intervals during malignant transformation were employed. Serum Raman spectra were subjected to multivariate analyses—principal component analysis, principal component‐based linear discriminant analysis (for stratification of study groups), and multivariate curve resolution‐alternating least squares (MCR‐ALS) (to comprehend biomolecular differences). Multivariate analysis revealed misclassifications between LTL and HTL at all week intervals. MCR‐ALS components showed statistically significant abundances between control versus LTL and control versus HTL, but could not discern LTL and HTL. MCR‐ALS components exhibited spectral mixtures of proteins, lipids, heme and nucleic acids. Thus, these findings support use of serum RS as a screening tool as varying tumour load is not a confounding factor influencing the technique. [ABSTRACT FROM AUTHOR]

The world is on the brink of facing coronavirus's (COVID‐19) fourth wave as the mutant forms of viruses are escaping neutralizing antibodies in spite of being vaccinated. As we have already witnessed that it has encumbered our health system, with hospitals swamped with infected patients observed during the viral outbreak. Rapid triage of patients infected with SARS‐CoV‐2 is required during hospitalization to prioritize and provide the best point of care. Traditional diagnostics techniques such as RT‐PCR and clinical parameters such as symptoms, comorbidities, sex and age are not enough to identify the severity of patients. Here, we investigated the potential of confocal Raman microspectroscopy as a powerful tool to generate an expeditious blood‐based test for the classification of COVID‐19 disease severity using 65 patients plasma samples from cohorts infected with SARS‐CoV‐2. We designed an easy manageable blood test where we used a small volume (8 μl) of inactivated whole plasma samples from infected patients without any extra solvent usage in plasma processing. Raman spectra of plasma samples were acquired and multivariate exploratory analysis PC‐LDA (principal component based linear discriminant analysis) was used to build a model, which segregated the severe from the non‐severe COVID‐19 group with a sensitivity of 83.87%, specificity of 70.60% and classification efficiency of 76.92%. Among the bands expressed in both the cohorts, the study led to the identification of Raman fingerprint regions corresponding to lipids (1661, 1742), proteins amide I and amide III (1555, 1247), proteins (Phe) (1006, 1034), and nucleic acids (760) to be differentially expressed in severe COVID‐19 patient's samples. In summary, the current study exhibits the potential of confocal Raman to generate simple, rapid, and less expensive blood tests to triage the severity of patients infected with SARS‐CoV‐2. [ABSTRACT FROM AUTHOR]

About two third of oral cancer cases present at advanced stages, emphasizing the need for screening tools for early diagnosis. Raman spectroscopy (RS) can prove an appropriate tool for early diagnosis as biochemical changes occur before morphological changes. Due to limitations with study of early oral cancer stages in human subjects, we performed a sequential carcinogenesis study in animal model. In the present study, we aim to probe early biochemical changes occurring during DMBA induced oral carcinogenesis in hamster buccal pouch model, using serum RS (SRS). Serum being systemic biofluid can reflect the true in vivo state, is minimally invasive and offers advantages of remote diagnosis and serial sampling. The female Syrian golden hamsters were treated with 0.5% DMBA and retro‐orbital blood collection was performed every week for a period of 14 weeks. An increase in classification efficiency as the DMBA treatment progressed, with distinct classification of >70% seen as early as week 5 (W5), appreciably early to development of visual changes (W8–W10) was observed. Furthermore, the early treatment weeks (W1–W4) and healthy condition (W0) show no misclassifications with weeks exhibiting frank tumours (W9–W14), demonstrating efficacy of SRS to stratify healthy versus carcinogen exposure. The findings suggest that as oral carcinogenesis progresses, biochemical alterations in serum such as increasing protein and loss of lipid content can be detected by RS, highlighting the feasibility and utility of SRS in early oral cancer diagnosis. To our knowledge, this is the first study exploring the ex vivo sequential approach to gain insights into oral cancer progression. [ABSTRACT FROM AUTHOR]

Minimally invasive cancer detection using bio-fluids has been actively pursued due to practical limitations, though there are better suited noninvasive and online in vivo methods. Saliva is one such clinically informative bio-fluid that offers the advantages of easy and multiple sample collection. Despite its potential in cancer diagnostics, saliva analysis is challenging due to its heterogeneous composition. Recently, there has been an upsurge in saliva exploration using optical techniques. Forms of saliva such as precipitate and supernatant have been monitored, but this sampling method needs to be standardized due to the obvious loss of analytes in processing. In that context, present work details the comparison of four different saliva sampling methodologies, i.e., air-dried, lyophilized, pellet, and supernatant using Raman spectroscopy collected from 10 healthy samples. Composition-driven spectral features of all forms were compared and classified using principal component analysis and linear discriminant analysis. Analysis was carried out on all four groups in the first step. In the second step, groups of pellet and supernatant, and air-dried and lyophilized were analyzed. Findings suggest that pellet and supernatant exhibit discrete spectroscopic features and demonstrate high classification efficiency, which is indicative of their distinctive biochemical composition. On the other hand, air-dried and lyophilized forms showed overlapping spectral features and low classification, suggesting these forms retain majority spectroscopic features of whole saliva and are less prone to sampling losses. Thus, this study indicates air-dried and lyophilized forms may be more appropriate for saliva sampling using Raman spectroscopy providing the comprehensive information required for cancer diagnosis. Furthermore, the method was also tested for the classification of oral cancer and healthy subjects (n = 27) which yielded 90% stratification. The findings of the study indicate the utility of minimally invasive salivary Raman-based diagnostics in oral cancers. [ABSTRACT FROM AUTHOR]