In type 2 diabetes mellitus (T2DM), changes in glucose and lipid levels in serum are the first signals of disease progression. In this article, Raman spectroscopy supported with machine learning (ML) ...

Spectrochemical imaging has emerged as a powerful, label-free modality for visualizing the bio-chemical composition of tissues based on intrinsic vibrational signatures. Specifically, mid-infrared spe...

Colorectal Cancer (CC) is recognized as the third most prevalent cancer worldwide and constitutes a major cause of cancer-related fatalities among both genders. Current diagnostic approaches for CC...

Synovial fluid analysis remains to be a critical aspect of the diagnosis of crystal arthropathies. The gold standard, compensated polarized light microscopy (CPLM), has significant shortcomings due t...

Raman spectroscopy is a nondestructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in R...

Statistical thinking is a core part of solid, trustworthy biology. However, many studies still include insufficient sample sizes, have poor experimental design or select an incorrect statistical metho...

Artificial intelligence (AI) systems that can analyse medical images, records, and claims are becoming accessible to everyone. Although these systems outperform physicians at specific tasks, such as d...

Biochemical analysis of living systems such as single cells benefits greatly from the label-free and low-invasive molecular mapping with Raman microspectroscopy. Sets of Raman spectra at different spatial points are analyzed to generate Raman molecular maps corresponding to specific chemical species. However, human error and subjective data analysis can be technical issues that limit interpretation and its validity. Here, we present an objective data analysis scheme for postprocessing large data sets of Raman spectra for molecular mapping of living cells. The process comprises three steps: (i) Denoising the spectral data set using low-rank approximation; (ii) obtaining an objective background from data points outside the target cell; (iii) subtracting the thus obtained background using Hypothetical Addition Multivariate Analysis with Numerical Differentiation (HAMAND) via an automatically determined coefficient. Through the present analysis, minor Raman peaks, as indiscernible as they are, can be identified and precisely mapped. We demonstrate a quantitative discussion of cellular components after extracting contributions only from a single target cell from a Raman mapping image where multiple cells or parts of other cells are present. This work opens an improved analysis workflow for accurate spectroscopic analysis of living cells with the advantage of identifying minor Raman peaks unambiguously.

Infrared spectroscopy of macroscopic samples can be calibrated against reference analysis, such as lipid profiles acquired by gas chromatography, and serve as a fast, low-cost, quantitative analytical...

There is an unmet clinical need for a rapid point-of-care tests to identify patients at high-risk of developing acute liver failure following a paracetamol overdose. Here, authors assess a diagnostic test, consisting of a lateral flow immunoassay paired with a handheld Raman reader, in performance evaluation studies.

Scientific Reports - ATR-FTIR and multivariate analysis for differential diagnosis of dengue and leptospirosis: a feasibility study

Discrete frequency infrared (IR) imaging is an exciting experimental technique that has shown promise in various applications in biomedical science. This techni...

We present a novel machine learning (ML)-based scoring technique for determining the similarity between experimental and predicted infrared (IR) spectra for identification purposes. IR spectroscopy is...

Raman spectroscopy is an important diagnostic method that extracts molecular-level information from biological specimens, with distinct potential for disease diagnoses. However, its clinical applic...