Infrared spectroscopy has emerged as a promising technique for studying the composition of biological samples like lipids that play important roles in cellular functions and are involved in various diseases. For this reason, lipids are a target of interest in many biomedical studies. The objective of the present study is to utilize Fourier-Transform Infrared (FT-IR) spectroscopy to examine the main lipid components of human cells (phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, phosphatidylinositol, C18 ceramide, sphingosine-1-phosphate, ceramide-1-phosphate, sphingomyelin, cholesterol, and triolein). FT-IR analysis on the previously mentioned lipid samples was performed in Attenuated Total Reflection (ATR) mode. The obtained spectra clearly evidence the contributions of the different functional groups that are present in the examined samples. Detailed assignments of spectral features were carried out in agreement with the literature. Similarities and differences among the different types of commercial lipid samples are evidenced and discussed, with particular attention to phospholipid and sphingolipid components. A quantitative analysis of phosphatidylinositol and sphingomyelin spectra using a ratiometric approach is reported. Moreover, a reconstruction procedure of FT-IR spectra of complex lipids useful for chemometrics applications is described. These representative examples of the potential use of the results of the present study can certainly contribute to a larger use of FT-IR spectroscopy in lipidomics. 

Probing Biochemical Differences in Lipid Components of Human Cells by Means of ATR-FTIR Spectroscopy

Machine learning (ML) is a broad term encompassing several methods that allow us to learn from data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient–provider decision-making. This paper presents a review of articles that discuss the use of Fourier transform infrared (FTIR) spectroscopy and ML for human blood analysis between the years 2019–2023. The literature review was conducted to identify published research of employed ML linked with FTIR for distinction between pathological and healthy human blood cells. The articles’ search strategy was implemented and studies meeting the eligibility criteria were evaluated. Relevant data related to the study design, statistical methods, and strengths and limitations were identified. A total of 39 publications in the last 5 years (2019–2023) were identified and evaluated for this review. Diverse methods, statistical packages, and approaches were used across the identified studies. The most common methods included support vector machine (SVM) and principal component analysis (PCA) approaches. Most studies applied internal validation and employed more than one algorithm, while only four studies applied one ML algorithm to the data. A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of ML methods. There is a need to ensure that multiple ML approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that the discrimination of human blood cells is being made with the highest efficient evidence.

/pdf/a-review-of-machine-learning-methods-recently-applied-to-3drc2r0x.pdf

A Review of Machine Learning Methods Recently Applied to FTIR Spectroscopy Data for the Analysis of Human Blood Cells

There is an increasing need for inexpensive and rapid screening tests in point-of-care clinical oncology settings. Herein, we develop a swab “dip” test in saliva obtained from consenting patients participating in a lung-cancer-screening programme being undertaken in North West England. In a pilot study, a total of 211 saliva samples (n = 170 benign, 41 designated cancer-positive) were randomly taken during the course of this prospective lung-cancer-screening programme. The samples (sterile Copan blue rayon swabs dipped in saliva) were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. An exploratory analysis using principal component analysis (PCA,) with or without linear discriminant analysis (LDA), was then undertaken. Three pairwise comparisons were undertaken including: (1) benign vs. cancer following swab analysis; (2) benign vs. cancer following swab analysis with the subtraction of dry swab spectra; and (3) benign vs. cancer following swab analysis with the subtraction of wet swab spectra. Consistent and remarkably similar patterns of clustering for the benign control vs. cancer categories, irrespective of whether the swab plus saliva sample was analysed or whether there was a subtraction of wet or dry swab spectra, was observed. In each case, MANOVA demonstrated that this segregation of categories is highly significant. A k-NN (using three nearest neighbours) machine-learning algorithm also showed that the specificity (90%) and sensitivity (75%) are consistent for each pairwise comparison. In detailed analyses, the swab as a substrate did not alter the level of spectral discrimination between benign control vs. cancer saliva samples. These results demonstrate a novel swab “dip” test using saliva as a biofluid that is highly applicable to be rolled out into a larger lung-cancer-screening programme.

/pdf/atr-ftir-spectroscopy-with-chemometrics-for-analysis-of-3pthx80a.pdf

ATR-FTIR Spectroscopy with Chemometrics for Analysis of Saliva Samples Obtained in a Lung-Cancer-Screening Programme: Application of Swabs as a Paradigm for High Throughput in a Clinical Setting

Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey

Characterization and differentiation of cervical cancer cell lines using ATR-FTIR spectroscopy and multivariate data analysis

Precise diagnosis of lung cancer enabled by improved FTIR-based machine learning

Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) of liquid biofluids enables the probing of biomolecular markers for disease diagnosis, characterized as a time and cost-effective approach. It remains poorly understood for fast and deep diagnosis of digestive tract cancers (DTC) to detect abundant changes and select specific markers in a broad spectrum of molecular species. Here, we present a diagnostic protocol of DTC in which the in-situ blood-based ATR-FTIR spectroscopic data mining pathway was designed for the identification of DTC triages in 252 blood serum samples, divided into the following groups: liver cancer (LC), gastric cancer (GC), colorectal cancer (CC), and their different three stages respectively. The infrared molecular fingerprints (IMFs) of DTC were measured and used to build a 2-dimensional second derivative spectrum (2D-SD-IR) feature dataset for classification, including absorbance and wavenumber shifts of FTIR vibration peaks. By comparison, the Partial Least-Squares Discriminant Analysis (PLS-DA) and backpropagation (BP) neural networks are suitable to differentiate DTCs and pathological stages with a high sensitivity and specificity of 100% and averaged more than 95%. Furthermore, the measured IMF data was mutually validated via clinical blood biochemistry testing, which indicated that the proposed 2D-SD-IR-based machine learning protocol greatly improved DTC classification performance.

https://www.mdpi.com/2218-273X/12/12/1815/pdf?version=1670325425

Fast and Deep Diagnosis Using Blood-Based ATR-FTIR Spectroscopy for Digestive Tract Cancers

Micro-tomographic and infrared spectral data mining for breast cancer diagnosis

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Precise diagnosis of lung cancer enabled by improved FTIR-based machine learning

Fast and Deep Diagnosis Using Blood-Based ATR-FTIR Spectroscopy for Digestive Tract Cancers

Micro-tomographic and infrared spectral data mining for breast cancer diagnosis