Macrophages are important cells of the innate immune system that play many different roles in host defense, a fact that is reflected by their polarization into many distinct subtypes. Depending on their function and phenotype, macrophages can be grossly classified into classically activated macrophages (pro-inflammatory M1 cells), alternatively activated macrophages (anti-inflammatory M2 cells), and non-activated cells (resting M0 cells). A fast, label-free and non-destructive characterization of macrophage phenotypes could be of importance for studying the contribution of the various subtypes to numerous pathologies. In this work, single cell Raman spectroscopic imaging was applied to visualize the characteristic phenotype as well as to discriminate between different human macrophage phenotypes without any label and in a non-destructive manner. Macrophages were derived by differentiation of peripheral blood monocytes of human healthy donors and differently treated to yield M0, M1 and M2 phenotypes, as confirmed by marker analysis using flow cytometry and fluorescence imaging. Raman images of chemically fixed cells of those three macrophage phenotypes were processed using chemometric methods of unmixing (N-FINDR) and discrimination (PCA-LDA). The discrimination models were validated using leave-one donor-out cross-validation. The results show that Raman imaging is able to discriminate between pro- and anti-inflammatory macrophage phenotypes with high accuracy in a non-invasive, non-destructive and label-free manner. The spectral differences observed can be explained by the biochemical characteristics of the different phenotypes.

Environmental pollution caused by plastic is a present problem. Polystyrene is a widely used packaging material (e.g., Styrofoam) that can be broken down into microplastics through abrasion. Once the plastic is released into the environment, it is dispersed by wind and atmospheric dust. In this study, we investigated the uptake of polystyrene particles into human cells using A549 cells as a model of the alveolar epithelial barrier, CaCo-2 cells as a model of the intestinal epithelial barrier, and THP-1 cells as a model of immune cells to simulate a possible uptake of microplastics by inhalation, oral uptake, and interaction with the cellular immune system, respectively. The uptake of fluorescence-labeled beads by the different cell types was investigated by confocal laser scanning microscopy in a semi-quantitative, concentration-dependent manner. Additionally, we used Raman spectroscopy as a complementary method for label-free qualitative detection and the visualization of polystyrene within cells. The uptake of polystyrene beads by all investigated cell types was detected, while the uptake behavior of professional phagocytes (THP-1) differed from that of adherent epithelial cells.

The need for novel techniques of rapid identification of pathogenic microorganisms arises from the massive spread of drug-resistant nosocomial strains and the emergence of centers for biohazard control. Fourier-transform infrared spectroscopy is a promising alternative to mass spectrometry as it is cost-effective, fast and suitable for field use. The aim of this work was to propose an algorithm for the identification of microorganisms in pure cultures based on the analysis of their Fourier transform infrared spectra. The algorithm is based on the automated principal component analysis of infrared spectra. Unlike its analogues described in the literature, the algorithm is capable of identifying bacteria regardless of the culture medium or growth phase. The training sample included the most prevalent causative agents of infections and sepsis in humans: Staphylococcus aureus (n = 67), Enterococcus faecalis (n = 10), Enterococcus faecium (n = 10), Klebsiella pneumoniae (n = 10), Escherichia coli (n = 10), Serratia marcescens (n = 10), Enterobacter cloacae (n = 10), Acinetobacter baumannii (n = 10), Pseudomonas aeruginosa (n = 10), and Candida albicans (n = 10). The model we built successfully passed a series of blind tests involving clinical isolates of 10 methicillin-resistant (MRSA) and 10 methicillin-sensitive (MSSA) Staphylococcus aureus strains as well as pair mixes of these cultures with clinical isolates of Pseudomonas aeruginosa, Escherichia coli, and Klebsiella pneumoniae.

A predictive model is presented which allows estimating six-month-risk of cardiovascular disease in patients discharged from hospital after acute coronary syndrome. A database, that has been collected from 16 medical centers in seven Russian cities during seven years, was used to create the model. The database contains a wide range of clinical, biochemical and genetic characteristics. The approaches based on the use of optimal partitioning, such as the method of optimal valid partitioning (OVD) and the modified method of statistically weighted syndromes (MSWS), were used in order to create the predictive model. The accuracy of the model is quite well and is estimated by the value of AUC=0.72. This model shows the better predictive ability in comparison with the most widely used methods such as logistic regression, usage of decision trees, neural networks etс.