Non-linear optical (NLO) microscopy has proven to be a powerful tool especially for tissue imaging with sub-cellular resolution, high penetration depth, endogenous contrast specificity, pinhole-less optical sectioning capability. In this review, we discuss label-free non-linear optical microscopes including the two-photon fluorescence (TPF), fluorescence lifetime imaging microscopy (FLIM), polarization-resolved second harmonic generation (SHG) and coherent anti-Stokes Raman scattering (CARS) techniques with various samples. The non-linear signals are generated from collagen in tissue (SHG), amylopectin from starch granules (SHG), sarcomere structure of fresh muscle (SHG), elastin in skin (TPF), nicotinamide adenine dinucleotide (NADH) in cells (TPF), and lipid droplets in cells (CARS). Again, the non-linear signals are very specific to the molecular structure of the sample and its relative orientation to the polarization of the incident light. Thus, polarization-resolved non-linear optical microscopy provides high image contrast and quantitative estimate of sample orientation. An overview of the advancements on polarization-resolved SHG microscopy including Stokes vector based polarimetry, circular dichroism, and susceptibility are also presented in this review article. The working principles and corresponding implements of above-mentioned microscopy techniques are elucidated. The potential of time-resolved TPF lifetime imaging microscopy (TP-FLIM) is explored by imaging endogenous fluorescence of NAD(P)H, a key coenzyme in cellular metabolic processes. We also discuss single laser source time-resolved multimodal CARS-FLIM microscopy using time-correlated single-photon counting (TCSPC) in combination with continuum generation from photonic crystal fiber (PCF). Using examples, we demonstrate that the multimodal NLO microscopy is a powerful tool to assess the molecular specificity with high resolution.

Pathogenesis of many diseases is associated with changes in the collagen spatial structure. Traditionally, the 3D structure of collagen in biological tissues is analyzed using histochemistry,...

The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient’s state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning.

The infrared laser photoacoustic spectroscopy (LPAS) and the pattern-recognition-based approach for noninvasive express diagnostics of pulmonary diseases on the basis of absorption spectra analysis of the patient’s exhaled air are presented. The study involved lung cancer patients ($N=9$), patients with chronic obstructive pulmonary disease ($N=12$), and a control group of healthy, nonsmoking volunteers ($N=11$). The analysis of the measured absorption spectra was based at first on reduction of the dimension of the feature space using principal component analysis; thereafter, the dichotomous classification was carried out using the support vector machine. The gas chromatography–mass spectrometry method (GC–MS) was used as the reference. The estimated mean value of the sensitivity of exhaled air sample analysis by the LPAS in dichotomous classification was not less than 90% and specificity was not less than 69%; the analogous results of analysis by GC–MS were 68% and 60%, respectively. Also, the approach to differential diagnostics based on the set of SVM classifiers usage is presented.

The results of in-vivo two-photon imaging of lymphedema tissue are presented. The study involved 36 image samples from II stage lymphedema patients and 42 image samples from healthy volunteers. The papillary layer of the skin with a penetration depth of about 100 μm was examined. Both the collagen network disorganization and increase of the collagen/elastin ratio in lymphedema tissue, characterizing the severity of fibrosis, was observed. Various methods of image characterization, including edge detectors, a histogram of oriented gradients method, and a predictive model for diagnosis using machine learning, were used. The classification by “ensemble learning” provided 96% accuracy in validating the data from the testing set.