Diabetes noninvasive diagnostics and monitoring through volatile biomarkers analysis in the exhaled breath using optical absorption spectroscopy.

The review is aimed on the analysis the abilities of noninvasive diagnostics and monitoring of diabetes mellitus (DM) and DM-associated complications through volatile molecular biomarkers detection in the exhaled breath. The specific biochemical reactions in the body of DM patients and their associations with volatile molecular biomarkers in the breath are considered. The applications of optical spectroscopy methods, including UV, IR, and terahertz spectroscopy for DM-associated volatile molecular biomarkers measurements, are described. The applications of similar technique combined with machine learning methods in DM diagnostics using the profile of DM-associated volatile molecular biomarkers in exhaled air or "pattern-recognition" approach are discussed.

Quantitative Assessment of Low-Dose Photodynamic Therapy Effects on Diabetic Wound Healing Using Raman Spectroscopy.

One of challenges that faces diabetes is the wound healing process. The delayed diabetic wound healing is caused by a complicated molecular mechanism involving numerous physiological variables. Low-dose photodynamic therapy (LDPDT) provides excellent results in rejuvenation and wound healing. In this study, the LDPDT effect on diabetic wounds in mice was studied using two photosensitizers, 5-aminolevulinic acid and methylene blue, and two laser dose expositions of 1 J/cm2 and 4 J/cm2 by Raman spectroscopy (RS). The latter was used as a noninvasive method, providing specific information about tissue state based on the fundamental vibrational modes of its molecular components. RS allows high spatial resolution acquisition of biochemical and structural information through the generation of point spectra or spectral images. An approach to in vivo quantitative assessment of diabetic wound healing state was developed. This approach is based on an application of the principal component analysis combined with the Mahalanobis metrics to skin Raman spectra, in particular, intensities of the amide I and CH2 bands.

A Criterion of Colorectal Cancer Diagnosis Using Exosome Fluorescence-Lifetime Imaging.

This study was aimed to investigate the applicability of the exosome fluorescence-lifetime imaging microscopy (FLIM) for colorectal cancer (CRC) diagnosis. Differential ultra-centrifugation was used to extract exosomes from the blood plasma of 11 patients with colon polyps (CPs) and 13 patients with CRC at the T2-4, N0-3, and M0-1 stages. Analysis was performed using a two-photon FLIM device. In total, 165 and 195 FLIM images were recorded for the CP and CCR patient groups, respectively. Two classes of exosomes differentiated by autofluorescence average lifetime tm were discovered in the samples. The first class of exosomes with tm = (0.21 ± 0.06) ns was mostly found in samples from CRC patients. The second class with tm = (0.43 ± 0.19) ns was mostly found in samples from CP patients. The relative number of "CRC-associated" exosomes Nch in the FLIM dataset was shown to be very small for the CP patient group and large for the CRC patient group. This difference was statistically significant. Therefore, the suggested CRS diagnostics criterion can be as follows. If Nch > 0.5, the probability of CRC is high. If Nch < 0.3, the probability of CRC is low.

Application of machine learning and laser optical-acoustic spectroscopy to study the profile of exhaled air volatile markers of acute myocardial infarction.

Conventional acute myocardial infarction (AMI) diagnosis is quite accurate and has proved its effectiveness. However, despite this, discovering more operative methods of this disease detection is underway. From this point of view, the application of exhaled air analysis for a similar diagnosis is valuable. The aim of the paper is to research effective machine learning algorithms for the predictive model for AMI diagnosis constructing, using exhaled air spectral data. The target group included 30 patients with primary myocardial infarction. The control group included 42 healthy volunteers. The 'LaserBreeze' laser gas analyzer (Special Technologies Ltd, Russia), based on the dual-channel resonant photoacoustic detector cell and optical parametric oscillator as the laser source, had been used. The pattern recognition approach was applied in the same manner for the set of extracted concentrations of AMI volatile markers and the set of absorption coefficients in a most informative spectral range 2.900 ± 0.125µm. The created predictive model based on the set of absorption coefficients provided 0.86 of the mean values of both the sensitivity and specificity when linear support vector machine (SVM) combined with principal component analysis was used. The created predictive model based on using six volatile AMI markers (C5H12, N2O, NO2, C2H4, CO, CO2) provided 0.82 and 0.93 of the mean values of the sensitivity and specificity, respectively, when linear SVM was used.

Application of multiphoton imaging and machine learning to lymphedema tissue analysis.

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.

Exhaled air analysis using wideband wave number tuning range infrared laser photoacoustic spectroscopy.

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 chromatography­mass spectrometry method (GC­MS) 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 GC­MS were 68% and 60%, respectively. Also, the approach to differential diagnostics based on the set of SVM classifiers usage is presented.

On the motion of a heavy rigid body in an ideal fluid with circulation.

We consider Chaplygin's equations [Izd. Akad. Nauk SSSR 3, 3 (1933)] describing the planar motion of a rigid body in an unbounded volume of an ideal fluid while circulation around the body is not zero. Hamiltonian structures and new integrable cases are revealed; certain remarkable partial solutions are found and their stability is examined. The nonintegrability of the system describing the motion of a body in the field of gravity is proved and the chaotic behavior of the system is illustrated.