Spectral analysis of the blood flow in the foot microvascular bed during thermal testing in patients with diabetes mellitus

Timely diagnostics of microcirculatory system abnormalities, which are the most severe diabetic complications, is one of the major problems facing modern health care. Functional abnormalities manifest themselves earlier than the structural ones, and therefore their assessment is the issue of primary importance. In this study Laser Doppler flowmetry, a noninvasive technique for the cutaneous blood flow monitoring, was utilized together with local temperature tests and wavelet analysis. The study of the blood flow in the microvascular bed of toes was carried out in the control group of 40 healthy subjects and in two groups of 17 type 1 and 23 type 2 diabetic patients. The local temperature tests demonstrated that the diabetic patients have impaired vasodilation in response to local heating. The tendency for impaired low frequency pulsations of the blood flow associated with endothelial and neurogenic activities in both diabetes groups was observed. Local thermal tests induced variations in perfusion and its spectral characteristics, which were different in the groups under study. In our opinion, the obtained preliminary results can be a basis for further research and provide a deeper understanding of pathological processes that drive microvascular abnormalities caused by diabetes mellitus

Application of shallow and deep convolutional neural networks to recognize the average flow rate of physiological fluids in a capillary

The aim of this work is to develop practical tools to recognize the average flow rate of physiological fluids in capillaries. This tool is represented by classification models in an artificial neural networks form. The flow rate data were obtained experimentally. Intralipid was used as the test liquid. Laser speckle contrast imaging was used to obtain images of liquid flow in a glass capillary. The experiment was carried out with an average flow rate of 0-2 mm/s with various concentrations of intralipid. The results of training of fully connected and convolutional neural networks for processing the received data are presented. The accuracy of determining the average flow rate of intralipid with different concentrations was comparable to the previously obtained results for a fixed concentration and amounted to approximately 97.5%

Interaction of oxidative stress and misfolded proteins in the mechanism of neurodegeneration

Aggregation of the misfolded proteins β-amyloid, tau, huntingtin, and α-synuclein is one of the most important steps in the pathology underlying a wide spectrum of neurodegenerative disorders, including the two most common ones—Alzheimer's and Parkinson's disease. Activity and toxicity of these proteins depends on the stage and form of aggregates. Excessive production of free radicals, including reactive oxygen species which lead to oxidative stress, is proven to be involved in the mechanism of pathology in most of neurodegenerative disorders. Both reactive oxygen species and misfolded proteins play a physiological role in the brain, and only deregulation in redox state and aggregation of the proteins leads to pathology. Here, we review the role of misfolded proteins in the activation of ROS production from various sources in neurons and glia. We discuss if free radicals can influence structural changes of the key toxic intermediates and describe the putative mechanisms by which oxidative stress and oligomers may cause neuronal death

Laser speckle contrast imaging and machine learning in application to physiological fluids flow rate recognition

The laser speckle contrast imaging allows the determination of the flow motion in a sequence of images. The aim of this study is to combine the speckle contrast imaging and machine learning methods to recognition of physiological fluids flow rate. Data on the flow of intralipid with average flow rate of 0-2 mm/s in a glass capillary were obtained using a developed experimental setup. These data were used to train a feed-forward artificial neural network. The accuracy of random image recognition was quite low due to pulsations and the uneven flow set by the pump. To increase the recognition accuracy, various methods for calculating speckle contrast were used. The best result was obtained when calculating the mean spatial speckle contrast. The application of the mean spatial speckle contrast imaging together with the proposed artificial neural network allowed to increase the fluid flow rate recognition accuracy from about 65 % to 89 % and make it possible to exclude an expert from the data processing

Wavelet analysis of laser speckle contrast reveals new feature space for transcranial assessment of cerebral blood flow

This work shows the application of LSCI for mapping the cerebral vessels of a laboratory animal, and also presents the time-frequency processing of the registered signal. Thus, we expand the capabilities of the existing LSCI approach and demonstrate spatial mapping of blood flow fluctuations

Blood flow oscillations as a signature of microvascular abnormalities

Laser Doppler flowmetry (LDF) was utilized for blood ow measurements. Wavelet analysis was used to identify spectral characteristics of the LDF signal in patients with rheumatic diseases and diabetes mellitus. Baseline measurements were applied for both pathological groups. Blood flow oscillations analyses were performed by means of the wavelet transform. Higher baseline perfusion was observed in both pathological groups in comparison to controls. Differences in the spectral properties between the groups studied were revealed. The results obtained demonstrated that spectral properties of the LDF signal collected in basal conditions may be the signature of microvasculature functional state

Strategies and innovations of socially responsible business

The purpose of social innovation in business is to solve social problems of modern society and ensure efficiency. The innovation should at least reduce the severity of the social problem. However, social innovations are effective when society needs them and they can improve the quality of life of the population. In this case, it is not just a corporate responsibility of the business. Business can count on government support. An innovative strategy and a social responsibility strategy together become the drivers of business development

Spatial heterogeneity of cutaneous blood flow respiratory-related oscillations quantified via laser speckle contrast imaging

LSCI technique provides experimental data which can be considered in the context of spatial blood flow coherency. Analysis of vascular tone oscillations gives additional information to ensure a better understanding of the mechanisms affecting microvascular physiology. The oscillations with different frequencies are due to different physiological mechanisms. The reasons for the generation of peripheral blood flow oscillations in the 0.14–0.6 Hz frequency band are as follows: cardio-respiratory interactions, pressure variations in the venous part of the circulatory system, and the effect of the sympathetic nervous system on the vascular tone. Earlier, we described the spatial heterogeneity of around 0.3 Hz oscillations and this motivated us to continue the research to find the conditions for the occurrence of spatial phase synchronization. For this purpose, a number of physiological tests (controlled respiration, breath holder, and venous occlusion tests) which influence the blood flow oscillations of 0.14–0.6 Hz were considered, an appropriate measurement system and the required data processing algorithms were developed. At spontaneous respiration, the oscillations with frequencies around 0.3 Hz were stochastic, whereas all the performed tests induced an increase in spatial coherence. The protocols and methods proposed here can help to clarify whether the heterogeneity of respiratory-related blood flow oscillations exists on the skin surface

Machine Learning Aided Photonic Diagnostic System for Minimally Invasive Optically Guided Surgery in the Hepatoduodenal Area

Abdominal cancer is a widely prevalent group of tumours with a high level of mortality if diagnosed at a late stage. Although the cancer death rates have in general declined over the past few decades, the mortality from tumours in the hepatoduodenal area has significantly increased in recent years. The broader use of minimal access surgery (MAS) for diagnostics and treatment can significantly improve the survival rate and quality of life of patients after surgery. This work aims to develop and characterise an appropriate technical implementation for tissue endogenous fluorescence (TEF) and assess the efficiency of machine learning methods for the real-time diagnosis of tumours in the hepatoduodenal area. In this paper, we present the results of the machine learning approach applied to the optically guided MAS. We have elaborated tissue fluorescence approach with a fibre-optic probe to record the TEF and blood perfusion parameters during MAS in patients with cancers in the hepatoduodenal area. The measurements from the laser Doppler flowmetry (LDF) channel were used as a sensor of the tissue vitality to reduce variability in TEF data. Also, we evaluated how the blood perfusion oscillations are changed in the tumour tissue. The evaluated amplitudes of the cardiac (0.6-1.6 Hz) and respiratory (0.2-0.6 Hz) oscillations was significantly higher in intact tissues (p < 0.001) compared to the cancerous ones, while the myogenic (0.2-0.06 Hz) oscillation did not demonstrate any statistically significant difference. Our results demonstrate that a fibre-optic TEF probe accompanied with ML algorithms such as k-Nearest Neighbours or AdaBoost is highly promising for the real-time in situ differentiation between cancerous and healthy tissues by detecting the information about the tissue type that is encoded in the fluorescence spectrum. Also, we show that the detection can be supplemented and enhanced by parallel collection and classification of blood perfusion oscillations