Microcirculatory Response to Photobiomodulation-Why Some Respond and Others Do Not: A Randomized Controlled Study.

BACKGROUND AND OBJECTIVES: Photobiomodulation (PBM), a non-ionizing, non-thermal irradiation, used clinically to accelerate wound healing and inhibit pain, was previously shown to increase blood flow. However, some individuals respond to PBM, but others do not. The purpose of this study was to investigate factors affecting this patient-specific response using advanced, noninvasive methods for monitoring microcirculatory activity. STUDY DESIGN/MATERIALS AND METHODS: In this prospective, randomized controlled clinical trial (NCT03357523), 20 healthy non-smoking volunteers (10:10 males:females, 30 ± 8 years old) were randomized to receive either red- (633 nm and 70 W/cm2 ) or near-infrared light (830 nm and 55 mW/cm2 ) over the wrist for 5 minutes. Photoplethysmography, laser Doppler flowmetry, and thermal imaging were used to monitor palm microcirculatory blood volume, blood flow, and skin temperature, respectively, before, during, and 20 minutes after irradiation. Participants with skin temperature change ≥0.5°C from baseline were considered "responders". RESULTS: Near-infrared PBM was found to induce a 27% increase in microcirculatory flow that increased to 54% during the 20-minute follow-up period (P = 0.049 and P = 0.004, respectively), but red light PBM did not increase the median flow. Only 10 of 20 participants were responders by thermal imaging (i.e., ≥0.5°C from baseline), and their initial skin temperature was between 33 and 37.5°C. The non-responders had either "hot" hands (≥37.5°C) or "cold" hands (≤33°C). In responders, the meantime to 20% increase in microcirculatory blood volume and blood flow was less than 2.5 minutes after initiation of PBM irradiation. CONCLUSIONS: We demonstrated that PBM induces arteriolar vasodilatation that results in both immediate and long-lasting increased capillary flow and tissue perfusion in healthy individuals. This response was wavelength-dependent and modified by skin temperature. These findings regarding physiological parameters associated with sensitivity or resistance to PBM provide information of direct relevance for patient-specific therapy. Lasers Surg. Med. © 2020 Wiley Periodicals, Inc.

Arpeggio: harmonic compression of ChIP-seq data reveals protein-chromatin interaction signatures.

Researchers generating new genome-wide data in an exploratory sequencing study can gain biological insights by comparing their data with well-annotated data sets possessing similar genomic patterns. Data compression techniques are needed for efficient comparisons of a new genomic experiment with large repositories of publicly available profiles. Furthermore, data representations that allow comparisons of genomic signals from different platforms and across species enhance our ability to leverage these large repositories. Here, we present a signal processing approach that characterizes protein-chromatin interaction patterns at length scales of several kilobases. This allows us to efficiently compare numerous chromatin-immunoprecipitation sequencing (ChIP-seq) data sets consisting of many types of DNA-binding proteins collected from a variety of cells, conditions and organisms. Importantly, these interaction patterns broadly reflect the biological properties of the binding events. To generate these profiles, termed Arpeggio profiles, we applied harmonic deconvolution techniques to the autocorrelation profiles of the ChIP-seq signals. We used 806 publicly available ChIP-seq experiments and showed that Arpeggio profiles with similar spectral densities shared biological properties. Arpeggio profiles of ChIP-seq data sets revealed characteristics that are not easily detected by standard peak finders. They also allowed us to relate sequencing data sets from different genomes, experimental platforms and protocols. Arpeggio is freely available at http://sourceforge.net/p/arpeggio/wiki/Home/.

Classification of hand and wrist movements via surface electromyogram using the random convolutional kernels transform.

Prosthetic devices are vital for enhancing personal autonomy and the quality of life for amputees. However, the rejection rate for electric upper-limb prostheses remains high at around 30%, often due to issues like functionality, control, reliability, and cost. Thus, developing reliable, robust, and cost-effective human-machine interfaces is crucial for user acceptance. Machine learning algorithms using Surface Electromyography (sEMG) signal classification hold promise for natural prosthetic control. This study aims to enhance hand and wrist movement classification using sEMG signals, treated as time series data. A novel approach is employed, combining a variation of the Random Convolutional Kernel Transform (ROCKET) for feature extraction with a cross-validation ridge classifier. Traditionally, achieving high accuracy in time series classification required complex, computationally intensive methods. However, recent advances show that simple linear classifiers combined with ROCKET can achieve state-of-the-art accuracy with reduced computational complexity. The algorithm was tested on the UCI sEMG hand movement dataset, as well as on the Ninapro DB5 and DB7 datasets. We demonstrate how the proposed approach delivers high discrimination accuracy with minimal parameter tuning requirements, offering a promising solution to improve prosthetic control and user satisfaction.

A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders.

Step length is an important diagnostic and prognostic measure of health and disease. Wearable devices can estimate step length continuously (e.g., in clinic or real-world settings), however, the accuracy of current estimation methods is not yet optimal. We developed machine-learning models to estimate step length based on data derived from a single lower-back inertial measurement unit worn by 472 young and older adults with different neurological conditions, including Parkinson's disease and healthy controls. Studying more than 80,000 steps, the best model showed high accuracy for a single step (root mean square error, RMSE = 6.08 cm, ICC(2,1) = 0.89) and higher accuracy when averaged over ten consecutive steps (RMSE = 4.79 cm, ICC(2,1) = 0.93), successfully reaching the predefined goal of an RMSE below 5 cm (often considered the minimal-clinically-important-difference). Combining machine-learning with a single, wearable sensor generates accurate step length measures, even in patients with neurologic disease. Additional research may be needed to further reduce the errors in certain conditions.

Incorporating artificial intelligence in portable infrared thermal imaging for the diagnosis and staging of nonalcoholic fatty liver disease.

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is one of the most prevalent chronic liver diseases worldwide. Thermal imaging combined with advanced image-processing and machine learning analysis accurately classified disease status in a study on mice; this study aimed to develop this tool for humans. This prospective study included 46 patients who underwent liver biopsy. Liver thermal imaging was performed on the same day as liver biopsy. We developed an image-processing algorithm that measured the relative spatial thermal variation across the skin covering the liver. The texture parameters obtained from the thermal images were input into the machine learning algorithm. Patients were diagnosed with MASLD and stratified according to nonalcoholic fatty liver disease activity score (NAS) and fibrosis stage using the METAVIR score. Twenty-one of 46 patients were diagnosed with MASLD. Using thermal imaging followed by processing, detection accuracy for patients with NAS >4 was 0.72.

Non-ionizing measurement and quantification of bell-shaped chests in spinal muscular atrophy: a pilot study.

Background: Spinal Muscular Atrophy (SMA) is manifested by deformation of the chest wall, including a bell-shaped chest. We determined the ability of a novel non-ionizing, non-volitional method to measure and quantify bell-shaped chests in SMA. Methods: A 3D depth camera and a chest x-ray (CXR) were used to capture chest images in 14 SMA patients and 28 controls. Both methods measure the distance between two points, but measurements performed by 3D analysis allow for the consideration of the curve of a surface (geodesic measurements), whereas the CXR allows solely for the determination of the shortest path between two points, with no regard for the surface (Euclidean measurements). The ratio of the upper to lower chest distances was quantified to distinguish chest shape in imaging by both the 3D depth camera and the CXR, and the ratios were compared between healthy and SMA patients. Results: The mean 3D Euclidean ratio of distances measured by 3D imaging was 1.00 in the control group and 0.92 in the SMA group (p = 0.01), the latter indicative of a bell-shaped chest. This result repeated itself in the ratio of geodesic measurements (0.99 vs. 0.89, respectively, p = 0.03). Conclusion: The herein-described novel, noninvasive 3D method for measuring the upper and lower chest distances was shown to distinguish the bell-shaped chest configuration in patients with SMA from the chests of controls. This method bears several advantages over CXR and may be readily applicable in clinical settings that manage children with SMA.

Smartphone-based detection of COVID-19 and associated pneumonia using thermal imaging and a transfer learning algorithm.

COVID-19-related pneumonia is typically diagnosed using chest x-ray or computed tomography images. However, these techniques can only be used in hospitals. In contrast, thermal cameras are portable, inexpensive devices that can be connected to smartphones. Thus, they can be used to detect and monitor medical conditions outside hospitals. Herein, a smartphone-based application using thermal images of a human back was developed for COVID-19 detection. Image analysis using a deep learning algorithm revealed a sensitivity and specificity of 88.7% and 92.3%, respectively. The findings support the future use of noninvasive thermal imaging in primary screening for COVID-19 and associated pneumonia.

Nonlinear intrinsic variables and state reconstruction in multiscale simulations.

Finding informative low-dimensional descriptions of high-dimensional simulation data (like the ones arising in molecular dynamics or kinetic Monte Carlo simulations of physical and chemical processes) is crucial to understanding physical phenomena, and can also dramatically assist in accelerating the simulations themselves. In this paper, we discuss and illustrate the use of nonlinear intrinsic variables (NIV) in the mining of high-dimensional multiscale simulation data. In particular, we focus on the way NIV allows us to functionally merge different simulation ensembles, and different partial observations of these ensembles, as well as to infer variables not explicitly measured. The approach relies on certain simple features of the underlying process variability to filter out measurement noise and systematically recover a unique reference coordinate frame. We illustrate the approach through two distinct sets of atomistic simulations: a stochastic simulation of an enzyme reaction network exhibiting both fast and slow time scales, and a molecular dynamics simulation of alanine dipeptide in explicit water.

Morphological features of the photoplethysmographic signal: a new approach to characterize the microcirculatory response to photobiomodulation.

Introduction and Objectives: Advanced analysis of the morphological features of the photoplethysmographic (PPG) waveform may provide greater understanding of mechanisms of action of photobiomodulation (PBM). Photobiomodulation is a non-ionizing, red to near-infrared irradiation shown to induce peripheral vasodilatation, promote wound healing, and reduce pain. Using laser Doppler flowmetry combined with thermal imaging we found previously in a clinical study that PBM stimulates microcirculatory blood flow and that baseline palm skin temperature determines, at least in part, why some individuals respond favorably to PBM while others do not. "Responders" (n = 12) had a skin temperature range of 33°C-37.5°C, while "non-responders" (n = 8) had "cold" or "hot" skin temperature (<33°C or >37.5°C respectively). The continuous PPG signals recorded from the index fingers of both hands in the original clinical study were subjected to advanced post-acquisitional analysis in the current study, aiming to identify morphological features that may improve the accuracy of discrimination between potential responders and non-responders to PBM. Methods: The PPG signals were detrended by subtracting the lower envelope from the raw signal. The Root Mean Square (RMS) and Entropy features were extracted as were two additional morphological features -- Smoothness and number of local extrema per PPG beat (#Extrema). These describe the signal jaggedness and were developed specifically for this study. The Wilcoxon test was used for paired comparisons. Correlations were determined by the Spearman correlation test (rs). Results: The PPG waveforms of responders to PBM had increased amplitude and decreased jaggedness (Baseline vs. 10' post-irradiation: Entropy, 5.0 ± 1.3 vs. 3.9 ± 1.1, p = 0.012; #Extrema, 4.0 ± 1.1 vs. 3.0 ± 1.6, p = 0.009; RMS, 1.6 ± 0.9 vs. 2.3 ± 1.2, p = 0.004; Smoothness, 0.10 ± 0.05 vs. 0.19 ± 0.16, p = 0.016). In addition, unilateral irradiation resulted in a bilateral response, although the response of the contralateral, non-irradiated hand was shorter in duration and lower in magnitude. Although subjects with 'cold,' or 'hot,' baseline skin temperature appeared to have morphologically distinct PPG waveforms, representing vasoconstriction and vasodilatation, these were not affected by PBM irradiation. Conclusion: This pilot study indicates that post-acquisitional analysis of morphological features of the PPG waveform provides new measures for the exploration of microcirculation responsiveness to PBM.

Assessment of blood distribution in response to post-surgical steal syndrome: A novel technique based on Thermo-Anatomical Segmentation.

The distal ischemic steal syndrome (ISS) is a complication following the construction of an arteriovenous (A-V) access for hemodialysis. The ability to non-invasively monitor changes in skin microcirculation improves both the diagnosis and treatment of vascular diseases. In this study, we propose a novel technique for evaluating the palms' blood distribution following arteriovenous access, based on thermal imaging. Furthermore, we utilize the thermal images to identify typical recovery patterns of patients that underwent this surgery and show that thermal images taken post-surgery reflect the patient's follow-up status. Thermal photographs were taken by a portable thermal camera from both hands before and after the A-V access surgery, and one month following the surgery, from ten dialysis patients. A novel term "Thermo-Anatomical Segmentation", which enables a functional assessment of palm blood distribution was defined. Based on this segmentation it was shown that the greatest change after surgery was in the most distal region, the fingertips (p < 0.05). In addition, the changes in palm blood distribution in both hands were synchronized, which indicates a bilateral effect. An unsupervised machine learning model revealed two variables that determine the recovery pattern following the surgery: the palms' temperature difference pre- and post-surgery and the post-surgery difference between the treated and untreated hand. Our proposed framework provides a new technique for quantitative assessment of the palm's blood distribution. This technique may improve the clinical treatment of patients with vascular disease, particularly the patient-specific follow-up, in clinics as well as in homecare.

Automated processing of thermal imaging to detect COVID-19.

Rapid and sensitive screening tools for SARS-CoV-2 infection are essential to limit the spread of COVID-19 and to properly allocate national resources. Here, we developed a new point-of-care, non-contact thermal imaging tool to detect COVID-19, based on advanced image processing algorithms. We captured thermal images of the backs of individuals with and without COVID-19 using a portable thermal camera that connects directly to smartphones. Our novel image processing algorithms automatically extracted multiple texture and shape features of the thermal images and achieved an area under the curve (AUC) of 0.85 in COVID-19 detection with up to 92% sensitivity. Thermal imaging scores were inversely correlated with clinical variables associated with COVID-19 disease progression. In summary, we show, for the first time, that a hand-held thermal imaging device can be used to detect COVID-19. Non-invasive thermal imaging could be used to screen for COVID-19 in out-of-hospital settings, especially in low-income regions with limited imaging resources.

Automated thermal imaging for the detection of fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) comprises a spectrum of progressive liver pathologies, ranging from simple steatosis to non-alcoholic steatohepatitis (NASH), fibrosis and cirrhosis. A liver biopsy is currently required to stratify high-risk patients, and predicting the degree of liver inflammation and fibrosis using non-invasive tests remains challenging. Here, we sought to develop a novel, cost-effective screening tool for NAFLD based on thermal imaging. We used a commercially available and non-invasive thermal camera and developed a new image processing algorithm to automatically predict disease status in a small animal model of fatty liver disease. To induce liver steatosis and inflammation, we fed C57/black female mice (8 weeks old) a methionine-choline deficient diet (MCD diet) for 6 weeks. We evaluated structural and functional liver changes by serial ultrasound studies, histopathological analysis, blood tests for liver enzymes and lipids, and measured liver inflammatory cell infiltration by flow cytometry. We developed an image processing algorithm that measures relative spatial thermal variation across the skin covering the liver. Thermal parameters including temperature variance, homogeneity levels and other textural features were fed as input to a t-SNE dimensionality reduction algorithm followed by k-means clustering. During weeks 3,4, and 5 of the experiment, our algorithm demonstrated a 100% detection rate and classified all mice correctly according to their disease status. Direct thermal imaging of the liver confirmed the presence of changes in surface thermography in diseased livers. We conclude that non-invasive thermal imaging combined with advanced image processing and machine learning-based analysis successfully correlates surface thermography with liver steatosis and inflammation in mice. Future development of this screening tool may improve our ability to study, diagnose and treat liver disease.

Non-invasive thermal imaging of cardiac remodeling in mice.

Thermal infrared imaging has been suggested as a non-invasive alternative to monitor physiological processes and disease. However, the use of this technique to image internal organs, such as the heart, has not yet been investigated. We sought to determine the ability of our novel thermal image-processing algorithm to detect structural and functional changes in a mouse model of hypertension and cardiac remodeling. Twelve mice were randomly assigned to receive either the pro-inflammatory, hypertensive hormone angiotensin-II (2 mg/kg/day, n = 6) or saline (n = 6) infusion for 28 days. We performed weekly blood pressure measurements, together with serial trans-thoracic echocardiography studies and histopathological evaluation of the hearts. Thermal images were captured with a commercially available thermal camera, and images were processed by our novel algorithm which analyzes relative spatial temperature variation across the animal's thorax. We assessed cardiac inflammation by measuring inflammatory cell infiltration through flow cytometry. Angiotensin infusion increased blood pressure together with cardiac hypertrophy and fibrosis. Thermal imaging at day 28 of the experiment detected an increase in the fraction of the skin heated by the heart in angiotensin-treated mice. Thermal image findings were significantly correlated to left ventricular volume and mass parameters seen on echocardiography (r = 0.8, p < 0.01 and r = 0.6, p = 0.07). We also identified distinct changes in the spatial heat profiles of all angiotensin-treated hearts, possibly reflecting remodeling processes in the hypertensive heart. Finally, a machine learning based model using thermal imaging parameters predicted intervention status in 10 out of 11 mice similar to a model using echocardiographic measurements. Our findings suggest, for the first time, that a new thermal image-processing algorithm successfully correlates surface thermography with cardiac structural changes in mice with hypertensive heart disease.

The response of peripheral microcirculation to gravity-induced changes.

BACKGROUND: The peripheral microcirculation supplies fresh blood to the small blood vessels, providing oxygen and nutrients to the tissues, removing waste, and maintaining normal homeostatic conditions. The goal of this study was to characterize the response of the peripheral microcirculation, in terms of blood flow and tissue oxygenation variables, to gravity-induced changes. METHODS: The study included 20 healthy volunteers and the experiment involved monitoring central and peripheral variables with the right hand positioned at different heights. These positions correspond to various gravitational levels. Peripheral perfusion and oxygenation were monitored using a laser Doppler flowmeter, photoplethysmograph, and transcutaneous oxygen tension monitor. Local blood pressure and respiration rate were also measured. FINDINGS: At the heart level, tissue oxygenation displayed a nadir, while capillary flow displayed a peak. Similar but weaker changes were observed at the control hand. In contrast, the photoplethysmograph's amplitude strongly decreased upon reducing the arm position below heart level. Both systolic and diastolic pressures decreased linearly between the highest to lowest arm position. INTERPRETATION: The results may reflect peripheral compensation mechanisms, as well as an interaction between the central and peripheral cardiovascular systems, in response to local changes in blood pressure. The observed dependence of the oxygenation pattern on height could lead to important new insights for the diagnosis and treatment of problems in the regulation of tissue perfusion.

Edge-enhancement postprocessing using artificial dissipation.

We present a method to enhance, by postprocessing, the performance of gradient-based edge detectors. It improves the performance of the edge detector by adding terms which are similar to the artificial dissipation that appear in the numerical solution of hyperbolic PDEs. This term is added to the output of the edge detector. The edges that are missed or blurred by the edge detector are reconstructed through the addition of the artificial dissipation terms. Edges that are detected correctly by the edge detector are preserved. We present the theory of the artificial dissipation and its improvement of the quality of the detected edges. We demonstrate the performance of the algorithm on diverse images.

Noninvasive monitoring of peripheral microcirculatory hemodynamics under varying degrees of hypoxia.

The effect of hypoxia on skin blood flow was examined in anesthetized rabbits during induction of various levels of hypoxia. Peripheral perfusion and oxygenation were monitoring using a combined system (LPT) composed of a laser Doppler flowmeter (LDF), a photoplatysmograph (PPG), and a transcutaneous oxygen tension monitor (tc-PO2). Central blood parameters (PaO2, HCO3(-), SaO2, pH, and lactate) were measured concomitantly throughout the experiment. A continuous decline was found in both peripheral and central values, depending on the severity of the hypoxia. The results clearly indicate that monitoring peripheral indices with the LPT system enables monitoring changes of vital blood parameters during hypoxia. The system has clinical potential for sensitive and noninvasive monitoring of vital variables during medical procedures in clinics, as well as for homecare for patients with respiratory diseases. Minimizing the system may be useful in various conditions of exposure to low oxygen levels, such as during mountain climbing.

Diffusion methods for aligning medical datasets: location prediction in CT scan images.

The purpose of this study is to introduce diffusion methods as a tool to label CT scan images according to their position in the human body. A comparative study of different methods based on a k-NN search is carried out and we propose a new, simple and efficient way of applying diffusion techniques that is able to give better location forecasts than methods that can be considered the current state-of-the-art.