Digital infrared thermography and machine learning for diabetic foot assessment: thermal patterns and classification.

Objectives: Digital infrared thermography is a noninvasive tool used for assessing diseases, including the diabetic foot. This study aims to analyze thermal patterns of the foot sole in patients with type 2 diabetes mellitus using thermography and explore correlations with clinical variables. Additionally, a machine learning approach was developed for classification.

Integrating fNIRS and machine learning: shedding light on Parkinson's disease detection.

The purpose of this research is to introduce an approach to assist the diagnosis of Parkinson's disease (PD) by classifying (fNIRS) studies as PD positive or negative. fNIRS is a non-invasive optical signal modality that conveys the brain's hemodynamic response, specifically changes in blood oxygenation in the cerebral cortex; and its potential as a tool to assist PD detection deserves to be explored since it is non-invasive and cost-effective as opposed to other neuroimaging modalities. Besides the integration of fNIRS and machine learning, a contribution of this work is that various approaches were implemented and tested to find the implementation that achieves the highest performance.

Parkinson's disease patients show delayed hemodynamic changes in primary motor cortex in fine motor tasks and decreased resting-state interhemispheric functional connectivity: a functional near-infrared spectroscopy study.

Significance: People with Parkinson's disease (PD) experience changes in fine motor skills, which is viewed as one of the hallmark signs of this disease. Due to its non-invasive nature and portability, functional near-infrared spectroscopy (fNIRS) is a promising tool for assessing changes related to fine motor skills.

Aim: We aim to compare activation patterns in the primary motor cortex using fNIRS, comparing volunteers with PD and sex- and age-matched control participants during a fine motor task and walking.

Fatty Acid-Binding Proteins Identification during the Evolution of Metabolic Syndrome: A Raman Spectroscopy-Based Approach.

Excess fat in abdominal deposits is a risk factor for multiple conditions, including metabolic syndrome (MetS); lipid metabolism plays an essential role in these pathologies; fatty acid-binding proteins (FABPs) are dedicated to the cytosolic transport of fat. FABP4, whose primary source is adipose tissue, is released into the circulation, acting as an adipokine, while FABP5 also accompanies the adverse effects of MetS. FABP4 and 5 are potential biomarkers of MetS, but their behavior during syndrome evolution has not been determined.

Response to Comment on "Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre-diabetes and diabetes".

This letter aims to reply to Bratchenko and Bratchenko's comment on our paper "Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre-diabetes and diabetes." Our paper analyzed the feasibility of using in vivo Raman measurements combined with machine learning techniques to screen diabetic and prediabetic patients. We argued that this approach yields high overall accuracy (94.

Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre-diabetes and diabetes.

In this article, we investigated the feasibility of using Raman spectroscopy and multivariate analysis method to noninvasively screen for prediabetes and diabetes in vivo. Raman measurements were performed on the skin from 56 patients with diabetes, 19 prediabetic patients and 32 healthy volunteers. These spectra were collected along with reference values provided by the standard glycated hemoglobin (HbA1c) assay.

Raman Spectroscopy Study of Skin Biopsies from Patients with Parkinson's Disease: Trends in Alpha-Synuclein Aggregation from the Amide I Region.

Parkinson's disease (PD) is one of the most common neurological pathologies with a high prevalence worldwide. PD is characterized by Lewy bodies, whose major component is the aggregates of α-synuclein (αSyn) protein. Interestingly, recent works have demonstrated that skin biopsy studies are a promising diagnostic tool for evaluating α-synucleinopathies.

Computational and Experimental Analysis of Gold Nanorods in Terms of Their Morphology: Spectral Absorption and Local Field Enhancement.

A nanoparticle's shape and size determine its optical properties. Nanorods are nanoparticles that have double absorption bands associated to surface plasmon oscillations along their two main axes. In this work, we analize the optical response of gold nanorods with numerical simulations and spectral absorption measurements to evaluate their local field enhancement-which is key for surface-enhanced Raman spectroscopic (SERS) applications.

Raman Spectroscopy for Adipose Tissue Assessment in Rat Models of Obesity and Type 1 Diabetes.

Adipose tissue presents structural and functional changes in obesity and type 1 diabetes mellitus (T1DM). In obesity, the size and number of adipocytes and adipokine secretion increases. In T1DM, a loss of adipose tissue suggests changes in the metabolic activity of this tissue.

Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools: reply to comment.

We show the spectra of advanced glycation products in response to recent comments made by Bratchenko . Our results suggest that information retrieved by Raman spectroscopy is relevant to screening diabetic patients, however, the comparison carried out in our paper, between ANN and SVM, was not fair, because of the erroneous PCA selection procedure and different sources of variation present in the analysis.

Prediction of epileptic seizures with convolutional neural networks and functional near-infrared spectroscopy signals.

There have been different efforts to predict epileptic seizures and most of them are based on the analysis of electroencephalography (EEG) signals; however, recent publications have suggested that functional Near-Infrared Spectroscopy (fNIRS), a relatively new technique, could be used to predict seizures. The objectives of this research are to show that the application of fNIRS to epileptic seizure detection yields results that are superior to those based on EEG and to demonstrate that the application of deep learning to this problem is suitable given the nature of fNIRS recordings. A Convolutional Neural Network (CNN) is applied to the prediction of epileptic seizures from fNIRS signals, an optical modality for recording brain waves.

Use of Raman spectroscopy in the assessment of skin after CO ablative fractional laser surgery on acne scars.

Background: Ablative fractional laser surgery is a common technique for treating acne scars. However, an in vivo and noninvasive analysis of the histologic variations between acne skin and the resulting resurfaced skin is needed in order to evaluate the wound healing process of the scars induced by the ablative fractional laser surgery.

Materials And Methods: Nine patients with acne scars underwent a single treatment with a CO ablative fractional laser surgery.

An automated method for the evaluation of breast cancer using infrared thermography.

Breast cancer is one of the major causes of death for women. Temperature measurement is advantageous because it is non-invasive, non-destructive, and cost-effective. Temperature measurement through infrared thermography is useful to detect changes in blood perfusion that can occur due to inflammation, angiogenesis, or other pathological causes.

Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools.

Type 2 diabetes mellitus (DM2) is one of the most widely prevalent diseases worldwide and is currently screened by invasive techniques based on enzymatic assays that measure plasma glucose concentration in a laboratory setting. A promising plan of action for screening DM2 is to identify molecular signatures in a non-invasive fashion. This work describes the application of portable Raman spectroscopy coupled with several supervised machine-learning techniques, to discern between diabetic patients and healthy controls (Ctrl), with a high degree of accuracy.

Altered Functional Connectivity Following an Inflammatory White Matter Injury in the Newborn Rat: A High Spatial and Temporal Resolution Intrinsic Optical Imaging Study.

Very preterm newborns have an increased risk of developing an inflammatory cerebral white matter injury that may lead to severe neuro-cognitive impairment. In this study we performed functional connectivity (fc) analysis using resting-state optical imaging of intrinsic signals (rs-OIS) to assess the impact of inflammation on resting-state networks (RSN) in a pre-clinical model of perinatal inflammatory brain injury. Lipopolysaccharide (LPS) or saline injections were administered in postnatal day (P3) rat pups and optical imaging of intrinsic signals were obtained 3 weeks later.

Imaging of an inflammatory injury in the newborn rat brain with photoacoustic tomography.

Background: The precise assessment of cerebral saturation changes during an inflammatory injury in the developing brain, such as seen in periventricular leukomalacia, is not well defined. This study investigated the impact of inflammation on locoregional cerebral oxygen saturation in a newborn rodent model using photoacoustic imaging.

Methods: 1 mg/kg of lipopolysaccharide(LPS) diluted in saline or saline alone was injected under ultrasound guidance directly in the corpus callosum of P3 rat pups.

Optical imaging of resting-state functional connectivity in a novel arterial stiffness model.

This study aims to assess the impact of unilateral increases in carotid stiffness on cortical functional connectivity measures in the resting state. Using a novel animal model of induced arterial stiffness combined with optical intrinsic signals and laser speckle imaging, resting state functional networks derived from hemodynamic signals are investigated for their modulation by isolated changes in stiffness of the right common carotid artery. By means of seed-based analysis, results showed a decreasing trend of homologous correlation in the motor and cingulate cortices.

Functional electrical stimulation post-spinal cord injury improves locomotion and increases afferent input into the central nervous system in rats.

Background: Functional electrical stimulation (FES) has been found to be effective in restoring voluntary functions after spinal cord injury (SCI) and stroke. However, the central nervous system (CNS) changes that occur in as a result of this therapy are largely unknown.

Objective: To examine the effects of FES on the restoration of voluntary locomotor function of the CNS in a SCI rat model.

Optical imaging of acute epileptic networks in mice.

The potential of intrinsic optical imaging and resting-state analysis under anesthetized conditions as a tool to study brain networks associated with epileptic seizures is investigated. Using an acute model of epileptiform activity, the 4-aminopyridine model in live mice, we observe the changes in resting-state networks with the onset of seizure activity and in conditions of spiking activity. Resting-state networks identified before and after the onset of epileptiform activity show both decreased and increased homologous correlations, with a small dependence on seizure intensity.

Carotid calcification in mice: a new model to study the effects of arterial stiffness on the brain.

Background: Arterial stiffness has been identified as an important risk factor for cognitive decline. However, its effects on the brain's health are unknown, and there is no animal model available to study the precise impact of arterial stiffness on the brain. Therefore, the objective of the study was to develop and characterize a new model specific to arterial stiffness in order to study its effects on the brain.

Laminar optical tomography of the hemodynamic response in the lumbar spinal cord of rats.

Intrinsic optical imaging (IOI) has emerged as a very powerful tool to assess neuronal function in small animals. Although it has been used extensively in the brain, its application to the spinal cord is rare. The inability of intrinsic optical techniques to resolve different depths and embedded gray matter hampers their capacity to distinguish larger vasculature contributions of hemodynamic signals originating from motoneuron and interneuron activation.