Time-domain heart rate dynamics in the prognosis of progressive atherosclerosis.

Background And Aim: The regular uptake of a high-fat diet (HFD) with changing lifestyle causes atherosclerosis leading to cardiovascular diseases and autonomic dysfunction. Therefore, the current study aimed to investigate the correlation of autonomic activity to lipid and atherosclerosis markers. Further, the study proposes a support vector machine (SVM) based model in the prediction of atherosclerosis severity.

Molecular characterization and in-depth genomic analysis to unravel the pathogenic features of an environmental isolate Enterobacter sp. S-33.

Enterobacter species represent widely distributed opportunistic pathogens, commonly associated with plants and humans. In the present study, we performed a detailed molecular characterization as well as genomic study of a type VI secretion system (T6SS) bacterium belonging to member of the family Enterobacteriaceae and named Enterobacter sp. S-33.

Secretome analysis of an environmental isolate Enterobacter sp. S-33 identifies proteins related to pathogenicity.

Enterobacter species are responsible for causing infections of the lower respiratory tract, urinary tract, meninges, etc. Proteins secreted by these species may act as determinants of host-pathogen interaction and play a role in virulence. Among the secreted proteins, the Type VI secretion system (T6SS) acts as a molecular nanomachine to deliver many effector proteins directly into prey cells in a contact-dependent manner.

Heart rate dynamics in the prediction of coronary artery disease and myocardial infarction using artificial neural network and support vector machine.

Background: Atherosclerosis leads to coronary artery disease (CAD) and myocardial infarction (MI), a major cause of morbidity and mortality worldwide. The computer-aided prognosis of atherosclerotic events with the electrocardiogram (ECG) derived heart rate variability (HRV) can be a robust method in the prognosis of atherosclerosis events.

Methods: A total of 70 male subjects aged 55 ± 5 years participated in the study.

Heart rate variability time domain features in automated prediction of diabetes in rat.

Diabetes is a very common occurring disease, diagnosed by hyperglycemia. The established mode of diagnosis is the analysis of blood glucose level with the help of a hand-held glucometer. Nowadays, it is also known for affecting multi-organ functions, particularly the microvasculature of the cardiovascular system.

Time-domain heart rate variability-based computer-aided prognosis of lung cancer.

Objective: Incidence of lung cancer (LC) is increasing day by day with exposure to smoke, radiation, and chemicals; LC is one of the leading causes of death. The major difficulty in treatment was delayed diagnosis. This study aims to propose a time-domain heart rate variability (HRV) feature-based automated system in LC prediction and its staging.

Mechanistic electronic model to simulate and predict the effect of heat stress on the functional genomics of HO-1 system: Vasodilation.

The present work is concerned to model the molecular signalling pathway for vasodilation and to predict the resting young human forearm blood flow under heat stress. The mechanistic electronic modelling technique has been designed and implemented using MULTISIM 8.0 and an assumption of 1V/ degrees C for prediction of forearm blood flow and the digital logic has been used to design the molecular signalling pathway for vasodilation.

Neural network detects the effects of p-CPA pre-treatment on brain electrophysiology in a rat model of focal brain injury.

Objective: To examine the performance of Artificial Neural Network (ANN) in evaluation of the effects of pretreatment of para-Chlorophenylalanine (p-CPA), a serotonin blocker, in experimental brain injury.

Methods: Continuous 4 h digital electroencephalogram (EEG) recordings from male Charles Foster rats and its power spectrum analysis by using fast Fourier transform (FFT) were performed in two experimental (i) drug untreated injury group; (ii) p-CPA pretreated injury group as well as a control group. The EEG power spectrum data were tested by ANN containing 60 nodes in input layer, weighted from the digital values of power spectrum from 0 to 30 Hz, 18 nodes in hidden layer and an output node.

An unsupervised neural network to predict the level of heat stress.

Heat stress is known to induce high mortality rate due to multi-system illness, which demands urgent attention to reduce the fatality rate in such patients. Further, for the diagnosis and supportive therapy, one needs to define the severity of heat stress that can be distinguished as mild, intermediate and severe. The objective of this work is to develop an automated unsupervised artificial system to analyze the clinical outcomes of different levels of heat related illnesses.

Computer simulation of heat transfer in different tissue layers of body extremities under heat stress in deep anesthetic condition.

Many mathematical models of thermoregulation in humans have been developed, so far. These models appeared to be very useful tools for studying temperature regulation in humans under adverse environmental conditions. However, no one discussed the heat transfer characteristics of denervated subjects.

Prediction of heat-illness symptoms with the prediction of human vascular response in hot environment under resting condition.

The thermoregulatory control of human skin blood flow is vital to maintain the body heat storage during challenges of thermal homeostasis under heat stress. Whenever thermal homeostasis disturbed, the heat load exceeds heat dissipation capacity, which alters the cutaneous vascular responses along with other body physiological variables. Whole body skin blood flow has been calculated from the forearm blood flow.

Neural network-based evaluation of chronic non-thermal effects of modulated 2450 MHz microwave radiation on electroencephalogram.

The effects of chronic exposure (2 h daily for 21 days) of 1 kHz square wave-modulated 2450 MHz microwave radiation (non-thermal) on sleep-EEG, open field behavior, and thyroid hormones (T(3), T(4), and TSH) have been analyzed in an animal model. Results revealed significant changes in these pathophysiological parameters (p < 0.05 or better), except body temperature, grooming behavior, and TSH levels.

Depletion of serotonin synthesis with p-CPA pretreatment alters EEG in urethane anesthetized rats under whole body hyperthermia.

Background: Serotonin is believed as an important factor in brain function. The role of serotonin in cerebral psycho-patho-physiology has already been well established. However, the function of serotonin antagonist in anesthetized subjects under hyperthermia has not been studied properly.

Backpropagation ANN-based prediction of exertional heat illness.

Exertional heat illness is primarily a multi-system disorder results from the combined effect of exertional and thermoregulation stress. The severity of exertional heat illness can be classified as mild, intermediate and severe from non-specific symptoms like thirst, myalgia, poor concentration, hysteria, vomiting, weakness, cramps, impaired judgement, headache, diarrhea, fatigue, hyperventilation, anxiety, and nausea to more severe symptoms like exertional dehydration, heat cramps, heat exhaustion, heat injury, heatstroke, rhabdomyolysis, and acute renal failure. At its early stage, it is quite difficult to find out the severity of disease with manual screening because of overlapping of symptoms.

Backpropagation artificial neural network classifier to detect changes in heart sound due to mitral valve regurgitation.

The phonocardiograph (PCG) can provide a noninvasive diagnostic ability to the clinicians and technicians to compare the heart acoustic signal obtained from normal and that of pathological heart (cardiac patient). This instrument was connected to the computer through the analog to digital (A/D) converter. The digital data stored for the normal and diseased (mitral valve regurgitation) heart in the computer were decomposed through the Coifman 4th order wavelet kernel.

Backpropagation artificial neural network detects changes in electro-encephalogram power spectra of syncopic patients.

This paper presents an effective application of backpropagation artificial neural network (ANN) in differentiating electroencephalogram (EEG) power spectra of syncopic and normal subjects. Digitized 8-channel EEG data were recorded with standard electrodes placement and amplifier settings from five confirmed syncopic and five normal subjects. The preprocessed EEG signals were fragmented in two-second artifact free epochs for calculation and analysis of changes due to syncope.