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. The minimum forearm blood flow has been observed at 35 degrees C (0 ml 100 ml(-1)min(-1)) and the maximum at 42 degrees C (18.7 ml 100 ml(-1)min(-1)) environmental temperature with respect to the base value of 2 ml 100 ml(-1)min(-1). This model may also enable to identify many therapeutic targets that can be used in the treatment of inflammations and disorders due to heat-related illnesses.

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. The Kohonen neural network program written in C++, which has seven normalized values of different clinical symptoms between 0-1 fed to the input layer of the network with 50 Kohonen output neurons, has been presented. The optimized initializing parameters such as neighborhood size and learning rate was set to 50 and 0.7, respectively, to simulate the network for 10 million iterations. The network was found smartly distinguishing all 51 patterns to three different states of heat illnesses. With the advent of these findings, it can be concluded that the Kohonen neural network can be used for automated classification of the severity of heat stress and other related psycho-patho-physiological disorders. However, to replace the expert clinicians with such type of smart diagnostic tool, extensive work is required to optimize the system with variety of known and hidden clinical and pathological parameters.

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. Thus, the present study is concerned with aspects of the passive system for denervated subjects: (1) modeling the human body extremities (2) modeling heat transport mechanism within the body and at its periphery. The present model was simulated using the software (Wintherm 8.0, Thermoanalytics, USA) for different body segments to predict the heat flow between body core and skin surface with changes in environmental temperature with fixed relative humidity and wind velocity. The simulated model for comparative study of internal temperature distribution of hand, arm, leg and feet segments yielded remarkably good results and observed to be in trends with previously cited work under ambient environmental condition and at controlled room temperature. Models could be used to measure the temperature distribution in human limbs during local hyperthermia and to investigate the interaction between limbs and the thermal environment.

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. Present model has been designed using electronics circuit simulator (Multisim 8.0, National Instruments, USA), is to execute a series of predictive equations for early prediction of physiological parameters of young nude subjects during resting condition at various level of dry heat stress under almost still air to avoid causalities associated with hot environmental. The users can execute the model by changing the environmental temperature in degrees C and exposure time in minutes. The model would be able to predict and detect the changes in human vascular responses along with other physiological parameters and from this predicted values heat related-illness symptoms can be inferred.

Digital-analog hybrid control model for eukaryotic heat shock response illustrating the dynamics of heat shock protein 70 on exposure to thermal stress.

We are introducing in this paper a digital-analog hybrid model approach for the study of a complete gene regulatory network; the heat shock response (HSR) network of eukaryotes. HSR is a crucial and widely studied cellular phenomenon occurring due to various stresses on the cell, and is characterised by the induction of heat shock genes resulting in the production of heat shock proteins (HSPs) which restores cellular homeostasis by maintaining protein integrity. We are proposing a model which incorporates simple digital and analog components which mimic the functioning of biological molecules involved in HSR and model their dynamics and behaviour. The simulation result of the circuit for the production of HSP70 has been found to be consistent with published experimental results. The qualitative behaviour of the HSR is expressed through a truth table. Through this novel approach, the authors have tried to develop a level of understanding of the interactions of the parts of the HSR system and of this system as a whole.

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. Therefore, one need to classify automatically the disease based on symptoms. The 7:10:1 backpropagation artificial neural network model has been used to predict the clinical outcome from the symptoms that are routinely available to clinicians. The model has found to be effective in differentiating the different stages of exertional heat-illness with an overall performance of 100%.