Dysregulation of oxygen hemodynamic responses to synaptic train stimulation in a rat hippocampal model of subarachnoid hemorrhage

We investigated microvascular reactivity to synaptic train stimulation after induction of subarachnoid hemorrhage in adult rats, analyzing tissue oxygen levels [pO₂] in intact hippocampus. In control rats, hippocampal pO₂ averaged 11.4 mm Hg whereas hemodynamic responses averaged 13.1 mm Hg (to a 25 s train). After subarachnoid hemorrhage (at 2 days), we recorded a dramatic elevation in baseline pO₂ in the hippocampus (to 68.4 mm Hg) accompanied by inverted pO₂ responses to synaptic train stimulation (−9.46 mm Hg). These significant changes in baseline hippocampal pO₂ and inverted pO₂ responses after subarachnoid hemorrhage indicate severe alterations of neurovascular coupling and neuronal viability.     

The B-Mode Image-Guided Ultrasound Attenuation Parameter Accurately Detects Hepatic Steatosis in Chronic Liver Disease

The purpose of our study was to evaluate the diagnostic accuracy of the ultrasound-guided attenuation parameter (UGAP) for the detection of hepatic steatosis in comparison with the controlled attenuation parameter (CAP), using histopathology as the reference standard. We prospectively analyzed 163 consecutive chronic liver disease patients who underwent UGAP, CAP, computed tomography and a liver biopsy on the same day between April 2016 and July 2017. Radiofrequency signals corresponding to the images were compensated by the reference signal previously measured from the uniform phantom with known attenuation (0.44 dB/cm/MHz). The attenuation coefficient was calculated from the signals’ decay slope. The median attenuation coefficient values in patients with S0 (n?=?62), S1 (n?=?63), S2 (n?=?23) and S3 grade (n?=?15) were 0.485, 0.560, 0.660 and 0.720, respectively. Significant correlations were found between attenuation coefficient and percentage steatosis, CAP values and liver-to-spleen computed tomography attenuation ratio (p < 0.001). The areas under the receiver operating characteristic curve of UGAP for identifying ≥S1, ≥S2 and ≥S3 were 0.900, 0.953 and 0.959, respectively, which were significantly better than the results obtained with CAP for identifying ≥S2 and ≥S3. In conclusion, UGAP had high diagnostic accuracy for detecting hepatic steatosis in patients with chronic liver disease     

Correlation approach for the detection of the heartbeat intervals using force sensors placed under the bed posts

Abstract

This study proposes a method for detecting the heartbeat intervals of a person lying on a bed from ballistocardiographic signals recorded unobtrusively with four dynamic force sensors located under the bed posts. The method does not recognize individual heartbeats, but the intervals where the correlation between two consecutive signal segments maximizes. This study evaluated the performance of the method with nine subjects in 1-h long recordings and achieved 91% beat-to-beat interval (BBI) recognition coverage; 98.6% of the detected BBIs differed less than 50?ms from the values calculated from a reference electrocardiogram signal. This study also evaluated the reliability of two parameters of heart rate variability that have been used in sleep quality assessment in several studies and are usually calculated for 30?s epochs. The results suggest that the method is able to provide sufficient reliability for using the data in evaluation of sleep quality.     

Beta prime regression with application to risky behavior frequency screening

Our aim is to model the frequency of certain behavioral acts, especially those that are likely to transmit communicable diseases between persons. We develop a generalized linear model on the basis of the beta prime distribution to model the responses to a survey question of the form, ‘When was the last time that you engaged in this behavior?’ Intuitively, individuals reporting more recent events are more likely to have greater frequency of the risky behavior. The beta prime distribution is especially suited to this application because of its long tail. We adjust for length‐biased sampling. We show how to use this distribution as the basis of a linear regression model that accounts for differences in demographic and psychological characteristics of the respondents. We discuss estimation of parameters, residuals, tests for heterogeneity of these parameters, and jackknife measures of influence. We apply the methods to a survey of alcohol abuse use among individuals who are at high risk for spreading HIV and other communicable diseases in a study conducted in Saint Petersburg, Russia. Copyright © 2013 John Wiley & Sons, Ltd.     

Physiological variables involved in heart valve substitute calcification

Biochemical, histological and genetic studies using in vitro/in vivo models have demonstrated that pathological calcification of bioprosthetic heart valves (BHV) is regulated by various mechanisms associated with physiological variables. The major objective of this review is to characterize physiological variables involved in BHV calcification. This review examines our understanding of the systemic cellular behavior and physiological regulation processes behind BHV calcification and its clinical applications.     

Perception or reality: Can thermal perceptions inform management of firefighters in the heat?

Accurately assessing the physiological status of firefighters during work in the heat is critical to ensuring their safety. Evaluating core temperatures (T_c) in the field is problematic due to cost and limitations in technology and accuracy. As such, fire services rely on individual perceptions of wellbeing. The present study aimed to establish whether perceptual responses measured using the perceptual strain index (PeSI), calculated from rate of perceived exertion (RPE) and thermal sensation (TS), could reliably predict the physiological strain (PSI) encountered by experienced firefighters working in a hot environment. We conducted two firefighting simulations (set-pace and self-paced) in a purposefully built heat chamber (100 ± 5°C) comprised of two 20-min periods separated by a 10-min recovery outside the chamber. Physiological strain was measured via heart rate (HR) and gastrointestinal temperature (T_gi) and compared with PeSI at 5-min intervals. To evaluate the predictive ability of the PeSI for PSI, mean differences and the 95% limits of agreement (LOA) were established, along with correlation coefficients at each 5-min interval. Moderately significant correlations occurred in the second work bout of the self-paced trial only (10 min: r = 0.335, 15 min: r = 0.498, 20 min r = 0.439) with no other correlations observed at any other time during either trial or during the rest periods. Bland-Altman analysis revealed mean differences of ?0.74 ± 2.70 (self-paced) and +0.04 ± 2.04 (set-paced) between PeSI and PSI with the 95% LOA being ?4.77 to 3.28 (self-paced) and ?4.01 to 2.01 (set-paced). The wide LOA and lack of correlations observed between perceptual and physiological strain in both self-paced and set-paced work trials indicate that PeSI is not sufficiently reliable as a sole measure of wellbeing for firefighters working in the heat. Hence, we recommend that fire services prioritise the development of reliable and effective monitoring tools for use in the field.     

Estimation of tissue blood flow from hyperthermia treatment data

During hyperthermia treatment of patients the delivered heat and the temperatures at several points in the tissue are often measured and recorded. These data contain information about thermal tissue parameters. A method for extracting this information, i.e. estimating the tissue parameters—in particular the blood perfusion rate—is described. The method applies a system identification technique, adjusting the unknown parameters in a thermal tissue model, until the predicted model output (temperature) coincides well with the measured temperature. Data from a number of patient treatments have been used to test the method, and although the accuracy of the method remains to be established conclusively it appears to give a good estimate of the model parameter representing blood flow. The obvious advantage of the method is that it requires no special transducers or experiments. The weak aspect is that it depends on the correctness of a thermal model of the perfused tissue.     

Combining the ‘bottom up’ and ‘top down’ approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data

Pharmacokinetic models range from being entirely exploratory and empirical, to semi-mechanistic and ultimately complex physiologically based pharmacokinetic (PBPK) models. This choice is conditional on the modelling purpose as well as the amount and quality of the available data. The main advantage of PBPK models is that they can be used to extrapolate outside the studied population and experimental conditions. The trade-off for this advantage is a complex system of differential equations with a considerable number of model parameters. When these parameters cannot be informed from in vitro or in silico experiments they are usually optimized with respect to observed clinical data. Parameter estimation in complex models is a challenging task associated with many methodological issues which are discussed here with specific recommendations. Concepts such as structural and practical identifiability are described with regards to PBPK modelling and the value of experimental design and sensitivity analyses is sketched out. Parameter estimation approaches are discussed, while we also highlight the importance of not neglecting the covariance structure between model parameters and the uncertainty and population variability that is associated with them. Finally the possibility of using model order reduction techniques and minimal semi-mechanistic models that retain the physiological-mechanistic nature only in the parts of the model which are relevant to the desired modelling purpose is emphasized. Careful attention to all the above issues allows us to integrate successfully information from in vitro or in silico experiments together with information deriving from observed clinical data and develop mechanistically sound models with clinical relevance.