Can Currently Available Advanced Combustion Biomass Cook-Stoves Provide Health Relevant Exposure Reductions? Results from Initial Assessment of Select Commercial Models in India

EcoHealth - Household air pollution from use of solid fuels is a major contributor to the national burden of disease in India. Currently available models of advanced combustion biomass cook-stoves...     

Can Force Feedback and Science Learning Enhance the Effectiveness of Neuro-Rehabilitation? An Experimental Study on Using a Low-Cost 3D Joystick and a Virtual Visit to a Zoo

In this paper, we demonstrate that healthy adults respond differentially to the administration of force feedback and the presentation of scientific content in a virtual environment, where they interact with a low-cost haptic device. Subjects are tasked with controlling the movement of a cursor on a predefined trajectory that is superimposed on a map of New York City’s Bronx Zoo. The system is characterized in terms of a suite of objective indices quantifying the subjects’ dexterity in planning and generating the multijoint visuomotor tasks. We find that force feedback regulates the smoothness, accuracy, and duration of the subject’s movement, whereby converging or diverging force fields influence the range of variations of the hand speed. Finally, our findings provide preliminary evidence that using educational content increases subjects’ satisfaction. Improving the level of interest through the inclusion of learning elements can increase the time spent performing rehabilitation tasks and promote learning in a new context.     

Evaluating Variations of Bladder Volume Using an Ultrasound Scanner in Rectal Cancer Patients during Chemoradiation: Is Protocol-Based Full Bladder Maintenance Using a Bladder Scanner Useful to Maintain the Bladder Volume?

Purpose

The maintenance of full bladder is important to reduce radiation-induced toxicities and maintain the therapeutic consistency in locally advanced rectal cancer patients who underwent radiotherapy (RT). So, the aim of this study was to evaluate the effectiveness of protocol-based full bladder maintenance by assessing bladder volume variation using an ultrasound bladder scanner to maintain bladder volume.

Materials and Methods

From March 2011 to May 2011, twenty consecutive rectal cancer patients receiving external beam RT participated in this prospective study. Protocol-based full bladder maintenance consisted of education, training and continuous biofeedback by measuring bladder volume. Bladder volume was measured by bladder scan immediately before simulation CT scan and before each treatment three times weekly during the RT period. The relative bladder volume change was calculated. Intra-patient bladder volume variations were quantified using interquartile range (IQR) of relative bladder volume change in each patient. We compared intra-patient bladder volume variations obtained (n=20) with data from our previous study patients (n=20) performing self-controlled maintenance without protocol.

Results

Bladder volumes measured by bladder scan highly correlated with those on simulation CT scan (R=0.87, p<0.001). Patients from this study showed lower median IQR of relative bladder volume change compared to patients of self-controlled maintenance from our previous study, although it was not statistically significant (median 32.56% vs. 42.19%, p=0.058). Upon logistic regression, the IQR of relative bladder volume change was significantly related to protocol-based maintenance [relative risk 1.045, 95% confidence intervals (CI) 1.004-1.087, p=0.033]. Protocol-based maintenance included significantly more patients with an IQR of relative bladder volume change less than 37% than self-controlled maintenance (p=0.025).

Conclusion

Our findings show that bladder volume could be maintained more consistently during RT by protocol-based management using a bladder scan.     

Predicting Fecundity of Fathead Minnows (Pimephales promelas) Exposed to Endocrine-Disrupting Chemicals Using a MATLAB?-Based Model of Oocyte Growth Dynamics

Fish spawning is often used as an integrated measure of reproductive toxicity, and an indicator of aquatic ecosystem health in the context of forecasting potential population-level effects considered important for ecological risk assessment. Consequently, there is a need for flexible, widely-applicable, biologically-based models that can predict changes in fecundity in response to chemical exposures, based on readily measured biochemical endpoints, such as plasma vitellogenin (VTG) concentrations, as input parameters. Herein we describe a MATLAB^® version of an oocyte growth dynamics model for fathead minnows (Pimephales promelas) with a graphical user interface based upon a previously published model developed with MCSim software and evaluated with data from fathead minnows exposed to an androgenic chemical, 17β-trenbolone. We extended the evaluation of our new model to include six chemicals that inhibit enzymes involved in steroid biosynthesis: fadrozole, ketoconazole, propiconazole, prochloraz, fenarimol, and trilostane. In addition, for unexposed fathead minnows from group spawning design studies, and those exposed to the six chemicals, we evaluated whether the model is capable of predicting the average number of eggs per spawn and the average number of spawns per female, which was not evaluated previously. The new model is significantly improved in terms of ease of use, platform independence, and utility for providing output in a format that can be used as input into a population dynamics model. Model-predicted minimum and maximum cumulative fecundity over time encompassed the observed data for fadrozole and most propiconazole, prochloraz, fenarimol and trilostane treatments, but did not consistently replicate results from ketoconazole treatments. For average fecundity (eggs•female^-1•day^-1), eggs per spawn, and the number of spawns per female, the range of model-predicted values generally encompassed the experimentally observed values. Overall, we found that the model predicts reproduction metrics robustly and its predictions capture the variability in the experimentally observed data.