Characterization of the physicochemical, antimicrobial, and drug release properties of thermoresponsive hydrogel copolymers designed for medical device applications

In this study, a series of hydrogels was synthesized by free radical polymerization, namely poly(2-(hydroxyethyl) methacrylate) (pHEMA), poly(4-(hydroxybutyl)methacrylate) (pHBMA), poly(6-(hydroxyhexyl)methacrylate) (pHHMA), and copolymers composed of N-isopropylacrylamide (NIPAA), methacrylic acid (MA), NIPAA, and the above monomers. The surface, mechanical, and swelling properties (at 20 and 37 degrees C, pH 6) of the polymers were determined using dynamic contact angle analysis, tensile analysis, and thermogravimetry, respectively. The T-g and lower critical solution temperatures (LCST) were determined using modulated DSC and oscillatory rheometry, respectively. Drug loading of the hydrogels with chlorhexidine diacetate was performed by immersion in a drug solution at 20 degrees C (

High-performance 3D median filter architecture for medical image despeckling

A high-sample rate 3D median filtering processor architecture is proposed, based on a novel 3D median filtering algorithm, that can reduce the computing complexity in comparison with the traditional bubble sorting algorithm. A 3 x 3 x 3 filter processor is implemented in VHDL, and the simulation verifies that the processor can process a 128 x 128 x 96 MRI image in 0.03 seconds while running at 50 MHz.

Handling uncertainty in a medical study of dietary intake during pregnancy

This paper is concerned with handling uncertainty as part of the analysis of data from a medical study. The study is investigating connections between the birth weight of babies and the dietary intake of their mothers. Bayesian belief networks were used in the analysis. Their perceived benefits include (i) an ability to represent the evidence emerging from the evolving study, dealing effectively with the inherent uncertainty involved; (ii) providing a way of representing evidence graphically to facilitate analysis and communication with clinicians; (iii) helping in the exploration of the data to reveal undiscovered knowledge; and (iv) providing a means of developing an expert system application.

Right-left discrimination among medical students: Getting it right all of the time?

Objective To determine medical students’ self awareness and ability to discriminate right from left; to identify characteristics associated with this ability; and to identify any techniques used to aid discrimination. Design Questionnaire and psychometric study. Setting Undergraduate medical school, Northern Ireland. Participants 290 first year undergraduate students. Main outcome measure Medical students’ ability to discriminate right from left using the Bergen right-left discrimination test. Results Test scores ranged from 31 to 143 on a scale of 0- 144 (mean 112 (standard deviation 22.2)). Male students significantly outperformed female students (117.18 (26.96) v 110.80 (28.94)). Students who wanted to be surgeons performed significantly better than those who wanted to be general practitioners or medical doctors (119.87 (25.15) v 110.55 (27.36) v 112.50 (26.88)). The interaction effect for sex and career wishes was not significant (P=0.370). Students who used learnt techniques to help them discriminate scored significantly less than those who did not (P