METHODS: The intrasubject coefficient of variation was estimated from the residual mean square error obtained from analysis of variance of the parameters AUC0-infinity, Cmax and Cmax/AUC0-infinity after logarithmic transformation. The test power in the analyses of the above parameters was subsequently estimated using nomograms provided by Diletti et al. [1991].
RESULTS AND CONCLUSION: Thirty products covering 16 drugs were studied in which 22 were immediate-release (including one dispersible tablet) and 8 were sustained-release formulations. The intrasubject coefficient of variation for the parameter AUC0-infinity was smaller than Cmax, and hence considerably more studies were able to attain a power of greater than 80% using 12 volunteers for the AUC0-infinity, compared to the Cmax. However, the variability in the Cmax could be reduced by using the parameter Cmax/ AUC0-infinity, and thus, provide a more realistic estimation of sample size, since the latter reflects only the rate of absorption and not both the rate and extent as in the case of Cmax [Endrenyi et al. 1991].
PURPOSE: The purpose of this clinical study was to formulate a custom-made, 2-color chewing gum for the mixing ability test and to develop an image-processing method for color mixing analysis.
MATERIAL AND METHODS: Specimens of red-green (RG) chewing gum were prepared as a test food. Twenty dentate participants (10 men, 10 women; mean age 21 years) took part in this study. Each participant masticated 1 piece of RG gum for 3, 6, 9, 15, and 25 cycles, and this task was repeated 3 times consecutively (total n=15 for each participant). The boluses were retrieved and flattened to 1-mm-thick wafers and scanned with a flatbed scanner. The digital images were analyzed using ImageJ software equipped with a custom-built plug-in to measure the geometric dispersion (GD) of baseline red segment. The predictive criterion validity of this method was determined by correlating GD to the number of mastication cycles. The hardness and mass of RG chewing gum were measured before and after mastication. Hardness loss (%) and mass loss (%) were then calculated and compared with those of a commercially available chewing gum.
RESULTS: The 2-way repeated-measures ANOVA with post hoc Bonferroni test showed that GD was able to discriminate among the groups of different numbers of mastication cycles (P
METHODS: We retrospectively reviewed two pictures both with white light (WL) and LCI for 54 consecutive neoplastic polyps 2-20 mm in size. All pictures were evaluated by four endoscopists according to a published polyp visibility score from four (excellent visibility) to one (poor visibility). Additionally, we calculated CD value between each polyp and surrounding mucosa in LCI and WL using an original software.
RESULTS: The mean polyp visibility scores of LCI (3.11 ± 1.05) were significantly higher than those of WL (2.50 ± 1.09, P
METHODS: A computer-based SG (CBSG) tool was developed using Microsoft® PowerPoint 2007 to value asthma-specific health states in Malaysia. Eight hypothetical health states were considered, including two anchor states (healthy and dead), three chronic (C) states and three temporary (T) states (each numbered 1 through 3, with increasing severity) in addition to the subject's current health state. Twenty adult asthma patients completed the CBSG tool in addition to paper-based Asthma Control Test, three health status measures (EQ-5D, EQ-VAS, and Mini Asthma Quality of Life Questionnaire (MiniAQLQ)), and VAS utility assessment tool. Patients and interviewers rated the difficulty of the VAS and CBSG tools. Correlations between current health state values derived from the various measures were determined.
RESULTS: The SG and the VAS received similar difficulty ratings. 17 patients completed the CBSG tool within 30 minutes. The mean utilities determined by the CBSG tool for the T1-T3 asthma health states met the expected logical order of 1>2>3, but those for the C1-C3 states did not. Correlation between current health state values derived from the CBSG tool and other measurement tools was poor.
CONCLUSION: The CBSG tool developed for measuring utilities of asthma health states showed acceptable feasibility and overall validity.
METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.
RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.
CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.