Background: The presence of high-density starry dots around the intracerebral hemorrhage (ICH), which we termed as a satellite sign, is occasionally observed in CT. The relationship between ICH with a satellite sign and its functional outcome has not been identified. This study aimed to determine whether the presence of a satellite sign could be an independent prognostic factor for patients with ICH. Methods: Patients with acute spontaneous ICH were retrospectively identified and their initial CT scans were reviewed. A satellite sign was defined as scattered high-density lesions completely separate from the main hemorrhage in at least the single axial slice. Functional outcome was evaluated using the modified Rankin Scale (mRS) at discharge. Poor functional outcome was defined as mRS scores of 3-6. Univariate and multivariate logistic regression analyses were applied to assess the presence of a satellite sign and its association with poor functional outcome. Results: A total of 241 patients with ICH were enrolled in the study. Of these, 98 (40.7%) had a satellite sign. Patients with a satellite sign had a significantly higher rate of poor functional outcome (95.9%) than those without a satellite sign (55.9%, p < 0.0001). Multivariate logistic regression analysis revealed that higher age (OR 1.06; 95% CI 1.03-1.10; p = 0.00016), large hemorrhage size (OR 1.06; 95% CI 1.03-1.11; p = 0.00015), and ICH with a satellite sign (OR 13.5; 95% CI 4.42-53.4; p < 0.0001) were significantly related to poor outcome. A satellite sign was significantly related with higher systolic blood pressure (p = 0.0014), higher diastolic blood pressure (p = 0.0117), shorter activated partial thromboplastin time (p = 0.0427), higher rate of intraventricular bleeding (p < 0.0001), and larger main hemorrhage (p < 0.0001). Conclusions: The presence of a satellite sign in the initial CT scan is associated with a significantly worse functional outcome in ICH patients.
S). The reliability of these optimal solutions was evaluated by a bootstrap resampling technique. Different levels of three causal factors were used as factors of response surface analysis: the lactose/cornstarch ratio (X 1 ), the amount of carmellose calcium (X 2 ), and the amount of hydroxypropylcellulose (X 3 ). The target responses were the dissolution ratio of theophylline for the first 15 min (Y 1 ) and the hardness (Y 2 ) of each of the prepared tablets. Similar optimal solutions were estimated in three different sizes of datasets. A bootstrap re-sampling with replacements from the original dataset was applied, and optimal solutions for each bootstrap dataset were estimated. The frequency of the distribution of the optimal solution generated by the bootstrap re-sampling technique demonstrated almost normal distribution. The average and standard deviation of the optimal solution distribution were calculated as evaluation indices reflecting the accuracy and reproducibility of the optimal solution. It was confirmed that the accuracy was sufficiently high, irrespective of the dataset size; however, the reproducibility worsened with a decrease in the number of the experimental datasets. Consequently, it was considered that the novel evaluation method based on the bootstrap re-sampling technique was suitable for evaluating the reliability of the optimal solution.
This work presents a decision support method for the choice between batch and continuous technologies in solid drug product manufacturing based on the economic evaluation. The method consists of four steps: (I) modeling of operating costs, (II) evaluation, (III) sensitivity analysis, and (IV) interpretation, with iterations. For a given design situation, manufacturing processes are modeled and evaluated with consideration for the characteristics of the two technologies. The sensitivity of the input parameters is analyzed; after interpreting all results, the economically preferable technology is suggested. As a case study, the method was applied to a situation where a new product was in the late development stage, and one of the two technologies needs to be chosen. After executing the four steps, the comparison result of the net present cost was obtained as the decision support information.
The optimal solutions of theophylline tablet formulations based on datasets from 4 experimental designs (Box and Behnken design, central composite design, D-optimal design, and full factorial design) were calculated by the response surface method incorporating multivariate spline interpolation (RSM S ). Reliability of these solutions was evaluated by a bootstrap (BS) resampling technique. The optimal solutions derived from the Box and Behnken design, D-optimal design, and full factorial design dataset were similar. The distributions of the BS optimal solutions calculated for these datasets were symmetrical. Thus, the accuracy and the reproducibility of the optimal solutions enabled quantitative evaluation based on the deviations of these distributions. However, the distribution of the BS optimal solutions calculated for the central composite design dataset were almost unsymmetrical, and the basic statistic of these distributions could not be conducted. The reason for this problem was considered to be the mixing of the global and local optima. Therefore, self-organizing map (SOM) clustering was applied to identify the global optimal solutions. The BS optimal solutions were divided into 4 clusters by SOM clustering, the accuracy and reproducibility of the optimal solutions in each cluster were quantitatively evaluated, and the cluster containing the global optima was identified. Therefore, SOM clustering was considered to reinforce the BS resampling method for the evaluation of the reliability of optimal solutions irrespective of the dataset style.
The design space of the granulation process of mefenamic acid tablets, based on Box and Behnken design datasets, was described by a response surface method incorporating multivariate spline interpolation. The reliability of the optimal solutions and the acceptance ranges were evaluated by a bootstrap (BS) resampling technique. The distribution of the BS optimal solutions was almost symmetrical; however, several solutions, which were quite different from the original solution, were mixed. The reason for this problem was considered to be the mixing of the global and the local optima. Therefore, we applied self-organizing map (SOM) clustering for dividing data into several clusters and identified the cluster containing the global optima. The accuracy and reproducibility of the optimal solution in the cluster containing the optimal solution were quantitatively evaluated. In addition, the response surfaces modeled from all the BS datasets contained in the cluster were plotted into the same coordinates with the original response surface. The plots of BS optimal solutions were distributed around the original solution. Moreover, the average of all the BS response surfaces sufficiently corresponded with the original response surface. The conservative limits of the 95% confidence intervals of the acceptance ranges in three response variables could be calculated using the standard deviations of the BS response surfaces. Consequently, it was considered that a novel evaluation method based on BS resampling and SOM could be used for quantitatively evaluating the precision of the nonlinear response surface model.
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