Lithium has been proved to be highly efficacious in the treatment of bipolar affective disorder, though a narrow therapeutic index and a high incidence of troublesome side effects often leads to poor compliance in patients. Therefore, there is a need to explore treatment strategies to improve the efficacy and side effect profile of lithium. We compared the efficacy and side effect profile of a once-daily versus twice-daily dosing schedule of lithium in mania. Eighty-three manic patients according to International Classification of Diseases, 10th Revision, Diagnostic Criteria for Research, giving informed consent were randomly allocated to receive regular lithium carbonate once daily or twice daily. They were assessed using Bech-Rafaelsen Mania Rating Scale, a lithium side effect scale, hemogram, renal function test, lipid profile, and a thyroid function test at baseline and Day 7, Day 21, and Day 42. Repeated-measures analysis of variance for Bech-Rafaelsen Mania Rating Scale scores showed a significant main effect, but interaction of treatment groups over time was not significant. Those subjects receiving twice-daily lithium experienced significantly higher urinary frequency on Day 21 (P = 0.008) and Day 42 (P = 0.035). They also required significantly higher total daily dose of lithium (P = 0.017) and had lower serum lithium levels (P < 0.001). There was a significant positive correlation between urinary frequency at Day 42 with lithium dose. A twice-daily dose of lithium was of similar efficacy as the once-daily schedule but produces higher renal adverse effects that may be dose-related. Therefore, a single daily dose of lithium can be a viable method to reduce the side effects of lithium, which may lead to better patient compliance.
Low contrast images and blurriness pose challenge in the over‐segmentation of image, which increases model complexities. In this work, a novel hybrid dermoscopic skin‐lesion segmentation method, namely SLICACO, is proposed incorporating the simple linear iterative clustering (SLIC) and ant colony optimization (ACO) algorithms. The working of proposed method is multifold. First, over‐segmentation of preprocessed image is generated using SLIC super‐pixel technique. Second, clusters of super‐pixels generated by SLIC are used by ACO with the pixels of similar intensity for edge detection and seek for the optimum pathway in a strained zone. Third, lesion area is segmented using the Convex Hull and Thresholding. Fourth, Erosion Filtering is used to obtain the final segmented image. The performance of SLICACO is assessed on five benchmark dermatoscopic datasets and compared with deep learning models to test its generalizing behavior. Promising results are obtained on the PH2 archive data set with an accuracy of 95.9%.
Background: Interview-based schizophrenia (SCZ) diagnostic methods are not completely valid. Moreover, SCZ-the disease entity is very heterogeneous. Supervised-Machine-Learning (sML) application of Artificial-Intelligence holds a tremendous promise in solving these issues. Aims: To sML-based discriminating validity of resting-state electroencephalographic (EEG) quantitative features in classifying SCZ from healthy and, positive (PS) and negative symptom (NS) subgroups, using a high-density recording. Settings and Design: Data collected at a tertiary care mental-health institute using a cross-sectional study design and analyzed at a premier Engineering Institute. Materials and Methods: Data of 38-SCZ patients and 20-healthy controls were retrieved. The positive-negative subgroup classification was done using Positive and Negative Syndrome Scale operational-criteria. EEG was recorded using 256-channel high-density equipment. Eight priori regions-of-interest were selected. Six-level wavelet decomposition and Kernel-Support Vector Machine (SVM) method were used for feature extraction and data classification. Statistical Analysis: Mann–Whitney test was used for comparison of machine learning-features. Accuracy, sensitivity, specificity, and area under receiver operating characteristics-curve were measured as discriminatory indices of classifications. Results: Accuracy of classifying SCZ from healthy and PS from NS SCZ, were 78.95% and 89.29%, respectively. While beta and gamma frequency related features most accurately classified SCZ from healthy controls, delta and theta frequency related features most accurately classified positive from negative SCZ. Inferior frontal gyrus features most accurately contributed to both the classificatory instances. Conclusions: SVM-based classification and sub-classification of SCZ using EEG data is optimal and might help in improving the “validity” and reducing the “heterogeneity” in the diagnosis of SCZ. These results might only be generalized to acute and moderately ill male SCZ patients.
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