Materials and Methods: The primary analysis was based on population control studies. Data were pooled by means of a random-effects model, and sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR-), diagnostic odds ratios (DOR), and areas under the summary receiver operating characteristic curve (AUC) were calculated.
Results: In 23 population control studies, HLA-B*15:02 was measured in 373 patients with CBZ-induced TEN/SJS and 3452 patients without CBZ-induced TEN/SJS. The pooled sensitivity, specificity, LR+, LR-, DOR, and AUC were 0.67 (95% confidence interval [CI] = 0.63-0.72), 0.98 (95% CI = 0.98-0.99), 19.73 (95% CI = 10.54-36.92), 0.34 (95% CI = 0.23-0.49), 71.38 (95% CI = 34.89-146.05), and 0.96 (95% CI = 0.92-0.98), respectively. Subgroup analyses for Han Chinese, Thai, and Malaysian populations yielded similar findings. Specifically, racial/ethnic subgroup analyses revealed similar findings with respect to DOR for Han Chinese (99.28; 95% CI = 22.20-443.88), Thai (61.01; 95% CI = 23.05-161.44), and Malaysian (30; 95% CI = 7.08-126.68) populations, which are similar to the pooled DOR for the relationship between the HLA-B*15:02 allele and CBZ-induced TEN/SJS across all populations (71.38; 95% CI = 34.89-146.05).
Conclusions: The present study reveals that CBZ is the leading cause of TEN/SJS in many countries. Screening of HLA-B*15:02 may help patients to prevent the occurrence of CBZ-induced TEN/SJS, especially in populations with a higher (≥5%) risk allele frequency.
Methods: This study included patients with biopsy-proven non-alcoholic fatty liver disease (NAFLD) diagnosed between November 2012 and October 2015. Serum cathepsin D levels were measured using the CatD enzyme-linked immunosorbent assay (USCN Life Science, Wuhan, China) using stored samples collected on the same day of the liver biopsy procedure. The performance of cathepsin D in the diagnosis and monitoring of NASH was evaluated using receiver operating characteristic analysis.
Results: Data for 216 liver biopsies and 34 healthy controls were analyzed. The mean cathepsin D level was not significantly different between NAFLD patients and controls; between NASH and non-NASH patients; and across the different steatosis, lobular inflammation, and hepatocyte ballooning grades. The area under receiver operating characteristic curve (AUROC) of cathepsin D for the diagnosis of NAFLD and NASH was 0.62 and 0.52, respectively. The AUROC of cathepsin D for the diagnosis of the different steatosis, lobular inflammation, and hepatocyte ballooning grades ranged from 0.51 to 0.58. Of the 216 liver biopsies, 152 were paired liver biopsies from 76 patients who had a repeat liver biopsy after 48 weeks. There was no significant change in the cathepsin D level at follow-up compared to baseline in patients who had histological improvement or worsening for steatosis, lobular inflammation, and hepatocyte ballooning grades. Cathepsin D was poor for predicting improvement or worsening of steatosis and hepatocyte ballooning, with AUROC ranging from 0.47 to 0.54. It was fair for predicting worsening (AUROC 0.73) but poor for predicting improvement (AUROC 0.54) of lobular inflammation.
Conclusion: Cathepsin D was a poor biomarker for the diagnosis and monitoring of NASH in our cohort of Asian patients, somewhat inconsistent with previous observations in Caucasian patients. Further studies in different cohorts are needed to verify our observation.
Methods: Six osteoporosis risk assessments tools (the Simple Calculated Osteoporosis Risk Estimation [SCORE], the Osteoporosis Risk Assessment Instrument, the Age Bulk One or Never Estrogen, the body weight, the Malaysian Osteoporosis Screening Tool, and the Osteoporosis Self-Assessment Tool for Asians) were used to screen postmenopausal women who had not been previously diagnosed with osteoporosis/osteopenia. These women also underwent a dual-energy X-ray absorptiometry (DXA) scan to confirm the absence or presence of osteoporosis.
Results: A total of 164/224 participants were recruited (response rate, 73.2%), of which only 150/164 (91.5%) completed their DXA scan. Sixteen participants (10.7%) were found to have osteoporosis, whilst 65/150 (43.3%) were found to have osteopenia. Using precision-recall curves, the recall of the tools ranged from 0.50 to 1.00, whilst precision ranged from 0.04 to 0.14. The area under the curve (AUC) ranged from 0.027 to 0.161. The SCORE had the best balance between recall (1.00), precision (0.04-0.12), and AUC (0.072-0.161).
Conclusions: We found that the SCORE had the best balance between recall, precision, and AUC among the 6 screening tools that were compared among Malaysian postmenopausal women.
METHODS: Valproic acid-encapsulated nanoemulsions were formulated and physically characterised (osmolarity, viscosity, drug content, drug encapsulation efficiency). Further investigations were also conducted to estimate the drug release, cytotoxic profile, in-vitro blood-brain barrier (BBB) permeability, pharmacokinetic parameter and the concentration of VPA and VANE in blood and brain.
KEY FINDINGS: Physical characterisation confirmed that VANE was suitable for parenteral administration. Formulating VPA into nanoemulsion significantly reduced the cytotoxicity of VPA. In-vitro drug permeation suggested that VANEs crossed the BBB as freely as VPA. Pharmacokinetic parameters of VANE-treated rats in plasma and brain showed F3 VANE had a remarkable improvement in AUC, prolongation of half-life and reduction in clearance compared to VPA. Given the same extent of in-vitro BBB permeation of VPA and VANE, the higher bioavailability of VANE in brain was believed to have due to higher concentration of VANE in blood. The brain bioavailability of VPA was improved by prolonging the half-life of VPA by encapsulating it within the nanoemulsion-T80.
CONCLUSIONS: Nanoemulsion containing VPA has alleviated the cytotoxic effect of VPA and improved the plasma and brain bioavailability for parenteral delivery of VPA.
METHODS: The pterygium screening system was tested on two normal eye databases (UBIRIS and MILES) and two pterygium databases (Australia Pterygium and Brazil Pterygium). This system comprises four modules: (i) a preprocessing module to enhance the pterygium tissue using HSV-Sigmoid; (ii) a segmentation module to differentiate the corneal region and the pterygium tissue; (iii) a feature extraction module to extract corneal features using circularity ratio, Haralick's circularity, eccentricity, and solidity; and (iv) a classification module to identify the presence or absence of pterygium. System performance was evaluated using support vector machine (SVM) and artificial neural network.
RESULTS: The three-step frame differencing technique was introduced in the corneal segmentation module. The output image successfully covered the region of interest with an average accuracy of 0.9127. The performance of the proposed system using SVM provided the most promising results of 88.7%, 88.3%, and 95.6% for sensitivity, specificity, and area under the curve, respectively.
CONCLUSION: A basic platform for computer-aided pterygium screening was successfully developed using the proposed modules. The proposed system can classify pterygium and non-pterygium cases reasonably well. In our future work, a standard grading system will be developed to identify the severity of pterygium cases. This system is expected to increase the awareness of communities in rural areas on pterygium.
METHODS: EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects.
RESULTS: The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] using SVM and k-NN machine learning classifiers.
CONCLUSION: The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.
Methods: A cross-sectional study on 50 patients of age 50 and above with contrast-enhanced CT (CECT) and dual-energy X-ray absorptiometry (DXA) was conducted from November 2018 to November 2019. Single region of interest (ROI) was placed at the anterior trabecular part of L1 vertebra on CECT to obtain HU value. Correlation of CT HU value of L1 vertebra and DXA T-score, interrater reliability agreement between HU value of L1 vertebra and T-score in determining groups of with and without osteoporosis, ROC curve analysis for diagnostic accuracy and cut-off value of CT for detection of osteoporosis were identified.
Results: Significant correlation between HU value of L1 vertebra and L1 T-score (r = 0.683)/lowest skeletal T-score (r = 0.703) (P < 0.001). Substantial agreement between HU value of L1 vertebra and DXA in determining the groups with and without osteoporosis (k = 0.8; P < 0.001). The area under the receiver operating characteristic (AUROC) curve was 0.93 (95% CI: 0.86, 1.00) using HU value (P < 0.001). Cut-off value for osteoporosis was 149 HU.
Conclusion: HU value of lumbar vertebra is an effective alternative for the detection of osteoporosis with high diagnostic accuracy in hospitals without DXA facility.