The research is aimed to explore the distinct molecular signatures in discriminating the rheumatoid arthritis patients with traditional Chinese medicine (TCM) cold pattern and heat pattern. Twenty patients with typical TCM cold pattern and heat pattern were included. Microarray technology was used to reveal gene expression profiles in CD4+ T cells. The signal intensity of each expressed gene was globally normalized using the R statistics program. The ratio of cold pattern to heat pattern in patients with RA at more or less than 1:2 was taken as the differential gene expression criteria. Protein–protein interaction information for these genes from databases was searched, and the highly connected regions were detected by IPCA algorithm. The significant pathways were extracted from these subnetworks by Biological Network Gene Ontology tool. Twenty-nine genes differentially regulated between cold pattern and heat pattern were found. Among them, 7 genes were expressed significantly more in cold pattern. Biological network of protein–protein interaction information for these significant genes were searched and four highly connected regions were detected by IPCA algorithm to infer significant complexes or pathways in the biological network. Particularly, the cold pattern was related to Toll-like receptor signaling pathway. The following related pathways in heat pattern were included: Calcium signaling pathway; cell adhesion molecules; PPAR signaling pathway; fatty acid metabolism. These results suggest that better knowledge of the main biological processes involved at a given pattern in TCM might help to choose the most appropriate treatment.

Two independent neuroradiologists determined their ASPECTS. We stratified patients using ASPECTS into 2 groups: large volume infarcts (ASPECTS≤7 points) and small volume infarcts . In addition, we evaluated a third group with very large volume infarcts (ASPECTS≤5 points). We then analyzed the 3 subgroups using the Maas, Tan, and ASPECTS-collaterals grading systems of the computed tomographic angiogram intracranial collaterals. Good outcomes were defined by modified Rankin Scale score of 0 to 2 at 3 months. Results-A total of 300 patients were included in the final analysis. For patients with very large volume infarcts (ASPECTS≤5 points), univariable analysis showed that younger age, male sex, lower National Institute of Health Stroke Scale (NIHSS), lower systolic blood pressure, and good collaterals by Maas, Tan, or ASPECTS-collaterals grading were predictors of good outcomes. On multivariate analysis, younger age (odds ratio, 0.93; 95% confidence interval, 0.89-0.97; P=0.002) and good collaterals by ASPECTS-collaterals system (odds ratio, 1.34; 95% confidence interval, 1.15-1.57; P<0.001) were associated with good outcomes. Conclusions-In patients with large and very large volume infarcts, good collaterals as measured by the ASPECTScollaterals system is associated with improved outcomes and can help select patients for intravenous thrombolysis.

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The 5-HTTLPR SS genotype may be a risk factor for PSD. The 5-HTTLPR LL genotype showed a significant negative association with PSD. Further research to assess the sensitivity and specificity of predicting the risk of developing PSD by screening for the 5-HTTLPR genotype in stroke patients is required.

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Introduction: Singapore has the world’s second most efficient healthcare system while costing less than 5% GDP. It remains unclear whether transcatheter aortic valve implantation (TAVI) is cost-effective for treating intermediate-low risk severe aortic stenosis (AS) patients in a highly efficient healthcare system. Materials and Methods: A two-phase economic model combining decision tree and Markov model was developed to assess the costs, effectiveness, and the incremental cost-effectiveness ratio (ICER) of transfemoral (TF) TAVI versus surgical aortic valve replacement (SAVR) in intermediate-low risk patients over an 8-year time horizon. Mortality and complications rates were based on PARTNER 2 trial cohort A and Singapore life table. Costs were mainly retrieved from Singapore National University Health System database. Health utility data were obtained from Singapore population based on the EuroQol-5D (EQ-5D). A variety of sensitivity analyses were conducted. Results: In base case scenario, the incremental effectiveness of TF-TAVI versus SAVR was 0.19 QALYs. The ICER of TF-TAVI was S$33,833/QALY. When time horizon was reduced to 5 years, the ICER was S$60,825/QALY; when event rates from the propensity analysis was used, the ICER was S$21,732/QALY and S$44,598/QALY over 8-year and 5-year time horizons, respectively. At a willingness to pay threshold of S$73,167/QALY, TF-TAVI had a 98.19% probability of being cost-effective after 100,000 simulations. The model was the most sensitive to the costs of TF-TAVI procedure. Conclusion: TF-TAVI is a highly cost-effective option compared to SAVR for intermediate-low risk severe AS patients from a Singapore healthcare system perspective. Increased procedure experience, reduction in device cost, and technology advance may have further increased the cost-effectiveness of TF-TAVI per scenario analysis. Keywords: Surgical aortic valve replacement, Quality of life, Transfemoral TAVI, Reimbursement

Background Axonal loss denervates muscle, leading to an increase of fat accumulation in the muscle. Therefore, fat fraction (FF) in whole limb muscle using MRI has emerged as a monitoring biomarker for axonal loss in patients with peripheral neuropathies. In this study, we are testing whether deep learning‐based model can automate quantification of the FF in individual muscles. While individual muscle is smaller with irregular shape, manually segmented muscle MRI images have been accumulated in this lab; and make the deep learning feasible. Purpose To automate segmentation on muscle MRI images through deep learning for quantifying individual muscle FF in patients with peripheral neuropathies. Study Type Retrospective. Subjects 24 patients and 19 healthy controls. Field Strength/Sequences 3T; Interleaved 3D GRE. Assessment A 3D U‐Net model was implemented in segmenting muscle MRI images. This was enabled by leveraging a large set of manually segmented muscle MRI images. B1+ and B1− maps were used to correct image inhomogeneity. Accuracy of the automation was evaluated using Pixel Accuracy (PA), Dice Coefficient (DC) in binary masks; and Bland‐Altman and Pearson correlation by comparing FF values between manual and automated methods. Statistical Tests PA and DC were reported with their median value and standard deviation. Two methods were compared using the ± 95% confidence intervals (CI) of Bland‐Altman analysis and the Pearson’s coefficient (r2). Results DC values were from 0.83 ± 0.17 to 0.98 ± 0.02 in thigh and from 0.63 ± 0.18 to 0.96 ± 0.02 in calf muscles. For FF values, the overall ± 95% CI and r2 were [0.49, –0.56] and 0.989 in thigh and [0.84, –0.71] and 0.971 in the calf. Data Conclusion Automated results well agreed with the manual results in quantifying FF for individual muscles. This method mitigates the formidable time consumption and intense labor in manual segmentations; and enables the use of individual muscle FF as outcome measures in upcoming longitudinal studies. Level of Evidence 3 Technical Efficacy Stage 1

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