Micro-level social structures and the success of COVID-19 national policies.

Similar policies in response to the COVID-19 pandemic have resulted in different success rates. Although many factors are responsible for the variances in policy success, our study shows that the micro-level structure of person-to-person interactions-measured by the average household size and in-person social contact rate-can be an important explanatory factor. To create an explainable model, we propose a network transformation algorithm to create a simple and computationally efficient scaled network based on these micro-level parameters, as well as incorporate national-level policy data in the network dynamic for SEIR simulations. The model was validated during the early stages of the COVID-19 pandemic, which demonstrated that it can reproduce the dynamic ordinal ranking and trend of infected cases of various European countries that are sufficiently similar in terms of some socio-cultural factors. We also performed several counterfactual analyses to illustrate how policy-based scenario analysis can be performed rapidly and easily with these explainable models.

MicroRNA-186-5p serves as a diagnostic biomarker in atherosclerosis and regulates vascular smooth muscle cell proliferation and migration.

OBJECTIVE: MicroRNA dysregulation occurs in many human diseases, including atherosclerosis. Here, we examined the serum expression and clinical significance of miR-186-5p in patients with atherosclerosis, and explored its influence on vascular smooth muscle cell (VSMC) proliferation and migration. METHODS: Blood samples were collected from 104 patients with asymptomatic atherosclerosis and 80 healthy controls. Quantitative real-time PCR was applied to measure the miR-186-5p level. An ROC curve was established to assess the discriminatory ability of the serum miR-186-5p level for identifying atherosclerosis from controls. CCK-8 and Transwell assays were used to evaluate the impact of miR-186-5p on cell behaviors. RESULTS: Serum expression of miR-186-5p was significantly higher in atherosclerosis patients than in the control group. The serum miR-186-5p level showed a positive correlation with CIMT and could be used to distinguish atherosclerosis patients from healthy controls, with an area under the curve (AUC) score of 0.891. In VSMCs, overexpression of miR-186-5p significantly promoted cell proliferation and migration, while the opposite results were observed when miR-186-5p was downregulated. CONCLUSION: Overexpression of miR-186-5p has a certain diagnostic significance for atherosclerosis. Upregulation of miR-186-5p stimulates VSMC proliferation and migration. Therefore, it is a possible target for atherosclerosis interventions.

Mixed-integer optimization approach to learning association rules for unplanned ICU transfer.

After admission to emergency department (ED), patients with critical illnesses are transferred to intensive care unit (ICU) due to unexpected clinical deterioration occurrence. Identifying such unplanned ICU transfers is urgently needed for medical physicians to achieve two-fold goals: improving critical care quality and preventing mortality. A priority task is to understand the crucial rationale behind diagnosis results of individual patients during stay in ED, which helps prepare for an early transfer to ICU. Most existing prediction studies were based on univariate analysis or multiple logistic regression to provide one-size-fit-all results. However, patient condition varying from case to case may not be accurately examined by such a simplistic judgment. In this study, we present a new decision tool using a mathematical optimization approach aiming to automatically discover rules associating diagnostic features with high-risk outcome (i.e., unplanned transfers) in different deterioration scenarios. We consider four mutually exclusive patient subgroups based on the principal reasons of ED visits: infections, cardiovascular/respiratory diseases, gastrointestinal diseases, and neurological/other diseases at a suburban teaching hospital. The analysis results demonstrate significant rules associated with unplanned transfer outcome for each subgroups and also show comparable prediction accuracy (>70%) compared to state-of-the-art machine learning methods while providing easy-to-interpret symptom-outcome information.