Cerebral ischemia-induced accumulation of unfolded proteins in vulnerable neurons triggers endoplasmic reticulum (ER) stress. Arginine-rich, mutated in early stage tumors (ARMET) is an ER stress-inducible protein and upregulated in the early stage of cerebral ischemia. The purposes of this study were to investigate the characteristics and implications of ARMET expression induced by focal cerebral ischemia. Focal cerebral ischemia in rats was induced by right middle cerebral artery occlusion with a suture; ischemic lesions were assessed by magnetic resonance imaging and histology; neuronal apoptosis was determined by TUNEL staining; the expressions of proteins were measured by immunohistochemistry, immunofluorescent labeling, and Western blotting. ARMET was found to be extensively upregulated in ischemic regions in a time-dependent manner. The expression of ARMET was neuronal in all examined structures in response to the ischemic insult. We also found that ARMET expression is earlier and more sensitive to ischemic stimulation than C/EBP homologous protein (CHOP). ER stress agent tunicamycin induced ARMET and CHOP expressions in the primary cultured neurons. Treatment with recombinant human ARMET promoted neuron proliferation and prevented from neuron apoptosis induced by tunicamycin. These results suggest that cerebral ischemia-induced ARMET expression may be protective to the neurons.

Objective To investigate the risk factors of medication nonadherence in patients with type 2 diabetes mellitus (T2DM) and to establish a risk nomogram model. Methods This retrospective study enrolled patients with T2DM, which were divided into two groups based on their scores on the Morisky Medication Adherence scale. Univariate and multivariate logistic regression analyses were used to screen for independent risk factors for medication nonadherence. A risk model was then established using a nomogram. The accuracy of the prediction model was evaluated using centrality measurement index and receiver operating characteristic curves. Internal verification was evaluated using bootstrapping validation. Results A total of 338 patients with T2DM who included in the analysis. Logistic regression analysis showed that the educational level, monthly per capita income, drug affordability, the number of drugs used, daily doses of drugs and the time spent taking medicine were all independent risk factors for medication nonadherence. Based on these six risk factors, a nomogram model was established to predict the risk of medication nonadherence, which was shown to be very reliable. Bootstrapping validated the nonadherence nomogram model for patients with T2DM. Conclusions This nomogram model could be used to evaluate the risks of drug nonadherence in patients with T2DM.