Effectiveness of Multidomain Dormitory Environment and Roommate Intervention for Improving Sleep Quality of Medical College Students: A Cluster Randomised Controlled Trial in China

Medical students are vulnerable to sleep disorders, which could be further exaggerated by poor dormitory environment and roommate behaviour. However, there is little evidence of whether dormitory environment intervention is effective in improving the sleep quality of medical college students in developing countries. The present study aimed to evaluate the effects of a comprehensive multidomain intervention on dormitory environment and roommate behaviour among medical college students in China. In this cluster randomised controlled trial, a total of 106 dormitories (364 students) were randomly allocated into an intervention group (55 dormitories, 193 students) and a control group (51 dormitories, 171 students). The intervention group received a three-month intervention with multiple components to improve or adapt to sleep environments in dormitories; the control group received no intervention. Primary and secondary outcomes were measured at study enrolment and three months later for both groups. The linear mixed-effects models showed that, compared with the control group, the intervention was associated with a significantly decreased Pittsburgh Sleep Quality Index (β = −0.67, p = 0.012), and a marginally significant effect on reducing roommates’ influence on sleep schedule (β = −0.21, p = 0.066). Students in the intervention group rated “making dormitory sleep rules” and “wearing eye masks” as the most effective intervention measures. These findings could contribute to the limited body of scientific evidence about sleep intervention in Chinese medical students and highlight the importance of dormitory sleep environments in maintaining sleep quality

Adapentpronitrile, a New Dipeptidyl Peptidase-IV Inhibitor, Ameliorates Diabetic Neuronal Injury Through Inhibiting Mitochondria-Related Oxidative Stress and Apoptosis

Our previous studies indicated that adapentpronitrile, a new adamantane-based dipeptidyl peptidase-IV (DPP-IV) inhibitor, has a hypoglycemic effect and ameliorates rat pancreatic β cell dysfunction in type 2 diabetes mellitus through inhibiting DPP-IV activity. However, the effect of adapentpronitrile on the neurodegenerative diseases has not been studied. In the present study, we first found that adapentpronitrile significantly ameliorated neuronal injury and decreased amyloid precursor protein (APP) and amyloid beta (Aβ) expression in the hippocampus and cortex in the high fat diet/STZ rat model of diabetes. Furthermore, adapentpronitrile significantly attenuated oxidative stress, downregulated expression of the pro-apoptotic proteins BAX, cytochrome c, caspase-9, and caspase-3, and upregulated expression of the anti-apoptotic protein Bcl-2, although there was no effect on GLP-1R expression. At 30 min post-injection of adapentpronitrile (50 mg/kg) via the tail vein, its concentration in normal rat brain was 0.2034 ± 0.0094 μg/g. Subsequently, we further confirmed the neuroprotective effects and mechanism of adapentpronitrile in HT22 cells treated with high glucose (HG) and aluminum maltolate [Al(mal)3] overload, respectively. Our results showed significant decreases in mitochondrial membrane potential (MTP) and Bcl-2 expression, accompanied by a significant increase in apoptosis, reactive oxygen species (ROS) generation, and the expression of pro-apoptotic proteins in HT22 cells exposed to these stimuli. Adapentpronitrile treatment protected against neuronal injury, suppressed ROS generation, and reduced MTP and mitochondrial apoptosis in HT22 cells; however, DPP-IV activity was not detected. Our results suggest that adapentpronitrile protects against diabetic neuronal injury, at least partially, by inhibiting mitochondrial oxidative stress and the apoptotic pathway in a DPP-IV-independent manner

A Distributed Blocking Flowshop Scheduling with Setup Times Using Multi-Factory Collaboration Iterated Greedy Algorithm

As multi-factory production models are more widespread in modern manufacturing systems, a distributed blocking flowshop scheduling problem (DBFSP) is studied in which no buffer between adjacent machines and setup time constraints are considered. To address the above problem, a mixed integer linear programming (MILP) model is first constructed, and its correctness is verified. Then, an iterated greedy-algorithm-blending multi-factory collaboration mechanism (mIG) is presented to optimize the makespan criterion. In the mIG algorithm, a rapid evaluation method is designed to reduce the time complexity, and two different iterative processes are selected by a certain probability. In addition, collaborative interactions between cross-factory and inner-factory are considered to further improve the exploitation and exploration of mIG. Finally, the 270 tests showed that the average makespan and RPI values of mIG are 1.93% and 78.35% better than the five comparison algorithms on average, respectively. Therefore, mIG is more suitable to solve the studied DBFSP_SDST