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AimsType 2 diabetes mellitus (T2DM) is a strong risk factor for complications of coronavirus disease 2019 (COVID-19). The effect of T2DM medications on COVID-19 outcomes remains unclear. In a retrospective analysis of a cohort of 131 patients with T2DM hospitalized for COVID-19 in Wuhan, we have previously found that metformin use prior to hospitalization is associated with reduced mortality. The current study aims to investigate the effects of inpatient use of T2DM medications, including metformin, acarbose, insulin and sulfonylureas, on the mortality of COVID-19 patients with T2DM during hospitalization.MethodsWe continue to carry out a retrospective analysis of a cohort of 131 patients with T2DM hospitalized for COVID-19 and treated with different combinations of diabetes medications.ResultsWe found that patients using metformin (p = .02) and acarbose (p = .04), alone or both together (p = .03), after admission were significantly more likely to survive than those who did not use either metformin or acarbose. 37 patients continued to take metformin after admission and 35 (94.6%) survived. Among the 57 patients who used acarbose after admission, 52 survived (91.2%). A total of 20 patients used both metformin and acarbose, while 57 used neither. Of the 20 dual-use patients, 19 (95.0%) survived.ConclusionOur analyses suggest that inpatient use of metformin and acarbose together or alone during hospitalization should be studied in randomized trials.

AimsType 2 diabetes mellitus (T2DM) is a strong risk factor for complications of coronavirus disease 2019 (COVID-19). The effect of T2DM medications on COVID-19 outcomes remains unclear. In a retrospective analysis of a cohort of 131 patients with T2DM hospitalized for COVID-19 in Wuhan, we have previously found that metformin use prior to hospitalization is associated with reduced mortality. The current study aims to investigate the effects of inpatient use of T2DM medications, including metformin, acarbose, insulin and sulfonylureas, on the mortality of COVID-19 patients with T2DM during hospitalization.MethodsWe continue to carry out a retrospective analysis of a cohort of 131 patients with T2DM hospitalized for COVID-19 and treated with different combinations of diabetes medications.ResultsWe found that patients using metformin (p = .02) and acarbose (p = .04), alone or both together (p = .03), after admission were significantly more likely to survive than those who did not use either metformin or acarbose. 37 patients continued to take metformin after admission and 35 (94.6%) survived. Among the 57 patients who used acarbose after admission, 52 survived (91.2%). A total of 20 patients used both metformin and acarbose, while 57 used neither. Of the 20 dual-use patients, 19 (95.0%) survived.ConclusionOur analyses suggest that inpatient use of metformin and acarbose together or alone during hospitalization should be studied in randomized trials.

Type 2 diabetes mellitus (T2DM) is a strong risk factor for complications of coronavirus disease 2019 (COVID-19). The effect of T2DM medications on COVID-19 outcomes remains unclear. In a retrospective analysis of a cohort of 131 patients with T2DM hospitalized for COVID-19 in Wuhan, we have previously found that metformin use prior to hospitalization is associated with reduced mortality. Here we continue to investigate the effects of inpatient use of T2DM medications, including metformin, acarbose, insulin, and sulfonylureas, on the mortality of COVID-19 patients with T2DM during hospitalization. We found that patients using metformin and acarbose, alone or both together, after admission were significantly more likely to survive than those who did not use either metformin or acarbose. Thus, our analyses suggest that inpatient use of metformin and acarbose together or alone during hospitalization should be studied in randomized trials.

The SARS-CoV-2 and its variants are still hitting the world. Ever since the outbreak, neurological involvements as headache, ageusia, and anosmia in COVID-19 patients have been emphasized and reported. But the pathogenesis of these new-onset neurological manifestations in COVID-19 patients is still obscure and controversial. As difficulty always lay in the diagnosis of neurological infection, current reports to validate the presence of SARS-CoV-2 in cerebrospinal fluid (CSF) almost relied on the basic methods and warranted improvement. Here we reported a case series of 8 patients with prominent new-onset neurological manifestations, who were screened out from a patch of 304 COVID-19 confirmed patients. Next-generation sequencing (NGS) and proteomics were conducted in the simultaneously obtained CSF and serum samples of the selected patients, with three non-COVID-19 patients with matched demographic features used as the controls for proteomic analysis. SARS-CoV-2 RNA was detected in the CSF of four COVID-19 patients and was suspicious in the rest four remaining patients by NGS, but was negative in all serum samples. Proteomic analysis revealed that 185 and 59 proteins were differentially expressed in CSF and serum samples, respectively, and that only 20 proteins were shared, indicating that the proteomic changes in CSF were highly specific. Further proteomic annotation highlighted the involvement of complement system, PI3K-Akt signaling pathway, enhanced cellular interaction, and macrophages in the CSF proteomic alterations. This study, equipped with NGS and proteomics, reported a high detection rate of SARS-CoV-2 in the CSF of COVID-19 patients and the proteomic alteration of CSF, which would provide insights into understanding the pathological mechanism of SARS-CoV-2 CNS infection.

ObjectiveAlthough type 2 diabetes mellitus (T2DM) has been reported as a risk factor for coronavirus disease 2019 (COVID-19), the effect of pharmacologic agents used to treat T2DM, such as metformin, on COVID-19 outcomes remains unclear. Metformin increases the expression of angiotensin converting enzyme 2, a known receptor for severe acute respiratory syndrome coronavirus 2. Data from people with T2DM hospitalized for COVID-19 were used to test the hypothesis that metformin use is associated with improved survival in this population.MethodsRetrospective analyses were performed on de-identified clinical data from a major hospital in Wuhan, China, that included patients with T2DM hospitalized for COVID-19 during the recent epidemic. One hundred and thirty-one patients diagnosed with COVID-19 and T2DM were used in this study. The primary outcome was mortality. Demographic, clinical characteristics, laboratory data, diabetes medications, and respiratory therapy data were also included in the analysis.ResultsOf these 131 patients, 37 used metformin with or without other antidiabetes medications. Among the 37 metformin-taking patients, 35 (94.6%) survived and 2 (5.4%) did not survive. The mortality rates in the metformin-taking group versus the non-metformin group were 5.4% (2/37) versus 22.3% (21/94). Using multivariate analysis, metformin was found to be an independent predictor of survival in this cohort (P = .02).ConclusionThis study reveals a significant association between metformin use and survival in people with T2DM diagnosed with COVID-19. These clinical data are consistent with potential benefits of the use of metformin for COVID-19 patients with T2DM.

Background: Health workers (HWs) faced considerable psychosocial hazards during the COVID-19 pandemic, which profoundly affected their occupational health and job performance. The potential indirect relationship between workplace violence (WPV) and burnout among HWs needs to be further explored. The purpose of this study is to examine the chain mediating effects of sleep disturbance and work ability in the relationship between WPV and burnout among HWs. Methods: A cross-sectional study was conducted in a secondary hospital two years after the COVID-19 outbreak in Wuhan, China. A sample of 571 HWs was recruited using a cluster sampling method, achieving a response rate of 80.06%. Participants completed self-report questionnaires that included demographic information and measures of WPV, burnout, sleep disturbance, and work ability. Results: The prevalence rates of burnout and WPV among HWs two years after the COVID-19 outbreak were 37.30% and 31.52%, respectively. WPV was significantly associated with burnout (β = 0.446, p < 0.001). Sleep disturbance was identified as a mediator in the relationship between WPV and burnout (β = 0.063, 95% CI: 0.027–0.105), accounting for 14.13% of the total effect. Similarly, work ability also played a mediating role in this relationship (β = 0.142, 95% CI: 0.065–0.225), accounting for 31.84%. Additionally, both sleep disturbance and work ability exhibited a chain mediation effect on the association between WPV and burnout (β = 0.020, 95% CI: 0.008–0.036), and the total indirect effect accounted for 50.67%. Conclusions: Among Chinese HWs, WPV exerts significant direct and indirect effects on burnout symptoms, mediated by sleep disturbance and work ability. This finding provides valuable empirical insights for designing interventions to mitigate the adverse effects of psychosocial factors such as WPV and burnout among HWs. After exposure to WPV, measures focused on reducing sleep disturbance and enhancing work ability may prove effective in alleviating burnout in subsequent interventions. [ABSTRACT FROM AUTHOR]

This paper analyzes and compares the four mainstream cloud desktop technologies: RDS, VDI, IDV and VOI. According to the practical application in colleges and universities, a hybrid cloud desktop technology scheme is designed and proposed, and the specific implementation method of the integration of VDI and VOI is studied. In view of the particularity and requirements of experimental courses and application scenarios, an application model based on this technical scheme is designed, which is suitable for all the common scenes of computer experimental teaching, realizes the deep integration of cloud desktop and teaching activities, and also provides reference for the construction and application of cloud desktop technology in colleges and universities.

In view of the problems existing in the current teaching of computer network technology and application course, this paper introduces the educational concept driven by innovative thinking into the teaching of the course, and puts forward specific teaching reform measures: the teaching method which adopts the engineering application of innovative thinking as the guidance and focuses the problems on the core knowledge points, combining MOOC, SPOC and flipped classroom teaching philosophy, it is student-centered and arouses students’ interest in learning. Practice has proved that the teaching reform has obvious practical effects: students can quickly grasp the basic theory of computer network with more goals, and meanwhile, their practical ability, teamwork ability and innovation ability can be better trained and improved, which is conducive to the cultivation of high-quality innovative talents.