Risk factors for post-cerebral infarction cognitive dysfunction in older adults: a retrospective study.

OBJECTIVE: Our research aims to elucidate the significance of type 2 diabetes (T2D) and provides an insight into a novel risk model for post-cerebral infarction cognitive dysfunction (PCICD). METHODS: Our study recruited inpatients hospitalized with cerebral infarction in Xijing hospital, who underwent cognitive assessment of Mini-Mental State Examination (MMSE) from January 2010 to December 2021. Cognitive status was dichotomized into normal cognition and cognitive impairment. Collected data referred to Demographic Features, Clinical Diseases, scale tests, fluid biomarkers involving inflammation, coagulation function, hepatorenal function, lipid and glycemic management. RESULTS: In our pooled dataset from 924 eligible patients, we included 353 in the final analysis (age range 65-91; 30.31% female). Multivariate logistic regression analysis was performed to show that Rural Areas (OR = 1.976, 95%CI = 1.111-3.515, P = 0.020), T2D (OR = 2.125, 95%CI = 1.267-3.563, P = 0.004), Direct Bilirubin (OR = 0.388, 95%CI = 0.196-0.769, P = 0.007), Severity of Dependence in terms of Barthel Index (OR = 1.708, 95%CI = 1.193-2.445, P = 0.003) that were independently associated with PCICD, constituting a model with optimal predictive efficiency. CONCLUSION: To the best of our knowledge, this study provides a practicable map of strategical predictors to robustly identify cognitive dysfunction at risk of post-cerebral infarction for clinicians in a broad sense. Of note, our findings support that the decline in serum direct bilirubin (DBil) concentration is linked to protecting cognitive function.

Risk factors for cognitive dysfunction and glycemic management in older adults with type 2 diabetes mellitus: a retrospective study.

BACKGROUND: Epidemiological evidence shows a robust relationship between cognitive dysfunction and type 2 diabetes mellitus (T2DM). This study identified major risk factors that might prevent or ameliorate T2DM-associated cognitive dysfunction in the realm of clinical practice. METHODS: Using Mini-mental State Examination (MMSE) in the light of education level, we identified older adults with T2DM on admission aged 50 and above. We conducted this case-control study when eligible participants were divided into Cognitively Normal (CN) group and Cognitively Impaired (CI) group. Analytical data referred to demographic characteristics, clinical features, fluid biomarkers, and scale tests. RESULTS: Of 596 records screened, 504 cases were included in the final analysis. Modified multivariate logistic regression analysis verified that homocysteine (OR = 2.048, 95%CI = 1.129-3.713), brain infarction (OR = 1.963, 95%CI = 1.197-3.218), dementia (OR = 9.430, 95%CI = 2.113-42.093), education level (OR = 0.605, 95%CI = 0.367-0.997), severity of dependence (OR = 1.996, 95%CI = 1.397-2.851), creatine kinase (OR = 0.514, 95%CI = 0.271-0.974) were significant risk factors of incident T2DM-related cognitive dysfunction in patients of advanced age. CONCLUSION: Our study supported a robust relationship between T2DM and cognitive dysfunction. Our results provide clinicians with major risk factors for T2DM-related cognitive dysfunction, in particular the protective role of creatine kinase.

Super-proximity routing in structured peer-to-peer overlay networks.

Peer-to-Peer systems are emerging as one of the most popular Internet applications. Structured Peer-to-Peer overlay networks use identifier based routing algorithms to allow robustness, load balancing, and distributed lookup needed in this environment. However, identifier based routing that is independent of Internet topology tends to be of low efficiency. Aimed at improving the routing efficiency, the super-proximity routing algorithms presented in this paper combine Internet topology and overlay routing table in choosing the next hop. Experimental results showed that the algorithms greatly improve the efficiency of Peer-to-Peer routing.