The predictive performance of artificial intelligence on the outcome of stroke: a systematic review and meta-analysis.

Objectives: This study aimed to assess the accuracy of artificial intelligence (AI) models in predicting the prognosis of stroke. Methods: We searched PubMed, Embase, and Web of Science databases to identify studies using AI for acute stroke prognosis prediction from the database inception to February 2023. Selected studies were designed cohorts and had complete data. We used the Quality Assessment of Diagnostic Accuracy Studies tool to assess the qualities and bias of included studies and used a random-effects model to summarize and analyze the data. We used the area under curve (AUC) as an indicator of the predictive accuracy of AI models. Results: We retrieved a total of 1,241 publications and finally included seven studies. There was a low risk of bias and no significant heterogeneity in the final seven studies. The total pooled AUC under the fixed-effects model was 0.872 with a 95% CI of (0.862-0.881). The DL subgroup showed its AUC of 0.888 (95%CI 0.872-0.904). The LR subgroup showed its AUC 0.852 (95%CI 0.835-0.869). The RF subgroup showed its AUC 0.863 (95%CI 0.845-0.882). The SVM subgroup showed its AUC 0.905 (95%CI 0.857-0.952). The Xgboost subgroup showed its AUC 0.905 (95%CI 0.805-1.000). Conclusion: The accuracy of AI models in predicting the outcomes of ischemic stroke is good from our study. It could be an assisting tool for physicians in judging the outcomes of stroke patients. With the update of AI algorithms and the use of big data, further AI predictive models will perform better.

An S-like curve relationship between systemic inflammation response index (SIRI) and respiratory failure in GBS patients.

BACKGROUND: Our study examined the association between the initial systemic inflammation response index (SIRI) and respiratory failure in patients with Guillain-Barré syndrome (GBS). METHODS: The weighted linear regression model, weighted chi-square test, logistic regression models, smooth curve fittings, and the two-piece linear regression model were utilized for data analysis. RESULTS: Among the 443 GBS patients, 75 (6.9%) had experienced respiratory failure. According to logistic regression models, there existed no consistent linear relationship between respiratory failure and SIRI in model 1 (OR = 1.2, p < 0.001), model 2 (OR = 1.2, p < 0.001), and model 3 (OR = 1.3, p = 0.017). However, smooth curve fittings found an S-like curve relationship between SIRI and respiratory failure. Furthermore, when SIRI was < 6.4, there existed a positive correlation between SIRI and respiratory failure in model 1 (OR = 1.5, 95% CI = (1.3, 1.8), p < 0.0001), higher correlation in model 2 (OR = 1.6, 95% CI = (1.3, 1.8), p < 0.0001), and highest correlation in model 3 (OR = 1.6, 95% CI = (1.3, 2.5), p < 0.0001). CONCLUSIONS: SIRI can be used as a predictor of respiratory failure in GBS, and an S-like relationship exists between SIRI and respiratory failure at an infliction point of 6.4. When the SIRI was less than 6.4 and increased, SIRI was associated with a higher occurrence of respiratory failure. The risk of respiratory failure was no longer increased when the SIRI was over 6.4.

Prevalence and risk factors for abnormal tandem gait in patients with essential tremor syndrome: A cross-sectional study in Southwest China.

Objectives: Patients with essential tremor (ET) syndrome have more prevalent and more serious gait and balance impairments than healthy controls. In this cross-sectional study, we explored whether balance impairments are associated with falls as well as more pronounced non-motor symptoms in patients with ET syndrome. Methods: We assessed the tandem gait (TG) test, as well as falls or near-falls that occurred over the previous year. Non-motor symptoms-including cognitive deficits, psychological and sleep disorders-were evaluated. In univariate analyses, statistical significance was corrected for multiple comparisons using the Benjamini-Hochberg method. Multiple logistic regression was utilized to evaluate the risk factors of poor TG performance in patients with ET syndrome. Results: A total of 358 patients with ET syndrome were divided into the abnormal TG (a-TG) and normal TG (n-TG) groups based on their performances in the TG test. We revealed that 47.2% of patients with ET syndrome had a-TG. The patients with a-TG were older, were more likely female, and were more likely present with cranial tremors and falls or near-falls (all adjusted P < 0.01). The patients with a-TG had significantly lower Mini-Mental Status Examination scores, as well as significantly higher Hamilton Depression/Anxiety Rating Scale and Pittsburgh Sleep Quality Index scores. Multiple logistic regression analysis demonstrated that female sex (OR 1.913, 95% CI: 1.180-3.103), age (OR 1.050, 95% CI: 1.032-1.068), cranial tremor scores (OR 1.299, 95% CI: 1.095-1.542), a history of falls or near-falls (OR 2.952, 95% CI: 1.558-5.594), and the presence of depressive symptoms (OR 1.679, 95% CI: 1.034-2.726) were associated with the occurrence of a-TG in patients with ET syndrome. Conclusion: TG abnormalities may be a predictor of fall risk in patients with ET syndrome and are associated with non-motor symptoms, especially depression.

Association Between Metformin and Alzheimer's Disease: A Systematic Review and Meta-Analysis of Clinical Observational Studies.

BACKGROUND: As one of the widely used drugs for the management of type 2 diabetes mellites (T2DM), metformin is increasingly believed to delay cognitive deterioration and therapeutically for Alzheimer's disease (AD) patients especially those with T2DM. However, studies of the potential neuroprotective effects of metformin in AD patients have reported contradictory results. OBJECTIVE: This study aimed to evaluate the association between metformin and the risk of developing AD. METHODS: We systematically searched the PubMed, EMBASE, Web of Science, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases to identify clinical observational studies on the relationship between AD risk and metformin use published before December 20, 2021. Two investigators independently screened records, extracted data, and assessed the quality of the studies. Pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were calculated using random-effect models. RESULTS: After screening a total of 1,670 records, we included 10 studies involving 229,110 participants. The meta-analysis showed no significant association between AD incidence and metformin exposure (OR 1.17, 95% CI 0.88-1.56, p = 0.291). However, subgroup analysis showed that among Asians, the risk of AD was significantly higher among metformin users than those who did not (OR 1.71, 95% CI 1.24-2.37, p = 0.001). CONCLUSION: The available evidence does not support the idea that metformin reduces risk of AD, and it may, in fact, increase the risk in Asians. Further well-designed randomized controlled trials are required to understand the role played by metformin and other antidiabetic drugs in the prevention of AD and other neurodegenerative diseases.

Exploring the dual character of metformin in Alzheimer's disease.

Alzheimer's disease (AD) is the most common neurodegenerative disease, which results in dementia typically in the elderly. The disease is mainly characterized by the deposition of amyloid beta (Aß) plaques and neurofibrillary tangles (NFTs) in the brain. However, only few drugs are available for AD because of its unknown pathological mechanism which limits the development of new drugs. Therefore, it is urgent to identify potential therapeutic strategies for AD. Moreover, research have showed that there is a significant association between Type 2 diabetes mellites (T2DM) and AD, suggesting that the two diseases may share common pathophysiological mechanisms. Such mechanisms include impaired insulin signaling, altered glucose metabolism, inflammation, oxidative stress, and premature aging, which strongly affect cognitive function and increased risk of dementia. Consequently, as a widely used drug for T2DM, metformin also has therapeutic potential for AD in vivo. It has been confirmed that metformin is beneficial on the brain of AD animal models. The mechanisms underlying the effects of metformin in Alzheimer's disease are complex and multifaceted. Metformin may work through mechanisms involving homeostasis of glucose metabolism, decrease of amyloid plaque deposition, normalization of tau protein phosphorylation and enhancement of autophagy. However, in clinical trials, metformin had little effects on patients with mild cognitive impairment or mild AD. Pathological effects and negative clinical results of metformin on AD make the current topic quite controversial. By reviewing the latest progress of related research, this paper summarizes the possible role of metformin in AD. The purpose of this study is not only to determine the potential treatment of AD, but also other related neurodegenerative diseases.

Exosomal MicroRNA Expression Profiling Analysis of the Effects of Lycium Barbarum Polysaccharide on Gestational Diabetes Mellitus Mice.

OBJECTIVE: Gestational diabetes mellitus (GDM) is a pathological condition, affecting an increasing number of pregnant women worldwide. Safe and effective treatment for GDM is very important for the public health. In this study, we utilized a high-fat diet-induced GDM model to evaluate the effects of LBP on GDM and examined the changes of exosomal microRNA expression profiling to decipher the potential underlying mechanism of LBP. METHODS: Female C57BL/6J mice were fed a control diet, HFD, or 150 mg/kg LBP-supplemented HFD for 6 weeks before conception and throughout gestation. Oral glucose tolerance test and plasma lipid levels were determined, and liver histopathology was assessed. Sequencing was used to define the microRNA expression profiling of plasma exosomes in the three groups of mice, and protein expression levels of the candidate target genes were analyzed. RESULTS: LBP significantly relieved glucose intolerance, abnormal plasma lipid levels, and pathomorphological changes of liver histopathology in HFD-induced GDM mice. Moreover, we found that this effect of LBP was mediated by downregulation of the increase of 6 miRNAs (miR-93-3p, miR-188-5p, miR-466k, miR-1188-5p, miR-7001-3p, and miR-7115-5p) and reversing the increase of the protein expression of CPT1A, which is the target gene of miR-188-5p. CONCLUSIONS: Our findings provide novel insights into the biological activities of LBP in the treatment of GDM.

Undergraduate laboratory experiment on determination of total protein content in milk powder by moving reaction boundary titration.

Laboratory exercises focused on protein quantification are frequently conducted in traditional undergraduate biochemistry laboratory curriculum. The laboratory course described here is designed to provide students with experience in measurement of protein content in milk powder by moving reaction boundary titration (MRBT), a new rapid technique for total protein content determination in milk. In addition, this approach is weakly influenced by nonprotein nitrogen reagents such as melamine and urea. The course was done as three weekly laboratory exercises. First, students established a standard curve for milk protein concentration by MRBT method. Then, students investigated the influence of nonprotein nitrogen reagents on MRBT method. Finally, students made a comparison among three different protein quantification methods (MRBT, Biuret, and Kjeldahl method). From the experiments, students grasped the concept and advantages of MRBT and deepened the understanding of protein quantification. This course offer students the opportunity to be exposed to an advanced technique, which may have practical significance to their future study and work in the life science field. © 2018 International Union of Biochemistry and Molecular Biology, 46(6):644-651, 2018.

Age at menarche and risks of gestational diabetes mellitus: a meta-analysis of prospective studies.

BACKGROUND: Accumulating evidence suggests that early menarche is associated with adult obesity, which in turn may increase the risk of insulin resistance and hyperglycemia. However, the relation of menarcheal age with gestational diabetes mellitus (GDM) remains inconsistent across studies. The objective of this meta-analysis was to evaluate the association between age at menarche and GDM risk. MATERIALS AND METHODS: We searched Medline (PubMed), Embase, Web of Knowledge and the Cochrane library through the end of May 2017. We pooled summary relative risks (RR) with 95% confidence intervals (CIs). Stata 12.0 software was used to analyse the data. RESULTS: Five prospective studies were eligible for inclusion. The results of meta-analysis showed that women in the early menarcheal age group (at < 12 years of age) had a higher risk of GDM compared with those in the "not early" menarcheal age group (at ≥ 13 years of age) (pooled RR = 1.31, 95% CI: 1.05, 1.56) with moderate heterogeneity (I2 = 47.5%, P = 0.107). However, there was no obvious protection of late menarche (at ≥ 15 years of age) versus median menarche (at 13 years of age) (pooled RR = 1.12, 95% CI: 0.92, 1.32; I2 = 0%). CONCLUSIONS: The findings support an association between earlier age at menarche and increased risk of GDM. Age at menarche may help identify women with increased risk of developing GDM. However, considering the potential limitations in this study, further larger prospective studies are warranted to verify our findings.