Development and validation of a machine learning-based model for postoperative ischemic stroke in middle-aged and elderly patients with hip or knee arthroplasty.

Postoperative ischemic stroke in middle-aged and elderly patients with hip or knee arthroplasty remains a major postoperative challenge, little is known about its incidence and risk factors. This study sought to create a nomogram for precise prediction of ischemic stroke after hip or knee arthroplasty. Discharge data of all middle-aged and elderly patients undergoing primary hip or knee arthroplasty from May 2013 to October 2020 were queried. These patients were then followed up over time to determine their risk of ischemic stroke. Clinical parameters and blood biochemical features were analyzed by the use of univariable and multivariable generalized logistic regression analysis. A nomogram to predict the risk of ischemic stroke was constructed and validated with bootstrap resampling. Eight hundred twenty-eight patients were included for analysis; Fifty-one were diagnosed with ischemic stroke. After final regression analysis, age, the neutrophil-to-lymphocyte ratio (NLR), a standard deviation of red blood cell distribution width, American Society of Anesthesiologists, low-density lipoprotein, and diabetes were identified and were entered into the nomogram. The nomogram showed an area under the receiver operating characteristic curve of 0. 841 (95% confidence interval [CI], 0.809-0.871). The calibration curves for the probability of ischemic stroke showed optimal agreement between the probability as predicted by the nomogram and the actual probability (Hosmer-Lemeshow test: P = .818). We developed a practical nomogram that can predict the risk of ischemic stroke for middle-aged and elderly patients with hip or knee arthroplasty. This model has the potential to assist clinicians in making treatment recommendations.

[Expression of glycerol dehydrogenase gene in Escherichia coli by codon optimization].

OBJECTIVE: To improve expression level of glycerol dehydrogenase gene gldA in Escherichia coli by means of codon optimization. METHODS: For immediately downstream region of initiation codon in gldA, we designed optimized sequence by choosing higher AT-content synonymous, in order that this region's AT-content was increased without changing the corresponding amino acids. Then we had wild gene gldA-WT site-directed mutagenesis depending on mega-primers PCR, so that physically optimized gene gldA-4 was acquired. We cloned gldA-4 into pET-32a(+) to construct expression plasmid pET-gldA4, which was transformed into Escherichia coli BL21 (DE3) for gaining engineering bacteria E. coli-4, by contrast engineering bacteria involved gldA-WT named E. coli-WT. After E. coli-4 and E. coli-WT were fermented in shake flasks,we measured enzyme activities of expression products with glycerol as substrate. RESULTS: Four gldA-4's bases in the second, fifth and sixth codon were different with gldA-WT, so AT-content of the optimized gene was up to 80.0% higher than the wild gene's 53.3%. Furthermore, enzyme activity of E. coli-4's crude extract was 191.3 U/mL more three times than E. coli-WT's 48.3 U/mL. CONCLUSION: This optimization scheme was quick and easy, but indeed increased dehydrogenase's activity. It possible becomes a universal method to improve heterogenous expression level of target genes.