Objective To investigate the distribution of CYP2C9*3 and methionine synthetase(MSA2756G) genes related to drug therapy in hyperlipidemia patients of Ningxia region as well as its relation with hyperlipidemia.Methods Genotype was determined by using amplication-created restriction sites(ACRS) and polymerase chain reaction-restriction fragment length polymorphism(PCR-RFLP) in hyperlipidemia patients.Results Among the 180 hyperlipidemia patients of Ningxia Hui population,the frequency of CYP2C9*3 alleles was 3.33% and mutation rate in men(3.05%) was significantly higher than that in women(0.28%)(P0.05).The frequency of MSA2756G(15.83%) alleles was significantly higher than that in healthy control group(10.25%)(P

Objective: To systematically evaluate risk factors for cardiometabolic multimorbidity (CMM), so as to provide the evidence for formulating CMM prevention and control strategies. Methods: Publications pertaining to the risk factors for CMM were retrieved from databases, including SinoMed, CNKI, Wanfang Data, VIP, PubMed and Cochrane Library from inception to March 31, 2023. Meta-analysis was performed using the software RevMan 5.4 and Stata 16.0, and sensitivity analysis was performed using the leave-one-out method. The publication bias was evaluated using Egger's test. Results: Totally 494 publications were screened, and 20 publications were included in the final analysis, including 13 cohort studies (covering 1 940 000 participants) and 7 cross-sectional studies (covering 13 000 000 participants). Meta-analysis revealed that female (OR=1.54, 95%CI: 1.40-1.71), middle age (OR=3.80, 95%CI: 3.33-4.34), elderly (OR=2.82, 95%CI: 1.48-5.37), urban resident (OR=1.41, 95%CI: 1.27-1.57), higher education level (OR=2.01, 95%CI: 1.35-3.01), higher economic level (OR=1.21, 95%CI: 1.16-1.25), overweight (OR=1.92, 95%CI: 1.64-2.26), obesity (OR=3.01, 95%CI: 2.30-3.93), central obesity (OR=1.70, 95%CI: 1.12-2.56), smoking (OR=1.27, 95%CI: 1.07-1.51), alcohol consumption (OR=1.27, 95%CI: 1.01-1.59), irregular diet (OR=1.10, 95%CI: 1.02-1.18), insufficient intake of vegetables and fruits (OR=1.12, 95%CI: 1.07-1.17), lack of sleep at night (OR=1.17, 95%CI: 1.08-1.27), and depression (OR=1.50, 95%CI: 1.33-1.69) were risk factors for CMM. Sensitivity analysis of effects of central obesity and alcohol consumption were not robust. No publication bias was examined by Egger's test. Conclusions Female, middle age, elderly, urban resident, higher education level, higher economic level, overweight, obesity, central obesity, smoking, alcohol consumption, irregular diet, insufficient intake of vegetables and fruits, lack of sleep at night and depression are risk factors for CMM.

Objective: To conduct a scoping review on prognostic prediction models for patients with comorbidity of chronic diseases, and understand modeling methods, predictive factors and predictive effect of the models, so as to provide the reference for prognostic evaluation on patients with comorbidity of chronic diseases. Methods: Literature on prognostic prediction models for patients with comorbidity of chronic diseases was collected through SinoMed, CNKI, Wanfang Data, VIP, PubMed, Embase, Cochrane Library and Web of Science published from the time of their establishment to November 1, 2023. The quality of literature was assessed using prediction model risk of bias assessment tool (PROBAST), then modeling methods, predictive factors and predictive effects were reviewed. Results: Totally 2 130 publications were retrieved, and nine publications were finally enrolled, with an overall high risk of bias. Thirteen models were involved, with three established using machine learning methods and ten established using logistic regression. The prediction results of four models were death, with main predictive factors being age, gender, body mass index (BMI), Barthel index and pressure ulcers; the prediction results of nine models were rehospitalization, with main predictive factors being age, BMI, hospitalization frequency, duration of hospital stay and hospitalization costs. Eleven models reported the area under the receiver operating characteristic curve (AUC), ranging from 0.663 to 0.991 6; two models reported the C-index, ranging from 0.64 to 0.70. Eight models performed internal validation, one model performed external validation, and four models did not reported verification methods. Conclusions The prognostic prediction models for patients with comorbidity of chronic diseases are established by logistic regression and machine learning methods with common nursing evaluation indicators, and perform well. Laboratory indicators should be considered to add in the models to further improve the predictive effects.

Objective To explore the event-related potentials (ERPs) P300 is changeable or not before and after treatment. Methods 99 cases of patients with first onset of depression diagnosed by DSM-Ⅳ as case group,and 100 cases matched with patients as control group were collected. P300 of two groups were obtained before and after treatment for 6 weeks,12 weeks,24 weeks. T test was used to analysis the difference of indicators of P300 among groups; repeated measure analysis of variance was used to analysis the longitudinal changes. Results shorter latency of N2-P3 ( (P ＜ 0.01 ); and lower amplitude of N2, P3, N2-P3 (P ＜ 0. 05 ), higher amplitude of P2-tency and a upward one in N2-P3 latency in the four periods; a upward trend could also be found in P3, N2-P3 amplitude, but there were no statistical differences(P ＞ 0. 05 ). The results of paired-samples t test: P3, N2-P3 amplitude in case group were higher after treatment for 6 weeks than before, the difference was significant (P ＜ 0.01 ); no significant results were found in P300 latency or amplitude between the 62 cases of depression after treatment for 24 weeks and the 65 normal controls selected (P ＞ 0. 05 ). Conclusion P300 latencies and amplitudes tend to be partly recovered after the acute treatment in patients with depression, but after the long-term therapy not clear.