Stroke, a major cerebrovascular disease, has high morbidity and mortality. Effective methods to reduce the risk and improve the prognosis are lacking. Currently, uric acid (UA) is associated with the pathological mechanism, prognosis, and therapy of stroke. UA plays pro/anti-oxidative and pro-inflammatory roles in vivo. The specific role of UA in stroke, which may have both neuroprotective and damaging effects, remains unclear. There is a U-shaped association between serum uric acid (SUA) levels and ischemic stroke (IS). UA therapy provides neuroprotection during reperfusion therapy for acute ischemic stroke (AIS). Urate-lowering therapy (ULT) plays a protective role in IS with hyperuricemia or gout. SUA levels are associated with the cerebrovascular injury mechanism, risk, and outcomes of hemorrhagic stroke. In this review, we summarize the current research on the role of UA in stroke, providing potential targets for its prediction and treatment.
Humans ; Uric Acid/metabolism* ; Stroke/drug therapy* ; Animals ; Hyperuricemia/drug therapy* ; Ischemic Stroke/blood* ; Biomarkers/blood*

Urinary incontinence is a common complication following robot-assisted radical prostatectomy (RARP). Urethral length has been identified as a factor affecting postoperative continence recovery. In this meta-analysis, we examined the association between use of the maximal urethral length preservation (MULP) technique and postoperative urinary continence in patients undergoing RARP. We conducted a comprehensive search of PubMed, Web of Science, Embase, and the Cochrane Library up to December 31, 2023. The quality of the literature was assessed using the Newcastle-Ottawa Scale. A random-effects meta-analysis was performed to synthesize data and calculate the odds ratio (OR) from eligible studies on continence and MULP. Six studies involving 1869 patients met the eligibility criteria. MULP was positively associated with both early continence (1 month after RARP; Z = 3.62, P = 0.003, OR = 3.10, 95% confidence interval [CI]: 1.68-5.73) and late continence (12 months after RARP; Z = 2.34, P = 0.019, OR = 2.10, 95% CI: 1.13-3.90). Oncological outcomes indicated that MULP did not increase the overall positive surgical margin rate or the positive surgical margin status at the prostate apex (both P > 0.05). In conclusion, the use of the MULP technique in RARP significantly improved both early and late postoperative continence outcomes without compromising oncological outcomes.
Humans ; Prostatectomy/adverse effects* ; Robotic Surgical Procedures/methods* ; Male ; Urethra/surgery* ; Urinary Incontinence/prevention & control* ; Postoperative Complications/etiology* ; Prostatic Neoplasms/surgery* ; Organ Sparing Treatments/methods*

Objective To investigate the predictive value of atherosclerotic index of plasma(AIP)for the risk of coronary heart disease(CHD)in patients with nonalcoholic fatty liver disease(NAFLD).Methods A retrospective analysis was conducted in 299 patients with NAFLD.Based on presence or absence of CHD,the patients were divided into NAFLD with CHD group(n=177)and NAFLD group(n=122).Clinical data were collected from both groups,and AIP was calculat-ed.Multivariate Logistic regression analysis was performed to explore the independent risk factors for CHD in patients with NAFLD.Receiver operating characteristic(ROC)curves were plotted to evalu-ate the predictive value of AIP for the risk of CHD in patients with NAFLD.Results The NAFLD with CHD group had a higher proportion of males,smokers,and higher levels of alanine aminotrans-ferase(ALT),aspartate aminotransferase(AST),fasting plasma glucose(FPG),triglycerides(TG),low-density lipoprotein cholesterol(LDL-C),γ-glutamyltransferase(GGT),uric acid(UA),and AIP than the NAFLD group.The NAFLD with CHD group also had lower levels of high-density lipoprotein cholesterol(HDL-C)than the NAFLD group(P＜0.05).Multivariate Logistic regression analysis revealed that males(OR=2.548,95％CI,1.402 to 4.632,P=0.002),high levels of AST(OR=1.038,95％CI,1.002 to 1.077,P=0.041),high levels of LDL-C(OR=1.811,95％CI,1.242 to 2.640,P=0.002),and high AIP(OR=16.117,95％CI,1.874 to 138.609,P=0.011)were independent risk factors for CHD in patients with NAFLD(P＜0.05).ROC curve analysis showed that AIP had an area under the curve of 0.746(95％CI,0.688 to 0.804)for pre-dicting CHD in patients with NAFLD,with a sensitivity of 76.3％and a specificity of 73.0％.Conclusion AIP is an independent influencing factor for CHD in patients with NAFLD and has certain predictive value for the risk of CHD in these patients.

Objective:To explore the relevant factors affecting the accuracy of scanning glucose monitoring (FGM) technology in critically ill patients.Methods:A total of 53 patients who were admitted to the Intensive Care Unit (ICU) of the Second Affiliated Hospital of Soochow University and used FGM glucose monitoring from September 2022 to December 2023 were selected by means of a convenience sampling method. The paired data of arterial blood glucose and FGM glucose were analysed. The accuracy of FGM blood glucose measurement in critically ill patients was evaluated using Bland-Altman analysis. The blood glucose data were divided into two groups: the FGM monitoring accuracy group and the FGM monitoring non-accuracy group.. The t-test and χ2 test were used for the comparison of one-way analysis of arterial versus FGM accuracy among critically ill patients with different demographic characteristics. Logistic regression analysis was used to analyze the factors influencing the accuracy of FGM. Results:The results of Bland-Altman analysis of the 53 patients indicated that the mean bias value of FGM blood glucose was elevated at 1.215 mmol/L in comparison with arterial-blood gas analysis blood glucose values. An incidence of exceeding the upper and lower limits of the range was observed, amounting to 6.349%. The discrepancy between the studies was found to be statistically significant (95% CI:1.0394~1.3908, P<0.001). The effects of the changes in height ( OR=0.877, 95% CI:0.780~0.987, P=0.029), changes in leukocyte counts( OR=0.917, 95% CI:0.868~0.969, P=0.002), changes in C-reactive protein( OR=1.009, 95% CI:1.002~1.017, P=0.016), changes in albumin counts( OR=0.986, 95% CI:0.974~0.999, P=0.031), and whether or not sepsis ( OR=3.937, 95% CI:1.192~13.008, P=0.025) on the accuracy of FGM had a statistically significant. Conclusion:The mean bias value of the accuracy of FGM was relatively higher compared with that of arterial blood gas analysis blood glucose values, and the influencing factors involved height, white blood cells, C-reactive protein, albumin, and whether or not sepsis was present. With the development of science and technology, applying the FGM system to critically ill patients has an absolute advantage in determining the overall glycemic trend, and the application value of FGM technology deserves further study.

Health behavior motivation significantly affects the quality of life of patients with cardiovascular diseases.Assessing the level of health behavior motivation can measure the health behavior of individuals,help to promote the participation rate of cardiac rehabilitation,reduce the readmission rate of patients,and promote patients'healthy lifestyles.This article reviews the content,characteristics and application of assessment tools for health behavior motivation of patients with cardiovascular diseases at home and abroad,with the aim of providing references for the development and application of such tools in China,and offering a basis for scientifically assessing their health behavior motivation level and formulating effective strategies to promote health behavior motivation.

The number of patients with chronic diseases in China has been increasing year by year,followed by the increase of treatment burden.It is particularly important to effectively evaluate the treatment burden of patients with chronic diseases.This paper reviews the main contents,application scope,advantages and disadvantages of the assessment tools for the treatment burden of patients with chronic diseases at home and abroad,so as to provide references and basis for medical staff to reasonably select assessment tools and conduct relevant clinical research.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Health behavior motivation significantly affects the quality of life of patients with cardiovascular diseases.Assessing the level of health behavior motivation can measure the health behavior of individuals,help to promote the participation rate of cardiac rehabilitation,reduce the readmission rate of patients,and promote patients'healthy lifestyles.This article reviews the content,characteristics and application of assessment tools for health behavior motivation of patients with cardiovascular diseases at home and abroad,with the aim of providing references for the development and application of such tools in China,and offering a basis for scientifically assessing their health behavior motivation level and formulating effective strategies to promote health behavior motivation.

The number of patients with chronic diseases in China has been increasing year by year,followed by the increase of treatment burden.It is particularly important to effectively evaluate the treatment burden of patients with chronic diseases.This paper reviews the main contents,application scope,advantages and disadvantages of the assessment tools for the treatment burden of patients with chronic diseases at home and abroad,so as to provide references and basis for medical staff to reasonably select assessment tools and conduct relevant clinical research.