Objective : To investigate the mechanism of prostaglandin E synthase 3(PTGES3)/heat shock protein 90(HSP90) in the medial prefrontal cortex regulating obesity-related cognitive dysfunction. Methods: This study consisted of clinical trials and animal experiments. In part one, obese patients scheduled for bariatric surgery, and healthy adults matching gender and age were recruited at the same time to reach 10 cases in each group. The cognitive level was assessed with trail making test part A(TMT-A) and victoria stroop tests(VST). Four-dimensional data-independent acquisition(4D-DIA) was used to screen the proteome changes in peripheral blood. In part two, forty SPF healthy male C57BL/6J mice were randomly divided into four groups: normal diet group(ND group), high fat diet induced obesity group(DIO group), DIO supplemented with the control virus group(DIO+Scramble group) and DIO supplemented with the interfering virus group(DIO+shPTGES3 group). The Morris water maze test was conducted to evaluate the cognitive behavior changes of the four groups of mice. The immunofluorescence staining was performed to detect the expression of PTGES3 and HSP90 in the medial prefrontal cortex and the activation of ionized calcium binding adapter molecule 1(IBA1)-labeled microglia. Results: In the case-control study, the cognitive function of obese patients significantly decreased, and the expression of PTGES3 in peripheral blood significantly increased, while the level of PTGES3 was negatively correlated with cognitive function. In animal experiments, compared with ND group, DIO group had significantly prolonged time reaching the target platform, otherwise, the residence time in the target quadrant was shortened in the Morris water maze test. Simultaneously, there were significant increase in the expression of PTGES3 and HSP90, and the activation of IBA1 in the medial prefrontal cortex. Compared with DIO+Scramble group, mice in the DIO+shPTGES3 group spent less time reaching the target platform, and stayed longer in the target quadrant. The expression and co-localization levels of PTGES3 and HSP90 in medial prefrontal cortex significantly decreased. The activation level of microglia cells was also attenuated by PTGES3 interference. Conclusion Obesity-related cognitive dysfunction may be attributed to PTGES3/HSP90 in the medial prefrontal cortex by mediating neural inflammation.

Objective To study the complexity of helical tomotherapy plans for different anatomical sites and explore its correlations with dosimetric verification.Methods A total of 71 complexity metrics of the helical tomotherapy plans executed by Accuray?planning system v.5.1.9.2 for nasopharyngeal carcinoma(n=52),breast cancer(n=19),cervical cancer(n=52)and central nervous system cancer(n=37)were analyzed.The correlations between metrics were analyzed,and principal component analysis was used for dimensionality reduction.The global complexity score(normalized plan complexity score,nPCS)was calculated.The top-contributing complexity metrics within the first 10 principal components were extracted,and a correlation analysis with the gamma passing rate was conducted.Results There was no significant correlation between most metrics.The global complexity score was different for different sites,with the highest median nPCS and the most complex plan in the head and neck cancer.The correlation analysis between the global complexity score and gamma passing rate revealed that in the central nervous system plans,CFNS90 and EPSTV1_0 had strong correlations with gamma passing rate for 3%/2 mm criterion,and a significant correlation was found between CFNS90 and gamma passing rate for 3%/3 mm criterion.Conclusion Complexity scores can quantize the complexity of helical tomotherapy plans for different sites,and certain metrics are correlated with gamma passing rate.

Objective To investigate the in vitro antiviral effects of Blumea balsamifera(L.)DC.extract against Coxsackievirus B5(CVB5).Methods A series of dilutions of Coxsackievirus were prepared and co-cultured with RD cells to determine the TCID50 value.Subsequently,different concentrations of the extract were added to a 96-well plate containing RD cells to evaluate their impact on cell viability.The ability of Blumea balsamifera extract to inhibit Coxsackievirus was further observed in the 96-well plate containing RD cells and the extract.Results The TCID50 value of Coxsackie virus solution was 10-7.67.The inhibition rate of Blumea balsamifera extract against Coxsackievirus increased with concentration,with an IC50 value of 7.26 mg/L.At a concen-tration of 50 mg/L,the extract caused a decrease in RD cell viability(P＜0.05),but within the concentration range of 6.25 to 50 mg/L,it increased the viability of virus-infected RD cells(P＜0.05),with a selectivity index(SI)exceeding 6.89.Conclusion Blumea balsamifera(L.)DC.extract exhibits in vitro activity against Coxsackievirus.

Objective To systematically evaluate the published models in prediction of the risk of lung infections in elderly patients with hip fracture so as to provide a guidance for medical workers in selection or development of suitable risk prediction models.Methods Relevant studies were searched from databases including CNKI,Wanfang Data,VIP,SinoMed,PubMed,Web of Science,Cochrane Library,Embase and CINAHL,from the inception to 31st January,2024.Data were extracted from the selected literature and a bias assessment tool of risk predictive model was used to evaluate the risk of bias and applicability of the included literature.Results A total of 1,035 articles were retrieved,of which seven studies involving 13 predictive models were finally included after screening.The sample sizes ranged from 305 to 2,669 cases and lung infection rates ranged from 5.40%to 20.02%.The repeatedly reported predictors included age,gender,chronic obstructive pulmonary disease,hypoproteinaemia,American Society of Anesthesiologists(ASA)Physical Status Classification and white blood cell count.In the 13 models constructed,the reported area under the curve(AUC)of subjects'job characteristics ranged from 0.667 to 0.996.Five out of seven studies had good overall applicability,but all with high risk of bias.Conclusion The predictive models for lung infections in elderly patients with hip fracture are still in the stage of development.Although the predictive models show some predictive performance,however they are still deficient,and all studies have been found with a high risk in bias.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. 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, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective To explore the effect of the horticultural therapy in the elderly with mild cognitive impairment in elderly care institutions.Methods A convenient cluster sampling method was used.The study was conducted among the elderly with mild cognitive impairment in 2 nursing homes with 5A-level in Guangzhou,from March to September 2024.Using a lottery method,subjects from 2 nursing areas across 2 elderly care institutions were allocated to an experimental group,with the other 2 nursing areas serving as a control group,each group comprising 55 cases.The experimental group participated in horticultural therapy on the basis of control group interventions,while the control group was given routine care and daily leisure activities.The cognitive function,basic psychological needs and quality of life were compared between the 2 groups after the intervention.Results Eventually,37 cases in the experimental group and 38 cases in the control group completed the study.After the intervention,the cognitive function,basic psychological needs and quality of life in the experimental group were all better than those in the control group,and the differences were statistically significant(P＜0.05).Conclusion The horticultural therapy program can delay the progression of cognitive decline in the elderly with mild cognitive impairment in nursing homes,meet their basic psychological needs and improve their quality of life.

A 66-year-old female patient self-administered Jingyaokang capsules (a compound preparation of traditional Chinese medicines, 3 capsules thrice daily orally) due to lumbar pain. The patient developed oliguria and edema of bilateral lower limbs after 3 doses of medication on the same day. The laboratory tests showed WBC 7.3×10 9/L, neutrophil percentage 0.70, blood urea 12.7 mmol/L, blood crea- tinine 179 μmol/L, and blood uric acid 461 μmol/L. The kidney function tests 15 days ago showed no abnormalities in the patient. Acute kidney injury caused by Jingyaokang capsules was considered. The drug was stopped and symptomatic treatments including torasemide and maintenance of fluid balance were given. The patient′s urine output gradually increased. Five days later, the patient′s edema of bilateral lower limbs disappeared, and her blood urea and creatinine decreased to normal range. The acute kidney injury in the patient may be related to strychni semen component in the Jingyaokang capsules.

Objective To systematically evaluate the published models in prediction of the risk of lung infections in elderly patients with hip fracture so as to provide a guidance for medical workers in selection or development of suitable risk prediction models.Methods Relevant studies were searched from databases including CNKI,Wanfang Data,VIP,SinoMed,PubMed,Web of Science,Cochrane Library,Embase and CINAHL,from the inception to 31st January,2024.Data were extracted from the selected literature and a bias assessment tool of risk predictive model was used to evaluate the risk of bias and applicability of the included literature.Results A total of 1,035 articles were retrieved,of which seven studies involving 13 predictive models were finally included after screening.The sample sizes ranged from 305 to 2,669 cases and lung infection rates ranged from 5.40%to 20.02%.The repeatedly reported predictors included age,gender,chronic obstructive pulmonary disease,hypoproteinaemia,American Society of Anesthesiologists(ASA)Physical Status Classification and white blood cell count.In the 13 models constructed,the reported area under the curve(AUC)of subjects'job characteristics ranged from 0.667 to 0.996.Five out of seven studies had good overall applicability,but all with high risk of bias.Conclusion The predictive models for lung infections in elderly patients with hip fracture are still in the stage of development.Although the predictive models show some predictive performance,however they are still deficient,and all studies have been found with a high risk in bias.

Objective To study the complexity of helical tomotherapy plans for different anatomical sites and explore its correlations with dosimetric verification.Methods A total of 71 complexity metrics of the helical tomotherapy plans executed by Accuray?planning system v.5.1.9.2 for nasopharyngeal carcinoma(n=52),breast cancer(n=19),cervical cancer(n=52)and central nervous system cancer(n=37)were analyzed.The correlations between metrics were analyzed,and principal component analysis was used for dimensionality reduction.The global complexity score(normalized plan complexity score,nPCS)was calculated.The top-contributing complexity metrics within the first 10 principal components were extracted,and a correlation analysis with the gamma passing rate was conducted.Results There was no significant correlation between most metrics.The global complexity score was different for different sites,with the highest median nPCS and the most complex plan in the head and neck cancer.The correlation analysis between the global complexity score and gamma passing rate revealed that in the central nervous system plans,CFNS90 and EPSTV1_0 had strong correlations with gamma passing rate for 3%/2 mm criterion,and a significant correlation was found between CFNS90 and gamma passing rate for 3%/3 mm criterion.Conclusion Complexity scores can quantize the complexity of helical tomotherapy plans for different sites,and certain metrics are correlated with gamma passing rate.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. 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, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.