ObjectiveTo investigate the potential machanism of Xiaozhong Zhitong Mixture （消肿止痛合剂， XZM） in the treatment of diabetes foot ulcer （DFU）. MethodsFifty SD rats were randomly divided into blank group， model group， XZM group， inhibitor group， XZM plus inhibitor group （combination group）， with 10 rats in each group. Except for the blank group， rats were fed with high-sugar， high-fat， high-cholesterol diet， intraperitoneally injected with streptozotocin， and subjected to skin defect to establish DFU model. After successful modeling， the XZM group and the combination group were given 1 ml/（100 g·d）of XZM by gavage， while the blank group， model group， and inhibitor group were all given an equal volume of 0.9% sodium chloride injection by gavage. Thirty minutes later， the inhibitor group and the combination group were intraperitoneally injected with 5 mg/（kg·d） of Notch1 inhibitor DAPT. All groups were treated once a day. After 14 days of administration， the skin tissue from the dorsal foot of the blank group rats and wound tissue from the other groups were collected. The pathological changes of granulation tissue in the wound were detected using hematoxylin eosin （HE） staining. The microvascular density （MVD） in wounds was detected through immunohistochemical staining. Real time fluorescence quantitative polymerase chain reaction （RT-PCR） and western blotting were used to detect the mRNA and protein levels of Notch1 homolog （Notch1）， Delta-like ligand 4 （Dll4）， Delta-like ligand 4 （VEGF）， and angiopoietin 2 （Ang-2）， respectively. ResultsHistological results showed that the epidermal structure in the dorsal foot skin tissue of the rats in the blank group was intact. In the wound tissue of the model group， the epidermis exhibited excessive keratinization， vacuolar cytoplasm， and a large number of inflammatory cells infiltrating the tissue， while in the XZM group， a large amount of scab formation was observed in the epidermis， with no significant inflammatory cell infiltration and a noticeable increase in fibroblasts. In the combination group and the inhibitor group， partial epidermal scab formation was observed in the wound tissue with a small amount of inflammatory cell infiltration. Compared to those in the blank group， the MVD in the wound tissue increased in the model group， as well as the mRNA expression and protein levels of Notch1 and Dll4， while VEGFA and Ang-2 mRNA expression and protein levels significantly decreased （P＜0.05 or P＜0.01）. Compared to those in the model group， the MVD in the wound tissue of all medication groups significantly increased， and the mRNA and protein levels of Notch1 and Dll4 decreased， while VEGFA and Ang-2 mRNA expression and protein levels increased （P＜0.05 or P＜0.01）. Compared to the XZM group， the inhibitor group and the combination group showed decreased MVD in wound tissue， increased Notch1 and Dll4 mRNA and protein levels， and decreased expression of VEGFA and Ang-2 mRNA and proteins （P＜0.05 or P＜0.01）. ConclusionXZM can effectively promote wound healing in DFU rats， and its mechanism of action may be related to the inhibition of Dll4/Notch1 signaling pathway in the wound tissue， therey promoting angiogenesis.

Objective To investigate the prognostic factors affecting survival in patients with natural killer T-cell lymphoma(NKTL),and then develop a prognostic model for predicting their overall survival(OS)based on random survival forest(RSF)algorithm.Methods Demographic and clinical pathological data of NKTL patients were collected from the SEER database during 2000 and 2020.The patients were divided into a training cohort(n=471)and a validation cohort(n=203)in a 7∶3 ratio.Cox regression analysis was performed to identify prognostic factors affecting OS,and a nomogram model was constructed based on the obtained factors.Meanwhile,RSF algorithm was used to determine prognostic factors affecting OS to build the RSF model.The models were evaluated using receiver operating characteristic(ROC)curve,calibration curve,decision curve,net reclassification improvement(NRI),and integrated discrimination improvement(IDI),and the predictive performances of the 2 models were compared.Risk scores for each patient were calculated using the 2 models.Then the patients were divided into high-and low-risk groups based on the median risk score,and survival curve was plotted for comparison.Results Ann Arbor stage,age,radiotherapy,combined treatment,and type of disease were identified as significant prognostic variables associated with OS.In the validation cohort,the area under the ROC curve(AUC)for the nomogram model at 1,3,and 5 years was 0.745,0.771,and 0.748,respectively,while the AUC for the RSF model was 0.764,0.792,and 0.761 at the same time points.ROC curve analysis indicated that both models demonstrated good accuracy and discrimination in predicting OS.Calibration curve analysis showed a strong consistency between the predicted and actual OS for both models.Both models effectively stratified the patients into poor and favorable prognosis groups,with the OS of patients in the poor prognosis group being significantly shorter than that of the favorable prognosis group(P＜0.000 1).Decision curve analysis revealed that the net benefit of the RSF model was superior to that of the nomogram model.Compared to the nomogram model,the NRI for the RSF model was 0.184(95％CI:0.098～0.267,P＜0.01),and the IDI was 0.300(95％CI:0.241～0.359,P＜0.01).Overall,the RSF model demonstrated superior predictive capability than the nomogram model.Conclusion Ann Arbor stage,age,radiotherapy,combined treatment,and type of disease are prognostic factors affecting the prognosis of NKTL patients.Our RSF model demonstrates strong predictive capability for the prognosis of NKTL patients and can effectively assess patient outcomes.

Objective To conduct an in-depth analysis and feature extraction of the transcriptomics data of 10 types of cancers in order to realize the staging diagnosis of cancer samples.Methods The transcriptomics data of the top 10 cancers having the highest incidence were amassed from the UCSC Xena website,which comprised 4 938 samples and 59 428 genes.With the aid of variational autoencoder,we developed an incremental feature ranking and selection variational autoencoder(IFRSVAE)based on feature importance ranking and incorporating the masking algorithm and the Incremental Feature Selection(IFS).Subsequently,the performance efficiency of our IFRSVAE model was evaluated in conjunction with Random Forest(RF),Support Vector Machine(SVM),and eXtreme Gradient Boosting(XGboost),and it was also compared with other methods.Results Our research extracted 21 features for the ensuing classification.In comparison to the conventional variational autoencoder,recursive feature elimination,and Lasso regression models,the IFRSVAE model attained more favorable performance across all 3 classifiers(highest AUC value,and well performed other indicators).Notably,the IFRSVAE-RF exhibited the most outstanding performance,with an AUC value reaching 85.49%(95%CI:83.24%～87.74%).Moreover,Shapley additive explanations(SHAP)interpretable model illustrated well contributions of the features in our model.Conclusion Our developed IFRSVAE shows certain effectiveness in feature extraction.The constructed IFRSVAE-RF model demonstrates relatively good performance in the task of cancer staging diagnosis,which providing a new and referable idea for research orientation of deep-learning-based diagnostic methods for cancer staging.

Objective To investigate the changes of 8 lipid biomarkers,4 complement biomarkers and albu-min(ALB)in serum of patients with colorectal cancer(CRC)and their value in CRC screening.Methods A total of 120 newly diagnosed CRC patients in Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine from August 2022 to January 2024 were selected as the CRC group,and 110 healthy subjects were selected as the healthy control(HC)group.A total of 8 lipid biomarkers including total cholesterol(TC),tri-glyceride(TG),high density lipoprotein cholesterol(HDL-C),low density lipoprotein cholesterol(LDL-C),apolipoprotein(Apo)A1,ApoA2,ApoB and ApoE,4 complement biomarkers including complement C3(C3),complement C4(C4),complement C1q(C1q)and complement C1 inhibitor(C1INH),3 intestinal tumor markers including carcinoembryonic antigen(CEA),carbohydrate antigen(CA)125,CA19-9,and ALB levels were detected in serum of each group.Independent sample t test and Mann-Whitney U test were used for com-parison between groups,and stepwise Fisher discriminant algorithm was used to fit each marker to establish a screening model.Receiver operating characteristic curve was used to analyze the diagnostic efficacy of each marker and the model.Results The serum levels of ApoA1,ApoA2,HDL-C,TC and ALB in CRC group were lower than those in HC group(P<0.05),while the serum levels of C1INH,C4 and CEA were higher than those in HC group(P<0.05).Among the single biomarkers,ALB had the highest diagnostic efficiency,the area under the curve(AUC)was 0.909,the sensitivity was 77.50%,and the specificity was 94.55%.The AUC of the screening model composed of ApoA2,C1INH and ALB was 0.978,the sensitivity was 91.67%,and the specificity was 98.86%.The diagnostic efficacy was higher than any single biomarker.Conclusion ApoA2,C1INH and ALB are abnormally expressed in the serum of CRC patients.The screening model composed of ApoA2,C1INH and ALB can provide reference for CRC screening and clinical auxiliary diagnosis.

Objective:To explore the effects of dopamine receptor D2(DRD2)on astrocytic dedifferentiation based on SOX2-regulated genes in neural stem cells(NSCs)and astrocytes.Methods:Immunofluorescence staining and SOX2-GFP mice were used to examine the lineage differentiation of SOX2-positive cells during the development of cere-bral cortex.Primary NSCs/astrocytes culture,ChIP-seq and Western Blot were adopted to analyze and verify the expres-sion of candidate genes.Pharmacological manipulation,neurosphere formation,photochemical ischemia,immunofluo-rescence staining and behavior tests were adopted to evaluate the effects of activating DRD2 signaling on astrocytic dedif-ferentiation.Results:Immunofluorescence staining demonstrated the NSC-astrocyte switch of SOX2-expression in the normal development of cerebral cortex.ChIP-seq revealed enrichment of DRD2 signaling by SOX2-bound enhancers in NSCs and SOX2-bound promoters in astrocytes.Western Blot and immunofluorescence staining verified the expression of DRD2 in NSCs and reactive astrocytes.Application of quinagolide hydrocholoride(QH),an agonist of DRD2,signifi-cantly promoted astrocytic dedifferentiation both in vitro and in vivo following ischemia.In addition,quinagolide hydro-choloride treatment improved locomotion recovery.Conclusion:Activating DRD2 signaling facilitates astrocytic dedif-ferentiation and may be used to treat ischemic stroke.

Objective To construct a time series analysis fusion tool using multisource internet data and then accurately predict the incidence trend of hepatitis in Chongqing.Methods The incidence rate of hepatitis were obtained from the database of the Centre for Health and Disease Control.Air pollutant data were obtained from the official website of the China Environmental Monitoring Station,climate data were obtained from the National Meteorological Galaxy Center,and network index data were obtained through Baidu search engine.The time duration was from November 2013 to May 2023.Based on existing time series analysis methods,multisource data were used to correct the residual part of the decomposition model.A delayed input neural network(DINN)was constructed based on the respective advantages of non autoregressive(NAR)and long short-term memory(LSTM)recurrent neural networks.Afterwards,optimization modules such as the Nutcracker Optimization Algorithm(NOA)and Joint Quantile Huber Loss(JQHL)were added to the foundation,and then DINN+was constructed.Results Compared to common single-input models and synchronous multi-input models,DINN achieved the best prediction performance.After adding hyperparameters and loss function optimization,the predictive performance of DINN+was further improved,with a mean-square error(MSE)of 0.170 9,a mean absolute error(MAE)of 0.461 2,a root-mean-square error(RMSE)of 0.582 1,a mean absolute percentage error(MAPE)of 0.062 6,and a R-square(R2)of 0.884 0 in a testing set.Conclusion Based on the ideas of diverse methods and multidimensional data fusion,we propose a DINN+optimization model with good accuracy and generalization ability on the basis of previous time series analysis.This model enriches and supplements the methodological research content of using multisource data to calibrate infectious disease time series prediction analysis and can serve as a new benchmark for future analysis of influencing factors and trend prediction of infectious disease public health.

Objective To construct a prognosis prediction model of primary liver cancer after surgical treatment based on synthetic minority over-sampling technique(SMOTE)algorithm and machine learning model.Methods A retrospective cohort study was conducted on 4 297 patients with primary liver cancer from the surveillance,epidemiology,and end results(SEER)database.One-Hot Encoding and Multiple Imputation were used to preprocess the collect data,and SMOTE algorithm was employed to solve the imbalance of data categories.The obtained clinical variables were included in the machine learning model.Based on decision tree(DT),random forest(RF),gradient boosting decision tree(GBDT)and eXtreme Gradient Boosting(XGBoost),a prognostic prediction model(SMOTE+DT/RF/GBDT/XGBoost)was build,and then the best prediction model was determined by comparing the performance of various models.Finally,a prognostic analysis system for primary liver cancer was developed based on the optimal model,which was then visualized.Results The combination model SMOTE+RF showed the best predictive performance,with higher area under the curve(0.895),accuracy(0.811)and precision(0.806)than those of other models in receiver operating characteristic curve(ROC)analysis.Conclusion The SMOTE+RF prognostic prediction model can effectively predict the survival outcome of patients with primary liver cancer.

Objective To investigate the influencing factors for tendency towards depression in college students having music listening habits with music APP,and develop a prediction model and further optimize it.Methods A total of 1 157 college students were subjected with convenient sampling and surveyed with questionaires between April and May 2023.Univariate analysis and logistic regression analysis were employed to identify the influencing factors.Then a prediction model was constructed based on these factors.SMOTEENN over-sampling algorithm was utilized to enhance the dataset and construct the prediction model.Results Logistic regression analysis revealed that female(OR=1.730,95％CI:1.257～2.396),senior grade(OR=2.649,95％CI:1.198～7.506),postgraduate grade(OR=2.041,95％CI:1.231～3.885),major in Science(OR=1.573,95％CI:1.052～2.350),listening for a duration of 0.5～2 h(OR=1.661,95％CI:1.011～2.695),music style of melancholy(OR=2.668,95％CI:1.701～4.226)and of nostalgia(OR=1.751,95％CI:1.086～2.837),and frequency of comments on 0～5％of songs(OR=2.938,95％CI:1.018～8.417)were independent risk factors for depressive tendency.Time since listening to music for 1～3 years(OR=0.547,95％CI:0.347～0.872),listening to music from 14:00 to 18:00(OR=0.375,95％CI:0.167～0.845)and 18:00 to 21:00(OR=0.313,95％CI:0.148～0.671),and preference for Chinese style songs(OR=0.711,95％CI:0.541～0.941)were independent protective factors.The logistic early warning model based on SMOTEENN algorithm demonstrated optimal predictive performance with an AUC value of 0.923.Conclusion Our constructed logistic regression model has identified 9 independent influencing factors associated with depression tendency among college students.The early warning model based on SMOTEENN algorithm can predict the depression tendency more accurately for college students.

Objective: To investigate the relationship between the use of earphone and hearing impairment and its influencing factors among students aged 14-28, so as to provide a reference for appropriate earphone usage and hearing impairment prevention. Methods: A cross sectional survey was conducted through the questionnaire star platform, and 983 students aged 14 to 28 were recruited across China by snowball sampling during April 3 to May 1, 2022. The χ 2 test was used to identify indicators affecting hearing, the Logistic regression model was used to further selection. Results: There were 366 students with hearing impairment, accounting for 37.23%. Univariate analysis showed significant differences in hearing impairment by gender, earphone usage duration and volume, wearing during sleep, and replacement frequency ( χ 2=6.03, 6.86, 14.87, 12.22, 11.15, P <0.05). The Logistic regression model analysis showed that girls ( OR=1.43, 95%CI =1.10-1.88), maximum earphone volume ( OR=3.08, 95%CI = 1.56- 6.08), earphone usage for >1.5-3 h each time ( OR=1.44, 95%CI =1.04-1.99), sleep with headphone ( OR= 1.53 , 95%CI = 1.11- 2.11) were positively associated with hearing impairment ( P <0.05), earphone replacement every 4-<6 months ( OR= 0.38, 95%CI =0.17-0.86) and earphone replacement every six months or longer ( OR=0.39, 95%CI =0.18-0.85) were negatively associated with hearing impairment ( P < 0.05 ). Conclusion Students aged 14 to 28 earphone usage shows adverse impact on hearing. When using earphone, it is recommended to limit time spent on earphone usage, low the volume of earphone, avoid sleeping with earphone and replace earphone frequently.