Objective:To investigate the survival outcomes and prognostic factors in patients undergoing radical resection for pancreatic body and tail cancer.Methods:A retrospective case series study was conducted on 992 patients who underwent radical resection for pancreatic body and tail cancer at the Pancreatic Center of the First Affiliated Hospital of Nanjing Medical University from January 2016 to June 2024. In this study, 577 (58.2%) were male and 415 (41.8%) were female,with an age of (65±9) years (range: 26 to 86 years). Follow-up continued until June 2024. Survival rates were estimated using the Kaplan-Meier method,and prognostic factors were identified using univariate and multivariate Cox proportional hazards models.Results:Among 992 patients,open surgery was the predominant approach (89.1%, 884/992), and radical antegrade modular pancreatosplenectomy (RAMPS) was performed in 317 patients (32.0%). Combined organ resection,venous resection,and arterial resection were performed in 23.5%, 9.3%,and 11.2% of patients,respectively. The rates of R0, R1-1 mm, and R1-direct resections were 49.8% (494/992),41.5% (412/992), and 8.7% (86/992),respectively. Stage ⅡB was the most common TNM stage (32.2%,319/992). A total of 801 patients (80.8%) received adjuvant chemotherapy. The median follow-up period was 32.0(8.8) months(range:3.2 to 105.3 months),during which 508 patients (51.2%) died. The overall median survival (OS) was 26.4 months,with 1-,3-, and 5-year survival rates of 79.0%,40.0%, and 29.0%, respectively. In the recent five years (from 2020 to 2024), the median OS improved significantly to 34.1 months compared to 20.0 months from 2016 to 2019 ( P<0.01). Histological subtype analysis showed that the median OS time was 26.7 months for pancreatic ductal adenocarcinoma (PDAC, n=855),58.9 months for invasive intraductal papillary mucinous carcinoma (IPMC, n=32),and 15.7 months for adenosquamous carcinoma of pancreas (ASCP, n=73) ( P=0.001). Among PDAC patients, adjuvant chemotherapy significantly improved survival (29.1 months vs. 14.4 months, P<0.01);in IPMC patients, adjuvant chemotherapy also extended survival (65.7 months vs. 58.9 months, P=0.047). Although ASCP patients receiving chemotherapy had a longer median OS time than those without (18.8 months vs. 8.9 months),the difference was not statistically significant ( P=0.151). Multivariate Cox regression analysis in PDAC patients indicated that adjuvant chemotherapy, R0 resection, T stage,N stage,and tumor differentiation were independent prognostic factors ( P<0.01). The median OS time by TNM stage was:not reached for stage ⅠA, 51.6 months for ⅠB, 25.5 months for ⅡA, 23.7 months for ⅡB, 23.0 months for Ⅲ, and 14.4 months for Ⅳ. The median OS time for R0,R1-1 mm,and R1-direct resections was 34.1,24.7,and 15.7 months,respectively ( P<0.01). Conclusion:Adjuvant chemotherapy,R0 resection,tumor stage,and differentiation are independent prognostic factors for pancreatic body and tail cancer.

Objective:To investigate the survival outcomes and prognostic factors in patients undergoing radical resection for pancreatic body and tail cancer.Methods:A retrospective case series study was conducted on 992 patients who underwent radical resection for pancreatic body and tail cancer at the Pancreatic Center of the First Affiliated Hospital of Nanjing Medical University from January 2016 to June 2024. In this study, 577 (58.2%) were male and 415 (41.8%) were female,with an age of (65±9) years (range: 26 to 86 years). Follow-up continued until June 2024. Survival rates were estimated using the Kaplan-Meier method,and prognostic factors were identified using univariate and multivariate Cox proportional hazards models.Results:Among 992 patients,open surgery was the predominant approach (89.1%, 884/992), and radical antegrade modular pancreatosplenectomy (RAMPS) was performed in 317 patients (32.0%). Combined organ resection,venous resection,and arterial resection were performed in 23.5%, 9.3%,and 11.2% of patients,respectively. The rates of R0, R1-1 mm, and R1-direct resections were 49.8% (494/992),41.5% (412/992), and 8.7% (86/992),respectively. Stage ⅡB was the most common TNM stage (32.2%,319/992). A total of 801 patients (80.8%) received adjuvant chemotherapy. The median follow-up period was 32.0(8.8) months(range:3.2 to 105.3 months),during which 508 patients (51.2%) died. The overall median survival (OS) was 26.4 months,with 1-,3-, and 5-year survival rates of 79.0%,40.0%, and 29.0%, respectively. In the recent five years (from 2020 to 2024), the median OS improved significantly to 34.1 months compared to 20.0 months from 2016 to 2019 ( P<0.01). Histological subtype analysis showed that the median OS time was 26.7 months for pancreatic ductal adenocarcinoma (PDAC, n=855),58.9 months for invasive intraductal papillary mucinous carcinoma (IPMC, n=32),and 15.7 months for adenosquamous carcinoma of pancreas (ASCP, n=73) ( P=0.001). Among PDAC patients, adjuvant chemotherapy significantly improved survival (29.1 months vs. 14.4 months, P<0.01);in IPMC patients, adjuvant chemotherapy also extended survival (65.7 months vs. 58.9 months, P=0.047). Although ASCP patients receiving chemotherapy had a longer median OS time than those without (18.8 months vs. 8.9 months),the difference was not statistically significant ( P=0.151). Multivariate Cox regression analysis in PDAC patients indicated that adjuvant chemotherapy, R0 resection, T stage,N stage,and tumor differentiation were independent prognostic factors ( P<0.01). The median OS time by TNM stage was:not reached for stage ⅠA, 51.6 months for ⅠB, 25.5 months for ⅡA, 23.7 months for ⅡB, 23.0 months for Ⅲ, and 14.4 months for Ⅳ. The median OS time for R0,R1-1 mm,and R1-direct resections was 34.1,24.7,and 15.7 months,respectively ( P<0.01). Conclusion:Adjuvant chemotherapy,R0 resection,tumor stage,and differentiation are independent prognostic factors for pancreatic body and tail cancer.

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 explore the clinical distribution characteristics and changes of common inhalant allergens and food allergens in all outpatient and inpatient children visiting Shanghai Children′s Hospital from 2020 to 2024, and to provide a basis for the diagnosis, treatment and prevention of allergic diseases in children.Methods:A retrospective cohort study was conducted to retrospectively enroll all outpatient and inpatient children who visited Shanghai Children′s Hospital and underwent serum allergen-specific IgE (sIgE) antibody testing from January 2020 to August 2024, and the characteristics and changing trends of allergens in the past 5 years were analyzed. A total of 127 310 tests were included. There were 76 776 male tests (60.31%) and 50 534 female tests (39.69%). There were 27 392 tests (21.52%) aged 0-3 years (infant group), 51 596 tests (40.53%) aged 4-6 years (preschool group), 44 574 tests (35.01%) aged 7-12 years (school-age group), and 3 748 tests (2.94%) aged 13-18 years (adolescent group). The χ2 test was used for statistical analysis. Results:The difference in total positivity rate between different years was statistically significant ( χ2=2 907.478, P<0.001). The positive rates of inhalant allergens such as house dust, Dermatophagoides pteronyssinus/ Dermatophagoides farinae, cockroach, cat dander, mugwort, Humulus scandens, mold fungi mix, and food allergens such as beef and mutton increased significantly with age (The χ2 values were 649.496, 3 414.686, 303.247, 1 277.408, 40.477, 189.952, 600.737, 203.198, and 15.301, respectively, and the P values were <0.001, <0.001, <0.001, <0.001, <0.001, <0.001,<0.001,<0.001, and 0.002, respectively). The positive rates of inhalant allergen such as Ambrosia elatior (1.59%) and food allergens such as egg white (17.36%), milk (30.48%), shrimp (8.27%), crab (8.13%), codfish (2.61%), salmon (0.66%), mytilus edulis (2.89%), lobster/scallop (5.27%), cashew nuts (5.09%), peanuts (3.54%), and soybean (1.73%) were highest at the age of 0-3 years and decreased significantly with age (The χ2 values were 10.365, 2 407.443, 139.085, 872.548, 870.245, 106.823, 47.674, 47.244, 559.422, 369.800, 384.788, 153.660, respectively, and the P values were 0.016, 0.000,<0.001,<0.001,<0.001,<0.001,<0.001,<0.001,<0.001,<0.001,<0.001,<0.001, respectively). Inhaled allergens mainly have a greater impact on children with respiratory-related diseases such as allergic rhinitis and asthma, while food allergens mainly have a greater impact on children with atopic dermatitis/eczema. The positive rate of sIgE of various allergens in the allergic rhinitis combined asthma group were higher than that of allergic rhinitis alone, and the sIgE positive rate of total allergens and inhaled allergens was significantly higher than that of allergic rhinitis alone ( χ2=20.851, 39.155, the P values were both<0.001). Among them, the sIgE positive rate of Ambrosia elatior and cashew nuts showed significant difference ( χ2=5.044, 8.420, P=0.025, 0.004); and the sIgE positive rate of Dermatophagoides pteronyssinus/ Dermatophagoides farinae, cat dander, grass pollens mix and mold fungi mix had extremely significant difference ( χ2=26.409, 25.990, 21.283, 16.411, the P values were all <0.001). The inhaled allergens and food allergens with the highest positive rates in the 5 years were Dermatophagoides pteronyssinus/ Dermatophagoides farinae (56.21%) and milk (47.47%), and as time went by, the positive rates gradually decreased. There is a moderate correlation between the three allergens of Ambrosia elatior, Amaranthus retroflexus, and tree pollens mix (0.55, 0.70, 0.63), and there is a moderate correlation between mango and tree pollens mix (0.50). Conclusion:Dermatophagoides pteronyssinus/ Dermatophagoides farinae, cat dander, dog dander, egg white, and milk may be important allergens for children in Shanghai City from 2020 to 2024. The positive rates vary among different genders, age groups, and disease groups, but the positive rates of Dermatophagoides pteronyssinus/ Dermatophagoides farinae, milk and cat dander allergens remain in the top three.

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 Hepatitis C is a kind of infectious disease with great harm and strong concealment.Accurate trend prediction is an important measure to ensure accurate intervention.This paper aims to confirm the effectiveness of multivariate time series prediction method and Internet data and provide a better data and method basis for hepatitis C prediction.Methods The data of the monthly incidence of hepatitis C in Chongqing from 2011 to 2018 were included,including infectious disease incidence data and Internet prediction data.To screen important features,this paper introduces the theoretical basis of feature entropy,and proposes an adaptive correlation entropy weight method(ACEW)for feature selection through the steps of collinearity removal,directional evaluation and information content evaluation.After that,this paper constructed a multivariate time series model(CNN-BILLSTM-Attention)and carried out the characteristic performance test(including prospective evaluation and posterior evaluation)and model efficiency exploration.Results Prospective evaluation revealed that the variables selected by ACEW had low consistency with each other,and the weight distribution calculated by each variable was relatively equal.The posterior evaluation revealed that the feature set screened by ACEW could obtain the best prediction information in each model.In the exploration of model effectiveness,the overall performance of multivariate time series prediction model is significantly better than that of univariate model.When ACEW and CNN-BILSTM-Attention are combined,the MSE,MAE,RMSE,MAPE and R2 on the test set are 0.0223,0.0649,0.0771,5.9285 and 0.9156,respectively.Conclusion In the study of predicting the incidence of hepatitis C,data fusion and method improvement are studied in this paper.The improved feature selection method(ACEW)can provide an opportunity for the regulation of hepatitis C,and the multivariate time series prediction model can improve the performance of hepatitis C trend prediction,to effectively control and prevent hepatitis C,which has better public health prevention and control significance.

Artificial intelligence methods are developing rapidly in the medical field.However,the effectiveness of model training relies heavily on the support of sufficient sample sizes.Due to various constraints such as privacy,security,ethics,and costs in the medical field,it is rather difficult to obtain a large number of labeled training samples.Problems like the scarcity of rare disease cases,the lack of biological data for drug molecule mining,and the shortage of high-quality annotations for medical images significantly reduce the ability of models to learn from observed data,which in turn leads to poor prediction performance.In this context,constructing efficient learning artificial intelligence models for small sample data is of far-reaching significance both theoretically and practically.On the one hand,it can help to explore potential patterns when samples are insufficient in the early stage of new research.On the other hand,high-quality models can effectively reduce the cost of manual annotation,shorten the research cycle,and provide opportunities for solving challenging problems in medical research where it is difficult to collect a sufficient number of samples.Driven by both the expected advantages and actual needs,the research on artificial intelligence for small sample data has gradually become a highly anticipated and important research direction.This review systematically collates and summarizes the principles,advantages,disadvantages,applicable scenarios,and principal challenges associated with six artificial intelligence methods currently employed in the context of small-sample medical data,namely generative adversarial networks,graph neural networks,transfer learning,reinforcement learning,and Meta-learning.Furthermore,the review provides an extensive outlook and in-depth contemplation on the future trajectory of artificial intelligence methodologies in the realm of small sample data in medicine.