Objective:To develop a deep learning model based on super-resolution endorectal ultrasound（ERUS）images for the preoperative prediction of perineural invasion（PNI）in patients with rectal cancer，thereby providing a reference for risk stratification and individualized treatment planning.Methods:A retrospective analysis was conducted on 382 patients with rectal cancer who underwent total mesorectal excision at Fujian Medical University Union Hospital between June 2019 and February 2024. Patients were randomly divided into a training set（ n=305）and a test set（ n=77）at a ratio of 8∶2，and further grouped into PNI-negative group and PNI-positive group subgroups based on pathological results. Super-resolution ultrasound images were generated from original ERUS images using a generative adversarial network（GAN）. Deep convolutional neural networks were developed based on features from intratumoral and peritumoral regions to identify the optimal region of interest（ROI）. The dSR5_ResNet18 and dSR5_ResNet50 models were constructed using the super-resolution images with a 5-pixel peritumoral extension. Representative clinical features were selected for subgroup analysis based on sample size and intergroup statistical differences between PNI-positive and PNI-negative patients. Forest plots were used to evaluate model applicability and robustness across subgroups. Results:The dSR5_ResNet18 model，built using super-resolution images of the tumor combined with a 5-pixel peritumoral region，achieved the best predictive performance，with an AUC of 0.867（95% CI=0.782 - 0.952）in the test set. Decision curve analysis demonstrated that the dSR5_ResNet18 model provided the greatest net clinical benefit. Forest plot analysis indicated strong generalizability of the models across subgroups such as pathological N stage，maximum lesion length，and lymph node enlargement，though relatively weaker performance was observed in the carcinoembryonic antigen（CEA）subgroup. Among all models，dSR5_ResNet18 exhibited the most consistent performance across subgroups，with the narrowest confidence intervals and highest robustness. Conclusions:The deep learning model incorporating ERUS-based super-resolution reconstruction demonstrated excellent performance in the preoperative prediction of PNI in rectal cancer. It offers significant advantages in image quality and generalizability，and may serve as a valuable tool to assist clinicians in formulating personalized treatment strategies.

Objective:To develop a deep learning model based on super-resolution endorectal ultrasound（ERUS）images for the preoperative prediction of perineural invasion（PNI）in patients with rectal cancer，thereby providing a reference for risk stratification and individualized treatment planning.Methods:A retrospective analysis was conducted on 382 patients with rectal cancer who underwent total mesorectal excision at Fujian Medical University Union Hospital between June 2019 and February 2024. Patients were randomly divided into a training set（ n=305）and a test set（ n=77）at a ratio of 8∶2，and further grouped into PNI-negative group and PNI-positive group subgroups based on pathological results. Super-resolution ultrasound images were generated from original ERUS images using a generative adversarial network（GAN）. Deep convolutional neural networks were developed based on features from intratumoral and peritumoral regions to identify the optimal region of interest（ROI）. The dSR5_ResNet18 and dSR5_ResNet50 models were constructed using the super-resolution images with a 5-pixel peritumoral extension. Representative clinical features were selected for subgroup analysis based on sample size and intergroup statistical differences between PNI-positive and PNI-negative patients. Forest plots were used to evaluate model applicability and robustness across subgroups. Results:The dSR5_ResNet18 model，built using super-resolution images of the tumor combined with a 5-pixel peritumoral region，achieved the best predictive performance，with an AUC of 0.867（95% CI=0.782 - 0.952）in the test set. Decision curve analysis demonstrated that the dSR5_ResNet18 model provided the greatest net clinical benefit. Forest plot analysis indicated strong generalizability of the models across subgroups such as pathological N stage，maximum lesion length，and lymph node enlargement，though relatively weaker performance was observed in the carcinoembryonic antigen（CEA）subgroup. Among all models，dSR5_ResNet18 exhibited the most consistent performance across subgroups，with the narrowest confidence intervals and highest robustness. Conclusions:The deep learning model incorporating ERUS-based super-resolution reconstruction demonstrated excellent performance in the preoperative prediction of PNI in rectal cancer. It offers significant advantages in image quality and generalizability，and may serve as a valuable tool to assist clinicians in formulating personalized treatment strategies.

Objective:To investigate the application value of shear wave elastography (SWE) in the evaluation of T re-staging after neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer.Methods:Clinical, endorectal ultrasound (ERUS) and SWE data of 271 patients with locally advanced rectal cancer who underwent nCRT and total mesorectal excision in Fujian Medical University Union Hospital from October 2021 to March 2023 were prospectively collected. The independent predictors for low T staging were analyzed and screened, and the Logistic regression model was constructed. An independent test set was used to validate the prediction performance of the models and compare them with the diagnostic results of sonographers.Results:Binary multivariate Logistic regression analysis showed that Emean of the mesentery around the lesion, thickness, and enlarged lymph nodes around the rectum were the independent predictors for low T staging, and the odds ratios were 1.089, 1.214, 0.183, respectively. The Logistic regression model A established by Emean, thickness and enlarged lymph nodes around the lesion and the Logistic regression model B established by Emean around the lesion had high diagnostic efficiencies (area under the ROC curve were 0.931, 0.918, respectively, the accuracy were 0.888 and 0.887, respectively). There was no significant difference in diagnostic accuracy between the two models ( P=1.000), and both models were significantly higher than that of sonographers (all P<0.001). Conclusions:SWE can effectively predict whether the tumor is of low T staging after nCRT in locally advanced rectal cancer, and can be used as an important supplement to ERUS in evaluating the T re-staging of rectal cancer after nCRT.

Objective To establish a quantitative method for the simultaneous measurement of the residual level of three pesticides in Nelumbinis semen by GC-MS/MS. Methods The samples were extracted by acetonitrile and purify by Cleanert TPH column. The samples were then tested by GC-MS/MS. Information on relative retention time and mass charge ratio was used for qualitative analysis. The peak area obtained by secondary ion MS of bifenthrin (181.1/166.1)was used as the reference peak to calculate the relative correction factor for the peak area of fenpropathrin (265.1/210.1) and deltamethrin (252.9/93.0), to establish a method using bifenthrin as the reference substance to determinate there sidual quantity of three pesticides in Nelumbinis semen by GC-MS/MS. Results When the injection quantity of the sample containing bifenthrin,fenpropathrin and deltamethrin in the range of 0.01-0.1 ng , there was a good linear relationship between the injection quantity and peak are a Limitation of quantification (LOQ) of bifenthrin , fenpropathrin and deltamethrin were 4.321×10-4 ng, 3.435×10-4 ng, 8.913×10-3 ng, respectively. The average recovery rates of bifenthrin, fenpropathrin and deltamethrin were 93.5％, 93.5％ and 93.8％, respectively. Conclusions The method of quantitative analysis of multi-components with a single-marker is simple, quick and accurate. It suitable for the detection of residual quantity of bifenthrin, fenpropathrin and deltamethrin in Nelumbinis semen.

OBJECTIVE:To evaluate the economic effectiveness in different pharmacotherapeutic schemes for Hp infection in children.METHODS:To analyze four therapeutic schemes for Hp infection in children with cost - effectiveness analysis.RESULTS: The cost- effectiveness ratios of four therapeutic schemes were 14.92,8.85,8.37 and 8.58 respectively. CONCLUSIONS: scheme C(clarithromycin + bismuth potassium citrate + metronidazole)is the best one.

Objective:To find out the current situation of and objectively evaluate the use of the antidiabetic agents so as to provide reference information to the departments of production, sales and consumption of the agents. 　Methods:A analytic review of 6,639 020 prescriptions during the period of 1995～1999 was made regarding the costs and DDDs.　Results: In recent 5 years, the annual increase rate of the expenses for was(36.03±11.92)%; and that of DDDs was 20.05%. The order of expenses for various antidiabetic agents from high to low was as follows: glicalazide, acarbose, glipizide and metformin, while that of DDDs was glicalazide, glipizide, metformin and acarbose. The percentage of expenditure for oral versus the injection antidiabetic agents was(87.08±3.50)% to(12.92±3.50)%. 　Conclusion: The antidiabetic agents is a kind of common drugs with a bright future of development.

OBJECTIVE:To evaluate the economic effectiveness in different pharmacotherapeutic schemes for Hp infection in children.METHODS: To analyze four therapeutic schemes for Hp infection in children with cost - effectiveness analy-sis. RESULTS: The cost - effectiveness ratios of four therapeutic schemes were 14.92, 8.85, 8.37 and 8.58 respective-ly. CONCLUSIONS: scheme C(clarithromycin + bismuth potassium citrate + metronidazole)is the best one.