Objective:To evaluate immunotherapy combined with neoadjuvant chemotherapy before radical total gastrectomy in microsatellite stable locally advanced gastric cancer in two cases.Methods:Two male patients with clinical stage cT3N 1M0 and microsatellite-stable locally advanced gastric cancer were treated with neoadjuvant chemotherapy with PD-1 inhibitor (Nivolumab) combined with SOX (Oxaliplatin+S-1) for 4 cycles before surgery. Standard laparoscopic assisted total gastrectomy with D 2 lymphadenectomy was performed on Feb 2023 and Oct 2023 respectively after the neoadjuvant treatment. Pathological tumor regression grade(TRG) was observed to assess the degree of tumor regression, and follow-up was conducted to monitor tumor markers and abdominal enhanced CT to detect recurrence. Results:Two patients achieved pathological complete response(TRG0). They were followed up until May 2024 and no recurrence was observed.Conclusion:Preoperative combination of chemotherapy and immunotherapy may provide survival benefit for microsatellite stable locally advanced gastric cancer patients.

Objective:To develop a preoperative differentiation model for colorectal mucinous adenocarcinoma and non-mucinous adenocarcinoma using a combination of contrast-enhanced CT radiomics and deep learning methods.Methods:This is a retrospective cohort study. Clinical data of colorectal cancer patients confirmed by postoperative pathological examination were retrospectively collected from January 2016 to December 2023 at Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Center 1, n=220) and the First Affiliated Hospital of Bengbu Medical University (Center 2, n=51). Among them, there were 108 patients diagnosed with mucinous adenocarcinoma, including 55 males and 53 females, with an age of (68.4±12.2) years (range: 38 to 96 years); and 163 patients diagnosed with non-mucinous adenocarcinoma, including 96 males and 67 females, with an age of (67.9±11.0) years (range: 43 to 94 years). The cases from Center 1 were divided into a training set ( n=156) and an internal validation set ( n=64) using stratified random sampling in a 7∶3 ratio, and the cases from Center 2 were used as an independent external validation set ( n=51). Three-dimensional tumor volume of interest was manually segmented on venous-phase contrast-enhanced CT images. Radiomics features were extracted using PyRadiomics, and deep learning features were extracted using the ResNet-18 network. The two sets of features were then combined to form a joint feature set. The consistency of manual segmentation was assessed using the intraclass correlation coefficient. Feature dimensionality reduction was performed using the Mann-Whitney U test and the least absolute shrinkage and selection operator regression. Six machine learning algorithms were used to construct models based on radiomics features, deep learning features, and combined features, including support vector machine, logistic regression, random forest, extreme gradient boosting, k-nearest neighbors, and decision tree. The discriminative performance of each model was evaluated using receiver operating characteristic curves, the area under the curve (AUC), DeLong test, and decision curve analysis. Results:After feature selection, 22 features with the most discriminative value were finally retained, among which 12 were traditional radiomics features and 10 were deep learning features. In the internal validation set, the Random Forest algorithm based on the combined features model achieved the best performance (AUC=0.938, 95% CI: 0.875 to 0.984), which was superior to the single-modality radiomics feature model (AUC=0.817, 95% CI: 0.702 to 0.913, P=0.048) and the deep learning feature model (AUC=0.832, 95% CI: 0.727 to 0.926, P=0.087); in the independent external validation set, the Random Forest algorithm with the combined features model maintained the highest discriminative performance (AUC=0.891, 95% CI: 0.791 to 0.969), which was superior to the single-modality radiomics feature model (AUC=0.770, 95% CI: 0.636 to 0.890, P=0.045) and the deep learning feature model (AUC=0.799, 95% CI: 0.652 to 0.911, P=0.169). Conclusion:The combined model based on radiomics and deep learning features from venous-phase enhanced CT demonstrates good performance in the preoperative differentiation of colorectal mucinous from non-mucinous adenocarcinoma.

Objective To establish a gas chromatography-mass spectrometry method for simultaneous determination of the contents of volatile components muscone,menthol,borneol,isoborneol,camphor,and carvone in Wuli Huichun pills,and to evaluate the quality of Wuli Huichun pills based on authenticity,effectiveness and safety.Methods The ethyl acetate extract was analyzed by HP-5MS capillary column(30 m×0.25 mm,0.25 μm)with temperature programming.The injection port temperature was set at 230℃,and the split ratio was set at 10∶1.The carrier gas was high-purity helium gas.The injection port was in constant flow mode,with a carrier gas flow rate of 1.0 mL·min-1.The detector was a mass spectrometer with a triple quadrupole in series.The ion source was an electron bombardment source(EI)with a temperature of 250℃.The collision gas was argon.The temperature of the mass spectrometry transmission interface was 250℃.Scanning mode was SIM mode,with a solvent delay of 2 minutes.Results The linear relationships of the six components were good(r>0.999 0),the average recoveries were 93.57%-97.96%,and the RSD was 0.73%-2.5%.Conclusion The method is simple,accurate,and efficient,and can be used for the quality evaluation of Wuli Huichun pills.

Objective To establish a gas chromatography-mass spectrometry method for simultaneous determination of the contents of volatile components muscone,menthol,borneol,isoborneol,camphor,and carvone in Wuli Huichun pills,and to evaluate the quality of Wuli Huichun pills based on authenticity,effectiveness and safety.Methods The ethyl acetate extract was analyzed by HP-5MS capillary column(30 m×0.25 mm,0.25 μm)with temperature programming.The injection port temperature was set at 230℃,and the split ratio was set at 10∶1.The carrier gas was high-purity helium gas.The injection port was in constant flow mode,with a carrier gas flow rate of 1.0 mL·min-1.The detector was a mass spectrometer with a triple quadrupole in series.The ion source was an electron bombardment source(EI)with a temperature of 250℃.The collision gas was argon.The temperature of the mass spectrometry transmission interface was 250℃.Scanning mode was SIM mode,with a solvent delay of 2 minutes.Results The linear relationships of the six components were good(r>0.999 0),the average recoveries were 93.57%-97.96%,and the RSD was 0.73%-2.5%.Conclusion The method is simple,accurate,and efficient,and can be used for the quality evaluation of Wuli Huichun pills.

Objective:To evaluate immunotherapy combined with neoadjuvant chemotherapy before radical total gastrectomy in microsatellite stable locally advanced gastric cancer in two cases.Methods:Two male patients with clinical stage cT3N 1M0 and microsatellite-stable locally advanced gastric cancer were treated with neoadjuvant chemotherapy with PD-1 inhibitor (Nivolumab) combined with SOX (Oxaliplatin+S-1) for 4 cycles before surgery. Standard laparoscopic assisted total gastrectomy with D 2 lymphadenectomy was performed on Feb 2023 and Oct 2023 respectively after the neoadjuvant treatment. Pathological tumor regression grade(TRG) was observed to assess the degree of tumor regression, and follow-up was conducted to monitor tumor markers and abdominal enhanced CT to detect recurrence. Results:Two patients achieved pathological complete response(TRG0). They were followed up until May 2024 and no recurrence was observed.Conclusion:Preoperative combination of chemotherapy and immunotherapy may provide survival benefit for microsatellite stable locally advanced gastric cancer patients.

Objective:To develop a preoperative differentiation model for colorectal mucinous adenocarcinoma and non-mucinous adenocarcinoma using a combination of contrast-enhanced CT radiomics and deep learning methods.Methods:This is a retrospective cohort study. Clinical data of colorectal cancer patients confirmed by postoperative pathological examination were retrospectively collected from January 2016 to December 2023 at Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Center 1, n=220) and the First Affiliated Hospital of Bengbu Medical University (Center 2, n=51). Among them, there were 108 patients diagnosed with mucinous adenocarcinoma, including 55 males and 53 females, with an age of (68.4±12.2) years (range: 38 to 96 years); and 163 patients diagnosed with non-mucinous adenocarcinoma, including 96 males and 67 females, with an age of (67.9±11.0) years (range: 43 to 94 years). The cases from Center 1 were divided into a training set ( n=156) and an internal validation set ( n=64) using stratified random sampling in a 7∶3 ratio, and the cases from Center 2 were used as an independent external validation set ( n=51). Three-dimensional tumor volume of interest was manually segmented on venous-phase contrast-enhanced CT images. Radiomics features were extracted using PyRadiomics, and deep learning features were extracted using the ResNet-18 network. The two sets of features were then combined to form a joint feature set. The consistency of manual segmentation was assessed using the intraclass correlation coefficient. Feature dimensionality reduction was performed using the Mann-Whitney U test and the least absolute shrinkage and selection operator regression. Six machine learning algorithms were used to construct models based on radiomics features, deep learning features, and combined features, including support vector machine, logistic regression, random forest, extreme gradient boosting, k-nearest neighbors, and decision tree. The discriminative performance of each model was evaluated using receiver operating characteristic curves, the area under the curve (AUC), DeLong test, and decision curve analysis. Results:After feature selection, 22 features with the most discriminative value were finally retained, among which 12 were traditional radiomics features and 10 were deep learning features. In the internal validation set, the Random Forest algorithm based on the combined features model achieved the best performance (AUC=0.938, 95% CI: 0.875 to 0.984), which was superior to the single-modality radiomics feature model (AUC=0.817, 95% CI: 0.702 to 0.913, P=0.048) and the deep learning feature model (AUC=0.832, 95% CI: 0.727 to 0.926, P=0.087); in the independent external validation set, the Random Forest algorithm with the combined features model maintained the highest discriminative performance (AUC=0.891, 95% CI: 0.791 to 0.969), which was superior to the single-modality radiomics feature model (AUC=0.770, 95% CI: 0.636 to 0.890, P=0.045) and the deep learning feature model (AUC=0.799, 95% CI: 0.652 to 0.911, P=0.169). Conclusion:The combined model based on radiomics and deep learning features from venous-phase enhanced CT demonstrates good performance in the preoperative differentiation of colorectal mucinous from non-mucinous adenocarcinoma.

Colorectal cancer is a common malignant tumor of the digestive system globally, with both its incidence and mortality rates increasing annually in China. In recent years, there has been exponential growth in technology based on artificial intelligence, leading to significant advancements in the field of medical imaging diagnosis. Particularly in the application of colonoscopy, CT and magnetic resonance imaging (MRI), artificial intelligence, leveraging its advanced image recognition and feature analysis capabilities, has provided new perspectives for the diagnosis of colorectal cancer, thereby driving the realization of precision medicine. Currently, various artificial intelligence algorithms are either under development or optimization, such as performance comparisons of various artificial intelligence-assisted systems, the collaborative application of multiple algorithms, and integration with other omics. Additionally, challenges persist in the integration difficulty, interpre-tability and credibility, as well as cost and resource limitations of AI in clinical practice, necessitating further standardization and improvement. The authors explore the current status and progress of artificial intelligence in endoscopic and imaging diagnosis of colorectal cancer from four aspects: colonoscopy, CT, MRI and other imaging examination for reference and reference by peers.

Objective To understand the differences in the demand,preference,and tendency for elderly care services between urban and rural areas in the Pearl River Delta(PRD),and to provide reference for the planning and balanced allocation of elderly care resources in urban and rural areas.Methods Using the multi-stage stratified random sampling method,we selected 7 community health service centers in 2 prefecture-level cities in the PRD and conducted a questionnaire survey on the elderly care service demand,preference,and tendency among 1919 regular residents aged 60 years and above who attended the centers.Results A total of 641 urban elderly residents(33.4%)and 1278 rural elderly residents(66.6%)were surveyed in the PRD.The urban and rural elderly residents showed differences in the child number(χ2 =43.379,P<0.001),willingness to purchase socialized elderly care services(χ2 =104.141,P<0.001),and attitudes to the concept of raising child to avoid elderly hardship(χ2 =65.632,P<0.001).The proportion(71.8%)of rural elderly residents who prefer family-based elderly care was higher than that(57.1%)of urban elderly residents(χ2 =41.373,P<0.001).The proportion(62.2%)of urban elderly residents clearly expressing their willingness to choose institutions for elderly care was higher than that(44.0%)of rural elderly residents(χ2 =57.007,P<0.001).Compared with family-based elderly care,the willingness to choose institutional or community-based in-house elderly care was low among the urban elderly residents with surplus monthly household income or balanced income and expenditure;urban males,those with college education background or above,and those who purchased so-cialized elderly care services tended to prefer community-based in-house elderly care.In rural areas,the elderly residents who had local household registry were prone to choose institutional or community-based in-house elderly care,while those who had more than one child and those who were satisfied with the current living conditions were less willing to choose community-based in-house elderly care.Conclusions It is suggested that the urban-rural differences in the elderly care service demand,preference and tendency should be fully considered in the planning and allocation of urban and rural elderly care resources.Efforts remain to be made to develop diversified social elderly care services tailored to the characteristics of urban and rural areas.

Objective To compare the different therapeutic efficacies between radiofrequency and cryoballoon ablation in atrial fibrillation(AF)patients with enlarged left atrium.Methods A total of 106 AF patients with enlarged left atrium(LAVI)>34ml/m2 who visited our center between December 2016 and May 2022 for catheter ablation were enrolled.They were divided into radiofrequency group(R-group)and cryoballoon group(C-group)according to the procedure.The recurrence of AF was compared between the two groups.Results The median follow-up time was 12 months and no death occurred.The rate of loss of follow-up was 3.8%.The overall recurrence rate of AF after ablation was 36.8%.There was no significant difference in clinical baseline between the two groups(P>0.05).The radiofrequency group had longer operation time(P<0.001),shorter X-ray time and less exposure(P<0.05);There was no significant difference in clinical baseline and postoperative complications between the two groups(P>0.05).The results of survival analysis showed that there was no significant difference in the survival rate of patients without atrial fibrillation/atrial flutter/atrial tachycardia between the two groups(P>0.05).Conclusion For AF patients with enlarged left atrium,the clinical effects of the two methods were similar.Cryoballoon takes a shorter time,while radiofrequency has a lower X-ray time and exposure dose for physicians and patients.Therefore,appropriate ablation methods should be selected according to the actual situation.

ObjectiveTo investigate the relationship between lower limb muscle strength and walking speed in older adults, and to analyze the mediating role of flexibility and dynamic balance. MethodsFrom November to December, 2021, a total of 155 older adults at the Shanghai Senior Sports and Health Home were included. Their basic health information was collected, and the lower limb muscle strength, flexibility, dynamic balance and walking speed were tested. A mediated effects analysis was conducted. ResultsThere was a pairwise correlation among lower limb muscle strength, flexibility, dynamic balance and walking speed in older adults (r > 0.210, P < 0.01). In the mediated effects model, after controlling for age and gender, lower limb muscle strength did not directly predict walking speed in older adults (β = 0.029, P = 0.699), however, lower limb muscle strength could influence walking speed through the partial mediation of dynamic balance (effect = 0.0130, 95% CI 0.0073~0.0197) and the chain mediation of lower flexibility and dynamic balance (effect = 0.0019, 95% CI 0.0003~0.0043). ConclusionLower limb muscle strength can indirectly affect walking speed in older adults through the mediators of flexibility and dynamic balance, or the dynamic balance alone.