Objective: To investigate the factors influencing the development of gastric cancer in Chinese populations, so as provide insights into creating a model for predicting gastric cancer incidence among Chinese populations. Methods: The case-control and cohort studies pertaining to factors affecting the development of gastric cancer were retrieved in electronic Chinese and English databases, including CNKI, Wanfang Data, VIP, PubMed, Web of Science and Embase from their inception until September 30, 2021. A meta-analysis was performed using R package version 4.1.0. Sensitivity analysis was performed using the “leave-one-out” evaluation procedure, and the publication bias was evaluated using the Egger regression test and the trim-and-fill procedure. Results: A total of 5 301 publications were screened and 116 eligible studies were included in the final analysis, including 103 case-control studies and 13 cohort studies, which covered approximately 3.23 million study subjects. A total of 45 factors affecting the development of gastric cancer were collected, and there were less than 4 publications reporting 7 factors, which were only qualitatively described. There were 38 factors included in the final meta-analysis. A total of 21 factors were identified as risk factors of gastric cancer, including a history of gastrointestinal diseases (pooled OR=4.85, 95%CI: 3.74-6.29), H. pylori infection (pooled OR=3.18, 95%CI: 2.35-4.32), binge eating and drinking (pooled OR=2.88, 95%CI: 2.09-3.97) and a family history of tumors (pooled OR=2.78, 95%CI: 2.17-3.56), and 10 factors as protective factors, including vegetable intake (pooled OR=0.48, 95%CI: 0.38-0.61), tea consumption (pooled OR=0.55, 95%CI: 0.47-0.64), administration of aspirin (pooled OR=0.53, 95%CI: 0.31-0.92) and administration of statins (pooled OR=0.59, 95%CI: 0.44-0.80). Sensitivity analyses of eating moldy food frequently, white meat intake, favoring spicy food and administration of sulfonylureas were not robust. Following correction with the trim-and-fill procedure, there was still a publication bias pertaining to high income, diabetes, administration of stains, alcohol consumption, tea consumption and white meat intake. Conclusions The development of gastric cancer is associated with a medical history of gastrointestinal disease, H. pylori infection, family history of tumors and poor dietary habits. Risk and protective factors of gastric cancer are recommended to be included in models used to predict gastric cancer incidence among Chinese populations.

Objective To compare the clinical outcomes between conventional percutaneous vertebroplasty (PVP) and targeted PVP in the treatment of osteoporotic vertebral compression fractures (OVCFs).Methods A retrospective cohort study was designed to review 215 cases of single level OVCFs hospitalized between January 2014 and December 2015.According to the procedure techniques,the patients were assigned to targeted PVP group (89 cases) and conventional PVP group (126 cases) which was further divided into sufficient filled subgroup (110 cases) and insufficient filled subgroup (16 cases) on basis of cement distribution.Key techniques of targeted PVP included accurate needle insertion to fractured area and cement injection using a push rob with a side opening.Operating time,cement injection volume,rate and types of cement leakage,cement distribution in the fractured area and visual analogue score (VAS) of back pain were compared between the two groups.Results Operating time in targeted PVP group was longer than that in conventional PVP group (P ＜ 0.05).There were no significant differences in cement injection volume and rate and types of cement leakage between the two groups (P ＞ 0.05).None in targeted PVP group showed insufficient cement distribution in fractured area,while 16 cases (12.7％) in conventional PVP group (P ＜ 0.05).No significant differences in preoperative VAS of back pain existed among targeted PVP group,sufficient subgroup and insufficient subgroup (P ＞ 0.05).VAS of back pain was significantly decreased after PVP in three groups (P ＜ 0.05).Difference in postoperative VAS of back pain between targeted PVP group and sufficient filled subgroup was insignificant (P ＞0.05).However,postoperative VAS of back pain in insufficient filled subgroup was significantly increased compared with targeted PVP group and sufficient filled subgroup (P ＜ 0.05).Conclusion Targeted PVP provides sufficient cement to fill the fractured area and decreases incidence of unsatisfactory clinical outcome compared with traditional PVP,indicating a secure and effective new technique in the treatment of OVCFs.

Objective:To investigate the application of combined deep learning and radiomic features derived from enhanced arterial phase CT imaging with clinical data to differentiate between T2 and T3 staging in patients with esophageal cancer.Methods:A retrospective study was conducted using clinical and CT data from 388 patients with pathologically confirmed esophageal cancer treated at The First Affiliated Hospital of Wannan Medical College between May 2015 and April 2024.The dataset was randomly divided into a training set(271 cases)and validation set(117 cases)in a 7:3 ratio.Radiomic and deep learning features were extracted from enhanced arterial phase CT images.The least absolute shrinkage and selection operator algorithm was employed for feature reduction and selection,leading to the development of radiomic(Radscore)and deep learning(Deepscore)scores.Univariate and multivariate Logistic regression analyses were conducted to identify independent risk factors,and clinical,radiomic,deep learning,and combined models were constructed.A nomogram was gener-ated for the combined model.The diagnostic performance of the models was evaluated using the area under the receiver operating charac-teristic curve(AUC)and compared using the DeLong test.Clinical net benefit was assessed through decision curve analysis,and model calib-ration was evaluated using calibration curves.Results:Nine radiomicand 12 deep learning features were selected after dimensionality reduc-tion.Multivariate Logistic regression identified tumor length,boundary,Radscore,and Deepscore as independent risk factors for distinguish-ing between T2 and T3 staging.In the training set,the AUC of the combined model was 0.867,which was significantly higher than that of the clinical(0.774,P<0.001),radiomic(0.795,P<0.001),and deep learning(0.821,P=0.001)models.In the validation set,the AUC of the com-bined model was 0.810,which was significantly higher than that of the clinical(0.653,P=0.002),radiomic(0.719,P=0.033),and deep learn-ing(0.750,P=0.009)models.The decision curve analysis indicated that the combined model provided the highest clinical benefit in both datasets.The calibration curves demonstrated a good fit for both datasets(P=0.084,0.053).Conclusion:The integration of deep learning and radiomic features obtained from enhanced arterial phase CT images with clinical data offers a reliable method for accurately distinguishing between preoperative T2 and T3 staging in esophageal cancer,thereby supporting clinical decision-making for treatment planning.

Red blood cell (RBC) transfusion is a clinically effective therapy in anemia, for example in patients with malignancies (Shander et al., 2020), bleeding (Odutayo et al., 2017), and preoperative anemia (Padmanabhan et al., 2019). The past few decades have witnessed a shortage of blood for transfusion due to limited health insurance coverage for blood use and the rapid expansion of hospitals (Chen et al., 2011; Shi et al., 2014). Blood donation levels may easily be affected by general changes in the environment, policy, major events such as disasters, and public sentiment (Hu et al., 2019). Meanwhile, the transfusion of allogeneic RBC is a double-edged sword, increasing the possibility of infectious and immunological complications, and also leading to higher morbidity and mortality after transfusion (Frank et al., 2012). Considering that the continual shortfall has been increasingly prominent, identifying the factors associated with RBC transfusion could help blood transfusion departments to improve their supply of blood products as well as their inventory management (O'Donnell et al., 2018).