No abstract

BackgroundTo compare short-term and long-term results of bariatric surgery vs non-surgical treatment for type 2 diabetes mellitus (T2DM).MethodsA systematic search was conducted in the PubMed, Embase, and Cochrane Library databases for randomized controlled trials (RCTs). All statistical analysis was performed using Review Manager version 5.3. The dichotomous data was calculated using risk ratio (RR) and continuous data was using mean differences (MD) along with 95% confidence intervals (CI).ResultsA total of 8 RCTs with 619 T2DM patients were analyzed. Compared with non-surgical treatment group, bariatric surgery group was associated with higher rate T2DM remission (RR = 5.76, 95%CI:3.15-10.55, P < 0.00001), more reduction HbA1C (MD = 1.29, 95%CI: -1.70 to -0.87, P < 0.00001), more decrease fasting plasma glucose (MD = -36.38, 95%CI: -51.76 to -21.01, P < 0.00001), greater loss body weight (MD = -16.93, 95%CI: 19.78 to -14.08, P < 0.00001), more reduction body mass index (MD = -5.80, 95%CI: -6.95 to -4.64, P < 0.00001), more decrease triglyceride concentrations (MD = -51.27, 95%CI: -74.13 to -28.41, P < 0.0001), and higher increase density lipoprotein cholesterol (MD = 9.10, 95%CI: 7.99 to 10.21; P < 0.00001). But total and low density lipoprotein cholesterol were no significant changes.ConclusionBariatric surgery for T2DM is efficacious and improves short- and long-term outcomes as compared with non-surgical treatment.

BackgroundLymph node metastasis is one of the most important prognostic factors for survival of patients with gastric cancer (GC) after surgical resection. Nevertheless, a considerable number of patients have node-negative disease. We performed the present systematic review to evaluate survival and identify prognostic factors in node-negative GC patients undergoing curative intent resection.Material/MethodsRelevant studies published between January 2000 and January 2015 were identified by searching the PubMed database and reviewed systematically. Summary relative risks (RR) and 95% confidence intervals (95% CI) were estimated using random-effects models.ResultsThirty observational studies involving 12 504 patients were included in the review. Median 5-year overall survival was 84.3% (range, 53–96.3%). Pooled analysis showed that old age (RR, 1.26; 95%CI, 1.13–1.42),

In the cross-silo federated learning setting, one kind of data partition according to features, which is so-called vertical federated learning (i.e. feature-wise federated learning) (Yang et al. 2019), is to apply to multiple datasets that share the same sample ID space but different feature spaces. Simultaneously, the image dataset can also be partitioned according to labels. To improve the model performance of the isolated parties based on feature-wise (i.e. label-wise) results, the most effective method is to federate the model results of the isolated parties together. However, it is a non-trivial task to allow the participating parties to share the model results without violating the data privacy of the parties. In this paper, within the framework of principal component analysis (PCA), we propose a Federated-PCA machine learning approach, in which the PCA method is used to reduce the dimensionality of sample data for all parties and extract the principal component feature information to improve the efficiency of subsequent training work. This process will not reveal the original data information of each party. The federal system can help each side build a common profit strategy. Under this federal mechanism, the identity and status of each party are the same. By comparing the federated results of the isolated parties and the result of the unseparated party through multiple sets of comparative experiments, we find that the experimental results of these two settings are close, and the proposed method can effectively improve the training model performance of most participating parties.

No abstract