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Polymorphisms in the MGMT gene have been implicated in susceptibility to cancer, but the published studies have reported inconclusive results. The objective of the current study was to investigate the genetic risk of polymorphisms in the MGMT gene for cancer. A meta-analysis was carried out to analyze the association between polymorphisms in the MGMT gene and cancer risk. Five polymorphisms (Leu84Phe, Leu53Leu, Ile143Val, Lys178Arg, and -485C/A) with 98 case-control studies from 49 articles were analyzed. The results indicated that individuals who carried the Phe/Phe homozygote genotype of Leu84Phe had a 31 % increased risk of cancer compared with the Leu allele (Leu + Leu/Phe) carriers (odds ratio [OR] = 1.32, 95 % confidence interval [CI] = 1.15-1.52, P < 0.0001 for Phe/Phe vs. Phe/Leu + Leu/Leu). However, there was no significant association between the risk of cancer and the other four polymorphisms (Leu53Leu, Ile143Val, Lys178Arg, and -485C/A). In further stratified analyses for the Leu84Phe and Ile143Val polymorphisms, the increased risk of cancer remained in subgroups of Caucasians, patients with esophageal cancer for the Leu84Phe polymorphism, and patients with lung cancer for the Ile143Val polymorphism. Results from the current meta-analysis suggested that Leu84Phe and Ile143Val in the MGMT gene are risk factors for cancer. In the future, more studies should be performed to validate our results.

Late major bleeding is one of the main complications after transcatheter aortic valve replacement (TAVR). We aimed to develop a risk prediction model based on deep learning to predict major or life-threatening bleeding complications (MLBCs) after TAVR. Patients and Methods: This was a retrospective study including TAVR patients from West China Hospital of Sichuan University Transcatheter Aortic Valve Replacement Registry (ChiCTR2000033419) between April 17, 2012 and May 27, 2020. A deep learning-based model named BLeNet was developed with 56 features covering baseline, procedural, and post-procedural characteristics. The model was validated with the bootstrap method and evaluated using Harrell's concordance index (c-index), receiver operating characteristics (ROC) curve, calibration curve, and Kaplan-Meier estimate. Captum interpretation library was applied to identify feature importance. The BLeNet model was compared with the traditional Cox proportional hazard (Cox-PH) model and the random survival forest model in the metrics mentioned above. Results: The BLeNet model outperformed the Cox-PH and random survival forest models significantly in discrimination [optimism-corrected c-index of BLeNet vs Cox-PH vs random survival forest: 0.81 (95% CI: 0.79-0.92) vs 0.72 (95% CI: 0.63-0.77) vs 0.70 (95% CI: 0.61-0.74)] and calibration (integrated calibration index of BLeNet vs Cox-PH vs random survival forest: 0.007 vs 0.015 vs 0.019). In Kaplan-Meier analysis, BLeNet model had great performance in stratifying high-and low-bleeding risk patients (p < 0.0001). Conclusion:Deep learning is a feasible way to build prediction models concerning TAVR prognosis. A dedicated bleeding risk prediction model was developed for TAVR patients to facilitate well-informed clinical decisions.

Background With the expanded utilization of transcatheter aortic valve implantation (TAVI) to younger and lower surgical risk patients with severe aortic stenosis (AS), optimal medical therapy after TAVI procedure has become the main concern. Renin-angiotensin system inhibitors (RASi) are widely utilized in the area of cardiovascular disease including heart failure and myocardial infarction and revealed the ability to reverse left ventricular (LV) remodeling. Interests have, thus, been drawn in investigating whether the prescription of RASi after the TAVI procedure can prevent or reverse cardiac remodeling and improve long-term clinical outcomes. No recommendation regarding the prescription of RASi after TAVI is proposed yet due to the lack of evidence from randomized controlled trials, especially in the Chinese population. We, therefore, designed this randomized controlled trial to explore the effect of adding fosinopril to standard care in patients who underwent a successful TAVI procedure on the LV remodeling. Methods A total of 200 post-TAVI patients from seven academic hospitals across China will be recruited and randomized with a ratio of 1:1 to receive standard care or standard care plus fosinopril. Follow-up visits will take place at 30 days, 3 months, 6 months, 12 months, and 24 months from randomization to assess the clinical symptoms, any adverse events, cardiac function, and quality of life. Cardiac magnetic resonance will be performed at baseline and repeated at the 24-month follow-up visit to assess LV remodeling. Discussion This study will provide evidence regarding medical therapy for AS patients who underwent TAVI and filling the gap in the Chinese population. Trial registration Chinese Clinical Trial Registry ChiCTR2100042266. Registered on 17 January 2021