Objective:To compare the dosimetric difference between knowledge-based planning (KBP) volumetric modulated arc therapy (VMAT) and intensity-modulated radiotherapy (IMRT) models for predicting the dose distribution during IMRT, aiming to investigate the feasibility of VMAT model to predict the IMRT plans.Methods:Fifty prostate cancer patients who had completed radiotherapy were selected. Manual planning was performed on each selected patient to generate the corresponding IMRT and VMAT plans. The IMRT and VMAT manual plans of the 40 randomly-selected patients were adopted to generate the KBP VMAT and IMRT models. The remaining 10 patients were utilized to predict IMRT plans. VMAT library-derived IMRT model (V-IMRT) and IMRT library-derived IMRT model (I-IMRT) were generated. Dosimetric parameters related to organ-at-risks (OARs) and planning target volume (PTV) were statistically compared among the manual IMRT (mIMRT), V-IMRT and I-IMRT plans.Results:Compared with the mIMRT plan, I-IMRT could significantly better control D max of the PTV ( P=0.039), whereas V-IMRT and I-IMRT plans could better protect the bladder and bilateral femoral heads (both P<0.05). V-IMRT plan could better protect the D max of bilateral femoral heads and the D 15% of the right femoral head (both P<0.05), whereas no significant difference was observed in other OARs and PTV (all P>0.05). Conclusions:Compared with the manual plans, KBP IMRT plan has significant advantages in protecting the OARs. KBP VMAT and IMRT models are both feasible in clinical practice, which yield equivalent accuracy for predicting IMRT plan.

Objective:To analyze the causal relationship between gut microbiota and gestational diabetes,and to clarify its mechanism.Methods:Two-sample Mendelian randomization(MR)analysis was conducted by using summary data from genome-wide association study(GWAS)for gut microbiota and gestational diabetes.The GWAS data of gut microbiota were obtained from a GWAS study from the MiBioGen consortium;the GWAS data on gestational diabetes were sourced from the FinnGen consortium's publicly available R8 dataset;inverse variance weighted(IVW)method was used as the primary method to detect the causal association between the gut microbiota and the gestational diabetes.Sensitivity analysis was performed by Weighted Median and MR Egger methods;heterogeneity and pleiotropy were detected by Cochran's Q,MR-PRESSO,Egger intercept tests and Leave-One-Out analysis;multivariable MR was used to adjust for the effect of body mass index(BMI);reverse MR was used to explore the presence of reverse causal associations;Gene Ontology(GO)fuctional and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling enrichment analyses were used to explore the potential pathways through which gut microbiota may have impact on gestational diabetes.Results:Four gut microbes were found to be causally associated with gestational diabetes:the genus Methanobrevibacter and the phylum Euryarchaeota displayed negative causal relationships with the risk of gestational diabetes,while the genus Olsenella and genus Lachnoclostridium exhibited positive causal associations.No significant heterogeneity or horizontal pleiotropy was detected in the analysis.The reverse MR analysis did not reveal any causal relationship.After adjusting for BMI,the multivariable MR analysis results showed there were the causal associations between the genus Olsenella and the phylum Euryarchaeota with the risk of gestational diabetes.The GO fuctional and KEGG signaling pathway enrichment analyses results showed that axon development,axon production,insulin secretion and other pathways were significantly enriched.Conclusion:There are causal associations between four gut microbes and gestational diabetes.Among them,the significant correlations with gestational diabetes are still observed in phylum Euryarchaeota and genus Olsenella after adjusting for BMI.