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Background: Anemia is a common complication of total hip arthroplasty (THA). In this study, we evaluated the preoperative risk factors for postoperative anemia after THA and developed a nomogram model based on related preoperative and intraoperative factors. Methods: From January 2020 to May 2023, 927 THA patients at the same medical center were randomly assigned to either the training or validation cohort. The correlation between preoperative and intraoperative risk factors and postoperative anemia after THA was evaluated using univariate and multivariate logistic regression analysis. A nomogram was developed using these predictive variables. The effectiveness and validation for the clinical application of this nomogram were evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: Through univariate and multivariate logistic regression analysis, 7 independent predictive factors were identified in the training cohort: Lower body mass index (BMI), extended operation time, greater intraoperative bleeding, lower preoperative hemoglobin level, abnormally high preoperative serum amyloid A (SAA) level, history of cerebrovascular disease, and history of osteoporosis. The C-index of the model was 0.871, while the AUC indices for the training and validation cohorts were 84.4% and 87.1%, respectively. In addition, the calibration curves of both cohorts showed excellent consistency between the observed and predicted probabilities. The DCA curves of the training and validation cohorts were high, indicating the high clinical applicability of the model. Conclusions: Lower BMI, extended operation time, increased intraoperative bleeding, reduced preoperative hemoglobin level, elevated preoperative SAA level, history of cerebrovascular disease, and history of osteoporosis were seven independent preoperative risk factors associated with postoperative anemia after THA. The nomogram developed could aid in predicting postoperative anemia, facilitating advanced preparation, and enhancing blood management. Furthermore, the nomogram could assist clinicians in identifying patients most at risk for postoperative anemia. [ABSTRACT FROM AUTHOR]

Background: N6-methyladenosine (m6A) is the most abundant modification of RNA in eukaryotic cells and play critical roles in cancer. While most related studies focus on m6A modifications in linear RNA, transcriptome-wide profiling and exploration of m6A modification in circular RNAs in cancer is still lacking. Methods: For the detection of m6A modification in circRNAs, we developed a new bioinformatics tools called Circm6A and applied it to the m6A-seq data of 77 tissue samples from 58 individuals with pancreatic ductal adenocarcinoma (PDAC). Results: Circm6A performs better than the existing circRNA identification tools, which achieved highest F1 score among these tools in the detection of circRNAs with m6A modifications. By using Circm6A, we identified a total of 8807 m6A-circRNAs from our m6A-seq data. The m6A-circRNAs tend to be hypermethylated in PDAC tumor tissues compared with normal tissues. The hypermethylated m6A-circRNAs were associated with a significant gain of circRNA-mRNA coexpression network, leading to the dysregulation of many important cancer-related pathways. Moreover, we found the cues that hypermethylated m6A-circRNAs may promote the circularization and translation of circRNAs. Conclusions: These comprehensive findings further bridged the knowledge gaps between m6A modification and circRNAs fields by depicting the m6A-circRNAs genomic landscape of PDAC patients and revealed the emerging roles played by m6A-circRNAs in pancreatic cancer. Circm6A is available at https://github.com/canceromics/circm6a. [ABSTRACT FROM AUTHOR]