Exhaustive-exercise-induced oxidative stress alteration of erythrocyte oxygen release capacity.

The aim of the present study was to explore the effect of exhaustive running exercise in the oxygen release capacity of rat erythrocytes. Rats were divided into sedentary control, moderate running exercise, and exhaustive running exercise groups. The thermodynamic and kinetic properties of the erythrocyte oxygen release process of the different groups were tested. We also determined the degree of band-3 oxidation and phosphorylation, anion transport activity, and carbonic anhydrase isoform II activity. Biochemical studies suggested that exhaustive running significantly increased oxidative injury parameters in thiobarbituric acid reactive substances and methaemoglobin levels. Furthermore, exhaustive running significantly decreased anion transport activity and carbonic anhydrase isoform II activity. Thermodynamic analysis indicated that erythrocytes oxygen release ability also significantly increased due to elevated 2,3-DPG level after exhaustive running. Kinetic analysis indicated that exhaustive running resulted in significantly decreased T50 value. We presented evidence that exhaustive running remarkably impacted thermodynamic and kinetic properties of RBC oxygen release. In addition, changes in 2,3-DPG levels and band-3 oxidation and phosphorylation could be the driving force for exhaustive-running-induced alterations in erythrocyte oxygen release thermodynamic and kinetic properties.

Prediction model for missed abortion of patients treated with IVF-ET based on XGBoost: a retrospective study.

Aim: In this study, we established a model based on XGBoost to predict the risk of missed abortion in patients treated with in vitro fertilization-embryo transfer (IVF-ET), evaluated its prediction ability, and compared the model with the traditional logical regression model. Methods: We retrospectively collected the clinical data of 1,017 infertile women treated with IVF-ET. The independent risk factors were screened by performing a univariate analysis and binary logistic regression analysis, and then, all cases were randomly divided into the training set and the test set in a 7:3 ratio for constructing and validating the model. We then constructed the prediction models by the traditional logical regression method and the XGBoost method and tested the prediction performance of the two models by resampling. Results: The results of the binary logistic regression analysis showed that several factors, including the age of men and women, abnormal ovarian structure, prolactin (PRL), anti-Müllerian hormone (AMH), activated partial thromboplastin time (APTT), anticardiolipin antibody (ACA), and thyroid peroxidase antibody (TPO-Ab), independently influenced missed abortion significantly (P < 0.05). The area under the receiver operating characteristic curve (AUC) score and the F1 score with the training set of the XGBoost model (0.877 ± 0.014 and 0.730 ± 0.019, respectively) were significantly higher than those of the logistic model (0.713 ± 0.013 and 0.568 ± 0.026, respectively). In the test set, the AUC and F1 scores of the XGBoost model (0.759 ± 0.023 and 0.566 ± 0.042, respectively) were also higher than those of the logistic model (0.695 ± 0.030 and 0.550 ± 049, respectively). Conclusions: We established a prediction model based on the XGBoost algorithm, which can accurately predict the risk of missed abortion in patients with IVF-ET. This model performed better than the traditional logical regression model.