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Abstract Our objective is to develop a prognostic model focused on cuproptosis, aimed at predicting overall survival (OS) outcomes among Acute myeloid leukemia (AML) patients. The model utilized machine learning algorithms incorporating stacking. The GSE37642 dataset was used as the training data, and the GSE12417 and TCGA-LAML cohorts were used as the validation data. Stacking was used to merge the three prediction models, subsequently using a random survival forests algorithm to refit the final model using the stacking linear predictor and clinical factors. The prediction model, featuring stacking linear predictor and clinical factors, achieved AUC values of 0.840, 0.876 and 0.892 at 1, 2 and 3 years within the GSE37642 dataset. In external validation dataset, the corresponding AUCs were 0.741, 0.754 and 0.783. The predictive performance of the model in the external dataset surpasses that of the model simply incorporates all predictors. Additionally, the final model exhibited good calibration accuracy. In conclusion, our findings indicate that the novel prediction model refines the prognostic prediction for AML patients, while the stacking strategy displays potential for model integration.

Abstract To investigate the gender-specific relationship between total bilirubin (TBIL) and fundus arteriosclerosis in the general population, and to explore whether there is a dose–response relationship between them. In a retrospective cohort study, 27,477 participants were enrolled from 2006 to 2019. The TBIL was divided into four groups according to the quartile. The Cox proportional hazards model was used to estimate the HRs with 95% CIs of different TBIL level and fundus arteriosclerosis in men and women. The dose–response relationship between TBIL and fundus arteriosclerosis was estimated using restricted cubic splines method. In males, after adjusting for potential confounders, the Q2 to Q4 level of TBIL were significantly associated with the risk of fundus arteriosclerosis. The HRs with 95% CIs were 1.217 (1.095–1.354), 1.255 (1.128–1.396) and 1.396 (1.254–1.555), respectively. For females, TBIL level was not associated with the incidence of fundus arteriosclerosis. In addition, a linear relationship between TBIL and fundus arteriosclerosis in both genders (P

Abstract Accurate forecasting of hospital outpatient visits is beneficial to the rational planning and allocation of medical resources to meet medical needs. Several studies have suggested that outpatient visits are related to meteorological environmental factors. We aimed to use the autoregressive integrated moving average (ARIMA) model to analyze the relationship between meteorological environmental factors and outpatient visits. Also, outpatient visits can be forecast for the future period. Monthly outpatient visits and meteorological environmental factors were collected from January 2015 to July 2021. An ARIMAX model was constructed by incorporating meteorological environmental factors as covariates to the ARIMA model, by evaluating the stationary $${R}^{2}$$ R 2 , coefficient of determination $${R}^{2}$$ R 2 , mean absolute percentage error (MAPE), and normalized Bayesian information criterion (BIC). The ARIMA $${(0, 1, 1) (0, 1, 0)}_{12}$$ ( 0 , 1 , 1 ) ( 0 , 1 , 0 ) 12 model with the covariates of $$\text{SO}_{2}$$ SO 2 , $${PM}_{2.5}$$ PM 2.5 , and $$\text{CO}$$ CO was the optimal model. Monthly outpatient visits in 2019 can be predicted using average data from past years. The relative error between the predicted and actual values for 2019 was 2.77%. Our study suggests that $$\text{SO}_{2}$$ SO 2 , $${PM}_{2.5}$$ PM 2.5 , and $$\text{CO}$$ CO concentration have a significant impact on outpatient visits. The model built has excellent predictive performance and can provide some references for the scientific management of hospitals to allocate staff and resources.