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The pharmacokinetic (PK) characteristics of drugs were altered under high‐altitude hypoxia. We aim to describe the population PK of atorvastatin (ATV) to identify patient characteristics that are predictive of variability in the PK parameters of the ATV and investigate the effects of high‐altitude hypoxia on the blood concentration of ATV in patients with hyperlipidemia. A total of 160 plasma concentrations were collected from 40 patients with hyperlipidemia in plateau areas and 40 in plain areas. The population pharmacokinetic model of patients with hyperlipidemia in plateau and plain areas of China was established by a nonlinear mixed‐effects model. The PK of ATV were described by a 1‐compartment model with first‐order elimination. The main PK parameters of ATV were the first‐order absorption rate (0.76 hour−1 fixed); clearance (174.22 L/h) and apparent volume of distribution (1119.62 L). The values of area and age were identified as significant covariates for the clearance, area, age, and urea for the volume of distribution. The steady‐state peak concentration in the plateau area was higher than that in the plain area. This study may suggest dose reduction is necessary for patients with hyperlipidemia in high altitudes. [ABSTRACT FROM AUTHOR]

Background: Radiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity region within nodules, affect the results. Methods: A total of 525 patients from two medical centers were retrospectively enrolled. The radiomics features were extracted according to two regions of interest (ROI) segmentation criteria. Multiple logistic regression models were trained to predict the pathology: (1) The clinical model relied on clinical‐radiological semantic features; (2) The radiomics models (radiomics+ and radiomics−) utilized radiomics features from different ROIs (including or excluding cavities); (3) the composite models (composite+ and composite−) incorporated both above. Results: In the testing set, the radiomics+/− models and the composite+/− models still possessed efficient prediction performance (AUC ≥ 0.94), while the AUC of the clinical model was 0.881. In the validation set, the AUC of the clinical model was only 0.717, while that of the radiomics+/− models and the composite+/− models ranged from 0.801 to 0.825. The prediction performance of all the radiomics+/− and composite+/− models were significantly superior to that of the clinical model (p < 0.05). Whether the ROI segmentation included or excluded the cavity had no significant effect on these models (radiomics+ vs. radiomics−, composite+ model vs. composite−) (p > 0.05). Conclusions: The present study established a machine learning‐based radiomics strategy for differentiating LUAD from TB lesions. The ROI segmentation including or excluding the cavity region may exert no significant effect on the predictive ability. [ABSTRACT FROM AUTHOR]