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Abstract Background This study aims to construct a model predicting the probability of RF in AECOPD patients upon hospital admission. Methods This study retrospectively extracted data from MIMIC-IV database, ultimately including 3776 AECOPD patients. The patients were randomly divided into a training set (n = 2643) and a validation set (n = 1133) in a 7:3 ratio. First, LASSO regression analysis was used to optimize variable selection by running a tenfold k-cyclic coordinate descent. Subsequently, a multifactorial Cox regression analysis was employed to establish a predictive model. Thirdly, the model was validated using ROC curves, Harrell’s C-index, calibration plots, DCA, and K-M curve. Result Eight predictive indicators were selected, including blood urea nitrogen, prothrombin time, white blood cell count, heart rate, the presence of comorbid interstitial lung disease, heart failure, and the use of antibiotics and bronchodilators. The model constructed with these 8 predictors demonstrated good predictive capabilities, with ROC curve areas under the curve (AUC) of 0.858 (0.836–0.881), 0.773 (0.746–0.799), 0.736 (0.701–0.771) within 3, 7, and 14 days in the training set, respectively and the C-index was 0.743 (0.723–0.763). Additionally, calibration plots indicated strong consistency between predicted and observed values. DCA analysis demonstrated favorable clinical utility. The K-M curve indicated the model’s good reliability, revealed a significantly higher RF occurrence probability in the high-risk group than that in the low-risk group (P

Due to their unique location, airway tumors have a significant impact on patient quality of life and survival. Current research has focused extensively on malignant airway tumors; however, benign airway tumors, especially rare ones, are less understood due to their low incidence. These tumors are often misdiagnosed and mistreated due to diagnostic challenges. Therefore, there is still a lack of consensus on the treatment of some rare benign airway tumors. Our center summarizes the diagnosis and treatment of four rare cases of benign airway stenosis in recent years, highlighting the bronchoscopic manifestations and therapeutic approaches to improve the understanding of these diseases.

Background: Pain is the most common cancer-related symptom, but it is often undertreated. Telemedicine is widely used in cancer treatment, but its effectiveness is uncertain. Objective: We aimed to evaluate the impact of telemedicine intervention on pain in patients with cancer. Design: Methodological quality and risk-of-bias evaluation were conducted, and the sources of heterogeneity were explored through subgroup analysis and sensitivity analysis. Data Sources and Methods: PubMed, Embase, Web of Science, Cochrane Library, and clinical trial databases were searched up to 16 August 2022. Randomized controlled trials of the impact of telemedicine intervention regarding pain in patients with cancer were included, and the results related to pain were extracted. Results: Twenty-one randomized controlled trials were selected from 1810 articles. A total of 1454 patients received telemedicine interventions, and 2213 received conventional medical services. Telemedical intervention had a positive effect on improving pain intensity [standard mean deviation (SMD) = −0.28, 95% confidence interval (CI): −0.49 to −0.06, p = 0.01] and pain interference (SMD = −0.41, 95% CI: −0.54 to −0.28, p

As a bioactive component extracted from the seed of milk thistle, silymarin has many pharmacological functions, such as liver-protective and cholagogic effect. It is categorized as a class of flavonoids, among which silybin is one of the main components. Efficient extraction of silybin with a type of new and green solvent natural deep eutectic solvent (NADES), as well as mechanism discussion were explored in this study. With the degreased milk thistle seed shell powder as raw material and using HPLC for silybin analysis, NADES composed of choline chloride/1,4-butanediol was screened out as optimal solvent for silybin extraction. On the basis of single factor experiments, Box-Behnken Design from response surface methodology was used for process optimization. The generated model predicated that the highest silybin yield of 4.29% would be achieved with solid-liquid ratio of 1:20 g/mL, at 77 °C for 5.6 h. The result from verification experiment was about 4.30%, which fitted well with the prediction. Then, based on conductor-like screening model for segment activity coefficient (COSMO-SAC), structure and energy optimization of silybin and solvent were attempted. The logarithms of infinite dilution activity coefficients in the two solvents, chloride/1,4-butanediol and the traditional organic solvent ethanol, were −6.922 and −6.043, and the intermolecular interaction energies were −51.62 and −25.47 kJ/mol, respectively, which would provide insight into the mechanism explaining performance difference of the solvents during extraction.