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ABSTRACT Targeted therapy and immunotherapy drugs for oncology have greater efficacy and tolerability than cytotoxic chemotherapeutic drugs. However, the cutaneous adverse drug reactions associated with these newer therapies are more common and remain poorly predicted. An effective prediction model is urgently needed and essential. This retrospective study included 1052 patients, divided into train set, test set, and external validation set. As a data‐driven study, a total of 76 variables were collected. Univariate logistic analysis, least absolute shrinkage and selection operator regression, and stepwise logistic regression were utilized for feature screening. Finally, nine machine‐learning models were constructed and compared, and grid search was performed to adjust the parameters. Model performance was evaluated using calibration curve and the area under the receiver operating characteristic curve (AUROC). Nine risk factors were eventually identified: age, treatment modality, cancer types, history of allergies, age‐corrected Charlson comorbidity index, percentage of eosinophils, absolute number of monocytes, Eastern Cooperative Oncology Group Performance Status, and C‐reactive protein. Among the models, the logistic model performed best, demonstrating strong performance in test set (AUROC = 0.734) and external validation set (AUROC = 0.817). This study identified nine significant risk factors and developed a nomogram prediction model. These findings have important implications for optimizing therapeutic efficacy and maintaining the quality of life of patients from the perspective of managing cutaneous adverse drug reactions. Trial Registration: ChiCTR2400088422

Abstract Objectives The risk and beneficial factors of early discharge after thoracoscopic anatomic lung cancer surgery are unknown, and this study aims to investigate predictors and associated 30‐day readmission for early discharge. Methods We performed a single‐center retrospective analysis of 10,834 consecutive patients who underwent thoracoscopic anatomic lung cancer surgery. Two groups were determined based on discharge date: “discharged by postoperative Day 2” and “discharged after postoperative Day 2.” Univariable and multivariable analysis were conducted to identify predictors for discharge. Using propensity score matching (PSM) to compare 30‐day readmission rate between two cohorts. Results A total of 1911 patients were discharged by postoperative Day 2. Multivariable analysis identified older age (odds ratio (OR) = 1.014, p

Abstract Background Sleeve lobectomy is recognized as an alternative surgical operation to pneumonectomy because it preserves the most pulmonary function and has a considerable prognosis. In this study, we aimed to investigate the implications of residual status for patients after sleeve lobectomy. Methods In this retrospective cohort study, we summarized 58 242 patients who underwent surgeries from 2015 to 2018 in Shanghai Chest Hospital and found 456 eligible patients meeting the criteria. The status of R2 was excluded. The outcomes were overall survival (OS) and recurrence‐free survival (RFS). We performed a subgroup analysis to further our investigation. Results After the propensity score match, the baseline characteristic was balanced between two groups. The survival analysis showed no significant difference of overall survival and recurrence‐free survival between R0 and R1 groups (OS: p = 0.053; RFS: p = 0.14). In the multivariate Cox analysis, we found that the margin status was not a dependent risk factor to RFS (p = 0.119) and OS (p = 0.093). In the patients of R1, N stage and age were closely related to OS, but we did not find any significant risk variable in RFS for R1 status. In the subgroup analysis, R1 status may have a worse prognosis on patients with more lymph nodes examination. On further investigation, we demonstrated no differences among the four histological types of margin status. Conclusion In our study, we confirmed that the margin status after sleeve lobectomies was not the risk factor to prognosis. However, patients with more lymph nodes resection should pay attention to the margin status.

Abstract There have been significant advancements in medical techniques in the present epoch, with the emergence of some novel operative substitutes. However, the treatment of tracheal defects still faces tremendous challenges and there is, as yet, no consensus on tracheal and carinal reconstruction. In addition, surgical outcomes vary in different individuals, which results in an ambiguous future for tracheal surgery. Although transplantation was once an effective and promising method, it is limited by a shortage of donors and immune rejection. The development of bioengineering has provided an alternative for the treatment of tracheal defects, but this discipline is full of ethical controversy and hindered by limited cognition in this area. Meanwhile, progression of this technique is blocked by a deficiency in ideal materials. The trachea together with the carina is still the last unpaired organ in thoracic surgery and propososal of a favorable scheme to remove this dilemma is urgently required. In this review, four main tracheal reconstruction methods, especially surgical techniques, are evaluated, and a thorough interpretation conducted.

With the construction of a new-type power system under the China “double carbon” target and the increasing diversification of the energy demand on the user side, the short-term load forecasting of the power system is facing new challenges. To fully exploit the massive information contained in data, based on the graph convolutional network (GCN) and long short-term memory network (LSTM), this paper presents a new short-term load forecasting method for power systems considering multiple factors. The Spearman rank correlation coefficient was used to analyse the correlation between load and meteorological factors, and a model including meteorology, dates, and regions was established. Secondly, GCN and LSTM are jointly used to extract the spatial and temporal characteristics of massive data, respectively, and finally achieve short-term power load prediction. Historical electrical load data from 2020 to 2022 public data of a real industrial park in southern China were selected to verify the validity of the proposed method from the aspects of forecasting accuracy, feature dimension, and training time.