Artificial intelligence (AI) technologies are being widely applied in radiotherapy. However, the integration of AI into clinical workflows of radiotherapy faces a series of challenges, such as poor model interpretability, domain shifts between clinical application and training data, and the inherent model uncertainties. Therefore, case-specific quality assurance (QA) is essential before deploying AI models in clinical practice. This paper reviews and summarizes QA methodologies for the application of AI models in radiotherapy across four key areas: image registration, image generation, region of interest segmentation, and treatment planning.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Artificial intelligence (AI) technologies are being widely applied in radiotherapy. However, the integration of AI into clinical workflows of radiotherapy faces a series of challenges, such as poor model interpretability, domain shifts between clinical application and training data, and the inherent model uncertainties. Therefore, case-specific quality assurance (QA) is essential before deploying AI models in clinical practice. This paper reviews and summarizes QA methodologies for the application of AI models in radiotherapy across four key areas: image registration, image generation, region of interest segmentation, and treatment planning.

Propofol,as a widely used intravenous anesthetic,has been found to have the potential of anti-tumor growth and metastasis in recent years.At present,the research on the mechanism of pro-pofol inhibiting tumor growth and metastasis is still relatively limited.In this paper,we summarize the recent studies on the role of propofol in inhibiting tumor growth and metastasis and its possible mo-lecular mechanisms,such as regulating specific sig-naling pathways or key molecules,and interacting with other molecules.At the same time,the appli-cation of propofol,including the optimization of ad-ministration route and dose,as well as the possibili-ty of being an adjunct therapy to radiotherapy and chemotherapy,was analyzed,and the recent stud-ies on the mechanism of propofol inhibiting tumor growth and metastasis were reviewed,providing a reference for further exploration of the application and research direction of propofol in clinical anti-tu-mor growth and metastasis therapy.

Objective:To investigate the prospective association of sleep duration with the development of chronic obstructive pulmonary disease (COPD) in adults in Suzhou.Methods:The study used the data of 53 269 participants aged 30-79 years recruited in the baseline survey from 2004 to 2008 and the follow-up until December 31, 2017 of China Kadoorie Biobank (CKB) conducted in Wuzhong District, Suzhou. After excluding participants with airflow limitation, self-reported chronic bronchitis/emphysema/coronary heart disease history at the baseline survey and abnormal or incomplete data, a total of 45 336 participants were included in the final analysis. The association between daily sleep duration and the risk for developing COPD was analyzed by using a Cox proportional hazard regression model, and the hazard ratio ( HR) values and their 95% CI were calculated. The analysis was stratified by age, gender and lifestyle factors, and cross-analysis was conducted according to smoking status and daily sleep duration. Results:The median follow-up time was 11.12 years, with a total of 515 COPD diagnoses in the follow-up. After adjusting for potential confounders, multifactorial Cox proportional hazard regression analysis showed that daily sleep duration ≥10 hours was associated with higher risk for developing COPD ( HR=1.42, 95% CI: 1.03-1.97). The cross analysis showed that excessive daily sleep duration increased the risk for COPD in smokers ( HR=2.49, 95% CI: 1.35-4.59, interaction P<0.001). Conclusion:Longer daily sleep duration (≥10 hours) might increase the risk for COPD in adults in Suzhou, especially in smokers.

Esketamine is an antagonist of the N-methyl-D-aspartate (NMDA) receptor and exerts antianxiety, hypnotic, sedative, and analgesic effects by interacting with NMDA receptors, opioid receptors, M - choline receptors, monoamine receptors, adenosine receptors, and other purine receptors. As the more potents isomer of ketamine, it is about twice as potent as ketamine. Compared with ketamine, esketamine has the characteristics of rapid onset and metabolism, strong analgesia, slight respiratory depression, rapid recovery of cognitive function, and low incidence of psychiatric side effects. It has become a new choice of pediatric anesthesia drugs. This article reviews the pharmacological properties of esketamine and its recent application in pediatric anesthesia, and provides reference for the safe use of esketamine in pediatric perioperative period.

Liver transplantation is an effective treatment for end-stage liver disease in children,and its clinical efficacy has been validated.Split liver transplantation(SLT)can effectively expand the donor liver pool for children.SLT for children has unique clinical characteristics and principles.Establishing technical operation specifications for pediatric SLT plays a significant role in improving clinical efficacy.In this paper,clinical practice guidelines on pediatric SLT were established in the aspect of donor and donor liver evaluation,donor-recipient matching,and ductal segmentation and reconstruction of donor liver,aiming to standardize the technical process,optimize surgical operational details,minimize the risk of com-plications of SLT for children,further promoting the rapid development of pediatric SLT in China.

Objective:To investigate a time series deep learning model for respiratory motion prediction.Methods:Eighty pieces of respiratory motion data from lung cancer patients were used in this study. They were divided into a training set and a test set at a ratio of 8∶2. The Informer deep learning network was employed to predict the respiratory motions with a latency of about 600 ms. The model performance was evaluated based on normalized root mean square errors (nRMSEs) and relative root mean square errors (rRMSEs).Results:The Informer model outperformed the conventional multilayer perceptron (MLP) and long short-term memory (LSTM) models. The Informer model yielded an average nRMSE and rRMSE of 0.270 and 0.365, respectively, at a prediction time of 423 ms, and 0.380 and 0.379, respectively, at a prediction time of 615 ms.Conclusions:The Informer model performs well in the case of a longer prediction time and has potential application value for improving the effects of the real-time tracking technology.