OBJECTIVE: To analyze the time characteristics of the Ethos online adaptive radiotherapy (OART) process in clinical practice and provide guidance for the comprehensive optimization of each stage of adaptive radiotherapy. METHODS: The study involved 61 patients with cervical, rectal, gastric, lung, esophageal, and breast cancers who underwent Ethos OART. The mean ± standard deviation of segmental time, total time, and target volume for these patients were tracked. The time characteristics for different cancer types were evaluated, and the average time for target and organ at risk (OAR) modifications was compared with the average target volume for each cancer type. RESULTS: Cervical cancer born the longest total treatment time, while breast cancer had the shortest. For all cancer types except breast cancer, the modification time for target and OAR was the most time-consuming segment. The average time for target and OAR modifications aligned with the trend of the average target volume. CONCLUSION The total treatment time for various cancers ranges from 15 to 35 minutes, indicating room for improvement.
Humans ; Radiotherapy Planning, Computer-Assisted/methods* ; Neoplasms/radiotherapy* ; Female

In ground-based experiments,ultraviolet irradiation is used to charge simulated lunar dust,aiming to quantitatively study its charging and discharging behavior in space environments.However,due to factors such as vacuum and dust disturbances,the charging and discharging processes of lunar dust are difficult to measure accurately through contact methods.Therefore,this paper designs and constructs a non-contact,in-situ surface potential measurement system based on the principle of vibrating capacitance,suitable for vacuum environments.The system combines moving average and standard deviation methods for data filtering and introduces a weighted KNN algorithm to predict and compensate for missing or anomalous data,thereby improving measurement accuracy and stability.Two typical simulated lunar dust samples,TYII-2 and CLRS-1A,were selected for charging and discharging experiments under ultraviolet irradiation.The surface potential distribution was obtained in real-time using an in-situ motion mechanism equipped with a vibrating capacitance sensor.The results show significant differences in the charging response and dissipation characteristics of the samples with different particle size distributions.

Objective To explore the perioperative therapeutic effects and follow-up manage-ment in 16 patients with left ventricular assist device(LVAD)implantation.Methods A retrospec-tive analysis was conducted in data of 16 patients who underwent LVAD implantation in the depart-ment of cardiovascular surgery.Data of 6-minute walk test(6MWT),European Quality of Life-5 Di-mension-5 Levels(EQ-5D-5L),New York Heart Association(NYHA)classification,echocardio-graphy,chest radiography,cardiothoracic ratio,and occurrence of complications(infection,bleed-ing,thrombosis,right heart failure,neurological issues)were collected via the electronic medical re-cord system before surgery and at 30,60,and 90 days postoperatively.Results All patients sur-vived with the pump at 90 days postoperatively.One patient with preoperative renal insufficiency un-derwent dialysis and received a heart transplant after 8 months.One patient developed a sterile granu-loma at the percutaneous lead site on the abdominal wall,which improved after treatment,no complica-tions occurred in other patients.At 90 days postoperatively,there was no statistically significant differ-ence in the right ventricular area change fraction and tricuspid annular plane systolic excursion com-pared with preoperative values(P＞0.05).The left ventricular ejection fraction,left ventricular end-diastolic diameter,and cardiothoracic ratio showed significant improvement compared with preoperative levels(P＜0.05).At 30 days postoperatively,30％of patients recovered to NYHA class Ⅰ and 70％to class Ⅱ;at 60 days,80％of patients to class Ⅰ and 20％to class Ⅱ;at 90 days,90％to classⅠ and 10％to class Ⅱ.The 6MWT and EQ-5D-5L scores of patients significantly increased within 90 days postoperatively(P＜0.01).Conclusion Through rigorous preoperative assessment by a multidisciplinary LVAD team,refinement of surgical techniques,and comprehensive management during hospitalization,self-management before discharge,daily follow-up after discharge,and fol-low-up management upon returning to the hospital for patients with LVAD implantation,the cardiac function and quality of life of patients are significantly improved at 90 days postoperatively.

Objective To systematically evaluate the effects of liraglutide combined with metformin on glucose and lipid metabolism,sex hormones,reproductive function,and gastrointestinal reactions in patients with polycystic ovary syndrome(PCOS).Methods We searched databases such as PubMed,WanFang Medical Network,WanFang Database,China National Knowledge Infrastructure(CNKI),and VIP Journals to collect randomized controlled trials published from January 2011 to August 2022 on the treatment of polycystic ovary syndrome with liraglutide combined with metformin,using Revman 5.4 for the meta-analysis.Results A total of 16 case-control studies were included,involving 1436 patients.Compared to metformin alone,the combination of liraglutide and metformin significantly lowered fasting blood sugar,postprandial blood glucoseafter 2 hours,HbA1c,HOMA-IR,FINS,BMI,as well as LH,FSH,and total testosterone in patients with PCOS.It improved lipid levels,helped establish menstrual cycles,and increased the normal ovulation rate and natural conception rate in these patients.However,there was a significantly higher incidence of gastrointestinal reactions in the liraglutide plus metformin group,and the difference was statistically significant(P＜0.05).Conclusion The combination of liraglutide and metformin can improve glucose and lipid metabolism in patients with polycystic ovary syndrome(PC OS),reduce levels of LH,FSH,and total testosterone,help establish regular menstrual cycles,and increase ovulation and pregnancy rates,but it significantly increases gastrointestinal side effects.

Objective:To aid in the detection of thyroid cancer by using deep learning to differentiate the unique bioimpedance parameter patterns of different thyroid tissues.Methods:An electrical impedance system was designed to measure 331 ex-vivo thyroid specimens from 321 patients during surgery. The impedance data was then analyzed with one dimensional convolution neural (1D-CNN) combining with long short-term memory (LSTM) network models of deep learning. In the process of analysis, we assigned 80% of the data to training set (1072/1340) and the remaining 20% data to the test set (268/1340). The performance of final model was assessed using receiver operating characteristic (ROC) curves. In addition, sensitivity, specificity, positive predictive value, negative predictive value, Youden index were applied to compare impedance model with ultrasound results.Results:The ROC curve of the two-classification (malignant /non-malignant tissue) model showed a good performance (area-under-the-curve AUC=0.94), with an overall accuracy of 91.4%. To better fit clinical practice, we further performed a three-classification (malignant/ benign/ normal tissue) model, of which the areas under ROC curve were 0.91, 0.85, 0.92 for normal, benign, and malignant group, respectively. The results indicated that the area under micro-average ROC curve and the macro-average ROC curve were 0.91 and 0.90, respectively. Moreover, compared with ultrasound, the impedance model exhibited higher specificity.Conclusions:A deep learning model (CNN-LSTM) trained by thyroid electrical impedance spectroscopy (EIS) parameters shows an excellent performance in distinguishing among different in-vitro thyroid tissues, which is promising for applications. In future clinical utility, our study does not replace existing tests, but rather complements others, thus contributing to therapeutic decision-making and management of thyroid disease.

Objective:To explore the application effect of the modified sandwich teaching method based on constructivism theory in clinical teaching of neurology nursing interns.Methods:A total of 29 nursing interns who practiced in the Department of Neurology of the First Affiliated Hospital of University of Science and Technology of China (Anhui Provincial Hospital) from June 2020 to May 2021 were selected as the control group, using the conventional clinical nursing teaching method, and 28 nursing interns who practiced from June 2021 to May 2022 were selected as the observation group, using the modified sandwich teaching method based on constructivism theory. Before graduating from the Department of Neurology, nursing interns in the two groups were compared in terms of the teaching effects, such as the score of learning initiative, theoretical examination, operation examination, and nursing round report. SPSS 25.0 was used for t test and chi-square test. Results:The score for each dimension (learning driving force, learning objectives, in-depth learning, controlled learning, and solid learning) and total score in the observation group were significantly higher than those in the control group ( P<0.05). There was no significant difference between the two groups in the scores of theoretical examination and operation examination (89.11±3.58 vs. 88.97±2.74, 93.79±2.48 vs. 93.86±2.20; P>0.05); the scores of nursing rounds in the observation group were significantly higher than those in the control group (88.61±2.60 vs. 83.38±3.97, P<0.05). Conclusions:The modified sandwich teaching method based on constructivism theory can enhance the learning initiative and comprehensive analysis ability of nursing interns in the Department of Neurology. It is suitable for clinical nursing teaching in the Department of Neurology and is worth popularizing.

Isolated pontine infarction (IPI) is the main type of acute brainstem infarction. Due to the application of high-resolution MRI, the research on the etiology of IPI has gradually increased in recent years. This article reviews the latest research progress on the characteristics of basilar artery plaques and disease progression mechanisms of IPI, aiming to provide reference for the etiology research of IPI.

Purpose To explore the artificial intelligence(AI)-assisted diagnosis system of thyroid cancer based on deep transfer learning and evaluate its clinical application value.Methods The HE sections of 682 cases thyroid disease patients(including benign lesions,papillary carcinoma,follicular carci-noma,medullary carcinoma and undifferentiated carcinoma)in the Pathology Department of the Renmin Hospital of Wuhan Uni-versity were collected,scanned into digital sections,divided into training sets and internal test sets according to the ratio of 8 ∶ 2,and the training sets were labeled at the pixel level by patholo-gists.The thyroid cancer classification model was established u-sing VGG image classification algorithm model.In the process of model training,the parameters of the breast cancer region recog-nition model were taken as the initial values,and the parameters of the thyroid cancer region recognition model were optimized through the transfer learning strategy.Then the test set and 633 intraoperative frozen HE section images of thyroid disease in Jianli County People's Hospital,Jingzhou City,Hubei Province wereused as the external test set to evaluate the performance of the established AI-assisted diagnostic model.Results In the internal test set,without the use of the breast cancer region rec-ognition model transfer learning,the accuracy of the AI-assisted diagnosis model was 0.882,and the area under the Receiver op-erating characteristic(AUC)valuewas0.938;However,inthe use of the Transfer learning model,the accuracy of the AI-assis-ted diagnosis model for was 0.926,and the AUC value was 0.956.In the external test set,the accuracy of the zero learning model was 0.872,the AUC value was 0.915,and the accuracy of the Transfer learning model was 0.905,the AUC value was 0.930.Conclusion The AI-assisted diagnosis method for thy-roid cancer established in this study has good accuracy and gen-eralization.With the continuous development of pathological AI research,transfer learning can help improve the performance and generalization ability of the model,and improve the accura-cy of the diagnostic model.

Objective:To develop a Self-rating Scale for Scientific Research Ability of Nursing Staff and conduct reliability and validity test.Methods:Based on literature review, qualitative interview and expert letter consultation, the first draft of the Self-rating Scale for Scientific Research Ability of Nursing Staff was formed. By the convenient sampling method, a total of 924 nursing staff from Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology were selected as the research objects from February to March 2021 for questionnaire survey to test the reliability and validity of the scale.Results:Two common factors were extracted from exploratory factor analysis, including 13 items, and the cumulative variance contribution rate was 82.088%. Confirmatory factor analysis suggested that the model fit well. Content validity index at item level was 0.833~1.000, and content validity index at scale level was 0.865. The Cronbach's α coefficient of the total scale was 0.971, and the Cronbach's α coefficient of each dimension was 0.913 and 0.974. The total split-half reliability coefficient of the scale was 0.941, and split-half reliability coefficient of each dimension was 0.932 and 0.964.Conclusions:The self-rating scale for scientific research ability of nursing staff has good reliability and validity, which can be used as an evaluation tool of research ability of nursing staff.

Klebsiella pneumoniae(K.pneumoniae)is an important pathogen that can cause severe hospital-and community-acquired infections.To systematically investigate its methylation features,we determined the whole-genome sequences of 14 K.pneumoniae strains covering varying serotypes,multilocus sequence types,clonal groups,viscosity/virulence,and drug resistance.Their methy-lomes were further characterized using Pacific Biosciences single-molecule real-time and bisulfite technologies.We identified 15 methylation motifs[13 N6-methyladenine(6mA)and two 5-methylcytosine(5mC)motifs],among which eight were novel.Their corresponding DNA methyl-transferases were also validated.Additionally,we analyzed the genomic distribution of GATC and CCWGG methylation motifs shared by all strains,and identified differential distribution pat-terns of some hemi-/un-methylated GATC motifs,which tend to be located within intergenic regions(IGRs).Specifically,we characterized the in vivo methylation kinetics at single-base resolu-tion on a genome-wide scale by simulating the dynamic processes of replication-mediated passive demethylation and MTase-catalyzed re-methylation.The slow methylation of the GATC motifs in the replication origin(oriC)regions and IGRs implicates the epigenetic regulation of replication initiation and transcription.Our findings illustrate the first comprehensive dynamic methylome map of K.pneumoniae at single-base resolution,and provide a useful reference to better understand epigenetic regulation in this and other bacterial species.