The chemical constituents in dried roots of Atractylodes macrocephala were investigated in this study. Through utilizing normal-phase silica gel and ODS column chromatography, TLC, and semi-preparative HPLC, 4 new sesquiterpenoids were purified from the ethyl acetate extract of A. macrocephala. By various spectroscopic techniques, such as 1D and 2D NMR, HR-ESI-MS, IR, UV, and CD, their structures were identified as atractylmacron A (1), 9β-hydroxyasterolide (2), atractylenolide H (3), atractylenolide J (4). Compound 1 possesses a rare 6/7 bicyclic skeleton.

Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

Objective To retrospectively investigate the efficacy of pelvic floor muscle training(PFMT)using APP-based home device in the real-world scenario.Methods A retrospective survey was conducted among 171 000 Chinese female users over 18 years old who freely registered APP and performed PFMT using a domestic manufactured home device from Oct 1,2019 to Mar 31,2021.The Patient Global Impression of Improvement(PGI-I)questionnaires concerning pelvic floor general condition,sexual satisfaction and other three major symptoms related to pelvic floor muscle laxity such as stress urinary incontinence(SUI),pelvic organ prolapse(POP),and vaginal laxity(VL)were broadcast public online for users to self-assess the improvement.The vaginal muscle strength values prior and post training recorded by APP were also reported.All data were collected anonymously for further stratified analysis.Results A total of 984 valid questionnaires were collected by systematic sampling.The PGI-I scores of pelvic floor general condition(P=0.000 1),sexual satisfaction(P=0.009),SUI(P=0.000 1),POP(P=0.044)and VL(P=0.034)were statistically significant in users who reported to use the device for 3 months or more compared with those less than 3 months.In addition,the increase of vaginal muscle strength was related to the improvement of PGI-I scores in SUI and POP with statistical significance.There were no significant difference in subgroups such as age,education,parity,and delivery mode.Conclusion The subjective benefit of pelvic floor function-related symptoms and the improvement of vaginal muscle strength could be observed after PFMT for three months or more using APP-based home device in Chinese women in the real-world scenario.

Objective:To retrospectively analyze a pediatric case of X-linked hypophosphatemic rickets treated with Burosumab and improve clinicians′ awareness of the safety and effectiveness of the drug.Methods:Clinical data of the child were collected. Whole-exon genetic testing after parental consent confirmed X-linked hypophosphatemic rickets. During 18 months of Burosumab treatment, fasting blood phosphorus, alkaline phosphate, calcium, and calcium phosphate product were monitored every 11-14 days. Parathyroid hormone and 25 hydroxyvitamin D were checked every 2-6 weeks, while knee spacing, liver and kidney function, urinary calcium creatinine ratio, electrocardiogram were assessed every 3 months. Radiological imaging was performed every 6 months, with continuous follow-up of the child.Results:Whole-exon sequencing results showed a c. 1080_1081insCAATGTTA(p.T361Qfs*3) spontaneous heterozygous frameshift mutation in the PHEX gene in the child, which has not been reported previously. After the patient was treated with Burosumab for 18 months, the biochemical indexes were significantly improved, and the rickets score was reduced, without gingival abscess or other adverse events.Conclusion:The variant c. 1080_1081insCAATGTTA(p.T361Qfs*3) in the PHEX gene was identified as the cause of the patient′s condition. Burosumab, as a targeted therapeutic agent for X-linked hypophosphatemic rickets, showed significant treatment efficacy.

Informed consent is an important ethical symbol in clinical research,and researchers have the responsibility to fully inform participants of the research information before conducting clinical research.However,it is difficult to obtain complete informed consent form participants or their guardians within a narrow treatment time period in clinical research conducted in emergency situations.Currently,in addition to traditional general informed consent,there are also reality-accepted informed consent,including exemption of informed consent,broad informed consent,and deferred informed consent.By introducing the origin and development process of deferred informed consent in clinical research,this paper sorted out the current application status of deferred informed consent,proposed the prerequisites for applying deferred informed consent in emergency situations,and explored the issues that need to be noted during the application process of deferred informed consent.It is hoped to provide an ethical defense and ethical procedure for the application of deferred informed consent in clinical research in emergency situations.

Objective To investigate the clinical efficacy of multidisciplinary team (MDT) model combined with Da Vinci robot-assisted thoracic surgery in the treatment of early non-small cell lung cancer (NSCLC). Methods From July 2020 to December 2021, the patients with NSCLC who received Da Vinci robot-assisted thoracic surgery in the Department of Thoracic Surgery, General Hospital of Northern Theater Command were collected. According to whether MDT were performed before hospitalization, the patients were divided into an MDT group and a common group. The recovery and clinical efficacy were compared between the two groups. Results A total of 187 patients were enrolled, including 81 males and 106 females, aged 63 (56, 67) years. There were 85 patients in the MDT group, and 102 patients in the common group. Compared with the common group, the MDT group had lower incidence of postoperative complications (9.4% vs. 29.4%, P=0.017), shorter intraoperative operation time [55 (45, 61) min vs. 79 (65, 90) min, P<0.001], and less intraoperative blood loss [25 (20, 30) mL vs. 30 (20, 50) mL, P=0.029] in the same operation mode. In addition, the drainage volume on the second postoperative day [270 (200, 350) mL vs. 215 (190, 300) mL, P=0.004], the number of dissected lymph nodes groups [6 (5, 6) groups vs. 5 (3, 6) groups, P=0.004] and the number of dissected lymph nodes [16 (13, 21) vs. 13 (9, 20), P=0.005] in the MDT group were significantly better than those in the common group. The differences in the postoperative intubation time and postoperative hospital stay between the two groups were not statistically significant (P>0.05). Conclusion MDT combined with Da Vinci robot-assisted thoracic surgery can further reduce the risk of surgery, improve the clinical treatment effect, reduce the incidence of postoperative complications, and accelerate the rehabilitation of patients.

Objective To analyze the learning curve of Da Vinci robotic segmentectomy. Methods Cumulative sum analysis (CUSUM) was used to analyze the learning curve of Da Vinci robotic segmentectomy performed by the General Hospital of Northern Theater Command from February 2018 to December 2020. The learning curve was obtained by fitting, and R2 was used to judge the goodness of fitting. The clinical data of patients in different stages of learning curve were compared and analyzed. Results The first 50 patients who received Da Vinci robotic segmentectomy were included, including 24 males and 26 females, with an average age of 61.9±10.6 years. The operation time decreased gradually with the accumulation of operation patients. The goodness of fitting coefficient reached the maximum value when R2=0.907 (P<0.001), CUSUM (n) =0.009×n3−0.953×n2+24.968×n−7.033 (n was the number of patients). The fitting curve achieved vertex crossing when the number of patients reached 17. Based on this, 50 patients were divided into two stages: a learning and improving stage and a mastering stage. There were statistical differences in the operation time, intraoperative blood loss, postoperative drainage volume, number of lymph node dissection, postoperative catheter time, postoperative hospital stay, and postoperative complications between the two stages (P<0.05). Conclusion It shows that the technical competency for assuring feasible perioperative outcomes can be achieved when the cumulative number of surgical patients reaches 17.

Objective: To construct a prediction model for preterm birth risk among pregnant women, so as to provide the reference for screening high-risk population and preventing preterm birth. Methods: Pregnant women who received antenatal examination and delivered at the Women's Hospital, School of Medicine, Zhejiang University from January 1 to December 31, 2019 were selected as the study subjects, among them, 80% were included in the modeling group, and 20% were included in the validation group. Demographic and clinical information were collected. A multivariable logistic regression model was used to analyze the predictive factors of preterm birth risk in the modeling group, and a preterm birth risk prediction model was established based on the OR values of predictive factors. The model was validated with the data from the validation group. The Youden index was used to determine the critical score for predicting preterm birth risk. The prediction performance of the model was evaluated using the receiver operating characteristic (ROC) curve. Results: A total of 15 197 pregnant women were surveyed, including 12 131 pregnant women in the observation group and 3 066 pregnant women in the validation group. There was no statistically significant difference in age, education level and gravidity between the two groups of pregnant women (all P<0.05). Multivariable logistic regression analysis identified the number of pregnancies, education level, place of residence, hypertension, diabetes, history of preterm birth, twin-pregnancy, placenta praevia, and gestational hypertension as risk prediction factors for preterm birth risk among pregnant women. The risk score system for preterm birth was established as follows: >2 pregnancies (2 points), high school education or below (4 points), college degree or above (-4 points), rural residence (5 points), hypertension (7 points), diabetes (11 points), history of preterm birth (11 points), twin-pregnancy (28 points), placenta previa (19 points), and gestational hypertension (12 points). The total score of the preterm birth risk scoring system ranged from -4 to 99 points. When the critical score was 8 points, the Youden index was the highest at 0.480, with an area under the ROC curve of 0.749 (95%CI: 0.732-0.767), a sensitivity of 0.610, and a specificity of 0.886, indicating good prediction performance of the model. Conclusion The preterm birth risk prediction model established in this study based on demographic and clinical characteristics of pregnant women can effectively predict the risk of preterm birth among pregnant women.

Objective The leptin receptor,encoded by the LEPR gene,is involved in tumorigenesis.A potential functional variant of LEPR,rs1137101(Gln223Arg),has been extensively investigated for its contribution to the risk of digestive system(DS)cancers,but results remain conflicting rather than conclusive.Here,we performed a case-control study and subsequent meta-analysis to examine the association between rs1137101 and DS cancer risk. Methods A total of 1,727 patients with cancer(gastric/liver/colorectal:460/480/787)and 800 healthy controls were recruited.Genotyping of rs1137101 was conducted using a polymerase chain reaction-restriction fragment length polymorphism(PCR-RFLP)assay and confirmed using Sanger sequencing.Twenty-four eligible studies were included in the meta-analysis. Results After Bonferroni correction,the case-control study revealed that rs1137101 was significantly associated with the risk of liver cancer in the Hubei Chinese population.The meta-analysis suggested that rs1137101 is significantly associated with the risk of overall DS,gastric,and liver cancer in the Chinese population. Conclusion The LEPR rs1137101 variant may be a genetic biomarker for susceptibility to DS cancers(especially liver and gastric cancer)in the Chinese population.

Aim To observe the behavioral effects of exogenous allopregnanolone(ALLO)and its inhibitor finasteride on the receptive period(R)and non-recep-tive period(NR)of PMDD liver-qi inversion model rats and the expression of GABAARα4,GABAARδ mR-NA and protein effects to explore its pathogenesis.Methods The PMDD liver-qi reverse syndrome rat model was prepared.The rats were divided into the normal group R and NR(control-R,control-NR),model group R and NR(Model-R,Model-NR),nor-mal group R+ALLO and NR+ALLO(Control+A-R,Control+A-NR),and model group R+ALLO and NR+ALLO(Model+A-R,Model+A-NR),model group R+finasteride and NR+finasteride(Model+F-R,Model+F-NR).The elevated cross labyrinth ex-periment and social interaction experiment were used to detect the behaviors of rats;fluorescence quantitative PCR and immunofluorescence were used to detect the expression of GABAARα4 and 8 mRNA and protein in rat amygdala and hippocampus.Results In the be-havioral evaluation,in the NR period,in the elevated cross maze test and in the social interaction test,the rats in the model group had anxiety behavior and de-creased social communication ability(P＜0.05),while the rats in the Model+A group could effectively relieve anxiety symptoms and improve their social com-munication ability(P＜0.05),and the rats in the Model+F group had increased anxiety behavior and social disorder(P＜0.05).In fluorescence quantita-tive PCR and immunofluorescence experiments,the ex-pression of GABAARα4 subunit in the model group was up-regulated in the hippocampus(P＜0.01),and the expression of δ subunit was down-regulated(P＜0.01);the expression of GABAARα4 subunit in the a-mygdala and hippocampus of the Model+A group de-creased(P＜0.01),and the expression of δ subunit increased in the hippocampus(P＜0.01).Conclu-sions The abnormal expression of GABAARα4 and 8 subunits mediated by ALLO improves the anxiety symptoms and social interaction ability of PMDD,which is the pathogenesis of PMDD liver-qi reverse syndrome,and provides basis and support for subse-quent exploration of the pathogenesis of PMDD liver-qi reverse syndrome.