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 study the effect of berberine on the damage of renal tubular epithelial cells(RETC)in diabetic in vitro.Methods RTEC were divided into control group,model group(high glucose),low dose experimental group(2 mg·L-1 berberine and high glucose),medium dose experimental group(4 mg·L-1 berberine and high glucose),high dose experimental group(8 mg·L-1 berberine and high glucose),BBM-H+miR-NC(transfected with mimics control group,8 mg·L-1 berberine and high glucose),BBM-H+miR-135b group(transfected with miR-135b mimics,8 mg·L-1 berberine and high glucose).Methyl thiazolyl tetrazolium(MTT)method was used to detect cell proliferation activity,flow cytometry was used to detect cell apoptosis rate,Western blot was used to detect the expression of B-cell lymphoma-2(Bcl-2)and Bel-associated X(Bax)proteins in cells,and reactive oxygen species(ROS)level were detected by chemical fluorescence method,thiobarbituric acid method was used to detect malondialdehyde(MDA)level,and xanthine oxidation method was used to detect superoxide dismutase(SOD)level,the levels of tumor necrosis factor-α(TNF-α),interleukin-8(IL-8)and interleukin-1β(IL-1 β)were detected by enzyme-linked immunosorbent assay(ELISA).Results The proliferative activity(OD value)of RTEC in control group,model group and low,medium,high dose experimental groups were 0.66±0.04,0.36±0.02,0.43±0.03,0.54±0.03,0.63±0.05;the apoptosis rates were(3.62±0.31)％,(29.41±2.33)％,(20.10±1.65)％,(15.02±1.25)％,(9.58±1.43)％;MDA were(1.04±0.12),(5.24±0.29),(3.45±0.22),(2.16±0.13),(1.60±0.11)nmol·mL-1;SOD were(240.22±12.06),(130.56±10.84),(169.62±12.50),(201.97±12.78),(236.74±14.52)U·mL-1;TNF-α were(31.25±2.51),(51.84±4.20),(44.52±2.61),(38.25±1.50),(32.10±1.78)mg·L-1;IL-8 were(10.59±1.14),(19.95±1.74),(16.10±1.03),(13.52±1.25),(11.17±0.92)mg·L-1;IL-1β were(23.01±1.45),(56.92±2.51),(43.20±1.96),(32.05±1.23),(26.37±2.48)mg·L-1.There were statistically significant differences between model group and control group(all P＜0.05).Compared with the model group,the above indexes in low,medium and high dose experimental group were statistically significant(all P＜0.05).The above indexes of BBM-H+miR-NC group were statistically significant compared with those of BBM-H+miR-135b group(all P＜0.05).Conclusion Berberine can reduce diabetic renal tubular epithelial cell damage by down-regulating miR-135b.

Due to the unique characteristics and complexity of administration of orally inhaled drug products,bioequivalence trial for orally inhaled generic drugs presents greater challenges in clinical implementation compared to conventional oral administration.This difficulty is particularly evident inmainly attributed to the variability during the self-administration inhalation of inhaled drugss by subjects.Therefore,effective management of subjects is crucial in clinical trials involving orally inhaled products.This paper,based on the experience in conducting bioequivalence trials for orally inhaled drug products,discusses key factors and measures for successful subject management in clinical trials.The aim is to enhance the clinical implementation capabilities of researchers in the evaluation of generic consistency for inhaled drug products and to ensure the quality of clinical trials.

Objective To explore the effects of dydrogesterone combined with granulocyte colony-stimulating factor(G-CSF)on blood flow parameters and sex hormone levels in patients with thin endometrial(TE)infertility.Methods TE infertility patients were randomly divided into control group and treatment group.All patients were treated with freezing-thawing embryo transfer.The control group was treated with desdrogesterone after ovulation monitoring by vaginal ultrasound,10 mg each time,bid,and stopped until the 14th day after ovulation.In treatment group,G-CSF was given on the basis of control group,and the endometrial thickness and hemodynamic parameters were monitored by vaginal ultrasound 3 days later,and then embryo transfer was performed.Blood flow parameters,ultrasound parameters,endometrial morphology,sex hormone levels[follicle stimulating hormone(FSH),luteinizing hormone(LH),estradiol(E2),progesterone(P)]and pregnancy status were compared between the two groups.Results There were 49 cases in treatment group and 49 cases in control group.After treatment,endometrial thickness in treatment group and control group were(9.59±1.35)and(7.89±1.14)mm;follicle diameter were(19.35±1.58)and(17.75±1.42)mm;uterine volume were(92.68±7.85)and(85.69±6.74)cm3;PI were 1.29±0.27 and 1.74±0.32;RI were 0.32±0.09 and 0.50±0.11;peak systolic/end-diastolic velocity(S/D)were 2.03±0.28 and 2.21±0.25;P were(45.98±5.75)and(39.15±5.78)nmol·L-1;E2 were(462.58±58.42)and(339.45±45.97)pg·mL-1;FSH were(12.18±2.58)and(9.42±1.42)U·L-1;LH were(29.42±4.18)and(22.25±3.40)U·L-1;there were statistically significant differences between the two groups(all P＜0.05).The clinical pregnancy rate(51.02％)and implantation rate(57.14％)in treatment group were higher than those in control group(30.61％and 36.73％),and the differences were statistically significant(all P＜0.05).Conclusion Dydrogesterone combined with G-CSF can improve blood flow parameters,endometrial thickness and sex hormone levels in TE infertility patients,and help to increase the embryo implantation rate.

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 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 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.

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.