ObjectiveA qualitative analysis method was established for the composition of Astragali Radix（AR） before and after rice stir-frying. On the basis of systematic characterization of the chemical compositions in AR and stir-fried AR with rice（ARR）， the structures of their major compounds were deduced and identified， and the differential compositions between them were analyzed. MethodsUltra performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS/MS） was used to detect the samples of AR and ARR in positive and negative ion modes， respectively. The compounds were analyzed and identified through self-constructed databases， literature， and reference standards， etc. And the data were analyzed by chemometrics， in order to screen for the differential components between AR and ARR. ResultsA total of 123 compounds were identified in AR and ARR， including 41 flavonoids， 19 terpenoids， 26 organic acids， 8 amino acids， 5 nucleotides， 5 carbohydrates and 19 other compounds. Among them， there were 95 common components in both， 18 unique components in AR， and 10 unique components in ARR. Principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA） results both showed that there were significant differences in the chemical constituents of AR before and after rice stir-frying， and a total of 26 constituents with differences in the content were screened out， including L-canavanine， L-pyroglutamic acid， L-phenylalanine， cis-caffeic acid， and malonylastragaloside Ⅰ. Among them， 19 constituents of ARR were down-regulated and 7 constituents were up-regulated by comparing with AR. ConclusionThis study clarifies that the chemical composition of AR and ARR is mainly composed of flavonoids， terpenoids， and organic acids， and analyzes the components with significant differences in content between the two in combination with chemometrics， and the differential components are dominated by amino acids， organic acids and terpenoids， which can provide reference for the subsequent quality control and material basis research.

Objective To investigate the correlation between the inflammatory burden index(IBI)and major adverse cardiovascular events(MACEs)in patients with ST-segment elevation myocardial infarction(STEMI)after percutaneous coronary intervention(PCI),and to assess the efficacy of IBI in predicting in-hospital and long-term MACEs.Methods This retrospective study included 465 STEMI patients who received PCI treatment in No.904 Hospital of Joint Logistics Support Force of PLA from Dec.2017 to Dec.2021.The IBI was calculated for each patient.The predictive value for in-hospital and long-term MACEs was evaluated using receiver operating characteristic(ROC)curves,and the area under curve(AUC)was calculated.The population was grouped based on the optimal IBI cut-off value for clinical characteristic analysis.Multivariate logistic regression and Cox regression analyses were used to identify factors independently associated with MACEs.The Kaplan-Meier estimator and log-rank test were used to assess the MACE risk of different IBI groups.Results The AUC value for predicting MACEs during hospitalization in STEMI patients using IBI was 0.687,and the AUC value for predicting long-term MACEs was 0.634.Multivariate logistic regression analysis revealed that a high IBI 102.33 mg/L)independently increased the risk of MACEs during hospitalization in STEMI patients(odds ratio=10.900,95％confidence interval[95％CI]4.273-29.180,P＜0.001).Multivariate Cox regression analysis further indicated that during long-term follow-up of STEMI patients,a high IBI(≥55.88 mg/L)independently predicted MACEs(hazard ratio=1.989,95％CI 1.128-3.506,P=0.018).Conclusion IBI is a valuable predictor for the occurrence of MACEs during hospitalization and long-term follow-up after PCI in STEMI patients.

Objective To validate the efficacy of the prediction model for difficulty of peroral endoscopic myotomy (POEM) through an independent cohort. Methods A total of 617 patients with achalasia who underwent POEM at the Endoscopy Center of Zhongshan Hospital, Fudan University from January 2021 to December 2023 were included. The general data of patients were collected, and the predictive value of the prediction model for POEM difficulty in the validation cohort was estimated. The stratified analysis was undergone according to the difficulty risk scores. Results In 617 consecutive patients, technical difficulty was observed in 90 cases (14.6%). The predictive model demonstrated moderate discriminatory capacity with an area under the receiver operating characteristic curve (AUC) of 0.711 (95%CI 0.643-0.780). Patients were stratified into three risk categories according to the difficulty risk scores: low-risk (＜0.1), medium-risk (0.1-0.25), and high-risk (≥0.25). The corresponding technical difficulty rates were 7.3%, 16.9%, and 51.6%, respectively. Conclusion The prediction model for POEM difficulty built by our center shows good stability and discrimination, and has good clinical application value.

AIM: To observe the clinical outcomes of Phorcides analytic engine-assisted topography-guided personalized laser assisted in situ keratomileusis(LASIK)for the treatment of myopia and astigmatism in virgin eyes with the refractive astigmatism significantly deviating from corneal topography.METHODS: Retrospective clinical study. A total of 32 cases(42 eyes)with myopia and astigmatism that received corneal topography-guided personalized LASIK in the Ophthalmology Refractive Surgery Center of the First Affiliated Hospital of Xi'an Jiaotong University from December 2019 to March 2021 were selected. The uncorrected distance visual acuity(UDVA), best corrected distance visual acuity(CDVA), refractive state and aberrations before and at 6 mo after surgery were recorded.RESULTS: There were 15 males and 17 females, with an age of 23.00(18.00, 29.25)years old; preoperative sphere was -5.75(-6.25, -4.00)D, and cylinder was -0.75(-1.38, -0.25)D. At 6 mo postoperatively, the UDVA exceeded the preoperative CDVA in 19 eyes(45%). The spherical equivalent(SEQ)of all eyes(100%)was -0.50 to +0.50 D at 6 mo postoperatively, and the postoperative SEQ of 23 eyes(55%)was -0.13 to +0.13 D. There were 33 eyes(79%)had a postoperative astigmatism ≤ 0.25 D, the target-induced astigmatism(TIA)was 0.94±0.96 D, and the surgically induced astigmatism(SIA)was 0.94±0.86 D, with no statistical significance between TIA and SIA(P>0.05). The astigmatism axial deviation ranged from -5° to +5° in 33 eyes(79%)at 6 mo postoperatively. Compared to pre-operation, the total higher-order aberrations and spherical aberrations within the central 6 mm diameter of the anterior corneal surface increased at 6 mo postoperatively(Z=-3.778, P<0.001; Z=-4.929, P<0.001); the postoperative coma aberrations had no change(Z=-1.763, P=0.078); the postoperative trefoil aberrations decreased(Z=-2.490, P=0.013). Compared to pre-operation, the Strehl ratio of the anterior corneal surface increased significantly at 6 mo after surgeries(t=-5.401, P=0.013).CONCLUSION: Using the Phorcides analytic engine to assist topography-guided personalized LASIK for the treatment of myopia and astigmatism in virgin eyes with the refractive astigmatism significantly deviating from topography-measured astigmatism can achieve good therapeutic effects. Postoperative UDVA exceeded preoperative CDVA in nearly half of the eyes, and the quality of postoperative corneal imaging was improved.

Activity cliffs (ACs) are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target. ACs offer crucial insights that aid medicinal chemists in optimizing molecular structures. Nonetheless, they also form a major source of prediction error in structure-activity relationship (SAR) models. To date, several studies have demonstrated that deep neural networks based on molecular images or graphs might need to be improved further in predicting the potency of ACs. In this paper, we integrated the triplet loss in face recognition with pre-training strategy to develop a prediction model ACtriplet, tailored for ACs. Through extensive comparison with multiple baseline models on 30 benchmark datasets, the results showed that ACtriplet was significantly better than those deep learning (DL) models without pre-training. In addition, we explored the effect of pre-training on data representation. Finally, the case study demonstrated that our model's interpretability module could explain the prediction results reasonably. In the dilemma that the amount of data could not be increased rapidly, this innovative framework would better make use of the existing data, which would propel the potential of DL in the early stage of drug discovery and optimization.

Activity cliffs(ACs)are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target.ACs offer crucial insights that aid medicinal chemists in optimizing molecular structures.Nonetheless,they also form a major source of prediction error in structure-activity relationship(SAR)models.To date,several studies have demonstrated that deep neural networks based on molecular images or graphs might need to be improved further in predicting the potency of ACs.In this paper,we integrated the triplet loss in face recognition with pre-training strategy to develop a prediction model ACtriplet,tailored for ACs.Through extensive comparison with multiple baseline models on 30 benchmark datasets,the results showed that ACtriplet was significantly better than those deep learning(DL)models without pre-training.In addition,we explored the effect of pre-training on data representation.Finally,the case study demonstrated that our model's interpretability module could explain the prediction results reasonably.In the dilemma that the amount of data could not be increased rapidly,this innovative framework would better make use of the existing data,which would propel the potential of DL in the early stage of drug discovery and optimization.

Objective:To systematically evaluate and integrate the qualitative studies on nurses' experiences with nursing information systems, so as to provide a basis for improving the use and management of nursing information systems.Methods:The qualitative study on nurses' experiences, feelings, and attitudes toward nursing information systems was electronically searched in China National Knowledge Infrastructure, Wanfang Data, VIP, China Biology Medicine disc, PubMed, Web of Science, Cochrane Library, Embase, and PsycINFO. The retrieval period was from database establishment to December 2023. The quality of the literature was evaluated according to the Australia Joanna Briggs Institute Evidence-Based Health Care Centre Quality Assessment Criteria for Qualitative Research (2020). Two researchers independently completed the extraction of data and applied the aggregative synthesis to integrate the results.Results:A total of 10 articles were included. A total of 36 results were extracted, and seven new categories were summarized to form three integrated results, namely, the vertical evolution of technology acceptance attitudes, the "mixed blessing" of information technology, and the future-oriented expectations of users.Conclusions:Managers need to dynamically assess nurses' acceptance of the nursing information system, emphasize nurses' experience of annoyance, reduce their psychological burden, establish a sound feedback mechanism and supportive work environment, and effectively promote the construction of information technology.

Objective Patients with acute coronary syndrome(ACS)combined with chronic kidney disease(CKD)are at high risk of major adverse cardiovascular events(MACE),and early prediction is crucial for improving prognosis.Non-invasive detection methods,due to their simplicity and safety,have become important tools for assessing risks in such patients.This study aims to evaluate the application value of non-invasive detection indicators in predicting MACE in ACS patients with CKD.Methods The study included 216 ACS patients with CKD,divided into a Non-MACE group(n=149)and a MACE group(n=67).General patient data,non-invasive detection indicators,electrocardiogram(ECG),and cardiac function indicators were collected.Univariate and multivariate logistic regression analyses were performed to explore the relationship between these indicators and MACE.A nomo-gram prediction model was constructed,and its performance was evaluated using ROC curve analysis,calibration curve,and decision curve analysis.Results Univariate analysis showed that age,BMI,SVR,SVRI,HRV,QTd,SDNN,LVEF,LVEDd,SCOPA-AUT score,and GRACE score were significantly associated with MACE.Multivariate analysis identified SVRI,QTd,SDNN,LVEF,LVEDd,SCOPA-AUT score,and GRACE score as independent risk factors for MACE.ROC curve analysis revealed that the model had an AUC value of 0.979,sensi-tivity of 0.925,specificity of 0.966,and accuracy of 0.9537,indicating high diagnostic accuracy.Calibration curve and decision curve analyses further confirmed the model's reliability.Conclusion Non-invasive detection indicators,including SVRI,QTd,SDNN,LVEF,LVEDd,SCOPA-AUT score,and GRACE score,have significant value in predicting MACE in ACS patients with CKD.The constructed prediction model provides an effective tool for clinical risk assessment.

Objective To analyze the influencing factors of postoperative live birth in patients with tubal ectopic pregnancy who had received fresh double cleavage-stage embryo transfer through in vitro fertilization/intracytoplasmic sperm injection(IVF/ICSI).Methods A retrospective cohort study was performed by analyzing clinical data of patients receiving laparoscopic salpingectomy for clinical diagnosed tubal ectopic pregnancy who had undergone double fresh cleavage-stage embryo transfer in our department from January 2016 to June 2018.A follow-up study was performed in terms of pregnancy outcomes in patients who were willing to conceive or attempt pregnancy for 6 years after the operation.The patients were categorized into two groups according to postoperative pregnancy outcomes.A logistic regression analysis was performed to analyze the influencing factors of postoperative live births.Results A total of 188 patients were included,110 of which had live birth within 6 years after surgery,with a live birth rate of 58.5％(110/188).A multivariate logistic regression analysis was conducted on variables with P＜0.2 in the univariate analysis(age,duration of infertility,ovarian reserve dysfunction,number of egg retrieval after laparoscopic resection,endometrial thickness,pelvic adhesions,and fimbrial morphology of the fallopian tube)and the baseline antral follicle count in the last transplantation cycle of the live birth cycle(P=0.205),which would affect the outcome of subsequent pregnancy.The analysis showed that pelvic single site and multiple site dense adhesions were independent prognostic factors affecting the live birth again(OR=0.185,95％ CI:0.046-0.736,P=0.017;OR=0.124,95％ CI:0.024-0.640,P=0.013).Conclusions Intraoperative pelvic adhesion negatively affects the pregnancy outcomes of patients diagnosed with tubal ectopic pregnancy who have received fresh double cleavage-stage embryo transfer,and is an independent prognosis factor for postoperative live birth within 6 years.Improving pelvic adhesion and preventing pelvic inflammatory disease are beneficial for refining fertility outcome after surgery.