The general models for intermediates quality analysis in the production process of Yaobitong capsule were established by near infrared spectroscopy (NIRS) combined with chemometrics, realizing the rapid determination of notoginsenoside R1, ginsenoside Rg1, ginsenoside Re, ginsenoside Rb1, ginsenoside Rd and moisture. The spray-dried fine powder and total mixed granule were selected as research objects. The contents of five saponins were determined by high performance liquid chromatography and the moisture content was determined by drying method. The measured contents were used as reference values. Meanwhile, NIR spectra were collected. After removing abnormal samples by Monte Carlo cross validation (MCCV), Monte Carlo uninformative variables elimination (MC-UVE) and competitive adaptive reweighted sampling (CARS) were used to select feature variables respectively. Based on the feature variables, quantitative models were established by partial least squares regression (PLSR), extreme learning machine (ELM) and ant lion optimization least squares support vector machine (ALO-LSSVM). The results showed that CARS-ALO-LSSVM model had the optimum effect. The correlation coefficients of the six index components were greater than 0.93, and the relative standard errors were controlled within 6%. ALO-LSSVM was more suitable for a large number of samples with rich information, and the prediction effect and stability of the model were significantly improved. The general models with good predicting effect can be used for the rapid quality determination of Yaobitong capsule intermediates.

For HCQ retinopathy with CME, acupuncture combined with periocular injection can be used to improve the CME and protect the central vision. Subsequent research endeavors involving a more extensive cohort and extended observation periods are warranted to evaluate the effectiveness and safety profile of the intervention.
Humans ; Macular Edema/drug therapy* ; Acupuncture Therapy/methods* ; Hydroxychloroquine/therapeutic use* ; Female ; Retinal Diseases/chemically induced* ; Middle Aged ; Combined Modality Therapy ; Male

Mycophenolic acid (MPA), the active moiety of both mycophenolate mofetil (MMF) and enteric-coated mycophenolate sodium (EC-MPS), serves as a primary immunosuppressant for maintaining solid organ transplants. Therapeutic drug monitoring (TDM) enhances treatment outcomes through tailored approaches. This study aimed to develop an evidence-based guideline for MPA TDM, facilitating its rational application in clinical settings. The guideline plan was drawn from the Institute of Medicine and World Health Organization (WHO) guidelines. Using the Delphi method, clinical questions and outcome indicators were generated. Systematic reviews, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) evidence quality evaluations, expert opinions, and patient values guided evidence-based suggestions for the guideline. External reviews further refined the recommendations. The guideline for the TDM of MPA (IPGRP-2020CN099) consists of four sections and 16 recommendations encompassing target populations, monitoring strategies, dosage regimens, and influencing factors. High-risk populations, timing of TDM, area under the curve (AUC) versus trough concentration (C₀), target concentration ranges, monitoring frequency, and analytical methods are addressed. Formulation-specific recommendations, initial dosage regimens, populations with unique considerations, pharmacokinetic-informed dosing, body weight factors, pharmacogenetics, and drug‍-‍drug interactions are covered. The evidence-based guideline offers a comprehensive recommendation for solid organ transplant recipients undergoing MPA therapy, promoting standardization of MPA TDM, and enhancing treatment efficacy and safety.
Mycophenolic Acid/administration & dosage* ; Drug Monitoring/methods* ; Humans ; Organ Transplantation ; Immunosuppressive Agents/administration & dosage* ; Delphi Technique

Objective To explore the effectiveness of an integrated laparoscopic simulation training course (best essential surgical technique training, BEST) in enhancing laparoscopic peritoneal suturing techniques in surgical residents.Methods As an integrated two-stage program, the BEST course applied basic laparoscopic training system with simple molds in phase Ⅰ training, and then adopted advanced laparoscopic training system, 3D Laparoscope and ex-vivo animal models in phase Ⅱ training. The laparoscopic suturing techniques were practiced in phase Ⅱ training. From August 2021 to July 2024, surgical residents in the second year of the national standardized training program were divided into pilot and control groups based on whether they had undergone the BEST course. Two cases of laparoscopic peritoneal suture were performed by the surgical residents under supervision in the department of gastrointestinal surgery. The operative time, quality of suture, and independent completion rate were compared between the two groups.Results A total of 33 surgical residents (19 in pilot group and 14 in control group) were included in this study, and a total of 66 cases of laparoscopic peritoneal suture were performed (38 in pilot group and 28 in control group). The operative time was significantly shorter in pilot group than that in control group (15.7 min vs. 17.5 min, P=0.025). The quality of suture was significantly better in pilot group compared to control group (P=0.023). In pilot group, all peritoneal sutures were performed by residents independently, whereas in control group, 3 cases (10.7%) were assisted by the supervisor, and the independent completion rate was different significantly (P=0.039).Conclusions The BEST course can help improve surgical residents′ laparoscopic peritoneal suturing techniques and could be promoted in the national standardized training program for surgical residents.

Objective To explore the value of dynamic nomogram constructed by multi spiral computed tomography(MSCT)features combined with inflammatory indicators in predicting the status of microvascular invasion(MVI)of hepatocellular carcinoma(HCC)before surgery.Methods The clinical and imaging data of 137 patients with postoperative pathologically confirmed HCC were analyzed retrospectively.According to the status of the MVI,they were divided into positive group(44 cases)and negative group(93 cases).Multivariate logistic regression analysis was used to screen independent risk factors for predicting the MVI status of HCC patients,and a joint prediction model was constructed,which was displayed in the form of a dynamic nomogram.The receiver operating characteristic(ROC)curve,calibration curve and Hosmer-Lemeshow test were used to evaluate the diagnostic efficiency,calibration and goodness of fit of the model,Akaike information criterion(AIC)and Bayesian information criterion(BIC)were used for comparison between the models,and a 5-fold cross-validation and decision curve analysis(DCA)were also used to evaluate the stability and clinical applicability of the model.Results Multivariate logistic regression analysis showed that necrosis and delayed-phase enhancement(DEd),and alkaline phosphatase to lymphocyte ratio(ALR)were independent risk factors for predicting MVI status in HCC patients.The area under the curve(AUC)of the dynamic nomogram was 0.721,with the sensitivity of 0.705 and the specificity of 0.656.The AIC and BIC values were 152.372 and 158.212,respectively.The calibration curve and the Hosmer-Lemeshow test showed that the model had a high degree of calibration and goodness of fit(χ2=2.372,P=0.967),the average AUC of the 5-fold cross-validation was 0.787,and the DCA showed that the nomogram model had a good clinical applicability.Conclusion The dynamic nomogram model constructed by MSCT features combined with inflammatory indicators is feasible to predict the MVI status of HCC patients before surgery,and the dynamic nomogram can directly generate the prediction results of different individuals.

ObjectiveBased on knowledge mapping， the studies on prediction models in the field of traditional Chinese medicine （TCM） were visually analyzed， which provided a reference basis for the excavation and evolution of the future research direction by combing the development process and summarizing the research hotspots and dynamic trends. MethodChina National Knowledge Infrastructure and Web of Science Core Collection databases were searched to obtain studies on prediction models in the field of TCM from inception to February 28， 2023. Endnote X20 software was used for document management. Knowledge mapping generated by CiteSpace software and VOSviewer software was used to visually analyze the characteristics of publication， institutional cooperation relationship， author cooperation network， co-citation， and keywords. ResultA total of 264 pieces of Chinese literature and 266 pieces of English literature were included， and the overall number of research publications showed an increasing trend year by year. The cooperation relationship between the issuing institutions showed obvious regional characteristics， with the closest cooperation relationship between the universities of TCM and their affiliated hospitals， as well as secondary units subordinate to scientific research institutions. The number of research teams and team members publishing papers in English was higher， and cooperation between different teams was more frequent. Groundbreaking and/or referential studies were widely cited and referred to. The highly cited literature was mainly published in complementary and alternative medicine journals and pharmaceutical journals. Research hotspots mainly focused on clinical prediction models of TCM， quantitative models of TCM， and specific modeling methods. The application of artificial intelligence technologies such as machine learning and deep learning in the field of TCM will be the most cutting-edge research direction in the future. ConclusionThe field of TCM is paying more and more attention to the studies on prediction models， while the research cooperation mode involving multiple organizations and teams has increasingly become the mainstream. With the continuous development of multi-disciplinary integration， studies on prediction models are closely related to the development and rise of innovative techniques and methods， and any breakthrough in theory or application will induce and guide a new round of research upsurge. Systematic reviews of topic-specific prediction models should be carried out in the future to provide evidence-based evidence.

This study， anchored in the traditional Chinese medicine （TCM） syndrome differentiation and treatment principles alongside the clinical characteristics of sepsis in Western medicine， extensively gathers and meticulously dissects the latest research findings on sepsis animal models from both Chinese and international sources. Adhering strictly to TCM syndrome diagnostic criteria for sepsis， the study conducts a thorough evaluation of various animal models across multiple dimensions， including clinical manifestations， pathological changes， and biomarker expressions， so as to reflect the degree of resemblance these models have to human sepsis TCM syndromes. The results reveal that the colon stent implantation model exhibits a higher degree of congruence with both TCM and Western medicine standards， particularly aligning with the diagnosis of the "Fu-Qi obstruction syndrome". Conversely， the extraperitoneal sepsis model shows a higher degree of congruence with TCM， fitting more closely with the diagnosis of "acute deficiency syndrome" and emphasizing the core pathogenesis of Qi deficiency in sepsis. These findings not only augment the diversity of sepsis animal models but also highlight the necessity and potential of integrated TCM and western medicine research. Current sepsis animal models predominantly focus on western pathophysiological mechanisms， with limited direct incorporation of TCM syndrome differentiation elements. This underscores the need， in future study designs， to actively explore integrating TCM syndrome classification and intervention principles into model development. This could be achieved by manipulating model-inducing factors and observing more TCM-specific symptoms and signs among other strategies， so as to establish sepsis models that more closely resemble clinical reality and incorporate both TCM and western medical perspectives.

Objective To observe the efficacy and safety of Morinda officinalis oligosaccharides in the continuation treatment of mild and moderate depression.Methods An open,single-arm,multi-center design was adopted in our study.Adult patients with mild and moderate depression who had received acute treatment of Morinda officinalis oligosaccharides were enrolled and continue to receive Morinda officinalis oligosaccharides capsules for 24 weeks,the dose remained unchanged during continuation treatment.The remission rate,recurrence rate,recurrence time,and the change from baseline to endpoint of Hamilton Depression Scale(HAMD),Hamilton Anxiety Scale(HAMA),Clinical Global Impression-Severity(CGI-S)and Arizona Sexual Experience Scale(ASEX)were evaluated.The incidence of treatment-related adverse events was reported.Results The scores of HAMD-17 at baseline and after treatment were 6.60±1.87 and 5.85±4.18,scores of HAMA were 6.36±3.02 and 4.93±3.09,scores of CGI-S were 1.49±0.56 and 1.29±0.81,scores of ASEX were 15.92±4.72 and 15.57±5.26,with significant difference(P＜0.05).After continuation treatment,the remission rate was 54.59％(202 cases/370 cases),and the recurrence rate was 6.49％(24 cases/370 cases),the recurrence time was(64.67±42.47)days.The incidence of treatment-related adverse events was 15.35％(64 cases/417 cases).Conclusion Morinda officinalis oligosaccharides capsules can be effectively used for the continuation treatment of mild and moderate depression,and are well tolerated and safe.

AIM To compare the in vivo pharmacokinetics of paeoniflorin,paeoniflorin,liquiritin,isoliquiritin,liquiritigenin and glycyrrhizic acid from Shaoyao Gancao Decoction in normal and gastric ulcer rats.METHODS Six rats were randomly assigned into two groups,after which the 75%ethanol-induced gastric ulcer model was established,the gastric tissues were collected.Twelve rats were randomly assigned into two groups and given intragastric administration(9.9 g/kg),after which blood collection was made at different time points,UPLC-MS/MS method was adopted in the determination of plasma concentrations,and main pharmacokinetic parameters were calculated.RESULTS Prolonged Tmax(P＜0.05,P＜0.01)of various active constituents,prolonged T1/2,MRT0-t(P＜0.05,P＜0.01),increased Cmax,AUC(P＜0.05,P＜0.01)and decreased Vd/F,CL/F(P＜0.05,P＜0.01)of paeoniflorin,increased Cmax,AUC(P＜0.05,P＜0.01)and decreased CL/F(P＜0.05)of albiflorin,prolonged MRT(P＜0.05),increased AUC(P＜0.05)and decreased CL/F(P＜0.01)of liquiritin,prolonged MRT(P＜0.05,P＜0.01)and decreased Vd/F(P＜0.05)of isoliquiritin,no obviously changed pharmacokinetic parameters(except for Tmax)of liquiritigenin(P＞0.05),and prolonged T1/2,MRT0-∞(P＜0.05,P＜0.01),increased Cmax,AUC(P＜0.05,P＜0.01)and decreased CL/F(P＜0.01)of glycyrrhizic acid were observable in the model group as compared with those in the normal group.CONCLUSION Gastric ulcer exhibits certain influences on the velocities and degrees of in vivo absorption and metabolism of active constituents from Shaoyao Gancao Decoction.

Objective To construct an artificial intelligence assisted diagnosis model based on deep learning for dynamically recognizing gastric lesions and their locations under gastroscopy in real time,and to evaluate its ability to detect and recognize gastric lesions and their locations.Methods The gastroscopy videos of 104 patients in our hospital was retrospectively analyzed,and the video frames were manually annotated.The annotated picture frames of lesion category were divided into the training set and the validation set according to the ratio of 8∶2,and the annotated picture frames of location category were divided into the training set and the validation set according to the patient sources at the ratio of 8∶2.These sets were utilized for training and validating the respective models.YoloV4 model was used for the training of lesion recognition,and ResNet152 model was used for the training of location recognition.The accuracy,sensitivity,specificity,positive predictive value,negative predictive value and location recognition accuracy of the auxiliary diagnostic model were evaluated.Results A total of 68 351 image frames were annotated,with 54 872 frames used as the training set,including 41 692 frames for lesion categories and 13 180 frames for location categories.The validation set consisted of 13 479 frames,comprising 10 422 frames for lesion categories and 3 057 frames for location categories.The lesion recognition model achieved an overall accuracy of 98.8%,with a sensitivity of 96.6%,specificity of 99.3%,positive predictive value of 96.3%,and negative predictive value of 99.3% in validation set.Meanwhile,the location recognition model demonstrated an top-5 accuracy of 87.1% .Conclusion The artificial intelligence assisted diagnosis model based on deep learning for real-time dynamic recognition of gastric lesions and their locations under gastroscopy has good ability in lesion detection and location recognition,and has great clinical application prospects.