Dendrobium officinale(DO) is a traditional Chinese medicinal and edible plant, while it is critically endangered worldwide. This article, primarily based on the original research findings of the author's team and available articles, provides a comprehensive overview of the factors contributing to the endangerment of DO and the key technologies for the conservation, efficient cultivation, and value-added utilization of this plant. The scarcity of wild populations, low seed-setting rates, lack of endosperm in seeds, and the need for symbiosis with endophytic fungi for seed germination under natural conditions are identified as the primary causes for the rarity and endangerment of DO. Artificial seed production and tissue culture are highlighted as key technologies for alleviating the endangered status. The physiological and ecological mechanisms underlying the adaptation of DO to epiphytic growth are explored, and it is proposed that breaking the coupling of high temperature and high humidity is essential for preventing southern blight, a devastating affliction of DO. The roles of endophytic fungi in promoting the growth, improving the quality, and enhancing the stress resistance of DO are discussed. Furthermore, the integration of variety breeding, environment selection, and co-culture with endophytic fungi is emphasized as a crucial approach for efficient cultivation. The value-added applications of DO in pharmaceuticals, health foods, food products, and daily chemicals-particularly in the food and daily chemical industries-are presented as key drivers for a 10-billion-RMB-level industry. This systematic review offers valuable insights for the further development, utilization, and industrialization of DO resources, as well as for the broader application of conservation strategies for other rare and endangered plant species.
Dendrobium/microbiology* ; Endangered Species ; Seeds/microbiology* ; Fungi/physiology*

[This corrects the article DOI: 10.1016/j.apsb.2024.09.010.].

Eightly-four novel thioheterocyclic nucleoside derivatives were designed, synthesized, and evaluated for antitumor activity in vitro and in vivo. Most of the compounds inhibited the growth of HCT116 and HeLa cancer cells in vitro, among them 33a and 36b exhibited potent activity against HCT116 cells (IC₅₀ = 0.27 and 0.49 μmol/L, respectively). Both compounds 33a and 36b inhibited cell metastasis, arrested the cell cycle in the G₂/M phase, and induced apoptosis in vitro. Mechanistic studies revealed that 33a and 36b increased ROS levels, led to DNA damage, ER stress, and mitochondrial dysfunction, and inhibited autophagy in HCT116 cells. Biological information analysis, RNA-sequencing, Gene Set Enrichment Analysis (GSEA), drug affinity responsive target stability (DARTS) assay, cellular thermal shift assay (CETSA), and SPR experiments identified that compounds 33a and 36b showed antitumor activity by suppressing the c-MYC pathway. c-MYC silencing assays indicated that c-MYC proteins participated in 33a-mediated anticancer activities in HCT116 cells. More importantly, compound 33a presented favorable pharmacokinetic properties in mice (T _1/2 = 6.8 h) and showed significant antitumor efficacy in vivo without obvious toxicity, showing promising potential for further clinical development.

AIM To explore the clinical effects of Huayu Ditan Linao Decoction combined with Suhexiang Pills on elderly patients with cerebral hemorrhage in convalescent stage.METHODS One hundred and ten patients were randomly assigned into control group(55 cases)for 1-month intervention of both conventional treatment and rehabilitation training,and observation group(55 cases)for 1-month intervention of Huayu Ditan Linao Decoction,Suhexiang Pills,conventional treatment and rehabilitation training.The changes in clinical effects,TCM syndrome score,NIHSS score,FMA score,BI index,cerebrovascular function indices(dynamic resistance,average flow velocity,peripheral resistance,average flow rate),serum inflammatory indices(IL-6,hs-CPR,TNF-α)and serum oxidative stress indices(SOD,MDA)were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P＜0.05).After the treatment,the two groups displayed decreased TCM syndrome score,NIHSS score,dynamic resistance,peripheral resistance,serum inflammatory indices,MDA(P＜0.05),and increased FMA score,BI index,average flow velocity,average flow rate,SOD(P＜0.05),especially for the observation group(P＜0.05).CONCLUSION For the elderly patients with cerebral hemorrhage in convalescent stage,Huayu Ditan Linao Decoction combined with Suhexiang Pills can enhance clinical effects,improve limb functions and nerve functions,regulate serum inflammatory and oxidative stress indice,and promote prognosis.

A growing body of research points out that gut microbiota plays a key role in tumor immunotherapy. By optimizing the composition of intestinal microbiota, it is possible to effectively improve immunotherapy resistance and enhance its therapeutic effect. This article comprehensively analyzes the mechanism of intestinal microbiota influencing tumor immunotherapy resistance, expounds the current strategies for targeted regulation of intestinal microbiota, such as traditional Chinese medicine and plant components, fecal microbiota transplantation, probiotics, prebiotics and dietary therapy, and explores the potential mechanisms of these strategies to improve patients' resistance to tumor immunotherapy. At the same time, the article also briefly discusses the prospects and challenges of targeting intestinal microbiota to improve tumor immunotherapy resistance, which provides a reference for related research to help the strategy research of reversing tumor immunotherapy resistance.

ObjectiveTo construct a large language model （LLM）-based intelligent pre-consultation system for traditional Chinese medicine （TCM） to improve efficacy of clinical practice. MethodsA TCM large language model was fine-tuned using DeepSpeed ZeRO-3 distributed training strategy based on YAYI 2-30B. A weighted undirected graph network was designed and an agent-based syndrome differentiation model was established based on relationship data extracted from TCM literature and clinical records. An agent collaboration framework was developed to integrate the TCM LLM with the syndrome differentiation model. Model performance was comprehensively evaluated by Loss function， BLEU-4， and ROUGE-L metrics， through which training convergence， text generation quality， and language understanding capability were assessed. Professional knowledge test sets were developed to evaluate system proficiency in TCM physician licensure content， TCM pharmacist licensure content， TCM symptom terminology recognition， and meridian identification. Clinical tests were conducted to compare the system with attending physicians in terms of diagnostic accuracy， consultation rounds， and consultation duration. ResultsAfter 100 000 iterations， the training loss value was gradually stabilized at about 0.7±0.08， indicating that the TCM-LLM has been trained and has good generalization ability. The TCM-LLM scored 0.38 in BLEU-4 and 0.62 in ROUGE-L， suggesting that its natural language processing ability meets the standard. We obtained 2715 symptom terms， 505 relationships between diseases and syndromes， 1011 relationships between diseases and main symptoms， and 1 303 600 relationships among different symptoms， and constructed the Agent of syndrome differentiation model. The accuracy rates in the simulated tests for TCM practitioners， licensed pharmacists of Chinese materia medica， recognition of TCM symptom terminology， and meridian recognition were 94.09%， 78.00%， 87.50%， and 68.80%， respectively. In clinical tests， the syndrome differentiation accuracy of the system reached 88.33%， with fewer consultation rounds and shorter consultation time compared to the attending physicians （P＜0.01）， suggesting that the system has a certain pre- consultation ability. ConclusionThe LLM-based intelligent TCM pre-diagnosis system could simulate diagnostic thinking of TCM physicians to a certain extent. After understanding the patients' natural language， it collects all the patient's symptom through guided questioning， thereby enhancing the diagnostic and treatment efficiency of physicians as well as the consultation experience of the patients.

Abdominal war trauma is a common and high-risk type of injury in the modern battlefield,with rapid changes in condition and a high mortality rate.There is an urgent need for emerging medical technologies to improve the efficiency and success rate of first aid for military casualties.With the development of artificial intelligence(AI),5G,and other emerging technologies,the concept of intelligent medical treatment is gradually forming and can assist in the diagnosis and treatment of abdominal trauma.This paper reviews the characteristics of abdominal war trauma in modern wars,discusses the application of intelligent medical treatment for abdominal war trauma and its drawbacks to be solved,aiming to provide reference for research related to abdominal war trauma.

Objective To explore the correlation between gallbladder stones and small intestinal bacterial overgrowth(SIBO).Methods A retrospective analysis was conducted on the clinical data of 393 patients who attended the Department of Gastroenterology of the Sixth Medical Center of Chinese PLA General Hospital from January 2021 to September 2023.They were divided into gallbladder stones group(n=190)and control group(n=203)based on the presence of gallbladder stones.Their general clinical data,laboratory test results,and abdominal symptoms were compared.Multivariate logistic regression was used to analyze the risk factors for gallbladder stones.Additionally,the total population was divided into SIBO-positive group(n=239)and SIBO-negative group(n=154),and their clinical characteristics were analyzed by logistic regression to explore the risk factors for SIBO.Results Univariate analysis revealed that gallbladder stones group had a higher rate of age,body mass index(BMI),fasting plasma glucose(FPG),glutaminase levels,prevalence of hypertension,diabetes,coronary heart disease,non-alcoholic fatty liver disease,gallbladder polyps,and SIBO,as well as a higher prevalence of CH4-positive and H2-positive in SIBO group than control group(P＜0.05).In terms of abdominal symptoms,the incidence of bad breath(48.4%vs.35.5%),dyspepsia(38.4%vs.28.6%),abdominal pain(30.5%vs.14.8%),bloating(42.1%vs.28.6%),diarrhea(20.5%vs.7.4%),and more exhaustion(46.8%vs.34.5%)were significantly higher in gallbladder stones group than those in control group(P＜0.05).Multivariate logistic regression analysis showed that independent positive determinants for incident gallbladder stones were age,BMI,FPG,total bilirubin(TBIL),coronary heart disease,gallbladder polyps,and SIBO.Univariate analysis revealed that age,prevalence of gallbladder stones,proportion of single stones,triglycerides(TG),total cholesterol(TC),and low-density lipoprotein cholesterol(LDL-C)were significantly higher in SIBO-positive group than those in SIBO-negative group(P＜0.05).Multivariate logistic regression analysis showed that the risk factors for SIBO were age,coronary heart disease,and gallbladder stones,while the protective factor for SIBO was high-density lipoprotein cholesterol(HDL-C).Conclusion There is a significant correlation between gallbladder stones and small SIBO;interventions on related factors of gallbladder stones and small SIBO may help reduce their incidence.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

Objective:This study aims to explore the risk factors of Multi-center Investigator-Initiated Clinical Trials (MIITs), and provide a basis for developing study management strategies.Methods:The original draft of MIIT risk evaluation factors was determined through literature analysis and internal discussions of the research group. Thirty five experts were consulted using the Delphi method, and then the MIIT risk evaluation elements were finally determined. Analytic Hierarchy Process (AHP) was used to calculate the weights of each index.Results:The recovery rates of both rounds of expert consultation were 100%, and the degree of expert authority was 0.856. The study ultimately formed an MIIT risk evaluation framework consisting of three first-class indexes, twelve second-class indexes, and thirty-eight third-class indexes. The weight values of the first-class indexes (start-up period, implementation period, and summary period) were 0.209 8, 0.710 6, and 0.079 6, respectively. Meanwhile, the weight values of the second-class indexes and third-class indexes were determined.Conclusions:Exploring the risk evaluation factors of MIIT provides valuable insights into identifying critical risk points, which, in turn, contributes to enhancing MIIT management efficiency, research progress, and quality.