This study was aimed at identifying the pathogenic bacteria responsible for the death of a young tiger at the Fujian Meihua Mountain South China Tiger Breeding Research Institute.Tissue samples from the lungs,liver,and intestines of the deceased tiger were collected,and the bacteria were cultured inasterile environment.The bacterial strains were characterized according to their morphological and molecular biological properties,including assessment of virulence genes and antibiotic resistance genes,mouse lethality tests,and antibiotic susceptibility evaluations.A predominant bacterial strain isolated from the liver of the deceased tiger was identified as enterotoxigenic Escherichia coli(ETEC)strain Tiger22513F.Phylogenetic analysis of the 16S rRNA gene revealed that the Tiger22513F strain exhibited close genetic similarity to the reference strain ETEC(MF919609.1),with 99.9%nucleotide similarity,and resided on the same evolutionary branch.The Tiger22513F strain contained 11 antibiotic resistance genes(tetA,sul1,sul3,cmlA,floR,blaTEM,blaSHV,blaCMY-2,qnrA,qnrS,and qnrD)along with five virulence genes(VT1,fyuA,tsh,iucD,and ST).Mouse lethality tests indicated significant pathogenicity toward mice,affecting primarily the lungs,liver,and intestines.Antibiotic susceptibility testing demonstrated that this strain exhibited resistance to various classes of beta-lactam antibiotics,as well as quinolones and aminoglycosides.This investigation successfully isolated a multi-drug resistant enterotoxigenic Escherichia coli strain with pronounced pathogenicity from the liver of a deceased tiger;thus providing valuable scientific insights for clinical diagnosis,as well as prevention and control measures,against ETEC infections in South China tigers.

Aim To investigate the role of FRMD4A in autophagy of placental trophoblast cells in preeclampsia(PE).Methods The placental tissues and clinical data of normal pregnancy and PE were obtained,and the histopathological changes were observed by HE staining.An in vitro model of hypoxia-induced HTR-8/SVneo trophoblast cells was established.The expres-sions of LC3B Ⅱ/Ⅰ and p62 in placental tissues and hypoxic cell models were analyzed by Western blot.The expression of FRMD4A was detected by qRT-PCR,Western blot and immunofluorescence,and the correlation between the expression level of FRMD4A and the clinical characteristics of the subjects was ana-lyzed by Pearson correlation analysis.Hypoxia induced trophoblast cells were transfected with si-FRMD4A,and the expression of LC3 B Ⅱ/Ⅰ and p62 was analyzed by Western blot.Results Compared with the normal group,the expression of LC3B Ⅱ/Ⅰ in PE placental tissues and hypoxia-induced trophoblast models was significantly upregulated,while the expression of p62 was significantly downregulated.Meanwhile,the ex-pression of FRMD4A increased significantly.Moreo-ver,its expression was positively correlated with the maternal systolic blood pressure,diastolic blood pres-sure,and platelet count,but negatively correlated with the neonatal weight(P＜0.01).In addition,hypoxia-induced trophoblast cells transfected with si-FRMD4A showed a significant decrease in LC3B Ⅱ/Ⅰ and an increase in p62 expression.Conclusions The expres-sion of FRMD4A is upregulated in PE placenta and hy-poxia-induced trophoblast cell model.Interfering with it can significantly hinder the autophagy process of trophoblast cells,suggesting that it may serve as a po-tential molecular target to participate in the pathologi-cal process of PE.

AIM To study the effect of Zuogui Pills on improving ovarian function in rats with premature ovarian failure(POF)by regulating autophagy.METHODS Seven rats were randomly selected as the blank group,and the remaining rats were injected with cisplatin(4.0 mg/kg)intraperitoneally to establish POF model.The success of the model was evaluated by observing the changes of estrous cycle.Twenty-one successful model of rats were randomly divided into model group and estradiol group(0.01 mg/mL)and Zuogui Pills group(1.85 g/kg),with 7 rats in each group,the drug was administered continuously for 21 days.Serum E2,FSH and LH levels were detected by ELISA;HE staining was used to observe the pathological morphology of ovarian tissue;immunohistochemical(IHC)method was used to detect the protein expressions of LC3B,SIRT1 and FoxO1 in ovarian tissues;RT-qPCR method was used to detect the mRNA expressions of LC3B,Beclin-1 and Atg5 in ovarian tissues;the protein expressions of SIRT1,FoxO1 and Ac-FoxO1 in ovarian tissue was detected by Western blot.RESULTS Compared with the blank group,the ovarian index of the model group decreased(P＜0.01);serum FSH and LH levels increased(P＜0.01)and E2 level decreased(P＜0.01);the structure of ovary is disordered,especially atresia follicle;the mRNA expressions of LC3B,Beclin-1 and Atg5 in ovarian tissue increased(P＜0.01),while the protein expressions of LC3B and Ac-FoxO1 increased(P＜0.01),while the protein expressions of SIRT1 and FoxO1 decreased(P＜0.01).Compared with the model group,the ovarian index of rats in estradiol group and Zuogui Pills group increased(P＜0.01);serum FSH and LH levels decreased(P＜0.01)and E2 level increased(P＜0.01);the number of primordial follicles in ovary increased and the number of atresia follicles decreased;the mRNA expressions of LC3B,Beclin-1 and Atg5 in ovarian tissue decreased(P＜0.01),while the protein expressions of LC3B and Ac-FoxO1 decreased(P＜0.01),while the protein expressions of SIRT1 and FoxO1 increased(P＜0.01).CONCLUSION Zuogui Pills may inhibit autophagy by activating SIRT1/FoxO1 signaling pathway,thus improving the pathological state of POF rats,regulating the level of sex hormones in rats,restoring endocrine balance,enhancing ovarian reserve function,promoting the normal development of follicles and delaying the progress of POF.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective To explore the predictive efficacy of several multimodal MRI-based machine learning models for the promoter methylation states of O6-methylguanine-DNA methyltransferase(MGMT)of glioblastoma muliforme(GBM)patients in terms of the GBM heterogeneity and the complexity of the tumor microenvironment.Methods Firstly,the multimodal MRI images of 317 GBM patients from The University of Pennsylvania Glioblastoma(UPENN-GBM)dataset were pre-processed,with four sequences involved in including T1-weighted imaging(T1WI)sequence,T1-weighted contrast-enhanced imaging(T1CE)sequence,T2-weighted imaging(T2WI)sequence and fluid-attenuated inversion recovery(FLAIR)sequence,and the radiomics features were extracted for two regions of interest(ROIs)such as the tumor core region and the tumor edema region.Secondly,the data of the 317 GBM patients were randomly divided into a training set(254 cases)and a test set(63 cases),which underwent normalization with Z-scores and feature selection and dimensionality reduction with Lasso regression.Finally,three models were established respectively with particle swarm optimization-support vector machine(PSO-SVM),C-support vector classification(C-SVC)and adaptive boosting(adaptive boosting(Adaboost)algorithms,and the predictive efficacy of the three models for glioblastoma multiforme MGMT promoter methylation states were evaluated in terms of accuracy and AUC.Results The Adaboost model based on T2WI sequence and radiomics features of the tumor core region had the highest predictive efficacy with accuracy and AUC values of 67％and 0.74,respectively,higher than those of other combinations of sequences,models and regions of interest.Conclusion The multimodal MRI-based machine learning models can be used for the prediction of glioblastoma multiforme MGMT promoter methylation states,which provides powerful support for personalized treatment and prognostic assessment of GBM.[Chinese Medical Equipment Journal,2025,46(6):7-13]

This paper aims to contribute to guaranteeing the stable development and enhancing the understanding of ecological agriculture of Chinese materia medica so that the national strategy and industrial demand can be better served. It first introduces current traditional Chinese medicine(TCM)policy and industrial development status from five aspects, including policy guarantee, theoretical support, technological innovation, standardization system, and brand influence. Then, the paper analyzes the development dilemma of TCM agriculture in production and quality increase and ecological environment protection. It also proposes the development goals of ecological agriculture of Chinese materia medica that meet the current industrial development demand, which are reducing chemical fertilizers, pesticides, and carbon emissions, improving quality, increasing efficiency, and protecting ecological environment. In addition, the new development goals are interpreted through case studies. Finally, this paper proposes four development strategies for ecological agriculture of Chinese materia medica: conducting research on the pattern and spatial and temporal variations of nationwide TCM production areas; studying the internal and external ecological memories of medicinal plant growth from the perspectives of genetic variations and environmental adaptation variations and elucidating their contributions to the formation of quality; carrying out selection and breeding of stress-resistant varieties for ecological agriculture of Chinese materia medica, the optimization of key technologies for soil improvement and restoration and green prevention and control against diseases and pests, and the improvement of quality; carrying out research on the quality assurance and value realization of ecological products made from TCM. This research can provide guidance for policy formulation, theoretical development of the discipline, and the enhancement of industrial technology for ecological agriculture of Chinese materia medica.
Agriculture/methods* ; China ; Drugs, Chinese Herbal ; Plants, Medicinal/chemistry* ; Ecosystem ; Materia Medica ; Medicine, Chinese Traditional

Traditional Chinese medicine(TCM) resources are an important foundation for the theory and practice of TCM. Rare and endangered TCM, as a significant component of these resources, plays an essential role. Conducting research on substitutes for rare and endangered TCM resources is of great significance for alleviating resource shortages, promoting the sustainable utilization of TCM, and advancing TCM modernization. This paper reviews the conservation achievements of rare and endangered Chinese medicinal materials in China and organizes the substitution methods for these materials. Currently, the main substitution approaches include introduction and domestication, tissue culture, varietal replacement, and artificial synthesis. Furthermore, this paper proposes the following approaches for researching the application scenarios of rare and endangered medicinal materials, i.e., tracing the historical context of their use to clarify foundational principles; verifying disease classifications to strengthen the clinical application scenarios of these materials; analyzing the evolution patterns of prescription formulations to strengthen the mining of the compatibility application scenarios of rare and endangered medicinal materials; scientifically evaluating to strengthen the application scenario research and development of endangered Chinese patent medicine industry. These efforts aim to promote the scientific substitution and sustainable utilization of rare and endangered medicinal materials and their substitutes.
Drugs, Chinese Herbal/chemistry* ; Humans ; Medicine, Chinese Traditional ; China ; Plants, Medicinal/growth & development* ; Endangered Species ; Conservation of Natural Resources ; Animals

Moringa oleifera, widely utilized in Ayurvedic medicine, is recognized for its leaves, seeds, and velamen possessing traditional effects such as vātahara(wind alleviation), sirovirecaka(brain clearing), and hridya(mental nourishment). This study aims to identify the medicinal part of ■ in the Sārasvata ghee formulation as described in the Bower Manuscript, while investigating the ameliorative effects of different medicinal parts of M. oleifera on learning and memory deficits in mice and elucidating the underlying molecular mechanisms. A total of 144 male ICR mice were randomly assigned to the following groups: control, model(scopolamine hydrobromide, Sco, 2 mg·kg~(-1)), donepezil(donepezil hydrochloride, Don, 3 mg·kg~(-1)), M. oleifera leaf low-, medium-, and high-dose groups(0.5, 1, 2 g·kg~(-1)), M. oleifera seeds low-, medium-, and high-dose groups(0.25, 0.5, 1 g·kg~(-1)), and M. oleifera velamen low-, medium-, and high-dose groups(0.31, 0.62, 1.24 g·kg~(-1)). Learning and memory abilities were assessed using the passive avoidance test and Morris water maze. Nissl and HE staining were employed to examine histopathological changes in the hippocampus. Transcriptomics and targeted metabolomics were used to screen differential genes and metabolites, with MetaboAnalyst 6.0 and O2PLS methods applied to identify key disease-related targets and pathways. RESULTS:: demonstrated that M. oleifera leaf(1 g·kg~(-1)) significantly ameliorated Sco-induced learning and memory deficits, outperforming M. oleifera seeds(0.25 g·kg~(-1)) and M. oleifera velamen(1.24 g·kg~(-1)). This was evidenced by improved behavioral performance, reversal of neuronal damage, and reduced acetylcholinesterase(AChE) activity. Multi-omics analysis revealed that M. oleifera leaf upregulated Tuba1c gene expression through the synaptic vesicle cycle, enhancing glutamate(Glu), dopamine(DA), and acetylcholine(ACh) release via Tuba1c-Glu associations for neuroprotection. M. oleifera seeds targeted the dopaminergic synapse pathway, promoting memory consolidation through Drd2-ACh associations. M. oleifera velamen was associated with the cocaine addiction pathway, modulating dopamine metabolism via Adora2a-DOPAC, with limited relevance to learning and memory. In conclusion, M. oleifera leaf exhibits superior efficacy and mechanistic advantages over M. oleifera seeds and velamen, suggesting that the ■ in the Sārasvata ghee formulation is likely M. oleifera leaf, providing scientific evidence for its identification in ancient texts.
Animals ; Moringa oleifera/chemistry* ; Male ; Mice ; Seeds/chemistry* ; Plant Leaves/chemistry* ; Mice, Inbred ICR ; Memory Disorders/psychology* ; Transcriptome/drug effects* ; Memory/drug effects* ; Learning/drug effects* ; Metabolomics ; Humans ; Drugs, Chinese Herbal/administration & dosage* ; Maze Learning/drug effects*

Gallbladder-duodenal fistula,a severe complication of cholecystitis caused by gallstones,is clinically rare.Its clinical presentation lacks specificity,and conventional preoperative imaging often fails to establish a definitive diagnosis.This report describes a case where a gallbladder-duodenal fistula was diagnosed using oral microbubble ultrasound contrast agent for gastric-duodenal contrast-enhanced ultrasound,providing a novel approach for diagnosing this condition.
Humans ; Biliary Fistula/diagnostic imaging* ; Contrast Media ; Duodenal Diseases/diagnostic imaging* ; Gallbladder Diseases/diagnostic imaging* ; Intestinal Fistula/diagnostic imaging* ; Ultrasonography