Genetic transformation is a fundamental tool in molecular biology research of medicinal plants. Tailoring transgenic technologies to each distinct medicinal plant would necessitate a substantial investment of time and effort. Here, we present a simple hairy root transformation method that does not require sterile conditions, utilizing Agrobacterium rhizogenes strain K599 and the visible RUBY reporter system. Transgenic hairy roots were obtained for six tested medicinal plant species, roots or rhizomes of which have recognized medicinal value, spanning four botanical families and six genera (Platycodon grandiflorus, Atractylodes macrocephala, Scutellaria baicalensis, Codonopsis pilosula, Astragalus membranaceus, and Glycyrrhiza uralensis). Furthermore, two previously identified Glycyrrhiza uralensis UGTs that convert liquiritigenin into liquiritin in heterologous systems were studied in planta using the method. Our results indicate that overexpression of GuUGT1 but not GuUGT10 and Cas9-mediated knockout of GuUGT1 profoundly influenced the accumulation of liquiritin and isoliquiritin in licorice roots. Therefore, the method described here represents a simple, rapid and widely applicable hairy root transformation method that enables fast gene functional study in medicinal plants.

OBJECTIVE: In Pakistan, traditional medicines are an important component of the medical system, with numerous varieties and great demands. However, due to the scattered resources and the lack of systematic collection and collation, adulteration of traditional Pakistani medicine (TPM) is common, which severely affects the safety of their medicinal use and the import and export trades. Therefore, it is urgent to systematically organize and unify the management of TPM and establish a set of standards and operable methods for the identification of TPM. METHODS: We collected and organized the information on 128 TPMs with regard to their medicinal parts, efficacy, usage, and genetic material, based on Pakistan Hamdard Pharmacopoeia of Eastern Medicine: Pharmaceutical Codex. The genetic information of TPM is summarized from national center for biotechnology information (NCBI) and global pharmacopoeia genome database (GPGD). Furthermore, we utilized bioinformatics technology to supplement the chloroplast genome (cp-genome) data of 12 TPMs. To build the web server, we used the Linux + Apache + MySQL + PHP (LAMP) system and constructed the webpage on a PHP: Hypertext Preprocessor (PHP) model view controller (MVC) framework. RESULTS: We constructed a new genomic database, the traditional Pakistani medicine genomic database (TPMGD). This database comprises five entries, namely homepage, medicinal species, species identification, basic local alignment search tool (BLAST), and download. Currently, TPMGD contains basic profiles of 128 TPMs and genetic information of 102 TPMs, including 140 cytochrome c oxidase subunit I (COI) sequences and 119 mitochondrial genome sequences from Bombyx mori, 1 396 internal transcribed spacer 2 (ITS2) sequences and 1 074 intergenic region (psbA-trnH) sequences specific to 92 and 83 plant species, respectively. Additionally, TPMGD includes 199 cp-genome sequences of 82 TPMs. CONCLUSION TPMGD is a multifunctional database that integrates species description, functional information inquiry, genetic information storage, molecular identification of TPM, etc. The database not only provides convenience for TPM information queries but also establishes the scientific basis for the medication safety, species identification, and resource protection of TPM.

Objective To observe hierarchical differences in brain functional networks in unilateral mesial temporal lobe epilepsy(mTLE)patients with different outcomes after surgery.Methods A total of 69 unilateral mTLE patients who underwent resection of epileptogenic lesion on the affected side were retrospectively enrolled.Based on Engel classification 1 year after surgery,the patients were divided into seizure free(SF)group and non-seizure free(NSF)group.Functional connectivity gradient analysis was employed to extract principal gradient explaining the highest variance on preoperative resting-state functional MRI(rs-fMRI),then the whole-brain gradient characteristics and principal gradient values within specific functional networks were compared between groups.Results Principal gradient connected default mode network(DMN)with sensorimotor network(SMN)along a continuous axis.Compared to SF group,NSF group showed a contracted gradient range at both ends(DMN and SMN)of the functional network and weakened hierarchical differentiation,and principal gradient value of DMN was higher,while that of SMN was lower than those in SF group(both P＜0.05).Conclusion Hierarchical differences in brain functional networks in unilateral mTLE patients with different outcomes after surgery mainly present as enhanced DMN and weakened SMN in NSF ones,and the latter two might serve as important neuroimaging markers for evaluating postoperative seizure recurrence.

Objective To observe the value of machine learning(ML)models based on brain functional network features combining clinical indicators for predicting postoperative outcomes of patients with drug-resistant mesial temporal lobe epilepsy(DR-mTLE).Methods Totally 84 patients with unilateral DR-mTLE who underwent surgery were retrospectively enrolled and classified into seizure free(SF)group(n=55)and non-seizure free(NSF)group(n=29)according to one-year postoperative follow-up.Clinical data were analyzed to screen independent predictors of postoperative outcomes.Based on brain preoperative resting-state functional MRI,brain functional networks were constructed using graph theory analysis,and 587 features were extracted.Five-fold cross validation was used to divide the data into training set and test set,then the optimal brain functional network features related to postoperative outcomes of DR-mTLE patients were selected.Combining with clinically relevant independent predictors,ML models were constructed using classifiers including Gaussian process(GP),logistic regression(LR),support vector machine(SVM)and quadratic discriminant analysis(QDA),respectively,and the prediction efficacy,calibration and clinical value of each ML model were evaluated.Results Both course of disease and lesion location were clinically relevant independent predictors of postoperative outcome of DR-mTLE patients(OR=0.928,5.710,P=0.010,0.016).Four optimal brain function network features were selected,including betweenness centrality of the third zone of cerebellar vermis,degree centrality of right globus pallidus,nodal efficiency of temporal left inferior temporal gyrus and nodal clustering coefficient of left inferior parietal lobule.The average area under the curve(AUC)of GP,LR,SVM and QDA models in test set was 0.868,0.864,0.875 and 0.870,respectively.Calibration curves and decision curve analysis indicated that each ML model had good calibration and high clinical net benefit.Conclusion ML models based on brain functional network features combining with clinical indicators could be used to effectively predict postoperative outcomes in DR-mTLE patients.

Objective A scoping review of studies on symptom clusters in patients with chronic heart failure(CHF)was conducted to provide reference for the treatment and management of CHF.Methods According to reporting framework of scoping review put forward by Arksey,the related literatures of Cochrane Library,CINAHL,Web of Science,PubMed,Embase,Wanfang Data Knowledge Service Platform,SinoMed,CNKI and VIP from January 2014 to July 2024 were searched,and the contents of the literatures were screened,extracted and analyzed.Results Ten articles were included,involving many symptom clusters,mainly including mood,ischemia,congestion,digestive tract and fatigue.Symptom group assessment tool mainly adopted Chinese version of Memorial symptom assessment scale-heart failure.Conclusion There are various types of symptom clusters in CHF patients,and they show dynamic changes in each disease stage.It is still necessary to strengthen the research on the evaluation tools,occurrence principles and standardized naming of symptom clusters.Medical staff can give first-class care to the main symptom clusters in each period,formulate personalized nursing intervention measures in advance,and improve the efficiency of symptom management in clinical nursing.

Objective To investigate the distribution of plantar pressures and bone stresses of the foot with high,normal and low arch morphologies,and reveal the influence of arch morphology on foot biomechanical properties.Methods A total of 127 young females were recruited.The foot type was classified by collecting the morphological data of the foot with the three-dimensional(3D)foot scanner,and three types of the foot arch morphology were selected for analysis.The geometric model of foot bone was obtained by CT scanning,so as to establish the biomechanical finite element model of the foot.A load of 50％human body weight was applied to the model to simulate the state of bipedal standing.Results The calculated plantar contact area was compared with the measured results,and the relative error values were smaller than 10％,which proved the validity of the finite element model.The peak plantar pressure under three types of arch morphologies was located in the hind foot region,and the heel pressure of high-and low-arched foot was higher than that of normal-arched foot.Compared with normal-arched foot,high-arched foot showed a significant increase in stress in the hind foot area,the peak stress of soft tissues was 299.45％higher,and the peak bone stress was 93.19％higher.For low-arched foot,the plantar contact area increased by 13.28％and calcaneal stress increased by 98.09％.The peak bone stresses of high-,normal-and low-arched foot were located at the talus,which were 9.903,19.921 and 36.308 MPa,respectively.Conclusions This study supports the association between abnormal arch morphology,foot pain and foot diseases,and provides a basis and direction for the design of orthopedic insoles and arch support structures for abnormal feet.

BACKGROUND:Total flavonoids from Sophora flavescens have a variety of pharmacological effects,including anti-inflammatory,immunomodulatory,antioxidant,and anti-hepatic injury,but the therapeutic effects and mechanisms in non-alcoholic fatty liver disease are not clear.OBJECTIVE:To reveal the mechanism of total flavonoids from Sophora flavescens in the treatment of non-alcoholic fatty liver disease using bioinformatics,network pharmacology and zebrafish experimental validation.METHODS:A zebrafish model of non-alcoholic fatty liver disease was constructed to observe lipid accumulation,pathomorphologic changes,and expression of inflammatory genes in the liver of zebrafish after treatment with total flavonoids from Sophora flavescens.The active ingredients of total flavonoids from Sophora flavescens and non-alcoholic fatty liver disease-related targets were obtained from TCMSP,Swiss Target Prediction,and Bat-man databases.STRING was used to perform protein-protein interaction network analysis,GO functional enrichment and KEGG pathway enrichment analysis.Based on the GSE33814 dataset,the differentially expressed genes of total flavonoids from Sophora flavescens and non-alcoholic fatty liver disease intersection targets were screened out.Correlation analysis and receiver operating characteristic curve were performed using R4.3.2 software.Core genes were verified by the validation set GSE89632.RT-qPCR and western blot assays were performed to verify the expression of core pathway-related genes and proteins.RESULTS AND CONCLUSION:(1)Total flavonoids from Sophora flavescens could improve lipid accumulation in the liver of zebrafish with non-alcoholic fatty liver disease,significantly inhibited the elevation of lipid and aminotransferase levels in zebrafish(P<0.05),and regulated the expression of genes related to inflammation and lipid metabolism.(2)A total of 168 common targets were obtained using the network pharmacology,and top 10 core genes,identified by Cytoscape topology analysis,were HSP90AA1,STAT3,PIK3R1,MAPK1,AKT1,RXRA,PIK3CA,EGFR,JAK2,and ESR1.GO and KEGG analysis pathways mainly included insulin resistance,lipids,and atherosclerosis.There were a total of 59 differentially expressed genes after intersection of total flavonoids from Sophora flavescens and non-alcoholic fatty liver disease targets.The receiver operating characteristic curve and validation set analyses yielded six core targets that were significantly different between healthy individuals and patients with non-alcoholic fatty liver disease(P<0.01).(3)RT-PCR and western blot results verified that total flavonoids from Sophora flavescens inhibited the activation of the JAK2/STAT3 signaling pathway in zebrafish.To conclude,total flavonoids from Sophora flavescens may alleviate the inflammatory response through the JAK2/STAT3 signaling pathway,thus inhibiting lipid accumulation and improving non-alcoholic fatty liver disease.

Objective To observe hierarchical differences in brain functional networks in unilateral mesial temporal lobe epilepsy(mTLE)patients with different outcomes after surgery.Methods A total of 69 unilateral mTLE patients who underwent resection of epileptogenic lesion on the affected side were retrospectively enrolled.Based on Engel classification 1 year after surgery,the patients were divided into seizure free(SF)group and non-seizure free(NSF)group.Functional connectivity gradient analysis was employed to extract principal gradient explaining the highest variance on preoperative resting-state functional MRI(rs-fMRI),then the whole-brain gradient characteristics and principal gradient values within specific functional networks were compared between groups.Results Principal gradient connected default mode network(DMN)with sensorimotor network(SMN)along a continuous axis.Compared to SF group,NSF group showed a contracted gradient range at both ends(DMN and SMN)of the functional network and weakened hierarchical differentiation,and principal gradient value of DMN was higher,while that of SMN was lower than those in SF group(both P＜0.05).Conclusion Hierarchical differences in brain functional networks in unilateral mTLE patients with different outcomes after surgery mainly present as enhanced DMN and weakened SMN in NSF ones,and the latter two might serve as important neuroimaging markers for evaluating postoperative seizure recurrence.

Objective To observe the value of machine learning(ML)models based on brain functional network features combining clinical indicators for predicting postoperative outcomes of patients with drug-resistant mesial temporal lobe epilepsy(DR-mTLE).Methods Totally 84 patients with unilateral DR-mTLE who underwent surgery were retrospectively enrolled and classified into seizure free(SF)group(n=55)and non-seizure free(NSF)group(n=29)according to one-year postoperative follow-up.Clinical data were analyzed to screen independent predictors of postoperative outcomes.Based on brain preoperative resting-state functional MRI,brain functional networks were constructed using graph theory analysis,and 587 features were extracted.Five-fold cross validation was used to divide the data into training set and test set,then the optimal brain functional network features related to postoperative outcomes of DR-mTLE patients were selected.Combining with clinically relevant independent predictors,ML models were constructed using classifiers including Gaussian process(GP),logistic regression(LR),support vector machine(SVM)and quadratic discriminant analysis(QDA),respectively,and the prediction efficacy,calibration and clinical value of each ML model were evaluated.Results Both course of disease and lesion location were clinically relevant independent predictors of postoperative outcome of DR-mTLE patients(OR=0.928,5.710,P=0.010,0.016).Four optimal brain function network features were selected,including betweenness centrality of the third zone of cerebellar vermis,degree centrality of right globus pallidus,nodal efficiency of temporal left inferior temporal gyrus and nodal clustering coefficient of left inferior parietal lobule.The average area under the curve(AUC)of GP,LR,SVM and QDA models in test set was 0.868,0.864,0.875 and 0.870,respectively.Calibration curves and decision curve analysis indicated that each ML model had good calibration and high clinical net benefit.Conclusion ML models based on brain functional network features combining with clinical indicators could be used to effectively predict postoperative outcomes in DR-mTLE patients.

Objective A scoping review of studies on symptom clusters in patients with chronic heart failure(CHF)was conducted to provide reference for the treatment and management of CHF.Methods According to reporting framework of scoping review put forward by Arksey,the related literatures of Cochrane Library,CINAHL,Web of Science,PubMed,Embase,Wanfang Data Knowledge Service Platform,SinoMed,CNKI and VIP from January 2014 to July 2024 were searched,and the contents of the literatures were screened,extracted and analyzed.Results Ten articles were included,involving many symptom clusters,mainly including mood,ischemia,congestion,digestive tract and fatigue.Symptom group assessment tool mainly adopted Chinese version of Memorial symptom assessment scale-heart failure.Conclusion There are various types of symptom clusters in CHF patients,and they show dynamic changes in each disease stage.It is still necessary to strengthen the research on the evaluation tools,occurrence principles and standardized naming of symptom clusters.Medical staff can give first-class care to the main symptom clusters in each period,formulate personalized nursing intervention measures in advance,and improve the efficiency of symptom management in clinical nursing.