ObjectiveTo investigate the liver-protecting and anti-liver fibrosis effects of astragaloside Ⅳ （AS-Ⅳ） in vitro and in vivo， as well as its mechanism of action in intervention against liver fibrosis. MethodsIn the animal experiment， C57BL/6J mice were divided into control group， model group， low-dose AS-Ⅳ （20 mg/kg） group， and high-dose AS-Ⅳ （80 mg/kg） group. The mice were given intraperitoneal injection of carbon tetrachloride for 6 weeks to induce liver fibrosis， and since week 3 of injection， the mice in the low-dose AS-Ⅳ group and the high-dose AS-Ⅳ group were given AS-Ⅳ by gavage at a dose of 20 mg/kg and 80 mg/kg， respectively. The serum levels of alanine aminotransferase （ALT） and aspartate aminotransferase （AST） were measured after 4 weeks of administration， as well as the serum levels of hyaluronic acid （HA）， laminin （LN）， procollagen Ⅲ N-terminal peptide （PⅢNP）， and collagen type Ⅳ （Col-Ⅳ）. HE staining， picrosirius red staining， and Masson staining were used to observe liver histopathology and collagen deposition； RT-qPCR was used to measure the mRNA expression levels of Acta2， Col1a1， and Col3a1 in liver tissue， and Western blot was used to measure the protein expression levels of α-smooth muscle actin （α-SMA）， collagen type Ⅲ （Col-Ⅲ）， phosphatidylinositol 3-kinase （PI3K）， phosphorylated PI3K （pPI3K）， protein kinase B （Akt）， and phosphorylated AKT （p-Akt） in liver tissue； transcriptome sequencing was performed for liver tissue to identify differentially expressed genes and perform a bioinformatics analysis. In the cell experiment， transforming growth factor-β （TGF-β） was used to induce the activation of LX-2 cells， and the PI3K inhibitor LY294002 and the PI3K activator 740 Y-P were used for intervention. The cells were divided into control group， model group， AS-Ⅳ group， LY294002 group， and AS-Ⅳ+740 Y-P group， and the cells were harvested after 36 hours of intervention. Changes in the protein expression levels of α-SMA， Col-Ⅲ， pPI3K/PI3K， and pAkt/Akt in LX-2 cells were measured， as well as changes in the relative mRNA expression levels of Acta2， Col1a1， and Col3a1. A one-way analysis of variance was used for comparison of continuous data between multiple groups， and the least significant difference t-test was used for further comparison between two groups. ResultsIn the animal experiment， compared with the model group， the AS-Ⅳ treatment group had significant reductions in the serum levels of ALT， AST， HA， LN， PⅢNP， and Col-Ⅳ （all P<0.01）， the mRNA expression levels of Acta2， Col1a1， and Col3a1 in liver tissue （all P<0.05）， and the protein expression levels of α-SMA， Col-Ⅲ， pPI3K， and pAkt （Ser473） in liver tissue （all P<0.05）. In the cell experiment， compared with the control group， the model group had significant increases in the protein expression levels of α-SMA， Col-Ⅲ， pPI3K， and pAkt （Ser473） after TGF-β induction （all P<0.05）； compared with the model group， the AS-Ⅳ group had significant reductions in the protein expression levels of α-SMA， Col-Ⅲ， pPI3K， and pAkt （Ser473） （all P<0.05）， and both the AS-Ⅳ group and the LY294002 group had significant reductions in the protein expression level of pPI3K and the relative mRNA expression levels of Acta2， Col1a1， and Col3a1 （all P<0.05）. Compared with the AS-Ⅳ group， there were significant increases in the protein expression level of pPI3K and the relative mRNA expression levels of Acta2， col1a1， and Col3a1 after 740 Y-P intervention （all P<0.05）. ConclusionAS-Ⅳ can inhibit hepatic stellate cell activation and improve liver fibrosis， possibly by inhibiting the PI3K/Akt signaling pathway.

Objective To summarize the clinical and genetic characteristics of end-stage renal disease caused by PLCE1 gene mutations.Methods A retrospective analysis of the clinical and genetic features of three children from a family with PLCE1 gene mutations was conducted,along with a literature review of hereditary kidney disease cases caused by PLCE1 gene mutations.Results The proband was an 8-year-old male presenting with nephrotic syndrome stage 4 chronic kidney disease.Renal biopsy showed focal segmental glomerulosclerosis.Two years and five months after kidney transplantation,the patient had persistent negative proteinuria and normal renal function.Whole-exome sequencing identified two pathogenic heterozygous variants:c.961C>T and c.3255_3256delinsT,with c.3255_3256delinsT being a novel mutation.Family screening revealed no renal involvement in the parents,but among five siblings,one brother died at age of 4 years from end-stage renal disease.A 7-year-old sister presented with proteinuria and bilateral medullary sponge kidney,with proteinuria resolving after one year of follow-up.A 3-year-old brother died after kidney transplantation due to severe pneumonia.The literature review included 45 patients with hereditary kidney disease caused by PLCE1 gene mutations.The main clinical phenotype was nephrotic syndrome(87%,39/45),and renal pathology predominantly showed focal segmental glomerulosclerosis(57%,16/28).No mutation hotspots were identified.Conclusions Compound heterozygous mutations in the PLCE1 gene can lead to rapid progression of the disease to end-stage renal disease,with favorable outcomes following kidney transplantation.Family screening is crucial for early diagnosis,and medullary sponge kidney may be a novel phenotype associated with these gene mutations.Citaion:[Chinese Journal of Contemporary Pediatrics,2025,27(5):580-587]

Traditional Chinese medicine(TCM)exerts integrative effects on complex diseases owing to the char-acteristics of multiple components with multiple targets.However,the syndrome-based system of diagnosis and treatment in TCM can easily lead to bias because of varying medication preferences among physicians,which has been a major challenge in the global acceptance and application of TCM.Therefore,a standardized TCM prescription system needs to be explored to promote its clinical application.In this study,we first developed a gradient weighted disease-target-herbal ingredient-herb network to aid TCM formulation.We tested its efficacy against intracerebral hemorrhage(ICH).First,the top 100 ICH targets in the GeneCards database were screened according to their relevance scores.Then,SymMap and Traditional Chinese Medicine Systems Pharmacology(TCMSP)databases were applied to find out the target-related ingredients and ingredient-containing herbs,respectively.The relevance of the resulting ingredients and herbs to ICH was determined by adding the relevance scores of the corresponding targets.The top five ICH therapeutic herbs were combined to form a tailored TCM prescriptions.The absorbed components in the serum were detected.In a mouse model of ICH,the new prescription exerted multifaceted effects,including improved neurological function,as well as attenuated neuronal damage,cell apoptosis,vascular leakage,and neuroinflammation.These effects matched well with the core pathological changes in ICH.The multi-targets-directed gradient-weighting strategy presents a promising avenue for tailoring precise,multipronged,unbiased,and standardized TCM prescriptions for complex diseases.This study provides a paradigm for advanced achievements-driven modern innovation in TCM concepts.

Objective:To investigate the value of coronary CTA (CCTA)-based traditional features and radiomics of plaque in the identification of culprit lesions that caused acute myocardial infarction (AMI).Methods:This was a retrospective multicenter study. From July 2016 to November 2023, a total of 344 patients from the Affiliated Hospital of Qingdao University (training cohort, n=184), Shandong Provincial Hospital Affiliated to Shandong First Medical University (validation cohort, n=88) and Qilu Hospital of Shandong University (test cohort, n=72) who received percutaneous coronary intervention (PCI) due to AMI and underwent CCTA within 48 hours of AMI were enrolled. The culprit plaques and non-culprit plaques were identified using a combination of electrocardiogram, CCTA, and angiographic findings. The vessel, plaque location, plaque type, Coronary Artery Disease-Reporting and Data System (CAD-RADS) score, high-risk plaque characteristics, plaque length, plaque volume, and burden were analyzed, and 1 904 radiomics features were extracted for each plaque. The traditional imaging model, the radiomics model, and the combined model were established by using multivariate Logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each model in identifying culprit lesions. The DeLong test was used for the comparison of AUC between every two models. The net reclassification index (NRI) was used to evaluate the incremental value of the combined model to the traditional imaging model and the radiomics model. The decision curve analysis (DCA) was used to assess the clinical net benefit of these models. A correlation heatmap was used to evaluate the correlation between the radiomics score and traditional CCTA factors. The interpretable analysis of the decision process of the combined model was performed by the Shapley Additive exPlanations (SHAP). Results:In the validation cohort and the test cohort, the AUC of the traditional imaging model developed by the vessel, plaque type, positive remodeling and CAD-RADS score was 0.898 (95% CI 0.869-0.922) and 0.881 (95% CI 0.848-0.910), respectively. The radiomics model developed by six radiomics features was 0.863 (95% CI 0.831-0.891) and 0.863 (95% CI 0.827-0.864), respectively. The AUC of the combined model was 0.930 (95% CI 0.905-0.950)and 0.919 (95% CI 0.889-0.942), respectively. In the validation cohort and the test cohort, the AUC of the combined model was higher than that of the traditional imaging model ( Z=4.013, 4.272, P<0.001) and that of the radiomics model ( Z=4.819, 3.784, P<0.001), respectively. In the validation cohort, the combined model yielded an NRI of 20.43% (95% CI 10.43%-30.44%, P<0.001) and 20.21% (95% CI 9.62%-30.80%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. In the test cohort, the combined model yielded an NRI of 28.05% (95% CI 16.72%-39.38%, P<0.001) and 23.57% (95% CI 13.58%-33.56%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. DCA showed the combined model had the highest clinical net benefit. The correlation heatmap showed the radiomics score was not correlated or only weakly correlated with traditional CCTA factors. SHAP indicated the radiomics and CAD-RADS score contributed significantly to the model. Conclusion:The CCTA-based traditional features and radiomics of plaque have favorable performance for the identification of culprit plaques in patients with AMI.

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.

An assay was established for detection of toxigenic Clostridioides difficile by combining microfluidic chip analysis with immunochromatography,and its performance was evaluated and compared with those of the Xpert C.difficile/Epi and VIDAS CD AB tests.Primer pairs were designed according to the tcdB and tpi genes in C.difficile.The specificity,limit of detection,reproducibility,and stability were evaluated.A total of 215 stool samples from patients with diarrhea were collected and tested in parallel with the Xpert C.difficile/Epi,VIDAS CDAB,and our assay.C.difficile was isolated from samples,and the tcdB gene was identified when discrepant results were obtained from the three above assays.Our assay showed no cross-reaction with other diarrhea-associated pathogens.Its reproducibility was 100％in testing of two standard plasmids containing tcdB and tpi genes at two concentrations(105 and 102 copies/μL).Two standard plasmids were detected after the PCR and immunochromatography reagents had been stored for 3,6,9,and 12 months,and all the results were posi-tive.The limit of detection was 10 copies/μL for toxigenic C.difficile.Testing of 33 samples positive for C.difficile with our assay(33/215,15.3％)yielded findings statistically coherent with those of the Xpert C.difficile/Epi test(kappa value=0.965).The sensitivity,specificity,positive predictive value,and negative predictive value of our assay,with respect to Xpert C.difficile/Epi as the standard,were 94.3％,100.0％,100.0％,and 98.9％;these values were significantly higher than those of VIDAS CDAB(60.0％,98.9％,91.3％,and 92.7％)(Kappa=0.714,OR=157.50,95％CI:62.03-847.28,P=0.013).In conclusion,our newly developed assay is specific,stable,and reproducible,and may be used for rapid and accu-rate detection of toxigenic C.difficile.The assay could be used for C.difficile infection screening in outpatient and emergen-cy,community medical service center,and epidemiological settings.

This study was aimed at exploring novel auxiliary diagnostic biomarkers for brucellosis and their potential miR-NA-mRNA regulatory networks.High-throughput sequencing was used to compare miRNA expression differences in serum ex-osomes between patients with brucellosis and healthy controls.Subsequently,RT-qPCR was used to validate the expression of significantly upregulated exosomal miRNAs.The diagnostic value of these miRNAs was assessed with ROC curves,and bioin-formatics analyses were performed to investigate the potential roles of the miRNAs in brucellosis infection.The ROC curve a-nalysis indicated that the area under the curve for exosomal hsa-miR-11400(P＜0.05),hsa-miR-199a-5p(P＜0.05),and hsa-miR-148a-5p(P＜0.05)was 0.79,0.81,and 0.74,respectively.A total of 465 differentially expressed miRNAs and their tar-get genes were predicted,including 25 immune-related target genes,most of which were closely associated with cancer-related proteoglycans,NF-kappa B signaling pathways,and IL-17 signaling pathways.The constructed differentially expressed gene network indicated that the immune genes PLXNA2,IL17RA,PRKCA,CD22,ACVR1B,and CBL might be regulated by hsa-miR-199a-5p and hsa-miR-148a-5p.These findings suggest that exosomal miRNAs might serve as auxiliary diagnostic indicators for brucellosis.Our exosomal miRNA-mRNA regulatory network provides new insights into the pathogenesis and treatment of brucellosis.

In this study,an ON/OFF type micro-valve with a sandwich(glass-silicon-glass)structure was designed and fabricated based on the micro-electro-mechanical system(MEMS)technique.The deformable membrane of this micro-valve was prepared on the silicon on insulator(SOI)substrate and sealed using Si-Si bonding and anodic bonding methods.The micro-valve had high-temperature stability and was suitable for integration with other gas chromatography components.The deformable membrane with a thickness of 10 μm was processed on the top silicon of the SOI substrate.The flow control of the micro-valve could be achieved by changing the driving pressure applied to the deformable membrane to deform it.Compared with polymer membranes,the deformable membrane prepared on the top layer silicon of SOI had better temperature stability and could be released using the deep reactive ion etching technique after silicon-silicon bonding,avoiding deformation during the preparation process.In addition,due to the small gap between the membrane and the inlet/outlet holes,the dead volume of the microvalve was very small.The test results indicated that the micro-valve achieved flow control and ON/OFF functions with good repeatability.

A monolithic integrated gas chromatography chip,consisting of a micro gas chromatography column(μGCC)and a micro helium discharge ionization detector(μHDID)was proposed.The chip was fabricated using micro electromechanical system(MEMS)technique,and its sensitivity was improved from two aspects.On one hand,open tubular column was selected as the separation device,and the auxiliary helium channel width of μHDID was modulated based on the microchannel width of the μGCC to match the flow rates of μHDID and μGCC.On the other hand,the electrode structure inside the μHDID collection zone was optimized,a bias electrode group around the collection electrode was constructed,and the ion collection efficiency was improved.After coating HKUST-1 as the stationary phase,the monolithic integrated gas chromatography chip could achieve baseline separation and detection of light hydrocarbon gas mixture(methane,ethane,propane,andn-butane),with a detection limit for propane as low as 25 pg.The chip could carried out test under temperature-programmed conditions,with a resolution of 9.24 for ethane and propane.