Diabetic kidney disease （DKD）， a common complication of diabetes mellitus， is a leading global cause of end-stage renal disease （ESRD）. Current therapeutic strategies primarily focus on symptomatic management but exhibit limited efficacy in halting disease progression to ESRD， and some drugs carry non-negligible toxic side effects. Traditional Chinese medicine （TCM） has a long history in treating DKD， with single TCM and TCM compounds demonstrating unique advantages in multi-target， multi-pathway， and multi-effect therapeutic interventions. Tripterygium wilfordii （TW）， known for its effects in promoting blood circulation， dredging collaterals， dispelling wind， removing dampness， reducing swelling， and alleviating pain， contains bioactive components such as Tripterygium glycosides （TWG）， triptolide （TPL）， tripdiolide （TPD）， and celastrol （CEL）. The active ingredients possess various functions， including regulating immune-inflammatory balance， ameliorating renal fibrosis and glomerulosclerosis， combating oxidative stress， protecting podocytes， and improving glucose and lipid metabolism， all of which play a significant role in the treatment of DKD. This review summarized the mechanisms underlying the therapeutic effects of T. wilfordii and its active ingredients on DKD， aiming to provide insights for clinical management and novel drug development of DKD.

Hepatocellular carcinoma （HCC）， the predominant subtype of primary liver cancer， ranks among the top in both incidence and mortality rates of malignant tumors in China. In its early stages， the disease may present with subtle or nonspecific symptoms， often leading to poor clinical prognosis and low patient survival rates， which makes it a significant public health concern. The pathogenesis is associated with multiple factors， including hepatitis virus infection， alcohol consumption， obesity， drug-induced liver injury， and immune disorders， which may interact synergistically to promote disease development. Currently， mainstream therapeutic approaches for HCC in modern medicine encompass surgical resection， liver transplantation， radiofrequency ablation， radiotherapy， and chemotherapy， but they all have certain limitations， such as large side effects and poor prognosis， imposing substantial psychological distress and financial strain on affected individuals. With a rich historical background in hepatic malignancy management， traditional Chinese medicine offers therapeutic benefits characterized by multi-targeted mechanisms， multi-level regulation， minimal adverse effects， and reduced likelihood of disease recurrence. It can not only enhance the curative effect， but also reduce the side effects of radiotherapy， chemotherapy， and surgery. Thus， it has attracted widespread attention. Extensive research has demonstrated that traditional Chinese medicine exhibits significant antitumor properties， along with notable anti-inflammatory and oxidative stress-reducing capabilities， and its mechanism may be related to the regulation of nuclear factor-kappa B （NF-κB） signaling pathway， which can affect multiple stages of hepatocarcinogenesis， such as cell proliferation， invasion， metastasis， and apoptosis. The mechanism of NF-κB signaling pathway in traditional Chinese medicine for HCC treatment has emerged as one of the pivotal research directions in current oncology studies. Based on the existing research foundation， a systematic literature review method was adopted to retrieve and analyze relevant Chinese and English literature in recent years. Integrating the molecular regulatory mechanisms of the NF-κB signaling pathway and its pivotal role in HCC pathogenesis and progression helped further explore the latest research advances in traditional Chinese medicine interventions targeting this pathway for HCC treatment. This approach may provide novel theoretical foundations and translational strategies for the prevention and management of HCC using traditional Chinese medicine.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

ObjectiveTo observe the influence and therapeutic effect of quercetin on cuproptosis in rheumatoid arthritis rats and to explore its possible mechanism based on the solute carrier family 31 member 1 （SLC31A1）/ferredoxin 1 （FDX1） pathway. MethodsSixty male SD rats were divided into six groups： A control group， a model group， high- and low-dose quercetin groups （150 and 50 mg·kg^-1）， a cuproptosis inhibitor （tetrathiomolybdate， TTM） group （10 mg·kg^-1）， and a methotrexate group （2 mg·kg^-1）， 10 rats in each group. Except for the control group， the model of rheumatoid arthritis （CIA） rats was established by type Ⅱ collagen induction method. After successful modeling， each drug group was intervened according to the corresponding dose of drugs， and the control group and the model group were given the same amount of normal saline by gavage， once a day， which lasted for 4 weeks. The swelling degree of rats' feet was observed， and the clinical arthritis scores were determined. The levels of serum rheumatoid factor （RF）， matrix metalloproteinase-3 （MMP-3）， tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， interleukin-10 （IL-10）， and ceruloplasmin （Cp） were detected by enzyme-linked immunosorbent assay （ELISA）. The content of copper ion （Cu）， malondialdehyde （MDA）， superoxide dismutase （SOD）， and glutathione （GSH） in joint tissue was detected. Hematoxylin-eosin （HE） staining was used to observe the pathological changes of joint tissue. The levels of reactive oxygen species （ROS） and dihydrolipoic acid transacetylase （DLAT） were detected by immunofluorescence （IF）. The protein and mRNA expression of SLC31A1， FDX1， lipoic acid synthase （LIAS）， heat shock protein 70 （HSP70）， pyruvate dehydrogenase E1 subunit β （PDHB）， and copper transporting P-type ATPase β （ATP7B） was detected by immunohistochemistry （IHC） and real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）. ResultsCompared to the control group， the model group exhibited joint swelling and deformity， significantly increased clinical arthritis scores， obvious bone destruction， synovial hyperplasia， and inflammatory cell infiltration in joint tissue. In addition， the serum levels of RF， MMP-3， TNF-α， IL-1β， and Cp showed significant elevation， while the level of IL-10 was significantly reduced. The levels of Cu， MDA， ROS， and DLAT in joint tissue were markedly increased， whereas SOD and GSH content was significantly decreased. The protein and mRNA expression of SLC31A1 and HSP70 was significantly up-regulated， while the protein and mRNA expression of FDX1， LIAS， PDHB， and ATP7B was significantly down-regulated （P<0.01）. Compared to the model group， each treatment group exhibited varying degrees of improvement in joint swelling and deformation as well as clinical arthritis scores in rats. Additionally， there was a reduction in joint bone destruction， inflammatory cell infiltration， and synovial hyperplasia in rats. Furthermore， the serum levels of RF， MMP-3， TNF-α， IL-1β， and Cp significantly decreased， while the level of IL-10 increased significantly. In joint tissue， the levels of Cu， MDA， ROS， and DLAT showed significant decreases， while SOD and GSH content exhibited significant increases. The protein and mRNA expression of SLC31A1 and HSP70 was down-regulated， while the protein and mRNA expression of FDX1， LIAS， PDHB， and ATP7B was up-regulated （P<0.05）. ConclusionQuercetin effectively reduces synovial hyperplasia and inflammatory infiltration in rats with rheumatoid arthritis， thereby alleviating pathological damage to joint tissue. This effect may be attributed to the blockade of the SLC31A1/FDX1 signaling pathway activation and inhibition of excessive cuproptosis.

Objective To draw the genome-wide distribution and remodeling characteristics of H3K27me3 silencers in signet-ring cell carcinoma of the stomach(SRCC)through epigenetic sequencing technology,and to investigate their roles in transcriptional regulation in order to elucidate the regulatory mechanism of SRCC malignant progression.Methods The study was conducted on 35 gastric samples obtained by gastroendoscopic biopsy(15 normal and 20 SRCC tissues)from Department of Gastroenterology of Army Medical Center of PLA between January 2021 and December 2023.Multi-omics analyses,including assay for transposase-accessible chromatin with high-throughput sequencing(ATAC-seq),cleavage under targets and tagmentation(CUT&Tag)and transcriptome sequencing(RNA-seq),were performed to identify chromatin accessibility,H3K27me3 silencer regions,and transcriptional changes,with aid of Illumina NovaSeq 6000.H3K27me3 related differentially expressed genes(|Log2FC|>1,FDR<0.05)were screened using DESeq2.Gene Ontology(GO)analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)analysis were employed to analyze the enrichment function,and Homer was employed to identify transcription factor motifs.A regulatory network was constructed using Cytoscape,and then validated using immunohistochemistry to explore its regulatory mechanism.Results H3K27me3 silencers were primarily located in distal intergenic regions(37.06%)in SRCC.Compared with the normal tissues,SRCC showed a significant reduction in H3K27me3 silencer signals(95%CI:1.34～2.30,P=0.007)with 6 257 lost sites(FDR<0.01).Integrating CUT&Tag and RNA-seq revealed 380 up-regulated immune-related genes,particularly in T cell receptor signaling(OR=4.2,95%CI:2.8～6.3,P=0.002).Immunohistochemistry confirmed elevated expression of transcription factor EHF(P<0.05).Conclusion There is the remodeling of H3K27me3 silencers in SRCC,and EHF may potentially play a crucial role in the SRCC malignant progression.

″Boundarics in biomedicine″(or″Biomedical boundarics″)is an emerging frontier interdisciplinary subject that focuses on addressing key scientific issues related to the formation,identification,and evolution of biological boundaries within living organisms.In this field,the study of tumor boundaries is of particular importance.Imaging tumor boundaries not only helps to reveal the molecular mechanisms of tumor boundary evolution and interaction with the microenvironment,tumor invasion and metastasis,but is also crucial for clinical tumor diagnosis,treatment decision-making,efficacy monitoring and prognosis evaluation.Molecular probes,as functional substances that enhance imaging signals,play a crucial role in tumor boundary recognition.In this article,the basic concepts and research significance of boundarics in biomedicine and tumor boundarics in biomedicine were summarized firstly.Then a comprehensive review of the research progress in tumor boundary imaging molecular probes was provided,covering areas such as magnetic imaging,optical imaging,acoustic imaging,nuclear imaging,and multimodal imaging.The strategies to regulate the sensitivity,specificity,and safety of molecular probes through chemical structure modifications,conjugation with targeting ligands,and tumor microenvironment-responsive designs were emphasized.Finally,the research trends of molecular probes for tumor boundary imaging were analyzed,and the challenges faced in this field and the future research directions were discussed.

After the wooden shipwreck was recovered from the marine underwater environment,the wooden components undergo varying degrees of degradation,therefore,accurately determining the extent of degradation is a fundamental scientific issue for implementing effective preservation strategies.In this work,the wooden remains of Pinus massoniana excavated from the"Nanhai No.1"shipwreck(Southern Song Dynasty)were investigated and compared with the modern wood to discriminate the degradation levels of archaeological wood using attenuated total reflection Fourier transform infrared(ATR-FTIR)spectroscopy.The residual sugar content within wood cell walls was determined using a non-invasive automated microscale ATR-FTIR method to extract chemical information from the wood tangential section.Microstructural characterization of wood samples was conducted by super depth of field microscopy and scanning electron microscopy.FTIR spectral analysis was performed to evaluate the degradation state and elucidate changes in cellulose crystallinity.Finally,the combination of FTIR spectroscopy with the sparse partial least squares discriminant analysis(sPLS-DA)model facilitated the rapid discrimination of degradation levels in shipwreck archaeological wood,and the performance of the model was evaluated using receiver operating characteristic(ROC)curves and area under the curve(AUC).The results showed that the higher the degree of wood degradation,the lower the residual sugar content in the wood cell wall,and the residual glucose content of highly degraded wood was only 4.7％.Significant differences were observed in both the tangential section microstructure and FTIR characteristic absorption patterns across degradation levels,and as the degradation advanced,progressive cell wall loosening occurred alongside selective removal of polysaccharide components,and the relative lignin content was increased,resulting in an elevated A1509/A1370 ratio in FTIR spectra.The sPLS-DA model achieved excellent discrimination performance with AUC values exceeding 0.9,confirming that the combination of FTIR spectroscopy with sPLS-DA enabled accurate assessment of degradation levels in shipwreck archaeological wood.This study developed a rapid and accurate methodology for assessing degradation levels in shipwreck archaeological wood based on microscale ATR-FTIR spectroscopy,which would help to promote the accurate assessment of the preservation state of waterlogged wooden artifacts.

Due to the poor ionization efficiency and the weak mass spectrometry(MS)intensity of weakly polar substances,direct analysis using the traditional electrospray ionization mass spectrometry(ESI-MS)is a big challenge.In this study,a novel rapid on-site detection method of four prohibited sex hormones in cosmetics was proposed using online derivatization strategy coupled with a miniature mass spectrometer.The target substances in the samples were extracted by a custom-made polyaniline/multi-walled carbon nanotube solid-phase microextraction(SPME)probe.The stirring speed was 200 r/min,the extraction temperature was 40℃,and the extraction time was 2 min.A pulled dual-channel θ borosilicate glass capillary emitter was used as the nano-ESI ion source.The SPME probe was inserted into the channel containing methanol in theθborosilicate glass capillary.When the spray voltage was applied,the four sex hormones were desorbed and formed spray microdroplets,which then collided with the hydroxylamine microdroplets generated from the other channel.The microdroplets of reaction product entered into the miniature mass spectrometer for direct analysis.The limits of detection(LOD)and limits of quantification(LOQ)for the four sex hormones were 10-20 ng/mL and 20-50 ng/mL,respectively.The recoveries were from 84.6%to 107.8%with the relative standard deviations(RSD)from 4.1%to 11.6%.Compared to detection without derivatization,the MS signals of the four target substances were increased by 3 to 15 times.This method was simple,rapid,highly efficient and sensitive,and suitable for on-site rapid analysis of weakly polar sex hormones in cosmetics.

Acute poisoning in children is a significant global public health challenge，posing serious threats to the health and safety of infants and preschool-aged children. This review systematically summarizes recent advances in the field，including trends in epidemiology，causes，diagnostic and therapeutic techniques，and prevention strategies. Studies indicate that pediatric acute poisoning is significantly influenced by factors such as age，sex，geographic region，and type of toxic agent. Accidental ingestion of medications and household chemicals is more common in infants and young children，whereas intentional poisoning predominates among adolescents. With the emergence of novel toxic substances，pediatric poisoning has garnered increasing attention. Rapid diagnostic techniques，biomarker identification，and intelligent medical interventions have markedly improved diagnostic efficiency. Advances in antidote development，blood purification therapies，and extracorporeal membrane oxygenation have further contributed to improved clinical outcomes. This review underscores the importance of establishing a comprehensive management system encompassing prevention，early recognition，and effective treatment，aiming to enhance the efficiency and safety of pediatric poisoning care through systematic and integrated approaches.