OBJECTIVE To predict and verify the mechanism of Jiawei qifang weitong granule in ameliorating precancerous lesions of gastric cancer （PLGC）. METHODS Transcriptomic data of PLGC-related were obtained from the GEO database to screen differentially expressed genes and conduct functional enrichment analysis. Targets related to gastric mucosal injury repair were screened via the GeneCards database. The active ingredient targets of Jiawei qifang weitong granule were predicted using the TCMSP and other databases. The intersection of differentially expressed genes， gastric mucosal repair targets and active ingredient targets was extracted. A protein-protein interaction network was constructed based on the STRING database， and the top 6 hub target genes with the highest connectivity were screened. A PLGC rat model was established by combining 1-methyl-3-nitro-1-nitrosoguanidine with alternating fasting and full feeding. Model rats were divided into model group， weifuchun group （positive control， 0.40 g/kg）， low-， medium- and high-dose Jiawei qifang weitong granule groups （9.73， 19.44， 38.90 g/kg）， with 10 rats in each group. Another 10 unmodeled rats were set as control group. Rats in each group were intragastrically administered corresponding liquid medicine or normal saline once a day for 30 consecutive days. After the last administration， pathological morphological changes of rat gastric tissues were observed， and the mRNA expression levels of hub target genes as well as partial protein expression levels in gastric tissues were detected. RESULTS Transcriptomics and network pharmacology analyses revealed that the top 6 hub target genes ranked by connectivity included MMP9 （matrix metalloproteinase 9）， BCL2（B-cell lymphoma 2）， IL10（interleukin 10），ICAM1（intercellular adhesion molecule 1）， PTGS2 （prostaglandin-endoperoxide synthase 2）， and HIF1A （hypoxia-inducible factor 1α subunit）. Animal experiments showed that compared with the model group， gastric mucosal pathological injury was significantly alleviated in medium- and high-dose Jiawei qifang weitong granule groups and weifuchun group. The pathological injury score of gastric mucosa， the mRNA expression levels of MMP9， PTGS2， ICAM1， BCL2 and HIF1A， as well as the protein expression levels of PTGS2 and BCL2 in gastric tissues were significantly decreased （P＜0.05）， while the mRNA expression level of IL10 was significantly increased （P＜0.05）. CONCLUSIONS Jiawei qifang weitong granule may ameliorate PLGC by targeted down-regulating the mRNA expression of MMP9， PTGS2， ICAM1， BCL2 and HIF1A， up-regulating IL10 mRNA expression， and regulating the imbalance between gastric mucosal inflammatory response and injury repair.

ObjectiveTo analyze the risk factors for long-term cardiovascular events in patients undergoing long-term peritoneal dialysis （PD）， and to construct and validate a visual nomogram prediction model based on multiple parameters. MethodsA prospective cohort study was conducted， consecutively enrolling 248 maintenance PD patients （dialysis duration ≥ 3 months）. Demographic characteristics， clinical indicators， laboratory parameters， and echocardiographic indices （including left ventricular ejection fraction ［LVEF］， ratio of early diastolic mitral inflow velocity to early diastolic mitral annular velocity （E/e’）， etc.） were collected. The composite endpoint was defined as the occurrence of cardiovascular events or cardiovascular death， with non-cardiovascular death as the competing risk and loss to follow-up or the end of follow-up as censoring events. Fine-Gray competing risks model was used to screen independent predictors， based on which a nomogram model was constructed. Internal validation was performed using the Bootstrap method （1 000 resamplings）， and the concordance index （C-index） and time-dependent receiver operating characteristic （time-dependent ROC） curve were calculated to evaluate the model performance. ResultsWith a median follow-up of 29 months （interquartile range： 24–35 months）， 88 patients （35.48%） reached the composite endpoint， including 80 cases of cardiovascular events and 8 cases of cardiovascular death， and 4 patients died of non-cardiovascular causes. Multivariate Fine-Gray analysis revealed that age， diabetes mellitus， hemoglobin （HGB） level and E/e' ratio were independent influencing factors of the composite endpoint. Specifically， each 1-year increase in age was associated with a 3.0% increase in the risk of the composite endpoint （HR=1.030， P=0.006）； patients with diabetes mellitus had a 167.9% higher risk compared with non-diabetic patients （HR=2.679， P=0.007）； each 1g/L increase in HGB level contributed to a 1.5% reduction in the risk （HR=0.985， P=0.003）； and each 0.1 increase in E/e' ratio led to a 7.2% increase in the risk （HR=1.072， P=0.045）. The nomogram model had a C-index of 0.76 （95% CI： 0.698–0.820）， and the AUC of the time-dependent ROC curve reached 0.849 at 23 months of follow-up. ConclusionIncreased age， complicated with diabetes mellitus， decreased HGB， and elevated E/e' ratio are independent risk factors of long-term occurrence of cardiovascular events and cardiovascular death in patients undergoing long-term PD. The nomogram model constructed based on the above variables has good predictive value and clinical applicability， which can provide a reference for cardiovascular risk stratification and individualized intervention in long-term PD patients.

This article reports the diagnosis and treatment course of a patient with granulomatosis with polyangiitis (GPA) complicated with Kennedy disease (KD). The patient presented with headache, purulent and bloody rhinorrhea, ocular pain, decreased hearing and vision, multiple nodular erythema with ulcers on both lower extremities, and lower limb weakness. Chest imaging revealed a nodule with cavity formation in the right lung, and cytoplasmic anti-neutrophil cytoplasmic antibody (c-ANCA) and proteinase 3-antineutrophil cytoplasmic antibody (PR3-ANCA) were positive. The patient was initially diagnosed with granulomatosis with polyangiitis. After treatment with glucocorticoids and cyclophosphamide, the vasculitis-related symptoms were relieved while lower limb weakness got worse. Genetic testing identified 50 CAG repeats in exon 1 of the androgen receptor (AR) gene, and the final diagnosis was GPA complicated with KD. After rehabilitation therapy, the patient′s lower limb weakness improved. Methylprednisolone combined with leflunomide was administered for maintenance treatment, and the condition remained stable during long-term follow-up.

The ultrasound probe is the most important accessory in ultrasound equipment,which adopted the piezoelectric vibrator as its core component,and it can complete ultrasonic emission and receiving echoes.A deep understanding for the operational characteristics of the probe's array elements is important premise that user can accurately judge the running situation of the probe.This study employed pulse-echo technique to detect sensitivity and capacitance of the array elements of ultrasonic probe.The detected data of 60 probes at China-Japan Friendship Hospital were collected,and 75%of the 60 probes existed residual couplant,and 80%of them existed indentation or damage to the outer skin.The tested results of leakage current indicated that 1 probe(1.7%)existed excessively high leakage current.In test for array elements,2 probes(3.4%)displayed partial damage at array elements,and 6 probes(10.2%)were irreparably damaged.The preventive test for the diagnostic equipment with ultrasound is helpful to establish a quality control system for ultrasound,and to provide analysis materials with reliability in using diagnostic equipment with ultrasound,and to also provide decision-making basis for equipment departments and clinical department of using equipment in hospital.

BACKGROUND: Recent studies have provided compelling evidence that exposure to brominated flame retardants (BFRs) can adversely affect human health. We aim to explore the potential impact of BFRs on adiposity and central obesity. METHODS: Data from the National Health and Nutrition Examination Surveys (NHANES) cycles conducted between 2009 and 2014 was used to study the connections between variables. After filtering, we analyzed a sample of 4,110 adults aged 20 years and above. Our goal was to examine the potential association between BFRs and consequences and investigate the part played by oxidative stress and inflammatory markers as intermediaries. To achieve this, we used advanced statistical methods such as weighted quantile sum (WQS) regression, quantile-based g-computation (QGC), and the Bayesian kernel machine regression (BKMR). RESULTS: The findings showed that among the examined chemicals, exposure to PBDE85 (weight: 41%), PBDE100 (24%), and PBB153 (23%) may be the dominant contributors to general obesity risk. Upon controlling for all variables that could impact the results, it was found that the QGC outcomes indicated a positive correlation between exposure to mixtures of brominated flame retardants and the occurrence of abdominal obesity (OR = 1.187, 95% CI: 1.056-1.334, p = 0.004). Significant contributions were made by PBDE85 (52%), PBB153 (27%), and PBDE100 (21%). Mediation analysis shows that lymphatic cells (LC) and albumin (ALB) partially mediate the link between brominated flame retardants and obesity. The results of BKMR are generally consistent with those of WQS and QGC. CONCLUSION At a population level, our research has revealed a noteworthy correlation between BFRs and obesity. However, further investigation is required through prospective cohort studies and in-depth mechanistic exploratory studies.
Humans ; Flame Retardants/adverse effects* ; Oxidative Stress/drug effects* ; Adult ; Male ; Female ; Middle Aged ; Inflammation/epidemiology* ; Obesity/chemically induced* ; Biomarkers/blood* ; Nutrition Surveys ; Mediation Analysis ; Young Adult ; United States/epidemiology* ; Environmental Exposure/adverse effects* ; Aged ; Environmental Pollutants/adverse effects* ; Halogenated Diphenyl Ethers/adverse effects*

OBJECTIVES: To investigate the therapeutic mechanism of 2,6-dimethoxy-1,4-benzoquinone (DMQ) for alleviating dextran sulfate sodium (DSS)-induced ulcerative colitis (UC) in mice. METHODS: Eighteen male C57BL/6J mice were equally randomized into control group, DSS group and DMQ treatment group. In DSS and DMQ groups, the mice were treated with DSS in drinking water to induce UC, and received intraperitoneal injections of sterile PBS or DMQ (20 mg/kg) during modeling. The changes in body weight, disease activity index (DAI), colon length, spleen weight, and colon histological scores of the mice were examined, and the percentages of Th17 and IFN-γ⁺ CD8⁺ T cells in the mesenteric lymph nodes and spleen were analyzed using flow cytometry. The expressions of tight junction proteins (Occludin and ZO-1), proteins associated with inflammasome activation (caspase-1 and p20), IL-1β and TNF-α in the colon tissues were detected using Western blotting or ELISA. In the cell experiment, mouse bone marrow-derived macrophages (BMDMs) primed with lipopolysaccharide (LPS) were treated with DMQ, followed by stmulation with nigericin to activate the classical NLRP3 inflammasome pathway. In cultured human peripheral blood mononuclear cells (PBMCs) treated with either LPS alone or LPS plus nigericin, the effects of DMQ on inflammasome activation, pyroptosis, and cytokine release were evaluated via Western blotting, ELISA, and flow cytometry. RESULTS: In DSS-treated mice, DMQ treatment significantly alleviated DSS-induced body weight loss, colon shortening, spleen enlargement, and colon inflammation. The DMQ-treated mice showed significantly reduced percentages of Th17 cells and IFN-γ⁺ CD8⁺ T cells in the mesenteric lymph nodes and spleen, with increased occludin and ZO-1 expressions and decreased caspase-1 expression in the colon tissue. DMQ obviously inhibited classical NLRP3 inflammasome activation in mouse BMDMs and both the classical and alternative pathways of NLRP3 activation in human PBMCs, causing also suppression of caspase-1-dependent pyroptosis. CONCLUSIONS DMQ ameliorates DSS-induced UC in mice by inhibiting NLRP3 inflammasome activation.
Animals ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Mice, Inbred C57BL ; Colitis, Ulcerative/metabolism* ; Dextran Sulfate/adverse effects* ; Male ; Inflammasomes/metabolism* ; Mice ; Benzoquinones/therapeutic use* ; Th17 Cells ; Caspase 1/metabolism*

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases. Accurately predicting microbe-disease interactions (MDIs) offers critical insights for disease intervention and pharmaceutical research. Current advanced AI-based technologies automatically generate robust representations of microbes and diseases, enabling effective MDI predictions. However, these models continue to face significant challenges. A major issue is their reliance on complex feature extractors and classifiers, which substantially diminishes the models' generalizability. To address this, we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs. Initially, we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation. Secondly, we employ decoupled representation learning technology, compelling the graph neural network (GNN) to independently learn the weights for each feature subspace, thus enhancing its expressive power. Finally, we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN, reducing information loss due to occlusion. Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models. This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research. Code and data are accessible at: https://github.com/shmildsj/MDI-IFDRL.

In general, bioassay-guided fractionation and isolation of bioactive constituents from botanical materials frequently ended up with the reward of a single compound. However, botanical materials typically exert their therapeutic actions through multi-pathway effects due to the intrinsic complex nature of chemical constituents. In addition, the content of bioactive compounds in botanical materials is largely dependent on humidity, temperature, soil, especially geographical origins, from which rapid and accurate identification of plant materials is pressingly needed. These long-standing obstacles collectively impede the deep exploitation and application of these versatile natural sources. To address the challenges, a new paradigm integrating biogravimetric analyses and machine learning-driven origin classification (BAMLOC) was developed. The biogravimetric analyses are based on absolute qHNMR quantification and in vivo zebrafish model-assisted activity index calculation, by which bioactive substance groups jointly responsible for the bioactivities in all fractions are pinpointed before any isolation effort. To differentiate origin-different botanical materials varying in the content of bioactive substance groups, principal component analysis, linear discriminant analysis, and hierarchical cluster analysis in conjunction with supervised support vector machine are employed to classify and predict production areas based on the detection of volatile organic compounds by E-nose and GC-MS. Expanding BAMLOC to Codonopsis Radix enables the identification of polyacetylenes and pyrrolidine alkaloids as the bioactive substance group for immune restoration effect and accurately determines the origins of plants. This study advances the toolbox for the discovery of bioactive compounds from complex mixtures and lays a more definitive foundation for the in-depth utilization of botanical materials.

Objective To compare the changes in biological indicators of human corneal epithe-lial(HCET)cells and rabbit corneas after exposure to different doses of ultraviolet B(UVB)radia-tion,so as to evaluate the impact of UVB radiation on corneal injury effects.Methods In cell exper-iment,HCET cells were divided into groups with radiation doses of 0,6,12,18,and 24 mJ/cm2.The effect of UVB radiation on HCET cell viability was detected using the CCK-8 assay,and the level of intracellular DNA damage was assessed by immunofluorescence.In the animal experiment,15 healthy New Zealand white rabbits(30 eyes)were randomly divided into groups with radiation doses of 0,1.35,2.16,4.32,and 6.48 J/cm2.The UVB exposure time for the radiation groups was 30 minutes per day for 3 consecutive days.Corneal injury was evaluated using methods such as slit-lamp microscopy,sodium fluorescein staining,central corneal thickness measurement,optical coherence tomography(OCT)imaging,and hematoxylin and eosin(HE)staining.Results Compared with the control group,cell viability in the radiation groups gradually decreased,and the level of DNA damage gradually increased with increasing radiation dose.As the radiation dose increased in the radiation groups,the degree of corneal opacity in rabbits gradually worsened,the central corneal area gradu-ally thickened,and OCT revealed high-intensity scattered light signals with the formation of shadow areas.Results from HE staining,immunohistochemistry,Western blot(WB),and sodium fluores-cein staining showed that the 1.35 J/cm2 group caused mild corneal injury,with damage reaching the corneal epithelial layer.In the 2.16 J/cm2 group,the corneal injury presented as dense punctate distribution,with damage extending from the epithelial layer to the superficial stroma.The number of ephrin type-A receptor 2(EphA2)protein-stained cells was relatively small,and the staining was light,showing a weak positive result.In the 4.32 J/cm2 and 6.48 J/cm2 groups,the corneal injury was irreversible,with damage gradually progressing from the corneal epithelial layer and superficial stroma to the endothelial layer.The number of EphA2 protein-stained cells was relatively large,and the staining was dark,showing a strong positive result.Conclusion This study comprehensively e-valuates the dose-dependent injury effects of UVB on HCET cells and New Zealand white rabbit cor-neas through cell and animal experiments.It elucidates that UVB radiation could induce corneal cell DNA damage,promote inflammatory responses,and trigger apoptosis by upregulating γ-phosphoryla-ted histone H2AX(γH2AX)and EphA2.The self-repair ability and process of corneal injury are preliminarily explored,providing a basis for further research on mechanisms of corneal injury caused by ultraviolet radiation and the development of protective drugs.

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insights for disease intervention and pharmaceutical research.Current advanced AI-based technologies automatically generate robust representations of microbes and diseases,enabling effec-tive MDI predictions.However,these models continue to face significant challenges.A major issue is their reliance on complex feature extractors and classifiers,which substantially diminishes the models' generalizability.To address this,we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer po-tential MDIs.Initially,we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation.Secondly,we employ decoupled representation learning technology,compelling the graph neural network(GNN)to independently learn the weights for each feature subspace,thus enhancing its expressive power.Finally,we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN,reducing information loss due to occlusion.Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models.This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research.Code and data are accessible at:https://github.com/shmildsj/MDI-IFDRL.