OBJECTIVE To investigate the therapeutic effect and mechanism of Qiwei dongqingye powder （QDP） on bronchial asthma in mice. METHODS The mice were divided into blank group （normal saline）， model group （normal saline）， dexamethasone group （2 mg/kg）， and QDP low-， medium-， and high-dose groups （200， 400， 800 mg/kg）， with 14 mice in each group. Except for the blank group， mice in all other groups were given ovalbumin via intraperitoneal injection followed by aerosol inhalation to induce a bronchial asthma model. During the modeling process， mice in each group were administered corresponding drug solutions or normal saline intragastrically/intraperitoneally. After the last medication， the number of cells in the bronchoalveolar lavage fluid （BALF） of the mice was observed and counted； the pathological changes of the bronchus and lung tissue were observed； the levels of malondialdehyde （MDA）， nitric oxide （NO）， total superoxide dismutase （T-SOD）， and glutathione peroxidase （GSH-Px） in the lung tissue of the mice were determined， and the level of interleukin-17 （IL-17） in the BALF and serum was determined. Transcriptomics was employed to predict and validate the mechanism of action of QDP against bronchial asthma. RESULTS Compared with the model group， the total cell count， neutrophil count， lymphocyte count， and macrophage counts in the BALF of the QDP high-dose group were all significantly reduced （ P ＜0.05）； the levels of MDA and NO in the lung tissue， and the levels of IL-17 in the BALF and serum were all decreased significantly （ P ＜0.05）； the levels of T-SOD and GSH-Px were significantly increased （ P ＜0.05）； the arrangement of lung tissue cells tended to normalize， with reduced infiltration of inflammatory cells and decreased exfoliation of bronchial simple columnar epithelial cells. The transcriptomic results revealed that the differentially expressed genes were B-cell receptor signaling pathway， nuclear factor κB （NF-κB） signaling pathway， ferroptosis signaling pathway， and others. Further validation revealed that， compared with the model group， the expression levels of NF-κB p65 and chemokine ligand 20， as well as the phosphorylation level of NF-κB inhibitor protein α， were significantly decreased in the lung tissues of the mice in all QDP groups （ P ＜0.05）. Conversely， the protein expression of nuclear factor erythroid 2-related factor 2 （Nrf2） and heme oxygenase 1 （HO-1） were significantly increased （ P ＜0.05）. CONCLUSIONS QDP can effectively alleviate bronchial asthma by inhibiting the NF-κB signaling pathway， activating the Nrf2/HO-1 signaling pathway， regulating oxidative stress， and reducing inflammatory responses.

Objective To investigate the role and regulatory mechanism of LINC00657 in the progression of cervical cancer. Methods Bioinformatics analysis predicted potential binding sites between LINC00657 and miR-30a-5p and between miR-30a-5p and Skp2. These sites were verified by using RNA immunoprecipitation and dual-luciferase reporter experiments. LINC00657, miR-30a-5p, and Skp2 mRNA expression levels in cervical cancer tissues and cell lines were assessed by utilizing RT-qPCR. Western blot analysis was employed to examine the protein levels of Skp2 in cells and subcutaneous xenograft tumor models in nude mice. Immunohistochemistry was applied to analyze Skp2 expression in animal tissues. The cellular processes of cervical cancer cell lines were evaluated through CCK-8, scratch, and Transwell assays. Results LINC00657 and Skp2 presented binding sites for miR-30a-5p. In cervical cancer, LINC00657 and Skp2 showed high expression levels (P<0.05), whereas miR-30a-5p displayed low expression (P<0.05). Functional experiments demonstrated that linc00657 upregulates Skp2 expression, a process that is dependent on its sequestration of miR-30a-5p. Conclusion LINC00657 promoted the malignant progression of cervical cancer by upregulating Skp2 expression through specifically sequestering miR-30a-5p, thereby relieving its inhibitory effect on the target gene Skp2.

Background Methicillin-resistant Staphylococcus aureus (MRSA) is an important pathogen for both human bloodstream infections and mastitis in cows. However, little attention has been paid to the cross-host transmission of MRSA from cows to high-risk groups in China. Objective To determine the MRSA colonization rates among dairy cows and dairy farm workers in Xinjiang, identify the antibiotic resistance profiles and molecular characteristics of the isolates, and provide scientific evidence for the formulation of targeted infection control strategies. Method A cross-sectional survey combined with laboratory pathogen analysis was conducted. From June to August 2024, large-scale dairy farms in Xinjiang region were selected as study sites. Nasal swabs (n=96) and skin swabs (n=39) were collected from workers, and bovine nasal swab samples (n=109) were collected simultaneously. All samples were subjected to MRSA isolation, cultivation, and identification, followed by antibiotic susceptibility testing to characterize resistance phenotypes. Staphylococcus aureus protein A (Spa) typing was performed to determine strain genotypes and elucidate MRSA colonization rates and molecular epidemiological patterns. Results A total of 35 MRSA strains was successfully isolated from 244 samples. The MRSA colonization rates among dairy farm workers and dairy cows were 20.83% (20/96) and 12.84% (14/109), respectively, with an overall isolation rate of 14.34% (35/244). Among the workers, the nasal colonization rate was 16.67% (16/96), and the skin colonization rate was 12.82% (5/39). One worker exhibited MRSA colonization at multiple body sites. All MRSA strains were resistant to cefoxitin (100%, 35/35). The resistance rates to erythromycin and clindamycin were 42.86% (15/35) and 34.29% (12/35), respectively. Thirteen strains showed a multidrug-resistant phenotype, whereas all strains were susceptible to vancomycin. The MRSA isolates exhibited high genetic diversity, with 13 Spa types identified, among which t441 was the most prevalent (8 strains). Both t441 and t034 types were detected in samples from both the dairy cows and their handlers. These two Spa types also carried and stably inherited specific resistance combinations, including erythromycin–clindamycin–cefoxitin and ciprofloxacin–erythromycin–clindamycin–gentamicin–cefoxitin–tetracycline, and a statistically significant association was also observed between the two resistance profiles and the bacterial types (P < 0.001). In addition, one novel Spa type strain was identified. Conclusion MRSA colonization rates among dairy cows and dairy farm workers in Xinjiang are relatively high, with evidence of multi-site colonization. The isolates exhibit high levels of multidrug resistance and genetic diversity, indicating a potential risk of cross-host transmission.

Objective: To explore the bidirectional causal relationship between 731 immune cell phenotypes and recurrent aphthous ulcers (RAU) using Mendelian randomization (MR). Methods: A two-sample bidirectional MR study was conducted using publicly available genome-wide association study (GWAS) summary statistics for 731 immune cell phenotypes and the RAU GWAS summary data from the FinnGen consortium. The inverse-variance weighted (IVW) method was used as the primary analysis tool, with supplementary analyses including the weighted median (WM) method, MR-Egger regression, weighted mode, and simple mode. Sensitivity analyses were conducted using Cochran’s Q test, the mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) method for detecting pleiotropy and outliers, and leave-one-out cross-validation. Furthermore, differential analysis was performed using a clinical cohort dataset from the Gene Expression Omnibus (GEO) to further validate the MR results. Results: In the forward MR analysis, 731 immune cell phenotypes were considered as exposures and RAU as the outcome. Among them, 52 immune cell phenotypes showed a significant causal effect on RAU (P<0.05). After false discovery rate (FDR) correction, two immune phenotypes remained significantly associated with RAU risk: with increased monocyte-derived myeloid suppressor cells (M-MDSC) (OR = 1.06; 95% CI: 1.03-1.09) and CD33 on granulocytic myeloid-derived suppressor cells (G-MDSC) (OR = 1.06; 95% CI: 1.03-1.09), the risk of RAU also increased. In reverse MR, RAU was found to have a significant causal effect on two immune cell phenotypes (P<0.05), but no significant effects were found after FDR correction. Sensitivity analysis showed no significant heterogeneity between SNPs (P>0.05). Differential analysis of the GEO dataset revealed that the characteristic genes of myeloid-derived suppressor cells (MDSC) (CTBS, IPMK, and UBA3) were significantly upregulated in RAU (P<0.05). Conclusion The MR results of 731 immune cell phenotypes suggest that M-MDSC and CD33 molecules on G-MDSC may be risk factors for RAU development. The clinical GEO dataset further validated that MDSC may play a role in RAU, while RAU did not show a significant causal association with the 731 immune cell phenotypes.

ObjectiveTo improve the therapeutic effect of Buyang Huanwutang（BYHW） on diabetic kidney disease （DKD） and explore new methods for developing new Chinese medicine decoctions，we utilized 5-hydroxymethylcytosine （5hmC）-Seal sequencing technology and network pharmacology to modify BYHW. MethodsWe selected 14 diabetes mellitus （DM） patients and 15 DKD patients hospitalized in the Department of Endocrinology of Peking University Third Hospital in 2021. Circulating free DNA （cfDNA） in the patients’ plasma was sequenced. After data processing and screening， we performed temporal clustering analysis to select a DKD 5hmC gene set， which was then cross-validated with a DKD database gene set to obtain the DKD gene set. We retrieved target genes of the seven herbal components of BYHW from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP） and the Encyclopedia of Traditional Chinese Medicine （ETCM）， and performed cross-analysis with the DKD gene set to identify common genes shared by the disease and the Chinese medicines. A protein-protein interaction （PPI） network was constructed for the common genes to screen out the key genes. Chinese medicines targeting these key genes were searched against ETCM to identify removable Chinese medicines. Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analysis was performed on non-common DKD genes， and key genes in DKD-related pathways were selected based on machine learning. The GSE30529 dataset was used to verify the expression trends of 5hmC-modified genes and the feasibility of target genes as drug targets. TCMBank was used to search for target genes and obtain compounds targeting these genes and the corresponding Chinese medicines to construct a "key target-compound-Chinese medicine" network. Molecular docking was employed to verify the binding affinity of compounds with key targets. TCMSP and ETCM were used to search and count the candidate Chinese medicines targeting DKD-related genes， and a new decoction was formed by adding the selected Chinese medicines. A mouse model of DKD was established to examine the efficacy of the new decoction based on the mouse body mass， random blood glucose， urinary microalbumin （mALB）， serum creatinine （Scr）， and blood urea nitrogen （BUN） and by hematoxylin-eosin staining， periodic acid-Schiff staining， Masson staining， immunofluorescence assay， and Real-time PCR. ResultsThe cross-analysis results showed that the DKD gene set included 507 genes， of which 30 were target genes of BYHW. The PPI analysis indicated that the top 15% target genes regarding the degree were interleukin-6 （IL-6）， Toll-like receptor 4 （TLR4）， lactotransferrin （LTF）， lipoprotein lipase （LPL）， and sterol regulatory element-binding transcription factor 1 （SREBF1）. Persicae Semen and Pheretima in BYHW were unrelated to key genes and removed. Machine learning identified 10 potential target genes， among which TBC1 domain family member 5 （TBC1D5）， RAD51 paralog B （RAD51B）， and proteasome 20S subunit alpha 6 （PSMA6） had expression trends consistent with the GSE30529 dataset and could serve as drug targets. The "key target-compound-Chinese medicine" network and molecular docking results indicated that the compounds with good binding affinity to target proteins were arginine， glycine， myristicin， serine， and tyrosine， corresponding to 121 Chinese medicines. The top 10 Chinese medicines targeting DKD-related genes were Lycii Fructus， Ginseng Radix et Rhizoma， Dioscoreae Rhizoma， Rehmanniae Radix Praeparata， Isatidis Radix， Glehniae Radix， Ophiopogonis Radix， Allii Sativi Bulbus， Isatidis Folium， and Bolbostemmatis Rhizoma. Based on traditional Chinese medicine theory， the new decoction was obtained after removal of Persicae Semen and Pheretima and addition of Rehmanniae Radix Praeparata and Dioscoreae Rhizoma. Animal experiment results indicated that the modified BYHW improved the kidney function and inhibited renal fibrosis in DKD mice， with better effects than the original decoction. ConclusionThe BYHW modified based on 5hmC-Seal sequencing demonstrates better performance in inhibiting fibrosis and ameliorating DKD than the original decoction. This elucidates the biomedical theory behind the epigenetic modification of traditional Chinese medicine prescriptions， potentially offering new perspectives for the exploration of these prescriptions

ObjectiveTo improve the therapeutic effect of Buyang Huanwutang（BYHW） on diabetic kidney disease （DKD） and explore new methods for developing new Chinese medicine decoctions，we utilized 5-hydroxymethylcytosine （5hmC）-Seal sequencing technology and network pharmacology to modify BYHW. MethodsWe selected 14 diabetes mellitus （DM） patients and 15 DKD patients hospitalized in the Department of Endocrinology of Peking University Third Hospital in 2021. Circulating free DNA （cfDNA） in the patients’ plasma was sequenced. After data processing and screening， we performed temporal clustering analysis to select a DKD 5hmC gene set， which was then cross-validated with a DKD database gene set to obtain the DKD gene set. We retrieved target genes of the seven herbal components of BYHW from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP） and the Encyclopedia of Traditional Chinese Medicine （ETCM）， and performed cross-analysis with the DKD gene set to identify common genes shared by the disease and the Chinese medicines. A protein-protein interaction （PPI） network was constructed for the common genes to screen out the key genes. Chinese medicines targeting these key genes were searched against ETCM to identify removable Chinese medicines. Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analysis was performed on non-common DKD genes， and key genes in DKD-related pathways were selected based on machine learning. The GSE30529 dataset was used to verify the expression trends of 5hmC-modified genes and the feasibility of target genes as drug targets. TCMBank was used to search for target genes and obtain compounds targeting these genes and the corresponding Chinese medicines to construct a "key target-compound-Chinese medicine" network. Molecular docking was employed to verify the binding affinity of compounds with key targets. TCMSP and ETCM were used to search and count the candidate Chinese medicines targeting DKD-related genes， and a new decoction was formed by adding the selected Chinese medicines. A mouse model of DKD was established to examine the efficacy of the new decoction based on the mouse body mass， random blood glucose， urinary microalbumin （mALB）， serum creatinine （Scr）， and blood urea nitrogen （BUN） and by hematoxylin-eosin staining， periodic acid-Schiff staining， Masson staining， immunofluorescence assay， and Real-time PCR. ResultsThe cross-analysis results showed that the DKD gene set included 507 genes， of which 30 were target genes of BYHW. The PPI analysis indicated that the top 15% target genes regarding the degree were interleukin-6 （IL-6）， Toll-like receptor 4 （TLR4）， lactotransferrin （LTF）， lipoprotein lipase （LPL）， and sterol regulatory element-binding transcription factor 1 （SREBF1）. Persicae Semen and Pheretima in BYHW were unrelated to key genes and removed. Machine learning identified 10 potential target genes， among which TBC1 domain family member 5 （TBC1D5）， RAD51 paralog B （RAD51B）， and proteasome 20S subunit alpha 6 （PSMA6） had expression trends consistent with the GSE30529 dataset and could serve as drug targets. The "key target-compound-Chinese medicine" network and molecular docking results indicated that the compounds with good binding affinity to target proteins were arginine， glycine， myristicin， serine， and tyrosine， corresponding to 121 Chinese medicines. The top 10 Chinese medicines targeting DKD-related genes were Lycii Fructus， Ginseng Radix et Rhizoma， Dioscoreae Rhizoma， Rehmanniae Radix Praeparata， Isatidis Radix， Glehniae Radix， Ophiopogonis Radix， Allii Sativi Bulbus， Isatidis Folium， and Bolbostemmatis Rhizoma. Based on traditional Chinese medicine theory， the new decoction was obtained after removal of Persicae Semen and Pheretima and addition of Rehmanniae Radix Praeparata and Dioscoreae Rhizoma. Animal experiment results indicated that the modified BYHW improved the kidney function and inhibited renal fibrosis in DKD mice， with better effects than the original decoction. ConclusionThe BYHW modified based on 5hmC-Seal sequencing demonstrates better performance in inhibiting fibrosis and ameliorating DKD than the original decoction. This elucidates the biomedical theory behind the epigenetic modification of traditional Chinese medicine prescriptions， potentially offering new perspectives for the exploration of these prescriptions

Objective To investigate the relationship between cyclin A2 (CCNA2) and the prognosis of colon cancer, and its possible mechanism from the perspective of immune infiltration. Methods We downloaded the transcriptome data of colon cancer patients from The Cancer Genome Atlas database. Clinicopathological feature analysis and survival analysis were performed based on the expression levels of CCNA2. A total of 75 specimens of colon cancer and normal tissues were collected, and the expression level of CCNA2 was analyzed using immunohistochemical methods. Multivariate analysis was conducted to explore its relationship with clinicopathological features. Gene Set Enrichment Analysis (GSEA) was used to assess the potential molecular functions of CCNA2 in colon cancer. CIBERSORT algorithm was applied to calculate the correlation between CCNA2 and immune-cell infiltration in colon cancer. Results Database and immunohistochemical analyses indicated that CCNA2 was expressed at a significantly higher level in colon cancer tissues than normal tissues (P<0.001). The overall survival, disease-specific survival, and progression-free interval were all longer in the group with high CCNA2 expression than the group with low expression (all P<0.05). In tumor tissues, the expression level of CCNA2 decreased with increased pathological and TNM stages (P<0.05). The expression level of CCNA2 in normal tissues was consistently lower than that in colon cancer tissues across all clinical stages (all P<0.001). GSEA suggested that Wnt/β-catenin, KRAS, and other signaling pathways were enriched when CCNA2 was lowly expressed. CIBERSORT analysis revealed an increase in the infiltration of immune cells such as regulatory T cells and macrophages M0 when CCNA2 expression was low. Conclusion CCNA2 is highly expressed in colon cancer and closely associated with grade of pathology and TNM stage. It may recruit regulatory T cells through the KRAS and Wnt/β-catenin pathways, thereby reducing immune-cell infiltration and promoting colon cancer progression, leading to poor prognosis.

OBJECTIVE To evaluate the long-term cost-effectiveness of canagliflozin or semaglutide in patients with type 2 diabetes mellitus（T2DM）poorly controlled with metformin. METHODS Based on the perspective of China’s health system， a Markov model was used to calculate the long-term costs and utilities of canagliflozin or semaglutide combined with metformin for T2DM patients in China for 30 years based on the data from SUSTAIN 8 study. The incremental cost-effectiveness ratio（ICER） and incremental net monetary benefit （INMB） were calculated using one time the 2024 per capita gross domestic product（GDP） as the willingness-to-pay（WTP） threshold. One-way sensitivity analysis， probability sensitivity analysis and scenario analysis were conducted to confirm the stability of the conclusions. RESULTS Compared with canagliflozin + metformin， ICER of semaglutide combined with metformin was 260 485.67 yuan/quality-adjusted life year （QALY），which was higher than the WTP threshold set in this study （95 749 yuan/QALY），and the corresponding INMB was －61 576.24 yuan，indicating that the canagliflozin + metformin regimen was more cost-effective. The cost of diabetes without complications treatment in the semaglutide + metformin group had the greatest influence on INMB，but changes in parameters within the selected range did not drive decision reversal. With the increasing of WTP threshold，the economic acceptability of semaglutide + metformin regimen increased. Under the current WTP threshold，the annual cost of semaglutide should be reduced by 42.95% to make the semaglutide + metformin regimen more cost- effective. CONCLUSIONS From the perspective of China’s health system， canagliflozin + metformin is more cost-effective than semaglutide + metformin for T2DM patients yueru. with poor glycemic control with metformin alone.

Knee osteoarthritis (KOA) is a prevalent degenerative joint disease characterized by synovial inflammation, cartilage loss. Often manifesting as joint pain and limited mobility, it severely affects the quality of life of patients. Traditional treatment methods such as pharmacological injections and surgical interventions primarily aim to alleviate symptoms but have limited effects on cartilage repair. Human umbilical cord mesenchymal stem cells (hUC-MSCs), due to their anti-inflammatory and chondrogenic capabilities, is considered a new hope for the treatment of KOA. This article synthesizes the latest research findings from both domestic and international sources to discuss the theoretical basis for the clinical application of hUC-MSCs in treating KOA, clinical study design, and efficacy evaluation. It also addresses the challenges in the clinical application of hUC-MSCs and explores future directions, in the hope of providing feasible theoretical support for the treatment of KOA with hUC-MSCs.

Objective To construct an optimal prediction model of chickenpox in Urumqi, and to provide reference for formulating the prevention and control strategies of chickenpox. Methods The multivariate autoregressive moving average model (ARIMAX) and random forest model (RF) were established based on the monthly incidence of chickenpox in Urumqi from 2014 to 2018, and the monthly incidence of chickenpox in 2019 was used to test the models and evaluate their prediction effect. The prediction performance of the two models was compared, and the best model was selected to predict the incidence of chickenpox in Urumqi. Results The incidence of chickenpox in Urumqi showed a regular bimodal distribution with obvious seasonality, and it showed a slow upward trend from July 2014 to December 2019. The fitting model was ARIMA(0,1,0)(0,1,1)₁₂, the root mean square error (RMSE) and mean absolute error (MAE) of ARIMAX model training set were 1.29 and 0.95, respectively, and the RMSE and MAE of the test set were 1.88 and 1.44, respectively. The training set RMSE and MAE of RF model were 1.56 and 1.56, respectively, and the test set RMSE and MAE were 4.83 and 3.96, respectively. Conclusion The performance of ARIMAX model is better than that of RF model, which can better predict the incidence trend of chickenpox in Urumqi. It is necessary to optimize the prediction model according to the actual situation and provide scientific guidance for the prevention and control of chickenpox.