Objective To investigate the regulatory mechanism of transforming growth factor-β1 (TGF-β1) in different types of mitral valvular disease (MVD) with atrial fibrillation (AF). Methods From August 2011 to August 2012, patients with moderate to severe MVD accompanied by AF who required mitral valve replacement at the Department of Cardiovascular Surgery, West China Hospital, Sichuan University, were included. Based on echocardiographic results, patients were divided into two groups: a mitral regurgitation (MR) with AF (MR-AF) group and a mitral stenosis (MS) with AF (MS-AF) group. Left atrial tissue samples were collected during surgery. Techniques such as enzyme-linked immunosorbent assay, real-time fluorescence quantitative polymerase chain reaction, immunohistochemistry, and Western blotting were used to detect key molecules in the TGF-β1/JNK pathway. Results Sixteen patients were enrolled. There were 8 patients in the MR-AF group, including 5 males and 3 females, with an average age of (41.38±11.19) years; and 8 patients in the MS-AF group, including 6 males and 2 females, with an average age of (43.12±5.30) years. The left atrial volume load was higher in MR-AF patients, while the left atrial pressure load was higher in MS-AF patients. In MS-AF patients, the relative expression levels of MAPK9, JUN, CASP3, BAX, and BCL2 mRNA in left atrial tissues were significantly upregulated. The serum TGF-β1 protein level and the relative expression levels of p-JNK, p-c-Jun, and Caspase-3 proteins in the left atrial tissues of the MR-AF group were higher. Myocardial cell damage was more severe in the MS-AF group, and the protein expression level of Bcl-2 was higher. Conclusion Different MVD have distinct hemodynamic characteristics. The myocardium of the left atrium in MR-AF patients is more prone to apoptosis, possibly through the activation of the TGF-β1/JNK signaling pathway.

A 51-year-old male presented with nasal obstruction, followed by progressive hearing loss and blurred vision. Imaging identified space-occupying lesions in the paranasal sinuses, orbits, and paraspinal regions, while laboratory tests confirmed positive anti-proteinase 3 anti-neutrophil cytoplasmic antibody(PR3- ANCA) immunoglobulin G (IgG)and markedly elevated serum IgG4. Despite treatment with corticosteroids, immunosuppressants, and radiotherapy, the patient exhibited steroid dependency with relentless disease progression. Following multidisciplinary consultation, a diagnosis of inflammatory myofibroblastic tumor (IMT) coexisting with ANCA- associated vasculitis (AAV) was favored, though IgG4-related disease remained a critical differential. Ultimately, profound immunosuppression precipitated a severe herpesvirus infection, leading to disseminated intravascular coagulation and multiple organ dysfunction syndrome. This case underscores the rarity and diagnostic complexity of concurrent IMT and AAV, highlights the therapeutic dilemma of balancing primary disease control against fatal opportunistic infections, and emphasizes the critical role of multidisciplinary collaboration in the diagnosis and treatment of complex diseases.

ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

ObjectiveTo reveal the effects of overexpression and knockdown of CDH18 gene on cervical cancer proliferation and migration levels through the Wnt/β-catenin signaling pathway in cervical cancer cells. MethodsA comprehensive analysis of CDH18-interacting molecules was performed via the bioinformatics STRING database （http：//cn.string-db.org）. Molecular docking between Wnt/β-catenin inhibitor XAV939 and β-catenin was performed using the bioinformatics tool AutoDock （http：//autodock.scripps.edu）. The effect of CDH18 on β-catenin subcellular distribution was detected by laser confocal microscopy. EdU assay， wound healing assay and Transwell assay were used to detect the effects of CDH18 gene overexpression， CDH18 gene knockdown and the XAV939 on cell proliferation and migration. Western blot was performed to measure the expression levels of Wnt/β-catenin pathway-related molecules， including β-catenin， transcription factor 4 （TCF4）， and cellular myelocytomatosis oncogene （c-Myc）， following CDH18 gene overexpression， CDH18 gene knockdown， or XAV939 treatment. ResultsIn SiHa and HeLa cells， CDH18 gene overexpression significantly increased the protein expression levels of β-catenin， TCF4 and c-Myc， decreased the protein expression of GSK-3β， and enhanced cell proliferation and migration， with statistically significant differences （P<0.05）. Conversely， CDH18 gene knockdown significantly reduced the protein expression of β-catenin， TCF4 and c-Myc， elevated GSK-3β expression， and suppressed cell proliferation and migration （P<0.05）.Treatment with XAV939 in SiHa and HeLa cells downregulated β-catenin， TCF4 and c-Myc， upregulated GSK-3β， and inhibited cell proliferation and migration， with significant differences （P<0.05）. ConclusionCDH18 promotes the proliferation and migration of cervical cancer cells via the Wnt/β-catenin signaling pathway.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

ObjectiveTo study the effects of applying different microbial fertilizers on the growth and rhizosphere soil environment of Codonopsis pilosula and provide a theoretical basis for ecological cultivation of this medicinal plant. MethodsSeven groups were designed， including CK （no application of microbial fertilizer）， T1 （Trichoderma longibrachiatum fertilizer）， T2 （Bacillus subtilis fertilizer）， T3 （Trichoderma viride fertilizer）， T4 （compound microbial fertilizer）， T5 （C. pilosula stems and leaves fermented with compound microbial fertilizer）， and T6 （Scutellaria baicalensis stems and leaves fermented with T. viride fertilizer）. The physiological indicators， yield， and quality of C. pilosula and the physicochemical properties， enzyme activities， and microbial diversity in the rhizosphere soil of different fertilizer treatments were measured. ResultsGroup T1 showed slight decreases in soluble protein content （SPC） and superoxide dismutase （SOD）. Groups T2-T6 showed increases in physiological indicators such as proline （Pro）， soluble solids content （SSC）， SPC， catalase （CAT）， and peroxidase （POD） and a decrease in malondialdehyde （MDA） in C. pilosula leaves. All the fertilizer treatments increased the yield of C. pilosula and the total polysaccharide content in the roots. T1， T2， T3， T4， and T5 increased the total flavonoid content in the roots. Meanwhile， T4 increased the total saponin content in the roots. All the fertilizer treatments reduced the pH and increased the electric conductivity （EC）， soil organic matter （SOM）， and alkaline nitrogen （AN） in the soil. T2 and T5 increased the available phosphorus （AP）， and T3， T4， T5， and T6 increased the available potassium （AK） in the soil. All the fertilizer treatments increased the activities of urease， sucrase， and CAT in the soil. Except that T1 decreased the bacterial diversity in the soil， other fertilizer treatments significantly increased bacterial and fungal diversity in the soil. Different fertilizer treatments significantly affected the composition of bacterial and fungal communities in the soil. At the phylum level， the dominant bacterial phyla included Proteobacteria， Acidobacteriota， and Bacteroideta， and the dominant fungal phyla were Ascomycota， Mortierellomycota， and unclassified_fungi in the rhizosphere soil of C. pilosula after bacterial fertilizer treatment. At the genus level， unclassified Gemmatimonadaceae， Sphingomonas， and unclassified Vicinamibacteraceae were the dominant bacterial genera， while unidentified， unclassified Fungi， and unclassified Sordariomycetes were the dominant fungal genera in the rhizosphere soil. The results of redundancy analysis indicated that the main physicochemical factors affecting changes of microbial communities in the rhizosphere soil of C. pilosula were pH， EC， AK， AN， AP， and soil organic matter （SOM） in the soil. The correlation heatmap showed that Bryobacter had significantly positive correlations with EC， AK， and AN. There was a significantly negative correlation between Fusarium and SOM. In summary， applying an appropriate amount of microbial fertilizer can promote the growth and improve the rhizosphere soil environment of C. pilosula. ConclusionThe compound microbial fertilizer and the C. pilosula stems and leaves fermented with compound microbial fertilizer can improve the soil nutrients， growth， development， yield， and quality of C. pilosula， and thus they can be applied to the artificial cultivation of C. pilosula.

RNA methylation is a key process in the epigenetic regulation of post-transcriptional gene expression.5-Methylcytosine(m⁵C)is a type of RNA methylation,commonly existing in eukaryotic mRNA and non-coding RNAs.It mainly regulates transfer RNA stability,ribosomal RNA assembly,and mRNA translation,stability,and translation.RNA methylation is dynamically reversible and regulated by methyltransferase,demethylase,and methylation recognition protein.It has been confirmed that aberrant m⁵C RNA methylation is involved in the pathogenesis of non-neoplastic kidney diseases.This article summarizes the current progress of m⁵C RNA methylation associated with non-neoplastic acute and chronic kidney diseases,aiming to provide potential targets for the diagnosis and treatment of such diseases.
Humans ; Methylation ; 5-Methylcytosine/metabolism* ; Kidney Diseases/metabolism* ; Epigenesis, Genetic ; RNA Methylation

Objective:To conduct a scoping review of risk prediction models for the development of intraoperative hypothermia in patients undergoing cancer surgery to inform clinical nursing practice and future research.Methods:Relevant literature on constructing or validating intraoperative hypothermia risk prediction models for cancer surgery patients in five foreign language databases (PubMed, Embase, Web of Science, Cochrane Library, CINAHL) and four Chinese language databases (China National Knowledge Infrastructure, Wanfang, VIP, Chinese Biomedical Database) were searched from the time of library construction to June 1, 2024, extracted information on the applicable target, incidence of intraoperative hypothermia, methodology of model construction, predictors and performance, etc. The Prediction model Risk Of Bias Assessment Tool was used to evaluate the risk of bias of the studies, and the included literature was analyzed and discussed.Results:A total of 15 pieces of literature involving 18 models were included, with the study population focussing on patients undergoing surgery for colorectal cancer. The rate of intraoperative hypothermia ranged from 15.14% to 61.5%. Model construction methods included 2 types of Logistic regression models and machine learning, and model presentation was based on column-line plots. There were 8 predictors that appeared with a frequency of ≥5, including age, body mass index, operation time, anaesthesia time, operating room temperature, intraoperative rehydration volume, intraoperative bleeding volume, and heat preservation method.Conclusions:The performance of the included model was good, but the risk of bias was high for the predictors and the analysis part, and nursing staff should pay close attention to the risk factors of intraoperative hypothermia in patients undergoing cancer surgery, construct a risk prediction model with low bias and high applicability, and validate and improve the existing risk prediction model.

OBJECTIVE: To investigate the biological behavior, differentiation ability, and differential gene expression of lymph node mesenchymal stem cells (MSCs) in patients with diffuse large B-cell lymphoma (DLBCL) and reactive lymphoid hyperplasia (RLH), providing a theoretical basis for clinical chemotherapy resistance. METHODS: Lymph node MSCs from patients with DLBCL and RLH were separated, passaged and cultured. The cell morphology and growth status were observed. Flow cytometry was performed to detect the immune phenotype of MSCs. The in vitro directed differentiation ability of the two types of MSCs was observed. High-throughput sequencing was used to analyze the differential gene expression and enrichment of two groups of MSCs. RESULTS: The lymph node MSCs of patients with DLBCL and RLH had similar cell morphology and growth characteristics, and both groups of MSCs expressed CD90, CD105, and CD73 on the cell surface. Compared with lymph node MSCs derived from patients with RLH, lymph node MSCs derived from DLBCL patients showed stronger osteogenic and adipogenic differentiation abilities. High-throughput sequencing results displayed that lymph node MSCs derived from DLBCL patients significantly upregulated some genes such as TOP2A, LFNG, GRIA3, SEC14L2, SPON2, AURKA, LRRC15, FOXD1, HOXC9, CDC20 and remarkably downregulated some genes such as TBC1D8, LDLR, PCDHAC2, POLH, PKP2, ANKRD37, DMKN, HSD11B1, ARHGAP20, PTGS1,etc. CONCLUSION Lymph node MSCs in DLBCL patients exhibit unique biological behavior and gene expression profiles, which may be closely related to clinical chemotherapy resistance.
Humans ; Mesenchymal Stem Cells/cytology* ; Lymphoma, Large B-Cell, Diffuse/pathology* ; Cell Differentiation ; Lymph Nodes/pathology* ; Pseudolymphoma/pathology*