ObjectiveTo explore the academic characteristics of contemporary renowned Chinese medicine masters in treating diabetic kidney disease （DKD） from the perspectives of principles， methods， formulas， and medications. MethodsIn strict accordance with the Systematic Review of Text and Opinion （SrTO） process developed by the Joanna Briggs Institute （JBI）， an Australian evidence-based healthcare center， the databases including China National Knowledge Infrastructure （CNKI）， VIP Database， Wanfang Data， and China Biomedical Literature Service System （SinoMed） were searched. Based on predefined inclusion and exclusion criteria， text information extraction， quality evaluation， and text information synthesis were conducted sequentially. The data were analyzed and presented in the form of text and figures. ResultsA total of 215 articles related to 43 contemporary renowned experts in the fields of Chinese medicine nephrology and endocrinology were included. The study found that the academic thoughts of these masters in the treatment of DKD are extensive， involving multiple levels such as disease understanding， therapeutic strategies， formula application， and medication use. In terms of disease understanding， the primary pathogenesis is characterized by deficiency in the root and excess in the manifestation. It is emphasized that internal factors， such as congenital endowment deficiency， interact with external factors such as improper diet， emotional disturbances， invasion of exogenous pathogens， and delayed or inappropriate treatment， to jointly induce the disease. This further gives rise to various pathogenetic theories， including obstruction of renal collaterals by blood stasis， toxin-induced damage to renal collaterals， latent wind disturbing the kidney， and internal heat leading to mass formation. In terms of therapeutic strategies and medication use， the principal treatment method is to replenish Qi and nourish Yin. Stage-based and syndrome-differentiated treatments are advocated. Flexible use of insect-derived drugs and wind-dispelling drugs is emphasized， along with proficiency in applying classical formulas and drug pairs. Integrated internal and external treatments， as well as the combined application of multiple therapeutic approaches， are commonly employed for comprehensive management. Meanwhile， the concept of "preventive treatment of disease" is upheld， and individualized long-term management of patients is advocated. ConclusionThrough the SrTO process， the academic thoughts of contemporary renowned Chinese medicine masters in the treatment of DKD have been systematically and standardly synthesized， providing a scientific and standardized basis for future theoretical exploration.

ObjectiveTo explore the academic characteristics of contemporary renowned Chinese medicine masters in treating diabetic kidney disease （DKD） from the perspectives of principles， methods， formulas， and medications. MethodsIn strict accordance with the Systematic Review of Text and Opinion （SrTO） process developed by the Joanna Briggs Institute （JBI）， an Australian evidence-based healthcare center， the databases including China National Knowledge Infrastructure （CNKI）， VIP Database， Wanfang Data， and China Biomedical Literature Service System （SinoMed） were searched. Based on predefined inclusion and exclusion criteria， text information extraction， quality evaluation， and text information synthesis were conducted sequentially. The data were analyzed and presented in the form of text and figures. ResultsA total of 215 articles related to 43 contemporary renowned experts in the fields of Chinese medicine nephrology and endocrinology were included. The study found that the academic thoughts of these masters in the treatment of DKD are extensive， involving multiple levels such as disease understanding， therapeutic strategies， formula application， and medication use. In terms of disease understanding， the primary pathogenesis is characterized by deficiency in the root and excess in the manifestation. It is emphasized that internal factors， such as congenital endowment deficiency， interact with external factors such as improper diet， emotional disturbances， invasion of exogenous pathogens， and delayed or inappropriate treatment， to jointly induce the disease. This further gives rise to various pathogenetic theories， including obstruction of renal collaterals by blood stasis， toxin-induced damage to renal collaterals， latent wind disturbing the kidney， and internal heat leading to mass formation. In terms of therapeutic strategies and medication use， the principal treatment method is to replenish Qi and nourish Yin. Stage-based and syndrome-differentiated treatments are advocated. Flexible use of insect-derived drugs and wind-dispelling drugs is emphasized， along with proficiency in applying classical formulas and drug pairs. Integrated internal and external treatments， as well as the combined application of multiple therapeutic approaches， are commonly employed for comprehensive management. Meanwhile， the concept of "preventive treatment of disease" is upheld， and individualized long-term management of patients is advocated. ConclusionThrough the SrTO process， the academic thoughts of contemporary renowned Chinese medicine masters in the treatment of DKD have been systematically and standardly synthesized， providing a scientific and standardized basis for future theoretical exploration.

ObjectiveTo explore the mechanism of Yishen Qubi Tongluo Formula （益肾祛痹通络方， YQTF） in the treatment of rheumatoid arthritis（RA）. MethodsNetwork pharmacology was employed to retrieve and screen the active components and potential targets of YQTF as well as RA-related targets using databases including TCMSP， BATMAN， ETCM and GEO. The intersection of targets related to active components and RA-related targets was identified， and a protein-protein interaction （PPI） network was constructed. Gene Ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis were performed， and a drug-active component-common target network of YQTF in the treatment of RA was established. The core components of YQTF were molecularly docked with key targets. Human rheumatoid arthritis synovial fibroblast cell line MH7A was divided into blank group， model group， methotrexate group and YQTF group. The blank group was cultured with 10% fetal bovine serum， while the other three groups were stimulated with 10 μg/L of recombinant human tumor necrosis factor-α （TNF-α） for 24 h to establish the RA cell model. On this basis， the methotrexate group was treated with methotrexate suspension at a concentration of 20 μmol/L， and the YQTF group was treated with 10% YQTF-medicated serum. After 48 h of intervention， the levels of TNF-α and interleukin-17A（IL-17A）contents in cell supernatants were detected by enzyme-linked immunosorbent assay （ELISA）， and mRNA expressions of phosphatidylinositol 3-kinase（PI3K）， protein kinase B（AKT） and mammalian target of rapamycin（mTOR） were detected by real-time quantitative polymerase chain reaction （RT-qPCR）. ResultsNetwork pharmacological analysis identified 209 active components and 583 potential target genes of YQTF， as well as 818 RA-related targets. A total of 29 common targets were obtained from the intersection of drug-related targets and RA-related targets. Quercetin，β-sitosterol， kaempferol， stigmasterol and luteolin were the core active components of YQTF for the treatment of RA， while matrix metalloproteinase-9 （MMP9）， prostaglandin-endoperoxide synthase 2 （PTGS2）， Toll-like receptor 4 （TLR4）， tumor protein p53 （TP53） and transcription factor AP-1 subunit JUN were the key targets. The GO and KEGG pathway enrichment analysis showed that the involved biological processes and pathways were mainly associated with antioxidant responses， PI3K-AKT signaling pathway and Toll-like receptor （TLR） signaling pathway. Molecular docking results showed that MMP9 and PTGS2 exhibited high binding affinities with quercetin， β-sitosterol， kaempferol， stigmasterol and luteolin； TLR4 exhibited high binding activities with β-sitosterol， stigmasterol and luteolin； and TP53 showed high binding affinity with luteolin. The results of cell experiments showed that compared with the control group， the contents of TNF-α and IL-17A as well as the mRNA expressions of AKT and mTOR in the model group significantly increased （P＜0.05 or P＜0.01）. Compared with the model group， all the above indicators significantly decreased in the YQTF group， while the contents of TNF-α and the mRNA expression of AKT significantly decreased in the methotrexate group （P＜0.05 or P＜0.01）. ConclusionThe mechanism of YQTF in the treatment of RA may be associated with reducing inflammatory cytokine secretion and inhibiting the activation of the PI3K-AKT-mTOR signaling pathway.

Asthma and cough variant asthma （CVA） are both chronic heterogeneous diseases characterized by airway microenvironment homeostasis disruption as their core pathological basis. In recent years， micro ribonucleic acid （miRNA）， as core post-transcriptional regulators， have been shown to finely modulate multiple critical signaling pathways， including Janus kinase/signal transducer and activator of transcription （JAK/STAT）， nuclear factor-κB （NF-κB）， transforming growth factor-β/Smad （TGF-β/Smad）， and phosphoinositide 3-kinase/protein kinase B （PI3K/Akt）， as well as various pathological processes such as airway epithelial barrier restoration， type 1 helper T cell（Th1）/Th2 immune balance， M1/M2 macrophage polarization， airway smooth muscle cell function， and airway hyperresponsiveness. miRNAs play a pivotal role in maintaining and disrupting airway microenvironment homeostasis. Based on recent Chinese and international literature， a logical framework centered on "airway microenvironment homeostasis disruption， miRNA regulation， and microenvironment restoration" was constructed. From the perspective of the airway microenvironment， the therapeutic roles of miRNA in asthma and CVA were systematically summarized， and the cascade regulatory mechanisms of miRNA throughout the entire disease course were elucidated. The hub miRNA was identified， and research progress on traditional Chinese medicine intervention strategies was explored. Furthermore， current clinical studies on RNA therapeutics and traditional Chinese medicine in achieving multi-target and multi-pathway integrated treatment by modulating miRNA were analyzed. The value of miRNA as biomarkers for diagnosis， phenotyping， and prognosis assessment， as well as the potential and application prospects of miRNA mimics and antagonists in precision therapy， were summarized， with the ultimate goal of advancing precision therapy for asthma and CVA.

Background The contamination of public baths with Legionella pneumophila contamination has become a growing public health concern in recent years. However, research on its association with bacterial community characteristics in water samples remains limited. The integration of 16S rRNA sequencing and random forest modeling provides a new approach to elucidate the bacterial community characteristics of public bath water and their association with Legionella pneumophila contamination. Objective To investigate the bacterial community structure and diversity of public bath water in Shanghai, explore the association between Legionella pneumophila contamination and bacterial community characteristics, and identify key bacterial genera associated with contamination, thereby providing a scientific basis for formulating hygiene management regulations for public bath water. Methods From February to March 2023, water samples were collected from ten public baths in Shanghai which were selected based on business scale, regional distribution, and functional differences. Water quality parameters were evaluated, and the samples were categorized into Legionella-positive and Legionella-negative groups based on the detection results of Legionella pneumophila. The bacterial community structure, α-diversity, and β-diversity were analyzed using 16S rRNA sequencing. Redundancy analysis (RDA) was employed to examine the relationship between physicochemical factors and bacterial community diversity. A random forest model was employed to identify key bacterial genera distinguishing the two groups, with the importance of genera being evaluated based on the mean decrease accuracy (MDA). Results The oxygen consumption in the Legionella-positive group was significantly lower than that in the Legionella-negative group (mean values: 1.85 mg·L⁻¹ vs. 6.81 mg·L⁻¹, P< 0.05), while no significant differences were observed in other physicochemical indicators. The sequencing results revealed a total of 27 bacterial phyla and 454 bacterial genera, with Proteobacteria (63.00%) being the dominant phylum. The dominant genera included Pelomonas (8.50%), Acidovorax (8.13%), Mycobacterium (7.93%), and Acinetobacter (6.59%). The α-diversity analysis indicated that bacterial community richness (Chao1 and ACE indices) was significantly higher in the Legionella-positive group than in the Legionella-negative group (P<0.01). The β-diversity analysis showed no significant difference in the bacterial community structure between the two groups (P>0.05). The RDA analysis demonstrated that the bacterial community diversity was positively correlated with pH and negatively correlated with oxygen consumption and free residual chlorine. The RDA1 and RDA2 explained 23.92% and 21.30% of the bacterial community diversity, respectively. The random forest model identified 20 key genera significantly influencing the microbial community distribution between the two groups, including unclassified_Bradyrhizobiaceae (MDA=2.42), Meiothermus (MDA=2.37), and Flavihumibacter (MDA=2.26). Conclusion The diversity of bacterial communities in public bath water is influenced by pH, oxygen consumption, and free residual chlorine. Samples contaminated with Legionella pneumophila exhibit greater microbial richness and contain characteristic key bacterial genera that contribute to community differences. Machine learning random forest technology helps identify these distinctive key bacterial genera. The findings provide a basis for carrying out risk early warning strategies in such settings.

BACKGROUND:It has been proved that induced pluripotent stem cells can differentiate into cardiomyocytes,but there are few reports on the maturity of differentiated cardiomyocytes.OBJECTIVE:To explore the effect of prolonging the induced differentiation time on the morphology,sarcomere length,binuclear cell content,cardiac gene expression,cardiac protein expression,and mitochondrial function of cardiomyocytes derived from induced pluripotent stem cells.METHODS:Bone morphogenetic protein 4,CH IR 99021,and IWR1 were used to induce pluripotent stem cells to differentiate into cardiomyocytes,and differentiated cardiomyocytes were collected on days 20 and 40 respectively.The expression levels of cardiac genes and proteins in differentiated cardiomyocytes were detected by RT-PCR and immunofluorescence,respectively.LAS X image analysis software was used to analyze the morphology and sarcomere length of differentiated cardiomyocytes.MitoTracker Green FM mitochondrial staining was used to detect total mitochondria.JC-1 mitochondrial staining was used to detect mitochondrial membrane potential.RESULTS AND CONCLUSION:Differentiated card io myocytes on day 40 had longer cell circumference and sarcomere length,and larger cell area than those on day 20(P＜0.05).The proportion of multinucleated cells rose sharply from about 16％on day 20 to about 29％on day 40(P＜0.05).Differentiated cardiomyocytes on day 40 had gene expression levels that were more similar to those of the primary cardiomyocytes,and the expression levels of SERCA2A,Cx-43,and α-MHC genes were significantly higher than on day 20(P＜0.05).Compared with the differentiated cardiomyocytes on day 20,the expression density of TNNT2 and α-MHC protein was relatively higher,the distribution density of mitochondria was larger,and the number of functional mitochondria was greater on day 40(P＜0.05).The results show that prolonging the induced differentiation time can increase the maturity of differentiated cardiomyocytes by increasing the length of sarcomere and the number of functional mitochondria,as well as improving the expression levels of cardiac genes and proteins.

Objective To compare the safety of cluster immunotherapy versus conventional immunotherapy for cat dander-induced rhinitis,with or without asthma,and to explore the main influencing factors for the occurrence of adverse reactions.Methods Patients with cat dander-induced rhinitis with or without asthma were recruited.Patients were divided into cluster immunotherapy group(cluster group)and conventional immunotherapy group(conventional group).The incidence of adverse reactions in both groups was compared based on individual patients and the number of injections(dose escalation phase),and influencing factors for the occurrence of adverse reactions were analyzed based on the patients' baseline information prior to treatment.Results A total of 78 patients were enrolled,with 45 in the cluster group and 33 in the conventional group.There was no statistically significant difference in the per-patient incidence of local(P=0.648)and systemic(P=1.000)adverse reactions between the two groups.The size of local reactions(P=0.321)also showed no significant difference.When the number of injections was considered during the dose escalation phase,no significant difference was observed in the per-shot incidence of local(P=0.705)and systemic adverse reactions(P=0.237)between the two groups.Pre-treatment levels of T-IgE(OR=1.001,95％CI:1.000-1.003,P=0.032),sIgE/T-IgE(％)(OR=1.079,95％CI:1.003-1.161,P=0.042),medication score(OR=1.338,95％CI:1.055-1.696,P=0.016),and symptom and medication score(OR=1.217,95％CI:1.028-1.440,P=0.022)were independent risk factors for the occurrence of local adverse reactions.Asthma patients had a 16.393-fold higher risk of systemic adverse reactions compared to non-asthma patients(OR=16.393,95％CI:1.076-249.752,P=0.044).Conclusion There were no significant differences in the per-patient or per-shot incidence of local or systemic adverse reactions between the cluster group and conventional group.Higher pre-treatment levels of T-IgE,sIgE/T-IgE,medication score,and symptom and medication score should alert clinicians to the risk of local adverse reactions,while asthma patients should be mo-nitored for the potential occurrence of systemic adverse reactions.

Objective:To develop CNN-Dinov2, a deep learning-based automatic classification model for Gram-stained images, enabling rapid diagnosis of three prevalent Gram-negative bacilli in bloodstream infections: Escherichia coli ( E.coli), Klebsiella pneumoniae ( K.pneumoniae), and Pseudomonas aeruginosa ( P.aeruginosa). Methods:This evaluation study analyzed 1 425 Gram-stained microscopic images from patients with bloodstream infections at Houjie Hospital, in Dongguan City, collected between January 2023 and January 2024. The images, all positive for blood culture and identified as target strains, were categorized into Escherichia coli (419 images), Klebsiella pneumoniae (411 images), Pseudomonas aeruginosa (413 images), and other Gram-negative bacilli (182 images). They were randomly split into a training set (1 141 images), a validation set (141 images), and a test set (143 images) in an 8∶1∶1 ratio. A hybrid CNN-Dinov2 model was developed by integrating ResNet′s local feature extraction with Dinov2′s global pre-trained features, followed by a fully connected layer. The model was optimized by inputting the preprocessed images and adjusting parameters through loss calculation and backpropagation. AlexNet, Dinov2, and ResNet18 served as control models. The models′ classification performance was assessed using accuracy, precision, weighted F1 score, and recall rate, derived from the confusion matrix. The PR curve and AP value further evaluated each model′s classification capability across the four image categories. Results:The CNN-Dinov2 model achieved a training accuracy of 99.74%, a validation accuracy of 98.12%, and a validation loss of 0.070 6, demonstrating robust generalization without overfitting. Validation metrics revealed superior performance with an accuracy of 98.60%, precision of 98.65%, a weighted F1 score of 98.60%, and a recall rate of 98.60%, outperforming other models. The confusion matrix confirmed its strong classification capability, with the highest sum of diagonal values for identifying four types of bacteria. The macro average precision (AP) values under the precision-recall (PR) curves were all 1, indicating excellent discrimination across all categories. Overall, the CNN-Dinov2 model exhibited the best performance among the four models evaluated.Conclusion:This study successfully developed CNN-Dinov2, an automated classification model for Gram staining images. It offers valuable support for the rapid diagnosis of bloodstream infections caused by Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa, demonstrating practical utility.

Objective:To identify the risk factors for ascending aortic dilatation and develop a prediction model using LASSO-Logistic regression in patients with normally functioning bicuspid aortic valves（BAV）.Methods:Eight-four adult patients with BAV diagnosed as having normal valve function by transthoracic echocardiography who attended to the General Hospital of Ningxia Medical University between June 2024 and April 2025 were prospectively selected，there were 42 patients with ascending aortic dilatation and 42 patients without dilatation.The patients were divided into a training set（60 cases）and a test set（24 cases）using stratified random sampling at a ratio of 7 to 3 via the R caret package. All subjects underwent transthoracic echocardiography，lipid and plasma matrix metalloproteinases（MMP）and tissue inhibitors（TIMP）levels detection. The inverse probability of treatment weighting（IPTW）method was employed to control for potential confounding factors. LASSO and multifactorial binary Logistic regression was applied to screen the independent risk factors for ascending aortic dilatation of BAV and development a nomogram prediction model. The accuracy，consistency and clinical applicability of the prediction model were evaluated by applying the ROC curve，calibration curve，and decision curve analysis（DCA），respectively.Results:①After adjustment for IPTW，LASSO-Logistic regression analysis identified left ventricular global longitudinal strain（LVGLS）and plasma MMP-2 levels as independent risk factors for ascending aortic dilatation in BAV patients with normal valve function.②The nomogram prediction model constructed based on the above screening features，and the area under the curve（AUC）of ROC for the training set and test set were 0.917 and 0.903，with specificities of 0.867 and 0.917，and sensitivities of 0.933 and 0.916，respectively. ③Calibration curves demonstrated satisfactory alignment，with C-indices of 0.908（95% CI=0.879～0.937）for the training set and 0.903（95% CI=0.874 - 0.932）for the test set. Hosmer-Lemeshow goodness-of-fit tests indicated strong consistency between predicted and observed outcomes，with P-values of 0.138 and 0.750 for the training and test sets，respectively.The DCA curve demonstrated that within a threshold probability range of 0.04 - 0.90 in the test set，the clinical decision-making model provided a higher net benefit rate for patients with BAV. Conclusions:LVGLS absolute value reduction and elevated plasma MMP-2 levels are independent risk factors for predicting ascending aortic dilatation in BAV patients with normal valve function. The prediction model based on LASSO-Logistic regression has good predictive value，providing a scientific basis for clinical decision-making in patients with BAV and aortic diseases.

Hereditary diffuse leukoencephalopathy with spheroids (HDLS) is a rare case caused by a mutation in the colony-stimu-lating factor 1 receptor ( CSF1R) gene on chromosome 5. In this paper, we report a case of a young female patient with HDLS, mainly characterized by memory loss, cognitive impairment, delayed movement, and abnormal mental and behavioral states. Genetic testing revealed a missense mutation in the CSF1R gene.