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.

AIM: To compare the effectiveness of foldable capsular body(FCB)with traditional scleral buckling(SB)in the treatment of experimental retinal detachment animal models.METHODS: After successfully establishing rhegmatogenous retinal detachment(RRD)animal models, 24 New Zealand white rabbits were randomly divided into three groups(RRD models group, SB group, and FCB group), with 8 rabbits in each group. The FCB and SB groups underwent SB and FCB surgeries for the RRD animal models, while the RRD models group only consists of RRD models without any surgical intervention during the follow-up period. The follow-up duration was 3 mo. Wide-field neonatal fundus imaging system and ophthalmic B-ultrasound were used to assess the fundus conditions before and after surgery. The Icare® TONOVET Plus tonometer was utilized to evaluate intraocular pressure changes before and after surgery. The Eaton and Draize scoring systems were selected to monitor postoperative inflammatory reactions.RESULTS: The retinal reattachment rates in the FCB and SB groups were 87.5% and 75.0%, respectively, with no statistically significant difference between the groups(P>0.05). The intraocular pressure in both the FCB and SB groups increased postoperatively compared to preoperative levels(P<0.01), and there were no significant differences in intraocular pressure at any time points during the follow-up period between the groups(P>0.05). The intraocular pressure in the RRD models group remained at a low level throughout the follow-up period. The average surgical time for the FCB group was 16.87±2.29 min, which was shorter than 46.25±4.74 min in the SB group(t=-15.166, P<0.001). According to the Eaton and Draize scoring systems, the FCB group had lower grades of conjunctival hyperemia and edema in the early postoperative period compared to the SB group, indicating milder inflammatory reactions(P<0.05).CONCLUSION: Both FCB and SB are effective in treating experimental RRD. Compared to SB, FCB is simpler to operate, and also has a shorter surgical time and milder postoperative inflammatory reactions.

The drug resistance of the carbapenem-resistant Enterobacteriaceae(CRE)strains was mainly induced by multiple approaches such as production of carbapenemases,increase of bacterial outer membrane permeability,activation of active efflux pump system,formation of biofilm and drug modifying mechanisms.Those mecha-nisms involve deletion,mutation,insertion and posttranscriptional modification of relevant encoding genes,which may affect the susceptibility of the CRE strains to antibiotics.At present,the conventional clinical thera-pies include the use of traditional antibiotics,either the one-drug use or combined use of drugs.The development of novel antibacterial therapy is under way.The epidemiological characteristics of CRE infections,drug resist-ance mechanisms,current and prospective treatment strategies for CRE infections(covering new application of the drugs in available,the novel drugs such as ceftazidime/avibactam,meropenem/vaborbactam and imipenem/rele-bactam)were deeply reviewed in this article,so as to provide reliable reference for clinical prevention,control and treatment of CRE infections.

The drug resistance of the carbapenem-resistant Enterobacteriaceae(CRE)strains was mainly induced by multiple approaches such as production of carbapenemases,increase of bacterial outer membrane permeability,activation of active efflux pump system,formation of biofilm and drug modifying mechanisms.Those mecha-nisms involve deletion,mutation,insertion and posttranscriptional modification of relevant encoding genes,which may affect the susceptibility of the CRE strains to antibiotics.At present,the conventional clinical thera-pies include the use of traditional antibiotics,either the one-drug use or combined use of drugs.The development of novel antibacterial therapy is under way.The epidemiological characteristics of CRE infections,drug resist-ance mechanisms,current and prospective treatment strategies for CRE infections(covering new application of the drugs in available,the novel drugs such as ceftazidime/avibactam,meropenem/vaborbactam and imipenem/rele-bactam)were deeply reviewed in this article,so as to provide reliable reference for clinical prevention,control and treatment of CRE infections.

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.

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.

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.

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.

To investigate the genomic features and perform cluster analysis of Carbapenem-resistant Klebsiella pneumoniae (CRKP) to provide an experimental basis for guiding the prevention and treatment of CRKP infections.A retrospective case-cohort study was conducted on 19 non-redundant CRKP strains isolated from the Tenth Affiliated Hospital of Southern Medical University between January and June 2023. Whole genome sequencing (WGS) and multilocus sequence typing (MLST) were performed to compare genomic features and analyze the resistance genes and homology of the strains.The results showed that the 19 CRKP strains were isolated from 8 different clinical departments, mainly from respiratory specimens. The whole genome sequencing revealed that the genomic lengths of CRKP ranged from 4.90 to 5.85 Mbp, with contigs N50 values>20 kb for each genome. The median overall GC content was 57.0% (50.4%-57.1%). Comparative genomic analysis identified three regions with high genomic variability. WGS detected 32 resistance genes across 11 categories. All 19 strains carried carbapenem resistance genes ( blaKPC-2 and blaOXA-48), blaTEM-1B extended-spectrum β-lactamase resistance genes, qnrS1 quinolone resistance gene, and fosA fosfomycin resistance gene, with each strain carrying only one carbapenemase gene. The detection rate of blaKPC-2 was 94.7% (18/19). MLST identified three sequence types: ST11, ST437 and ST147, with ST11 being predominant (89.5%, 17/19). Clustering analysis based on acquired resistance genes revealed three clonal transmission patterns among strains 72 and 90, and strains 88, 84, 66 and 79.In conclusion, CRKP strains carry multiple resistance genes, and clustering analysis indicating that nosocomial clonal transmission is closely related to acquired resistance genes. The ST11- blaKPC-2 type strain is the predominant clone. Strengthened surveillance and effective control strategies are necessary to reduce nosocomial transmission of CRKP.