[Objective] To evaluate the risk of bacterial contamination of platelets apheresis and improve the clinical diagnosis rate of transfusion-transmitted bacterial infections. [Methods] A retrospective analysis was conducted on 11 cases involving bacterial contamination detected in apheresis platelets during quality inspections at our center from 2021 to 2023, as well as cases of transfusion-transmitted bacterial infection (TTBI) caused by platelet transfusion. The return of positive platelet bacteria test results and clinical transfusion adverse reactions were statistically analyzed. [Results] There were 9 donors with bacteria-contaminated platelets, of which 3 were reported as clinical transfusion reaction, 4 were detected by quality sampling, and 2 were identified by appearance detection before transfusion. The bacterial contamination rate of platelets was about 0.08% (9/10 762). The contaminated platelets were involved in 11 cases of TTBI, with an incidence of TTBI of approximately 0.05% (11/21 916). Only 3 cases of transfusion reactions were clinically reported, while the rest were case tracking with positive results of platelet bacterial test from quality sampling. The clinical return rate of TTBI was 27.27% (3/11), with an average reporting time of 8.12 hours after the occurrence of transfusion reactions. The majority of the contaminated platelets were detected at the end stage of storage, with 55.56% (5/9) of platelets collected on the 4th day after collection. Partial contaminated platelets were identified through quality sampling, with a positive rate of 2.78% (4/144). [Conclusion] The platelet bacterial contamination rate is high, but with low clinical return rate. It is recommended to conduct routine platelet bacterial monitoring and improve clinical diagnostic level.

Objective To investigate the influence of comorbidities on the prognosis of patients with status epilepticus， to develop Improved Comorbidity Burden Index （ICBI） based on Comorbidity Burden Index （CBI）， and to analyze the practicality of ICBI in assessing nonconvulsive status epilepticus in western China.Methods A total of 396 patients with status epilepticus who were treated in Department of Neurology， Intensive Care Unit， and Emergency Department， West China Hospital， Sichuan University， from December 2016 to December 2022 were enrolled， and all patients met the latest diagnostic criteria for status epilepticus issued by the International League Against Epilepsy （2015 edition）. SPSS 22.0 was used to perform a statistical analysis， and a Logistic regression analysis was used to investigate the influencing factors for in-hospital death and poor prognosis （with a Glasgow Outcome Scale score of 1-3）. CBI score was modified into ICBI score， with the addition of three comorbidities （immune system disorder， thyroid dysfunction， and hypoproteinemia） and consolidation of overlapping items， and the total score was calculated with each comorbidity contributing 1 score. The MedCalc-generated receiver operating characteristic （ROC） curve was used to analyze predictive value， and the area under the ROC curve （AUC） was used to reflect the diagnostic value of ICBI scale.Results Among the 396 patients with status epilepticus included in the study， 43 （10.9%） died in hospital and 114 （28.8%） had a poor prognosis. Digestive system diseases， respiratory system diseases， kidney and urinary system diseases， electrolyte/acid-base imbalance， infection， and immune system disorders were risk factors for in-hospital death of patients with status epilepticus. Digestive system diseases， respiratory system diseases， kidney and urinary system diseases， electrolyte/acid-base imbalance， hypoglycemia/hyperglycemia， infection， coagulation and blood disorders， nervous system disease， cardiovascular diseases， musculoskeletal disorders， and immune system disorders were risk factors for poor prognosis in patients with status epilepticus. The ROC curve analysis showed that ICBI>3 had an AUC of 0.914 in predicting in-hospital death， with a specificity of 71.37% and a sensitivity of 97.67% （P<0.000 1）， and ICBI>3 had an AUC of 0.882 in predicting poor prognosis， with a specificity of 81.56% and a sensitivity of 79.82% （P<0.000 1）. There were 327 patients with convulsive status epilepticus， among whom 41 patients died， and ICBI>3 had an AUC of 0.915 in predicting in-hospital death （P<0.000 1）. There were 100 patients with a poor prognosis， and ICBI>3 had an AUC of 0.867 in predicting poor prognosis （P<0.000 1）.Conclusion The in-hospital mortality rate is 10.9% in patients with status epilepticus in Sichuan， China， and ICBI>3 has a certain value in predicting in-hospital death and poor prognosis in patients with status epilepticus. There is no significant difference in ICBI score between convulsive status epilepticus and nonconvulsive status epilepticus.
Prognosis

OBJECTIVES: Osteoarthritis (OA) and sarcopenia are significant health concerns in the elderly, substantially impacting their daily activities and quality of life. However, the relationship between them remains poorly understood. This study aims to uncover common biomarkers and pathways associated with both OA and sarcopenia. METHODS: Gene expression profiles related to OA and sarcopenia were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control groups were identified using R software. Common DEGs were extracted via Venn diagram analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify biological processes and pathways associated with shared DEGs. Protein-protein interaction (PPI) networks were constructed, and candidate hub genes were ranked using the maximal clique centrality (MCC) algorithm. Further validation of hub gene expression was performed using 2 independent datasets. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of key genes for OA and sarcopenia. Mouse models of OA and sarcopenia were established. Hematoxylin-eosin and Safranin O/Fast Green staining were used to validate the OA model. The sarcopenia model was validated via rotarod testing and quadriceps muscle mass measurement. Real-time reverse transcription PCR (real-time RT-PCR) was employed to assess the mRNA expression levels of candidate key genes in both models. Gene set enrichment analysis (GSEA) was conducted to identify pathways associated with the selected shared key genes in both diseases. RESULTS: A total of 89 common DEGs were identified in the gene expression profiles of OA and sarcopenia, including 76 upregulated and 13 downregulated genes. These 89 DEGs were significantly enriched in protein digestion and absorption, the PI3K-Akt signaling pathway, and extracellular matrix-receptor interaction. PPI network analysis and MCC algorithm analysis of the 89 common DEGs identified the top 17 candidate hub genes. Based on the differential expression analysis of these 17 candidate hub genes in the validation datasets, AEBP1 and COL8A2 were ultimately selected as the common key genes for both diseases, both of which showed a significant upregulation trend in the disease groups (all P<0.05). The value of area under the curve (AUC) for AEBP1 and COL8A2 in the OA and sarcopenia datasets were all greater than 0.7, indicating that both genes have potential value in predicting OA and sarcopenia. Real-time RT-PCR results showed that the mRNA expression levels of AEBP1 and COL8A2 were significantly upregulated in the disease groups (all P<0.05), consistent with the results observed in the bioinformatics analysis. GSEA revealed that AEBP1 and COL8A2 were closely related to extracellular matrix-receptor interaction, ribosome, and oxidative phosphorylation in OA and sarcopenia. CONCLUSIONS AEBP1 and COL8A2 have the potential to serve as common biomarkers for OA and sarcopenia. The extracellular matrix-receptor interaction pathway may represent a potential target for the prevention and treatment of both OA and sarcopenia.
Sarcopenia/genetics* ; Osteoarthritis/genetics* ; Computational Biology/methods* ; Humans ; Protein Interaction Maps/genetics* ; Animals ; Mice ; Gene Expression Profiling ; Gene Ontology ; Transcriptome ; Male ; Signal Transduction/genetics* ; Gene Regulatory Networks

OBJECTIVES: Osteoarthritis (OA) is one of the most common chronic degenerative diseases, with chondrocyte apoptosis and extracellular matrix (ECM) degradation as the major pathological changes. The mechanical stimulation can attenuate chondrocyte apoptosis and promote ECM synthesis, but the underlying molecular mechanisms remain unclear. This study aims to investigate the role of primary cilia (PC) in mediating the effects of mechanical stimulation on OA progression. METHODS: In vivo, conditional knockout mice lacking intraflagellar transport 88 (IFT88^flox/flox IFT88 knockout; i.e., primary cilia-deficient mice) were generated, with wild-type mice as controls. OA models were established via anterior cruciate ligament transection combined with destabilization of the medial meniscus, followed by treadmill exercise intervention. OA progression was evaluated by hematoxylin-eosin staining, safranin O-fast green staining, and immunohistochemistry; apoptosis was assessed by TUNEL staining; and limb function by rotarod testing. In vitro, primary articular chondrocytes were isolated from mice and transfected with lentiviral vectors to suppress IFT88 expression, thereby constructing a primary cilia-deficient cell model. Interleukin-1β (IL-1β) was used to induce an inflammatory environment, while cyclic tensile strain (CTS) was applied via a cell stretcher to mimic mechanical loading on chondrocytes. Immunofluorescence and Western blotting were used to detect the protein expression levels of type II collagen α1 chain (COL2A1), primary cilia, IFT88, and caspase-12; reverse transcription polymerase chain reaction was performed to assess COL2A1 mRNA levels; and flow cytometry was used to evaluate apoptosis. RESULTS: In vivo, treadmill exercise significantly reduced Osteoarthritis Research Society International (OARSI) scores and apoptotic cell rates, and improved balance ability in wild-type OA mice, whereas IFT88-deficient OA mice showed no significant improvement. In vitro, CTS inhibited IL-1β-induced ECM degradation and apoptosis in primary chondrocytes; however, this protective effect was abolished in cells with suppressed primary cilia expression. CONCLUSIONS Mechanical stimulation delays OA progression by mediating signal transduction through primary cilia, thereby inhibiting cartilage degeneration and chondrocyte apoptosis.
Animals ; Chondrocytes/cytology* ; Apoptosis/physiology* ; Mice ; Cilia/metabolism* ; Osteoarthritis/pathology* ; Extracellular Matrix/metabolism* ; Mice, Knockout ; Disease Progression ; Interleukin-1beta ; Male ; Cells, Cultured

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation and joint damage, accompanied by the accumulation of plasma cells, which contributes to its pathogenesis. Understanding the genetic alterations occurring during plasma cell differentiation in RA can deepen our comprehension of its pathogenesis and guide the development of targeted therapeutic interventions. Here, our study elucidates the intricate molecular mechanisms underlying plasma cell differentiation by demonstrating that PRDX1 interacts with DOK3 and modulates its degradation by the autophagy-lysosome pathway. This interaction results in the inhibition of plasma cell differentiation, thereby alleviating the progression of collagen-induced arthritis. Additionally, our investigation identifies Salvianolic acid B (SAB) as a potent small molecular glue-like compound that enhances the interaction between PRDX1 and DOK3, consequently impeding the progression of collagen-induced arthritis by inhibiting plasma cell differentiation. Collectively, these findings underscore the therapeutic potential of developing chemical stabilizers for the PRDX1-DOK3 complex in suppressing plasma cell differentiation for RA treatment and establish a theoretical basis for targeting PRDX1-protein interactions as specific therapeutic targets in various diseases.

Mitochondrial DNA (mtDNA) acts as a damage-associated molecular pattern to activate the stimulator of interferon genes (STING) signaling in macrophages, promoting tissue inflammation. However, its role in acute myocardial infarction (AMI) remains unclear. Macrophage-specific Sting1 knockout mice were used to validate STING's pathological role in AMI. Cardiac and liver mtDNA were used to activate macrophages in co-culture systems with cardiomyocytes to assess fibrosis and hypertrophy. Panaxatriol saponin (PTS) was tested for its ability to block mtDNA-driven macrophage activation and subsequent cardiomyocyte damage. STING-PTS binding ability was analyzed. AMI rats received PTS to evaluate its effects on myocardial inflammation and ventricular remodeling. In vivo, macrophage-specific Sting1 knockout reduced myocardial inflammation and injury after AMI. In vitro, mtDNA-activated macrophages induced cardiomyocyte fibrosis and hypertrophy through STING signaling. PTS suppressed mtDNA-driven macrophage activation by directly binding STING, thereby blocking inflammatory cascades. In AMI rats, PTS treatment attenuated acute inflammation and reversed ventricular remodeling. These findings establish the mtDNA-STING axis in macrophages as a critical driver of post-AMI inflammation and identify pharmacological STING inhibition with PTS as a promising therapeutic strategy. The study bridges genetic validation with translational applications, highlighting macrophage STING as a novel target for ischemic heart disease management.

OBJECTIVES: To investigate the effect of high glucose on macrophage polarization and the role of immune-responsive gene 1 (IRG1) in mediating its effect. METHODS: RAW264.7 cells were transfected with IRG1-overexpressing plasmid or IRG1 siRNA via electroporation and cultured in either normal or high glucose for 72 h to observe the changes in cell viability and morphology using CCK-8 assay and phase contrast microscopy. The protein levels of IRG1, iNOS, Arg-1, IL-1β and IL-10 in the treated cells were detected with Western blotting, and the fluorescence intensities of iNOS and Arg-1 were detected using immunofluorescence assay. The protein levels of IL-1β and IL-10 in the culture medium were determined with ELISA. RESULTS: High glucose exposure significantly reduced IRG1 and Arg-1 expressions, increased iNOS and IL-1β expressions and IL-1β secretion, and decreased IL-10 level in RAW264.7 cells. Transfection with the IRG1-overexpressing plasmid provided the cells with obvious resistance to high glucose-induced changes in iNOS, Arg-1, IL-1β and IL-10, whereas IRG1 knockdown further enhanced the effects of high glucose exposure on Arg-1 expression and the expression and secretion of IL-10. CONCLUSIONS High glucose promotes M1 polarization of the macrophages possibly through a mechanism to inhibit the expression of IRG1 protein, thus leading to chronic inflammatory response.
Animals ; Mice ; Macrophages/drug effects* ; Glucose/pharmacology* ; Interleukin-10/metabolism* ; Nitric Oxide Synthase Type II/metabolism* ; RAW 264.7 Cells ; Interleukin-1beta/metabolism* ; Arginase/metabolism* ; RNA, Small Interfering/genetics* ; Transfection ; Inflammation

Natural antimicrobial peptides (AMPs) are promising candidates for the development of a new generation of antimicrobials to combat antibiotic-resistant pathogens. They have found extensive applications in the fields of medicine, food, and agriculture. However, efficiently screening AMPs from natural sources poses several challenges, including low efficiency and high antibiotic resistance. This review focuses on the action mechanisms of AMPs, both through membrane and non-membrane routes. We thoroughly examine various highly efficient AMP screening methods, including whole-bacterial adsorption binding, cell membrane chromatography (CMC), phospholipid membrane chromatography binding, membrane-mediated capillary electrophoresis (CE), colorimetric assays, thin layer chromatography (TLC), fluorescence-based screening, genetic sequencing-based analysis, computational mining of AMP databases, and virtual screening methods. Additionally, we discuss potential developmental applications for enhancing the efficiency of AMP discovery. This review provides a comprehensive framework for identifying AMPs within complex natural product systems.