Journal of Inflammation Research最新文献

Objective: To analyze the efficacy of combined lamotrigine and levetiracetam therapy in epileptic patients and its impact on hippocampal volume and inflammatory response.

Methods: A retrospective analysis was conducted on the clinical data of 120 epileptic patients admitted to our hospital from December 2022 to February 2024. All patients met the inclusion and exclusion criteria. Based on the treatment received, patients were divided into a control group (n=60, treated with lamotrigine) and an observation group (n=60, treated with lamotrigine combined with levetiracetam). Clinical efficacy, bilateral hippocampal volume, inflammatory factors, brain injury markers, and adverse reactions were compared between the two groups.

Results: ① Clinical efficacy: The observation group had higher effective rates in seizure control and EEG epileptiform discharge improvement than the control group (P<0.05). ② Hippocampal volume: No significant differences were observed in bilateral hippocampal volume changes between the two groups before and after treatment (P>0.05). ③ Inflammatory factors: TNF-α, hs-CRP, and IL-6 levels decreased in both groups after treatment, with greater reductions in the observation group (P<0.05). ④ Brain injury markers: S-100β and HMGB-1 levels decreased in both groups after treatment, with greater reductions in the observation group (P<0.05). ⑤ Adverse reactions: The incidence of adverse reactions was comparable between the two groups (P>0.05).

Conclusion: Compared with lamotrigine monotherapy, lamotrigine combined with levetiracetam further improves treatment efficacy in epileptic patients, reduces inflammatory response and brain injury, and does not increase the risk of adverse reactions.

Background: Osteoarthritis (OA) is a chronic inflammatory disease characterized by cartilage damage, but its pathogenesis remains unclear.

Objective: This study aims to identify potential therapeutic targets for OA and explore associated mechanistic pathways.

Methods: OA-related data from GWAS, eQTLGen, and GEO databases were analyzed. Differential expression analysis, WGCNA, and PPI network analysis identified OA-associated genes. Machine learning algorithms determined diagnostic markers, validated by ROC curve analysis. Mendelian randomization assessed causal relationships, and single-cell sequencing explored gene dynamics in OA cartilage. In vitro and in vivo experiments validated the findings.

Results: We identified 282 differentially expressed genes (DEGs) associated with OA, with 52 hub genes, including FGF1, as a key candidate. Machine learning identified FGF1 as a diagnostic biomarker, validated by ROC curve analysis (AUCs up to 1.000 in the training set, and 0.790 and 0.761 in validation sets). Mendelian randomization suggested a potential causal relationship between FGF1 expression and OA risk (95% CI = 1.002-1.081, p = 0.041). Single-cell sequencing explored the dynamics of diagnostic marker genes in OA cartilage progression. In vitro and in vivo experiments confirmed FGF1's role in inflammation and matrix degradation via the RAS-MAPK pathway.

Conclusion: This study confirms FGF1 as a diagnostic biomarker for OA, with a key role in pathogenesis through RAS-MAPK pathway activation. MR analysis provides suggestive causal evidence. FGF1 induces a pro-inflammatory and catabolic state in chondrocytes by upregulating MMP13 and TNFα, while inhibiting Aggrecan synthesis, driving irreversible cartilage matrix destruction. These findings support targeting FGF1 as a novel therapeutic strategy for OA.

Purpose: To investigate if protein STK11 alleviates lipopolysaccharide-induced acute lung injury (ALI), hypothesizing it activates autophagy (harmful substance clearance) via AMPK's "start signal".

Methods: Researchers used a genomics-first approach: they integrated datasets GSE66890, GSE10474, and GSE32707, screened autophagy-related differentially expressed genes in ALI via bioinformatics, and confirmed STK11 as a key regulator through protein-protein interaction analysis. They treated human lung cells with 50 μg/mL lipopolysaccharide for 24 hours to establish ALI models, then overexpressed or silenced STK11 in the cells. They assessed cell function, apoptosis, inflammatory factors, autophagy activity, and AMPK activation.

Results: Researchers verified STK11 as a key regulator of autophagy in ALI. STK11 overexpression significantly improved cell function, reduced apoptosis (lower pro-apoptotic/higher anti-apoptotic proteins), decreased IL-6/IL-8/TNF-α (mRNA/protein levels), enhanced autophagy (elevated LC3B-II, reduced P62, more autophagosomes), and activated AMPK. STK11 silencing reversed these protective effects and inhibited AMPK.

Conclusion: STK11 mitigates lipopolysaccharide-induced lung cell damage by activating AMPK-mediated autophagy, emerging as a potential therapeutic target for ALI.

Background: Ocular graft-versus-host disease (oGVHD) often presents with subtle and nonspecific symptoms following allogeneic hematopoietic stem cell transplantation (allo-HSCT). Current diagnostic methods primarily depend on subjective clinical evaluations with limited sensitivity. This study aimed to identify serum pyroptosis-related cytokines associated with oGVHD and to develop a cytokine-based diagnostic model.

Methods: In this prospective case-control study, 116 allo-HSCT recipients (61 with oGVHD and 55 without) and 47 healthy controls were enrolled. A sandwich antibody array was used to screen differentially expressed proteins in a pilot cohort (n = 4 per group), followed by pathway enrichment analysis. Ocular surface parameters and serum levels of NLRP3, TLR4, CCL2, IL-18, IL-6, and TNF-α were measured. Linear correlation, logistic regression, receiver operating characteristic (ROC) analyses, and internal bootstrap validation were performed to evaluate diagnostic performance. A cytokine-based risk score model was established.

Results: Thirty-four upregulated proteins were enriched in immune and inflammatory pathways. Patients with oGVHD exhibited severe dry eye features and significantly higher serum levels of NLRP3, TLR4, CCL2, IL-18, and IL-6 (all p < 0.001), which inversely correlated with ocular surface parameters. A logistic model combining these five cytokines achieved excellent diagnostic accuracy (AUC = 0.960). Internal 10-fold cross-validation with 1,000 bootstrap iterations yielded a consistent mean AUC of 0.953, confirming model robustness. A simplified risk score stratified patients into low-, intermediate-, and high-risk categories with strong discriminatory power (p < 0.001).

Conclusion: A serum panel of NLRP3, TLR4, CCL2, IL-18, and IL-6 demonstrates high diagnostic accuracy and stability for oGVHD. As the diagnostic cutoffs were derived from the same dataset, potential overfitting cannot be excluded, and independent validation in larger multicenter cohorts is warranted.

Background: Glioblastoma (GBM) is the most aggressive primary tumor of the adult central nervous system, not only characterized by rapid proliferation and diffuse brain infiltration but also by a pronounced pro-inflammatory microenvironment that fuels tumor progression and therapeutic resistance. Current standard-of-care, surgical resection followed by radiotherapy and chemotherapy, offers limited survival benefit, partly due to inflammation-driven invasion and immune evasion. The Hippo signaling pathway, a critical regulator of cell proliferation, apoptosis, and tissue homeostasis, has recently been implicated in inflammatory signaling, making it an attractive therapeutic target.

Purpose: In this study, we investigated the anti-inflammatory and anti-invasive properties of 1,3,6-tri-O-galloyl-α-D-glucose (αTGG), the α-anomer of βTGG from Terminalia chebula, in comparison with pharmacological Hippo pathway inhibitors IAG933, VT107, and GNE7883.

Results: To mimic the inflammatory milieu associated with GBM, U87 cells were treated with Concanavalin A (ConA), which induced phosphorylation of ERK and IκB, key mediators of MAPK and NF-κB inflammatory pathways. Both αTGG and Hippo pathway inhibitors effectively suppressed these phosphorylation events, with VT107 showing the strongest effect. ConA exposure downregulated Hippo pathway downstream effectors (AXL, CTGF, CYR61) in a TEAD-dependent manner, highlighting the interplay between Hippo signaling and inflammatory transcriptional control. Importantly, αTGG and VT107 also significantly attenuated ConA-induced activation of proMMP-2 to MMP-2 and reduced the expression of multiple pro-inflammatory mediators, including COX2, CCL22, CCR2, CCR4, CXCL10, CXCL12, CXCR1, FASLG, IFNG, IL13, and IL17A.

Conclusion: These findings underscore the dual anti-inflammatory and anti-invasive actions of αTGG, positioning it as a promising candidate for targeting inflammation-driven GBM progression through modulation of Hippo pathway activity.

Background: Spinal cord injury (SCI) triggers a intense inflammatory response that hinders the success of cell transplantation therapies. Immortalised Schwann cells (iSCs) offer a renewable cell source, but their response to inflammation is poorly understood. Wnt3a regulates neural stem cells, but its role in modulating inflammatory responses in iSCs remains unclear.

Methods: Rat Schwann cells (SCs) were immortalised using SV40Tag. An inflammatory model was established by treating iSCs with LPS, in the presence or absence of Wnt3a protein. The inflammatory response, apoptosis, proliferation, and migration were assessed using quantitative PCR (qPCR), Western blotting, immunofluorescence, CCK-8 assay, TUNEL staining, flow cytometry, and scratch wound healing assay. An acute spinal cord injury model in rats was utilised for in vivo validation.

Results: This study shows that immortalized Schwann cells share some genotypic similarity with primary Schwann cells, but have a much faster proliferation rate than Schwann cells, which can be better used for neurological disease-related research. In addition, in the LPS-induced inflammatory environment, Wnt3a was able to inhibit the expression of IL-1β in immortalized Schwann cells, and enhance the expression of TGF-β by activating NF-κB. More importantly, Wnt3a inhibited the level of apoptosis in the inflammatory environment and promoted the proliferation and migration ability of cells, which also enhanced the function of immortalized Schwann cells.

Conclusion: Wnt3a modulates the inflammatory response in iSCs, primarily through NF-κB-mediated upregulation of TGF-β, and promotes iSC survival and function. The combination of iSCs and Wnt3a presents a promising strategy for improving cell transplantation therapy for SCI.

Purpose: Rheumatoid arthritis (RA) affects millions of people worldwide and causes chronic joint pain with incompletely understood pathogenesis and challenging management. Myosin light chain 1 (MYL1) a muscle regulatory protein, has an unknown role in RA pathogenesis.

Methods: We analyzed transcriptomic data from RA patients and healthy controls in the Gene Expression Omnibus (GEO). Using least absolute shrinkage and selection operator (LASSO) regression and random forest, we identified differentially expressed genes (DEGs). Diagnostic performance was assessed using receiver operating characteristic (ROC) curves and area under the curve (AUC). To explore potential mechanisms, we conducted functional enrichment and immune-infiltration analyses. We further validated MYL1 expression in a rat RA model using qRT-PCR and Western blotting.

Results: We found MYL1 significantly downregulated in RA with high diagnostic value (AUC > 0.8). Enrichment analyses revealed its involvement in muscle structure development, immune regulation, and calcium signaling pathways. CIBERSORT analysis indicated associations with immune cell infiltration, particularly regulatory T cells, activated natural killer (NK) cells, and M1 macrophages. The rat model confirmed reduced MYL1 expression at both mRNA (p < 0.001) and protein (p = 0.009) levels, consistent with human data.

Conclusion: MYL1 is consistently downregulated in RA and may serve as a potential diagnostic biomarker. The results indicate that MYL1 may be involved in the pathological process of RA through calcium signaling, muscle function, and immune cell regulation. However, further clinical and mechanistic studies are warranted.

Background: In the wake of COVID-19, a resurgence of Mycoplasma pneumoniae pneumonia (MPP) has emerged globally since mid-2023. However, the clinical manifestations and immune responses following infection vary across different age groups of pediatric patients (infants, preschoolers and school-aged children), increasing the complexity of diagnosis and treatment.

Methods: This study retrospectively analyzed serum cytokine levels in 40 healthy children and 87 MPP patients, with additional cytokine profiling of bronchoalveolar lavage fluid (BALF) in severe cases, combining KEGG pathway analysis to investigate age-related immune patterns. SARS-CoV-2 antibody levels were further detected in these MPP patients, followed by Spearman correlation analysis to assess their correlation with cytokines in MPP children.

Results: Age-specific cytokine patterns emerged in MPP children. In 0-2 years, cytokines enriched in IL-17, TLR, and TNF pathways were upregulated in MPP groups compared to controls, while in 6-12 years, cytokines enriched in TLR, RLR, and JAK-STAT pathways were downregulated in MPP groups. Serum patterns in 3-5 years resembled those in 0-2 years, but BALF aligned with 6-12 years. SARS-CoV-2 IgG positively correlated with TWEAK, IL-22, IL-16, IL-12p40, CCL7, and CD152 in 0-2 years (P < 0.05), but negatively with CCL13 in 6-12 years (P < 0.01).

Conclusion: Overall, the immune pattern in children with MPP is age-specific and severity-dependent. Higher levels of SARS-CoV-2 IgG are associated with a more robust and mature anti-infective immune response in younger MPP patients, while in older children, besides providing immune memory against pathogens, SARS-CoV-2 IgG also appears to plant a landmine of immune exhaustion.

Purpose: To develop and externally validate an interpretable machine learning model for preoperative prediction of Gangrenous cholecystitis (GC) using multicenter clinical data.

Patients and methods: This retrospective multicenter study included 744 patients with cholecystitis who underwent cholecystectomy at one institution, split into training (n=521) and testing (n=223) cohorts, and a temporal external validation cohort of 300 patients from a second center. Twenty preoperative variables were screened by LASSO regression and Boruta algorithm; predictors selected by both were used to construct six machine learning models. Model performance was assessed via AUC, calibration, and decision curve analysis. SHAP analysis provided model interpretability.

Results: The Random Forest (RF) model demonstrated superior predictive performance, achieving an AUC of 0.893 in the training set, 0.875 in the testing set, and 0.818 in external validation. Calibration and decision curve analyses indicated excellent agreement and clinical benefit. SHAP analysis identified gallbladder wall thickening, C-reactive protein, pericholecystic fluid, white blood cell count, and impacted stone as the most influential predictors, ensuring transparency of model decisions.

Conclusion: In our multicenter cohorts, this interpretable machine learning model showed good discrimination for preoperative risk stratification of gangrenous cholecystitis and acceptable generalizability between centers. By integrating clinical, laboratory, and imaging features and providing explainability, the approach may assist perioperative decision-making when used alongside clinical judgment. Prospective, multicenter evaluations and clinical impact studies are warranted before routine clinical adoption.