Nafeisa Dilixiati

Secreted phosphoprotein 1-positive (SPP1+) macrophages play a critical role in the progression of silicosis, but their regulatory mechanisms remain unclear. In this study, we integrated single-cell RNA sequencing and spatial transcriptomics to characterize crucial macrophage subpopulations and their associated regulatory pathways during silicosis. Our results demonstrate that silica exposure significantly promotes the accumulation of SPP1+ macrophages within silicotic nodules. These macrophages contribute to inflammation by releasing pro-inflammatory cytokines such as TNF-α, MCP-1, and IL-6. Concurrently, they exhibit elevated ACTA2 RNA transcription levels, which enhance TGF-β production and drive fibrotic progression. These functional transitions are regulated by the ERK1/2 and JNK signaling pathways. Inhibition of SPP1 markedly attenuated silica-induced lung inflammation and fibrosis. Moreover, plasma SPP1 levels in silicosis patients were strongly correlated with disease onset and severity. Collectively, our findings indicate that SPP1+ macrophages are involved throughout the entire course of silicosis development, under the regulation of ERK/JNK signaling. Targeting SPP1+ macrophages may thus represent a promising therapeutic strategy for silicosis.

The purpose of this study was to establish a nomogram for predicting community-acquired pneumonia (CAP) in hospitalized patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD). The retrospective cohort study included 1249 hospitalized patients with AECOPD between January 2012 and December 2019. The patients were divided into pneumonia-complicating AECOPD (pAECOPD) and non-pneumonic AECOPD (npAECOPD) groups. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were utilized to identify prognostic factors. A prognostic nomogram model was established, and the bootstrap method was used for internal validation. Discrimination and calibration of the nomogram model were evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Logistic and LASSO regression analysis showed that C-reactive protein (CRP) >10 mg/L, albumin (Alb) 50 U/L, fever, bronchiectasis, asthma, previous hospitalization for pAECOPD in the past year (Pre-H for pAECOPD), and age-adjusted Charlson score (aCCI) & GE;6 were independent predictors of pAECOPD. The area under the ROC curve (AUC) of the nomogram model was 0.712 (95% CI: 0.682-0.741). The corrected AUC of internal validation was 0.700. The model had well-fitted calibration curves and good clinical usability DCA curve. A nomogram model was developed to assist clinicians in predicting the risk of pAECOPD.