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Purpose: Older patients with cancer are underrepresented in pivotal trials of immune checkpoint inhibitors (ICIs). This study aimed to investigate immune-related adverse events (irAEs) that occur in older patients with lung cancer treated with ICIs, and explore predictors of the occurrence of irAEs. Methods: A prospective analysis was performed on older patients with lung cancer aged ≥ 65 years who were treated with anti-programmed cell death-1/-ligand 1 (PD-1/PD-L1) inhibitors in Beijing Hospital from January 2018 to December 2022. The incidence and risk factors of irAEs were estimated by the Chi-square test or Wilcoxon rank-sum tests. The predictive power of Geriatric-8 (G-8) for irAEs was tested by receiver operating characteristic (ROC) curve analysis. Lymphocyte counts were measured by flow cytometry. Cytokine levels were tested by Enzyme-linked immunosorbent assay, respectively. Kaplan–Meier method was used to calculated progression-free survival (PFS) curves, and the log-rank test was used to evaluate differences. Results: A total of 201 older patients aged ≥ 65 years with lung cancer were enrolled in this study. The most common irAEs were interstitial pneumonia, dermatological toxicity and hypothyroidism, with rates of 17.2%, 16.1% and 5.6%, respectively. ROC showed that G-8 could predict the occurrence of irAEs in patients aged 65–71 years (≥ G2 irAEs: AUC = 0.757, p < 0.001; ≥ G3 irAEs: AUC = 0.862, p < 0.001), but not for patients aged ≥ 71 years. NLR, LMR, PNI, hypertension and diabetes were associated with irAEs. Lower CD4 + T cells and B cells, and lower levels of IL-10 were associated with the development of irAEs. Conclusion: Our study confirmed the accuracy of G-8 for predicting irAEs in older patients. We also identified several predictors of irAEs in older patients with lung cancer. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization, road dust poses a significant threat to ecological systems. To explore the source and ecological risks associated with heavy metals in surface dust, 36 road dust samples were collected in Beijing in May, August, and November 2018, and February 2019. The results indicated that the concentrations of Cd, Cr, Cu, Hg, Ni, Pb, and Zn exceeded their background values, and the mean concentration of Cd was nearly 10 times the background value. Three main sources were identified by Principal Component Analysis (PCA): industrial emissions, fuel combustion, and traffic exhaust. According to the Genetic Algorithm optimized Back Propagation (GA-BP) neural network, heavy metals from industrial sources and fuel combustion originate from suburban districts, while heavy metals from traffic exhaust originate from the city center. After careful screening of species and corresponding toxicity data, the concentration of pollutants corresponding to a 5% cumulative probability on the curve (HC5) of eight heavy metals was calculated by species sensitivity distribution (SSD), and the results indicate that most HC5 values are consistent with national standards. However, the HC5 values of Ni, Cu, and As differed from the national standards, which can be used to supplement existing standards. From the risk analysis based on HC5 values, the high-risk area of Ni (12.70–30.97%) was much larger than that of As (1.55–4.95%), and the proportions of high-risk areas of Ni and As reached their highest values in spring and autumn, respectively. Uncertainty existed between different geostatistical methods. The areas of uncertainty of Ni and As were small (less than 5.58%). The heavy metals in the surface dust in Beijing had potential ecological risks that vary with seasons and regions. Therefore, it is critical to implement targeted management measures based on pollution sources and risk distribution. [ABSTRACT FROM AUTHOR]