ObjectiveTo further supplement the previous research on the relationship between neutrophil–lymphocyte ratio (NLR) and all-cause and cardiovascular mortality, and construct clinical models to predict mortality.MethodsA total number of 2,827 observers were included from the National Health and Nutrition Examination Survey (NHANES) database in our research. NLR was calculated from complete blood count. According to the quartile of baseline NLR, those observers were divided into four groups. A multivariate weighted Cox regression model was used to analyze the association of NLR with mortality. We constructed simple clinical prognosis models by nomograms. Kaplan–Meier survival curves were used to depict cause-specific mortality. Restricted cubic spline regression was used to make explicit relationships between NLR and mortality.ResultsThis study recruited 2,827 subjects aged ≥ 18 years from 2005 to 2014. The average age of these observers was 51.55 ± 17.62, and 57.69% were male. NLR is still an independent predictor, adjusted for age, gender, race, drinking, smoking, dyslipidemia, and other laboratory covariates. The area under the receiver operating characteristic curves (AUCs) of NLR for predicting all-cause mortality and cardiovascular mortality were 0.632(95% CI [0599, 0.664]) and 0.653(95% CI [0.581, 0.725]), respectively, which were superior to C-reactive protein (AUCs: 0.609 and 0.533) and WBC (AUCs: 0.522 and 0.513). The calibration and discrimination of the nomograms were validated by calibration plots and concordance index (C-index), and the C-indexes (95% CIs) of nomograms for all-cause and cardiovascular mortality were 0.839[0.819,0.859] and 0.877[0.844,0.910], respectively. The restricted cubic spline showed a non-linear relationship between NLR and mortality. NLR > 2.053 might be a risk factor for mortality.ConclusionThere is a non-linear relationship between NLR and mortality. NLR is an independent factor related to mortality, and NLR > 2.053 will be a risk factor for prognosis. NLR and nomogram should be promoted to medical use for practicality and convenience.

Lung cancer is the leading cause of cancer‐related death worldwide due to diagnosis in the advanced stage and drug resistance in the subsequent treatments. Development of novel diagnostic and therapeutic methods is urged to improve the disease outcome. Exosomes are nano‐sized vehicles which transport different types of biomolecules intercellularly, including DNA, RNA and proteins, and are implicated in cross‐talk between cells and their surrounding microenvironment. Tumor‐derived exosomes (TEXs) have been revealed to strongly influence the tumor microenvironment, antitumor immunoregulatory activities, tumor progression and metastasis. Potential of TEXs as biomarkers for lung cancer diagnosis, prognosis and treatment prediction is supported by numerous studies. Moreover, exosomes have been proposed to be promising drug carriers. Here, we review the mechanisms of exosomal formation and uptake, the functions of exosomes in carcinogenesis, and potential clinical utility of exosomes as biomarkers, tumor vaccine and drug delivery vehicles in the diagnosis and therapeutics of lung cancer.

BackgroundIPF is an undetermined, progressive lung disease. Necroptosis is a type of programmed apoptosis, which involved in the pathogenesis of lung diseases like COPD and ARDS. However, necroptosis in IPF have not been adequately studied. This study aimed to investigate the necroptosis in IPF and the relationship between necroptosis and immune infiltration, to construct a prognostic prediction model of IPF based on necroptosis-related genes.MethodsGSE110147 was downloaded from the GEO database and utilized to analyze the expression of necroptosis-related differentially expressed genes (NRDEGs). Then NRDEGs were used to construct protein-protein interaction (PPI) networks in the STRING database, and Cytoscape software was used to identify and visualize hub genes. Necroptosis-related prognosticgenes were explored in GSE70866, and a prognostic prediction model was constructed. The ImmuCellAI algorithm was utilized to analyze the landscape of immune infiltration in GSE110147. The single-cell RNA sequencing dataset GSE122960 was used to explore the association between necroptosis and type II alveolar epithelial cells (AT II) in IPF. The GSE213001 and GSE93606 were used for external validation. The expression of prognostic genes was quantified using RT-qPCRin the IPF A549 cell model, and was further verified by western blotting in the bleomycin-induced pulmonary fibrosis mouse model.ResultsIt was observed that necroptosis-related signaling pathways were abundantly enriched in IPF. 29 NRDEGs were screened, of which 12 showed consistent expression trends in GSE213001. Spearman correlation analysis showed that the expression of NRDEGs was positively correlated with the infiltration of proinflammatory immune cells, and negatively correlated with the infiltration of anti-inflammatory immune cells. NRDEGs, including MLKL, were highly expressed in AT II of fibrotic lung tissue. A necroptosis-related prediction model was constructed based on 4 NRDEGsby the cox stepwise regression. In the validation dataset GSE93606, the prognostic prediction model showed good applicability. The verification results of RT-qPCR and western blotting showed the reliability of most of the conclusions.ConclusionsThis study revealed that necroptosis existed in IPF and might occur in AT II. Necroptosis was associated with immune infiltration, suggesting that necroptosis of AT II might involve in IPF by activating immune infiltration and immune response.

A growing number of ground-glass opacity (GGO) nodules are screened out in lungs. Small GGOs are frequently neither visible nor palpable, thus undetectable during operation. Various nodule localization techniques have been developed to facilitate the intraoperative detection of GGO nodules; however, general localization techniques are infeasible or inappropriate in some cases. The detection of small GGO is a great challenge, even within a surgical specimen in the absence of preoperative localization.A localization-independent approach for GGO detection is urgently needed. Herein, we report two cases with invisible and impalpable small GGO which were not appropriate for preoperative localization. The lesions were anatomically resected under the guidance of three-dimensional (3D) reconstruction and got an adequate margin distance. A vessel (artery, vein, or bronchus) which had advanced into or immediately adjacent to the nodule was assigned as a reference vessel. By dissecting and tracing the reference vessel from proximal to distal, the GGO lesions were successfully detected in the surgical specimens, to the eventual obtainment of an accurate pathological diagnosis. Via the two case reports, we introduced an easily handled approach, namely dissecting and tracing a reference vessel, for GGO detection. The novel approach was first described. Combined with precise anatomical segmentectomy guided by 3D reconstruction, it provides an alternative scheme for GGO resection with no need for preoperative localization.