Single-cell sequencing (SCS) which has an unprecedentedly high resolution is an advanced technique for cancer research. Lung cancer still has a high mortality and morbidity. For further understanding the lung cancer, SCS is also been applied to lung cancer research to investigate its heterogeneity, metastasis, drug resistance, tumor microenvironment and many other issues. In this review, we summarized lung cancer research using SCS and their research achievements.
Objective: This review was conducted to systematically summarize the progress made by artificial intelligence technology in early screening based medical imaging, pathological diagnosis, genomics inspection, prognostic evaluation, and individual treatment of lung cancer.Background: Lung cancer has a high mortality rate in China, which is closely related to the fact that most lung cancer patients are not diagnosed until the malignancy is advanced. Challenges remain in the early detection, accurate diagnosis, monitoring and individual treatment of lung cancers. Artificial intelligence has developed to process large amounts of data, from clinical presentations to physiological images, which is essential for solving the complex issues with clinical medicine. Increasing evidence has suggested that artificial intelligence technology provides novel, promising strategies for the diagnosis and treatment of lung cancer.Methods: A review of literature was conducted in PubMed, EMBASE and Cochrane to identify the latest research on artificial intelligence and lung cancer, and ultimately to generate a narrative review.Conclusions: Artificial intelligence plays an important role in the imaging inspection, histopathology examination and genomics inspection of lung cancer. In addition, artificial intelligence has the ability to detect a small number of biomarkers, which is conducive to lung cancer monitoring. Moreover, the intelligent treatment of lung cancer has gradually become the trend of future development, whether in internal medicine or surgical treatment. It is believed that artificial intelligence could improve the early diagnosis of lung cancer and assist doctors in treating lung cancer patients individually.
Introduction Nowadays, immune checkpoint blockades (ICBs) have been extensively applied in non-small cell lung cancer (NSCLC) treatment. However, the outcome of anti-program death-1/program death ligand-1 (anti-PD-1/PD-L1) therapy is not satisfying in EGFR -mutant lung adenocarcinoma (LUAD) patients and its exact mechanisms have not been fully understood. Since tumor mutation burden (TMB) and tumor immune phenotype had been thought as potential predictors for efficacy of ICBs, we further studied the TMB and immune phenotype in LUAD patients to explore potential mechanisms for poor efficacy of ICBs in EGFR positive mutated patients and to find possible factors that could impact the tumor immune phenotype which might uncover some new therapeutic strategies or combination therapies. Methods We enrolled 223 LUAD patients who underwent surgery in our hospital. We evaluated TMB through targeted panel sequencing. The tumor immune phenotype, which could be divided into non-inflamed, intermediate and inflamed, was determined through immunohistochemistry using formalin-fixed paraffin-embedded samples. Enumeration data were analyzed by Chi-square test or Fisher exact test and shown as number (proportion). Logistic regression model was employed for univariate and multivariate analysis of the association between TMB levels and clinical characteristics. Results The median TMB level was 4.0445 mutations/Mb. Multivariate analysis showed the TMB level was significantly associated with age ( P =0.026), gender ( P =0.041) and EGFR mutation status ( P =0.015), and in EGFR -mutant patients we found a lower proportion of patients with mutated KRAS and BRCA2 . Furthermore, we found patients with or without metastatic lesions would have different immune phenotype ( P =0.007). And the mutational frequencies of ALK, CDKN2A, MAP2K1, IDH2 and PTEN were significantly different among three immune phenotypes. Conclusion Low TMB level could be the reason for the poor efficacy of ICBs in patients having EGFR mutation. And mutational frequencies of KRAS and BRCA2 were lower in EGFR -mutant patients. Furthermore, ALK, CDKN2A, MAP2K1, IDH2 and PTEN might involve in the formation of immune phenotypes.
Nitrifiers in chloraminated drinking water distribution systems can trigger severe nitrification, resulting in subsequent water quality deteriorations. However, the occurrence and dynamics of nitrifiers in secondary water supply systems (SWSSs), an important water supply component beyond the distribution mains, remain largely unexplored. This study investigated the density, distribution, and diversities of nitrifiers in different microbial habitats (water, biofilm, and sediment) in SWSSs with different characteristics. Quantitative polymerase chain reaction analysis indicated higher gene copy numbers of ammonia-oxidizing archaea, ammonia-oxidizing bacteria, comammox clade A, and strict nitrite-oxidizing bacteria in SWSSs relative to the distribution mains (1.3–2.1 log10GC/mL increase, P < 0.05). Comammox were preferentially enriched in biofilm and sediments of SWSS tanks, highlighting biofilm and sediment as important ecological niches for comammox. Illumina sequencing of 16S rRNA genes revealed the presence of diverse nitrifiers (e.g., Nitrospira, Nitrosomonas, Nitrotoga, and Nitrobacter) in SWSSs, with distinct bacterial nitrifier communities noted in SWSS water compared to the distribution main water (analysis of similarity (ANOSIM), P < 0.05). Different bacterial nitrifier communities were noted between water and sediments in SWSS tanks, indicating niche segregations of nitrifiers (ANOSIM, P < 0.05). The results highlight SWSS as an important reservoir of nitrifiers and provide insights into nitrification monitoring and control strategies in SWSSs.
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