Characterization of bronchoalveolar lavage fluid microbiota in acute exacerbations of bronchiectasis with non-tuberculous mycobacterial detection.

OBJECTIVES: Non-tuberculous mycobacteria (NTM) frequently colonize the airways of patients with bronchiectasis; however, there has been limited research into airway microbiota composition and predisposing factors for NTM detection during acute bronchiectasis exacerbations. METHODS: This study enrolled 34 patients with bronchiectasis experiencing acute exacerbations. Metagenomic next-generation sequencing was used to detect microbiota in bronchoalveolar lavage fluid (BALF), and bioinformatics methods were used for the comparative analysis of meaningful microbiota in the BALF of patients with acute exacerbations of bronchiectasis. A correlation analysis was conducted to identify susceptibility factors for NTM in patients with bronchiectasis. RESULTS: Compared with patients with community-acquired pneumonia, patients with bronchiectasis had higher detection rates of NTM (38.2%), Pseudomonas aeruginosa, and Haemophilus influenzae. Patients with NTM-positive bronchiectasis had lower body mass index and lipid profiles than patients who were NTM-negative. Metagenomic next-generation sequencing of BALF revealed patients who were NTM-positive had increased relative abundance of Rothia and other anaerobic genera compared with patients who were NTM-negative. Patients who were NTM-positive also showed higher levels of Streptococcus parasanguinis at the species level. Elevated Rothia mucilaginosa and S. parasanguinis correlated with decreased percentages of clusters of differentiation 3+ T lymphocytes and clusters of differentiation 3+ T-cell subgroups in peripheral blood. CONCLUSIONS: NTM colonization increases the risk of acute bronchiectasis exacerbations. Low body mass index, lipid levels, and isolation of R. mucilaginosa and S. parasanguinis in BALF are susceptibility factors for NTM colonization in patients with bronchiectasis.

Gene features of tumor-specific T cells relevant to immunotherapy, targeted therapy and chemotherapy in lung cancer.

1 Background: In lung cancer, the use of small-molecule inhibitors, chemotherapy and immunotherapy has led to unprecedented survival benefits in selected patients. Considering most patients will experience a relapse within a short period of time due to single drug resistance, combination therapy is also particularly important to improve patient prognosis. Therefore, more robust biomarkers to predict responses to immunotherapy, targeted therapy, chemotherapy and rationally drug combination therapies may be helpful in clinical treatment choices. 2 Methods: We defined tumor-specific T cells (TSTs) and their features (TSTGs) by single-cell RNA sequencing. We applied LASSO regression to filter out the most survival-relevant TSTGs to form the Tumor-specific T cell score (TSTS). Immunological characteristics, enriched pathways, and mutation were evaluated in high- and low TSTS groups. 3 Results: We identified six clusters of T cells as TSTs in lung cancer, and four most robust genes from 9 feature genes expressed only on tumor-specific T cells were screened to construct a tumor-specific T cells score (TSTS). TSTS was positively correlated with immune infiltration and angiogenesis and negatively correlated with malignant cell proliferation. Moreover, potential vascular-immune crosstalk in lung cancer provides the theoretical basis for combined anti-angiogenic and immunotherapy. Noticeable, patients in high TSTS had better response to ICB and targeted therapy and patients in the low TSTS group often benefit from chemotherapy. 4 Conclusion: The proposed TSTS is a promising indicator to predict immunotherapy, targeted therapy and chemotherapy responses in lung cancer patients for helping clinical treatment choices.

A comparative bibliometric analysis of Omicron and Delta variants during the COVID-19 pandemic.

BACKGROUND: To compare the research hotspots of infections with the Delta and Omicron variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 2019 (COVID-19) pandemic and to identify future research trends. METHODS: Studies about Delta and Omicron variant infections published over the last 3 years were retrieved from the Web of Science (WoS) database. A comparative bibliometric analysis was conducted through machine learning and visualization tools, including VOSviewer, Bibliographic Item Co-Occurrence Matrix Builder, and Graphical Clustering Toolkit. Research hotspots and trends in the field were analyzed, and the contributions and collaborations of countries, institutions, and authors were documented. A cross-sectional analysis of the relevant studies registered at ClinicalTrials.gov was also performed to clarify the direction of future research. RESULTS: A total of 1,787 articles distributed in 107 countries and 374 publications from 77 countries focused on the Delta and Omicron variants were included in our bibliometric analysis. The top five productive countries in both variants were the USA, China, the UK, India, and Germany. In 5,999 and 1,107 keywords identified from articles on the Delta and Omicron, the top two frequent keywords were the same: "COVID-19" (occurrence: 713, total link strength: 1,525 in Delta; occurrence: 137, total link strength: 354 in Omicron), followed by "SARS-CoV-2" (occurrence: 553, total link strength: 1,478 in Delta; occurrences 132, total link strength: 395 in Omicron). Five theme clusters from articles on Delta variant were identified: transmission, molecular structure, activation mode, epidemiology, and co-infection. While other three theme clusters were recognized for the Omicron variant: vaccine, human immune response, and infection control. Meanwhile, 21 interventional studies had been registered up to April 2022, most of which aimed to evaluate the immunogenicity and safety of different kinds of vaccines in various populations. CONCLUSIONS: Publications and clinical trials related to COVID-19 increased annually. As the first comparative bibliometric analysis for Delta and Omicron variants, we noticed that the relevant research trends have shifted from vaccine development to infection control and management of complications. The ongoing clinical studies will verify the safety and efficacy of promising drugs.

Development and validation of a multivariable predictive model for EGFR gene mutation status in patients with lung adenocarcinoma.

Detection of epidermal growth factor receptor (EGFR) is one real dilemma owing to the non-sufficient tissue for testing EGFR mutations in lung adenocarcinoma. A model for predicting EGFR mutations would be helpful for clinical decisions in those patients. A retrospective cohort of 1,196 patients diagnosed with lung adenocarcinoma was investigated between December 1, 2017, and December 31, 2019, in Renji Hospital, Shanghai, China. All patients were tested for EGFR mutations (amplification refractory mutation system, n=1,144; next-generation sequencing, n=52). Of 1,196 patients with lung adenocarcinoma, 944 met the inclusion criteria. A nomogram model was developed based on 567 patients and validated in 377 patients. Variables associated with EGFR mutations were age, sex, smoking history, lepidic predominant subtype, solid predominant subtype, mucinous adenocarcinoma, Ki67 expression, lobulation, solid texture in radiology, and pleural retraction. The nomogram based on the model performed well in the development group (c-index 0.789, 95% CI: 0.751-0.827), and the validation group (c-index 0.809, 95% CI: 0.771-0.847). At the probability cut-point of 0.7, the diagnostic efficiency was 82.7% in patients with NGS liquid biopsy. Decision curve analysis further confirmed the clinical usefulness of the nomogram, which showed that predicting the EGFR mutations probability applying this nomogram would be better than having all patients or none patients use this nomogram. A high probability group (>0.7) by nomogram model may suggest a high possibility of EGFR mutation, if tissue is limited, NGS-based ctDNA with liquid biopsy could be implemented effectively.

The predicting roles of carcinoembryonic antigen and its underlying mechanism in the progression of coronavirus disease 2019.

BACKGROUND: The coronavirus disease 2019 (COVID-19) has induced a worldwide epidemiological event with a high infectivity and mortality. However, the predicting biomarkers and their potential mechanism in the progression of COVID-19 are not well known. OBJECTIVE: The aim of this study is to identify the candidate predictors of COVID-19 and investigate their underlying mechanism. METHODS: The retrospective study was conducted to identify the potential laboratory indicators with prognostic values of COVID-19 disease. Then, the prognostic nomogram was constructed to predict the overall survival of COVID-19 patients. Additionally, the scRNA-seq data of BALF and PBMCs from COVID-19 patients were downloaded to investigate the underlying mechanism of the most important prognostic indicators in lungs and peripherals, respectively. RESULTS: In total, 304 hospitalized adult COVID-19 patients in Wuhan Jinyintan Hospital were included in the retrospective study. CEA was the only laboratory indicator with significant difference in the univariate (P < 0.001) and multivariate analysis (P = 0.020). The scRNA-seq data of BALF and PBMCs from COVID-19 patients were downloaded to investigate the underlying mechanism of CEA in lungs and peripherals, respectively. The results revealed the potential roles of CEA were significantly distributed in type II pneumocytes of BALF and developing neutrophils of PBMCs, participating in the progression of COVID-19 by regulating the cell-cell communication. CONCLUSION: This study identifies the prognostic roles of CEA in COVID-19 patients and implies the potential roles of CEACAM8-CEACAM6 in the progression of COVID-19 by regulating the cell-cell communication of developing neutrophils and type II pneumocyte.

A bibliometric analysis using VOSviewer of publications on COVID-19.

BACKGROUND: As a global pandemic, COVID-19 has aroused great concern in the last few months and a growing number of related researches have been published. Therefore, a bibliometric analysis of these publications may provide a direction of hot topics and future research trends. METHODS: The global literatures about COVID-19 published between 2019 and 2020 were scanned in the Web of Science collection database. "COVID-19" "Novel Coronavirus" "2019-nCoV" and "SARS-CoV-2" were used as the keywords to reach the relevant publications. VOSviewer was applied to perform the bibliometric analysis of these articles. RESULTS: Totally 3,626 publications on the topic of COVID-19 were identified and "COVID-19" with a total link strength of 2,649 appeared as the most frequent keyword, which had a strong link to "pneumonia" and "epidemiology". The mean citation count of the top 100 most cited articles was 96 (range, 26-883). Most of them were descriptive studies and concentrated on the clinical features. The highest-ranking journal was British medical journal with 211 publications and the most cited journal was Lancet with 2,485 citation counts. Eleven articles written by Christian Drosten from Berlin Institute of Virology have been cited for 389 times and 40 articles from Chinese Academy of Sciences have been cited for 1,597 times which are the most cited author and organization. The number of collaborators with China is 44 and the total link strength is 487. The main partners of China are USA, England and Germany. The published literatures have focused on three topics: disease management, clinical features and pathogenesis. CONCLUSIONS: The current growth trends predict a large increase in the number of global publications on COVID-19. China made the most outstanding contribution within this important field. Disease treatment, spike protein and vaccine may be hotspots in the future.