Human genetic associations of the airway microbiome in chronic obstructive pulmonary disease.

Little is known about the relationships between human genetics and the airway microbiome. Deeply sequenced airway metagenomics, by simultaneously characterizing the microbiome and host genetics, provide a unique opportunity to assess the microbiome-host genetic associations. Here we performed a co-profiling of microbiome and host genetics with the identification of over 5 million single nucleotide polymorphisms (SNPs) through deep metagenomic sequencing in sputum of 99 chronic obstructive pulmonary disease (COPD) and 36 healthy individuals. Host genetic variation was the most significant factor associated with the microbiome except for geography and disease status, with its top 5 principal components accounting for 12.11% of the microbiome variability. Within COPD individuals, 113 SNPs mapped to candidate genes reported as genetically associated with COPD exhibited associations with 29 microbial species and 48 functional modules (P < 1 × 10-5), where Streptococcus salivarius exhibits the strongest association to SNP rs6917641 in TBC1D32 (P = 9.54 × 10-8). Integration of concurrent host transcriptomic data identified correlations between the expression of host genes and their genetically-linked microbiome features, including NUDT1, MAD1L1 and Veillonella parvula, TTLL9 and Stenotrophomonas maltophilia, and LTA4H and Haemophilus influenzae. Mendelian randomization analyses revealed a potential causal link between PARK7 expression and microbial type III secretion system, and a genetically-mediated association between COPD and increased relative abundance of airway Streptococcus intermedius. These results suggest a previously underappreciated role of host genetics in shaping the airway microbiome and provide fresh hypotheses for genetic-based host-microbiome interactions in COPD.

Targeting BRD4 with PROTAC degrader ameliorates LPS-induced acute lung injury by inhibiting M1 alveolar macrophage polarization.

OBJECTIVES: Acute lung injury (ALI) is a highly inflammatory condition with the involvement of M1 alveolar macrophages (AMs) polarization, eventually leading to the development of non-cardiogenic edema in alveolar and interstitial regions, accompanied by persistent hypoxemia. Given the significant mortality rate associated with ALI, it is imperative to investigate the underlying mechanisms of this condition so as to identify potential therapeutic targets. The therapeutic effects of the inhibition of bromodomain containing protein 4 (BRD4), an epigenetic reader, has been proven with high efficacy in ameliorating various inflammatory diseases through mediating immune cell activation. However, little is known about the therapeutic potential of BRD4 degradation in acute lung injury. METHODS: This study aimed to assess the protective efficacy of ARV-825, a novel BRD4-targeted proteolysis targeting chimera (PROTAC), against ALI through histopathological examination in lung tissues and biochemical analysis in bronchoalveolar lavage fluid (BALF). Additionally, the underlying mechanism by which BRD4 regulated M1 AMs was elucidated by using CUT & Tag assay. RESULTS: In this study, we found the upregulation of BRD4 in a lipopolysaccharide (LPS)-induced ALI model. Furthermore, we observed that intraperitoneal administration of ARV-825, significantly alleviated LPS-induced pulmonary pathological changes and inflammatory responses. These effects were accompanied by the suppression of M1 AMs. In addition, our findings revealed that the administration of ARV-825 effectively suppressed M1 AMs by inhibiting the expression of IRF7, a crucial transcriptional factor involved in M1 macrophages. CONCLUSION: Our study suggested that targeting BRD4 using ARV-825 is a potential therapeutic approach for ALI.

Acute exacerbation prediction of COPD based on Auto-metric graph neural network with inspiratory and expiratory chest CT images.

Chronic obstructive pulmonary disease (COPD) is a widely prevalent disease with significant mortality and disability rates and has become the third leading cause of death globally. Patients with acute exacerbation of COPD (AECOPD) often substantially suffer deterioration and death. Therefore, COPD patients deserve special consideration regarding treatment in this fragile population for pre-clinical health management. Based on the above, this paper proposes an AECOPD prediction model based on the Auto-Metric Graph Neural Network (AMGNN) using inspiratory and expiratory chest low-dose CT images. This study was approved by the ethics committee in the First Affiliated Hospital of Guangzhou Medical University. Subsequently, 202 COPD patients with inspiratory and expiratory chest CT Images and their annual number of AECOPD were collected after the exclusion. First, the inspiratory and expiratory lung parenchyma images of the 202 COPD patients are extracted using a trained ResU-Net. Then, inspiratory and expiratory lung Radiomics and CNN features are extracted from the 202 inspiratory and expiratory lung parenchyma images by Pyradiomics and pre-trained Med3D (a heterogeneous 3D network), respectively. Last, Radiomics and CNN features are combined and then further selected by the Lasso algorithm and generalized linear model for determining node features and risk factors of AMGNN, and then the AECOPD prediction model is established. Compared to related models, the proposed model performs best, achieving an accuracy of 0.944, precision of 0.950, F1-score of 0.944, ad area under the curve of 0.965. Therefore, it is concluded that our model may become an effective tool for AECOPD prediction.

Wogonoside Ameliorates Airway Inflammation and Mucus Hypersecretion via NF-κB/STAT6 Signaling in Ovalbumin-Induced Murine Acute Asthma.

Asthma is recognized as a chronic respiratory illness characterized by airway inflammation and airway hyperresponsiveness. Wogonoside, a flavonoid glycoside, is reported to significantly alleviate the inflammation response and oxidative stress. Herein, this study aimed to investigate the therapeutic effect and underlying mechanism of wogonoside on airway inflammation and mucus hypersecretion in a murine asthma model and in human bronchial epithelial cells (16HBE). BALB/c mice were sensitized and challenged with ovalbumin (OVA). Pulmonary function and the number of cells in the bronchoalveolar lavage fluid (BALF) were examined. Pathological changes in lung tissue in each group were evaluated via hematoxylin and eosin and periodic acid-Schiff staining, and changes in levels of cytokines in BALF and of immunoglobulin E in serum were determined via an enzyme-linked immunosorbent assay. The expression of relevant genes in lung tissue was analyzed via real-time PCR. Western blotting and immunofluorescence were employed to detect the expression of relevant proteins in lung tissue and 16HBE cells. Treatment with 10 and 20 mg/kg wogonoside significantly attenuated the OVA-induced increase of inflammatory cell infiltration, mucus secretion, and goblet cell percentage and improved pulmonary function. Wogonoside treatment reduced the level of T-helper 2 cytokines including interleukin (IL)-4, IL-5, and IL-13 in BALF and of IgE in serum and decreased the mRNA levels of cytokines (IL-4, IL-5, IL-6, IL-13, and IL-1ß and tumor necrosis factor-α), chemokines (CCL-2, CCL-11, and CCL-24), and mucoproteins (MUC5AC, MUC5B, and GOB5) in lung tissues. The expression of MUC5AC and the phosphorylation of STAT6 and NF-κB p65 in lung tissues and 16HBE cells were significantly downregulated after wogonoside treatment. Thus, wogonoside treatment may effectively decrease airway inflammation, airway remodeling, and mucus hypersecretion via blocking NF-κB/STAT6 activation.

Association between cooking fuel exposure and respiratory health: Longitudinal evidence from the China Health and Retirement Longitudinal Study (CHARLS).

The epidemiological evidences for the association between cooking fuel exposure and respiratory health were inconsistent, and repeated-measures prospective evaluation of cooking fuel exposure was still lacking. We assessed the longitudinal association of chronic lung disease (CLD) and lung function with cooking fuel types among Chinese adults aged ≥ 40 years. In this prospective, nationwide representative cohort of the China Health and Retirement Longitudinal Study from 2011 to 2018, 9004 participants from 28 provinces in China were included. CLD was identified based on self-reported physician diagnosis in 2018. Lung function was assessed by peak expiratory flow (PEF) in 2011, 2013 and 2015. Multivariable logistic and linear mixed-effects repeated-measures models were conducted to measure the associations of CLD and PEF with cooking fuel types. Three-level mixed-effects model was performed as sensitivity analysis. Among the participants, 3508 and 3548 participants used persistent solid and clean cooking fuels throughout the survey, and 1948 participants who used solid cooking fuels at baseline switched to clean cooking fuels. Use of persistent clean cooking fuels (adjusted odds ratio [aOR] = 0.73, 95 % confidence interval [CI]: 0.61, 0.88) and switch of solid fuels to clean fuels (aOR = 0.81, 95 % CI: 0.67, 0.98) were associated with lower risk of CLD. The use of clean cooking fuels throughout the survey and switch of solid fuels to clean fuels in 2013 were also significantly associated with higher PEF level. Similar results were observed in stratified analyses and different statistical models. The evidence from CHARLS cohort suggested that reducing solid cooking fuel exposure was associated with lower risk of CLD and better lung function. Given the recent evidence, improving household air quality will reduce the burden of chronic lung diseases.

Prognostic analysis of high-flow nasal cannula therapy and non-invasive ventilation in mild to moderate hypoxemia patients and construction of a machine learning model for 48-h intubation prediction-a retrospective analysis of the MIMIC database.

Background: This study aims to investigate the clinical outcome between high-flow nasal cannula (HFNC) and non-invasive ventilation (NIV) therapy in mild to moderate hypoxemic patients on the first ICU day and to develop a predictive model of 48-h intubation. Methods: The study included adult patients from the MIMIC III and IV databases who first initiated HFNC or NIV therapy due to mild to moderate hypoxemia (100 < PaO2/FiO2 ≤ 300). The 48-h and 30-day intubation rates were compared using cross-sectional and survival analysis. Nine machine learning and six ensemble algorithms were deployed to construct the 48-h intubation predictive models, of which the optimal model was determined by its prediction accuracy. The top 10 risk and protective factors were identified using the Shapley interpretation algorithm. Result: A total of 123,042 patients were screened, of which, 673 were from the MIMIC IV database for ventilation therapy comparison (HFNC n = 363, NIV n = 310) and 48-h intubation predictive model construction (training dataset n = 471, internal validation set n = 202) and 408 were from the MIMIC III database for external validation. The NIV group had a lower intubation rate (23.1% vs. 16.1%, p = 0.001), ICU 28-day mortality (18.5% vs. 11.6%, p = 0.014), and in-hospital mortality (19.6% vs. 11.9%, p = 0.007) compared to the HFNC group. Survival analysis showed that the total and 48-h intubation rates were not significantly different. The ensemble AdaBoost decision tree model (internal and external validation set AUROC 0.878, 0.726) had the best predictive accuracy performance. The model Shapley algorithm showed Sequential Organ Failure Assessment (SOFA), acute physiology scores (APSIII), the minimum and maximum lactate value as risk factors for early failure and age, the maximum PaCO2 and PH value, Glasgow Coma Scale (GCS), the minimum PaO2/FiO2 ratio, and PaO2 value as protective factors. Conclusion: NIV was associated with lower intubation rate and ICU 28-day and in-hospital mortality. Further survival analysis reinforced that the effect of NIV on the intubation rate might partly be attributed to the other impact factors. The ensemble AdaBoost decision tree model may assist clinicians in making clinical decisions, and early organ function support to improve patients' SOFA, APSIII, GCS, PaCO2, PaO2, PH, PaO2/FiO2 ratio, and lactate values can reduce the early failure rate and improve patient prognosis.

COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images.

Chronic obstructive pulmonary disease (COPD) is a common lung disease that can lead to restricted airflow and respiratory problems, causing a significant health, economic, and social burden. Detecting the COPD stage can provide a timely warning for prompt intervention in COPD patients. However, existing methods based on inspiratory (IN) and expiratory (EX) chest CT images are not sufficiently accurate and efficient in COPD stage detection. The lung region images are autonomously segmented from IN and EX chest CT images to extract the [Formula: see text] lung radiomics and [Formula: see text] 3D CNN features. Furthermore, a strategy for concatenating and selecting features was employed in COPD stage detection based on radiomics and 3D CNN features. Finally, we combine all the radiomics, 3D CNN features, and factor risks (age, gender, and smoking history) to detect the COPD stage based on the Auto-Metric Graph Neural Network (AMGNN). The AMGNN with radiomics and 3D CNN features achieves the best performance at 89.7[Formula: see text] of accuracy, 90.9[Formula: see text] of precision, 89.5[Formula: see text] of F1-score, and 95.8[Formula: see text] of AUC compared to six classic machine learning (ML) classifiers. Our proposed approach demonstrates high accuracy in detecting the stage of COPD using both IN and EX chest CT images. This method can potentially establish an efficient diagnostic tool for patients with COPD. Additionally, we have identified radiomics and 3D CNN as more appropriate biomarkers than Parametric Response Mapping (PRM). Moreover, our findings indicate that expiration yields better results than inspiration in detecting the stage of COPD.

Single-cell analysis reveals alterations in cellular composition and cell-cell communication associated with airway inflammation and remodeling in asthma.

BACKGROUND: Asthma is a heterogeneous disease characterized by airway inflammation and remodeling, whose pathogenetic complexity was associated with abnormal responses of various cell types in the lung. The specific interactions between immune and stromal cells, crucial for asthma pathogenesis, remain unclear. This study aims to determine the key cell types and their pathological mechanisms in asthma through single-cell RNA sequencing (scRNA-seq). METHODS: A 16-week mouse model of house dust mite (HDM) induced asthma (n = 3) and controls (n = 3) were profiled with scRNA-seq. The cellular composition and gene expression profiles were assessed by bioinformatic analyses, including cell enrichment analysis, trajectory analysis, and Gene Set Enrichment Analysis. Cell-cell communication analysis was employed to investigate the ligand-receptor interactions. RESULTS: The asthma model results in airway inflammation coupled with airway remodeling and hyperresponsiveness. Single-cell analysis revealed notable changes in cell compositions and heterogeneities associated with airway inflammation and remodeling. GdT17 cells were identified to be a primary cellular source of IL-17, related to inflammatory exacerbation, while a subpopulation of alveolar macrophages exhibited numerous significantly up-regulated genes involved in multiple pathways related to neutrophil activities in asthma. A distinct fibroblast subpopulation, marked by elevated expression levels of numerous contractile genes and their regulators, was observed in increased airway smooth muscle layer by immunofluorescence analysis. Asthmatic stromal-immune cell communication significantly strengthened, particularly involving GdT17 cells, and macrophages interacting with fibroblasts. CXCL12/CXCR4 signaling was remarkedly up-regulated in asthma, predominantly bridging the interaction between fibroblasts and immune cell populations. Fibroblasts and macrophages could jointly interact with various immune cell subpopulations via the CCL8/CCR2 signaling. In particular, fibroblast-macrophage cell circuits played a crucial role in the development of airway inflammation and remodeling through IL1B paracrine signaling. CONCLUSIONS: Our study established a mouse model of asthma that recapitulated key pathological features of asthma. ScRNA-seq analysis revealed the cellular landscape, highlighting key pathological cell populations associated with asthma pathogenesis. Cell-cell communication analysis identified the crucial ligand-receptor interactions contributing to airway inflammation and remodeling. Our findings emphasized the significance of cell-cell communication in bridging the possible causality between airway inflammation and remodeling, providing valuable hints for therapeutic strategies for asthma.

ErbB3 Governs Endothelial Dysfunction in Hypoxia-Induced Pulmonary Hypertension.

BACKGROUND: Pulmonary hypertension, characterized by vascular remodeling, currently lacks curative therapeutic options. The dysfunction of pulmonary artery endothelial cells plays a pivotal role in the initiation and progression of pulmonary hypertension (PH). ErbB3 (human epidermal growth factor receptor 3), also recognized as HER3, is a member of the ErbB family of receptor tyrosine kinases. METHODS: Microarray, immunofluorescence, and Western blotting analyses were conducted to investigate the pathological role of ErbB3. Blood samples were collected for biomarker examination from healthy donors or patients with hypoxic PH. The pathological functions of ErbB3 were further validated in rodents subjected to chronic hypoxia- and Sugen-induced PH, with or without adeno-associated virus-mediated ErbB3 overexpression, systemic deletion, or endothelial cell-specific ErbB3 knockdown. Primary human pulmonary artery endothelial cells and pulmonary artery smooth muscle cells were used to elucidate the underlying mechanisms. RESULTS: ErbB3 exhibited significant upregulation in the serum, lungs, distal pulmonary arteries, and pulmonary artery endothelial cells isolated from patients with PH compared with those from healthy donors. ErbB3 overexpression stimulated hypoxia-induced endothelial cell proliferation, exacerbated pulmonary artery remodeling, elevated systolic pressure in the right ventricle, and promoted right ventricular hypertrophy in murine models of PH. Conversely, systemic deletion or endothelial cell-specific knockout of ErbB3 yielded opposite effects. Coimmunoprecipitation and proteomic analysis identified YB-1 (Y-box binding protein 1) as a downstream target of ErbB3. ErbB3 induced nuclear translocation of YB-1 and subsequently promoted hypoxia-inducible factor 1/2α transcription. A positive loop involving ErbB3-periostin-hypoxia-inducible factor 1/2α was identified to mediate the progressive development of this disease. MM-121, a human anti-ErbB3 monoclonal antibody, exhibited both preventive and therapeutic effects against hypoxia-induced PH. CONCLUSIONS: Our study reveals, for the first time, that ErbB3 serves as a novel biomarker and a promising target for the treatment of PH.

A preliminary study of dynamic interactive simulation and computational CT scan of the ideal alveolus model.

BACKGROUND: While the development of CT imaging technique has brought cognition of in vivo organs, the resolution of CT images and their static characteristics have gradually become barriers of microscopic tissue research. PURPOSE: Previous research used the finite element method to study the airflow and gas exchange in the alveolus and acinar to show the fate of inhaled aerosols and studied the diffusive, convective, and sedimentation mechanisms. Our study combines these techniques with CT scan simulation to study the mechanisms of respiratory movement and its imaging appearance. METHODS: We use 3D fluid-structure interaction simulation to study the movement of an ideal alveolus under regular and forced breathing situations and ill alveoli with different tissue elasticities. Additionally, we use the Monte Carlo algorithm within the OpenGATE platform to simulate the computational CT images of the dynamic process with different designated resolutions. The resolutions show the relationship between the kinematic model of the human alveolus and its imaging appearance. RESULTS: The results show that the alveolus and the wall thickness can be seen with an image resolution smaller than 15.6 µm. With ordinary CT resolution, the alveolus is expressed with four voxels. CONCLUSIONS: This is a preliminary study concerning the imaging appearance of the dynamic alveolus model. This technique will be used to study the imaging appearance of the dynamic bronchial tree and the lung lobe models in the future.

Differentiation between lung allograft rejection and infection using donor-derived cell-free DNA and pathogen detection by metagenomic next-generation sequencing.

Background: In lung transplant recipients (LTRs), the primary causes of mortality are rejection and infection, which often present similar symptoms, making differentiation challenging. This study aimed to explore the diagnostic efficacy of plasma donor-derived cell-free DNA (dd-cfDNA) in conjunction with metagenomic next-generation sequencing (mNGS) for pathogen detection in differentiation between lung allograft rejection and infection in LTRs experiencing new-onset pulmonary complications. Methods: We conducted a retrospective study on 188 LTRs who underwent lung or heart-lung transplantation at our institution from 2015 to 2021. The LTRs were categorized into three groups: stable, rejection, and infection. We measured plasma dd-cfDNA levels and utilized both mNGS and culture methods to identify pathogens in the bronchoalveolar lavage fluid (BALF). Results: The rejection group exhibited the highest levels of plasma dd-cfDNA (median 1.34 %, interquartile range [IQR] 1.06-2.19 %) compared to the infection group (median 0.72 %, IQR 0.62-1.07 %) and the stable group (median 0.69 %, IQR 0.58-0.78 %) (both p < 0.001). Within the infection group, a significantly higher level of dd-cfDNA was observed in the cytomegalovirus infection subgroup (p < 0.001), but not in the fungal (p > 0.05) or bacterial infection subgroups (p > 0.05), when compared to the stable group. Elevated dd-cfDNA levels, in combination with negative mNGS results, strongly indicated lung allograft rejection, with a positive predictive value and negative predictive value of 88.7 % and 99.2 %, respectively. Conclusions: Plasma dd-cfDNA in combination with BALF pathogen detection by mNGS shows satisfactory accuracy in differentiating lung allograft rejection from infectious complications.

A novel approach to pulmonary bronchial tree model construction and performance index study.

The demand for respiratory disease and dynamic breathing studies has continuously driven researchers to update the pulmonary bronchial tree's morphology model. This study aims to construct a bronchial tree morphology model efficiently and effectively with practical algorithms. We built a performance index system using failure branch rate, volume ratio, and coefficient of variation of terminal volumes to evaluate the model performance. We optimized the parameter settings and found the best options to build the morphology model, and we constructed a 14th-generation bronchial tree model with a decent performance index. The dimensions of our model closely matched published data from anatomic in vitro measurements. The proposed model is adjustable and computable and will be used in future dynamic breathing simulations and respiratory disease studies.

Guidelines of integrated Chinese and western medicine for diagnosis and treatment of chronic obstructive pulmonary disease (2022).

Chronic obstructive pulmonary disease (COPD), with high prevalence rate, mortality, disability rate, and heavy disease burden, has become a critical chronic disease seriously threatening public health worldwide. Traditional Chinese medicine and Western medicine both have shown advantages in diagnosing and treating COPD, which has been widely applied in the clinics. In order to improve the diagnostic and treatment level for COPD with integrated traditional Chinese and Western medicine, the Guidelines of Integrated Chinese and Western Medicine for Diagnosis and Treatment of COPD were developed by the Internal Medicine Committee of the World Federation of Chinese Medicine Societies. First, a multidisciplinary working group was established. Development methods and processes of international clinical practice guidelines were adopted in the whole research. In final, a total of 13 recommendations for the diagnosis and treatment of COPD were established based on available evidence with the best quality. Meanwhile, characteristics of integrated traditional Chinese and Western medicine in treating COPD were taken into account with pros and cons of each intervention. The guidelines could be used as a reference for physicians in respiratory medicine departments (traditional Chinese medicine, integrated traditional Chinese and Western medicine, and Western medicine) at various levels of medical institutions in their diagnosis and treatment.

Efficacy of different durations of antifungal prophylaxis with nebulized amphotericin B after lung transplantation: a retrospective cohort study.

Background: Nebulized amphotericin B (NAB) is recommended for preventing invasive fungal diseases (IFDs) after lung transplantation. However, the optimal duration of NAB treatment is still unknown. This study aimed to compare the effectiveness of three different durations of antifungal prophylaxis with NAB after lung transplantation: a prolonged course beyond post-transplant 3 months, a medium course of 2 weeks to 3 months, and a short course of less than 2 weeks. Methods: This a single-center retrospective cohort study analyzed 333 patients who underwent lung or heart-lung transplantation between January 2015 and November 2021. Results: A prolonged course of NAB treatment was associated with a significantly lower incidence of IFDs (12.6%) at 1 year post-transplant compared with a short (50.9%) or a medium course (28.0%) (P<0.001). There was no significant difference in the rates of adverse effects among the three durations of NAB treatment (P>0.05). A prolonged course of NAB treatment was associated with a significantly higher 1-year survival rate (94.7%) compared with a short (36.8%) or a medium course (72.0%) (P<0.001). Conclusions: A prolonged course of NAB treatment provided better protection against IFDs than a short or medium course after lung transplantation. Prolonged use of NAB did not significantly increase the incidence of adverse effects.

Longitudinal reciprocal association between rheumatoid arthritis and chronic obstructive pulmonary disease and mediation of inflammation.

OBJECTIVES: To elucidate the longitudinal reciprocal association between rheumatoid arthritis (RA) and chronic obstructive pulmonary disease (COPD), and the mediating role of systemic inflammation in the association. METHODS: 403045 participants from UK Biobank were enrolled in this study. A cross-lagged panel model was used to investigate the longitudinal reciprocal association between RA and COPD. Cox-proportional hazard regression and logistic regression models were also conducted to examine the association between baseline RA and COPD during follow-up, and vice versa. Causal mediation analysis was then performed to explore the mediating roles of 160 systemic inflammatory biomarkers in the bidirectional association. RESULTS: At baseline, 4755 (1.2%) and 6989 (1.7%) individuals were diagnosed with RA and COPD, respectively. After adjusting for the covariates, the result of cross-lagged panel model revealed a bidirectional association between RA and COPD (ß = 0.018, P < 0.001 for RA→COPD path; ß = 0.010, P < 0.001 for COPD→RA path). In the non-COPD population, the risk of future COPD was increased in RA patients (Cox: HR = 1.65, 95% CI, 1.50-1.83; logistic: OR = 1.85, 95% CI, 1.66-2.07). In the non-RA population, baseline COPD was associated with a higher risk of RA during follow-up (Cox: HR = 1.67, 95% CI, 1.44-1.92; logistic: OR = 1.70, 95% CI, 1.47-1.97). Five inflammatory factors mediated the RA→COPD path, and C-reactive protein mediated the COPD→RA path (FDR < 0.05). CONCLUSIONS: A significant bidirectional association exists between RA and COPD, and it is partially mediated by systemic inflammation.

Multi-Layer Perceptron Classifier with the Proposed Combined Feature Vector of 3D CNN Features and Lung Radiomics Features for COPD Stage Classification.

Computed tomography (CT) has been regarded as the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Therefore, chest CT images should provide more information for COPD diagnosis, such as COPD stage classification. This paper proposes a features combination strategy by concatenating three-dimension (3D) CNN features and lung radiomics features for COPD stage classification based on the multi-layer perceptron (MLP) classifier. First, 465 sets of chest HRCT images are automatically segmented by a trained ResU-Net, obtaining the lung images with the Hounsfield unit. Second, the 3D CNN features are extracted from the lung region images based on a truncated transfer learning strategy. Then, the lung radiomics features are extracted from the lung region images by PyRadiomics. Third, the MLP classifier with the best classification performance is determined by the 3D CNN features and the lung radiomics features. Finally, the proposed combined feature vector is used to improve the MLP classifier's performance. The results show that compared with CNN models and other ML classifiers, the MLP classifier with the best classification performance is determined. The MLP classifier with the proposed combined feature vector has achieved accuracy, mean precision, mean recall, mean F1-score, and AUC of 0.879, 0.879, 0.879, 0.875, and 0.971, respectively. Compared to the MLP classifier with the 3D CNN features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.8% (accuracy), 5.3% (mean precision), 5.8% (mean recall), 5.4% (mean F1-score), and 2.5% (AUC). Compared to the MLP classifier with lung radiomics features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.0% (accuracy), 5.1% (mean precision), 5.0% (mean recall), 5.1% (mean F1-score), and 2.1% (AUC). Therefore, it is concluded that our method is effective in improving the classification performance for COPD stage classification.

Impacts of short-term low-level exposure to air pollutants on hospital admissions for pulmonary sepsis in elderly patients.

BACKGROUND: Acute exposures to high levels of air pollutants are thought to be associated with hospitalization of patients with lung infection, while relatively little is known about the association between air pollutants and HOSPITAL ADMISSIONS FOR pulmonary sepsis. OBJECTIVES: To assess the correlation between low-level exposure to air pollutants and the hospitalizations for pulmonary sepsis in elderly patients. METHODS: A total of 249 elderly patients with pulmonary sepsis from January 2018 to December 2020 in Shenzhen people's hospital were included. The data regarding hospitalizations for pulmonary sepsis, meteorological factors, and daily average levels of air pollutants on single-day lags (Lag0 to Lag7) in Shenzhen were collected. Low-level exposure was defined as the annual means of air pollutants below the levels of the Ambient Air Quality Standard (AAQS) in China (NO. GB3095-2012) and/or Global Air Quality Guidelines (AQG). A time-stratified case-crossover study design approach was used to evaluate the associations between exposure to air pollutants and incidence of the disease, univariate and multivariate logistic regression analysis to analyze the association between levels of air pollutants and hospitalizations for pulmonary sepsis in elderly patients. RESULTS: Exposure to PM1(P = 0.007, Lag 2 day; P = 0.038, Lag6 day), PM2.5(P = 0.046, Lag2 day), PM10(P = 0.048, Lag4 day), and O3(P = 0.044, Lag6 day) was positively correlated with elevated risk of hospitalizations for pulmonary sepsis. In addition, logistic regression analysis revealed that exposure to PM1 (OR = 1.833, 95%CI:1.032 ~ 3.256, Lag6 day) and O3 (OR = 2.091, 95%CI:1.019 ~ 4.289, Lag6 day) were the independent risk factors of pulmonary sepsis in elderly patients. CONCLUSION: Our results demonstrate that short-term low-level exposure to PM1 and O3 could elevate the risk of hospitalizations for pulmonary sepsis in elderly patients in Shenzhen, providing evidence for developing early warning and screening systems for pulmonary sepsis.

Exploring the Effectiveness of Multi-Objective Training for Organ Substructure Segmentation in Medical Imaging.

Chronic obstructive pulmonary disease (COPD) is closely related to the right ventricle and lung lobes. This study focuses on the segmentation of the right ventricle and lung lobes. We conducted experiments using the MMWHS and our lung lobe datasets and evaluated the segmentation using different training models. We observed that the multi-objective segmentation approach has advantages over single-objective segmentation in segmenting the right ventricle and lung lobes. For the segmentation of the right ventricle, the multi-objective segmentation approach yielded an improvement of 2.0% in the Dice coefficient and 2.5% in the Jaccard index compared to single-objective segmentation. For the segmentation of five lung lobes, the multi-objective segmentation outperformed the single-objective segmentation with Dice coefficient improvements of 1.4%, 1.0%, 1.5%, 0.7%, and 1.3%, respectively.

Prophylactic antibiotics for preventing ventilator-associated pneumonia: a pairwise and Bayesian network meta-analysis.

BACKGROUND: The role of prophylactic antibiotics in preventing ventilator-associated pneumonia (VAP) in patients undergoing invasive mechanical ventilation (IMV) remains unclear. This network meta-analysis compared the efficacy and safety of antibiotic prophylaxis in preventing VAP in an IMV population in intensive-care units (ICUs). METHODS: We searched the PubMed, Web of Science, Embase, and Cochrane Library databases from inception to December 2021, to identify relevant studies assessing the impact of prophylactic antibiotics on the incidence of VAP, the mortality, and the duration of ICU stays and hospitalization to perform a meta-analysis. RESULTS: Thirteen studies (2144 patients) were included, 12 of which were selected for the primary analysis, which revealed that treatment with prophylactic antibiotics resulted in a lower VAP rate compared with control groups [risk ratio (RR) = 0.62]. Bayesian network meta-analysis indicated that aerosolized tobramycin and intravenous ampicillin-sulbactam presented the greatest likelihood being the most efficient regimen for reducing VAP. CONCLUSIONS: Antibiotic prophylaxis may reduce the incidence of VAP, but not the mortality, for adult patients undergoing IMV in ICUs. Tobramycin via nebulization and ampicillin-sulbactam via intravenous administration presented the greatest likelihood of being the most efficient regimen for preventing VAP. However, well-designed randomized studies are warranted before definite recommendations can be made.