Artificial intelligence in COPD CT images: identification, staging, and quantitation.

Chronic obstructive pulmonary disease (COPD) stands as a significant global health challenge, with its intricate pathophysiological manifestations often demanding advanced diagnostic strategies. The recent applications of artificial intelligence (AI) within the realm of medical imaging, especially in computed tomography, present a promising avenue for transformative changes in COPD diagnosis and management. This review delves deep into the capabilities and advancements of AI, particularly focusing on machine learning and deep learning, and their applications in COPD identification, staging, and imaging phenotypes. Emphasis is laid on the AI-powered insights into emphysema, airway dynamics, and vascular structures. The challenges linked with data intricacies and the integration of AI in the clinical landscape are discussed. Lastly, the review casts a forward-looking perspective, highlighting emerging innovations in AI for COPD imaging and the potential of interdisciplinary collaborations, hinting at a future where AI doesn't just support but pioneers breakthroughs in COPD care. Through this review, we aim to provide a comprehensive understanding of the current state and future potential of AI in shaping the landscape of COPD diagnosis and management.

Respiratory physiological mechanism of two types of equal-intensity inspiratory muscle training in stable patients with chronic obstructive pulmonary disease.

PURPOSE: This study aimed to investigate the respiratory physiological changes resulting from short-term inspiratory resistance training (R-IMT) and inspiratory threshold training (T-IMT) in patients with chronic obstructive pulmonary disease (COPD) and to compare the mechanisms of the two training methods. PATIENTS AND METHODS: A total of 75 stable patients with COPD combined with inspiratory muscle weakness were randomly allocated to three groups: R-IMT (n = 26), T-IMT (n = 24), and control (n = 25). Before and after 8 weeks of inspiratory muscle training(IMT), cardiopulmonary exercise tests were conducted to assess respiratory patterns, respiratory central drive, exercise tolerance, and ventilation efficiency. RESULTS: After 8 weeks of IMT, Inspiratory muscle strength, represented by MIP (maximum inspiratory mouth pressure) and exercise capacity increased during exercise in both IMT groups (P < 0.05). In the R-IMT group, inspiratory time (Ti) prolonged (P < 0.05), tidal volume (Vt) increased (P < 0.05), ventilation efficiency (represented by ventilation-center coupling) increased (P < 0.05) during exercise. Conversely, the T-IMT group did not exhibit any of these changes after IMT (P > 0.05). CONCLUSION: In summary, the improvement in exercise tolerance was associated with an increase in inspiratory muscle reserve in both R-IMT and T-IMT. However, only R-IMT was associated with deeper and slower breathing, as well as improved ventilation efficiency.

[Antitumor Study of Neoantigen-reactive T Cells Co-expressing IL-7 and CCL19 
in Mouse Lung Cancer].

BACKGROUND: Neoantigen reactive T cell (NRT) has the ability to inhibit the growth of tumors expressing specific neoantigens. However, due to the difficult immune infiltration and the inhibition of tumor microenvironment, the therapeutic effect of NRT in solid tumors is limited. In this study, we designed NRT cells (7×19 NRT) that can express both interleukin-7 (IL-7) and chemokine C-C motif ligand 19 (CCL19) in mouse lung cancer cells, and evaluated the difference in anti-tumor effect between 7×19 NRT cells and conventional NRT cells. METHODS: We performed next-generation sequencing and neoantigen prediction for mouse Lewis lung carcinoma (LLC), prepared RNA vaccine, cultured NRT cells, constructed retroviral vectors encoding IL-7 and CCL19, transduced NRT cells and IL-7 and CCL19 were successfully expressed, and 7×19 NRT was successfully obtained. The anti-tumor effect was evaluated in vivo and in vitro in mice. RESULTS: The 7×19 NRT cells significantly enhanced the proliferation and invasion ability of T cells by secreting IL-7 and CCL19, achieved significant tumor inhibition in the mouse lung cancer and extended the survival period of mice. The T cell infiltration into tumor tissue and the necrosis of tumor tissue increased significantly after 7×19 NRT treatment. In addition, both 7×19 NRT treatment and conventional NRT treatment were safe. CONCLUSIONS: The anti-solid tumor ability of NRT cells is significantly enhanced by the arming of IL-7 and CCL19, which is a safe and effective genetic modification of NRT.

Predicting Acute Exacerbation Phenotype in Chronic Obstructive Pulmonary Disease Patients using VGG-16 Deep Learning.

Introduction：Exacerbations of chronic obstructive pulmonary disease (COPD) have a significant impact on hospitalizations, morbidity, and mortality of patients. This study aimed to develop a model for predicting acute exacerbation in COPD patient (AECOPD) based on deep learning (DL) features. METHODS: We performed a retrospective study on 219 patients with COPD who underwent inspiratory and expiratory HRCT scans. By recording the acute respiratory events of the previous year, these patients were further divided into Non-AECOPD group and AECOPD group according to the presence of acute exacerbation events. 69 Quantitative CT (QCT) parameters of emphysema and airway were calculated by NeuLungCARE software and 2000 DL features were extracted by VGG-16 method. The Logistic regression method was employed to identify AECOPD patients and 29 patients of external validation cohort were used to access the robustness of the results. RESULTS: The Model 3-B achieved an AUC of 0.933, and 0.865 in the testing cohort and external validation cohort respectively. Model 3-I obtained AUC of 0.895 in the testing cohort and AUC of 0.774 in the external validation cohort. Model 7-B combined clinical characteristics, QCT parameters, and DL features achieved the best performance with an AUC of 0.979 in the testing cohort and demonstrating robust predictability with an AUC of 0.932 in the external validation cohort. Likewise, Model 7-I achieving an AUC of 0.938, and 0.872 in the testing cohort and external validation cohort respectively. CONCLUSIONS: DL features extracted from HRCT scans can effectively predict acute exacerbation phenotype in COPD patients.

Largescale multicenter study of a serum metabolite biomarker panel for the diagnosis of breast cancer.

Breast cancer (BC) is currently the most prevalent malignancy worldwide, and finding effective non-invasive biomarkers for routine clinical detection of BC remains a significant challenge. Here, we performed non-targeted and targeted metabolomics analysis on the screening, training and validation cohorts of serum samples from 1,947 participants. A metabolite biomarker model including glutamate, erythronate, docosahexaenoate, propionylcarnitine, and patient's age was established for detecting BC. This model demonstrated better diagnostic performance than carbohydrate antigen 15-3 (CA15-3) and carcinoembryonic antigen (CEA) alone in discriminating BC from healthy controls both in the training and validation cohorts [area under the curve (AUC), 0.954; sensitivity, 87.1% and specificity, 93.5% for the training cohort and 0.834, 68.3%, and 85.2%, respectively, for the validation cohort 1]. This study has established a noninvasive approach for the detection of BC, which shows potential as a suitable supplement to the clinical screening methods currently employed for BC.

Clinical features and associated factors of impaired ventilatory efficiency: findings from the ECOPD study in China.

BACKGROUND: Impaired ventilatory efficiency during exercise is a predictor of mortality in chronic obstructive pulmonary disease. However, little is known about the clinical features and associated factors of impaired ventilatory efficiency in China. METHODS: We conducted a cross-sectional community-based study in China and collected demographic and clinical information, cardiopulmonary exercise testing, spirometry, and CT data. Impaired ventilatory efficiency was defined by a nadir ventilatory equivalent for CO2 production above the upper limit of normal. Multivariable linear and logistic regression models were used to explore the clinical features and associated factors of impaired ventilatory efficiency. RESULTS: The final analyses included 941 subjects, 702 (74.6%) of whom had normal ventilatory efficiency and 239 (25.4%) had impaired ventilatory efficiency. Participants with impaired ventilatory efficiency had more chronic respiratory symptoms, poorer lung function and exercise capacity, and more severe emphysema (natural logarithm transformation of the low-attenuation area of the lung with attenuation values below -950 Hounsfield units, logLAA-950: 0.19±0.65 vs -0.28±0.63, p<0.001) and air trapping (logLAA-856: 1.03±0.65 vs 0.68±0.70, p<0.001) than those with normal ventilatory efficiency. Older age (60-69 years, OR 3.10 (95% CI 1.33 to 7.21), p=0.009 and 70-80 years, OR 6.48 (95% CI 2.56 to 16.43), p<0.001 vs 40-49 years) and smoking (former, OR 3.19 (95% CI 1.29 to 7.86), p=0.012; current, OR 4.27 (95% CI 1.78 to 10.24), p=0.001 vs never) were identified as high risk factors of impaired ventilatory efficiency. CONCLUSIONS: Impaired ventilatory efficiency was associated with poorer respiratory characteristics. Longitudinal studies are warranted to explore the progression of individuals with impaired ventilatory efficiency.

Forsythoside B alleviates cerebral ischemia-reperfusion injury via inhibiting NLRP3 inflammasome mediated by SIRT1 activation.

BACKGROUND: The inflammatory response is a key factor in the pathogenesis of cerebral ischemia/reperfusion injury (CIRI), and anti-inflammatory interventions may offer a promising therapeutic strategy. Forsythoside B (FB) is a phenylethanoid glycoside isolated from Forsythiae fructus, which has been reported to have anti-inflammatory effects. However, the mechanism of the neuroprotective effect of FB on CIRI remains unclear. METHODS: Adult male Sprague-Dawley rats were subjected to transient middle cerebral artery occlusion/reperfusion (MCAO/R). FB was administered intraperitoneally for 3 days prior to MCAO/R. Cerebral infarct volume and neurological deficit score were used as indices to evaluate MCAO/R injury. The serum levels of inflammatory factors and antioxidant enzymes were measured. The activation of silent information regulator 2 homolog 1 (Sirt1) and the inhibition of the nucleotide-binding oligomerization domain-like receptor with a pyrin domain 3 (NLRP3) pathway were assessed through western blot and immunohistochemistry analysis. Furthermore, the rats were treated with Sirt1 shRNA 3 days before MCAO/R by stereotactical injection into the ipsilateral hemispheric region to assess the impact of Sirt1 knockdown on the protection of FB during MCAO/R. RESULTS: FB reduced cerebral infarct volume and neurological deficit score in MCAO/R rats. FB reduced pathological changes and cell apoptosis in the hippocampal CA1 region and cortex on the ischemic side of rats. FB inhibited the serum levels of inflammatory factors and increased the activities of antioxidant enzymes. Further study showed that FB inhibited the activation of the NLRP3 pathway and induced Sirt1 activation. CONCLUSION: FB demonstrated neuroprotective and anti-inflammatory effects by inhibiting the NLRP3 pathway through Sirt1 activation in CIRI.

Learning and depicting lobe-based radiomics feature for COPD Severity staging in low-dose CT images.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a prevalent and debilitating respiratory condition that imposes a significant healthcare burden worldwide. Accurate staging of COPD severity is crucial for patient management and treatment planning. METHODS: The retrospective study included 530 hospital patients. A lobe-based radiomics method was proposed to classify COPD severity using computed tomography (CT) images. First, we segmented the lung lobes with a convolutional neural network model. Secondly, the radiomic features of each lung lobe are extracted from CT images, the features of the five lung lobes are merged, and the selection of features is accomplished through the utilization of a variance threshold, t-Test, least absolute shrinkage and selection operator (LASSO). Finally, the COPD severity was classified by a support vector machine (SVM) classifier. RESULTS: 104 features were selected for staging COPD according to the Global initiative for chronic Obstructive Lung Disease (GOLD). The SVM classifier showed remarkable performance with an accuracy of 0.63. Moreover, an additional set of 132 features were selected to distinguish between milder (GOLD I + GOLD II) and more severe instances (GOLD III + GOLD IV) of COPD. The accuracy for SVM stood at 0.87. CONCLUSIONS: The proposed method proved that the novel lobe-based radiomics method can significantly contribute to the refinement of COPD severity staging. By combining radiomic features from each lung lobe, it can obtain a more comprehensive and rich set of features and better capture the CT radiomic features of the lung than simply observing the lung as a whole.

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.

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.

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 1 , 781 × 2 lung radiomics and 13 , 824 × 2 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 % of accuracy, 90.9 % of precision, 89.5 % of F1-score, and 95.8 % 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.