Bronchi

The current COVID-19 pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has an enormous impact on human health and economy. In search for therapeutic options, researchers have proposed resveratrol, a food supplement with known antiviral, anti-inflammatory, and antioxidant properties as an advantageous antiviral therapy for SARS-CoV-2 infection. Here, we provide evidence that both resveratrol and its metabolically more stable structural analog, pterostilbene, exhibit potent antiviral properties against SARS-CoV-2 in vitro. First, we show that resveratrol and pterostilbene antiviral activity in African green monkey kidney cells. Both compounds actively inhibit virus replication within infected cells as reduced virus progeny production was observed when the compound was added at post-inoculation conditions. Without replenishment of the compound, antiviral activity was observed up to roughly five rounds of replication, demonstrating the long-lasting effect of these compounds. Second, as the upper respiratory tract represents the initial site of SARS-CoV-2 replication, we also assessed antiviral activity in air-liquid interface (ALI) cultured human primary bronchial epithelial cells, isolated from healthy volunteers. Resveratrol and pterostilbene showed a strong antiviral effect in these cells up to 48 h post-infection. Collectively, our data indicate that resveratrol and pterostilbene are promising antiviral compounds to inhibit SARS-CoV-2 infection. Because these results represent laboratory findings in cells, we advocate evaluation of these compounds in clinical trials before statements are made whether these drugs are advantageous for COVID-19 treatment.

Ferroptosis was reported to be involved in the occurrence and development of asthma. However, the potential mechanism underlying the role of ferroptosis in asthma remains unclear. In this study, we established the mouse asthma model following the ovalbumin (OVA) method in C57BL/6 mice and the cell model with IL-13 induction in bronchial epithelial cells (BEAS-2B cells). Treatment of ferrostatin-1 (Ferr-1) and 3-methyladenine (3-MA) decreased iron deposition in IL-13-induced BEAS-2B cells and lung tissues of asthma mice, opposite to that in bronchoalveolar lavage fluid (BALF). Meanwhile, excessive lipid peroxidation asthma model in vivo and in vitro was alleviated by Ferr-1 or 3-MA treatment. In addition, Ferr-1 and 3-MA inhibited the expression of LC-3 in these cells and lung tissues of mice. Moreover, Ferr-1 and 3-MA also suppressed the production of inflammatory cytokines (IL-1β, IL-6, and TNF-α) and oxidative stress factors (ROS and MDA), while promoting the level of SOD, in vivo and in vitro. Furthermore, application of Ferr-1 exhibited a greater inhibitory effect on iron release and lipid peroxidation in IL-13-induced BEAS-2B cells and asthma mice than 3-MA, accompanied with a weaker effect on ferritinophagy than 3-MA. Collectively, Ferr-1 and 3-MA ameliorated asthma in vivo and in vitro through inhibiting ferroptosis, providing a new strategy for the clinical treatment of asthma.

Anesthesiologists commonly use video bronchoscopy to facilitate intubation or confirm the location of the endotracheal tube; however, depth and orientation in the bronchial tree can often be confused because anesthesiologists cannot trace the airway from the oropharynx when it is performed using an endotracheal tube. Moreover, the decubitus position is often used in certain surgeries. Although it occurs rarely, the misinterpretation of tube location can cause accidental extubation or endobronchial intubation, which can lead to hyperinflation. Thus, video bronchoscopy with a decision supporting system using artificial intelligence would be useful in the anesthesiologic process. In this study, we aimed to develop an artificial intelligence model robust to rotation and covering using video bronchoscopy images. We collected video bronchoscopic images from an institutional database. Collected images were automatically labeled by an optical character recognition engine as the carina and left/right main bronchus. Except 180 images for the evaluation dataset, 80% were randomly allocated to the training dataset. The remaining images were assigned to the validation and test datasets in a 7:3 ratio. Random image rotation and circular cropping were applied. Ten kinds of pretrained models with < 25 million parameters were trained on the training and validation datasets. The model showing the best prediction accuracy for the test dataset was selected as the final model. Six human experts reviewed the evaluation dataset for the inference of anatomical locations to compare its performance with that of the final model. In the experiments, 8688 images were prepared and assigned to the evaluation (180), training (6806), validation (1191), and test (511) datasets. The EfficientNetB1 model showed the highest accuracy (0.86) and was selected as the final model. For the evaluation dataset, the final model showed better performance (accuracy, 0.84) than almost all human experts (0.38, 0.44, 0.51, 0.68, and 0.63), and only the most-experienced pulmonologist showed performance comparable (0.82) with that of the final model. The performance of human experts was generally proportional to their experiences. The performance difference between anesthesiologists and pulmonologists was marked in discrimination of the right main bronchus. Using bronchoscopic images, our model could distinguish anatomical locations among the carina and both main bronchi under random rotation and covering. The performance was comparable with that of the most-experienced human expert. This model can be a basis for designing a clinical decision support system with video bronchoscopy.

BackgroundIdiopathic pulmonary fibrosis (IPF) is a fibrosing interstitial pneumonia with restrictive ventilation. Recently, the structural and functional defects of small airways have received attention in the early pathogenesis of IPF. This study aimed to elucidate the characteristics of small airway epithelial dysfunction in patients with IPF and explore novel therapeutic interventions to impede IPF progression by targeting the dysfunctional small airways.MethodsAirway trees spanning the proximal-distal axis were harvested from control lungs and explanted lungs with end-stage IPF undergoing transplant. Qualified basal cells (BCs, p63/Krt5/ITGA6/NGFR) were expanded, and their cellular functions, feasibility, safety and efficacy for transplantation therapy in IPF were validated with experiments in vitro and mouse model. Single-cell RNA-sequencing was employed to elucidate the underlying mechanisms governing the BCs based therapy. Based upon these evidences, three patients with advanced IPF and small airway dysfunction received autologous-BCs transplantation. Post-transplantation assessments included lung function, exercise capacity and high resolution computed tomography (HRCT) scans were analyzed to quantify the clinical benefits conferred by the BCs transplantation.FindingsAn overall landscape of senescent phenotype in airway epithelial cells and airway stem/progenitor cells along the proximal-distal axis of the airway tree in IPF were outlined. In contrast to the cells situated in distal airways, BCs located in small bronchi in IPF displayed a non-senescent phenotype, with comparable proliferative, differentiative capabilities, and similar transcriptomic profiles to normal controls. In a mouse model of pulmonary fibrosis, BCs exhibited promising protective efficacy and safety for transplantation therapy. Autologous BCs transplantation in three advanced IPF patients with small airway dysfunction yielded significant clinical improvements in pulmonary function, particularly evidence in lung volume and small airway function.InterpretationEpithelia of small bronchi in IPF contain functional and expandable basal stem cells, which exert therapeutic benefits via bronchoscopic implantation. Our findings offer a potential for IPF treatment by targeting small airways.FundingNational Natural Science Foundation of China (82430001, 81930001, and 81900059), Shanghai Shenkang Hospital Development Center (SHDC2020CR3063B), Department of Science and Technology of Shandong Province (2024HWYQ-058).