Protective effect of Palmijihwanghwan in a mouse model of cigarette smoke and lipopolysaccharide-induced chronic obstructive pulmonary disease

Long non-coding RNA Small Nucleolar RNA Host Gene 4 ameliorates cigarette smoke-induced proliferation, apoptosis, inflammation, and airway remodeling in alveolar epithelial cells through the modulation of the mitogen-activated protein kinase signaling pathway via the microRNA-409-3p/Four and a Half LIM Domains 1 axis

The long non-coding RNA (lncRNA) Small Nucleolar RNA Host Gene 4 (SNHG4) has been demonstrated to be significantly downregulated in various inflammatory conditions, yet its role in chronic obstructive pulmonary disease (COPD) remains elusive. This study aims to elucidate the biological function of SNHG4 in COPD and to unveil its potential molecular targets. Our findings reveal that both SNHG4 and Four and a Half LIM Domains 1 (FHL1) were markedly downregulated in COPD, whereas microRNA-409-3p (miR-409-3p) was upregulated. Importantly, SNHG4 exhibited a negative correlation with inflammatory markers in patients with COPD, but a positive correlation with forced expiratory volume in 1s percentage (FEV1%). SNHG4 distinguished COPD patients from non-smokers with high sensitivity, specificity, and accuracy. Overexpression of SNHG4 ameliorated cigarette smoke extract (CSE)-mediated inflammation, apoptosis, oxidative stress, and airway remodeling in 16HBE bronchial epithelial cells. These beneficial effects of SNHG4 overexpression were reversed by the overexpression of miR-409-3p or the silencing of FHL1. Mechanistically, SNHG4 competitively bound to miR-409-3p, mediating the expression of FHL1, and consequently improving inflammation, apoptosis, oxidative stress, and airway remodeling in 16HBE cells. Additionally, SNHG4 regulated the miR-409-3p/FHL1 axis to inhibit the activation of the mitogen-activated protein kinase (MAPK) pathway induced by CSE. In a murine model of COPD, knockdown of SNHG4 exacerbated CSE-induced pulmonary inflammation, apoptosis, and oxidative stress. In summary, our data affirm that SNHG4 mitigates pulmonary inflammation, apoptosis, and oxidative damage mediated by COPD through the regulation of the miR-409-3p/FHL1 axis.

Camellia sinensis L. Alleviates Pulmonary Inflammation Induced by Porcine Pancreas Elastase and Cigarette Smoke Extract

Cigarette smoke (CS) is the major factor in the development of chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide. Furthermore, although Camellia sinensis (CN) has been known as an anti-inflammatory material, the effect of CN has not yet been known on pulmonary inflammation in COPD. Thus, we investigated the protective effects of Camellia sinensis L. extract (CLE) against pulmonary inflammation in porcine pancreas elastase (PPE) and a cigarette smoke extract (CSE)-induced COPD mouse model. Oral administration of CLE suppressed the symptoms such as infiltration of immune cells, cytokines/chemokines secretion, mucus hypersecretion, and injuries of the lung parenchyma. Increased inflammatory responses in COPD are mediated by various immune cells such as airway epithelial cells, neutrophils, and alveolar macrophages. Thus, we investigated the effect and mechanisms of CLE in H292, HL-60, and MH-S cells. The CLE inhibited the expression of IL-6, IL-8, MUC5AC and MUC5B on CSE/LPS-stimulated H292 cells and also suppressed the formation of neutrophil extracellular traps and secretion of neutrophil elastase by inhibiting reactive oxygen species in PMA-induced HL-60 cells. In particular, the CLE suppressed the release of cytokines and chemokines caused by activating the nuclear factor kappa-light-chain-enhancer of activated B via the activation of nuclear factor erythroid-2-related factor 2 and the heme oxygenase-1 pathway in CSE/LPS-stimulated MH-S cells. Therefore, we suggest that the CLE administration be the effective approach for treating or preventing chronic pulmonary diseases such as COPD induced by CS.

Artificial Intelligence-Assisted Image Analysis of Acetaminophen-Induced Acute Hepatic Injury in Sprague-Dawley Rats

Although drug-induced liver injury (DILI) is a major target of the pharmaceutical industry, we currently lack an efficient model for evaluating liver toxicity in the early stage of its development. Recent progress in artificial intelligence-based deep learning technology promises to improve the accuracy and robustness of current toxicity prediction models. Mask region-based CNN (Mask R-CNN) is a detection-based segmentation model that has been used for developing algorithms. In the present study, we applied a Mask R-CNN algorithm to detect and predict acute hepatic injury lesions induced by acetaminophen (APAP) in Sprague-Dawley rats. To accomplish this, we trained, validated, and tested the model for various hepatic lesions, including necrosis, inflammation, infiltration, and portal triad. We confirmed the model performance at the whole-slide image (WSI) level. The training, validating, and testing processes, which were performed using tile images, yielded an overall model accuracy of 96.44%. For confirmation, we compared the model’s predictions for 25 WSIs at 20× magnification with annotated lesion areas determined by an accredited toxicologic pathologist. In individual WSIs, the expert-annotated lesion areas of necrosis, inflammation, and infiltration tended to be comparable with the values predicted by the algorithm. The overall predictions showed a high correlation with the annotated area. The R square values were 0.9953, 0.9610, and 0.9445 for necrosis, inflammation plus infiltration, and portal triad, respectively. The present study shows that the Mask R-CNN algorithm is a useful tool for detecting and predicting hepatic lesions in non-clinical studies. This new algorithm might be widely useful for predicting liver lesions in non-clinical and clinical settings.