MicroRNA-145 plays a role in mitochondrial dysfunction in alveolar epithelial cells in lipopolysaccharide-induced acute respiratory distress syndrome

BACKGROUND: Acute respiratory distress syndrome (ARDS) causes substantial mortalities.Alveolar epithelium is one of the main sites of cell injuries in ARDS. As an important kind of microRNAs(miRNAs), microRNA-145 (miR-145) has been studied in various diseases, while its role in ARDS has notbeen investigated.METHODS: Lipopolysaccharide (LPS) was intratracheally instilled to establish a rat ARDS model.Cytokines from bronchoalveolar lavage fl uid (BALF) were measured using rat tumor necrosis factor-α andinterleukin-6 enzyme-linked immunosorbent assay kits (R&D Systems), and the pathological structureswere evaluated using hematoxylin and eosin (H&E) staining and transmission electron microscope;thelung miR-145 messenger RNA (mRNA) was detected using quantitative polymerase chain reaction.Bioinformatics focused on the target genes and possible pathways of gene regulation.RESULTS: A rat model of LPS-induced ARDS was successfully established. The miR-145was down-regulated in the LPS-induced ARDS lung, and mitochondrial dysfunction was observedin alveolar epithelial cells, most obviously at 72 hours after LPS. TargetScan and miRDB databaseswere used to predict the target genes of miR-145. A total of 428 overlapping genes were identifi ed,seven genes were associated with mitochondrial function, and Ogt, Camk2d, Slc8a3, and Slc25a25were verifi ed. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched in themitogen-activated protein kinase (MAPK) signaling pathway, and Gene Ontology (GO) biologicalprocess was mainly enriched in signal transduction and transcription regulation.CONCLUSIONS: The miR-145 is down-regulated in LPS-induced ARDS, and affects itsdownstream genes targeting mitochondrial functions.

Artificial intelligence computed tomography helps evaluate the severity of COVID-19 patients:A retrospective study

BACKGROUND:Computed tomography(CT)is a noninvasive imaging approach to assist the early diagnosis of pneumonia.However,coronavirus disease 2019(COVID-19)shares similar imaging features with other types of pneumonia,which makes differential diagnosis problematic.Artificial intelligence(AI)has been proven successful in the medical imaging field,which has helped disease identification.However,whether AI can be used to identify the severity of COVID-19 is still underdetermined.METHODS:Data were extracted from 140 patients with confirmed COVID-19.The severity of COVID-19 patients(severe vs.non-severe)was defined at admission,according to American Thoracic Society(ATS)guidelines for community-acquired pneumonia(CAP).The AI-CT rating system constructed by Hangzhou YITU Healthcare Technology Co.,Ltd.was used as the analysis tool to analyze chest CT images.RESULTS:A total of 117 diagnosed cases were enrolled,with 40 severe cases and 77 non-severe cases.Severe patients had more dyspnea symptoms on admission(12 vs.3),higher acute physiology and chronic health evaluation(APACHE)II(9 vs.4)and sequential organ failure assessment(SOFA)(3 vs.1)scores,as well as higher CT semiquantitative rating scores(4 vs.1)and AI-CT rating scores than non-severe patients(P<0.001).The AI-CT score was more predictive of the severity of COVID-19(AUC=0.929),and ground-glass opacity(GGO)was more predictive of further intubation and mechanical ventilation(AUC=0.836).Furthermore,the CT semiquantitative score was linearly associated with the AI-CT rating system(Adj R2=75.5%,P<0.001).CONCLUSIONS:AI technology could be used to evaluate disease severity in COVID-19 patients.Although it could not be considered an independent factor,there was no doubt that GGOs displayed more predictive value for further mechanical ventilation.