A recent outbreak of pneumonia in Wuhan, China, was caused by the 2019 novel coronavirus (2019-nCoV). There have been some reports of imaging findings regarding the disease’s characteristic features. Here, we report three cases of coronavirus disease 2019 (COVID-19) with dynamic pulmonary CT evaluation. The CT scan showed multiple regions of ground-glass opacities and patchy consolidation in COVID-19 patients and the CT scan was useful in tracking the progression or regression of COVID-19.

Cigarette smoke can induce pulmonary vascular remodeling, which involves pulmonary artery smooth muscle cell (PASMC) proliferation, resulting in pulmonary hypertension in chronic obstructive pulmonary disease. FHL1 is a member of the FHL subfamily, characterized by an N‑terminal half LIM domain, followed by four complete LIM domains, and has been suggested to be critical in cell proliferation. However, the effects of FHL1 on cigarette smoke‑induced PASMC proliferation and the precise molecular mechanism remain to be elucidated. The present study demonstrated that the protein expression of FHL1 correlated with cigarette smoke extract (CSE)‑induced PASMC proliferation. Knockdown of the expression of FHL1 using siRNA significantly suppressed cell proliferation and inhibited the cell cycle transition between the G1 and S phase by regulating the cyclin‑dependent kinase pathway at the basal level and following CSE stimulation. By contrast, overexpressing FHL1 using an adenovirus increased cell proliferation and promoted the cell cycle transition between the G1 and S phase. Furthermore, CSE significantly increased the protein expression of FHL1, however, exerted no effect on the mRNA expression levels. This alteration was due to the prolonged FHL1 half‑life, leading to the antagonizing of protein degradation. Collectively, these data suggested that FHL1 may be involved in excessive cell proliferation and may represent a potential therapeutic target for pulmonary hypertension.

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors. How to effectively diagnose LUAD at an early stage and make an accurate judgement of the occurrence and progression of LUAD are still the focus of current research. Support vector machine (SVM) is one of the most effective methods for diagnosing LUAD of different stages. The study aimed to explore the dynamic change of differentially expressed genes (DEGs) in different stages of LUAD, and to assess the risk of LUAD through DEGs enriched pathways and establish a diagnostic model based on SVM method. Based on TMN stages and gene expression profiles of 517 samples in TCGA-LUAD database, coefficient of variation (CV) combined with one-way analysis of variance (ANOVA) were used to screen out feature genes in different TMN stages after data standardization. Unsupervised clustering analysis was conducted on samples and feature genes. The feature genes were analyzed by Pearson correlation coefficient to construct a co-expression network. Fisher exact test was conducted to verify the most enriched pathways, and the variation of each pathway in different stages was analyzed. SVM networks were trained and ROC curves were drawn based on the predicted results so as to evaluate the predictive effectiveness of the SVM model. Unsupervised hierarchical clustering analysis results showed that almost all the samples in stage III/IV were clustered together, while samples in stage I/II were clustered together. The correlation of feature genes in different stages was different. In addition, with the increase of malignant degree of lung cancer, the average shortest path of the network gradually increased, while the closeness centrality gradually decreased. Finally, four feature pathways that could distinguish different stages of LUAD were obtained and the ability was tested by the SVM model with an accuracy of 91%. Functional level differences were quantified based on the expression of feature genes in lung cancer patients of different stages, so as to help the diagnosis and prediction of lung cancer. The accuracy of our model in differentiating between stage I/II and stage III/IV could reach 91%.

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