Background: Long non-coding RNAs (lncRNAs) can remarkably regulate human malignancies in terms of the development and the progression. Previously, lncRNA LINC00847 (LINC00847) has been reported to present dysregulation in several tumors. However, the expression and function of LINC00847 in non-small cell lung cancer (NSCLC) have not been investigated.Methods: RT-qPCR was performed to determine the expressions of LINC00847 in collected tissue samples and cell lines. The clinical significance of LINC00847 was statistically analyzed. CCK-8 test, cell scratch test and trans-well test were used to evaluate the proliferation, invasion and migration abilities of NSCLC cells, respectively. The xenograft tumor model was constructed to confirm the effects of LINC00847 knockdown on NSCLC in vivo. Further, luciferase reporter assays and Western blot were performed to explore molecular mechanisms underlying the functions of LINC00847.Results: Increased expressions of LINC00847 were observed in NSCLC samples as well as cell lines. Additionally, E2F1 could be capable of directly binding to the LINC00847 promoter region, followed by promoting its expression. Clinically, LINC00847 high-expression could lead to poor prognosis of NSCLC patients. Functionally, LINC00847 knockdown noticeably repressed NSCLC cell growth and metastasis. Mechanically, miR-147a/IFITM1 axis was a downstream target of LINC00847, and silencing of miR-147a could rescue the anti-cancer effects of LINC00847 knockdown on NSCLC cell behaviors.Conclusion: Overall, up regulation of LINC00847 induced by E2F1 promoted the progression of NSCLC by modulating miR-147a/IFITM1 axis, representing a novel regulatory mechanism for NSCLC progression.

The present study aimed to investigate the diagnostic value of automatic DNA image cytometry (DNA-ICM) for diagnosing lung cancer. A total of three different types of samples from 465 cases were included: Bronchoalveolar lavage fluid (BALF), 386 samples; pleural effusion cases, 70 samples; and fine-needle aspiration procedures, 9 samples. Two methods, liquid-based cytology (LBC) and automatic DNA-ICM, were used to assess the samples, and the pathological results of 120/465 cases were reviewed. The results of DNA-ICM were compared with those of LBC and pathology. There were 57 cases of lung cancer without aneuploidy and 49 cases without evidence of malignant tumor, but with the presence of heteroploid cells. The positive diagnostic rate for BALF samples using LBC was significantly higher compared with that for DNA-ICM (P<0.05). No statistically significant difference was observed in the positive diagnostic rate between DNA-ICM and LBC in pleural effusion samples. For DNA-ICM in BALF, pleural effusion and all samples, no statistically significant differences were identified between the positive diagnostic rates of lung squamous carcinoma and lung adenocarcinoma. The positive diagnostic rate of LBC combined with DNA-ICM was not significantly improved. In non-small cell lung cancer (NSCLC) cases, the difference in the maximum value of DNA (DNAmax) was positively correlated with tumor stage (P<0.05), but no significant correlations were observed among DNA max, tumor type and tumor location. In small-cell lung cancer (SCLC) cases, no significant correlations were observed among DNAmax, tumor staging or tumor location. The differences in the DNAmax values of squamous cell carcinoma, adenocarcinoma, SCLC and NSCLC were not statistically significant. In the present study, the area under the receiver operating characteristic curve for LBC (0.936) was significantly greater compared with that for DNA-ICM (0.766) (P<0.05). DNA-ICM has medium diagnostic value in lung cancer, and the DNAmax was positively correlated with tumor stage in NSCLC. DNA-ICM may serve as a supplement to LBC, but it is not recommended as a sole procedure for lung cancer screening.

The new coronavirus epidemic (COVID-19) has received widespread attention, causing the health crisis across the world. Massive information about the COVID-19 has emerged on social networks. However, not all information disseminated on social networks is true and reliable. In response to the COVID-19 pandemic, only real information is valuable to the authorities and the public. Therefore, it is an essential task to detect rumors of the COVID-19 on social networks. In this paper, we attempt to solve this problem by using an approach of machine learning on the platform of Weibo. First, we extract text characteristics, user-related features, interaction-based features, and emotion-based features from the spread messages of the COVID-19. Second, by combining these four types of features, we design an intelligent rumor detection model with the technique of ensemble learning. Finally, we conduct extensive experiments on the collected data from Weibo. Experimental results indicate that our model can significantly improve the accuracy of rumor detection, with an accuracy rate of 91% and an AUC value of 0.96.

Railway freight is at risk of losing customers due to intense competition in the market. Effectively managing potential customer loss is a long-term problem for railway freight. Big data technology has been widely researched and applied in recent years, and using data mining techniques to fully extract information from railway freight ticket data and discover potential customer loss is a research topic. In this study, a mining algorithm is proposed to improve the accuracy of predicting customer churn in railway freight using Long Short-Term Memory (LSTM) network. The algorithm involves three steps: constructing the time series of railway freight volume, predicting customer churn trends using a modified LSTM model, and analyzing the characteristics of customers with different degrees of loss and the characteristics of transportation demand of lost and potential lost customers. Experimental verification was conducted using customer freight ticket data from the Shanghai Railway Bureau in China and compared the proposed modified LSTM model was compared with other commonly used machine learning algorithms for churn prediction. The results showed that the present algorithm demonstrated good accuracy and adaptability. The study proposed in this paper enriches the theoretical basis of railway freight customer management, provides an effective method for predicting customer loss in railway freight, and offers technical support for railway freight management practices.