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Objective: lncRNAs are recently thought to play a significant role in cellular homeostasis during pathological process of diseases by competing inhibiting miRNA function. The aim of present study was to assess the function of long non-coding RNA (lncRNA) MEG3 and its functional interaction with microRNA-181b in cerebral ischemic infarct of mice and hypoxia-induced neurons apoptosis.Methods: To address this question, we performed the experiments with in vivo middle cerebral artery occlusion (MCAO) mice model and in vitro oxygen-glucose deprivation (OGD)-cultured neuronal HT22 cell line. Relative expression of MEG3, miR-181b, and 12/15-LOX (lipoxygenase) mRNA was determined using quantitative RT-PCR. Western blot was used to evaluate 12/15-LOX protein expression. TUNEL assay was performed to assess cell apoptosis.Results: In both MCAO mice and OGD-cultured HT22 cell, ischemia, or hypoxia treatment results in a time-dependent increase in MEG3 and 12/15-LOX expression and decrease in miR-181b expression. Knockdown of MEG3 contributes to attenuation of hypoxia-induced apoptosis of HT22 cell. Also, expression level of MEG3 negatively correlated with miR-181b expression and positively correlated with 12/15-LOX expression. In contrary to MEG3, miR-181b overexpression attenuated hypoxia-induced HT22 cell apoptosis, as well as suppressed hypoxia-induced increase in 12/15-LOX expression. By luciferase reporter assay, we concluded that miR-181b directly binds to 12/15-LOX 3′-UTR, thereby negatively regulates 12/15-LOX expression.Conclusion: Our data suggested that long non-coding RNA MEG3 functions as a competing endogenous RNA for miR-181b to regulate 12/15-LOX expression in middle cerebral artery occlusion-induced ischemic infarct of brain nerve cells.

Abstract. Annexins are associated with metastasis and infiltration of cancer cells. Proteomic analysis and immunohistochemical staining were used to understand whether several annexins play important roles in cancer alone and/ or synergistically. Seven fresh breast cancer samples with 23 paraffin specimens, three fresh pancreatic samples and five fresh laryngeal carcinoma samples with 25 paraffin specimens were obtained from humans, as well as ten golden hamster pancreatic cancer tissue samples, and they were used to observe differential expression of annexins compared with normal tissues using proteomics and immunohistochemical staining. Annexin A2, A4 and A5 were overexpressed in human breast cancer and laryngeal carcinoma tissues and in golden hamster pancreatic cancer tissue samples, respectively, as shown by proteomics and immunohistochemical staining. In addition, annexin A4 and A5 were expressed in breast cancer tissues, while annexin A1 was not expressed. Annexin A1, A2 and A4 were expressed in human laryngeal carcinoma tissues as shown by immunohistochemical staining. Annexin A1, A2, A4 and A5 played important roles in breast cancer, pancreatic cancer and laryngeal carcinoma, alone and/or synergistically, and they may be targets of therapy for malignant tumors. The choice of which annexins to target should depend on their respective biological behaviors.

Background Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. Methods This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). Results Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. Conclusions The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.