Isolation of putative progenitor endothelial cells for angiogenesis

We previously found that both the guide and passenger strands of the miR-139 duplex (miR-139-5p and miR-139-3p, respectively) were downregulated in cancer tissues. Analysis of TCGA datasets revealed that low expression of miR-139-5p (p < 0.0001) and miR-139-3p (p < 0.0001) was closely associated with 5-year survival rates of patients with renal cell carcinoma (RCC). Ectopic expression assays showed that miR-139-5p and miR-139-3p acted as tumor-suppressive miRNAs in RCC cells. Here, 19 and 22 genes were identified as putative targets of miR-139-5p and miR-139-3p in RCC cells, respectively. Among these genes, high expression of PLXDC1, TET3, PXN, ARHGEF19, ELK1, DCBLD1, IKBKB, and CSF1 significantly predicted shorter survival in RCC patients according to TCGA analyses (p < 0.05). Importantly, the expression levels of four of these genes, PXN, ARHGEF19, ELK1, and IKBKB, were independent prognostic factors for patient survival (p < 0.05). We focused on PXN (paxillin) and investigated its potential oncogenic role in RCC cells. PXN knockdown significantly inhibited cancer cell migration and invasion, possibly by regulating epithelial-mesenchymal transition. Involvement of the miR-139-3p passenger strand in RCC molecular pathogenesis is a new concept. Analyses of tumor-suppressive-miRNA-mediated molecular networks provide important insights into the molecular pathogenesis of RCC.

https://www.mdpi.com/2227-9059/8/12/599/pdf

Regulation of Oncogenic Targets by the Tumor-Suppressive miR-139 Duplex (miR-139-5p and miR-139-3p) in Renal Cell Carcinoma.

BACKGROUND: From December 2019, atypical pneumonia termed COVID-19 has been increasing exponentially across the world. It poses a great threat and challenge to world health and the economy. Medical specialists face uncertainty in making decisions based on their judgment for COVID-19. Thus, this study aimed to establish an intelligent model based on artificial neural networks (ANNs) for diagnosing COVID-19. MATERIALS AND METHODS: Using a single-center registry, we studied the records of 250 confirmed COVID-19 and 150 negative cases from February 9, 2020, to October 20, 2020. The correlation coefficient technique was used to determine the most significant variables of the ANN model. The variables at P < 0.05 were used for model construction. We applied the back-propagation technique for training a neural network on the dataset. After comparing different neural network configurations, the best configuration of ANN was acquired, then its strength has been evaluated. RESULTS: After the feature selection process, a total of 18 variables were determined as the most relevant predictors for developing the ANN models. The results indicated that two nested loops' architecture of 9-10-15-2 (10 and 15 neurons used in layer 1 and layer 2, respectively) with the area under the curve of 0.982, the sensitivity of 96.4%, specificity of 90.6%, and accuracy of 94% was introduced as the best configuration model for COVID-19 diagnosis. CONCLUSION: The proposed ANN-based clinical decision support system could be considered as a suitable computational technique for the frontline practitioner in early detection, effective intervention, and possibly a reduction of mortality in patients with COVID-19.

Developing an artificial neural network for detecting COVID-19 disease

Angiogenesis plays a great role on tumor growth, metastasis. A lot of angiogenesis factors and inhibitors regulate angiogenesis. VEGF is the most important one and through the research of the regulatory mechanism of VEGF during tumor angiogenesis , a new effective way to prevent and cure cancer was found. This thesis gives a review on tumor angiogenesis, the features of VEGF family, the application of VEGF on anti- tumor drugs .

VEGF and tumor angiogenesis and its application on anti-tumor drug exploration

Neuroblastoma is the most common solid tumor malignancy in the first year of life. We present a rare case of a 5-month-old girl with an infraorbital tumor that simulated an infantile hemangioma clinically but was ultimately diagnosed as metastatic neuroblastoma.

Metastatic neuroblastoma mimicking an infantile hemangioma

Orbital neuroblastoma metastasis: A case report and literature review

Torsional and burst mode phacoemulsification for patients with hard nuclear cataract: A randomized control study.

Identifying and studying the molecular mechanisms of neovascularization biomarkers are critical for conquering many diseases, such as corneal diseases and cancer. Paxillin is an important cell scaffold and cellular signaling protein, especially a key molecule of the Integrin-mediated downstream signaling transduction. This review summarizes the structure and functions of paxillin, and the research progress of its roles in neovascularization. Although there are still some problems to be solved, paxillin may become an important target of anti-neovascularization therapies.

The structure and functions of paxillin and its roles in neovascularization.

Supervised machine-learning (ML) models were employed to predict the occurrence of dry eye disease (DED) after vitrectomy in this study. The clinical data of 217 patients receiving vitrectomy from April 2017 to July 2018 were used as training dataset; the clinical data of 33 patients receiving vitrectomy from August 2018 to September 2018 were collected as validating dataset. The input features for ML training were selected based on the Delphi method and univariate logistic regression (LR). LR and artificial neural network (ANN) models were trained and subsequently used to predict the occurrence of DED in patients who underwent vitrectomy for the first time during the period. The area under the receiver operating characteristic curve (AUC-ROC) was used to evaluate the predictive accuracy of the ML models. The AUCs with use of the LR and ANN models were 0.741 and 0.786, respectively, suggesting satisfactory performance in predicting the occurrence of DED. When the two models were compared in terms of predictive power, the fitting effect of the ANN model was slightly superior to that of the LR model. In conclusion, both LR and ANN models may be used to accurately predict the occurrence of DED after vitrectomy.

/pdf/the-application-of-artificial-neural-networks-and-logistic-15p6283pkw.pdf

Wan-Ju Yang

Papers

Orbital neuroblastoma metastasis: A case report and literature review

Torsional and burst mode phacoemulsification for patients with hard nuclear cataract: A randomized control study.

The structure and functions of paxillin and its roles in neovascularization.

The Application of Artificial Neural Networks and Logistic Regression in the Evaluation of Risk for Dry Eye after Vitrectomy.