Chronic CSE Treatment Induces the Growth of Normal Oral Keratinocytes via PDK2 Upregulation, Increased Glycolysis and HIF1α Stabilization

Exposure to cigarette smoke is a major risk factor for head and neck squamous cell carcinoma (HNSCC). We have previously established a chronic cigarette smoke extract (CSE)-treated human oral normal keratinocyte model, demonstrating an elevated frequency of mitochondrial mutations in CSE treated cells. Using this model we further characterized the mechanism by which chronic CSE treatment induces increased cellular proliferation.We demonstrate that chronic CSE treatment upregulates PDK2 expression, decreases PDH activity and thereby increases the glycolytic metabolites pyruvate and lactate. We also found that the chronic CSE treatment enhanced HIF1α accumulation through increased pyruvate and lactate production in a manner selectively reversible by ascorbate. Use of a HIF1α small molecule inhibitor blocked the growth induced by chronic CSE treatment in OKF6 cells. Furthermore, chronic CSE treatment was found to increase ROS (reactive oxygen species) production, and application of the ROS scavengers N-acetylcysteine abrogated the expression of PDK2 and HIF1α. Notably, treatment with dichloroacetate, a PDK2 inhibitor, also decreased the HIF1α expression as well as cell proliferation in chronic CSE treated OKF6 cells.Our findings suggest that chronic CSE treatment contribute to cell growth via increased ROS production through mitochondrial mutations, upregulation of PDK2, attenuating PDH activity thereby increasing glycolytic metabolites, resulting in HIF1α stabilization. This study suggests a role for chronic tobacco exposure in the development of aerobic glycolysis and normoxic HIFα activation as a part of HNSCC initiation. These data may provide insights into development of chemopreventive strategies for smoking related cancers

Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method for Road Updating from Remote Sensing Imagery and Historical Vector Maps

A road is the skeleton of a city and is a fundamental and important geographical component. Currently, many countries have built geo-information databases and gathered large amounts of geographic data. However, with the extensive construction of infrastructure and rapid expansion of cities, automatic updating of road data is imperative to maintain the high quality of current basic geographic information. However, obtaining bi-phase images for the same area is difficult, and complex post-processing methods are required to update the existing databases.To solve these problems, we proposed a road detection method based on semi-supervised learning (SRUNet) specifically for road-updating applications; in this approach, historical road information was fused with the latest images to directly obtain the latest state of the road.Considering that the texture of a road is complex, a multi-branch network, named the Map Encoding Branch (MEB) was proposed for representation learning, where the Boundary Enhancement Module (BEM) was used to improve the accuracy of boundary prediction, and the Residual Refinement Module (RRM) was used to optimize the prediction results. Further, to fully utilize the limited amount of label information and to enhance the prediction accuracy on unlabeled images, we utilized the mean teacher framework as the basic semi-supervised learning framework and introduced Regional Contrast (ReCo) in our work to improve the model capacity for distinguishing between the characteristics of roads and background elements.We applied our method to two datasets. Our model can effectively improve the performance of a model with fewer labels. Overall, the proposed SRUNet can provide stable, up-to-date, and reliable prediction results for a wide range of road renewal tasks.Comment: 22 pages, 8 figure

Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure

Urban public transport and air quality: Empirical study of China cities

Abstract(#br)To analyze the impact of the increase of public transport on the urban air quality will contribute to the sustainable development of urbanization. But many existing studies have not paid attention to the potential endogeneity of estimation, which comes from the fact that the deterioration of air quality would in turn affect the policies of public transport investment. This paper attempts to control this endogeneity by introducing an instrument variable of the urban built-up area into the empirical models. Using city-level data from China, our study adopts 2SLS method and conducts a series of robustness tests to ensure the estimation results more convincing and robust. The results show that the urban air quality could be improved if the city provides more buses for public transport. Moreover, after controlling the endogeneity, the marginal improving effect of increasing the public transport on urban air quality could be larger from 0.082 to 0.678. This finding indicates that the endogeneity bias is likely to cause the underestimation of the improving effect, and may result in some errors of the policy decisions of urban investment

The construction of a nomogram to predict the prognosis and recurrence risks of UPJO

ObjectiveThis study was conducted to explore the risk factors for the prognosis and recurrence of ureteropelvic junction obstruction (UPJO).MethodsThe correlation of these variables with the prognosis and recurrence risks was analyzed by binary and multivariate logistic regression. Besides, a nomogram was constructed based on the multivariate logistic regression calculation. After the model was verified by the C-statistic, the ROC curve was plotted to evaluate the sensitivity of the model. Finally, the decision curve analysis (DCA) was conducted to estimate the clinical benefits and losses of intervention measures under a series of risk thresholds.ResultsPreoperative automated peritoneal dialysis (APD), preoperative urinary tract infection (UTI), preoperative renal parenchymal thickness (RPT), Mayo adhesive probability (MAP) score, and surgeon proficiency were the high-risk factors for the prognosis and recurrence of UPJO. In addition, a nomogram was constructed based on the above 5 variables. The area under the curve (AUC) was 0.8831 after self cross-validation, which validated that the specificity of the model was favorable.ConclusionThe column chart constructed by five factors has good predictive ability for the prognosis and recurrence of UPJO, which may provide more reasonable guidance for the clinical diagnosis and treatment of this disease

Integrative Discovery of Epigenetically Derepressed Cancer Testis Antigens in NSCLC

Background: Cancer/testis antigens (CTAs) were first discovered as immunogenic targets normally expressed in germline cells, but differentially expressed in a variety of human cancers. In this study, we used an integrative epigenetic screening approach to identify coordinately expressed genes in human non-small cell lung cancer (NSCLC) whose transcription is driven by promoter demethylation. Methodology/Principal Findings: Our screening approach found 290 significant genes from the over 47,000 transcripts incorporated in the Affymetrix Human Genome U133 Plus 2.0 expression array. Of the top 55 candidates, 10 showed both differential overexpression and promoter region hypomethylation in NSCLC. Surprisingly, 6 of the 10 genes discovered by this approach were CTAs. Using a separate cohort of primary tumor and normal tissue, we validated NSCLC promoter hypomethylation and increased expression by quantitative RT-PCR for all 10 genes. We noted significant, coordinated coexpression of multiple target genes, as well as coordinated promoter demethylation, in a large set of individual tumors that was associated with the SCC subtype of NSCLC. In addition, we identified 2 novel target genes that exhibited growth- promoting effects in multiple cell lines. Conclusions/Significance: Coordinated promoter demethylation in NSCLC is associated with aberrant expression of CTAs and potential, novel candidate protooncogenes that can be identified using integrative discovery techniques. These findings have significant implications for discovery of novel CTAs and CT antigen directed immunotherapy. © 2009 Glazer et al

ssDNA-binding protein 2 is frequently hypermethylated and suppresses cell growth in human prostate cancer

Purpose: Prostate cancer is a major cause of cancer death among men and the development of new biomarkers is important to augment current detection approaches. Experimental Design: We identified hypermethylation of the ssDNA-binding protein 2 (SSBP2) promoter as a potential DNA marker for human prostate cancer based on previous bioinformatics results and pharmacologic unmasking microarray. We then did quantitative methylation-specific PCR in primary prostate cancer tissues to confirm hypermethylation of the SSBP2 promoter, and analyzed its correlation with clinicopathologic data. We further examined SSBP2 expression in primary prostate cancer and studied its role in cell growth. Results: Quantitative methylation-specific PCR results showed that the SSBP2 promoter was hypermethylated in 54 of 88 (61.4%) primary prostate cancers versus 0 of 23 (0%) in benign prostatic hyperplasia using a cutoff value of 120. Furthermore, we found that expression of SSBP2 was down-regulated in primary prostate cancers and cancer cell lines. Hypermethylation of the SSBP2 promoter and its expression were closely associated with higher stages of prostate cancer. Reactivation of SSBP2 expression by the demethylating agent 5-aza-2'-deoxycytidine in prostate cancer cell lines confirmed epigenetic inactivation as one major mechanism of SSBP2 regulation. Moreover, forced expression of SSBP2 inhibited prostate cancer cell proliferation in the colony formation assay and caused cell cycle arrest. Conclusion: SSBP2 inhibits prostate cancer cell proliferation and seems to represent a novel prostate cancer - specific DNA marker, especially in high stages of human prostate cancer

Development and external validation of a mixed-effects deep learning model to diagnose COVID-19 from CT imaging

BackgroundThe automatic analysis of medical images has the potential improve diagnostic accuracy while reducing the strain on clinicians. Current methods analyzing 3D-like imaging data, such as computerized tomography imaging, often treat each image slice as individual slices. This may not be able to appropriately model the relationship between slices.MethodsOur proposed method utilizes a mixed-effects model within the deep learning framework to model the relationship between slices. We externally validated this method on a data set taken from a different country and compared our results against other proposed methods. We evaluated the discrimination, calibration, and clinical usefulness of our model using a range of measures. Finally, we carried out a sensitivity analysis to demonstrate our methods robustness to noise and missing data.ResultsIn the external geographic validation set our model showed excellent performance with an AUROC of 0.930 (95%CI: 0.914, 0.947), with a sensitivity and specificity, PPV, and NPV of 0.778 (0.720, 0.828), 0.882 (0.853, 0.908), 0.744 (0.686, 0.797), and 0.900 (0.872, 0.924) at the 0.5 probability cut-off point. Our model also maintained good calibration in the external validation dataset, while other methods showed poor calibration.ConclusionDeep learning can reduce stress on healthcare systems by automatically screening CT imaging for COVID-19. Our method showed improved generalizability in external validation compared to previous published methods. However, deep learning models must be robustly assessed using various performance measures and externally validated in each setting. In addition, best practice guidelines for developing and reporting predictive models are vital for the safe adoption of such models

Comparison of Promoter Hypermethylation Pattern in Salivary Rinses Collected with and without an Exfoliating Brush from Patients with HNSCC

Background: Salivary rinses have been recently proposed as a valuable resource for the development of epigenetic biomarkers for detection and monitoring of head and neck squamous cell carcinoma (HNSCC). Both salivary rinses collected with and without an exfoliating brush from patients with HNSCC are used in detection of promoter hypermethylation, yet their correlation of promoter hypermethylation has not been evaluated. This study was to evaluate the concordance of promoter hypermethylation between salivary rinses collected with and without an exfoliating brush from patients with HNSCC. Methodolgy: 57 paired salivary rinses collected with or without an exfoliating brush from identical HNSCC patients were evaluated for promoter hypermethylation status using Quantitative Methylation-Specific PCR. Target tumor suppressor gene promoter regions were selected based on our previous studies describing a panel for HNSCC screening an