Molecular liver cancer prevention in cirrhosis by organ transcriptome analysis and lysophosphatidic acid pathway inhibition

Cirrhosis is a milieu that develops hepatocellular carcinoma (HCC), the second most lethal cancer worldwide. HCC prediction and prevention in cirrhosis are key unmet medical needs. Here we have established an HCC risk gene signature applicable to all major HCC etiologies: hepatitis B/C, alcohol, and non-alcoholic steatohepatitis. A transcriptome meta-analysis of >500 human cirrhotics revealed global regulatory gene modules driving HCC risk and the lysophosphatidic acid pathway as a central chemoprevention target. Pharmacological inhibition of the pathway in vivo reduced tumors and reversed the gene signature, which was verified in organotypic ex vivo culture of patient-derived fibrotic liver tissues. These results demonstrate the utility of clinical organ transcriptome to enable a strategy, namely, reverse-engineering precision cancer prevention

Role of leukocyte cell-derived chemotaxin 2 as a biomarker in hepatocellular carcinoma

We sought to identify a secreted biomarker for β-catenin activation commonly seen in hepatocellular carcinoma (HCC). By examination of our previously published genearray of hepatocyte-specific β-catenin knockout (KO) livers, we identified secreted factors whose expression may be β-catenin-dependent. We verified expression and secretion of the leading factor in HCC cells transfected with mutated (Hep3BS33Y)-β- catenin. Serum levels of biomarker were next investigated in a mouse model of HCC with β-catenin gene (Ctnnb1) mutations and eventually in HCC patients. Leukocyte cell-derived chemotaxin-2 (LECT2) expression was decreased in KO livers. Hep3BS33Y expressed and secreted more LECT2 in media as compared to Hep3BWT. Mice developing HCC with Ctnnb1 mutations showed significantly higher serum LECT2 levels. However patients with CTNNB1 mutations showed LECT2 levels of 54.28±22.32 ng/mL (Mean ± SD; n = 8) that were insignificantly different from patients with non-neoplastic chronic liver disease (32.8±21.1 ng/mL; n = 15) or healthy volunteers (33.2±7.2 ng/mL; n = 11). Intriguingly, patients without β-catenin mutations showed significantly higher serum LECT2 levels (54.26 ± 22.25 ng/mL; n = 46). While β-catenin activation was evident in a subset of non-mutant β-catenin HCC group with high LECT2 expression, serum LECT2 was unequivocally similar between β-catenin-active and -normal group. Further analysis showed that LECT2 levels greater than 50 ng/ml diagnosed HCC in patients irrespective of β-catenin mutations with specificity of 96.1% and positive predictive value of 97.0%. Thus, LECT2 is regulated by β-catenin in HCC in both mice and men, but serum LECT2 reflects β-catenin activity only in mice. Serum LECT2 could be a potential biomarker of HCC in patients. © 2014 Okabe et al

Development of Input Libraries With Intel XLSDK to Capture Data for App Start Prediction

Using Intel's System Usage Reporting library and the accompanying XLSDK, we can create "monitors" of user and computer activity, known as collectors or Input Libraries, and create activity logs which we can then analyze to provide preloading solutions for the user. We have focused on the creation of these input libraries to have data to use in future collaboration with Intel, in which we would use the data we collected for analysis. We developed four different Input Libraries, each using a different template and measuring different categories of inputs from the user and computer. The first, the mouse input Input Library, keeps a log of the cursor coordinates as the user moves the mouse around. Second, the user waiting Input Library keeps a timer based log of the cursor icon as the user uses the computer. The third, a foreground window Input Library, creates a log entry whenever the foreground window (the window in front of all other windows) changes whether it be automatically (such as a notification pop up) or by user input (clicking the taskbar). Finally, the fourth Input Library is the desktop mapper, which, when triggered by a change in the foreground window, maps all the windows on the desktop in z-order and stores pertinent information about each window e.g. position and size. Each of these Input Libraries are coded differently in fundamental ways, and measure changes in different ways as well. By using the data provided by Libraries like these, we can determine preloading schedules for the individual user

Development of Input Libraries With Intel XLSDK to Capture Data for App Start Prediction

Using Intel's System Usage Reporting library and the accompanying XLSDK, we can create "monitors" of user and computer activity, known as collectors or Input Libraries, and create activity logs which we can then analyze to provide preloading solutions for the user. We have focused on the creation of these input libraries to have data to use in future collaboration with Intel, in which we would use the data we collected for analysis. We developed four different Input Libraries, each using a different template and measuring different categories of inputs from the user and computer. The first, the mouse input Input Library, keeps a log of the cursor coordinates as the user moves the mouse around. Second, the user waiting Input Library keeps a timer based log of the cursor icon as the user uses the computer. The third, a foreground window Input Library, creates a log entry whenever the foreground window (the window in front of all other windows) changes whether it be automatically (such as a notification pop up) or by user input (clicking the taskbar). Finally, the fourth Input Library is the desktop mapper, which, when triggered by a change in the foreground window, maps all the windows on the desktop in z-order and stores pertinent information about each window e.g. position and size. Each of these Input Libraries are coded differently in fundamental ways, and measure changes in different ways as well. By using the data provided by Libraries like these, we can determine preloading schedules for the individual user

INTELlinext: A Fully Integrated LSTM and HMM-Based Solution for Next-App Prediction With Intel SUR SDK Data Collection

International audienceAs the power of modern computing devices increases, so too do user expectations for them. Despite advancements in technology, computer users are often faced with the dreaded spinning icon waiting for an application to load. Building upon our previous work developing data collectors with the Intel System Usage Reporting (SUR) SDK, we introduce INTELlinext, a comprehensive solution for next-app prediction for application preload to improve perceived system fluidity. We develop a Hidden Markov Model (HMM) for prediction of the k most likely next apps, achieving an accuracy of 70% when k = 3. We then implement a long short-term memory (LSTM) model to predict the total duration that applications will be used. After hyperparameter optimization leading to an optimal lookback value of 5 previous applications, we are able to predict the usage time of a given application with a mean absolute error of ~45 seconds. Our work constitutes a promising comprehensive application preload solution with data collection based on the Intel SUR SDK and prediction with machine learning

INTELlinext: A Fully Integrated LSTM and HMM-Based Solution for Next-App Prediction With Intel SUR SDK Data Collection

International audienceAs the power of modern computing devices increases, so too do user expectations for them. Despite advancements in technology, computer users are often faced with the dreaded spinning icon waiting for an application to load. Building upon our previous work developing data collectors with the Intel System Usage Reporting (SUR) SDK, we introduce INTELlinext, a comprehensive solution for next-app prediction for application preload to improve perceived system fluidity. We develop a Hidden Markov Model (HMM) for prediction of the k most likely next apps, achieving an accuracy of 70% when k = 3. We then implement a long short-term memory (LSTM) model to predict the total duration that applications will be used. After hyperparameter optimization leading to an optimal lookback value of 5 previous applications, we are able to predict the usage time of a given application with a mean absolute error of ~45 seconds. Our work constitutes a promising comprehensive application preload solution with data collection based on the Intel SUR SDK and prediction with machine learning

Parathyroid hormone and premature thymus ageing in patients with chronic kidney disease

Abstract Premature immune ageing, including thymic atrophy, is observed in patients with chronic kidney disease (CKD). Parathyroid hormone (PTH) and fibroblast growth factor 23 (FGF23), which are mineral and bone disorder (MBD)-related factors, affect immune cells and possibly cause thymic atrophy. We examined the cross-sectional association between thymic atrophy, evaluated as the number of CD3+CD4+CD45RA+CD31+ cells [recent thymic emigrants (RTE)/μL], and MBD-related factors [(serum PTH, FGF23, and alkaline phosphatase (ALP) level] in 125 patients with non-dialysis dependent CKD. Median estimated glomerular filtration rate (eGFR) was 17 mL/min/1.73 m2. Older age (r = −0.46), male sex (r = −0.34), lower eGFR (r = 0.27), lower serum-corrected calcium (r = 0.27), higher PTH (r = −0.36), and higher ALP level (r = −0.20) were identified as determinants of lower number of RTE. In contrast, serum concentrations of FGF23 and phosphorus were not correlated with RTE. Multivariate non-linear regression analysis indicated a negative association between serum PTH and log-transformed RTE (P = 0.030, P for non-linearity = 0.124). However, the serum levels of FGF23 and ALP were not associated with RTE. In patients with CKD, serum PTH concentrations were related to thymic atrophy which contributes to immune abnormality

Cell type-specific pharmacological kinase inhibition for cancer chemoprevention

Safety is prerequisite for preventive medicine, but non-toxic agents are generally ineffective as clinical chemoprevention. Here we propose a strategy overcoming this challenge by delivering molecular-targeted agent specifically to the effector cell type to achieve sufficient potency, while circumventing toxicity in the context of cancer chemoprevention. Hepatic myofibroblasts drive progressive fibrosis that results in cirrhosis and liver cancer. In a rat model of cirrhosis-driven liver cancer, a small molecule epidermal growth factor receptor inhibitor, erlotinib, was delivered specifically to myofibroblasts by a versatile nanoparticle-based system, targeting platelet-derived growth factor receptor-beta uniquely expressed on their surface in the liver. With systemic administration of erlotinib, tumor burden was reduced to 31%, which was further improved to 21% by myofibroblast-targeted delivery even with reduced erlotinib dose (7.3-fold reduction with equivalent erlotinib dose) and less hepatocyte damage. These findings demonstrate a strategy, cell type-specific kinase inhibition, for more effective and safer precision cancer chemoprevention. (c) 2017 Elsevier Inc. All rights reserved

Cell type-specific pharmacological kinase inhibition for cancer chemoprevention

Safety is prerequisite for preventive medicine, but non-toxic agents are generally ineffective as clinical chemoprevention. Here we propose a strategy overcoming this challenge by delivering molecular-targeted agent specifically to the effector cell type to achieve sufficient potency, while circumventing toxicity in the context of cancer chemoprevention. Hepatic myofibroblasts drive progressive fibrosis that results in cirrhosis and liver cancer. In a rat model of cirrhosis-driven liver cancer, a small molecule epidermal growth factor receptor inhibitor, erlotinib, was delivered specifically to myofibroblasts by a versatile nanoparticle-based system, targeting platelet-derived growth factor receptor-beta uniquely expressed on their surface in the liver. With systemic administration of erlotinib, tumor burden was reduced to 31%, which was further improved to 21% by myofibroblast-targeted delivery even with reduced erlotinib dose (7.3-fold reduction with equivalent erlotinib dose) and less hepatocyte damage. These findings demonstrate a strategy, cell type-specific kinase inhibition, for more effective and safer precision cancer chemoprevention

Utilization of transposable element mPing as a novel genetic tool for modification of the stress response in rice.

Transposable elements (TEs) are DNA fragments that have the ability to move from one chromosomal location to another. The insertion of TEs into gene-rich regions often affects changes in the expression of neighboring genes. Miniature Ping (mPing) is an active miniature inverted-repeat TE discovered in the rice genome. It has been found to show exceptionally active transposition in a few japonica rice varieties, including Gimbozu, where mPing insertion rendered adjacent genes stress-inducible. In the Gimbozu population, it is highly possible that several genes with modified expression profiles are segregating due to the de novo mPing insertions. In our study, we utilized a screening system for detecting de novo mPing insertions in the upstream region of target genes and evaluated the effect of mPing on the stress response of the target genes. Screening for 17 targeted genes revealed five genes with the mPing insertion in their promoters. In most cases, the alteration of gene expression was observed under stress conditions, and there was no change in the expression levels of those five genes under normal conditions. These results indicate that the mPing insertion can be used as a genetic tool to modify an expression pattern of a target gene under stress conditions without changing the expression profiles of those under natural conditions