AGE-BSA down-regulates endothelial connexin43 gap junctions

<p>Abstract</p> <p>Background</p> <p>Advanced glycation end products generated in the circulation of diabetic patients were reported to affect the function of vascular wall. We examined the effects of advanced glycation end products-bovine serum albumin (AGE-BSA) on endothelial connexin43 (Cx43) expression and gap-junction communication.</p> <p>Results</p> <p>In human aortic endothelial cells (HAEC) treated with a series concentrations of AGE-BSA (0-500 μg/ml) for 24 and 48 hours, Cx43 transcript and Cx43 protein were reduced in a dose dependent manner. In addition, gap-junction communication was reduced. To clarify the mechanisms underlying the down-regulation, MAPKs pathways in HAEC were examined. Both a MEK1 inhibitor (PD98059) and a p38 MAPK inhibitor (SB203580) significantly reversed the reductions of Cx43 mRNA and protein induced by AGE-BSA. Consistently, phosphorylation of ERK and p38 MAPK was enhanced in response to exposure to AGE-BSA. However, all reversions of down-regulated Cx43 by inhibitors did not restore the functional gap-junction communication.</p> <p>Conclusions</p> <p>AGE-BSA down-regulated Cx43 expression in HAEC, mainly through reduced Cx43 transcription, and the process involved activation of ERK and p38 MAPK.</p

Estimating quality weights for EQ-5D (EuroQol-5 dimensions) health states with the time trade-off method in Taiwan

Background/PurposeEQ-5D (EuroQol-5 dimensions) is a preference-based measure of health, which is widely used in cost–utility analyses. It has been suggested that each country should develop its own value set. We therefore sought to develop the quality weights of the EQ-5D health states with the time trade-off (TTO) method in Taiwan.MethodsA total of 745 respondents consisting of employees and volunteers in 17 different hospitals were recruited and interviewed. Each of them valued 13 of 73 EQ-5D health states using the TTO method. Based on the three exclusion criteria for valuation data, only 456 (61.21%) respondents were considered eligible for data analysis. The quality weights for all EQ-5D health states were modeled by generalized estimating equations (GEEs).ResultsOver half of the responses were given negative values, and the medical personnel seemed to have a significantly higher TTO value (+0.1) than others after controlling for other predictors. The N3 model (level 3 occurred within at least 1 dimension) yielded an acceptable fit for the observed OTT data [mean absolute error (MAE) = 0.056, R2 = 0.35]. The magnitude of mean absolute differences (MADs) between Taiwan data and those from the UK, Japan, and South Korea ranged from 0.146 to 0.592, but the rank correlation coefficients were all above 0.811.ConclusionThis study reaffirms the differences in health-related preference values across countries. The high proportion of negative values might indicate that we have also partially measured the intensity of fear in addition to the utility of different health states

The Arabidopsis Malectin-Like/LRR-RLK IOS1 is Critical for BAK1-Dependent and BAK1-Independent Pattern-Triggered Immunity

Plasma membrane-localized pattern recognition receptors (PRRs) such as FLAGELLIN SENSING2 (FLS2), EF-TU RECEPTOR (EFR) and CHITIN ELICITOR RECEPTOR KINASE 1 (CERK1) recognize microbe-associated molecular patterns (MAMPs) to activate pattern-triggered immunity (PTI). A reverse genetics approach on genes responsive to the priming agent beta-aminobutyric acid (BABA) revealed IMPAIRED OOMYCETE SUSCEPTIBILITY1 (IOS1) as a critical PTI player. Arabidopsis thaliana ios1 mutants were hyper-susceptible to Pseudomonas syringae bacteria. Accordingly, ios1 mutants showed defective PTI responses, notably delayed up-regulation of the PTI-marker gene FLG22-INDUCED RECEPTOR-LIKE KINASE1 (FRK1), reduced callose deposition and mitogen-activated protein kinase activation upon MAMP treatment. Moreover, Arabidopsis lines over-expressing IOS1 were more resistant to bacteria and showed a primed PTI response. In vitro pull-down, bimolecular fluorescence complementation, co-immunoprecipitation, and mass spectrometry analyses supported the existence of complexes between the membrane-localized IOS1 and BRASSINOSTEROID INSENSITIVE1-ASSOCIATED KINASE1 (BAK1)-dependent PRRs FLS2 and EFR, as well as with the BAK1-independent PRR CERK1. IOS1 also associated with BAK1 in a ligand-independent manner, and positively regulated FLS2-BAK1 complex formation upon MAMP treatment. In addition, IOS1 was critical for chitin-mediated PTI. Finally, ios1 mutants were defective in BABA-induced resistance and priming. This work reveals IOS1 as a novel regulatory protein of FLS2-, EFR- and CERK1-mediated signaling pathways that primes PTI activation

NERBio: using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition

BACKGROUND: Biomedical named entity recognition (Bio-NER) is a challenging problem because, in general, biomedical named entities of the same category (e.g., proteins and genes) do not follow one standard nomenclature. They have many irregularities and sometimes appear in ambiguous contexts. In recent years, machine-learning (ML) approaches have become increasingly common and now represent the cutting edge of Bio-NER technology. This paper addresses three problems faced by ML-based Bio-NER systems. First, most ML approaches usually employ singleton features that comprise one linguistic property (e.g., the current word is capitalized) and at least one class tag (e.g., B-protein, the beginning of a protein name). However, such features may be insufficient in cases where multiple properties must be considered. Adding conjunction features that contain multiple properties can be beneficial, but it would be infeasible to include all conjunction features in an NER model since memory resources are limited and some features are ineffective. To resolve the problem, we use a sequential forward search algorithm to select an effective set of features. Second, variations in the numerical parts of biomedical terms (e.g., "2" in the biomedical term IL2) cause data sparseness and generate many redundant features. In this case, we apply numerical normalization, which solves the problem by replacing all numerals in a term with one representative numeral to help classify named entities. Third, the assignment of NE tags does not depend solely on the target word's closest neighbors, but may depend on words outside the context window (e.g., a context window of five consists of the current word plus two preceding and two subsequent words). We use global patterns generated by the Smith-Waterman local alignment algorithm to identify such structures and modify the results of our ML-based tagger. This is called pattern-based post-processing. RESULTS: To develop our ML-based Bio-NER system, we employ conditional random fields, which have performed effectively in several well-known tasks, as our underlying ML model. Adding selected conjunction features, applying numerical normalization, and employing pattern-based post-processing improve the F-scores by 1.67%, 1.04%, and 0.57%, respectively. The combined increase of 3.28% yields a total score of 72.98%, which is better than the baseline system that only uses singleton features. CONCLUSION: We demonstrate the benefits of using the sequential forward search algorithm to select effective conjunction feature groups. In addition, we show that numerical normalization can effectively reduce the number of redundant and unseen features. Furthermore, the Smith-Waterman local alignment algorithm can help ML-based Bio-NER deal with difficult cases that need longer context windows