MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature

<p>Abstract</p> <p>Background</p> <p>DNA methylation is regarded as a potential biomarker in the diagnosis and treatment of cancer. The relations between aberrant gene methylation and cancer development have been identified by a number of recent scientific studies. In a previous work, we used co-occurrences to mine those associations and compiled the MeInfoText 1.0 database. To reduce the amount of manual curation and improve the accuracy of relation extraction, we have now developed MeInfoText 2.0, which uses a machine learning-based approach to extract gene methylation-cancer relations.</p> <p>Description</p> <p>Two maximum entropy models are trained to predict if aberrant gene methylation is related to any type of cancer mentioned in the literature. After evaluation based on 10-fold cross-validation, the average precision/recall rates of the two models are 94.7/90.1 and 91.8/90% respectively. MeInfoText 2.0 provides the gene methylation profiles of different types of human cancer. The extracted relations with maximum probability, evidence sentences, and specific gene information are also retrievable. The database is available at <url>http://bws.iis.sinica.edu.tw:8081/MeInfoText2/</url>.</p> <p>Conclusion</p> <p>The previous version, MeInfoText, was developed by using association rules, whereas MeInfoText 2.0 is based on a new framework that combines machine learning, dictionary lookup and pattern matching for epigenetics information extraction. The results of experiments show that MeInfoText 2.0 outperforms existing tools in many respects. To the best of our knowledge, this is the first study that uses a hybrid approach to extract gene methylation-cancer relations. It is also the first attempt to develop a gene methylation and cancer relation corpus.</p

Allele Size Miscalling due to the Pull-Up Effect Influencing Size Standard Calibration in Capillary Electrophoresis: A Case Study Using HEX Fluorescent Dye in Microsatellites

Microsatellites are important genetic markers and have been broadly employed in many genetic studies. Currently, polymorphisms in microsatellites are often detected by an automated system of capillary electrophoresis with fluorescent dyes. In this situation, different dye combinations may cause pull-up/bleed-through problems, which introduce noise signals from one dye channel into another, causing genotyping errors. Here, we report the detection of such a problem at two microsatellite loci that used the HEX dye. Using three datasets, we tested for noise effects in four allele-scoring programmes: Genemapper, Genemarker, Gelquest and Fragman. We found that, because some allele sizes were identical or close to the size of one of the internal size standards, all four programmes gave allele size calling errors due to wrongly identifying pull-up signals as the internal size standard. In addition, because allele miscalling in this study was caused by the fluorescent dye that the microsatellites used introducing noise of the same colour as the internal size standard used, the pull-up correction function in Genemapper, Genemarker and Fragman failed to deal with this. Considering that pull-up peak scoring errors can occur with any dye colour, the phenomenon is not limited to the current HEX dye. Using different software and visual scoring of each result will allow accurate sizing of microsatellite alleles

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

Stapled peptide PROTAC induced significantly greater anti-PD-L1 effects than inhibitor in human cervical cancer cells

IntroductionImmune checkpoint inhibitors (ICIs) are monoclonal antibodies that target immune checkpoints that suppress immune cell activity. Low efficiency and high resistance are currently the main barriers to their clinical application. As a representative technology of targeted protein degradation, proteolysis-targeting chimeras (PROTACs) are considered to have potential for addressing these limitations.MethodsWe synthesized a stapled peptide-based PROTAC (SP-PROTAC) that specifically targeted palmitoyltransferase ZDHHC3 and resulted in the decrease of PD-L1 in human cervical cancer cell lines. Flow cytometry, confocal microscopy, protein immunoblotting, Cellular Thermal Shift Assay (CETSA), and MTT assay analyses were conducted to evaluate the effects of the designed peptide and verify its safety in human cells.ResultsIn cervical cancer celllines C33A and HeLa, the stapled peptide strongly downregulated PD-L1 to &lt; 50% of baseline level at 0.1 μM. DHHC3 expression decreased in both dosedependentand time-dependent manners. MG132, the proteasome inhibitor, can alleviate the SP-PROTAC mediated degradation of PD-L1 in human cancer cells. In a co-culture model of C33A and T cells, treatment with the peptide induced IFN-γ and TNF-α release in a dose-dependent manner by degrading PD-L1. These effects were more significant than that of the PD-L1 inhibitor, BMS-8.ConclusionsCells treated with 0.1 μM of SP-PROTAC or BMS-8 for 4 h revealed that the stapled peptide decreased PD-L1 more effectively than BMS-8. DHHC3-targeting SP-PROTAC decreased PD-L1 in human cervical cancer more effectively than the inhibitor BMS-8

Axin downregulates TCF-4 transcription via β-catenin, but not p53, and inhibits the proliferation and invasion of lung cancer cells

<p>Abstract</p> <p>Background</p> <p>We previously reported that overexpression of Axin downregulates T cell factor-4 (TCF-4) transcription. However, the mechanism(s) by which Axin downregulates the transcription and expression of TCF-4 is not clear. It has been reported that β-catenin promotes and p53 inhibits TCF-4 transcription, respectively. The aim of this study was to investigate whether β-catenin and/or p53 is required for Axin-mediated downregulation of TCF-4.</p> <p>Results</p> <p>Axin mutants that lack p53/HIPK2 and/or β-catenin binding domains were expressed in lung cancer cells, BE1 (mutant p53) and A549 (wild type p53). Expression of Axin or AxinΔp53 downregulates β-catenin and TCF-4, and knock-down of β-catenin upregulates TCF-4 in BE1 cells. However, expression of AxinΔβ-ca into BE1 cells did not downregulate TCF-4 expression. These results indicate that Axin downregulates TCF-4 transcription via β-catenin. Although overexpression of wild-type p53 also downregulates TCF-4 in BE1 cells, cotransfection of p53 and AxinΔβ-ca did not downregulate TCF-4 further. These results suggest that Axin does not promote p53-mediated downregulation of TCF-4. Axin, AxinΔp53, and AxinΔβ-ca all downregulated β-catenin and TCF-4 in A549 cells. Knock-down of p53 upregulated β-catenin and TCF-4, but cotransfection of AxinΔβ-ca and p53 siRNA resulted in downregulation of β-catenin and TCF-4. These results indicate that p53 is not required for Axin-mediated downregulation of TCF-4. Knock-down or inhibition of GSK-3β prevented Axin-mediated downregulation of TCF-4. Furthermore, expression of Axin and AxinΔp53, prevented the proliferative and invasive ability of BE1 and A549, expression of AxinΔβ-ca could only prevented the proliferative and invasive ability effectively.</p> <p>Conclusions</p> <p>Axin downregulates TCF-4 transcription via β-catenin and independently of p53. Axin may also inhibits the proliferation and invasion of lung cancer cells via β-catenin and p53.</p

Service utilization in community health centers in China: a comparison analysis with local hospitals

BACKGROUND: Being an important part of China's Urban Health Care Reform System, Community Health Centers (CHCs) have been established throughout the entire country and are presently undergoing substantial reconstruction. However, the services being delivered by the CHCs are far from reaching their performance targets. In order to assess the role of the CHCs, we examined their performance in six cities located in regions of South-East China. The purpose of this investigation was to identify the utilization and the efficiency of community health resources that are able to provide basic medical and public health services. METHODS: The study was approved by Peking University Health Science Center Institutional Reviewing Board (NO: IRB00001052-T1). Data were collected from all the local health bureaux and processed using SPSS software. Methods of analysis mainly included: descriptive analysis, paired T-test and one-way ANOVA. RESULTS: The six main functions of the CHCs were not fully exploited and the surveys that were collected on their efficiency and utilization of resources indicate that they have a low level of performance and lack the trust of local communities. Furthermore, the CHCs seriously lack funding support and operate under difficult circumstances, and residents have less positive attitudes towards them. CONCLUSION: The community health service must be adjusted according to the requirements of urban medical and health reform, taking into account communities' health needs. More research is required on the living standards and health needs of residents living within the CHC's range, taking into consideration the users' needs in expanding the newly implemented service, and at the same time revising the old service system so as to make the development of CHCs realistic and capable of providing a better service to patients. Several suggestions are put forward for an attainable scheme for developing a community health service

BIOSMILE web search: a web application for annotating biomedical entities and relations

BIOSMILE web search (BWS), a web-based NCBI-PubMed search application, which can analyze articles for selected biomedical verbs and give users relational information, such as subject, object, location, manner, time, etc. After receiving keyword query input, BWS retrieves matching PubMed abstracts and lists them along with snippets by order of relevancy to protein–protein interaction. Users can then select articles for further analysis, and BWS will find and mark up biomedical relations in the text. The analysis results can be viewed in the abstract text or in table form. To date, BWS has been field tested by over 30 biologists and questionnaires have shown that subjects are highly satisfied with its capabilities and usability. BWS is accessible free of charge at http://bioservices.cse.yzu.edu.tw/BWS