Research Participants' Attitudes towards Receiving Information on Genetic Susceptibility to Arsenic Toxicity in Rural Bangladesh.

BACKGROUND: In human genetics research, it has become common practice for researchers to consider returning genetic information to participants who wish to receive it. Research participants in lower-resource settings may have barriers or competing interests that reduce the benefit or relevance of such information. Thus, the decision to return genetic information in these settings may involve special considerations of participants' interests and preferences. In this project, our goal was to assess Bangladeshi research participants' attitudes towards receiving information regarding genetic susceptibility to the effects of consuming arsenic-contaminated drinking water, a serious environmental health concern in Bangladesh and other countries. METHODS: We administered a short questionnaire to 200 individuals participating in the Health Effects of Arsenic Longitudinal Study. Associations between survey responses and participant characteristics were estimated using logistic regression. RESULTS: Overall, 100% of our participants were interested in receiving information regarding their genetic susceptibility to arsenic toxicities, and 91% indicated that being at increased genetic risk would motivate them to make efforts to reduce their exposure. Lower levels of education showed evidence of association with less concern regarding the health effects of arsenic and lower levels of motivation to reduce exposure in response to genetic information. CONCLUSIONS: Research participants in this low-resource setting appeared interested in receiving information on their genetic susceptibility to arsenic toxicity and motivated to reduce exposure in response to such information. Additional research is needed to understand how best to communicate genetic information in this population and to assess the impact of such information on individuals' behaviors and health.

Genomics of Detoxification: How Genomics can be Used for Targeting Potential Intervention and Prevention Strategies Including Nutrition for Environmentally Acquired Illness.

Due to their genomic variants, some individuals are more highly affected by toxicants than others. Toxicant metabolizing and activating variants have been linked with a wide variety of health issues including an increased risk of miscarriages, birth defects, Alzheimer's, benzene toxicity, mercury toxicity and cancer. The study of genomics allows a clinician to identify pathways that are less effective and then gives the clinician the opportunity to counsel their patients about diet, supplements and lifestyle modifications that can improve the function of these pathways or compensate to some extent for their deficits. This article will review a few of these critical pathways relating to phase I and phase 2 detox such as GSTP1, GPX1, GSTT1 deletions, PON1 and some of the CYP 450 system as examples of how an individual's genomic vulnerabilities to toxicants can be addressed by upregulating or downregulating specific pathways via genomically targeted use of foods, supplements and lifestyle changes.

DigChem: Identification of disease-gene-chemical relationships from Medline abstracts.

Chemicals interact with genes in the process of disease development and treatment. Although much biomedical research has been performed to understand relationships among genes, chemicals, and diseases, which have been reported in biomedical articles in Medline, there are few studies that extract disease-gene-chemical relationships from biomedical literature at a PubMed scale. In this study, we propose a deep learning model based on bidirectional long short-term memory to identify the evidence sentences of relationships among genes, chemicals, and diseases from Medline abstracts. Then, we develop the search engine DigChem to enable disease-gene-chemical relationship searches for 35,124 genes, 56,382 chemicals, and 5,675 diseases. We show that the identified relationships are reliable by comparing them with manual curation and existing databases. DigChem is available at http://gcancer.org/digchem.

Effects of Prolonged GRP78 Haploinsufficiency on Organ Homeostasis, Behavior, Cancer and Chemotoxic Resistance in Aged Mice.

GRP78, a multifunctional protein with potent cytoprotective properties, is an emerging therapeutic target to combat cancer development, progression and drug resistance. The biological consequences of prolonged reduction in expression of this essential chaperone which so far has been studied primarily in young mice, was investigated in older mice, as older individuals are likely to be important recipients of anti-GRP78 therapy. We followed cohorts of Grp78+/+ and Grp78+/- male and female mice up to 2 years of age in three different genetic backgrounds and characterized them with respect to body weight, organ integrity, behavioral and memory performance, cancer, inflammation and chemotoxic response. Our results reveal that body weight, organ development and integrity were not impaired in aged Grp78+/- mice. No significant effect on cancer incidence and inflammation was observed in aging mice. Interestingly, our studies detected some subtle differential trends between the WT and Grp78+/- mice in some test parameters dependent on gender and genetic background. Our studies provide the first evidence that GRP78 haploinsufficiency for up to 2 years of age has no major deleterious effect in rodents of different genetic background, supporting the merit of anti-GRP78 drugs in treatment of cancer and other diseases affecting the elderly.

Sieve-based coreference resolution enhances semi-supervised learning model for chemical-induced disease relation extraction.

The BioCreative V chemical-disease relation (CDR) track was proposed to accelerate the progress of text mining in facilitating integrative understanding of chemicals, diseases and their relations. In this article, we describe an extension of our system (namely UET-CAM) that participated in the BioCreative V CDR. The original UET-CAM system's performance was ranked fourth among 18 participating systems by the BioCreative CDR track committee. In the Disease Named Entity Recognition and Normalization (DNER) phase, our system employed joint inference (decoding) with a perceptron-based named entity recognizer (NER) and a back-off model with Semantic Supervised Indexing and Skip-gram for named entity normalization. In the chemical-induced disease (CID) relation extraction phase, we proposed a pipeline that includes a coreference resolution module and a Support Vector Machine relation extraction model. The former module utilized a multi-pass sieve to extend entity recall. In this article, the UET-CAM system was improved by adding a 'silver' CID corpus to train the prediction model. This silver standard corpus of more than 50 thousand sentences was automatically built based on the Comparative Toxicogenomics Database (CTD) database. We evaluated our method on the CDR test set. Results showed that our system could reach the state of the art performance with F1 of 82.44 for the DNER task and 58.90 for the CID task. Analysis demonstrated substantial benefits of both the multi-pass sieve coreference resolution method (F1 + 4.13%) and the silver CID corpus (F1 +7.3%).Database URL: SilverCID-The silver-standard corpus for CID relation extraction is freely online available at: https://zenodo.org/record/34530 (doi:10.5281/zenodo.34530).

Combining machine learning, crowdsourcing and expert knowledge to detect chemical-induced diseases in text.

Drug toxicity is a major concern for both regulatory agencies and the pharmaceutical industry. In this context, text-mining methods for the identification of drug side effects from free text are key for the development of up-to-date knowledge sources on drug adverse reactions. We present a new system for identification of drug side effects from the literature that combines three approaches: machine learning, rule- and knowledge-based approaches. This system has been developed to address the Task 3.B of Biocreative V challenge (BC5) dealing with Chemical-induced Disease (CID) relations. The first two approaches focus on identifying relations at the sentence-level, while the knowledge-based approach is applied both at sentence and abstract levels. The machine learning method is based on the BeFree system using two corpora as training data: the annotated data provided by the CID task organizers and a new CID corpus developed by crowdsourcing. Different combinations of results from the three strategies were selected for each run of the challenge. In the final evaluation setting, the system achieved the highest Recall of the challenge (63%). By performing an error analysis, we identified the main causes of misclassifications and areas for improving of our system, and highlighted the need of consistent gold standard data sets for advancing the state of the art in text mining of drug side effects.Database URL: https://zenodo.org/record/29887?ln»en#.VsL3yDLWR_V.