Biomaterials text mining: A hands-on comparative study of methods on polydioxanone biocompatibility.

Scientific information extraction is fundamental for research and innovation, but is currently mostly a manual, time-consuming process. Text Mining tools (TMTs) enable automated, accurate and quick information extraction from text, but there is little precedent of their use in the biomaterials field. Here, we compare the ability of various TMTs to extract useful information from biomaterials abstracts. Focusing on the biocompatibility of polydioxanone, a biodegradable polymer for which there are relatively few scientific publications, we tested several tools ranging from machine learning approaches and statistical text analysis to MeSH indexing and domain-specific semantic tools for Named Entity Recognition. We also evaluated their output alongside a manual review of systematic reviews and meta-analyses. The findings show that TMTs can be highly efficient and powerful for mapping biomaterials texts and rapidly yield up-to-date information. Here, TMTs enable one to identify dominating themes, see the evolution of specific terms and topics, and learn about key medical applications in biomaterials literature over the years. The analysis also shows that ambiguity around biomaterials nomenclature is a significant challenge in mining biomedical literature that is yet to be tackled. This research showcases the potential value of using Natural Language Processing and domain-specific tools to extract and organize biomaterials data.

DEBBIE: The Open Access Database of Experimental Scaffolds and Biomaterials Built Using an Automated Text Mining Pipeline.

Biomaterials research output has experienced an exponential increase over the last three decades. The majority of research is published in the form of scientific articles and is therefore available as unstructured text, making it a challenging input for computational processing. Computational tools are becoming essential to overcome this information overload. Among them, text mining systems present an attractive option for the automated extraction of information from text documents into structured datasets. This work presents the first automated system for biomaterial related information extraction from the National Library of Medicine's premier bibliographic database (MEDLINE) research abstracts into a searchable database. The system is a text mining pipeline that periodically retrieves abstracts from PubMed and identifies research and clinical studies of biomaterials. Thereafter, the pipeline identifies sixteen concept types of interest in the abstract using the Biomaterials Annotator, a tool for biomaterials Named Entity Recognition (NER). These concepts of interest, along with the abstract and relevant metadata are then deposited in DEBBIE, the Database of Experimental Biomaterials and their Biological Effect. DEBBIE is accessible through a web application that provides keyword searches and displays results in an intuitive and meaningful manner, aiming to facilitate an efficient mapping and organization of biomaterials information.

Novel behavioral assays of spontaneous and precipitated THC withdrawal in mice.

BACKGROUND: A subset of cannabis users develop some degree of Cannabis Use Disorder (CUD). Although behavioral therapy has some success in treating CUD, many users relapse, often citing altered sleep, mood, and irritability. Preclinical animal tests of cannabinoid withdrawal focus primarily on somatic-related behaviors precipitated by a cannabinoid receptor antagonist. The goal of the present study was to develop novel cannabinoid withdrawal assays that are either antagonist-precipitated or spontaneously induced by abstinence. METHODS: C57BL/6 J mice were repeatedly administered the phytocannabinoid Δ9-tetrahydrocannabinol (THC; 1, 10 or 50 mg/kg, s.c.), the synthetic cannabinoid receptor agonist JWH-018 (1 mg/kg, s.c.), or vehicle (1:1:18 parts ethanol:Kolliphor EL:saline, s.c.) for 6 days. Withdrawal was precipitated with the cannabinoid receptor inverse agonist rimonabant (3 mg/kg, i.p.) or elicited via abstinence (i.e., spontaneous withdrawal), and putative stress-related behavior was scored. Classic somatic signs of cannabinoid withdrawal were also quantified. RESULTS: Precipitated THC withdrawal significantly increased plasma corticosterone. Precipitated withdrawal from either THC or JWH-018 suppressed marble burying, increased struggling in the tail suspension test, and elicited somatic withdrawal behaviors. The monoacylglycerol lipase inhibitor JZL184 attenuated somatic precipitated withdrawal but had no effect on marble burying or struggling. Spontaneous THC or JWH-018 withdrawal-induced paw tremors, head twitches, and struggled in the tail suspension test after 24-48 h abstinence. JZL184 or THC attenuated these spontaneous withdrawal-induced behaviors. CONCLUSION: Outcomes from tail suspension and marble burying tests reveal that THC withdrawal is multifaceted, eliciting and suppressing behaviors in these tests, in addition to inducing well-documented somatic signs of withdrawal.