Extraction of Conditional Probabilities of the Relationships Between Drugs, Diseases, and Genes from PubMed Guided by Relationships in PharmGKB

Guided by curated associations between genes, treatments (i.e., drugs), and diseases in pharmGKB, we constructed n-way Bayesian networks based on conditional probability tables (cpt’s) extracted from co-occurrence statistics over the entire Pubmed corpus, producing a broad-coverage analysis of the relationships between these biological entities. The networks suggest hypotheses regarding drug mechanisms, treatment biomarkers, and/or potential markers of genetic disease. The cpt’s enable Trio, an inferential database, to query indirect (inferred) relationships via an SQL-like query language

The Effect of Map Boundary on Estimates of Landscape Resistance to Animal Movement

BACKGROUND: Artificial boundaries on a map occur when the map extent does not cover the entire area of study; edges on the map do not exist on the ground. These artificial boundaries might bias the results of animal dispersal models by creating artificial barriers to movement for model organisms where there are no barriers for real organisms. Here, we characterize the effects of artificial boundaries on calculations of landscape resistance to movement using circuit theory. We then propose and test a solution to artificially inflated resistance values whereby we place a buffer around the artificial boundary as a substitute for the true, but unknown, habitat. METHODOLOGY/PRINCIPAL FINDINGS: We randomly assigned landscape resistance values to map cells in the buffer in proportion to their occurrence in the known map area. We used circuit theory to estimate landscape resistance to organism movement and gene flow, and compared the output across several scenarios: a habitat-quality map with artificial boundaries and no buffer, a map with a buffer composed of randomized habitat quality data, and a map with a buffer composed of the true habitat quality data. We tested the sensitivity of the randomized buffer to the possibility that the composition of the real but unknown buffer is biased toward high or low quality. We found that artificial boundaries result in an overestimate of landscape resistance. CONCLUSIONS/SIGNIFICANCE: Artificial map boundaries overestimate resistance values. We recommend the use of a buffer composed of randomized habitat data as a solution to this problem. We found that resistance estimated using the randomized buffer did not differ from estimates using the real data, even when the composition of the real data was varied. Our results may be relevant to those interested in employing Circuitscape software in landscape connectivity and landscape genetics studies

Medication treatment for opioid use disorder in expectant mothers (MOMs): Design considerations for a pragmatic randomized trial comparing extended-release and daily buprenorphine formulations.

Opioid use disorder (OUD) in pregnant women has increased significantly in recent years. Maintaining these women on sublingual (SL) buprenorphine (BUP) is an evidence-based practice but BUP-SL is associated with several disadvantages that an extended-release (XR) BUP formulation could eliminate. The National Drug Abuse Treatment Clinical Trials Network (CTN) is conducting an intent-to-treat, two-arm, open-label, pragmatic randomized controlled trial, Medication treatment for Opioid-dependent expectant Mothers (MOMs), to compare mother and infant outcomes of pregnant women with OUD treated with BUP-XR, relative to BUP-SL. A second aim is to determine the relative economic value of utilizing BUP-XR. Approximately 300 pregnant women with an estimated gestational age (EGA) of 6-30 weeks, recruited from 12 sites, will be randomized in a 1:1 ratio to BUP-XR or BUP-SL, balancing on site, EGA, and BUP-SL status (taking/not taking) at the time of randomization. Participants will be provided with study medication and attend weekly medication visits through 12 months postpartum. Participants will be invited to participate in two sub-studies to evaluate the: 1) mechanisms by which BUP-XR may improve mother and infant outcomes; and 2) effects of prenatal exposure to BUP-XR versus BUP-SL on infant neurodevelopment. This paper describes the key design decisions for the main trial made during protocol development. This Investigational New Drug (IND) trial uniquely uses pragmatic features where feasible in order to maximize external validity, hence increasing the potential to inform clinical practice guidelines and address multiple knowledge gaps for treatment of this patient population