Maternal perception of fetal movements: onset and associated factors.

Objectives: The purpose of this study was to determine the onset of fetal movements' perception and to identify parameters that affect this timing.

Methods: This was a prospective cohort study including singleton pregnancies that attended routine prenatal care, in a tertiary care center, in northern Greece, between January 2020 and July 2021. We collected data on medical and obstetric history, and invited women to record the time that they perceived the fetal movements for the first time, that being the primary outcome of the study.

A term-based and citation network-based search system for COVID-19.

The COVID-19 pandemic resulted in an unprecedented production of scientific literature spanning several fields. To facilitate navigation of the scientific literature related to various aspects of the pandemic, we developed an exploratory search system. The system is based on automatically identified technical terms, document citations, and their visualization, accelerating identification of relevant documents.

Depression in pregnant women hospitalized due to intrauterine growth restriction: Prevalence and associated factors.

Objective: Pregnancies complicated with intrauterine growth restriction (IUGR) may require hospitalization in a high-risk pregnancy unit (HRPU). A complicated pregnancy and hospital admission may negatively affect the pregnant woman's mental health. Several factors have been identified as possible risk factors for depression, which is proven to lead to several adverse perinatal outcomes.

LitPathExplorer: a confidence-based visual text analytics tool for exploring literature-enriched pathway models.

Motivation: Pathway models are valuable resources that help us understand the various mechanisms underpinning complex biological processes. Their curation is typically carried out through manual inspection of published scientific literature to find information relevant to a model, which is a laborious and knowledge-intensive task. Furthermore, models curated manually cannot be easily updated and maintained with new evidence extracted from the literature without automated support.

Using uncertainty to link and rank evidence from biomedical literature for model curation.

Motivation: In recent years, there has been great progress in the field of automated curation of biomedical networks and models, aided by text mining methods that provide evidence from literature. Such methods must not only extract snippets of text that relate to model interactions, but also be able to contextualize the evidence and provide additional confidence scores for the interaction in question. Although various approaches calculating confidence scores have focused primarily on the quality of the extracted information, there has been little work on exploring the textual uncertainty conveyed by the author.