Characterization of healing tissue in a tooth extraction socket in a rabbit model

The histology of healing in a tooth extraction socket has been described in many studies. The focus of research in bone biology and healing is now centered on molecular events that regulate repair of injured tissue. Rapid progress in cellular and molecular biology has resulted in identification of many signaling molecules (growth factors and cytokines) associated with formation and repair of skeletal tissues. Some of these include members of the transforming growth factor-β superfamily (including the bone morphogenetic proteins), fibroblast growth factors, platelet derived growth factors and insulin like growth factors. ^ Healing of a tooth extraction socket is a complex process involving tissue repair and regeneration. It involves chemotaxis of appropriate cells into the wound, transformation of undifferentiated mesenchymal cells to osteoprogenitor cells, proliferation and differentiation of committed bone forming cells, extracellular matrix synthesis, mineralization of osteoid, maturation and remodeling of bone. Current data suggests that these cellular events are precisely controlled and regulated by specific signaling molecules. A plethora of cytokines; have been identified and studied in the past two decades. Some of these like transforming growth factor beta (TGF-β), vascular endothelial growth factor (VEGF), platelet derived growth factor (PDGF) and fibroblast growth factors (FGFs) are well conserved proteins involved in the initial response to injury and repair in soft and hard tissue. ^ The purpose of this study was to characterize the spatial and temporal localization of TGF-βl, VEGF, PDGF-A, FGF-2 and BMP-2, and secretory IgA in a tooth extraction socket model, and evaluate correlation of spatial and temporal changes of these growth factors to histological events. The results of this study showed positive correlation of histological events to spatial and temporal localization of TGF-β1, BMP-2, FGF-2, PDGF-A, and VEGF in a rabbit tooth extraction model. ^

BIOMEDICAL LANGUAGE UNDERSTANDING AND EXTRACTION (BLUE-TEXT): A MINIMAL SYNTACTIC, SEMANTIC METHOD

Clinical text understanding (CTU) is of interest to health informatics because critical clinical information frequently represented as unconstrained text in electronic health records are extensively used by human experts to guide clinical practice, decision making, and to document delivery of care, but are largely unusable by information systems for queries and computations. Recent initiatives advocating for translational research call for generation of technologies that can integrate structured clinical data with unstructured data, provide a unified interface to all data, and contextualize clinical information for reuse in multidisciplinary and collaborative environment envisioned by CTSA program. This implies that technologies for the processing and interpretation of clinical text should be evaluated not only in terms of their validity and reliability in their intended environment, but also in light of their interoperability, and ability to support information integration and contextualization in a distributed and dynamic environment. This vision adds a new layer of information representation requirements that needs to be accounted for when conceptualizing implementation or acquisition of clinical text processing tools and technologies for multidisciplinary research. On the other hand, electronic health records frequently contain unconstrained clinical text with high variability in use of terms and documentation practices, and without commitmentto grammatical or syntactic structure of the language (e.g. Triage notes, physician and nurse notes, chief complaints, etc). This hinders performance of natural language processing technologies which typically rely heavily on the syntax of language and grammatical structure of the text. This document introduces our method to transform unconstrained clinical text found in electronic health information systems to a formal (computationally understandable) representation that is suitable for querying, integration, contextualization and reuse, and is resilient to the grammatical and syntactic irregularities of the clinical text. We present our design rationale, method, and results of evaluation in processing chief complaints and triage notes from 8 different emergency departments in Houston Texas. At the end, we will discuss significance of our contribution in enabling use of clinical text in a practical bio-surveillance setting.