inhibitory-synapse-formation-co-culture-model-incorporating-gabaergic

Inhibitory Synapse Formation in a Co-culture Model Incorporating GABAergic Medium Spiny Neurons and HEK293 Cells Stably Expressing GABA<sub>A</sub> Receptors

Inhibitory neurons act in the central nervous system to regulate the dynamics and spatio-temporal co-ordination of neuronal networks. GABA ...

University College London

1). To study the mechanisms of anemia, erythroid hyperplasia, and red blood cell (RBC) clearance in murine models of sickle cell disease (Sickle) and beta-thalassemia (Th1/Th1); 2) To determine the contribution of the phospholipid scramblase enzyme to phosphatidylserine (PS) exposure and RBC death in Sickle and Th1/Th1 mice.

Comparison of mechanisms of anemia in mice with sickle cell disease and beta-thalassemia: peripheral destruction, ineffective erythropoiesis, and phospholipid scramblase-mediated phosphatidylserine exposure.

Bayesian Networks (BN) is a knowledge representation formalism that has been proven to be valuable in biomedicine for constructing decision support systems and for generating causal hypotheses from data. Given the emergence of datasets in medicine and biology with thousands of variables and that current algorithms do not scale more than a few hundred variables in practical domains, new efficient and accurate algorithms are needed to learn high quality BNs from data. We present a new algorithm called Max-Min Hill-Climbing (MMHC) that builds upon and improves the Sparse Candidate (SC) algorithm; a state-of-the-art algorithm that scales up to datasets involving hundreds of variables provided the generating networks are sparse. Compared to the SC, on a number of datasets from medicine and biology, (a) MMHC discovers BNs that are structurally closer to the data-generating BN, (b) the discovered networks are more probable given the data, (c) MMHC is computationally more efficient and scalable than SC, and (d) the generating networks are not required to be uniformly sparse nor is the user of MMHC required to guess correctly the network connectivity

A novel algorithm for scalable and accurate Bayesian network learning.

To apply and compare common machine learning techniques with an expert-built Bayesian Network to determine eligibility for asthma guidelines in pediatric emergency department patients.

Comparing decision support methodologies for identifying asthma exacerbations.

According to the American Diabetes Association, nearly 24 million Americans live with type 2 diabetes, and clinical data project that this figure will increase 165% from 2000 to 2050. Thus, there is a pressing need to understand not only the physiological and medical needs, but also the psychological demands associated with the illness. Even though uncertainty has been explicated in various other illness contexts and potentially shapes the experience of type 2 diabetes, very little attention has been paid to the nature of uncertainty in this context. The present study examines the nature of uncertainty through qualitative interviews with 49 individuals diagnosed with type 2 diabetes. Using grounded theory methods, various themes emerged relative to the sources of uncertainty. These themes are discussed in light of their potential extension of health communication theory and application to intervention and educational programs for individuals living with type 2 diabetes.

Sources of uncertainty in type 2 diabetes: explication and implications for health communication theory and clinical practice.

We investigated communication and care in the context of lung cancer through qualitative interviews with 35 adult children. Participants described two core influences on communication: situational and relational influences. Participants also suggested that the ways in which support was communicated impacted how they managed challenges and how they were able to cope with the loss of their parent. This research adds to the literature on caregiving and communicating social support by exploring the experiences of families coping with lung cancer. We suggest theoretical and practical implications for the ways family members might communicate about illness.

Caring for a parent with lung cancer: caregivers' perspectives on the role of communication.

We previously demonstrated immune activation in the maternal peripheral circulation associated with preterm labor (PTL). There was an elevation in WBC mRNA of anti-inflammatory complement decay-accelerating factor (CD55) and the innate-immune response activating toll-like receptor 4 (TLR4). These findings suggested that collectively, these two molecules might serve as useful biomolecules to aid in the diagnosis of PTL. In this study, we used a combined marker approach to determine whether a dual marker model utilizing both CD55 and TLR4 mRNA levels to classify PTL would increase diagnostic accuracy compared to either molecule alone. Two methods were evaluated; a linear discriminant (LD) method and a distribution free (DF) method, in order to find the optimal linear combination of TLR4 and CD55 data to diagnose PTL accurately. Our results indicated that a combined CD55-TLR4 dual marker model could provide statistically significant improvements compared to CD55 or TLR4 single marker models for PTL classification performance.

Accurate preterm labor diagnosis using a CD55-TLR4 combination biomarker model.

Building on scholarship indicating that uncertainty is a fundamental component of the cancer experience, this study focuses on an understudied population: adolescents and young adults (AYAs) with cancer. Because AYAs' health outcomes lag behind those of older and younger people with cancer, scholars have recommended that the subjective experiences of AYAs be better understood. Using the tripartite model of uncertainty sources as a guiding framework, we analyzed naturally occurring messages from an online discussion forum for AYA cancer survivors. The majority of messages communicating uncertainty expressed medical uncertainty regarding the complexities of understanding treatment options and sequelae. Results indicated that several overarching areas of AYA uncertainty correspond to themes reported by other cancer populations, but that some distinctive concerns arise amid the normative complexities of late adolescence and young adulthood.

Article	Year
Comparison of mechanisms of anemia in mice with sickle cell disease and beta-thalassemia: peripheral destruction, ineffective erythropoiesis, and phospholipid scramblase-mediated phosphatidy... Experimental hematology\| PubMed ID: 12031645	2002
A novel algorithm for scalable and accurate Bayesian network learning. Studies in health technology and informatics\| PubMed ID: 15360905	2004
Comparing decision support methodologies for identifying asthma exacerbations. Studies in health technology and informatics\| PubMed ID: 17911842	2007
Sources of uncertainty in type 2 diabetes: explication and implications for health communication theory and clinical practice. Health communication\| PubMed ID: 22260354	2012
Caring for a parent with lung cancer: caregivers' perspectives on the role of communication. Qualitative health research\| PubMed ID: 22645222	2012
Accurate preterm labor diagnosis using a CD55-TLR4 combination biomarker model. Journal of biomedical science and engineering\| PubMed ID: 23667715	2013
"The Uncertainty Is What Is Driving Me Crazy": The Tripartite Model of Uncertainty in the Adolescent and Young Adult Cancer Context. Health communication\| PubMed ID: 25144578	2014

Laura E. Brown

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