Development and testing of a procedure for systematically assigning gestational age for clinical and research use

A graphing method was developed and tested to estimate gestational ages pre-and postnatally in a consistent manner for epidemiological research and clinical purposes on feti/infants of women with few consistent prenatal estimators of gestational age. Each patient's available data was plotted on a single page graph to give a comprehensive overview of that patient. A hierarchical classification of gestational age determination was then applied in a systematic manner, and reasonable gestational age estimates were produced. The method was tested for validity and reliability on 50 women who had known dates for their last menstrual period or dates of conception, and multiple ultrasound examinations and other gestational age estimating measures. The feasibility of the procedure was then tested on 1223 low income women with few gestational age estimators. The graphing method proved to have high inter- and intrarater reliability. It was quick, easy to use, inexpensive, and did not require special equipment. The graphing method estimate of gestational age for each infant was tested against the last menstrual period gestational age estimate using paired t-Tests, F tests and the Kolmogorov-Smirnov test of similar populations, producing a 98 percent probability or better that the means and data populations were the same. Less than 5 percent of the infants' gestational ages were misclassified using the graphing method, much lower than the amount of misclassification produced by ultrasound or neonatal examination estimates. ^

Biomedical informatics applications for epilepsy management: A systematic review

Epilepsy is a very complex disease which can have a variety of etiologies, co-morbidities, and a long list of psychosocial factors4. Clinical management of epilepsy patients typically includes serological tests, EEG's, and imaging studies to determine the single best antiepileptic drug (AED). Self-management is a vital component of achieving optimal health when living with a chronic disease. For patients with epilepsy self-management includes any necessary actions to control seizures and cope with any subsequent effects of the condition9; including aspects of treatment, seizure, and lifestyle. The use of computer-based applications can allow for more effective use of clinic visits and ultimately enhance the patient-provider relationship through focused discussion of determinants affecting self-management. ^ The purpose of this study is to conduct a systematic literature review on informatics application in epilepsy self-management in an effort to describe current evidence for informatics applications and decision support as an adjunct to successful clinical management of epilepsy. Each publication was analyzed for the type of study design utilized. ^ A total of 68 publications were included and categorized by the study design used, development stage, and clinical domain. Descriptive study designs comprised of three-fourths of the publications and indicate an underwhelming use of prospective studies. The vast majority of prospective studies also focused on clinician use to increase knowledge in treating patients with epilepsy. ^ Due to the chronic nature of epilepsy and the difficulty that both clinicians and patients can experience in managing epilepsy, more prospective studies are needed to evaluate applications that can effectively increase management activities. Within the last two decades of epilepsy research, management studies have employed the use of biomedical informatics applications. While the use of computer applications to manage epilepsy has increased, more progress is needed.^

Advanced Protein Modeling Method: Benchmarking and Applications in Computer-Aided Drug Discovery

Development of homology modeling methods will remain an area of active research. These methods aim to develop and model increasingly accurate three-dimensional structures of yet uncrystallized therapeutically relevant proteins e.g. Class A G-Protein Coupled Receptors. Incorporating protein flexibility is one way to achieve this goal. Here, I will discuss the enhancement and validation of the ligand-steered modeling, originally developed by Dr. Claudio Cavasotto, via cross modeling of the newly crystallized GPCR structures. This method uses known ligands and known experimental information to optimize relevant protein binding sites by incorporating protein flexibility. The ligand-steered models were able to model, reasonably reproduce binding sites and the co-crystallized native ligand poses of the β2 adrenergic and Adenosine 2A receptors using a single template structure. They also performed better than the choice of template, and crude models in a small scale high-throughput docking experiments and compound selectivity studies. Next, the application of this method to develop high-quality homology models of Cannabinoid Receptor 2, an emerging non-psychotic pain management target, is discussed. These models were validated by their ability to rationalize structure activity relationship data of two, inverse agonist and agonist, series of compounds. The method was also applied to improve the virtual screening performance of the β2 adrenergic crystal structure by optimizing the binding site using β2 specific compounds. These results show the feasibility of optimizing only the pharmacologically relevant protein binding sites and applicability to structure-based drug design projects.