Gastroesophageal reflux symptoms in infants in a rural population: longitudinal data over the first six months

<p>Abstract</p> <p>Background</p> <p>Increasing numbers of infants are receiving prescription medications for symptoms associated with gastroesophageal reflux. Our aim was to prospectively measure reported gastroesophageal reflux symptoms in healthy term infants for the first six months of life.</p> <p>Methods</p> <p>In a prospective cohort study in the rural Upper Peninsula of Michigan, 128 consecutive maternal-infant pairs were followed for six months and administered the Infant Gastroesophageal Reflux Questionnaire Revised (I-GERQ-R) at the one-month, two-month, four-month, and six-month well-child visits.</p> <p>Results</p> <p>The I-GERQ-R scores decreased with age. Average scores were 11.74 (SE = 5.97) at one-month, 9.97(4.92) at two-months, 8.44(4.39) at four-months, and 6.97(4.05) at six months. Symptoms associated with colic were greatest at one month of age.</p> <p>Conclusion</p> <p>Symptoms of gastroesophageal reflux as measured by the I-GERQ-R decrease with age in the first six months of life in otherwise healthy infants; however the I-GERQ-R may have difficulty differentiating gastroesophageal reflux disease from colic in those under 3 months of age.</p

Advanced Modeling of Cellular Proliferation: Toward a Multi-scale Framework Coupling Cell Cycle to Metabolism by Integrating Logical and Constraint-Based Models

Biological functions require a coherent cross talk among multiple layers of regulation within the cell. Computational efforts that aim to understand how these layers are integrated across spatial, temporal, and functional scales represent a challenge in Systems Biology. We have developed a computational, multi-scale framework that couples cell cycle and metabolism networks in the budding yeast cell. Here we describe the methodology at the basis of this framework, which integrates on off-the-shelf logical (Boolean) models of a minimal yeast cell cycle with a constraint-based model of metabolism (i.e., the Yeast 7 metabolic network reconstruction). Models are implemented in Python code using the BooleanNet and COBRApy packages, respectively, and are connected through the Boolean logic. The methodology allows for incorporation of interaction data, and validation through -omics data. Furthermore, evolutionary strategies may be incorporated to explore regulatory structures underlying coherent cross talks among regulatory layers