Complex genetic dependencies among growth and neurological phenotypes in healthy children: Towards deciphering developmental mechanisms.

The genetic mechanisms of childhood development in its many facets remain largely undeciphered. In the population of healthy infants studied in the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) program, we have identified a range of dependencies among the observed phenotypes of fetal and early childhood growth, neurological development, and a number of genetic variants. We have quantified these dependencies using our information theory-based methods. The genetic variants show dependencies with single phenotypes as well as pleiotropic effects on more than one phenotype and thereby point to a large number of brain-specific and brain-expressed gene candidates. These dependencies provide a basis for connecting a range of variants with a spectrum of phenotypes (pleiotropy) as well as with each other. A broad survey of known regulatory expression characteristics, and other function-related information from the literature for these sets of candidate genes allowed us to assemble an integrated body of evidence, including a partial regulatory network, that points towards the biological basis of these general dependencies. Notable among the implicated loci are RAB11FIP4 (next to NF1), MTMR7 and PLD5, all highly expressed in the brain; DNMT1 (DNA methyl transferase), highly expressed in the placenta; and PPP1R12B and DMD (dystrophin), known to be important growth and development genes. While we cannot specify and decipher the mechanisms responsible for the phenotypes in this study, a number of connections for further investigation of fetal and early childhood growth and neurological development are indicated. These results and this approach open the door to new explorations of early human development.

A joint design for functional data with application to scheduling ultrasound scans.

A joint design for sampling functional data is proposed to achieve optimal prediction of both functional data and a scalar outcome. The motivating application is fetal growth, where the objective is to determine the optimal times to collect ultrasound measurements in order to recover fetal growth trajectories and to predict child birth outcomes. The joint design is formulated using an optimization criterion and implemented in a pilot study. Performance of the proposed design is evaluated via simulation study and application to fetal ultrasound data.

Neurofibromatosis Type 1-Associated MPNST State of the Science: Outlining a Research Agenda for the Future.

Malignant peripheral nerve sheath tumor (MPNST) is an aggressive soft tissue sarcoma for which the only effective therapy is surgery. In 2016, an international meeting entitled "MPNST State of the Science: Outlining a Research Agenda for the Future" was convened to establish short- and long-term research priorities. Key recommendations included the: 1) development of standardized, cost-efficient fluorodeoxyglucose positron emission tomography and whole-body magnetic resonance imaging guidelines to evaluate masses concerning for MPNST; 2) development of better understanding and histologic criteria for the transformation of a plexiform neurofibroma to MPNST; 3) establishment of a centralized database to collect genetic, genomic, histologic, immunohistochemical, molecular, radiographic, treatment, and related clinical data from MPNST subspecialty centers in a standardized manner; 4) creation of accurate mouse models to study the plexiform neurofibroma-to-MPNST transition, MPNST metastasis, and drug resistance; 5) use of trial designs that minimize regulatory requirements, maximize availability to patients, consider novel secondary end points, and study patients with newly diagnosed disease. Lastly, in order to minimize delays in developing novel therapies and promote the most efficient use of research resources and patient samples, data sharing should be incentivized.

White matter maturation profiles through early childhood predict general cognitive ability.

Infancy and early childhood are periods of rapid brain development, during which brain structure and function mature alongside evolving cognitive ability. An important neurodevelopmental process during this postnatal period is the maturation of the myelinated white matter, which facilitates rapid communication across neural systems and networks. Though prior brain imaging studies in children (4 years of age and above), adolescents, and adults have consistently linked white matter development with cognitive maturation and intelligence, few studies have examined how these processes are related throughout early development (birth to 4 years of age). Here, we show that the profile of white matter myelination across the first 5 years of life is strongly and specifically related to cognitive ability. Using a longitudinal design, coupled with advanced magnetic resonance imaging, we demonstrate that children with above-average ability show differential trajectories of myelin development compared to average and below average ability children, even when controlling for socioeconomic status, gestation, and birth weight. Specifically, higher ability children exhibit slower but more prolonged early development, resulting in overall increased myelin measures by ~3 years of age. These results provide new insight into the early neuroanatomical correlates of cognitive ability, and suggest an early period of prolonged maturation with associated protracted white matter plasticity may result in strengthened neural networks that can better support later development. Further, these results reinforce the necessity of a longitudinal perspective in investigating typical or suspected atypical cognitive maturation.

A comprehensive hepatitis C viral kinetic model explaining cure.

We propose a model that characterizes and links the complexity and diversity of clinically observed hepatitis C viral kinetics to sustained virologic response (SVR)-the primary clinical end point of hepatitis C treatment, defined as an undetectable viral load at 24 weeks after completion of treatment)-in patients with chronic hepatitis C (CHC) who have received treatment with peginterferon alpha-2a +/- ribavirin. The new attributes of our hepatitis C viral kinetic model are (i) the implementation of a cure/viral eradication boundary, (ii) employment of all hepatitis C virus (HCV) RNA measurements, including those below the lower limit of quantification (LLOQ), and (iii) implementation of a population modeling approach. The model demonstrated excellent positive (99.3%) and negative (97.1%) predictive values for SVR as well as high sensitivity (96.6%) and specificity (99.4%). The proposed viral kinetic model provides a framework for mechanistic exploration of treatment outcome and permits evaluation of alternative CHC treatment options with the ultimate aim of developing and testing hypotheses for personalizing treatments in this disease.