Feeding tube use is associated with severe scoliosis in patients with cerebral palsy and limited ambulatory ability.

PURPOSE: Cerebral palsy (CP) is the most common motor disorder in childhood. Scoliosis is a common complication of CP that can reach clinically severe levels, but predictors for scoliosis in CP are not well understood. Some variables identified in the literature involve the severity of the brain injury and the presence of hip deformity. We aimed to identify associations with developing severe scoliosis in a prospective cohort of patients with cerebral palsy at higher risk for severe curve progression. METHODS: This study reviewed a prospectively collected database at a tertiary children's hospital. We evaluated a panel of potential associations with severe scoliosis-including age, sex, Gross Motor Function Classification System (GMFCS) class, history of hip surgery, epilepsy, and feeding tube presence-in a population of children with limited ambulatory ability defined as GMFCS level IV or V CP. Univariate analysis and multivariate logistic regression with stepwise selection was used for analysis. RESULTS: Descriptive analysis showed that female sex, higher GMFCS class, history of hip surgery, non-upright seating, pelvic obliquity, presence of epilepsy, and presence of a feeding tube were associated with an increased risk for scoliosis. Multivariate logistic regression analysis revealed that the presence of a feeding tube was associated with severe scoliosis even when controlling for GMFCS and age. CONCLUSIONS: Feeding tube use may stratify risk for severe scoliosis progression in patients with GMFCS IV or V CP.

Unexpected Poor Growth in Pediatric Patients on Food-based Enteral Therapy: Case Series and Suggested Practice Changes.

INTRODUCTION: Enteral feeding pumps at times may deliver different volumes than are prescribed, which can negatively impact growth, nutrition, and well-being. This study sought to assess whether challenges with pump accuracy for patients on food-based formulas contributed to challenges with weight gain. METHODS: Chart review identified complex feeding patients receiving food-based enteral nutrition via feeding pump with unexpected weight loss. Relevant data, such as enteral formula type, and anthropometric information were extracted. RESULTS: Five complex pediatric feeding patients were identified and 2 of these cases were summarized as representative examples, showing weight loss in children following the introduction of enteral food-based formulas because of feeding pump inaccuracy. CONCLUSIONS: Complex pediatric feeding patients may display unexpected and poor weight gain and growth while receiving food-based enteral feeding interventions because of pump errors. It is vital for providers to be aware of these challenges for timely intervention.

Single-Cell Co-expression Analysis Reveals Distinct Functional Modules, Co-regulation Mechanisms and Clinical Outcomes.

Co-expression analysis has been employed to predict gene function, identify functional modules, and determine tumor subtypes. Previous co-expression analysis was mainly conducted at bulk tissue level. It is unclear whether co-expression analysis at the single-cell level will provide novel insights into transcriptional regulation. Here we developed a computational approach to compare glioblastoma expression profiles at the single-cell level with those obtained from bulk tumors. We found that the co-expressed genes observed in single cells and bulk tumors have little overlap and show distinct characteristics. The co-expressed genes identified in bulk tumors tend to have similar biological functions, and are enriched for intrachromosomal interactions with synchronized promoter activity. In contrast, single-cell co-expressed genes are enriched for known protein-protein interactions, and are regulated through interchromosomal interactions. Moreover, gene members of some protein complexes are co-expressed only at the bulk level, while those of other complexes are co-expressed at both single-cell and bulk levels. Finally, we identified a set of co-expressed genes that can predict the survival of glioblastoma patients. Our study highlights that comparative analyses of single-cell and bulk gene expression profiles enable us to identify functional modules that are regulated at different levels and hold great translational potential.