RESUMEN
Rationale: Hyper IgE syndrome (STAT3-HIES), also known as Job's syndrome, is a rare immunodeficiency disease typically caused by dominant-negative STAT3 mutations. STAT3-HIES syndrome is characterized by chronic pulmonary infection and inflammation, suggesting impairment of pulmonary innate host defense. Objectives: To identify airway epithelial host defense defects consequent to STAT3 mutations that, in addition to reported mutant STAT3 immunologic abnormalities, produce pulmonary infection. Methods: STAT3-HIES sputum was evaluated for biochemical/biophysical properties. STAT3-HIES excised lungs were harvested for histology; bronchial brush samples were collected for RNA sequencing and in vitro culture. A STAT3-HIES-specific mutation (R382W), expressed by lentiviruses, and a STAT3 knockout, generated by CRISPR/Cas9, were maintained in normal human bronchial epithelia under basal or inflammatory (IL1ß) conditions. Effects of STAT3 deficiency on transcriptomics, and epithelial ion channel, secretory, antimicrobial, and ciliary functions were assessed. Measurements and Main Results: Mucus concentrations and viscoelasticity were increased in STAT3-HIES sputum. STAT3-HIES excised lungs exhibited mucus obstruction and elevated IL1ß expression. STAT3 deficiency impaired CFTR-dependent fluid and mucin secretion, inhibited expression of antimicrobial peptides, cytokines, and chemokines, and acidified airway surface liquid at baseline and post-IL1ß exposure in vitro. Notably, mutant STAT3 suppressed IL1R1 expression. STAT3 mutations also inhibited ciliogenesis in vivo and impaired mucociliary transport in vitro, a process mediated via HES6 suppression. Administration of a γ-secretase inhibitor increased HES6 expression and improved ciliogenesis in STAT3 R382W mutant cells. Conclusions: STAT3 dysfunction leads to multi-component defects in airway epithelial innate defense, which, in conjunction with STAT3-HIES immune deficiency, contributes to chronic pulmonary infection.
RESUMEN
PURPOSE: The purpose is to examine predictors of intervention non-compliance and develop a risk stratification score. DESIGN: Prospective cohort. SETTING: Early care and education (ECE). SUBJECTS: Early care and education programs (n = 3883) randomly allocated (3:1) to a development (n = 2909) or validation (n = 974) sample. INTERVENTION: Go NAPSACC provides a structured, web-based process to help improve the health of children around 7 modules (nutrition, physical activity, oral health, breast/infant feeding, farm to ECE, outdoor play, and screen time). MEASURES: Program characteristics and participation data are collected via Go NAPSACC tool. ANALYSIS: Multivariable Lasso logistic regression was used to identify predictors. Discriminative ability was based on area under the ROC curve (AUC). RESULTS: Overall, ECE program non-compliance (lack of valid pre-/post self-assessment) was 65.5%. Six predictors were retained in the final development model: type of program (P = .002), Child and Adult Care Food Program (CACFP) participation (P = .065), acceptance of subsidies (P < .001), past modules attempted (P < .001), past modules completed (P < .001), and action plans created (P < .001). These factors generated a non-compliance risk score which showed good discrimination in the validation sample (AUC: .922, 95% CI: .903-.940). CONCLUSION: Lack of qualitative data limits the ability to fully understand the context of non-compliance; however, this study demonstrates readily available data captured by Go NAPSACC are strong predictors of future success. Early identification of high-risk programs will inform targets for future implementation strategies geared toward improving program success.