New Americans: Child Care Decision-Making of Refugee and Immigrant Parents of English Language Learners

The immigrant and refugee communities in the United States continue to increase. Denver, Colorado and Portland, Maine are two U.S. cities that reflect the changing demographics across the country. As these cities evolve and adjust to serve new populations, it becomes necessary to rethink deep-rooted culturally constructed patterns and traditions that do not take into account the beliefs and practices of these new cultures. One such tradition is child care. Because of the important link between preschool experiences and later school success, understanding refugee and immigrant families’ beliefs and decisions about child care is extremely important. From a policy perspective, understanding these beliefs can guide professional development training for child care providers serving these linguistically and culturally diverse families

Time in treatment: examining mental illness trajectories across inpatient psychiatric treatment

Early discharge or reduced length of stay for inpatient psychiatric patients is related to increased readmission rates and worse clinical outcomes including increased risk for suicide. Trajectories of mental illness outcomes have been identified as an important method for predicting the optimal length of stay but the distinguishing factors that separate trajectories remain unclear. We sought to identify the distinct classes of patients who demonstrated similar trajectories of mental illness over the course of inpatient treatment, and we explore the patient characteristics associated with these mental illness trajectories. We used data (N = 3406) from an inpatient psychiatric hospital with intermediate lengths of stay. Using growth mixture modeling, latent mental illness scores were derived from six mental illness indicators: psychological flexibility, emotion regulation problems, anxiety, depression, suicidal ideation, and disability. The patients were grouped into three distinct trajectory classes: (1) High-Risk, Rapid Improvement (HR-RI); (2) Low-Risk, Gradual Improvement (LR-GI); and (3) High-Risk, Gradual Improvement (HR-GI). The HR-GI was significantly younger than the other two classes. The HR-GI had significantly more female patients than males, while the LR-GI had more male patients than females. Our findings indicated that younger females had more severe mental illness at admission and only gradual improvement during the inpatient treatment period, and they remained in treatment for longer lengths of stay, than older males

Child Care and Children With Special Needs: Challenges for Low Income Families [Report]

Findings from this mixed methods study include: Parents of young children with special needs face significant challenges finding and keeping child care arrangements for their child. Parents report significant problems with the child care arrangements they have used for their child with special needs. There are significant programmatic and financial barriers to supporting parents of children with special needs so they can work, and balance work and family. The combination of all of these problems and the particular demands of caring for a child with special needs often result in employment problems and job instability. Families of children with special needs face more economic difficulties (poverty, food and rent insecurity, lack of health insurance) than do families of children without special needs. Certain types of disabilities have a greater impact on the number of child care and work problems than others. Having a child with multiple special needs or having more than one child with special needs significantly increases the likelihood of employment difficulties and job instability

Predictors of short and long term recurrence of suicidal behavior in borderline personality disorder

Objective: To evaluate the incidence of suicidal outcomes and risk factors for short- and long-term recurrence of suicidal behavior (SB) among high-risk borderline personality disorder (BPD) patients during a 24-month prospective follow-up period. Methods: A multicenter prospective cohort study was designed to compare data obtained from 136 patients admitted to the emergency department for current suicidal ideation (SI) or a recent suicide attempt (SA). Subjects were clinically evaluated and monitored for a new SA or suicide. Results: The incidence of a new SA was 25.63 events/100 persons-year, and one patient died by suicide. Child sexual abuse (CSA) was the only significant predictor throughout the complete follow-up period. The absence of prior psychiatric treatment predicts the recurrence of SB in the first 6 months of follow-up. Patient age, poor psychosocial functioning before hospitalization, age at first SA, and having multiple suicide attempts increased risk of SB recurrence at the long-term period (24th months). In addition, there was an interaction between CSA and poor psychosocial functioning that increased risk of SB. Conclusion: The risk of recurrence was higher during the first 6 months. Risk factors at 6 and 24 months vary. These findings are important for implementing suicide strategies.Fil: Rodante, Demián E.. Universidad de Buenos Aires. Facultad de Medicina. Hospital de Clínicas General San Martín; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Farmacologia; ArgentinaFil: Grendas, Leandro. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Farmacologia; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital Municipal "José Tiburcio Borda"; ArgentinaFil: Puppo, Soledad. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Farmacologia; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Hospital de Clínicas General San Martín; ArgentinaFil: Vidjen, Patricia. Gobierno de la Ciudad de Buenos Aires. Hospital Municipal "José Tiburcio Borda"; ArgentinaFil: Portela, Alicia. Gobierno de la Ciudad de Buenos Aires. Hospital Municipal "José Tiburcio Borda"; ArgentinaFil: Rojas, Sasha M.. University of Arkansas for Medical Sciences; Estados UnidosFil: Chiapella, Luciana Carla. Universidad Nacional de Rosario. Facultad de Cs.bioquímicas y Farmaceuticas. Departamento de Cs.fisiologicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Daray, Federico Manuel. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Farmacologia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay; Argentin

A dynamic network approach for the study of human phenotypes

The use of networks to integrate different genetic, proteomic, and metabolic datasets has been proposed as a viable path toward elucidating the origins of specific diseases. Here we introduce a new phenotypic database summarizing correlations obtained from the disease history of more than 30 million patients in a Phenotypic Disease Network (PDN). We present evidence that the structure of the PDN is relevant to the understanding of illness progression by showing that (1) patients develop diseases close in the network to those they already have; (2) the progression of disease along the links of the network is different for patients of different genders and ethnicities; (3) patients diagnosed with diseases which are more highly connected in the PDN tend to die sooner than those affected by less connected diseases; and (4) diseases that tend to be preceded by others in the PDN tend to be more connected than diseases that precede other illnesses, and are associated with higher degrees of mortality. Our findings show that disease progression can be represented and studied using network methods, offering the potential to enhance our understanding of the origin and evolution of human diseases. The dataset introduced here, released concurrently with this publication, represents the largest relational phenotypic resource publicly available to the research community.Comment: 28 pages (double space), 6 figure

A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis

Background: The course of disease for patients with idiopathic pulmonary fibrosis (IPF) is highly heterogeneous. Prognostic models rely on demographic and clinical characteristics and are not reproducible. Integrating data from genomic analyses may identify novel prognostic models and provide mechanistic insights into IPF. Methods: Total RNA of peripheral blood mononuclear cells was subjected to microarray profiling in a training (45 IPF individuals) and two independent validation cohorts (21 IPF/10 controls, and 75 IPF individuals, respectively). To identify a gene set predictive of IPF prognosis, we incorporated genomic, clinical, and outcome data from the training cohort. Predictor genes were selected if all the following criteria were met: 1) Present in a gene co-expression module from Weighted Gene Co-expression Network Analysis (WGCNA) that correlated with pulmonary function (p 1.5 and false discovery rate (FDR) < 2 %; and 3) Predictive of mortality (p < 0.05) in univariate Cox regression analysis. "Survival risk group prediction" was adopted to construct a functional genomic model that used the IPF prognostic predictor gene set to derive a prognostic index (PI) for each patient into either high or low risk for survival outcomes. Prediction accuracy was assessed with a repeated 10-fold cross-validation algorithm and independently assessed in two validation cohorts through multivariate Cox regression survival analysis. Results: A set of 118 IPF prognostic predictor genes was used to derive the functional genomic model and PI. In the training cohort, high-risk IPF patients predicted by PI had significantly shorter survival compared to those labeled as low-risk patients (log rank p < 0.001). The prediction accuracy was further validated in two independent cohorts (log rank p < 0.001 and 0.002). Functional pathway analysis revealed that the canonical pathways enriched with the IPF prognostic predictor gene set were involved in T-cell biology, including iCOS, T-cell receptor, and CD28 signaling. Conclusions: Using supervised and unsupervised analyses, we identified a set of IPF prognostic predictor genes and derived a functional genomic model that predicted high and low-risk IPF patients with high accuracy. This genomic model may complement current prognostic tools to deliver more personalized care for IPF patients

A massive human co-expression-network and its medical applications

Network-based analysis is indispensable in analyzing high throughput biological data. Based on the assumption that the variation of gene interactions under given biological conditions could be better interpreted in the context of a large-scale and wide variety of developmental, tissue, and disease, we leverage the large quantity of publicly-available transcriptomic data \u3e 40,000 HG U133A Affymetrix microarray chips stored in ArrayExpress (http://www.ebi.ac.uk/arrayexpress/) using MetaOmGraph (http://metnet.vrac.iastate.edu/MetNet_MetaOmGraph.htm). From this data, 18,637 chips encompassing over 500 experiments containing high quality data (18637Hu-dataset) were used to create a globally stable gene co-expression network (18637Hu-co-expressionnetwork). Regulons, groups of highly and consistently co-expressed genes, were obtained by partitioning the 18637Hu-co-expression-network using an MCL clustering algorithm. The regulon were demonstrated to be statistically significant using a gene ontology (GO) term overrepresentation test combined with evaluation of the effects of gene permutations. The regulons include approximately 12% of human genes, interconnected by 31,471 correlations. All network data and metadata is publically available (http://metnet.vrac.iastate.edu/ MetNet_MetaOmGraph.htm). Text mining of these metadata, GO term overrepresentation analysis, and statistical analysis of transcriptomic experiments across multiple environmental, tissue, and disease conditions, has revealed novel fingerprints distinguishing central nervous system (CNS)-related conditions. This study demonstrates the value of mega-scale network-based analysis for biologists to further refine transcriptomic data derived from a particular condition, to study the global relationships between genes and diseases, and to develop hypotheses that can inform future research

Modular reorganization of the global network of gene regulatory interactions during perinatal human brain development.

BACKGROUND During early development of the nervous system, gene expression patterns are known to vary widely depending on the specific developmental trajectories of different structures. Observable changes in gene expression profiles throughout development are determined by an underlying network of precise regulatory interactions between individual genes. Elucidating the organizing principles that shape this gene regulatory network is one of the central goals of developmental biology. Whether the developmental programme is the result of a dynamic driven by a fixed architecture of regulatory interactions, or alternatively, the result of waves of regulatory reorganization is not known. RESULTS Here we contrast these two alternative models by examining existing expression data derived from the developing human brain in prenatal and postnatal stages. We reveal a sharp change in gene expression profiles at birth across brain areas. This sharp division between foetal and postnatal profiles is not the result of pronounced changes in level of expression of existing gene networks. Instead we demonstrate that the perinatal transition is marked by the widespread regulatory rearrangement within and across existing gene clusters, leading to the emergence of new functional groups. This rearrangement is itself organized into discrete blocks of genes, each targeted by a distinct set of transcriptional regulators and associated to specific biological functions. CONCLUSIONS Our results provide evidence of an acute modular reorganization of the regulatory architecture of the brain transcriptome occurring at birth, reflecting the reassembly of new functional associations required for the normal transition from prenatal to postnatal brain development

Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort

BACKGROUND: Idiopathic pulmonary fibrosis is a progressive fibrotic lung disease with a variable clinical trajectory. Decline in forced vital capacity (FVC) is the main indicator of progression; however, missingness prevents long-term analysis of patterns in lung function. We aimed to identify distinct clusters of lung function trajectory among patients with idiopathic pulmonary fibrosis using machine learning techniques. METHODS: We did a secondary analysis of longitudinal data on FVC collected from a cohort of patients with idiopathic pulmonary fibrosis from the PROFILE study; a multicentre, prospective, observational cohort study. We evaluated the imputation performance of conventional and machine learning techniques to impute missing data and then analysed the fully imputed dataset by unsupervised clustering using self-organising maps. We compared anthropometric features, genomic associations, serum biomarkers, and clinical outcomes between clusters. We also performed a replication of the analysis on data from a cohort of patients with idiopathic pulmonary fibrosis from an independent dataset, obtained from the Chicago Consortium. FINDINGS: 415 (71%) of 581 participants recruited into the PROFILE study were eligible for further analysis. An unsupervised machine learning algorithm had the lowest imputation error among tested methods, and self-organising maps identified four distinct clusters (1-4), which was confirmed by sensitivity analysis. Cluster 1 comprised 140 (34%) participants and was associated with a disease trajectory showing a linear decline in FVC over 3 years. Cluster 2 comprised 100 (24%) participants and was associated with a trajectory showing an initial improvement in FVC before subsequently decreasing. Cluster 3 comprised 113 (27%) participants and was associated with a trajectory showing an initial decline in FVC before subsequent stabilisation. Cluster 4 comprised 62 (15%) participants and was associated with a trajectory showing stable lung function. Median survival was shortest in cluster 1 (2·87 years [IQR 2·29-3·40]) and cluster 3 (2·23 years [1·75-3·84]), followed by cluster 2 (4·74 years [3·96-5·73]), and was longest in cluster 4 (5·56 years [5·18-6·62]). Baseline FEV1 to FVC ratio and concentrations of the biomarker SP-D were significantly higher in clusters 1 and 3. Similar lung function clusters with some shared anthropometric features were identified in the replication cohort. INTERPRETATION: Using a data-driven unsupervised approach, we identified four clusters of lung function trajectory with distinct clinical and biochemical features. Enriching or stratifying longitudinal spirometric data into clusters might optimise evaluation of intervention efficacy during clinical trials and patient management. FUNDING: National Institute for Health and Care Research, Medical Research Council, and GlaxoSmithKline