54 research outputs found

    Connecting the Dots: Stages of Implementation, Wraparound Fidelity and Youth Outcomes

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    Several necessary system and organizational support conditions for wraparound have been identified (Walker et al. 2003). Yet, the relationship between these necessary system level conditions and wraparound fidelity has only recently begun to be examined. Similarly, few studies have included a measure of wraparound fidelity when examining the relationship between wraparound implementation and youth outcomes. The statewide implementation of a wraparound demonstration grant offers the opportunity to explore these relationships and to identify factors that predict improvement in functioning for youth receiving wraparound. Findings suggest that significant relationships exist between (1) the stage of development of necessary support conditions for wraparound and wraparound fidelity and (2) wraparound fidelity and improvement in youth outcomes. Specific elements of wraparound (i.e., outcomes based and community based) and baseline needs and strengths (e.g., high levels of anxiety and conduct issues, poor functioning at home and in school, judgment, and risks) were found to predict a reduction in youth needs. Other unexpected relationships between youth outcomes and the cultural competence element of wraparound and being multi-racial were also discovered. These findings reinforce the importance of supporting high fidelity wraparound for youth and their families in a recovery focused behavioral health system

    Moving knowledge into action for more effective practice, programmes and policy: protocol for a research programme on integrated knowledge translation

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    MSC-related gene expression can be observed in whole tumor single cell RNAseq.

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    UMAP visualization of 130,246 cells analyzed by scRNA-seq and integrated across 26 primary breast tumors (from Wu et al [34]). Clusters were annotated for their cell types as predicted using canonical markers for epithelial cells (EPCAM), proliferating cells (MKI67), T cells (CD3D), myeloid cells (CD68), B cells (MS4A1), plasmablasts (JCHAIN), endothelial cells (PECAM1) and mesenchymal cells (fibroblasts/perivascular-like cells; PDGFRB) and gene signature-based annotation. A) Non-tumor MSC, perivascular and endothelial cells cluster on left side of plot demonstrated the majority of THY1 (CD90), CXCL12 and ACTA2 expression in the whole tumor. B) UMAP visualization of reclustered mesenchymal cells, including CAFs (6,573 cells), PVL cells (5,423 cells), endothelial cells (7,899 cells), lymphatic endothelial cells (203 cells) and cycling PVL cells, demonstrating that the majority of CD90 (THY1) positive cells residing in the assigned MSC cluster. C) Feature plots of gene expression of COL1A1, COL8A1 in whole tumor UMAP demonstrating gene expression restricted to MSC-associated clusters, and D) MSC UMAP demonstrating COL10A1 gene expression restricted to MSC/CAFS.</p

    S3 Data -

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    The tumor microenvironment is a complex mixture of cell types that bi-directionally interact and influence tumor initiation, progression, recurrence, and patient survival. Mesenchymal stromal cells (MSCs) of the tumor microenvironment engage in crosstalk with cancer cells to mediate epigenetic control of gene expression. We identified CD90+ MSCs residing in the tumor microenvironment of patients with invasive breast cancer that exhibit a unique gene expression signature. Single-cell transcriptional analysis of these MSCs in tumor-associated stroma identified a distinct subpopulation characterized by increased expression of genes functionally related to extracellular matrix signaling. Blocking the TGFβ pathway reveals that these cells directly contribute to cancer cell proliferation. Our findings provide novel insight into communication between breast cancer cells and MSCs that are consistent with an epithelial to mesenchymal transition and acquisition of competency for compromised control of proliferation, mobility, motility, and phenotype.</div

    Single cell analysis of patient-derived MSCs.

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    A) CD90+ cells were isolated from normal donors or patients with invasive breast cancer and subjected to single cell analysis. Gene expression profiles from single cells were clustered using tSNE, and 7 distinct cell clusters were observed. B) Candidate gene expression profiles were used to functionally characterize MSCs into 3 main subclasses (osteogenic, chondrogenic or adipogenic). C) Comparison of cells derived from healthy donors or breast cancer patients demonstrated proportional changes in number of cells contributing to specific clusters. D) Ontology categories associated with single cell populations.</p
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