Filtering by
- Language: English
![128785-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-04/128785-Thumbnail%20Image.png?versionId=o.hi9CzoU71LU787cDvRinFlisPAFDoS&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T002855Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=f785936b9da634751d7105b415b76935c726492b60e0293c2b3be806c99f1b29&itok=spBozlu4)
Strategies are needed to improve repopulation of decellularized lung scaffolds with stromal and functional epithelial cells. We demonstrate that decellularized mouse lungs recellularized in a dynamic low fluid shear suspension bioreactor, termed the rotating wall vessel (RWV), contained more cells with decreased apoptosis, increased proliferation and enhanced levels of total RNA compared to static recellularization conditions. These results were observed with two relevant mouse cell types: bone marrow-derived mesenchymal stromal (stem) cells (MSCs) and alveolar type II cells (C10). In addition, MSCs cultured in decellularized lungs under static but not bioreactor conditions formed multilayered aggregates. Gene expression and immunohistochemical analyses suggested differentiation of MSCs into collagen I-producing fibroblast-like cells in the bioreactor, indicating enhanced potential for remodeling of the decellularized scaffold matrix. In conclusion, dynamic suspension culture is promising for enhancing repopulation of decellularized lungs, and could contribute to remodeling the extracellular matrix of the scaffolds with subsequent effects on differentiation and functionality of inoculated cells.
![128789-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-04/128789-Thumbnail%20Image.png?versionId=_9glVryQBbQusEUsmNRqTzPeDTqDHaAq&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T025416Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=a2c6b5a7c93a9e94028cb79c703f96cb7319562c145a1765d504ab554fe952e4&itok=mtFKYUkp)
Resource-poor social environments predict poor health, but the mechanisms and processes linking the social environment to psychological health and well-being remain unclear. This study explored psychosocial mediators of the association between the social environment and mental health in African American adults. African American men and women (n = 1467) completed questionnaires on the social environment, psychosocial factors (stress, depressive symptoms, and racial discrimination), and mental health. Multiple-mediator models were used to assess direct and indirect effects of the social environment on mental health. Low social status in the community (p < .001) and U.S. (p < .001) and low social support (p < .001) were associated with poor mental health. Psychosocial factors significantly jointly mediated the relationship between the social environment and mental health in multiple-mediator models. Low social status and social support were associated with greater perceived stress, depressive symptoms, and perceived racial discrimination, which were associated with poor mental health. Results suggest the relationship between the social environment and mental health is mediated by psychosocial factors and revealed potential mechanisms through which social status and social support influence the mental health of African American men and women. Findings from this study provide insight into the differential effects of stress, depression and discrimination on mental health. Ecological approaches that aim to improve the social environment and psychosocial mediators may enhance health-related quality of life and reduce health disparities in African Americans.
![128808-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-04/128808-Thumbnail%20Image.png?versionId=eLxXz5o_xnll63OJWTx1GLE6ypcTOn6V&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240615/us-west-2/s3/aws4_request&X-Amz-Date=20240615T195843Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=041c8e373a11fcfc1aebe86f842c86a42325cf0b4450ea21b78410446034d876&itok=_U6dTlr_)
Extra-intestinal pathogenic E. coli (ExPEC), including avian pathogenic E. coli (APEC), pose a considerable threat to both human and animal health, with illness causing substantial economic loss. APEC strain χ7122 (O78∶K80∶H9), containing three large plasmids [pChi7122-1 (IncFIB/FIIA-FIC), pChi7122-2 (IncFII), and pChi7122-3 (IncI2)]; and a small plasmid pChi7122-4 (ColE2-like), has been used for many years as a model strain to study the molecular mechanisms of ExPEC pathogenicity and zoonotic potential. We previously sequenced and characterized the plasmid pChi7122-1 and determined its importance in systemic APEC infection; however the roles of the other pChi7122 plasmids were still ambiguous. Herein we present the sequence of the remaining pChi7122 plasmids, confirming that pChi7122-2 and pChi7122-3 encode an ABC iron transport system (eitABCD) and a putative type IV fimbriae respectively, whereas pChi7122-4 is a cryptic plasmid. New features were also identified, including a gene cluster on pChi7122-2 that is not present in other E. coli strains but is found in Salmonella serovars and is predicted to encode the sugars catabolic pathways. In vitro evaluation of the APEC χ7122 derivative strains with the three large plasmids, either individually or in combinations, provided new insights into the role of plasmids in biofilm formation, bile and acid tolerance, and the interaction of E. coli strains with 3-D cultures of intestinal epithelial cells. In this study, we show that the nature and combinations of plasmids, as well as the background of the host strains, have an effect on these phenomena. Our data reveal new insights into the role of extra-chromosomal sequences in fitness and diversity of ExPEC in their phenotypes.
![135538-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/135538-Thumbnail%20Image.png?versionId=v2FN1sRw4.J6etzqmepmrWrjzupqFn4a&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T033756Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=8067f16dc40d7a92d60c1c8c4c6dd8eafefa8b4e225ca55110caad152921510e&itok=_kXra5S4)
![135994-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/135994-Thumbnail%20Image.png?versionId=65Cxcxw5BTmp8h4j1UO3qV1b5eRj3wxy&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T012034Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=e16981f61eaa6fbdaae3d1355128c5b52d9fd9321607f7ed63defdc5b6d8b561&itok=AePdrwSP)
![135948-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/135948-Thumbnail%20Image.png?versionId=lKQ.z4ek2.3HLoHLQnt8EejnQfy4UgmY&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T013709Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=fa0327d21f72e757ea403e191aa6e64de3b76d7fad2ccd7465a4c317b354ab17&itok=n9D0Rrag)
![141494-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-06/141494-Thumbnail%20Image.png?versionId=n4nEc680x.QZqZCX9n2YFe9yVUUQFCwM&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T030120Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=363a545ba46f0c3cee527145bafb71d3701fc0574688b02afa353c7409b05646&itok=peuimSmY)
Background:
Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.
Results:
We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise.
Conclusions:
The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling.