7 research outputs found
Modulation of Chondrogenic and Osteogenic Differentiation of Mesenchymal Stem Cells through Signals in the Extracellular Microenvironment
Damage to synovial joints results in osteochondral defects that only heal with inferior fibrous repair tissue. Since mesenchymal stem cells (MSCs) play a vital role in the natural development, maintenance, and repair of cartilage and bone, tissue engineering strategies to enhance functional regeneration by modulating MSC differentiation are a promising alternative to the limitations and potential complications associated with current conventional therapies. In this work, signals present in the native microenvironment were utilized in fabricating polymer/extracellular matrix composite scaffolds to guide chondrogenic and osteogenic differentiation. In an osteochondral defect environment, interactions exist between bone marrow cell populations. Although MSCs have been extensively utilized for their ability to support hematopoietic stem and progenitor cells (HSPCs), the role of HSPCs in regulating the osteogenic differentiation of MSCs in the bone marrow niche is not well understood, and thus was explored via direct contact co-culture. HSPCs in a low dose with sustained osteogenic induction by dexamethasone accelerated osteogenesis and enhanced mineral deposition, whereas the lack of induction signals affected the spatial distribution of cell populations and minerals. Thus, HSPCs presumably play an active role in modulating the development and maintenance of the osteogenic niche. Since physical signals affect cellular activity, flow perfusion culture was employed to deposit mineralized extracellular matrix (ECM) with different maturity and composition on electrospun poly([varepsilon]-caprolactone) (PCL) microfibers in fabricating mineralized PCL/ECM composite scaffolds. The presence of mineralized matrix induced the osteogenic differentiation of MSCs even in the absence of dexamethasone, and a more mature matrix with higher quantities of collagen and minerals improved osteogenesis by accelerating alkaline phosphatase expression and matrix mineralization. To determine whether PCL/ECM scaffolds can be applied to support the chondrogenic differentiation of MSCs, cartilaginous PCL/ECM composite scaffolds were fabricated. The presence of cartilaginous matrix reduced fibroblastic phenotype and in combination with transforming growth factor-β1 (TGF-β1), further promoted chondrogenesis as evident in elevated levels of glycosaminoglycan synthetic activity. While further investigation is necessary to optimize and test these scaffolds to induce the regeneration of cartilage and bone, this work demonstrates the importance of harnessing signals present in the native microenvironment to modulate chondrogenic and osteogenic differentiation
An extended patch-dynamic framework for food chains in fragmented landscapes
Habitat destruction, a key determinant of species loss, can be characterized by two components, patch loss and patch fragmentation, where the former refers to the reduction in patch availability, and the latter to the division of the remaining patches. Classical metacommunity models have recently explored how food web dynamics respond to patch loss, but the effects of patch fragmentation have largely been overlooked. Here we develop an extended patch-dynamic model that tracks the patch occupancy of the various trophic links subject to colonization-extinction-predation dynamics by incorporating species dispersal with patch connectivity. We found that, in a simple food chain, species at higher trophic level become extinct sooner with increasing patch loss and fragmentation due to the constraint in resource availability, confirming the trophic rank hypothesis. Yet, effects of fragmentation on species occupancy are largely determined by patch loss, with maximal fragmentation effects occurring at intermediate patch loss. Compared to the spatially explicit simulations that we also performed, the current model with pair approximation generates similar community patterns especially in spatially clustered landscapes. Overall, our extended framework can be applied to model more complex food webs in fragmented landscapes, broadening the scope of existing metacommunity theory
Comparison of ANN and LR models for predicting Carbapenem-resistant Klebsiella pneumoniae isolates from a southern province of China's RNSS data
ABSTRACT: Objectives: Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a serious threat to public health due to its limited treatment options and high mortality rate. This study aims to identify the risk factors of carbapenem resistance in patients with K. pneumoniae isolates and develop CRKP prediction models using logistic regression (LR) and artificial neural network (ANN) methods. Methods: We retrospectively analysed the data of 49,774 patients with Klebsiella pneumoniae isolates from a regional nosocomial infection surveillance system (RNSS) between 2018 and 2021. We performed logistic regression analyses to determine the independent predictors for CRKP. We then built and evaluated LR and ANN models based on these predictors using calibration curves, ROC curves, and decision curve analysis (DCA). We also applied the Synthetic Minority Over-Sampling Technique (SMOTE) to balance the data of CRKP and non-CRKP groups. Results: The LR model showed good discrimination and calibration in both training and validation sets, with areas under the ROC curve (AUROC) of 0.824 and 0.825, respectively. The DCA indicated that the LR model had clinical usefulness for decision making. The ANN model outperformed the LR model both in the training set and validation set. The SMOTE technique improved the performance of both models for CRKP detection in training set, but not in the validation set. Conclusion: We developed and validated LR and ANN models for predicting CRKP based on RNSS data. Both models were feasible and reliable for CRKP inference and could potentially assist clinicians in selecting appropriate empirical antibiotics and reducing unnecessary medical resource utilization