Platelet-Rich Plasma as an Autologous and Proangiogenic Cell Delivery System.

Angiogenesis is a key factor in early stages of wound healing and is crucial for the repair of vascularized tissues such as the bone. However, supporting timely revascularization of the defect site still presents a clinical challenge. Tissue engineering approaches delivering endothelial cells or prevascularized constructs may overcome this problem. In the current study, we investigated platelet-rich plasma (PRP) gels as autologous, injectable cell delivery systems for prevascularized constructs. PRP was produced from human thrombocyte concentrates. GFP-expressing human umbilical vein endothelial cells (HUVECs) and human bone marrow-derived mesenchymal stem cells (MSCs) were encapsulated in PRP gels in different proportions. The formation of cellular networks was assessed over 14 days by time-lapse microscopy, gene expression analysis, and immunohistology. PRP gels presented a favorable environment for the formation of a three-dimensional (3D) cellular network. The formation of these networks was apparent as early as 3 days after seeding. Networks increased in complexity and branching over time but were only stable in HUVEC-MSC cocultures. The high cell viability together with the 3D capillary-like networks observed at early time points suggests that PRP can be used as an autologous and proangiogenic cell delivery system for the repair of vascularized tissues such as the bone.

Discriminating cell line specific features of antibiotic-resistant strains of Escherichia coli from Raman spectra via machine learning analysis.

While Raman spectroscopy can provide label-free discrimination between highly similar biological species, the discrimination is often marginal, and optimal use of spectral information is imperative. Here, we compare two machine learning models, an artificial neural network and a support vector machine, for discriminating between Raman spectra of 11 bacterial mutants of Escherichia coli MDS42. While we find that both models discriminate the 11 bacterial strains with similarly high accuracy, sensitivity and specificity, it is clear that the models form different class boundaries. By extracting strain-specific (and function-specific) spectral features utilized by the models, we find that both models utilize a small subset of high intensity peaks while separate subsets of lower intensity peaks are utilized by only one method or the other. This analysis highlights the need for methods to use the complete spectral information more effectively, beginning with a better understanding of the distinct information gained from each model.