EGF–FGF2 stimulates the proliferation and improves the neuronal commitment of mouse epidermal neural crest stem cells (EPI-NCSCs)

Epidermal neural crest stem cells (EPI-NCSCs), which reside in the bulge of hair follicles, are attractive candidates for several applications in cell therapy, drug screening and tissue engineering. As suggested remnants of the embryonic neural crest (NC) in an adult location, EPI-NCSCs are able to generate a wide variety of cell types and are readily accessible by a minimally invasive procedure. Since the combination of epidermal growth factor (EGF) and fibroblast growth factor type 2 (FGF₂) is mitogenic and promotes the neuronal commitment of various stem cell populations, we examined its effects in the proliferation and neuronal potential of mouse EPI-NCSCs. By using a recognized culture protocol of bulge whiskers follicles, we were able to isolate a population of EPI-NCSCs, characterized by the migratory potential, cell morphology and expression of phenotypic markers of NC cells. EPI-NCSCs expressed neuronal, glial and smooth muscle markers and exhibited the NC-like fibroblastic morphology. The treatment with the combination EGF and FGF₂, however, increased their proliferation rate and promoted the acquisition of a neuronal-like morphology accompanied by reorganization of neural cytoskeletal proteins βIII-tubulin and nestin, as well as upregulation of the pan neuronal marker βIII-tubulin and down regulation of the undifferentiated NC, glial and smooth muscle cell markers. Moreover, the treatment enhanced the response of EPI-NCSCs to neurogenic stimulation, as evidenced by induction of GAP43, and increased expression of Mash-1 in neuron-like cell, both neuronal-specific proteins. Together, the results suggest that the combination of EGF–FGF2 stimulates the proliferation and improves the neuronal potential of EPI-NCSCs similarly to embryonic NC cells, ES cells and neural progenitor/stem cells of the central nervous system and highlights the advantage of using EGF–FGF₂ in neuronal differentiation protocols.     

Parametric modal regression with varying precision

In this paper, we propose a simple parametric modal linear regression model where the response variable is gamma distributed using a new parameterization of this distribution that is indexed by mode and precision parameters, that is, in this new regression model, the modal and precision responses are related to a linear predictor through a link function and the linear predictor involves covariates and unknown regression parameters. The main advantage of our new parameterization is the straightforward interpretation of the regression coefficients in terms of the mode of the positive response variable, as is usual in the context of generalized linear models, and direct inference in parametric mode regression based on the likelihood paradigm. Furthermore, we discuss residuals and influence diagnostic tools. A Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples with a discussion of the results. Finally, we illustrate the usefulness of the new model by two applications, to biology and demography.     

Birnbaum–Saunders frailty regression models: Diagnostics and application to medical data

In survival models, some covariates affecting the lifetime could not be observed or measured. These covariates may correspond to environmental or genetic factors and be considered as a random effect related to a frailty of the individuals explaining their survival times. We propose a methodology based on a Birnbaum–Saunders frailty regression model, which can be applied to censored or uncensored data. Maximum‐likelihood methods are used to estimate the model parameters and to derive local influence techniques. Diagnostic tools are important in regression to detect anomalies, as departures from error assumptions and presence of outliers and influential cases. Normal curvatures for local influence under different perturbations are computed and two types of residuals are introduced. Two examples with uncensored and censored real‐world data illustrate the proposed methodology. Comparison with classical frailty models is carried out in these examples, which shows the superiority of the proposed model.