RESUMO
Endothelial progenitor cells (EPCs) are a vasculogenic subset of progenitors, which play a key role in maintenance of endothelial integrity. These cells are exercise-responsive, and thus exercise may play a key role in vascular repair and maintenance via mobilization of such cells. Blood flow restriction exercise, due to the augmentation of local tissue hypoxia, may promote exercise-induced EPC mobilization. Nine, healthy, young (18-30 years) males participated in the study. Participants undertook 2 trials of single leg knee extensor (KE) exercise, at 60% of thigh occlusion pressure (4 sets at 30% maximal torque) (blood flow restriction; BFR) or non- blood flow restriction (non-BFR), in a fasted state. Blood was taken prior, immediately after, and 30 min after exercise. Blood was used for the quantification of hematopoietic progenitor cells (HPCs: CD34+CD45dim), EPCs (CD34+VEGFR2+/CD34+CD45dimVEGFR2+) by flow cytometry. Our results show that unilateral KE exercise did not affect circulating HPC levels (p = 0.856), but did result in increases in both CD34+VEGFR2+ and CD34+CD45dimVEGFR2+ EPCs, but only in the non-BFR trial (CD34+VEGFR2+: 269 ± 42 cells mL-1 to 573 ± 90 cells mL-1, pre- to immediately post-exercise, p = 0.008; CD34+CD45dimVEGFR2+: 129 ± 21 cells mL-1 to 313 ± 103 cells mL-1, pre- to 30 min post-exercise, p = 0.010). In conclusion, low load BFR exercise did not result in significant circulating changes in EPCs in the post-exercise recovery period and may impair exercise-induced EPC mobilization compared to non-BFR exercise.
RESUMO
BACKGROUND: One important aspect of cellular function, which is at the basis of tissue homeostasis, is the delivery of proteins to their correct destinations. Significant advances in live cell microscopy have allowed tracking of these pathways by following the dynamics of fluorescently labelled proteins in living cells. OBJECTIVES: This paper explores intelligent data analysis techniques to model the dynamic behavior of proteins in living cells as well as to classify different experimental conditions. METHODS: We use a combination of decision tree classification and hidden Markov models. In particular, we introduce a novel approach to "align" hidden Markov models so that hidden states from different models can be cross-compared. RESULTS: Our models capture the dynamics of two experimental conditions accurately with a stable hidden state for control data and multiple (less stable) states for the experimental data recapitulating the behaviour of particle trajectories within live cell time-lapse data. CONCLUSIONS: In addition to having successfully developed an automated framework for the classification of protein transport dynamics from live cell time-lapse data our model allows us to understand the dynamics of a complex trafficking pathway in living cells in culture.
