Hypoxic expansion promotes the chondrogenic potential of articular chondrocytes

For cell-based cartilage repair strategies, an ex vivo expansion phase is required to obtain sufficient numbers of cells needed for therapy. Although recent reports demonstrated the central role of oxygen for the function and differentiation of chondrocytes, a beneficial effect of low oxygen concentrations during the expansion of the cells to further improve their chondrogenic capacity has not been investigated.Therefore, freshly harvested bovine articular chondrocytes were grown in two-dimensional monolayer cultures at 1.5% and 21% O2 and redifferentiation was subsequently induced in three-dimensional micromass cultures at 1.5%, 5%, and 21% O2. Cells expanded at 1.5% O2 were characterized by low citrate synthase (aerobic energy metabolism)--and high LDH (anaerobic energy metabolism-activities,suggesting an anaerobic energy metabolism. Collagen type II mRNA was twofold higher in cells expanded at 1.5% as compared to expansion at 21% O2. Micromass cultures grown at 21% O2 showed up to a twofold increase in the tissue content of glycosaminoglycans when formed with cells expanded at 1.5% instead of 21% O2. However, no differences in the levels of transcripts and in the staining for collagen type II protein were observed in these micromass cultures. Hypoxia (1.5% and 5% O2) applied during micromass cultures gave rise to tissues with low contents of glycosaminoglycans only. In vivo, the chondrocytes are adapted to a hypoxic environment. Taking this into account, by applying 1.5% O2 in the expansion phase in the course of cell-based cartilage repair strategies, may result in a repair tissue with higher quality by increasing the content of glycosaminoglycans.

Parameter-free extraction of the functional network topology from intracranial EEG recordings: a time-resolved study of graph properties in focal onset seizures

Rationale: Focal onset epileptic seizures are due to abnormal interactions between distributed brain areas. By estimating the cross-correlation matrix of multi-site intra-cerebral EEG recordings (iEEG), one can quantify these interactions. To assess the topology of the underlying functional network, the binary connectivity matrix has to be derived from the cross-correlation matrix by use of a threshold. Classically, a unique threshold is used that constrains the topology [1]. Our method aims to set the threshold in a data-driven way by separating genuine from random cross-correlation. We compare our approach to the fixed threshold method and study the dynamics of the functional topology. Methods: We investigate the iEEG of patients suffering from focal onset seizures who underwent evaluation for the possibility of surgery. The equal-time cross-correlation matrices are evaluated using a sliding time window. We then compare 3 approaches assessing the corresponding binary networks. For each time window: * Our parameter-free method derives from the cross-correlation strength matrix (CCS)[2]. It aims at disentangling genuine from random correlations (due to finite length and varying frequency content of the signals). In practice, a threshold is evaluated for each pair of channels independently, in a data-driven way. * The fixed mean degree (FMD) uses a unique threshold on the whole connectivity matrix so as to ensure a user defined mean degree. * The varying mean degree (VMD) uses the mean degree of the CCS network to set a unique threshold for the entire connectivity matrix. * Finally, the connectivity (c), connectedness (given by k, the number of disconnected sub-networks), mean global and local efficiencies (Eg, El, resp.) are computed from FMD, CCS, VMD, and their corresponding random and lattice networks. Results: Compared to FMD and VMD, CCS networks present: *topologies that are different in terms of c, k, Eg and El. *from the pre-ictal to the ictal and then post-ictal period, topological features time courses that are more stable within a period, and more contrasted from one period to the next. For CCS, pre-ictal connectivity is low, increases to a high level during the seizure, then decreases at offset. k shows a ‘‘U-curve’’ underlining the synchronization of all electrodes during the seizure. Eg and El time courses fluctuate between the corresponding random and lattice networks values in a reproducible manner. Conclusions: The definition of a data-driven threshold provides new insights into the topology of the epileptic functional networks.