Umbilical cord matrix (UCM)-derived mesenchymal stem/stromal cells (MSCs) are promising therapeutic candidates for regenerative medicine settings. UCM MSCs have advantages over adult cells as these can be obtained through a non-invasive harvesting procedure and display a higher proliferative capacity. However, the high cell doses required in the clinical setting make large-scale manufacturing of UCM MSCs mandatory. A commercially available human platelet lysate-based culture supplement (UltraGRO , AventaCell BioMedical) (5%(v/v)) was tested to effectively isolate UCM MSCs and to expand these cells under (1) static conditions, using planar culture systems and (2) stirred culture using plastic microcarriers in a spinner flask. The MSC-like cells were isolated from UCM explant cultures after 11 ± 2 days. After five passages in static culture, UCM MSCs retained their immunophenotype and multilineage differentiation potential. The UCM MSCs cultured under static conditions using UltraGRO -supplemented medium expanded more rapidly compared with UCM MSCs expanded using a previously established protocol. Importantly, UCM MSCs were successfully expanded under dynamic conditions on plastic microcarriers using UltraGRO -supplemented medium in spinner flasks. Upon an initial 54% cell adhesion to the beads, UCM MSCs expanded by >13-fold after 5-6 days, maintaining their immunophenotype and multilineage differentiation ability. The present paper reports the establishment of an easily scalable integrated culture platform based on a human platelet lysate supplement for the effective isolation and expansion of UCM MSCs in a xenogeneic-free microcarrier-based system. This platform represents an important advance in obtaining safer and clinically meaningful MSC numbers for clinical translation. Copyright © 2016 John Wiley & Sons, Ltd.

SUMMARYOwing to limited wireless network resources, network applications must provide an adaptive qualityguaranteed service to satisfy user requirements. Different applications are associated with different quality of service (QoS) concerns, as well as different QoS control parameters. This work presents an adaptive QoS algorithm by discussing the QoS specifications of three wireless access technologies, i.e. 3G, WiMAX and WiFi. Based on cross-layer and cognition concepts, these environmental parameters are integrated with the sensing of spectral and received signal strength from a cognitive radio paradigm. An adaptive QoS algorithm is then proposed to select the optimal access network for services. Simulation results indicate that the proposed adaptive QoS algorithm outperforms available ones in real-time applications. Compared with traditional algorithms, the proposed algorithm reduces not only the average delay time and jitter for VoIP services to 0.16 s and 0.09 ms, respectively, but also the packet loss ratio for high-definition video streaming by 3.4%.

Collision tumors of the stomach are uncommon. To the best of our knowledge, this is the first case report of gastric collision tumor composed of gastrointestinal stromal tumor (GIST) intermixed with primary adenocarcinoma in the English literature. The adenocarcinoma was determined to be the primary tumor based on histologic features. The tumor cells of the GIST were diffusely and strongly positive for CD34 and CD117, weakly positive for smooth muscle actin (5% of cells), and negative for desmin, S-100 protein, synaptophysin, and cytokeratin. There was no transition between the different components. We hypothesized that the stomach was influenced by the same unknown carcinogen, resulting in a simultaneous proliferation of different cell lines (epithelial and stromal cell). This case represents an example of two independent tumors in a unique one-on-another pattern, namely growth of adenocarcinoma on GIST.

In this paper, a hybrid neural network model is developed to predict and control the blood glucose (BG) of the patient who has type 1 diabetes mellitus (T1DM). The proposed model consists of two parts: a linear finite impulse response (FIR) model and a nonlinear autoregressive exogenous input (NARX) network. A recently developed and well-acknowledged meal simulation model of the glucose-insulin system for T1DM is employed to create virtual subjects. Data from virtual subjects are used to identify an intermediate physiological model, and then our proposed hybrid model is trained and validated based on this intermediate model. The key features of the resulting hybrid model are that it reveals satisfactory accuracy of long-term prediction and does not require an immeasurable state for model initialization. The developed hybrid model is then embedded in a nonlinear model predictive control (MPC) controller with zone penalty weights, and this closed-loop controller is implemented on these virtual subjects for simulation-based preclinical testing. The results show that promising glycemic control performance can be achieved. Moreover, this overall BG control methodology is easily portable and has the ability to arbitrarily start the therapeutic control at any initial point.