BackgroundDeveloping new methods to deliver cells to the injured tissue is a critical factor in translating cell therapeutics research into clinical use; therefore, there is a need for improved cell homing capabilities.Materials and methodsIn this study, we demonstrated the effects of labeling rat bone marrow-derived mesenchymal stem cells (MSCs) with fabricated polydopamine (PDA)-capped Fe3O4 (Fe3O4@PDA) superparticles employing preassembled Fe3O4 nanoparticles as the cores.ResultsWe found that the Fe3O4@PDA composite superparticles exhibited no adverse effects on MSC characteristics. Moreover, iron oxide nanoparticles increased the number of MSCs in the S-phase, their proliferation index and migration ability, and their secretion of vascular endothelial growth factor relative to unlabeled MSCs. Interestingly, nanoparticles not only promoted the expression of C-X-C chemokine receptor 4 but also increased the expression of the migration-related proteins c-Met and C-C motif chemokine receptor 1, which has not been reported previously. Furthermore, the MSC-loaded nanoparticles exhibited improved homing and anti-inflammatory abilities in the absence of external magnetic fields in vivo.ConclusionThese results indicated that iron oxide nanoparticles rendered MSCs more favorable for use in injury treatment with no negative effects on MSC properties, suggesting their potential clinical efficacy.

Advances in non-volatile resistive switching random access memory (RRAM) have made it a promising memory technology with potential applications in low-power and embedded in-memory computing devices owing to a number of advantages such as low-energy consumption, low area cost and good scaling. There have been proposals to employ RRAM in architecting chips for neuromorphic computing and artificial neural networks where matrix-vector multiplication can be computed in the analog domain in a single timestep. However, it is challenging to employ RRAM devices in neuromorphic chips owing to the non-ideal behavior of RRAM. In this article, we propose a cycle-accurate and scalable system-level simulator that can be used to study the effects of using RRAM devices in neuromorphic computing chips. The simulator models a spatial neuromorphic chip architecture containing many neural cores with RRAM crossbars connected via a Network-on-Chip (NoC). We focus on system-level simulation and demonstrate the effectiveness of our simulator in understanding how non-linear RRAM effects such as stuck-at-faults (SAFs), write variability, and random telegraph noise (RTN) can impact an application's behavior. By using our simulator, we show that RTN and write variability can have adverse effects on an application. Nevertheless, we show that these effects can be mitigated through proper design choices and the implementation of a write-verify scheme. INTRODUCTIONNeuromorphic computing is a domain-specific computing approach that uses analog, digital, or mixed-mode integrated circuits to mimic biological architectures of the neural system, including neurons, axons, synapses, and dendrites [40]. Neurons whose inputs and outputs are spikes are used in neuromorphic computing; the resulting spike-based or spiking neural networks (SNNs) are often regarded as third-generation neural networks [39]. Special-purpose built hardware for neuromorphic computing includes the HiCANN chip [12], NeuroGrid [7], SpiNNaker [9], and IBM's TrueNorth chip [17]. SpiNNaker and TrueNorth are fully digital; HiCANN and NeuroGrid are analog or partially analog in design. In TrueNorth, 4096 neurosynaptic cores of size 256 × 256 are interconnected by an intra-chip network. Using TrueNorth to implement SNNs, Esser et al. demonstrated good accuracies in real-world application benchmarks [22].Concurrent with the developments in neuromorphic computing, advances in non-volatile resistive switching random access memory (RRAM) have made it a suitable memory technology for realizing neuromorphic computing architectures [11]. For instance, RRAM-based neuromorphic computing hardware has been proposed in [19,23,25]. Apart from advantages such as low operating power, high speed and density, memristive and RRAM-based crossbars have been proposed as energy-efficient dot-product engines. These can be used to perform matrix-vector multiplication operations efficiently in the analog domain through current sums [4,6,15]. Such approaches are suitable for low-power embedded devices targeting ne...

The enzyme carboxyl ester lipase (CEL), known as bile salt-dependent lipase (BSDL) or bile saltstimulated lipase (BSSL), is mainly expressed in pancreatic acinar cells and lactating mammary glands. To investigate the link between CEL expression of breast cancer (BC) tissues and the survival of BC patients by analyzing The Cancer Genome Atlas Breast Carcinoma (TCGA-BRCA) level 3 data. Methods: The clinical information and RNA-sequencing (RNA-Seq) expression data were downloaded from TCGA. Patients were divided into a high CEL expression group and a low CEL expression group using the optimal cutoff value (5.611) identified from the ROC curve. Chi-square test and Fisher exact test were used to find the correlation between the expression of CEL and clinicopathologic features. To assess the diagnostic capability, the receiver operating characteristic (ROC) curve of CEL was drawn. The survival differences between high and low CEL expression groups were compared by Cox regression analysis. Logrank test was applied to the calculation of p values and the comparison of the Kaplan-Meier curves. Furthermore, Gene Expression Omnibus (GEO) datasets were used for external data validation. Results: Analysis of 1104 cases of tumor data showed that CEL was over-expressed in breast cancer. There were relationships between high CEL expression and clinicopathologic features. The high CEL expression group had a lower survival. By analyzing the area under the ROC curve (AUC) of CEL, it was found to have a limited diagnostic capability. CEL expression may be an independent prognostic factor for breast cancer survival through the multivariate analysis. The validation in GEO datasets also showed that CEL expression was higher in breast tumor tissues than in normal breast tissues. High CEL expression was associated with the poor overall survival of breast cancer. Conclusions: High CEL expression may be an independent prognostic factor for the poor survival of breast cancer.

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