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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Purpose: LCN1 (lipocalin-1), a gene that encodes tear lipocalin (or von Ebner’s gland protein), is mainly expressed in secretory glands and tissues, such as the lachrymal and lingual gland, and nasal, mammary, and tracheobronchial mucosae. Analysis of the Cancer Genome Atlas (TCGA) Breast Carcinoma (BRCA) level 3 data revealed a relationship between LCN1 expression and survival in breast cancer patients. Methods: The χ2 test and Fisher exact test were applied to analyze the clinical data and RNA sequencing expression data, and the association between LCN1 expression and clinicopathologic features was determined. The receiver-operating characteristic (ROC) curve of LCN1 was drawn to assess its ability as a diagnostic marker, and the optimal cutoff value was obtained from the ROC curve to distinguish groups with high and low LCN1 expression. Cox regression was used to compare both groups, and a log-rank test was applied to calculate p values and compare the ­Kaplan-Meier curves. Furthermore, GEO datasets were employed for external data validation. Results: Analysis of 1,104 breast cancer patients with a primary tumor revealed that LCN1 was overexpressed in breast cancer. High LCN1 expression was associated with clinicopathologic features and poor survival. Analyzing the area under the ROC curve (AUC) of LCN1, it was found that its diagnostic ability was limited. Multivariate analysis indicated that LCN1 expression is an independent predictor of survival in breast cancer patients. Through validation in GEO datasets, LCN1 expression was higher in tumor than normal tissue of the breast. High LCN1 expression was associated with poor survival in breast cancer patients. Conclusions: High LCN1 expression is an independent prognosticator of a poor prognosis in breast cancer.

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Breast cancer (Bc) is the most common female malignant tumor worldwide. The mechanism of tumorigenesis is still unclear. Ras-related proteins in brain (Rab)22a belongs to the Ras superfamily, which may act as an oncogene and participate in carcinogenesis. The present study aims to identify whether Rab22a could be a novel biomarker of prognosis and determine the effects of Rab22a on Bc cell progression. A total 258 BC and 56 para-tumor or non-tumor formalin fixed paraffin embedded tissues were stained through immunohistochemistry. The association between Rab22a expression and clinicopathological features, as well as overall survival status were analyzed. The expression level of Rab22a in breast cell lines were detected using reverse transcription-quantitative PcR and western blotting. SK-BR-3 cells were infected with Rab22a short hairpin RNA lenti-virus and the ability of cell proliferation, migration and invasion were measured. Gene Set Enrichment Analysis (GSEA) was employed to analyze the pathways involved in the Rab22a mRNA high level group. Rab22a was found to be overexpressed in BC tissues and upregulated in BC cells. High expression of Rab22a was related to a poor prognosis of patients with Bc. Knockdown of Rab22a decreased the proliferation, migration and invasion ability of Bc cells. GSEA indicated that certain pathways, including mammalian target of rapamycin complex 1 and protein secretion were upregulated, while pathways, such as hypoxia and KRas were downregulated in the Rab22a high level group. Rab22a is of prognostic value for Bc and necessary for Bc cell proliferation.

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