Stem cell transplantation offers a potentially transformative approach to treating neurodegenerative disorders. The safety of cellular therapies is established in multiple clinical trials, including our own in amyotrophic lateral sclerosis. To initiate similar trials in Alzheimer’s disease, efficacious cell lines must be identified. Here, we completed a preclinical proof-of-concept study in the APP/PS1 murine model of Alzheimer’s disease. Human neural stem cell transplantation targeted to the fimbria fornix significantly improved cognition in two hippocampal-dependent memory tasks at 4 and 16 weeks post-transplantation. While levels of synapse-related proteins and cholinergic neurons were unaffected, amyloid plaque load was significantly reduced in stem cell transplanted mice and associated with increased recruitment of activated microglia. In vitro, these same neural stem cells induced microglial activation and amyloid phagocytosis, suggesting an immunomodulatory capacity. Although long-term transplantation resulted in significant functional and pathological improvements in APP/PS1 mice, stem cells were not identified by immunohistochemistry or PCR at the study endpoint. These data suggest integration into native tissue or the idea that transient engraftment may be adequate for therapeutic efficacy, reducing the need for continued immunosuppression. Overall, our results support further preclinical development of human neural stem cells as a safe and effective therapy for Alzheimer’s disease.

A human cortex-derived neural stem cell (NSC) line modified to express insulin-like growth factor-I (IGF-I), HK532-IGF-I, is characterized in this report. The cell line is under study as a cellular therapy for Alzheimer’s disease (AD). HK532-IGF-I cells preferentially differentiated into gamma-aminobutyric acid-ergic neurons, a subtype dysregulated in AD; produced increased vascular endothelial growth factor levels; and displayed an increased neuroprotective capacity in vitro. HK532-IGF-I cells survived peri-hippocampal transplantation in a murine AD model and exhibited long-term persistence in targeted brain areas.

A high-fat diet (HFD), one of the major factors contributing to metabolic syndrome, which is associated with an increased risk of neurodegenerative diseases, leads to insulin resistance and cognitive impairment. It is not known whether these alterations are improved with dietary intervention. To investigate the long-term impact of a HFD on hippocampal insulin signaling and memory, C57BL6 mice were placed into one of three groups based on the diet: a standard diet (control), a HFD, or a HFD for 16 weeks and then the standard diet for 8 weeks (HF16). HFD-induced impairments in glucose tolerance and hippocampal insulin signaling occurred concurrently with deficits in both short- and long-term memory. Furthermore, these conditions were improved with dietary intervention; however, the HFD-induced decrease in insulin receptor expression in the hippocampus was not altered with dietary intervention. Our results demonstrate that memory deficits due to the consumption of a HFD at an early age are reversible.

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The hippocampus has been the target of stem cell transplantations in preclinical studies focused on Alzheimer's disease, with results showing improvements in histological and behavioral outcomes. The corpus callosum is another structure that is affected early in Alzheimer's disease. Therefore, we hypothesize that this structure is a novel target for human neural stem cell transplantation in transgenic Alzheimer's disease mouse models. This study demonstrates the feasibility of targeting the corpus callosum and identifies an effective immunosuppression regimen for transplanted neural stem cell survival. These results support further preclinical development of the corpus callosum as a therapeutic target in Alzheimer's disease.

A: Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural networks, can be used for inverse problems such as plasma tomography. In essence, plasma tomography consists in reconstructing the 2D plasma profile on a poloidal cross-section of a fusion device, based on line-integrated measurements from multiple radiation detectors. Since the reconstruction process is computationally intensive, a deconvolutional neural network trained to produce the same results will yield a significant computational speedup, at the expense of a small error which can be assessed using different metrics. In this work, we discuss the design principles behind such networks, including the use of multiple layers, how they can be stacked, and how their dimensions can be tuned according to the number of detectors and the desired tomographic resolution for a given fusion device. We describe the application of such networks at JET and COMPASS, where at JET we use the bolometer system, and at COMPASS we use the soft X-ray diagnostic based on photodiode arrays. K : Computerized Tomography (CT) and Computed Radiography (CR); Plasma diagnostics -interferometry, spectroscopy and imaging 1Corresponding author. 2See the author list of Overview of the JET preparation for Deuterium-Tritium Operation by E. Joffrin et al. in Nucl.

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