Saunderssutton8568

After an injury to the central nervous system (CNS), functional recovery is limited by the inability of severed axons to regenerate and form functional connections with appropriate target neurons beyond the injury. Despite tremendous advances in our understanding of the mechanisms of axon growth, and of the inhibitory factors in the injured CNS that prevent it, disappointingly little progress has been made in restoring function to human patients with CNS injuries, such as spinal cord injury (SCI), through regenerative therapies. Clearly, the large number of overlapping neuron-intrinsic and -extrinsic growth-inhibitory factors attenuates the benefit of neutralizing any one target. More daunting is the distances human axons would have to regenerate to reach some threshold number of target neurons, e.g., those that occupy one complete spinal segment, compared to the distances required in most experimental models, such as mice and rats. However, the difficulties inherent in studying mechanisms of axon regeneratioto how CNS axons respond to injury, and how this might affect the development of regenerative therapies for SCI and other CNS injuries.We have developed a deep learning-based computer algorithm to recognize and predict retinal differentiation in stem cell-derived organoids based on bright-field imaging. The three-dimensional "organoid" approach for the differentiation of pluripotent stem cells (PSC) into retinal and other neural tissues has become a major in vitro strategy to recapitulate development. We decided to develop a universal, robust, and non-invasive method to assess retinal differentiation that would not require chemical probes or reporter gene expression. We hypothesized that basic-contrast bright-field (BF) images contain sufficient information on tissue specification, and it is possible to extract this data using convolutional neural networks (CNNs). Retina-specific Rx-green fluorescent protein mouse embryonic reporter stem cells have been used for all of the differentiation experiments in this work. The BF images of organoids have been taken on day 5 and fluorescent on day 9. To train the CNN, we utilized a transfer learning approach ImageNet pre-trained ResNet50v2, VGG19, Xception, and DenseNet121 CNNs had been trained on labeled BF images of the organoids, divided into two categories (retina and non-retina), based on the fluorescent reporter gene expression. The best-performing classifier with ResNet50v2 architecture showed a receiver operating characteristic-area under the curve score of 0.91 on a test dataset. A comparison of the best-performing CNN with the human-based classifier showed that the CNN algorithm performs better than the expert in predicting organoid fate (84% vs. 67 ± 6% of correct predictions, respectively), confirming our original hypothesis. Overall, we have demonstrated that the computer algorithm can successfully recognize and predict retinal differentiation in organoids before the onset of reporter gene expression. This is the first demonstration of CNN's ability to classify stem cell-derived tissue in vitro.Techniques that allow the manipulation of specific neural circuits have greatly increased in the past few years. DREADDs (Designer receptors exclusively activated by designer drugs) provide an elegant way to manipulate individual brain structures and/or neural circuits, including neuromodulatory pathways. learn more Considerable efforts have been made to increase cell-type specificity of DREADD expression while decreasing possible limitations due to multiple viral vectors injections. In line with this, a retrograde canine adenovirus type 2 (CAV-2) vector carrying a Cre-dependent DREADD cassette has been recently developed. In combination with Cre-driver transgenic animals, the vector allows one to target neuromodulatory pathways with cell-type specificity. In the present study, we specifically targeted catecholaminergic pathways by injecting the vector in knock-in rat line containing Cre recombinase cassette under the control of the tyrosine hydroxylase promoter. We assessed the efficacy of infection of the nigrostriatal pathway and the catecholaminergic pathways ascending to the orbitofrontal cortex (OFC) and found cell-type-specific DREADD expression.Background In Alzheimer's disease (AD) neuronal degeneration is associated with gliosis and infiltration of peripheral blood mononuclear cells (PBMCs), which participate in neuroinflammation. Defects at the blood-brain barrier (BBB) facilitate PBMCs migration towards the central nervous system (CNS) and in particular CD4+ T cells have been found in areas severely affected in AD. However, the role of T cells, once they migrate into the CNS, is not well defined. CD4+ cells interact with astrocytes able to release several factors and cytokines that can modulate T cell polarization; similarly, astrocytic properties are modulated after interaction with T cells. Methods In in vitro models, astrocytes were primed with β-amyloid (Aβ; 2.5 μM, 5 h) and then co-cultured with magnetically isolated CD4+ cells. Cytokines expression was evaluated both in co-cultured CD4+ cells and astrocytes. The effects of this crosstalk were further evaluated by co-culturing CD4+ cells with the neuronal-like SH-SY5Y cell line and astrocytre the first cells that lymphocytes interact with and are among the principal players in neuroinflammation occurring in AD, understanding this crosstalk may disclose new potential targets of intervention in the treatment of neurodegeneration.Alzheimer's disease (AD) is characterized by amyloid beta (Aβ) plaques in the brain detectable by highly invasive in vivo brain imaging or in post-mortem tissues. A non-invasive and inexpensive screening method is needed for early diagnosis of asymptomatic AD patients. The shared developmental origin and similarities with the brain make the retina a suitable surrogate tissue to assess Aβ load in AD. Using curcumin, a FluoroProbe that binds to Aβ, we labeled and measured the retinal fluorescence in vivo and compared with the immunohistochemical measurements of the brain and retinal Aβ load in the APP/PS1 mouse model. In vivo retinal images were acquired every 2 months using custom fluorescence scanning laser ophthalmoscopy (fSLO) after tail vein injections of curcumin in individual mice followed longitudinally from ages 5 to 19 months. At the same time points, 1-2 mice from the same cohort were sacrificed and immunohistochemistry was performed on their brain and retinal tissues. Results demonstrated cortical and retinal Aβ immunoreactivity were significantly greater in Tg than WT groups.