Immediate operation for acute type A aortic dissection complicated by malperfusion is associated with good results.

Rod and cone photoreceptors differ in their shape, photopigment expression, synaptic connection patterns, light sensitivity, and distribution across the retina. Although rods greatly outnumber cones, human vision is mostly dependent on cone photoreceptors since cones are essential for our sharp visual acuity and color discrimination. In humans and other primates, the fovea centralis (fovea), a specialized region of the central retina, contains the highest density of cones. Despite the vast importance of the fovea for human vision, the molecular mechanisms guiding the development of this region are largely unknown. MicroRNAs (miRNAs) are small post-transcriptional regulators known to orchestrate developmental transitions and cell fate specification in the retina. Here, we have characterized the transcriptional landscape of the developing rhesus monkey retina. Our data indicates that non-human primate fovea development is significantly accelerated compared to the equivalent retinal region at the other side of the optic nerve head, as described previously. Notably, we also identify several miRNAs differentially expressed in the presumptive fovea, including miR-15b-5p, miR-342-5p, miR-30b-5p, miR-103-3p, miR-93-5p as well as the miRNA cluster miR-183/-96/-182. Interestingly, miR-342-5p is enriched in the nasal primate retina and in the peripheral developing mouse retina, while miR-15b is enriched in the temporal primate retina and increases over time in the mouse retina in a central-to-periphery gradient. Together our data constitutes the first characterization of the developing rhesus monkey retinal miRNome and provides novel datasets to attain a more comprehensive understanding of foveal development.

Axon injury is a hallmark of many neurodegenerative diseases, often resulting in neuronal cell death and functional impairment. Dual leucine zipper kinase (DLK) has emerged as a key mediator of this process. However, while DLK inhibition is robustly protective in a wide range of neurodegenerative disease models, it also inhibits axonal regeneration. Indeed, there are no genetic perturbations that are known to both improve long-term survival and promote regeneration. To identify such a neuroprotective target, we conducted a set of complementary high-throughput screens using a protein kinase inhibitor library in human stem cell-derived retinal ganglion cells (hRGCs). Overlapping compounds that promoted both neuroprotection and neurite outgrowth were bioinformatically deconvoluted to identify specific kinases that regulated neuronal death and axon regeneration. This work identified the role of germinal cell kinase four (GCK-IV) kinases in cell death and additionally revealed their unexpected activity in suppressing axon regeneration. Using an adeno-associated virus (AAV) approach, coupled with genome editing, we validated that GCK-IV kinase knockout improves neuronal survival, comparable to that of DLK knockout, while simultaneously promoting axon regeneration. Finally, we also found that GCK-IV kinase inhibition also prevented the attrition of RGCs in developing retinal organoid cultures without compromising axon outgrowth, addressing a major issue in the field of stem cell-derived retinas. Together, these results demonstrate a role for the GCK-IV kinases in dissociating the cell death and axonal outgrowth in neurons and their druggability provides for therapeutic options for neurodegenerative diseases.

Motivation: There are current programs and plugins that automatically count the number of cells in a given image. However, many of these processes are not entirely automatic, as they require user input to specify a region of interest and are also frequently inaccurate. Results: This project presents laocoön , a Python package specifically designed to automatically and efficiently count the number of fluorescently-labelled cells in images. This package not only allows for reliable cell counting, but returns the proportion of cells in each cell cycle relative to all the cells in the DAPI channel, which is currently used for research purposes, but could ultimately be utilized for clinical purposes. Availability and Implementation: This package, its corresponding execution instructions, and further information about the underlying algorithms, are currently available in the GitHub repository https://github.com/edukait/laocoon under the MIT license and can be run on the command terminal of any operating system. Alternatively, laocoön is available in the Python Package Index (PyPi), so the user can use the pip command to immediately download the package. Contact: kaitlin.y.