Paxillin facilitates timely neurite initiation on soft-substrate environments by interacting with the endocytic machinery.

Neurite initiation is the first step in neuronal development and occurs spontaneously in soft tissue environments. Although the mechanisms regulating the morphology of migratory cells on rigid substrates in cell culture are widely known, how soft environments modulate neurite initiation remains elusive. Using hydrogel cultures, pharmacologic inhibition, and genetic approaches, we reveal that paxillin-linked endocytosis and adhesion are components of a bistable switch controlling neurite initiation in a substrate modulus-dependent manner. On soft substrates, most paxillin binds to endocytic factors and facilitates vesicle invagination, elevating neuritogenic Rac1 activity and expression of genes encoding the endocytic machinery. By contrast, on rigid substrates, cells develop extensive adhesions, increase RhoA activity and sequester paxillin from the endocytic machinery, thereby delaying neurite initiation. Our results highlight paxillin as a core molecule in substrate modulus-controlled morphogenesis and define a mechanism whereby neuronal cells respond to environments exhibiting varying mechanical properties

Nonparetic Knee Extensor Strength Is the Determinant of Exercise Capacity of Community-Dwelling Stroke Survivors

Objective. To investigate the relationship among walking speed, exercise capacity, and leg strength in community dwelling stroke subjects and to evaluate which one was the leading determinant factor of them. Design. This is a descriptive, cross-sectional study. Thirty-five chronic stroke patients who were able to walk independently in their community were enrolled. Walking speed was evaluated by using the 12-meter walking test. A maximal exercise test was used to determine the stroke subjects’ exercise capacity. Knee extensor strength, measured as isokinetic torque, was assessed by isokinetic dynamometer. Results. The main walking speed of our subjects was 0.52 m/s. Peak oxygen uptake (VO2 peak) was 1.21±0.43 L/min. Knee extensor strength, no matter whether paretic or nonparetic side, was significantly correlated to 12-meter walking speed and exercise capacity. Linear regression also showed the strength of the affected knee extensor was the determinant of walking speed and that of the nonparetic knee extensor was the determinant of exercise capacity in community dwelling stroke subjects. Conclusions. Walking speed and peak oxygen uptake were markedly decreased after stroke. Knee extensor strength of nonparetic leg was the most important determinant of exercise capacity of the community-dwelling stroke subjects. Knee extensor strengthening should be emphasized to help stroke patient to achieve optimal community living

GPER-induced signaling is essential for the survival of breast cancer stem cells.

G protein-coupled estrogen receptor-1 (GPER), a member of the G protein-coupled receptor (GPCR) superfamily, mediates estrogen-induced proliferation of normal and malignant breast epithelial cells. However, its role in breast cancer stem cells (BCSCs) remains unclear. Here we showed greater expression of GPER in BCSCs than non-BCSCs of three patient-derived xenografts of ER- /PR+ breast cancers. GPER silencing reduced stemness features of BCSCs as reflected by reduced mammosphere forming capacity in vitro, and tumor growth in vivo with decreased BCSC populations. Comparative phosphoproteomics revealed greater GPER-mediated PKA/BAD signaling in BCSCs. Activation of GPER by its ligands, including tamoxifen (TMX), induced phosphorylation of PKA and BAD-Ser118 to sustain BCSC characteristics. Transfection with a dominant-negative mutant BAD (Ser118Ala) led to reduced cell survival. Taken together, GPER and its downstream signaling play a key role in maintaining the stemness of BCSCs, suggesting that GPER is a potential therapeutic target for eradicating BCSCs

Reduced expression of alpha-1,2-mannosidase I extends lifespan in Drosophila melanogaster and Caenorhabditis elegans

Exposure to sub-lethal levels of stress, or hormesis, was a means to induce longevity. By screening for mutations that enhance resistance to multiple stresses, we identified multiple alleles of alpha-1,2-mannosidase I (mas1) which, in addition to promoting stress resistance, also extended longevity. Longevity enhancement is also observed when mas1 expression is reduced via RNA interference in both Drosophila melanogaster and Caenorhabditis elegans. The screen also identified Edem1 (Edm1), a gene downstream of mas1, as a modulator of lifespan. As double mutants for both mas1 and Edm1 showed no additional longevity enhancement, it appeared that both mutations function within a common pathway to extend lifespan. Molecular analysis of these mutants revealed that the expression of BiP, a putative biomarker of dietary restriction (DR), is down-regulated in response to reductions in mas1 expression. These findings suggested that mutations in mas1 may extend longevity by modulating DR

Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs

Common dental diseases include caries, periodontitis, missing teeth and restorations. Dentists still use manual methods to judge and label lesions which is very time-consuming and highly repetitive. This research proposal uses artificial intelligence combined with image judgment technology for an improved efficiency on the process. In terms of cropping technology in images, the proposed study uses histogram equalization combined with flat-field correction for pixel value assignment. The details of the bone structure improves the resolution of the high-noise coverage. Thus, using the polynomial function connects all the interstitial strands by the strips to form a smooth curve. The curve solves the problem where the original cropping technology could not recognize a single tooth in some images. The accuracy has been improved by around 4% through the proposed cropping technique. For the convolutional neural network (CNN) technology, the lesion area analysis model is trained to judge the restoration and missing teeth of the clinical panorama (PANO) to achieve the purpose of developing an automatic diagnosis as a precision medical technology. In the current 3 commonly used neural networks namely AlexNet, GoogLeNet, and SqueezeNet, the experimental results show that the accuracy of the proposed GoogLeNet model for restoration and SqueezeNet model for missing teeth reached 97.10% and 99.90%, respectively. This research has passed the Research Institution Review Board (IRB) with application number 202002030B0