ImageNet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever et al.
17
Citations
1
Influential Citations
Small
Venue
2024
Year
Abstract Tissue engineering scaffolds can mediate the maneuverability of neural stem cell (NSC) niche to influence NSC behavior, such as cell self‐renewal, proliferation, and differentiation direction, showing the promising application in spinal cord injury (SCI) repair. Here, dual‐network porous collagen fibers (PCFS) are developed as neurogenesis scaffolds by employing biomimetic plasma ammonia oxidase catalysis and conventional amidation cross‐linking. Following optimizing the mechanical parameters of PCFS, the well‐matched Young's modulus and physiological dynamic adaptability of PCFS (4.0 wt%) have been identified as a neurogenetic exciter after SCI. Remarkably, porous topographies and curving wall‐like protrusions are generated on the surface of PCFS by simple and non‐toxic CO 2 bubble‐water replacement. As expected, PCFS with porous and matched mechanical properties can considerably activate the cadherin receptor of NSCs and induce a series of serine‐threonine kinase/yes‐associated protein mechanotransduction signal pathways, encouraging cellular orientation, neuron differentiation, and adhesion. In SCI rats, implanted PCFS with matched mechanical properties further integrated into the injured spinal cords, inhibited the inflammatory progression and decreased glial and fibrous scar formation. Wall‐like protrusions of PCFS drive multiple neuron subtypes formation and even functional neural circuits, suggesting a viable therapeutic strategy for nerve regeneration and functional recovery after SCI.
Spinal cord injury (SCI) leads to permanent loss of function due to limited regenerative capacity. Tissue engineering scaffolds offer a promising approach by providing physical and biochemical cues to guide neural stem cell (NSC) behavior. This paper introduces a novel biomimetic scaffold—dual-network porous collagen fibers (PCFS)—that combines optimized mechanical properties with porous topography to reconstruct the NSC niche. The significance lies in its systematic approach: the authors not only fabricate a scaffold but also identify the specific mechanotransduction pathways (AKT/YAP) that mediate its effects, bridging material science and molecular biology.
The work is particularly notable for its use of a simple, non-toxic CO2 bubble-water replacement method to create porous surface features, which is scalable and avoids harsh chemicals. By tuning the collagen concentration to 4.0 wt%, they achieve a Young's modulus that matches native spinal cord tissue, which is critical for cellular response. This study underscores the importance of mechanical cues in stem cell regulation, a concept that is gaining traction in regenerative medicine.
The study reports that PCFS with matched mechanical properties significantly activated cadherin receptors and induced AKT/YAP mechanotransduction, leading to enhanced NSC orientation, differentiation, and adhesion. In SCI rats, implanted PCFS integrated into the injured spinal cord, inhibited inflammatory progression, and decreased glial and fibrous scar formation. The wall-like protrusions drove the formation of multiple neuron subtypes and even functional neural circuits, suggesting improved functional recovery. However, the abstract lacks specific quantitative metrics (e.g., percentage of functional recovery, gene expression fold changes), which would strengthen the claims.
This research advances the field of neural tissue engineering by demonstrating that scaffold mechanical properties and topography can be precisely tuned to control stem cell behavior via specific signaling pathways. The findings have broader implications for designing biomimetic scaffolds for other tissues, where mechanotransduction plays a role. The use of a simple, non-toxic fabrication method also enhances translational potential. For the AI community, this work exemplifies how data-driven optimization of material parameters could be applied, though the paper itself does not employ machine learning. Future work could integrate computational models to predict optimal scaffold properties for various injury contexts.
Alex Krizhevsky, Ilya Sutskever et al.
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Diederik P. Kingma, Jimmy Ba