Advanced strategies for 3D-printed neural scaffolds:materials,structure,and nerve remodeling

Nerve regeneration holds significant potential in the treatment of various skeletal and neurological disorders to restore lost sensory and motor functions.The potential of nerve regeneration in ameliorating neurological diseases and injuries is critical to human health.Three-dimensional(3D)printing offers versatility and precision in the fabrication of neural scaffolds.Complex neural structures such as neural tubes and scaffolds can be fabricated via 3Dprinting.This reviewcomprehensively analyzes the current state of 3D-printed neural scaffolds and explores strategies to enhance their design.It highlights therapeutic strategies and structural design involving neural materials and stem cells.First,nerve regeneration materials and their fabrication techniques are outlined.The applications of conductive materials in neural scaffolds are reviewed,and their potential to facilitate neural signal transmission and regeneration is highlighted.Second,the progress in 3D-printed neural scaffolds applied to the peripheral and central nerves is comprehensively evaluated,and their potential to restore neural function and promote the recovery of different nervous systems is emphasized.In addition,various applications of 3D-printed neural scaffolds in peripheral and neurological diseases,as well as the design strategies of multifunctional biomimetic scaffolds,are discussed.

Numerical Study of the Biomechanical Behavior of a 3D Printed Polymer Esophageal Stent in the Esophagus by BP Neural Network Algorithm

Esophageal disease is a common disorder of the digestive system that can severely affect the quality of life andprognosis of patients. Esophageal stenting is an effective treatment that has been widely used in clinical practice.However, esophageal stents of different types and parameters have varying adaptability and effectiveness forpatients, and they need to be individually selected according to the patient’s specific situation. The purposeof this study was to provide a reference for clinical doctors to choose suitable esophageal stents. We used 3Dprinting technology to fabricate esophageal stents with different ratios of thermoplastic polyurethane (TPU)/(Poly-ε-caprolactone) PCL polymer, and established an artificial neural network model that could predict the radial forceof esophageal stents based on the content of TPU, PCL and print parameter. We selected three optimal ratios formechanical performance tests and evaluated the biomechanical effects of different ratios of stents on esophagealimplantation, swallowing, and stent migration processes through finite element numerical simulation and in vitrosimulation tests. The results showed that different ratios of polymer stents had different mechanical properties,affecting the effectiveness of stent expansion treatment and the possibility of postoperative complications of stentimplantation.

3D Ice Shape Description Method Based on BLSOM Neural Network

When checking the ice shape calculation software,its accuracy is judged based on the proximity between the calculated ice shape and the typical test ice shape.Therefore,determining the typical test ice shape becomes the key task of the icing wind tunnel tests.In the icing wind tunnel test of the tail wing model of a large amphibious aircraft,in order to obtain accurate typical test ice shape,the Romer Absolute Scanner is used to obtain the 3D point cloud data of the ice shape on the tail wing model.Then,the batch-learning self-organizing map(BLSOM)neural network is used to obtain the 2D average ice shape along the model direction based on the 3D point cloud data of the ice shape,while its tolerance band is calculated using the probabilistic statistical method.The results show that the combination of 2D average ice shape and its tolerance band can represent the 3D characteristics of the test ice shape effectively,which can be used as the typical test ice shape for comparative analysis with the calculated ice shape.

Human-induced pluripotent stem cell-derived neural stem cell exosomes improve blood-brain barrier function after intracerebral hemorrhage by activating astrocytes via PI3K/AKT/MCP-1 axis

Cerebral edema caused by blood-brain barrier injury after intracerebral hemorrhage is an important factor leading to poor prognosis.Human-induced pluripotent stem cell-derived neural stem cell exosomes(hiPSC-NSC-Exos)have shown potential for brain injury repair in central nervous system diseases.In this study,we explored the impact of hiPSC-NSC-Exos on blood-brain barrier preservation and the underlying mechanism.Our results indicated that intranasal delivery of hiPSC-NSC-Exos mitigated neurological deficits,enhanced blood-brain barrier integrity,and reduced leukocyte infiltration in a mouse model of intracerebral hemorrhage.Additionally,hiPSC-NSC-Exos decreased immune cell infiltration,activated astrocytes,and decreased the secretion of inflammatory cytokines like monocyte chemoattractant protein-1,macrophage inflammatory protein-1α,and tumor necrosis factor-αpost-intracerebral hemorrhage,thereby improving the inflammatory microenvironment.RNA sequencing indicated that hiPSC-NSC-Exo activated the PI3K/AKT signaling pathway in astrocytes and decreased monocyte chemoattractant protein-1 secretion,thereby improving blood-brain barrier integrity.Treatment with the PI3K/AKT inhibitor LY294002 or the monocyte chemoattractant protein-1 neutralizing agent C1142 abolished these effects.In summary,our findings suggest that hiPSC-NSC-Exos maintains blood-brain barrier integrity,in part by downregulating monocyte chemoattractant protein-1 secretion through activation of the PI3K/AKT signaling pathway in astrocytes.

Review of Artificial Intelligence for Oil and Gas Exploration: Convolutional Neural Network Approaches and the U-Net 3D Model

Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.

瑞马唑仑调节HIF-1α/BNIP3信号通路对OGD/R诱导神经细胞自噬和凋亡的影响

目的探讨瑞马唑仑对OGD/R诱导的神经细胞自噬和凋亡的影响及作用机制。方法体外培养小鼠海马神经元细胞(HT22)并进行神经细胞氧糖剥夺/再复氧(OGD/R),筛选实验用瑞马唑仑浓度;将HT22细胞分为对照组、OGD/R组、瑞马唑仑组、2-ME2组、瑞马唑仑+2-ME2组;CCK8法检测5组HT22细胞活力;流式细胞术检测5组HT22细胞凋亡率;透射电子显微镜观察5组HT22细胞自噬小体的形成;Western blot检测5组HT22细胞HIF-1α、BNIP3、LC3-Ⅱ/LC3-Ⅰ的表达。结果确定实验用瑞马唑仑浓度为50μg/mL;与对照组比较,OGD/R组HT22细胞OD450值、HIF-1α、BNIP3、LC3-Ⅱ/LC3-Ⅰ蛋白水平下调,凋亡率上调(P<0.05);与OGD/R组比较,瑞马唑仑组HT22细胞自噬小体增加,OD450值、HIF-1α、BNIP3、LC3-Ⅱ/LC3-Ⅰ蛋白水平上调,凋亡率下调(P<0.05);2-ME2组HT22细胞OD450值、HIF-1α、BNIP3、LC3-Ⅱ/LC3-Ⅰ蛋白水平下调,凋亡率上调(P<0.05)。与瑞马唑仑组比较,瑞马唑仑+2-ME2组HT22细胞自噬小体数量减少,OD450值、HIF-1α、BNIP3、LC3-Ⅱ/LC3-Ⅰ蛋白水平下调,凋亡率上调(P<0.05);与2-ME2组比较,瑞马唑仑+2-ME2组HT22细胞OD450值、HIF-1α、BNIP3、LC3-Ⅱ/LC3-Ⅰ蛋白水平上调,凋亡率下调(P<0.05)。结论瑞马唑仑可通过激活HIF-1α/BNIP3信号通路促进OGD/R诱导的神经细胞自噬,抑制细胞凋亡,从而减轻OGD/R诱导的神经细胞损伤。

Low-temperature 3D-printed collagen/chitosan scaffolds loaded with exosomes derived from neural stem cells pretreated with insulin growth factor-1 enhance neural regeneration after traumatic brain injury

There are various clinical treatments for traumatic brain injury,including surgery,drug therapy,and rehabilitation therapy;howeve r,the therapeutic effects are limited.Scaffolds combined with exosomes represent a promising but challenging method for improving the repair of traumatic brain injury.In this study,we determined the ability of a novel 3D-printed collagen/chitosan scaffold loaded with exosomes derived from neural stem cells pretreated with insulin-like growth factor-1(3D-CC-INEXOS) to improve traumatic brain injury repair and functional recove ry after traumatic brain injury in rats.Composite scaffolds comprising collagen,chitosan,and exosomes derived from neural stem cells pretreated with insulin-like growth fa ctor-1(INEXOS) continuously released exosomes for 2weeks.Transplantation of 3D-CC-INExos scaffolds significantly improved motor and cognitive functions in a rat traumatic brain injury model,as assessed by the Morris water maze test and modified neurological seve rity scores.In addition,immunofluorescence staining and transmission electron microscopy showed that3D-CC-INExos implantation significantly improved the recove ry of damaged nerve tissue in the injured area.In conclusion,this study suggests that transplanted3D-CC-INExos scaffolds might provide a potential strategy for the treatment of traumatic brain injury and lay a solid foundation for clinical translation.

3D printing of functional bioengineered constructs for neural regeneration: a review

Three-dimensional(3D)printing technology has opened a new paradigm to controllably and reproducibly fabricate bioengineered neural constructs for potential applications in repairing injured nervous tissues or producing in vitro nervous tissue models.However,the complexity of nervous tissues poses great challenges to 3D-printed bioengineered analogues,which should possess diverse architectural/chemical/electrical functionalities to resemble the native growth microenvironments for functional neural regeneration.In this work,we provide a state-of-the-art review of the latest development of 3D printing for bioengineered neural constructs.Various 3D printing techniques for neural tissue-engineered scaffolds or living cell-laden constructs are summarized and compared in terms of their unique advantages.We highlight the advanced strategies by integrating topographical,biochemical and electroactive cues inside 3D-printed neural constructs to replicate in vivo-like microenvironment for functional neural regeneration.The typical applications of 3D-printed bioengineered constructs for in vivo repair of injured nervous tissues,bio-electronics interfacing with native nervous system,neural-on-chips as well as brain-like tissue models are demonstrated.The challenges and future outlook associated with 3D printing for functional neural constructs in various categories are discussed.

Loss of SHROOM3 affects neuroepithelial cell shape through regulating cytoskeleton proteins in cynomolgus monkey organoids

Neural tube defects(NTDs)are severe congenital neurodevelopmental disorders arising from incomplete neural tube closure.Although folate supplementation has been shown to mitigate the incidence of NTDs,some cases,often attributable to genetic factors,remain unpreventable.The SHROOM3 gene has been implicated in NTD cases that are unresponsive to folate supplementation;at present,however,the underlying mechanism remains unclear.Neural tube morphogenesis is a complex process involving the folding of the planar epithelium of the neural plate.To determine the role of SHROOM3 in early developmental morphogenesis,we established a neuroepithelial organoid culture system derived from cynomolgus monkeys to closely mimic the in vivo neural plate phase.Loss of SHROOM3 resulted in shorter neuroepithelial cells and smaller nuclei.These morphological changes were attributed to the insufficient recruitment of cytoskeletal proteins,namely fibrous actin(F-actin),myosin II,and phospho-myosin light chain(PMLC),to the apical side of the neuroepithelial cells.Notably,these defects were not rescued by folate supplementation.RNA sequencing revealed that differentially expressed genes were enriched in biological processes associated with cellular and organ morphogenesis.In summary,we established an authentic in vitro system to study NTDs and identified a novel mechanism for NTDs that are unresponsive to folate supplementation.

Long non-coding RNA H19 regulates neurogenesis of induced neural stem cells in a mouse model of closed head injury

Stem cell-based therapies have been proposed as a potential treatment for neural regeneration following closed head injury.We previously reported that induced neural stem cells exert beneficial effects on neural regeneration via cell replacement.However,the neural regeneration efficiency of induced neural stem cells remains limited.In this study,we explored differentially expressed genes and long non-coding RNAs to clarify the mechanism underlying the neurogenesis of induced neural stem cells.We found that H19 was the most downregulated neurogenesis-associated lnc RNA in induced neural stem cells compared with induced pluripotent stem cells.Additionally,we demonstrated that H19 levels in induced neural stem cells were markedly lower than those in induced pluripotent stem cells and were substantially higher than those in induced neural stem cell-derived neurons.We predicted the target genes of H19 and discovered that H19 directly interacts with mi R-325-3p,which directly interacts with Ctbp2 in induced pluripotent stem cells and induced neural stem cells.Silencing H19 or Ctbp2 impaired induced neural stem cell proliferation,and mi R-325-3p suppression restored the effect of H19 inhibition but not the effect of Ctbp2 inhibition.Furthermore,H19 silencing substantially promoted the neural differentiation of induced neural stem cells and did not induce apoptosis of induced neural stem cells.Notably,silencing H19 in induced neural stem cell grafts markedly accelerated the neurological recovery of closed head injury mice.Our results reveal that H19 regulates the neurogenesis of induced neural stem cells.H19 inhibition may promote the neural differentiation of induced neural stem cells,which is closely associated with neurological recovery following closed head injury.

基于传感器阵列及神经网络算法的NH_(3)和NO_(2)混合气体体积分数识别

针对电阻型气体传感器具有的交叉敏感性,开发了基于WO_(3)传感器阵列及神经网络算法的NH_(3),NO_(2)混合气体体积分数预测技术。采用火焰合成法合成了La掺杂的WO_(3)敏感材料并制备了气体传感器,与商用MQ—137电阻型气体传感器组成阵列。通过提取特征值、神经网络训练,构建传感器阵列输出与气体体积分数的映射模型,并使用该模型由传感器阵列的响应结果对NH_(3),NO_(2)混合气体进行体积分数预测。实验结果表明:经训练后的神经网络能对NH_(3),NO_(2)混合气体中各组分体积分数进行有效预测,平均预测误差分别为3.64%和2.48%。本文所开发的传感器阵列及神经网络算法有效避免了电阻型传感器选择性差的局限,实现了对NH_(3)和NO_(2)混合气体的高效识别和体积分数测量。

RWNeRF:Robust Watermarking Scheme for Neural Radiance Fields Based on Invertible Neural Networks

As neural radiance fields continue to advance in 3D content representation,the copyright issues surrounding 3D models oriented towards implicit representation become increasingly pressing.In response to this challenge,this paper treats the embedding and extraction of neural radiance field watermarks as inverse problems of image transformations and proposes a scheme for protecting neural radiance field copyrights using invertible neural network watermarking.Leveraging 2D image watermarking technology for 3D scene protection,the scheme embeds watermarks within the training images of neural radiance fields through the forward process in invertible neural networks and extracts them from images rendered by neural radiance fields through the reverse process,thereby ensuring copyright protection for both the neural radiance fields and associated 3D scenes.However,challenges such as information loss during rendering processes and deliberate tampering necessitate the design of an image quality enhancement module to increase the scheme’s robustness.This module restores distorted images through neural network processing before watermark extraction.Additionally,embedding watermarks in each training image enables watermark information extraction from multiple viewpoints.Our proposed watermarking method achieves a PSNR(Peak Signal-to-Noise Ratio)value exceeding 37 dB for images containing watermarks and 22 dB for recovered watermarked images,as evaluated on the Lego,Hotdog,and Chair datasets,respectively.These results demonstrate the efficacy of our scheme in enhancing copyright protection.

MarkNeRF:Watermarking for Neural Radiance Field

This paper presents a novel watermarking scheme designed to address the copyright protection challenges encountered with Neural radiation field(NeRF)models.We employ an embedding network to integrate the watermark into the images within the training set.Then,theNeRFmodel is utilized for 3Dmodeling.For copyright verification,a secret image is generated by inputting a confidential viewpoint into NeRF.On this basis,design an extraction network to extract embedded watermark images fromconfidential viewpoints.In the event of suspicion regarding the unauthorized usage of NeRF in a black-box scenario,the verifier can extract the watermark from the confidential viewpoint to authenticate the model’s copyright.The experimental results demonstrate not only the production of visually appealing watermarks but also robust resistance against various types of noise attacks,thereby substantiating the effectiveness of our approach in safeguarding NeRF.

(3+1)维Hirota双线性方程的lump解

非线性发展方程是现代数学的一重要分支,其精确解的计算一直都是非线性科学领域的主流与焦点问题.lump解是精确解析解的一种特殊形式,以(3+1)维Hirota双线性方程为例对此展开研究.首先,利用Hirota双线性方法研究其经典lump解.其次,以双线性神经网络方法为基础,借助符号计算方法,得到方程的高阶lump解,主要是4阶lump解的计算.最后,通过对参数赋予一些特殊值,借助Maple软件,绘制出相关的三维图、密度图、相图以及传播图等,得到一些新的现象,同时展示了所求出的解的动力学行为.

Regression Method for Rail Fastener Tightness Based on Center-Line Projection Distance Feature and Neural Network

In the railway system,fasteners have the functions of damping,maintaining the track distance,and adjusting the track level.Therefore,routine maintenance and inspection of fasteners are important to ensure the safe operation of track lines.Currently,assessment methods for fastener tightness include manual observation,acoustic wave detection,and image detection.There are limitations such as low accuracy and efficiency,easy interference and misjudgment,and a lack of accurate,stable,and fast detection methods.Aiming at the small deformation characteristics and large elastic change of fasteners from full loosening to full tightening,this study proposes high-precision surface-structured light technology for fastener detection and fastener deformation feature extraction based on the center-line projection distance and a fastener tightness regression method based on neural networks.First,the method uses a 3D camera to obtain a fastener point cloud and then segments the elastic rod area based on the iterative closest point algorithm registration.Principal component analysis is used to calculate the normal vector of the segmented elastic rod surface and extract the point on the centerline of the elastic rod.The point is projected onto the upper surface of the bolt to calculate the projection distance.Subsequently,the mapping relationship between the projection distance sequence and fastener tightness is established,and the influence of each parameter on the fastener tightness prediction is analyzed.Finally,by setting up a fastener detection scene in the track experimental base,collecting data,and completing the algorithm verification,the results showed that the deviation between the fastener tightness regression value obtained after the algorithm processing and the actual measured value RMSE was 0.2196 mm,which significantly improved the effect compared with other tightness detection methods,and realized an effective fastener tightness regression.

TPO、CD56、Galectin-3辅助诊断甲状腺乳头状癌的价值分析

目的:分析甲状腺过氧化物酶(TPO)、神经细胞黏附分子56(CD56)和β-半乳糖凝集素-3(Galectin-3)辅助诊断甲状腺乳头状癌的价值。方法:选取2018年3月—2020年12月于克拉玛依市中心医院接受手术切除的PTC患者104例作为研究对象,分析患者PTC癌变组织及距离癌灶>2 cm的癌旁组织TPO、CD56、Galectin-3表达情况,评估TPO、CD56、Galectin-3单项及联合检测辅助诊断PTC的效能。结果:TPO、CD56、Galectin-3单项及联合诊断PTC的特异度比较,差异无统计学意义(P>0.05);三项联合诊断PTC的准确度高于TPO、CD56,Galectin-3及TPO均高于CD56,差异有统计学意义(P<0.05);Galectin-3、三项联合诊断PTC的灵敏度高于TPO、CD56,TPO高于CD56,差异有统计学意义(P<0.05)。结论:TPO、CD56、Galectin-3三项联合检测可用于辅助诊断PTC。

Automatic detection of breast lesions in automated 3D breast ultrasound with cross-organ transfer learning

Background Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications,particularly in visual recognition tasks such as image and video analyses.There is a growing interest in applying this technology to diverse applications in medical image analysis.Automated three dimensional Breast Ultrasound is a vital tool for detecting breast cancer,and computer-assisted diagnosis software,developed based on deep learning,can effectively assist radiologists in diagnosis.However,the network model is prone to overfitting during training,owing to challenges such as insufficient training data.This study attempts to solve the problem caused by small datasets and improve model detection performance.Methods We propose a breast cancer detection framework based on deep learning(a transfer learning method based on cross-organ cancer detection)and a contrastive learning method based on breast imaging reporting and data systems(BI-RADS).Results When using cross organ transfer learning and BIRADS based contrastive learning,the average sensitivity of the model increased by a maximum of 16.05%.Conclusion Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced,and contrastive learning method based on BI-RADS can improve the detection performance of the model.

Ifitm3敲除抑制小鼠神经干细胞增殖和分化

目的建立干扰素诱导跨膜蛋白3基因(Ifitm3)敲除小鼠模型,探究Ifitm3对小鼠神经干细胞增殖和分化的影响。方法利用CRISPR/Cas9技术,建立Ifitm3敲除小鼠模型,通过基因型鉴定和Western Blot检测IFITM3的敲除效果;利用苏木素-伊红(HE)染色和流式细胞术等方法分析Ifitm3敲除小鼠和野生型小鼠的表型差异。分离并培养野生型和Ifitm3敲除小鼠成体神经干细胞,统计神经球数量和大小,qRT-PCR、Western Blot、免疫荧光技术检测神经干细胞增殖和分化的能力。结果成功建立Ifitm3敲除小鼠模型,Ifitm3敲除小鼠发育正常,组织病理学及免疫系统未见明显异常。体外实验显示Ifitm3敲除抑制小鼠神经干细胞的自我更新潜能,导致神经干细胞增殖能力下降,并且抑制神经干细胞向未成熟神经元和星形胶质细胞分化。结论IFITM3缺失导致神经干细胞增殖和分化能力下降,IFITM3可能参与了神经干细胞的功能调节。

基于神经网络的未来3天Kp指数预报建模与可解释AI应用

当前业务中对未来3天Kp指数预报需求强烈.但地磁暴中多参数耦合导致难以量化各预报因子对Kp值的贡献,制约了预报精度提升.本文构建了神经网络3天Kp指数预报模型,并使用人工智能(AI)可解释性算法定量化各因子贡献.结果显示,行星际磁场南向分量在提前3 h对Kp指数的贡献为37.15%,为主要因子,说明模型能捕捉符合物理特征的主要预报因子.Kp指数历史特征贡献随提前量逐渐增加,提前3天总体贡献占68.06%,验证了对冕洞高速流引起的地磁暴事件的预报能力.对2015和2017年特大地磁暴进行贡献分析,模型准确捕捉了地磁暴多参数耦合的复杂特性.研究表明,可解释AI算法在一定程度上能定量化各预报因子对Kp指数的预报贡献,有助于改进未来3天Kp指数AI预报模型.

轴承表面 Al_(2)O_(3) 基陶瓷绝缘涂层的粗糙度预测

为了提升轴承表面Al_(2)O_(3)基陶瓷绝缘涂层的粗糙度预测精度,提出基于光谱共焦原理的砂轮表面测量及磨粒特征参数量化方法,以砂轮表面的磨粒特征参数K,砂轮线速度vs,工件进给速度f,切削深度ap及法向磨削力F为输入参数,建立能够直接反映砂轮表面时变状态的工件表面粗糙度BP神经网络预测模型,并通过已知磨削样本及砂轮磨损后的4组未知样本对网络预测模型性能进行验证。结果表明:已知样本的BP网络模型粗糙度预测结果与实际结果的规律及数值较为一致,其网络输出误差均<±0.04μm;4组未知样本的网络预测精度下降,但其相对误差最大值的绝对值不超过20.00%。建立的包含砂轮表面磨粒特征参数的神经网络预测模型,可以适应砂轮磨粒磨损时变状态下的轴承表面Al_(2)O_(3)基陶瓷绝缘涂层的粗糙度预测,且其对未知样本具有一定的泛化能力。