Rat Anti-Mouse CD105
- Known as:
- Rat Antibody toMouse CD105
- Catalog number:
- 129-10086
- Product Quantity:
- 250
- Category:
- -
- Supplier:
- Ray Biotech
- Gene target:
- Rat Anti-Mouse CD105
Related genes to: Rat Anti-Mouse CD105
- Gene:
- ENG NIH gene
- Name:
- endoglin
- Previous symbol:
- ORW1, ORW
- Synonyms:
- END, HHT1, CD105
- Chromosome:
- 9q34.11
- Locus Type:
- gene with protein product
- Date approved:
- 1993-03-03
- Date modifiied:
- 2019-04-23
Related products to: Rat Anti-Mouse CD105
Related articles to: Rat Anti-Mouse CD105
- Overcoming oxidative stress resulting from reactive oxygen species (ROS) overload in the microenvironment of bone injury is crucial for bone repair. This study developed a multifunctional composite scaffold composed of zein, adenosine-calcium phosphate (ACP), and epigallocatechin gallate (EGCG) for enhanced bone regeneration through antioxidant activity. For the preparation of the Zein/ACP/EGCG (Z/A/E) composite scaffold, ACP nanoparticles were first synthesized using an enzymatic reaction and characterized by SEM, EDS, FTIR, and XRD. ACP nanoparticles showed great biocompatibility and osteoinductivity within a certain dose range in vitro. A three-dimensional (3D) porous Zein/ACP (Z/A) composite scaffold was fabricated by compression molding and particulate leaching, followed by the adsorption of EGCG onto its surface. The final Z/A/E composite scaffold exhibited enhanced surface hydrophilicity, which facilitated the adhesion, proliferation, and differentiation of osteoblasts. Mechanistically, the Z/A/E composite scaffold promoted osteogenesis while concurrently inhibiting osteoclastogenesis, primarily through its ROS-scavenging capability. The bidirectional regulation of bone remodeling by the Z/A/E composite scaffold was demonstrated by significant repair of hydrogen peroxide-induced calvarial defects in ex vivo cultured explants from neonatal mice, confirmed by μ-CT and histological evaluation. Collectively, this Z/A/E composite scaffold, which leverages antioxidant activity to rebalance bone homeostasis, represents a promising strategy for bone regeneration. - Source: PubMed
Publication date: 2026/10/06
Han JingQiu JianyuanGong ZhaoqingWang WenyingZhou GuojianGe KunLu LiqingZhou Guoqiang - Recent advances in self-supervised learning for EEG representation have largely relied on masked reconstruction, where models are trained to recover randomly masked signal segments. While effective at modeling local dependencies, the training objective of masked reconstruction does not compel the model to capture global generative constraints essential for characterizing neural activity. To address this limitation, we propose EEGDM, a novel self-supervised framework that leverages latent diffusion models to generate EEG signals as an objective. - Source: PubMed
Publication date: 2026/10/06
Wang ShaocongLiu TongLi YihanLi MingWen KairuiYang PeiJi WenqiYu MinjingLiu Yong-Jin - Accurate electromagnetic head phantoms are important tools for validating electroencephalography (EEG) and magnetoencephalography (MEG) devices and techniques. A combined M/EEG phantom would be particularly useful for its ability to test and validate multimodal imaging methods. However, EEG and combined M/EEG phantoms remain commercially unavailable, and phantoms in published literature require materials research and development processes that are difficult to complete or replicate. Here, we detail steps to create a highly realistic four-layer, desktop M/EEG phantom and demonstrate simultaneous EEG and MEG recordings of signals generated from within the phantom. - Source: PubMed
Publication date: 2026/10/06
Alexander Kevin EEstepp Justin R - Decoding event-related potentials (ERPs) from Rapid Serial Visual Presentation (RSVP) EEG offers a promising approach for target recognition in brain-computer interfaces (BCIs). However, auditory noise in real-world environments degrades target-ERP decoding, substantially reducing accuracy and robustness. - Source: PubMed
Publication date: 2026/10/06
Zhang JiachenLi FuFu BoxunCai XiaohuiZhao YifanLi YangZhao ZhifuDong MinghaoChen Yuanfang - Accurately characterizing higher-order coordination among distributed brain regions is important for understanding brain disorders from fMRI data. Existing methods mainly rely on pairwise connectivity or low-order motifs, which may overlook disease-related alterations in collective multi-region communication and higher-dimensional topological organization. To address this limitation, we propose Quadra-Brain, a computational framework for modeling quadruplet higher-order interactions in fMRI-based brain disease diagnosis. Quadra-Brain first estimates time-resolved quadruplet co-fluctuations using the multiplication of temporal derivatives, enabling the detection of transient four-region coordination patterns. It then constructs weighted brain simplicial complexes and employs two persistent-homology-based filtration processes to extract higher-dimensional neural organizations from complementary spatiotemporal perspectives. Finally, a three-branch feature fusion architecture with mutual cross-attention integrates lower-order edge features, quadruplet interaction features, and topological invariants for disease classification and interpretation. Experiments on ADNI, TaoWu, and PPMI datasets demonstrate the effectiveness and interpretability of Quadra-Brain. The learned higher-order patterns reveal disease-specific topological alterations: Alzheimer's disease progression is associated with reduced quadruplet coordination and increased topological voids, whereas Parkinson's disease shows an opposite trend. Group-level statistical analyses further indicate that the identified regions and structures are consistent with neuroscience findings. These results suggest that quadruplet interactions provide a meaningful representation of collective brain coordination and offer complementary insights beyond conventional connectivity-based analysis. The source code is publicly available at: https://github.com/Zdy12/Quadra-Brain. - Source: PubMed
Publication date: 2026/10/06
Zhan DengyiZhou ZhihengQi Xingqin