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相关概念视频

Bone Remodeling01:40

Bone Remodeling

Bone remodeling is a continuous and balanced process of bone resorption by osteoclasts and bone formation by osteoblasts. In adults, it helps maintain bone mass and calcium homeostasis. While mechanical stress can stimulate turnover as part of the normal maintenance and reparative process, several hormones also regulate bone remodeling.
Classification of Bones01:18

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...
Gross Anatomy of Bone01:17

Gross Anatomy of Bone

The two main features of a long bone are the diaphysis and the epiphysis.
The diaphysis is the tubular shaft that runs between the proximal and distal ends of the bone. The walls of the diaphysis are composed of dense and hard compact bone made of numerous osteons — the functional unit of the compact bone. The hollow region in the diaphysis is called the medullary cavity, which harbors the bone marrow. In infants and children, this marrow cavity is filled with red marrow, whereas in adults, it...
Bone Cells and Tissue01:30

Bone Cells and Tissue

Bones contain a relatively small number of cells entrenched in a matrix of organic and inorganic components. Although bone cells compose only a small amount of the bone volume, they are crucial to its function. Four types of cells are found within the bone tissue— osteoblasts, osteocytes, osteogenic cells, and osteoclasts.
Osteoblasts and Osteocytes
The osteoblast is the bone cell responsible for forming new bone tissue. It is found in the growing portions of bone, including the periosteum and...
Bone Formation by Intramembranous Ossification01:29

Bone Formation by Intramembranous Ossification

Intramembranous ossification is one of the two processes involved in the development of bones within an embryo. The flat bones of the face, most of the cranial bones, and the clavicles are formed via this process. During intramembranous ossification, the bones develop directly from sheets of undifferentiated mesenchymal connective tissue.
The process begins when mesenchymal cells in the embryonic skeleton gather together and differentiate into osteogenic cells, which then develop into...
Bone Remodeling and Repair01:31

Bone Remodeling and Repair

Osteoclasts are cells responsible for bone resorption and remodeling. They originate from hematopoietic progenitor cells present in the bone marrow. Numerous progenitor cells fuse to form multinucleated cells, each with 10-20 nuclei. A single osteoclast has a diameter of 150 to 200 µM. These cells have ruffled borders that break down the underlying bone tissue and release minerals such as calcium into the blood in bone resorption. Osteoclasts cling to bones with their ruffled edges during bone...

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Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
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在细胞分辨率下将骨细胞的转录组和形态与生成AI联系起来.

Lu Lu1, Noriaki Ono2, Joshua D Welch1,3

  • 1Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Ave. Ann Arbor, MI 48109, United States.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research
|September 20, 2024
PubMed
概括

生成型人工智能 (AI) 可以从单细胞测序和空间转录学分析复杂的骨细胞数据. 解决数据限制是解锁AI的关键.

关键词:
人工智能的人工智能是人工智能.骨细胞 骨细胞 骨细胞深度学习是一种深度学习.单细胞转录组学 单细胞转录组学空间转录学 空间转录学

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科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 人工智能的人工智能

背景情况:

  • 深度学习 (DL) 和人工智能 (AI) 擅长分析大而复杂的数据集.
  • 单细胞测序和空间转录组学产生了大量的分子和形态数据.
  • 这些数据集类似于自然语言处理和计算机视觉中使用的多式联络数据.

研究的目的:

  • 探索生成AI在分析骨生物学数据集中的进展和潜力.
  • 突出生成AI在骨科研究中的关键应用,包括预测细胞分化,链接分子和形态特征,以及对干扰的细胞反应的建模.
  • 确定应用生成性AI到骨生物学中的挑战和未来方向.

主要方法:

  • 综述当前在生成性AI方面的进展.
  • 来自单细胞测序和空间转录组学的大规模数据集的分析.
  • 在骨细胞研究中识别潜在的AI应用.

主要成果:

  • 生成型人工智能具有显著的潜力,可以揭示骨细胞中复杂的生物过程.
  • 关键应用包括预测细胞分化动态,整合分子和形态数据,预测细胞对刺激的反应.
  • 必须克服一些挑战,包括技术偏见,不完整的细胞类型分析和当前数据集中缺乏空间信息.

结论:

  • 生成性AI提供了一种强大的方法,通过分析复杂的奥米克数据来推进骨生物学研究.
  • 解决数据质量,完整性和空间分辨率对于成功实施AI至关重要.
  • 未来的研究应该专注于生成高质量的空间转录学数据集,并通过实验验证AI预测.