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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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Knee Joint01:23

Knee Joint

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The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris...
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Classification of Bones01:18

Classification of Bones

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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...
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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Ankle Joint01:10

Ankle Joint

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The ankle is formed by the talocrural joint (crural = leg). It consists of the articulations between the talus bone of the foot and the distal ends of the tibia and fibula of the leg. The superior aspect of the talus bone is square-shaped and has three areas of articulation. The top of the talus articulates with the inferior tibia. This is the portion of the ankle joint that carries the body weight between the leg and foot. The sides of the talus are firmly held in position by the articulations...
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Updated: Jan 13, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Knowledge-graph embeddings for osteoarthritis candidate prediction.

Zhenggang Wang1, Zhengyu Lu2,3, Meng Li4

  • 1Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

NPJ Digital Medicine
|January 10, 2026
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Summary
This summary is machine-generated.

This study introduces a novel knowledge-graph approach, CausalPathKG, to discover new osteoarthritis treatments by integrating genetic data. The method successfully identified promising drug candidates by analyzing causal gene associations, advancing therapeutic discovery for this disabling joint disease.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Genetics

Background:

  • Osteoarthritis (OA) is a widespread and debilitating joint condition lacking disease-modifying treatments.
  • Current therapeutic strategies primarily manage OA symptoms rather than addressing underlying disease mechanisms.
  • Integrating large-scale biomedical data offers a promising avenue for discovering novel OA treatments.

Purpose of the Study:

  • To develop and validate a knowledge-graph based computational approach for identifying candidate OA treatments.
  • To leverage causal genetic associations from genome-wide association studies (GWAS) to enhance drug repurposing for OA.
  • To create a specialized Osteoarthritis Knowledge-graph (OKG) integrating diverse biomedical information.

Main Methods:

  • Construction of the Osteoarthritis Knowledge-graph (OKG) by combining the Drug Repurposing Knowledge-graph (DRKG) with causal genetic associations from a large-scale OA GWAS.
  • Development of CausalPathKG, a novel knowledge-graph embedding model based on RotatE, incorporating weighted edges, path-based regularization, multi-hop graph attention, and adversarial negative sampling.
  • Training and evaluation of CausalPathKG for link prediction, specifically predicting missing OA treatments, using held-out known OA-related edges for testing.

Main Results:

  • CausalPathKG demonstrated superior performance in predicting held-out OA treatments compared to baseline models (TransE, RotatE), achieving higher link prediction accuracy.
  • The model exhibited strong classification performance in identifying potential OA therapeutics.
  • Case studies confirmed that top-ranked repurposed drugs identified by CausalPathKG target key OA-associated genes and pathways implicated by human genetics.

Conclusions:

  • Integrating human genetic evidence into knowledge-graph models significantly enhances the discovery of therapeutics for complex diseases like OA.
  • CausalPathKG provides a robust computational strategy for drug repurposing by effectively bridging genomic data with existing drug knowledge.
  • This approach offers a promising pathway to accelerate the development of disease-modifying treatments for osteoarthritis.