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Related Concept Videos

Structural Joints: Synovial Joints01:16

Structural Joints: Synovial Joints

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Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
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Structural Joints: Fibrous Joints01:03

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Fibrous joints are a type of joint where the bones are connected by fibrous connective tissue. These joints provide stability and minimal to no movement between the articulating bones. There are three types of fibrous joints.
Suture
All the bones of the skull, except for the mandible, are joined to each other by a fibrous joint called a suture. The fibrous connective tissue found at a suture strongly unites the adjacent skull bones and thus helps to protect the brain and form the face. In...
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Structural Joints: Cartilaginous Joints01:17

Structural Joints: Cartilaginous Joints

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As the name indicates, at a cartilaginous joint, the adjacent bones are united by cartilage, a tough but flexible type of connective tissue. Unlike synovial joints, these types of joints lack a joint cavity and involve bones joined together by either hyaline cartilage or fibrocartilage.
There are two types of cartilaginous joints:
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A synchondrosis ("joined by cartilage") is a cartilaginous joint where bones are connected by hyaline cartilage. Synchondrosis may be temporary...
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Joints01:26

Joints

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Joints, also called articulations or articular surfaces, are points at which ligaments or other tissues connect adjacent bones. Joints permit movement and stability, and can be classified based on their structure or function.
Structural joint classifications are based on the material that makes up the joint as well as whether or not the joint contains a space between the bones. Joints are structurally classified as fibrous, cartilaginous, or synovial.
Fibrous Joints Are Immovable
The bones of a...
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Components of Stress01:23

Components of Stress

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Stress analysis under multiple loading conditions is intricate, necessitating a comprehensive grasp of normal and shearing stresses. Consider a small cube at point O, subjected to stress on all six faces, visible or not. Normal stress components σx, σy, σz act perpendicularly to the x, y, and z axes. Shearing stress components τxy and τxz are exerted on faces perpendicular to these axes.
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Joint Spatio-Temporal Shared Component Model with an Application in Iran Cancer Data

Behzad Mahaki1, Yadollah Mehrabi, Amir Kavousi

  • 1Department of Biostatistics, School of Public Health, Kermanshah University of Medical Sciences, Kermanshah, Iran.

Asian Pacific Journal of Cancer Prevention : APJCP
|June 26, 2018
PubMed
Summary

This study introduces a novel spatio-temporal model for joint disease mapping, identifying high-risk areas and risk factors for seven prevalent cancers in Iran. The model effectively captures geographical and temporal variations in cancer incidence rates.

Keywords:
Spatial statisticsdisease mappingbayesian modellingshared component modelIrancancer

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS)

Background:

  • Shared component models are increasingly popular for joint disease mapping.
  • Integrating temporal aspects into spatial models enhances disease data inference.
  • Seven prevalent cancers in Iran, accounting for 50% of all cancer cases, were analyzed.

Purpose of the Study:

  • To combine multivariate shared components with spatio-temporal modeling for joint disease mapping.
  • To apply the model to incidence rates of seven prevalent cancers in Iran.
  • To identify geographical and temporal variations and shared risk factors for these cancers.

Main Methods:

  • Development of a joint disease mapping model incorporating multivariate shared components and spatio-temporal trends.
  • Each component is shared by different subsets of diseases, with spatial and temporal trends estimated for each.
  • Estimation of the relative weight of these trends for each component and disease.

Main Results:

  • Identified high-risk provinces for specific cancers (e.g., Northern for esophagus/stomach, Northwest for bladder/lung, specific provinces for colorectal, prostate, and breast cancers).
  • Determined the geographical distribution and impact of shared risk factors: smoking (esophagus, stomach, bladder, lung), overweight/obesity (esophagus, colorectal, prostate, breast), and low physical activity (colorectal, breast).
  • Quantified the differential importance of risk factors across diseases (e.g., smoking for stomach vs. esophagus, overweight/obesity for colorectal vs. esophagus).

Conclusions:

  • The proposed spatio-temporal joint disease mapping model effectively captures geographical and temporal variations among diseases.
  • The model offers valuable insights into shared risk factors and their influence across different cancer types.
  • This approach provides benefits over existing joint disease mapping models for epidemiological research.