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Related Concept Videos

Self-Evaluation Maintenance Model01:29

Self-Evaluation Maintenance Model

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The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...
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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

Structural Joints: Fibrous Joints

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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:
Synchondrosis
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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Drug Classes and Categories01:25

Drug Classes and Categories

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Drugs can be classified according to their chemical composition or their intended therapeutic application. For instance, anti-infective agents that possess the ability to eliminate pathogens or suppress their growth and reproduction can be grouped based on the organisms they target or their chemical structure. Furthermore, drugs can be divided into prescription, nonprescription, or controlled substances. Prescription medications, such as antibiotics, require oversight from a licensed healthcare...
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PREPARE ALL: An Artificial Intelligence Tool for Predicting Relapse in Children With Acute Lymphoblastic Leukemia.

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Related Experiment Video

Updated: Feb 7, 2026

Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes
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A joint latent-class Bayesian model with application to ALL maintenance studies.

Damitri Kundu1,2, Sevantee Basu1, Manash Pratim Gogoi3

  • 1Statistical Science Division, Indian Statistical Institute, Kolkata, India.

Journal of Applied Statistics
|February 6, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian model to analyze childhood Acute Lymphocytic Leukemia (ALL) treatment data. The model identifies distinct patient groups, revealing significant differences in non-relapse probabilities for effective pediatric cancer research.

Keywords:
Acute lymphocytic leukemia (ALL)MCMCjoint modelinglatent class modellongitudinal outcomes

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Area of Science:

  • Biostatistics
  • Pediatric Oncology
  • Computational Biology

Background:

  • Acute Lymphocytic Leukemia (ALL) remains a leading cause of childhood cancer mortality globally.
  • Survival rates for ALL have improved in developed nations but lag in developing countries.
  • Clinical trials require advanced statistical models to analyze complex longitudinal biomarker data.

Purpose of the Study:

  • To develop and validate a joint latent-class Bayesian model for analyzing pediatric ALL patient data.
  • To investigate the relationship between longitudinal biomarkers and treatment outcomes in ALL.
  • To identify distinct patient subgroups based on lymphocyte count trajectories.

Main Methods:

  • A joint latent-class Bayesian model integrating longitudinal biomarkers (lymphocyte, neutrophil, platelet counts) and time-to-event data.
  • Latent-class modeling for lymphocyte count, linear mixed models for other biomarkers.
  • Semi-parametric proportional hazards model for time-to-event, linked via shared Gaussian random effects.

Main Results:

  • The proposed model identified two distinct latent classes for lymphocyte count in pediatric ALL patients.
  • Significant differences in class-specific non-relapse probabilities were observed over the study period.
  • The joint model demonstrated superior accuracy and practical utility compared to traditional methods via simulation.

Conclusions:

  • The joint latent-class Bayesian model provides a powerful tool for analyzing complex ALL data.
  • Identifying distinct patient subgroups can lead to personalized treatment strategies and improved outcomes.
  • This approach offers valuable insights for pediatric cancer research, particularly in resource-limited settings.