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Related Concept Videos

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...

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Related Experiment Video

Updated: Jul 6, 2026

The Colon-26 Carcinoma Tumor-bearing Mouse as a Model for the Study of Cancer Cachexia
08:55

The Colon-26 Carcinoma Tumor-bearing Mouse as a Model for the Study of Cancer Cachexia

Published on: November 30, 2016

Computational modeling of cancer cachexia.

Kevin D Hall1, Vickie E Baracos

  • 1Laboratory of Biological Modeling, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892-5621, USA. kevinh@niddk.nih.gov

Current Opinion in Clinical Nutrition and Metabolic Care
|April 12, 2008
PubMed
Summary
This summary is machine-generated.

Computational modeling offers a new approach to understanding cancer cachexia, a condition causing involuntary weight loss. This method integrates metabolic data to predict how nutritional support and therapies might impact body composition changes.

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Area of Science:

  • Metabolic research
  • Computational biology
  • Oncology

Background:

  • Cancer cachexia leads to involuntary weight loss, impacting body composition.
  • Quantitative metabolic studies are challenging in late-stage cancer patients.
  • Computational models are crucial for understanding cancer-associated weight loss.

Purpose of the Study:

  • To utilize a computational model of human macronutrient metabolism.
  • To integrate metabolic data from cancer cachexia patients.
  • To simulate and understand involuntary weight loss in cancer.

Main Methods:

  • A computational model simulating normal metabolic adaptations was employed.
  • The model was adapted to integrate data on metabolic changes in cancer cachexia.
  • Simulations were run to analyze weight loss, body composition, and therapeutic interventions.

Main Results:

  • Simulations demonstrated how metabolic disturbances and reduced intake cause progressive loss of weight, fat mass, and fat-free mass.
  • The model predicted the effects of nutritional support on cancer cachexia.
  • Inhibition of lipolysis versus proteolysis was investigated as a therapeutic strategy.

Conclusions:

  • Computational modeling provides a novel tool for integrating clinical data in cancer cachexia.
  • It offers a conceptual framework for understanding involuntary weight loss.
  • The approach can predict the efficacy of potential therapeutic strategies for cancer cachexia.