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Related Concept Videos

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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.
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Transmission-based precautions are for patients infected or suspected to be infected (or colonized) with organisms posing a significant risk to others. The transmission precautions include airborne and protective environment precautions.
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Atoms and molecules interact with each other through intermolecular forces. These electrostatic forces arise from attractive or repulsive interactions between particles with permanent, partial, or temporary charges. The intermolecular forces between neutral atoms and molecules are ion–dipole, dipole–dipole, and dispersion forces, collectively known as van der Waals forces.
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Related Experiment Video

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A Biomimetic Model for Liver Cancer to Study Tumor-Stroma Interactions in a 3D Environment with Tunable Bio-Physical Properties
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Histopathological Imaging⁻Environment Interactions in Cancer Modeling.

Yaqing Xu1, Tingyan Zhong2, Mengyun Wu3

  • 1Department of Biostatistics, Yale University, New Haven, CT 06520, USA. yaqing.xu@yale.edu.

Cancers
|April 27, 2019
PubMed
Summary

This study introduces a novel approach to cancer modeling by integrating histopathological imaging with clinical and environmental risk factors. We identified interaction effects, enhancing lung adenocarcinoma (LUAD) prognosis prediction.

Keywords:
cancer modelingclinical/environmental factorshistopathological imaginginteraction

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Area of Science:

  • Oncology
  • Medical Imaging
  • Biostatistics

Background:

  • Histopathological imaging is crucial for cancer diagnosis and increasingly used for predicting cancer outcomes like prognosis.
  • Clinical and environmental factors are established in cancer modeling, but their interactions with imaging features remain underexplored.
  • Existing cancer models often analyze imaging and clinical/environmental data separately, limiting comprehensive understanding.

Purpose of the Study:

  • To investigate the potential interactions between histopathological imaging features and clinical/environmental risk factors in cancer modeling.
  • To explore novel statistical methods for joint interaction analysis in this context.
  • To enhance the predictive accuracy of cancer outcomes, specifically prognosis in lung adenocarcinoma.

Main Methods:

  • Employed statistical methods adapted from gene-environment interaction analysis for marginal and joint interaction analysis.
  • Utilized The Cancer Genome Atlas (TCGA) lung adenocarcinoma (LUAD) dataset for the study.
  • Examined the association between histopathological imaging features, a lung function biomarker, and overall survival.

Main Results:

  • Identified significant interaction effects between histopathological imaging features and clinical/environmental factors.
  • Demonstrated the feasibility of combining these diverse data types for improved cancer modeling.
  • The analysis revealed specific interactions influencing lung adenocarcinoma prognosis.

Conclusions:

  • The integration of histopathological imaging with clinical/environmental data offers a promising alternative for cancer modeling.
  • This combined approach can lead to more accurate prediction of cancer outcomes, such as patient survival.
  • Novel statistical methodologies are effective for uncovering complex interactions in multi-modal cancer data.