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Related Concept Videos

Factors Affecting Illness01:18

Factors Affecting Illness

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When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
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Causality in Epidemiology01:21

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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Classification of Illness01:17

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
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Interactions Between Signaling Pathways01:19

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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
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Combined Effects of Drugs: Synergism01:27

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Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
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Exploring disease comorbidity in a module-module interaction network.

Soyoun Hwang1, Taekeon Lee2, Youngmi Yoon2

  • 1Department of IT Convergence Engineering, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Korea.

Journal of Bioinformatics and Computational Biology
|May 15, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel module-module interaction network to identify disease comorbidities. The findings reveal that analyzing functional module interactions offers deeper insights into disease mechanisms and predicts comorbidities more effectively than gene-based networks.

Keywords:
Network biologydisease comorbiditygene functionnetwork clustering

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Disease comorbidity impacts patient quality of life.
  • Understanding biological function associations is key to exploring comorbidities.
  • Current methods may not fully capture complex disease relationships.

Purpose of the Study:

  • To propose a novel method for identifying disease comorbidity using a module-based network.
  • To represent biological function interactions for improved comorbidity analysis.
  • To elucidate disease mechanisms and predict comorbid relationships.

Main Methods:

  • Constructed a module-module interaction (MMI) network representing biological function influences.
  • Detected gene modules associated with specific biological functions.
  • Built disease-related networks within the MMI network and calculated comorbidity scores using Gene Ontology (GO) terms.

Main Results:

  • Interactions between functional modules provide richer insights into disease mechanisms compared to gene-gene interactions.
  • The MMI network effectively predicts comorbid disease pairs.
  • Predicted comorbidities based on the MMI network are more significant than those from protein-protein interaction (PPI) networks.

Conclusions:

  • The MMI network approach enhances the understanding of disease comorbidity mechanisms.
  • This method offers a more comprehensive view of disease pathogenesis.
  • The findings can guide further research into complex diseases and their underlying biological functions.