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Related Concept Videos

Acute Inflammation I: Inflammatory Response01:26

Acute Inflammation I: Inflammatory Response

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Acute inflammation is a rapid, short-lived physiological response to tissue injury or infection, designed to eliminate harmful agents and initiate repair. This tightly regulated process typically lasts from minutes to several days and is triggered by factors such as microbial invasion, physical trauma, or chemical injury.Recognition and Mediator ReleaseThe inflammatory response begins when resident immune cells—such as mast cells, macrophages, and dendritic cells—detect...
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Acute Inflammation I: Cellular Phase01:26

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The cellular phase of acute inflammation is a tightly orchestrated sequence of events that recruits leukocytes, primarily neutrophils, to sites of tissue injury or infection. Following the initial vascular changes, this phase ensures effective immune cell migration, activation, and function at the affected site to eliminate pathogens and initiate tissue repair.Leukocyte Recruitment CascadeLeukocyte recruitment happens in four steps: margination, adhesion, transmigration, and chemotaxis. Reduced...
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Acute Inflammation II: Local and Systemic Effects01:25

Acute Inflammation II: Local and Systemic Effects

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Acute inflammation produces a coordinated set of local and systemic changes that limit injury, eliminate pathogens, and initiate repair. These responses arise within minutes of infection, trauma, or chemical insult and are driven by vascular alterations and leukocyte-derived mediators. When the stimulus resolves, the reaction typically abates within days.Local EffectsAt the site of injury, arteriolar vasodilation increases blood flow, resulting in redness and warmth. Simultaneously, increased...
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Tumor Necrosis Factor (TNF), a proinflammatory cytokine, contributes significantly to the inflammation seen in Crohn's disease. It exists as soluble TNF and membrane-bound TNF, with actions mediated through TNF receptors (TNFR). TNFR activation leads to the release of proinflammatory cytokines, T-cell activation, collagen production, and leukocyte migration, all contributing to inflammation in Crohn's disease. Anti-TNF monoclonal antibodies, namely infliximab (Remicade), adalimumab...
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Inflammatory Response I: Vascular and Cellular01:30

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The inflammatory response is the body's defense against infection, injury, or irritation from bacteria, trauma, toxins, or heat. Inflammation helps locate and destroy pathogens and remove damaged tissue elements to heal the body. During this initial phase, fluid, blood products, and nutrients migrate to the injured area, resulting in redness, heat, swelling, ache, and loss of function. Moreover, signs of systemic inflammation include fever, increased WBC count, malaise, anorexia, nausea,...
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Induction of Ocular Surface Inflammation and Collection of Involved Tissues
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A data-driven acute inflammation therapy.

Vladan Radosavljevic, Kosta Ristovski, Zoran Obradovic

    BMC Medical Genomics
    |February 26, 2014
    PubMed
    Summary
    This summary is machine-generated.

    A new data-driven method using machine learning significantly improves acute inflammation treatment outcomes, saving 88% of patients compared to 73% with existing methods. This AI approach optimizes medication timing and dosage for better patient recovery.

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    Area of Science:

    • Computational Biology
    • Artificial Intelligence in Medicine
    • Clinical Decision Support

    Background:

    • Acute inflammation is a critical condition and a leading cause of death, necessitating rapid clinical decisions.
    • Current therapeutic strategies for acute inflammation are limited, despite significant annual treatment costs.
    • Developing advanced methods to guide optimal therapy is crucial for improving patient outcomes.

    Purpose of the Study:

    • To develop and evaluate a data-driven machine learning method for suggesting optimal therapies for acute inflammation.
    • To enhance clinical decision-making by providing personalized treatment recommendations.
    • To reduce treatment failure rates and healthcare costs associated with acute inflammation.

    Main Methods:

    • A machine learning model was trained on historical patient data to predict optimal therapeutic interventions.
    • A mathematical model simulating inflammatory responses was used to generate virtual patients for method evaluation.
    • The proposed method's performance was compared against existing clinical practices and literature-based approaches.

    Main Results:

    • The data-driven method significantly increased the percentage of recovered patients, saving 88% compared to 73% with the best alternative.
    • The method demonstrated efficacy even with incomplete or noisy patient data and therapy delays.
    • Optimal dosage regimens suggested by the AI method utilized lower medication doses than alternative approaches.

    Conclusions:

    • Machine learning offers a powerful tool for developing personalized treatment strategies for acute inflammation.
    • The proposed data-driven approach shows significant potential for improving patient survival rates and treatment efficiency.
    • AI-based decision support systems can enhance clinical management of severe inflammatory conditions.