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Related Concept Videos

Rolling With Slipping01:14

Rolling With Slipping

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Rolling with slipping is a physical phenomenon that occurs when a rolling object experiences both rotational and linear motion but also experiences frictional forces that cause slipping. This phenomenon can occur in various situations, such as when a tire rolls on a wet road or a ball rolls on a rough surface.
An object's rolling motion is characterized by its rotation around its axis, while linear motion refers to the object's translational motion along a surface. Frictional forces can...
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Rolling Without Slipping01:09

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People have observed the rolling motion without slipping ever since the invention of the wheel. For example, one can look at the interaction between a car's tires and the surface of the road. If the driver presses the accelerator to the floor so that the tires spin without the car moving forward, there must be kinetic friction between the wheels and the road's surface. If the driver slowly presses the accelerator, causing the car to move forward, the tires roll without slipping. It is...
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Rolling Resistance01:21

Rolling Resistance

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When a solid cylinder rolls steadily on a rigid surface, the normal force applied by the surface on the cylinder is perpendicular to the tangent at the contact point. However, since no materials are entirely rigid, the surface's reaction to the cylinder involves a range of normal pressures.
For instance, imagine a hard cylinder rolling on a comparatively soft surface. The cylinder's weight compresses the surface beneath it. As the cylinder moves, the material in front of it slows down...
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The Calvin Benson Cycle01:46

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Ribulose 1,5- bisphosphate carboxylase/oxygenase (RuBisCo) is a critical enzyme that catalyzes carbon dioxide assimilation during photosynthesis. However, it is an inefficient enzyme, having an extremely slow catalytic rate. A typical enzyme can process about a thousand molecules per second; however, RuBisCo fixes only around three-carbon dioxides per second. Photosynthetic cells compensate for this slow rate by synthesizing very high amounts of RuBisCo, making it the most abundant single...
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Otto and Diesel Cycle01:27

Otto and Diesel Cycle

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An Otto engine is a four-stroke engine that uses a mixture of gasoline and air as the working fuel. The fuel is injected into the cylinder, and the piston is moved completely down so that the cylinder is at maximum volume. By moving the piston up, adiabatic compression takes place. The spark plug ignites the gasoline-air mixture, and the burning fuel adds heat to the system at a constant volume. The heated mixture expands adiabatically and gets further cooled by exhausting heat, and this cyclic...
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Doppler Effect - I00:56

Doppler Effect - I

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The Doppler effect and Doppler shift were named after the Austrian physicist and mathematician Christian Johann Doppler in 1842, who conducted experiments with both moving sources and moving observers. Consider an observer standing on a street corner, observing an ambulance with a siren sound passing by at a constant speed. The observer experiences two characteristic changes in the sound of the siren. Initially, the sound increases in loudness as the ambulance approaches and decreases in...
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Related Experiment Video

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Riding the Roller Coaster: A Year of Change for One DIO.

Cynthia Kelley1

  • 1is Designated Institutional Official and Vice President, Medical Education, Summa Health, Akron, Ohio, USA.

Journal of Graduate Medical Education
|August 20, 2025
PubMed
Summary

This study found that artificial intelligence (AI) can effectively predict patient responses to cancer immunotherapy. AI models offer a promising tool for personalizing cancer treatment strategies.

Area of Science:

  • Oncology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Cancer immunotherapy has revolutionized treatment but predicting patient response remains a challenge.
  • Biomarkers for predicting immunotherapy response are limited.
  • Developing predictive models is crucial for optimizing treatment selection.

Observation:

  • A novel artificial intelligence (AI) model was developed using radiomic features from pre-treatment CT scans.
  • The AI model analyzed patterns invisible to the human eye.
  • The model was trained and validated on independent patient cohorts.

Findings:

  • The AI model demonstrated high accuracy in predicting patient response to immune checkpoint inhibitors.
  • Radiomic features extracted by AI were significantly associated with treatment outcomes.

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  • The model outperformed traditional clinical and demographic predictors.

    • Implications:

    • AI-driven prediction can guide clinicians in selecting appropriate immunotherapy for individual cancer patients.
    • This approach may reduce treatment failures and improve patient survival rates.
    • Further research can integrate AI with other data types for even more precise predictions.