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Related Concept Videos

PD Controller: Design01:26

PD Controller: Design

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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
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VSEPR Theory for Determination of Electron Pair Geometries
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Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Frequency-Domain Interpretation of PD Control01:24

Frequency-Domain Interpretation of PD Control

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Proportional-Derivative (PD) controllers are widely used in fan control systems to improve stability and performance. A fan control system can be effectively represented using a Bode plot to illustrate the impact of a PD controller through its transfer function. The Bode plot visually conveys how PD control modifies the fan's response across various frequencies, providing a frequency domain interpretation of the controller's behavior.
The proportional control gain, combined with the...
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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[Predictive value of PD-L1 diagnostics].

H-U Schildhaus1

  • 1Institut für Pathologie, Universitätsmedizin Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Deutschland. Hans-Ulrich.Schildhaus@med.uni-goettingen.de.

Der Pathologe
|October 28, 2018
PubMed
Summary
This summary is machine-generated.

Immuno-oncology treatments utilize biomarkers like PD-L1 (programmed death-ligand 1) for cancer therapy decisions. Accurate PD-L1 testing via immunohistochemistry (IHC) is crucial for guiding treatment, with various scoring methods applied across different cancers.

Keywords:
BiomarkersImmune checkpoint inhibitorImmuno-oncologyNSCLCUrothelial carcinoma

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Area of Science:

  • Oncology
  • Immunology
  • Pathology

Background:

  • Immuno-oncology therapies are standard for many cancers, with ongoing trials expanding indications.
  • Predictive biomarkers such as microsatellite instability and tumor mutational burden are key.
  • PD-L1 (programmed death-ligand 1) testing by immunohistochemistry (IHC) is an established biomarker for treatment decisions in non-small cell lung cancer, head and neck cancer, and urothelial carcinomas.

Purpose of the Study:

  • To highlight the importance of PD-L1 testing in clinical routine and complementary diagnostics.
  • To emphasize the technical requirements for accurate PD-L1 staining and the need for quality assurance.
  • To differentiate between various scoring algorithms used for PD-L1 assessment in different tumor types.

Main Methods:

  • Immunohistochemistry (IHC) for PD-L1 staining.
  • Development and validation of sensitive primary antibodies and staining protocols.
  • Application of different scoring algorithms: Tumor Proportion Score (TPS), Combined Positivity Score (CPS), and Inflammatory Cell (IC) scoring.

Main Results:

  • PD-L1 IHC is widely used for treatment decisions in specific cancers.
  • PD-L1 testing provides valuable information even when not mandatory.
  • Different scoring systems (TPS, CPS, IC) are employed based on tumor type and clinical context.

Conclusions:

  • Accurate PD-L1 testing requires high specificity, a broad dynamic range, and robust quality assurance.
  • The choice of scoring algorithm (TPS, CPS, IC) is critical and tumor-specific.
  • PD-L1 testing is essential for optimizing immuno-oncology treatment strategies and is expected to expand to more indications.