Related Experiment Video
Updated: Feb 24, 2026

08:24
Fourier-Based Diffraction Analysis of Live Caenorhabditis elegans
Published on: September 13, 2017
8.3K
Summary
The FOURIER study shows evolocumab, a PCSK9 inhibitor, significantly reduces cardiovascular events in high-risk patients. This treatment lowers LDL cholesterol, decreasing heart attacks and strokes, and is safe for long-term use.
Area of Science:
- Cardiology
- Pharmacology
Background:
- High residual cardiovascular risk persists in patients treated with statins.
- PCSK9 protein inhibitors represent a new class of hypolipidemic agents.
- Evolocumab is a PCSK9 antibody developed to address this unmet need.
Purpose of the Study:
- To evaluate the efficacy and safety of evolocumab in reducing cardiovascular events.
- To determine if further reduction of LDL cholesterol impacts cardiovascular outcomes.
- To assess the impact of evolocumab in patients with elevated cardiovascular risk.
Main Methods:
- The FOURIER study was an "event" study involving patients with elevated cardiovascular risk.
- Participants received either evolocumab or a placebo.
- Key outcomes included cardiovascular mortality, heart attacks, strokes, and revascularization procedures.
Main Results:
- Evolocumab significantly reduced the composite primary endpoint by 15% (p < 0.001).
- A significant 20% reduction in the secondary endpoint (cardiovascular mortality, heart attacks, strokes) was observed (p < 0.001).
- Treatment benefits increased with longer treatment duration, and the treatment was found to be safe.
Conclusions:
- Further reduction of LDL cholesterol with evolocumab significantly decreases cardiovascular events in high-risk patients.
- Evolocumab is a safe and effective treatment for individuals with elevated cardiovascular risk.
- PCSK9 inhibition with evolocumab represents a significant advancement in managing cardiovascular disease.
Related Concept Videos
Properties of Fourier Transform I
708
The application of Fourier Transform properties in radio broadcasting is multifaceted, enabling significant advancements in the way signals are transmitted and received. Key areas where these properties are utilized include simultaneous multi-channel transmission, audio clip speed adjustments, live broadcast delays for different time zones, audio frequency adjustments, and signal demodulation.
In radio broadcasting, multiple audio signals often need to be transmitted simultaneously. The Fourier...
In radio broadcasting, multiple audio signals often need to be transmitted simultaneously. The Fourier...
708
Basic signals of Fourier Transform
1.0K
The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
1.0K
Fast Fourier Transform
1.0K
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
The computational efficiency of the FFT becomes...
1.0K
Properties of Fourier Transform II
820
The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
820
Parseval's Theorem for Fourier transform
2.3K
Parseval's theorem is a fundamental principle in signal processing that enables the calculation of a signal's energy in either the time domain or the frequency domain. This theorem is pivotal in demonstrating energy conservation between these two domains, ensuring that the computed energy value remains consistent regardless of the domain of analysis.
To understand Parseval's theorem, it is essential to first comprehend how signal energy is typically calculated. When considering a...
To understand Parseval's theorem, it is essential to first comprehend how signal energy is typically calculated. When considering a...
2.3K
Discrete-Time Fourier Series
750
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
For a discrete-time periodic signal x[n]...
750

