Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

422
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
422
Multi-Step Reactions02:31

Multi-Step Reactions

8.8K
Chemical reactions often occur in a stepwise fashion involving two or more distinct reactions taking place in a sequence. A balanced equation indicates the reacting species and the product species, but it reveals no details about how the reaction occurs at the molecular level. The reaction mechanism (or reaction path) provides details regarding the precise, step-by-step process by which a reaction occurs. Each of the steps in a reaction mechanism is called an elementary reaction. These...
8.8K
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

4.8K
Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
4.8K
Autoregulation of Blood Flow01:17

Autoregulation of Blood Flow

8.1K
Autoregulation mechanisms are characterized by their inherent capacity for self-regulation without necessitating specific nervous stimulation or endocrine control. These mechanisms facilitate the adjustment of blood flow and, therefore, perfusion specific to each tissue region. This self-regulation encompasses chemical signals and myogenic controls.
Chemical Signaling in Autoregulation
Chemical signaling operates at the precapillary sphincter level, inciting either contraction or relaxation....
8.1K
Insertion of Multi-pass Transmembrane Proteins in the RER01:29

Insertion of Multi-pass Transmembrane Proteins in the RER

18.4K
The rough ER membrane synthesizes, assembles, and embeds transmembrane proteins in diverse topologies. These proteins function as transporters or channels and can remain in the ER membrane or are sent to the Golgi complex, lysosome, and cell membrane.
The multipass transmembrane proteins are the type IV integral membrane proteins with multiple topogenic sequences determining their spatial arrangement in the ER membrane. Nearly all multipass proteins lack a cleavable signal sequence and use...
18.4K
Multi-pass Transmembrane Proteins and β-barrels01:09

Multi-pass Transmembrane Proteins and β-barrels

6.6K
In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
Multi-pass transmembrane proteins such as...
6.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

AI in primary care: secretary, not physician.

The British journal of general practice : the journal of the Royal College of General Practitioners·2026
Same author

Correction: Retrieving interpretability to support vector machine regression models in dynamic system identification.

Frontiers in artificial intelligence·2026
Same author

Publisher Correction: Reliability of LLMs as medical assistants for the general public: a randomized preregistered study.

Nature medicine·2026
Same author

Effect of Capsaicin (8%) Topical System on Sensory Function in Patients With Diabetic Peripheral Neuropathy: Analysis of the PACE Study.

Muscle & nerve·2026
Same author

Botulinum toxin use in patients with neurological disorders: A U.S.-based claims database analysis.

PM & R : the journal of injury, function, and rehabilitation·2026
Same author

Review of multimodal machine learning approaches in healthcare.

An international journal on information fusion·2026

Related Experiment Video

Updated: Feb 1, 2026

Evaluation of Cerebral Blood Flow Autoregulation in the Rat Using Laser Doppler Flowmetry
07:12

Evaluation of Cerebral Blood Flow Autoregulation in the Rat Using Laser Doppler Flowmetry

Published on: January 19, 2020

9.9K

Reproducibility of dynamic cerebral autoregulation parameters: a multi-centre, multi-method study.

Marit L Sanders1, Jurgen A H R Claassen1, Marcel Aries2

  • 1Department of Geriatric Medicine, Radboudumc Alzheimer Centre and Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.

Physiological Measurement
|December 8, 2018
PubMed
Summary
This summary is machine-generated.

Reproducibility of dynamic cerebral autoregulation (dCA) parameters is poor due to physiological variability, not signal analysis methods. This study highlights the need for standardized dCA methods to improve clinical reliability.

More Related Videos

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

12.8K
ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth
11:19

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

Published on: July 3, 2017

8.7K

Related Experiment Videos

Last Updated: Feb 1, 2026

Evaluation of Cerebral Blood Flow Autoregulation in the Rat Using Laser Doppler Flowmetry
07:12

Evaluation of Cerebral Blood Flow Autoregulation in the Rat Using Laser Doppler Flowmetry

Published on: January 19, 2020

9.9K
Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

12.8K
ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth
11:19

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

Published on: July 3, 2017

8.7K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Physiology

Background:

  • Dynamic cerebral autoregulation (dCA) parameter calculation methods lack reproducibility, limiting clinical utility.
  • Inter-center variability in protocols, modeling, and settings hinders standardization and comparability of dCA studies.
  • Assessing systematic errors in surrogate data is crucial for understanding dCA reproducibility limitations.

Purpose of the Study:

  • To evaluate the reproducibility of various dCA parameter calculation methods.
  • To identify the primary sources of poor reproducibility in dCA measurements.
  • To assess the impact of different modeling techniques on dCA parameter reliability.

Main Methods:

  • Fourteen centers analyzed 22 datasets using surrogate cerebral blood flow velocity and blood pressure signals.
  • dCA methods were categorized into Transfer Function Analysis (TFA)-like, Autoregulation Index (ARI)-like, and correlation coefficient-like outputs.
  • Reproducibility was quantified using one-way intraclass correlation coefficient (ICC) analysis.

Main Results:

  • TFA-like methods showed good ICC for gain (VLF: 0.71, LF: 0.80) and excellent ICC for phase (VLF: 0.53, LF: 0.92).
  • ARI-like methods yielded an ICC of 0.84, while correlation-like methods had a significantly lower ICC of 0.21.
  • ICC values for surrogate data were higher than those reported for physiological data, suggesting physiological variability as a key factor.

Conclusions:

  • The poor reproducibility of dCA parameters in clinical settings may stem primarily from the inherent physiological variability of cerebral blood flow regulation.
  • Signal analysis methods and stationary noise appear to contribute less to reproducibility issues than previously thought.
  • Further research into standardized dCA methodologies is warranted to enhance clinical reliability and comparability across studies.