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Related Concept Videos

Cardiac Output II: Effect of Stroke Volume on Cardiac Output01:22

Cardiac Output II: Effect of Stroke Volume on Cardiac Output

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Cardiac output (CO), the amount of blood the heart pumps per minute, is a parameter in cardiovascular physiology determined by stroke volume and heart rate. Stroke volume, the amount of blood pushed from one of the ventricles per heartbeat, is influenced by preload, afterload, and contractility.
Preload
Preload refers to the initial elongation of the cardiac myocytes before contraction and is related to the volume of blood filling the heart at the end of diastole, or end-diastolic volume. The...
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Cardiac Output
Cardiac output (CO) refers to the total amount of blood ejected by one of the ventricles in liters per minute (L/min). In a resting adult, CO ranges from 5 to 6 L/min, adjusting according to the body's metabolic requirements.
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Cardiac output adapts to metabolic demands during stress, physical activity, or illness. The autonomic nervous system regulates heart rate via the sinoatrial node. The parasympathetic nervous system decreases heart...
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Regular physical activity is essential for maintaining cardiovascular health, with aerobic exercises being particularly effective. According to the American Heart Association, 150 minutes of moderate to intense aerobic exercise per week is recommended for a healthy heart. Aerobic activities may include brisk walking, running, bicycling, cross-country skiing, and swimming, ideally performed three to five times per week.
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The heart's primary function is to pump blood throughout the body, maintaining a balance between blood sent out (cardiac output) and blood returning (venous return). If this balance is disrupted, it can result in congestive heart failure (CHF), a severe condition where the heart becomes an inefficient pump, leading to inadequate blood circulation.
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The human body predominantly expels water through the urinary system. On average, an individual generates around 1.5 liters of urine each day. This amount can fluctuate based on how well a person is hydrated, but a critical minimum quantity of urine must be produced to ensure the body's proper functioning. Daily, the kidneys remove 600 to 1200 milliosmoles of dissolved substances, effectively excreting excess minerals and water-soluble toxins such as creatinine, urea, and uric acid from the...
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The citric acid cycle is termed an amphibolic pathway as it operates both anabolically and catabolically. The cyclic reactions balance the flux of the substrates to provide an optimal concentration of NADH and ATP to the cell.
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The citric acid cycle is regulated in several ways, including feedback inhibition, regulation of enzyme activities, and associated anaplerotic or cataplerotic pathways.
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Author Spotlight: Assessment of Cardiac Output Calculation by Thermodilution in Pigs for Effective Perfusion Flow During EVLP
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Determining minimal output sets that ensure structural identifiability.

D Joubert1, J D Stigter1, J Molenaar1

  • 1Wageningen University and Research, Biometris, Department of Mathematical and Statistical Methods, Wageningen, The Netherlands.

Plos One
|November 13, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces an algorithm to identify minimal experimental outputs for unique parameter inference in models. This optimizes data collection, saving time and resources in scientific research.

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Area of Science:

  • Mathematical Modeling
  • Systems Biology
  • Computational Science

Background:

  • Parameter inference from experimental data is often complex and resource-intensive.
  • Determining which data to collect is crucial for efficient model development.

Purpose of the Study:

  • To identify minimal sets of experimental outputs ensuring unique model parameter calculation.
  • To develop a method for determining structural identifiability of model parameters.

Main Methods:

  • An algorithm based on iterative structural identifiability analysis was developed.
  • The method identifies minimal output sets for parameter calculation.

Main Results:

  • The algorithm efficiently determines minimal output sets for large differential equation models.
  • Multiple minimal output sets can be identified, offering experimental design flexibility.

Conclusions:

  • The developed algorithm simplifies experimental design by specifying essential measurements.
  • This approach reduces the time and cost associated with parameter inference.