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Related Concept Videos

Regulation of Stroke Volume01:27

Regulation of Stroke Volume

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The regulation of stroke volume, which is the amount of blood the heart pumps out during each heartbeat, is critical for maintaining a healthy circulatory system. Stroke volume is influenced by three main factors: preload, contractility, and afterload.
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Cardiac output (CO) is an integral aspect of human physiology, reflecting the heart's efficiency and responsiveness to the body's needs. It represents the volume of blood that the left or right ventricle ejects into the aorta or pulmonary trunk each minute. The CO is calculated by multiplying the heart rate (HR)—the number of heartbeats per minute—by the stroke volume (SV)—the amount of blood pumped out with each heartbeat.
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Cardiac Output II: Effect of Stroke Volume on Cardiac Output01:22

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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
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lncRNA - Long Non-coding RNAs02:39

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Areas Within Irregular Boundaries01:26

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Calculating areas within irregular boundaries, such as along rivers or curved roads, is crucial in various fields, including surveying, engineering, and environmental management. Surveyors often begin by creating a traverse, a connected series of straight lines approximating the area's boundary. The coordinates of each traverse point are essential for calculating the enclosed area. The double meridian distance formula is a widely used technique for this purpose. This method utilizes the...
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Measurement: Standard Units03:38

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Every measurement provides three kinds of information: the size or magnitude of the measurement (a number), a standard of comparison for the measurement (a unit), and an indication of the uncertainty of the measurement. While the number and unit are explicitly represented when a quantity is written, the uncertainty is an aspect of the errors in the measurement results.
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Googling Boundaries for Operating Mobile Stroke Unit for Stroke Codes.

Thanh G Phan1, Richard Beare2, Mark Parsons3

  • 1Stroke Unit, Clinical Trials Imaging and Informatics Division of Stroke and Aging Research Group, Monash Medical Centre, Monash University, Clayton, VIC, Australia.

Frontiers in Neurology
|April 26, 2019
PubMed
Summary
This summary is machine-generated.

Mobile stroke units (MSUs) can expedite treatment for stroke patients by delivering recombinant tissue plasminogen activator (tPA) and enabling endovascular clot retrieval (ECR). A new app models MSU operations to optimize deployment in metropolitan areas.

Keywords:
Google Mapmodelingoptimizationstroketransport

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Area of Science:

  • Neurology
  • Emergency Medicine
  • Health Services Research

Background:

  • Mobile stroke units (MSUs) are proposed to improve timeliness of acute stroke treatments, including recombinant tissue plasminogen activator (tPA) and endovascular clot retrieval (ECR).
  • Key operational parameters for MSUs, such as maximal distance, imaging time limits (CT, CT Angiography, CT Perfusion), and telemedicine integration, require further investigation.
  • A computational model, developed as a user-friendly app, was created to address these operational questions within the geographical context of a major metropolitan city.

Purpose of the Study:

  • To define optimal operating parameters for a Mobile Stroke Unit (MSU) in Melbourne, a large metropolitan city.
  • To utilize spatial simulations and a computational app to analyze the impact of travel time and on-scene processing times on treatment delivery.
  • To compare the efficiency of MSU-based treatment delivery against traditional ambulance services for acute stroke care.

Main Methods:

  • A computational model was developed as an app, incorporating geographical data (suburb geocodes) and travel times to designated stroke centers (Royal Melbourne Hospital/RMH, Monash Medical Center/MMC) in Melbourne.
  • Simulations were based on the MSU operating from RMH, delivering tPA at the patient's location, and then transporting the patient to the nearest ECR-capable hospital.
  • The app allowed for adjustable parameters including on-scene processing time, CT angiography (CTA) time, and telemedicine consultation duration, with comparisons made against standard ambulance metrics.

Main Results:

  • The MSU demonstrated superiority in delivering tPA to all simulated Melbourne suburbs, with potential time savings up to 76 minutes compared to standard care.
  • If CT angiography or on-scene processing times increased by 20 minutes, the MSU's superiority for ECR provision decreased, serving only 74.9% of suburbs when based at RMH.
  • Incorporating CT Perfusion or telemedicine, if limited to 20 minutes, impacted ECR capability but not tPA delivery for a single hospital-based MSU.

Conclusions:

  • The developed app serves as a valuable tool for defining and optimizing the deployment strategy of MSUs within Melbourne.
  • The model's adaptability allows for its modification and application to optimize MSU operational characteristics in other urban settings globally.
  • The study highlights the critical balance between MSU operational efficiency, geographical reach, and the timely delivery of advanced stroke therapies.