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Related Concept Videos

Titration Calculations: Strong Acid - Strong Base02:28

Titration Calculations: Strong Acid - Strong Base

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Calculating pH for Titration Solutions: Strong Acid/Strong Base
A titration is carried out for 25.00 mL of 0.100 M HCl (strong acid) with 0.100 M of a strong base NaOH. The pH at different volumes of added base solution can be calculated as follows:
(a) Titrant volume = 0 mL. The solution pH is due to the acid ionization of HCl. Because this is a strong acid, the ionization is complete and the hydronium ion molarity is 0.100 M. The pH of the solution is then:
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Titration Calculations: Weak Acid - Strong Base03:55

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Calculating pH for Titration Solutions: Weak Acid/Strong Base
For the titration of 25.00 mL of 0.100 M CH3CO2H with 0.100 M NaOH, the reaction can be represented as:
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Nonlinear Pharmacokinetics: Causes of Nonlinearity01:22

Nonlinear Pharmacokinetics: Causes of Nonlinearity

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Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
Nonlinear drug absorption can occur when the process is rate-limited by solubility, carrier-mediated transport systems, or saturation of the presystemic gut wall or hepatic metabolism. For instance, high doses of riboflavin...
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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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Pulse amplitude and quality01:17

Pulse amplitude and quality

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Pulse amplitude is a crucial indicator of cardiac health because it provides valuable insights into the strength of left ventricular contractions and the overall uniformity of blood circulation within the vasculature. The strength of the pulse is directly related to the force with which the heart contracts and the volume of blood being pumped.
A weak or absent pulse may indicate reduced cardiac output or poor left ventricular contraction, which can be signs of cardiovascular dysfunction or...
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Calculating pH Changes in a Buffer Solution02:45

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A buffer can prevent a sudden drop or increase in the pH of a solution after the addition of a strong acid or base up to its buffering capacity; however, such addition of a strong acid or base does result in the slight pH change of the solution. The small pH change can be calculated by determining the resulting change in the concentration of buffer components, i.e., a weak acid and its conjugate base or vice versa. The concentrations obtained using these stoichiometric calculations can be used...
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Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm
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Calculating the Mean Amplitude of Glycemic Excursions from Continuous Glucose Data Using an Open-Code Programmable

Xuefei Yu1, Liangzhuo Lin1, Jie Shen2

  • 1Southern Medical University, Guangzhou, China.

Computational and Mathematical Methods in Medicine
|May 1, 2018
PubMed
Summary

Calculating mean amplitude of glycemic excursions (MAGE) is crucial for diabetes management. A new computer program, MAGECAA v1.0, offers a robust and validated method for accurate MAGE assessment, improving upon manual calculations and existing software.

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Area of Science:

  • Biomedical Engineering
  • Clinical Informatics
  • Diabetes Management

Background:

  • Glycemic variability is critical for diabetes management, with Mean Amplitude of Glycemic Excursions (MAGE) being a key assessment index.
  • Traditional manual MAGE calculation is labor-intensive and error-prone due to large datasets.
  • Existing software solutions for MAGE calculation exhibit poor inter-program agreement, necessitating further research.

Purpose of the Study:

  • To develop a robust, computer-aided program for accurate MAGE calculation.
  • To validate the developed program against manual methods and existing software.
  • To provide an open-code algorithm for future research in glycemic variability assessment.

Main Methods:

  • Development of a novel mathematical algorithm based on integer nonlinear programming.
  • Implementation of the algorithm into an open-code computer program, MAGECAA v1.0.
  • Validation of MAGECAA v1.0 through statistical analysis and comparison with manual methods and other software.

Main Results:

  • The developed MAGECAA v1.0 program demonstrated robustness compared to the traditional manual calculation method.
  • Satisfactory agreement was observed between MAGECAA v1.0 and currently available popular software.
  • The study addresses concerns regarding inter-software discrepancies in MAGE calculation.

Conclusions:

  • MAGECAA v1.0 provides a reliable and validated tool for assessing glycemic variability.
  • The developed open-code algorithm serves as a valuable resource for researchers in glycemic variability methodology.
  • The findings suggest that concerns about significant disagreement among MAGE calculation software may be overstated with validated tools.