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Related Concept Videos

Titration Calculations: Weak Acid - Strong Base03:55

Titration Calculations: Weak Acid - Strong Base

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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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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:
34.0K
Calculating pH Changes in a Buffer Solution02:45

Calculating pH Changes in a Buffer Solution

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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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Drug Dosing in Renal Diseases: Dose Adjustments Based on Drug Clearance and Elimination Rate Constant01:25

Drug Dosing in Renal Diseases: Dose Adjustments Based on Drug Clearance and Elimination Rate Constant

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In patients with renal disease, dosage adjustments are necessary to maintain therapeutic plasma drug concentrations and prevent toxicity or subtherapeutic exposure. Renal impairment alters drug pharmacokinetics, especially in conditions like uremia, where changes such as prolonged elimination half-life and altered apparent volume of distribution can significantly affect drug disposition. These changes require careful modification of the dosing regimen to achieve the desired clinical...
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Calculating the Equilibrium Constant02:46

Calculating the Equilibrium Constant

38.1K
The equilibrium constant for a reaction is calculated from the equilibrium concentrations (or pressures) of its reactants and products. If these concentrations are known, the calculation simply involves their substitution into the Kc expression.
For example, gaseous nitrogen dioxide forms dinitrogen tetroxide according to this equation:
38.1K
Calculating Standard Free Energy Changes02:49

Calculating Standard Free Energy Changes

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The free energy change for a reaction that occurs under the standard conditions of 1 bar pressure and at 298 K is called the standard free energy change. Since free energy is a state function, its value depends only on the conditions of the initial and final states of the system. A convenient and common approach to the calculation of free energy changes for physical and chemical reactions is by use of widely available compilations of standard state thermodynamic data. One method involves the...
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Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
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A robust intensity modulated proton therapy optimizer based on Monte Carlo dose calculation.

Jiasen Ma1, Hok Seum Wan Chan Tseung1, Michael G Herman1

  • 1Department of Radiation Oncology, Mayo Clinic, 200 First Street Southwest, Rochester, MN, 55905, USA.

Medical Physics
|July 19, 2018
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A new all-scenario robust intensity modulated proton therapy (IMPT) optimization improves treatment planning by enhancing accuracy and robustness against uncertainties, outperforming traditional methods.

Keywords:
Monte Carlorobust IMPT

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Biology

Background:

  • Intensity modulated proton therapy (IMPT) quality relies on accurate dose calculation and robustness against uncertainties.
  • Optimizing IMPT plans requires sophisticated methods to handle variations in patient setup and proton range.

Purpose of the Study:

  • To develop an all-scenario robust IMPT optimization method.
  • To enhance dose calculation accuracy and plan robustness using Monte Carlo (MC) methods.
  • To mitigate the impact of uncertainties in clinical IMPT planning.

Main Methods:

  • Implemented an all-scenario robust IMPT optimization incorporating MC dose calculation.
  • Utilized dynamic DVH weighting across all scenarios throughout optimization.
  • Extended an adaptive quasi-Newton method for proton optimization, incorporating robustness.
  • Employed GPU acceleration for MC dose calculation and optimization.
  • Compared the all-scenario approach with single scenario (OTV-based) and worst-case optimization methods.

Main Results:

  • The all-scenario robust IMPT optimization demonstrated superior sparing of organs at risk (OARs) compared to the OTV-based method.
  • Achieved maintained target coverage and improved robustness for both targets and OARs.
  • Outperformed worst-case optimization methods by converging faster and yielding tighter DVH robustness spread.
  • Resulted in better target coverage and lower OAR doses than worst-case methods.

Conclusions:

  • Developed a GPU-accelerated, MC-based all-scenario robust IMPT treatment planning system.
  • The novel optimization method, using an adaptive quasi-Newton approach, showed improved performance in clinical cases.
  • The all-scenario robust optimization offers a significant advancement over existing worst-case optimization techniques for IMPT.