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Drug Classes and Categories01:25

Drug Classes and Categories

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Drugs can be classified according to their chemical composition or their intended therapeutic application. For instance, anti-infective agents that possess the ability to eliminate pathogens or suppress their growth and reproduction can be grouped based on the organisms they target or their chemical structure. Furthermore, drugs can be divided into prescription, nonprescription, or controlled substances. Prescription medications, such as antibiotics, require oversight from a licensed healthcare...
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Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
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Thiazide diuretics are sulfonamide derivatives featuring a benzothiadiazine ring system in their molecular structure. Based on this structure, thiazide diuretics can be categorized into two groups: thiazide-type and thiazide-like diuretics. Thiazide-type diuretics, including hydrochlorothiazide and chlorothiazide, consist of a benzothiadiazine backbone with an attached sulfonamide group. Thiazide-like diuretics, such as chlorthalidone and indapamide, lack the thiazide ring but demonstrate...
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Adrenergic stimulation generally impacts cardiac rate and rhythm. Specifically, stimulation of the β-adrenoceptors triggers an increase in intracellular calcium ion influx and pacemaker currents, which may cause arrhythmias. Catecholamines like adrenaline also demonstrate β2-adrenoceptor-mediated hypokalemia, impacting cardiac action potential and disrupting the normal cardiac rhythm. Class II antiarrhythmic drugs are β-adrenoceptor antagonists or β-blockers, which...
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Class I antiarrhythmic drugs are used to treat various types of arrhythmias or irregular heart rhythms. These drugs block the sodium (Na+) channels in the cardiac cells, thereby affecting the movement of electrical impulses across the heart. Class I antiarrhythmic drugs are divided into three subgroups: Class IA, Class IB, and Class IC, each with distinct mechanisms of action and effects on the heart.
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Class III antiarrhythmic drugs are a group of medications that can prolong action potentials in the heart. They achieve this by blocking potassium channels or enhancing inward currents from sodium channels. However, these drugs have a unique property of "reverse use-dependence," which is most pronounced at slower heart rates and can lead to torsades de pointes—a specific type of arrhythmia. However, it is essential to note that excessive QT interval prolongation—a measure of...
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Bayesian Latent Class Analysis Tutorial.

Yuelin Li1, Jennifer Lord-Bessen2, Mariya Shiyko3

  • 1a Department of Psychiatry & Behavioral Sciences , Memorial Sloan Kettering Cancer Center.

Multivariate Behavioral Research
|February 10, 2018
PubMed
Summary
This summary is machine-generated.

This guide simplifies Bayesian computation using Gibbs sampling for Latent Class Analysis (LCA). It offers a practical R tool and detailed explanations for quantitative psychology students to easily implement complex models.

Keywords:
Bayesian analysisGibbs samplingLatent Class AnalysisMarkov chain Monte Carlo

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Area of Science:

  • Quantitative Psychology
  • Computational Statistics
  • Bayesian Inference

Background:

  • Bayesian computation is often presented with complex mathematical details, posing challenges for students.
  • Existing tutorials may lack practical, step-by-step guidance for implementing Bayesian models in R.
  • Latent Class Analysis (LCA) can benefit from accessible Bayesian computational methods.

Purpose of the Study:

  • To provide an accessible, self-contained tutorial on Bayesian computation using Gibbs sampling.
  • To demonstrate the application of Gibbs sampling within the context of Latent Class Analysis (LCA).
  • To offer a practical computational tool in R for implementing Bayesian LCA.

Main Methods:

  • Detailed explanation of Bayesian computation broken down into simpler, manageable calculations.
  • Worked-out example addressing technical difficulties in Bayesian modeling.
  • Development and line-by-line explanation of an R program for Bayesian LCA implementation.

Main Results:

  • Demonstrated ease of implementing Bayesian LCA through a practical R program.
  • Successful application of the computational tool to a large, real-world dataset.
  • Explicit mathematical details provided to demystify Bayesian modeling for students.

Conclusions:

  • The tutorial and R tool effectively simplify Bayesian computation for LCA.
  • The approach provides a foundation for extending Bayesian methods to other statistical applications.
  • Further readings are suggested for continued learning in Bayesian modeling.