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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Updated: Sep 3, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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A Bayesian multivariate mixture model for high throughput spatial transcriptomics.

Carter Allen1,2, Yuzhou Chang1,2, Brian Neelon3

  • 1Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA.

Biometrics
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

SPRUCE, a new Bayesian model, identifies distinct cell populations in high throughput spatial transcriptomics data by accounting for spatial patterns and gene expression skewness. This method improves biological discovery from complex tissue samples.

Keywords:
Bayesian modelsconditionally autoregressive modelsmixture modelsskew-normalspatial transcriptomics

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Area of Science:

  • Genomics
  • Computational Biology
  • Biostatistics

Background:

  • High throughput spatial transcriptomics (HST) enables gene expression profiling with spatial context at near single-cell resolution.
  • Identifying cellular sub-populations within tissues is crucial for understanding biological phenomena.
  • Existing computational methods often overlook spatial heterogeneity, statistical features like skewness, or lack robust statistical inference.

Purpose of the Study:

  • To develop a novel statistical model for identifying distinct cellular sub-populations in HST data.
  • To address limitations of existing methods by incorporating spatial correlation and gene expression skewness.
  • To provide an accessible R package for applying the developed model.

Main Methods:

  • Developed SPRUCE, a Bayesian spatial multivariate finite mixture model utilizing multivariate skew-normal distributions.
  • Implemented Pólya-Gamma data augmentation and spatial random effects for inferring spatially correlated mixture component membership.
  • Validated the model through simulation studies and application to human brain and breast cancer HST data.

Main Results:

  • Simulation studies demonstrated the negative impact of ignoring skewness or spatial correlation in HST data analysis.
  • SPRUCE outperformed existing methods in accurately identifying annotated brain layers in human brain HST data.
  • Application to human breast cancer data revealed SPRUCE's ability to distinguish distinct cell populations within the tumor microenvironment.

Conclusions:

  • SPRUCE provides a statistically robust framework for analyzing high throughput spatial transcriptomics data.
  • The model effectively identifies cellular sub-populations by accounting for spatial heterogeneity and statistical distributions.
  • SPRUCE offers improved performance over existing methods, facilitating deeper biological insights from spatial transcriptomics.