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相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

56
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...
56
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

456
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
456
Uncertainty: Overview00:59

Uncertainty: Overview

504
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
504
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
3.1K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

632
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
632
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

43
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
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相关实验视频

Updated: May 30, 2025

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
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bneR:使用贝叶斯非参数集的空气污染暴露建模和不确定性表征的协作工作流程.

Jaime Benavides1, Carlos Carrillo-Gallegos2, Vijay Kumar1

  • 1Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA.

Journal of environmental management
|January 28, 2025
PubMed
概括

一个新的bneR框架使用贝叶斯非参数组 (BNE) 建模来估计空气污染,如二氧化 (NO2),及其不确定性. 这种方法提高了公共卫生研究和政策评估的准确性.

关键词:
空气污染 大气污染协作式的合作方式模型组合 模型组合没有参数的非参数.

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09:50

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An Air-liquid Interface Bronchial Epithelial Model for Realistic, Repeated Inhalation Exposure to Airborne Particles for Toxicity Testing
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科学领域:

  • 环境科学 环境科学
  • 公共卫生 公共卫生
  • 数据科学数据科学数据科学

背景情况:

  • 空气污染对全球公共卫生构成重大风险.
  • 当前的暴露模型经常忽视空气污染物度估计中的不确定性.
  • 准确的空气质量数据对于健康研究,监管行动和政策制定至关重要.

研究的目的:

  • 引入bneR建模框架,用于估计空气污染物度及其时空不确定性.
  • 为使用贝叶斯非参数组合 (BNE) 组合多个暴露模型提供一个强大的方法.
  • 提高空气污染数据的准确性和可靠性,用于科学和政策应用.

主要方法:

  • 该bneR框架协调空气污染物数据集用于标准化的BNE算法输入.
  • 它应用了BNE算法来生成污染物度的后期预测分布.
  • 创建可视化来表示时空估计和不确定性.

主要成果:

  • 该框架用于估计2015年纽约州的每日NO2度 (分辨率为1平方公里).
  • 每日平均NO2度为6.0ppb,平均不确定性 (SD) 为1.2ppb.
  • BNE模型表现出强的性能,交叉验证的RMSE为2.84ppb,R2为0.80.

结论:

  • 利益相关者参与强调了关于不确定性估计和解释的清晰沟通的重要性.
  • bneR框架为生成更可靠的空气污染估计提供了一个有价值的工具.
  • 有效的沟通策略对于相关社区采用和使用bnR数据产品至关重要.