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相关概念视频

Mixtures of Acids03:27

Mixtures of Acids

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The pH of a solution containing an acid can be determined using its acid dissociation constant and its initial concentration. If a solution contains two different acids, then its pH can be determined using one of several methods depending upon the relative strength of the acids and their dissociation constants.
A Mixture of a Strong Acid and a Weak Acid
In a mixture of a strong acid and a weak acid, the strong acid dissociates completely and becomes a source of almost all the hydronium ions...
22.1K
Mixtures of Acids01:19

Mixtures of Acids

1.1K
The pH of a solution containing an acid can be determined using its acid dissociation constant and initial concentration. If a solution contains two different acids, then its pH can be determined using one of several methods depending on the relative strength of the acids and their dissociation constants.
In a strong and weak acid mixture, the strong acid dissociates completely and becomes a source of almost all the hydronium ions present in the solution. In contrast, the weak acid shows...
1.1K
Racemic Mixtures and the Resolution of Enantiomers02:30

Racemic Mixtures and the Resolution of Enantiomers

21.9K
A racemic mixture, or racemate, is an equimolar mixture of enantiomers of a molecule that can be separated using their unique interaction with chiral molecules or media. Racemic mixtures are denoted by the (±)- prefix. This ‘optical rotation descriptor’ applies to the whole solution of a racemic mixture rather than a specific stereoisomer. Enantiomers typically have the same physical and chemical properties. Hence, they are not easily separable. However, enantiomers can exhibit...
21.9K
Cluster Sampling Method01:20

Cluster Sampling Method

14.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
14.9K
Vesicular Tubular Clusters01:45

Vesicular Tubular Clusters

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After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
With the help of motor proteins such...
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Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

65
In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
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相关实验视频

Updated: Feb 15, 2026

Basics of Multivariate Analysis in Neuroimaging Data
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在临床环境中使用多变量生长混合模型进行概率集群 - - 一个结核病例子.

Ji Soo Kim1, Yizhen Xu2, Rachel S Wallwork1

  • 1Division of Rheumatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Statistics in medicine
|February 13, 2026
PubMed
概括
此摘要是机器生成的。

这项研究确定了两个多发性硬化症 (全身性硬化症;SSc) 患者子组:一个稳定组和一个肺功能下降的进展组. 开发的算法预测了SSc的进展,以便做出更好的临床决策.

关键词:
贝叶斯的等级模型是贝叶斯的等级模型.多变量增长混合模型模型硬化皮质硬化皮质 (scleroderma) 是一种疾病.连续更新算法 连续更新算法基于趋势的集群成员资格

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科学领域:

  • 免疫学 免疫学 免疫学
  • 类风湿病学 类风湿病学
  • 肺部病理学 肺部病理学

背景情况:

  • 硬化症 (全身性硬化症;SSc) 是一种异质的自身免疫性疾病,在各器官系统中具有可变的进展.
  • 准确的患者分层对于指导临床护理和管理SSc至关重要.
  • 了解疾病轨迹有助于预测结果和量身定制治疗.

研究的目的:

  • 将SSc患者分类为具有临床意义的亚群.
  • 开发基于基线特征和疾病进展模式的实时分类框架.
  • 通过识别有迅速疾病进展风险的患者,指导临床护理.

主要方法:

  • 用贝叶斯的多变量生长混合模型来分析肺功能轨迹.
  • 强迫生命能力 (FVC) 和一氧化碳扩散能力 (DLCO) 在289名SSc患者中联合建模.
  • 开发了一个框架,使用纵向数据顺序更新患者子组概率.

主要成果:

  • 确定了两个不同的患者子组:a.
  • 稳定 稳定的 稳定的 稳定的
  • 组 (n=150) 在10年内肺功能变化最小.
  • 这是一个很好的选择.
  • 这是一个进步或进步.
  • 组 (n=139) 在疾病发作后不久,FVC和DLCO显著下降.
  • 该算法使用基线数据和纵向FVC/DLCO测量计算属于进步者组的概率.

结论:

  • 开发的方法可以计算出快速进展的基线概率,并随着患者数据的累积而连续更新.
  • 这种方法有助于早期识别可能经历疾病迅速衰退的患者.
  • 顺序的数据整合和分类有可能改善SSc的临床决策和患者结果.