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相关概念视频

Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
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Wind Turbine Machine Models01:24

Wind Turbine Machine Models

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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
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The kidneys are two large bean-shaped organs located in the upper abdomen. They filter the blood several times a day to remove toxins and rebalance water and electrolytes of the circulatory system via the renal veins. The kidneys receive blood directly from the heart via the renal arteries. These arteries enter the kidney at the hilum, the concave surface of the bean, where they branch and divide into smaller vessels and capillaries.
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Survival Curves01:18

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Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
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相关实验视频

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通过基于因果活检转录学的机器学习模型预测移植存活率.

Valbert Oliveira Costa Filho1, Pedro Robson Costa Passos2, Luis Gustavo Modelli de Andrade3

  • 1Center of Research and Drug Development, Federal University of Ceará, Fortaleza, CE, Brazil. valbertoliveiraf@gmail.com.

Scientific reports
|January 24, 2026
PubMed
概括

我们开发了一种机器学习模型,使用活检的基因表达来预测移植患者的长期移植存活率. 这种模型准确地识别高风险患者,改善移植护理.

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科学领域:

  • 腎臟病學 (nephrology) 是一種醫學專業.
  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 长期移植移植移植的存活仍然是一个挑战,尽管脏疾病末期管理的改善.
  • 传统的预后模型对移植结果的预测准确性有限.
  • 针对病因的活检是常规进行的,为预后数据提供了潜在的来源.

研究的目的:

  • 开发和比较机器学习 (ML) 模型来预测移植存活率.
  • 为了利用指示活检中的基因表达特征用于预后建模.
  • 评估开发模型在识别拒绝时的诊断性能.

主要方法:

  • 从六个基因表达综合 (GEO) 队列的因果脏活检中收集了基因表达数据.
  • 使用差异表达和Cox回归识别了与移植损失相关的预后基因.
  • 训练并验证了117个ML模型,重点是梯度提升机 (GBM).
  • 在四个独立的队列中进行诊断任务的外部验证.

主要成果:

  • 确定了与移植损失相关的11个关键基因.
  • GBM模型实现了高C指数 (>0.85) 并通过移植存活率准确地分层患者.
  • 外部验证显示,整体排斥的诊断性能很强 (AUC 0.760-0.826).
  • 高风险患者表现出减少的移植存活率和明显的免疫通路丰富.

结论:

  • 一个基于转录基因组的GBM模型准确地预测移植存活率,使用因果活检样本.
  • 该模型显示了跨多样化的队列强大的预后和诊断能力.
  • 这些发现强调了该模型因其实用性和生物相关性而被纳入常规移植护理的潜力.