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中文摘要
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描述(由申请人提供):尿潴留是一种泌尿道疾病,阻止成千上万的人正确排空膀胱内容物。尿潴留可能由许多因素引起,如自然衰老,急性创伤(特别是在外科手术期间),糖尿病,多发性硬化症等。尽管有各种原因,但大多数尿潴留病例的共同特征是检测尿道中液体流动的神经敏感性降低。这种敏感性的降低反过来又限制了自然控制膀胱排尿的反射的有效性。我们假设,增强尿道敏感性将减轻许多症状与保留允许反射再次正常运作。这项工作的建议将证明,这是可能的,以提高尿道的敏感性。为了实现这种增强,我们将使用一种称为随机共振的过程,在这种过程中,我们将亚阈值水平的电噪声(数量足够小,以至于感觉神经元无法检测到)直接注入尿道。这将在大鼠泌尿道模型中进行。随机共振在其他生物感觉系统中已经取得了成功,我们假设这些不可检测的扰动将降低尿道感觉神经元的激活阈值,有效地增加它们对流量的敏感性。我们将通过记录阴部神经来证明我们的发现,阴部神经将感觉信息从尿道传递到脊髓。我们将比较阴部神经对尿道中的流体流动的反应,在一定范围的流速下有和没有刺激。我们可以得出结论,如果在刺激存在下观察到比没有刺激更多的神经活动,则灵敏度增强,并且如果可以检测到较慢的流速,则刺激也将作为灵敏度增强的证据。最后,在下尿路随机共振的数学模型将开发与动物实验。该模型将允许我们模拟许多不同类型的刺激和流速,以更好地指导动物工作。使用该模型,我们可以研究广泛的实验参数,比我们可以使用生物系统测试更多。我们将使用最佳刺激参数和流速的模型估计值来为我们自己提供成功增强尿道敏感性的最佳机会。
英文摘要
DESCRIPTION (provided by applicant): Urinary retention is a disease of the urinary tract that prevents hundreds of thousands of people from properly emptying the contents of their bladder. Urinary retention can be caused by many things such as natural aging, acute trauma (especially during surgical procedures), diabetes, multiple sclerosis and others. Despite the range of causes, a feature common to most cases of urinary retention is a reduced neural sensitivity for detecting fluid flow in the urethra. This reduction in sensitivity, in turn, limits the effectivenes of reflexes that naturally control bladder voiding. We hypothesize that enhancing urethral sensitivity will mitigate many of the symptoms associated with retention by allowing the reflexes to once again function properly. The work in this proposal will demonstrate that it is possible to enhance urethral sensitivity. To accomplish this enhancement we will use a process called stochastic resonance, where we inject sub-threshold levels of electrical noise (amounts small enough that they cannot be detected by the sensory neurons) directly into the urethra. This will be done in a rat model of the urinary tract. Stochastic resonance has been successful in other biological sensory systems and we hypothesize that these undetectable perturbations will lower the activation threshold of the urethral sensory neurons, effectively increasing their sensitivity o flow. We will demonstrate our findings by recording from the pudendal nerve, which carries sensory information from the urethra to the spinal cord. We will compare the pudendal nerve's response to fluid flow in the urethra both with and without the stimulation at a range of flowrates We can conclude that sensitivity was enhanced if more neural activity is observed in the presence of the stimulation than without it, and if slower flowrates can be detected with the stimulation it will also serve as evidence of enhanced sensitivity. Finally, a mathematical model of stochastic resonance in the lower urinary tract will be developed in tandem with the animal experiments. This model will allow us to simulate many different types of stimulation and flowrates to better guide the animal work. Using the model we can investigate a wide range of experiment parameters, many more than we could test using the biological system. We will use the model estimates of the optimal stimulation parameters and flowrates to give ourselves the best chance of success for enhancing urethral sensitivity.
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A new hybrid modeling framework combining biophysics and deep learning to predict and optimize peripheral neuromodulation outcomes in lower urinary tract disease
  • 批准号:
    10705188
  • 项目类别:
  • 资助金额:
    $60.29万
  • 财政年份:
    2022
  • 负责人:
    Zachary C Danziger
  • 依托单位:
A new hybrid modeling framework combining biophysics and deep learning to predict and optimize peripheral neuromodulation outcomes in lower urinary tract disease
  • 批准号:
    10502727
  • 项目类别:
  • 资助金额:
    $66.03万
  • 财政年份:
    2022
  • 负责人:
    Zachary C Danziger
  • 依托单位:
A New Paradigm for Systems Physiology Modeling: Biomechanistic Learning Augmentation with Deep Differential Equation Representations (BLADDER)
  • 批准号:
    10206953
  • 项目类别:
  • 资助金额:
    $102.51万
  • 财政年份:
    2020
  • 负责人:
    Zachary C Danziger
  • 依托单位:
A New Paradigm for Systems Physiology Modeling: Biomechanistic Learning Augmentation with Deep Differential Equation Representations (BLADDER)
  • 批准号:
    10472818
  • 项目类别:
  • 资助金额:
    $87.2万
  • 财政年份:
    2020
  • 负责人:
    Zachary C Danziger
  • 依托单位:
海外基金