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相关概念视频

Cholinergic Receptors: Muscarinic01:25

Cholinergic Receptors: Muscarinic

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The pharmacological actions of acetylcholine are elicited via its binding to two families of cholinergic receptors or cholinoceptors, namely, muscarinic and nicotinic receptors. Muscarinic receptors are G protein-coupled receptors and have five subtypes, M1–M5. All mAChR subtypes are activated by acetylcholine and blocked by the antagonist, atropine. 
The subtypes M1, M3, and M5 couple with the Gq subunit and activate the phospholipase C (PLC) activity, mobilizing intracellular Ca2+....
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Antiasthma Drugs: Muscarinic Receptor Antagonists01:20

Antiasthma Drugs: Muscarinic Receptor Antagonists

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Muscarinic receptor antagonists, also known as antimuscarinic agents, are a class of bronchodilators used to treat asthma, although they are more commonly used to treat COPD. They work by inhibiting the action of acetylcholine (ACh), a neurotransmitter, on muscarinic receptors found in the airways.
Antimuscarinic agents compete with ACh for the same binding site on the muscarinic receptors. By binding to these receptors, they inhibit the downstream effects of ACh and block the parasympathetic...
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Myasthenia Gravis: Overview and Treatment01:20

Myasthenia Gravis: Overview and Treatment

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Myasthenia gravis is a neuromuscular transmission disorder characterized by weakness and increased fatigability of skeletal muscles. It is an autoimmune disease affecting approximately one in 2000 people, where antibodies against the α1 subunit of nicotinic acetylcholine receptors are produced.
These antibodies interfere with the function of the nicotinic receptors in three ways: by binding to the receptor and disrupting acetylcholine binding; by causing cross-linking of receptors which...
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Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
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Cholinergic Receptors: Nicotinic01:15

Cholinergic Receptors: Nicotinic

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Nicotinic receptors are ligand-gated ion channels that are activated by acetylcholine and nicotine. Upon activation, they cause a rapid increase in the permeability of cells to K+, Na+, and Ca2+, followed by depolarization and excitation. They are in the autonomic ganglia, skeletal neuromuscular junction, CNS, and adrenal medulla.
There are two types of nicotinic receptors: neuromuscular (NM/NM/N1) and neuronal (NN/NN/N2). The two families differ based on their location and selectivity to...
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Direct-Acting Cholinergic Agonists: Pharmacological Actions00:59

Direct-Acting Cholinergic Agonists: Pharmacological Actions

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Direct-acting cholinergic agonists exert their pharmacological actions by mimicking the effects of acetylcholine on postsynaptic muscarinic receptors to generate parasympathetic responses. These agents elicit a range of physiological responses, including cardiovascular effects. For example, activation of muscarinic receptors induces bradycardia, decreased cardiac output, reduced peripheral resistance, and consequent hypotension. In the eye, stimulation of M3 receptors leads to smooth muscle...
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开发使用转移学习和生成AI技术的肌受体M1分类模型.

Souvik Dey1,2, Anders Wallqvist3, Mohamed Diwan M AbdulHameed4,5

  • 1Department of Defense Biotechnology High Performance Computing Software Applications Institute, Defense Health Agency Research and Development, Medical Research and Development Command, 504 Scott Street, Fort Detrick, MD, 21702-5012, USA.

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开发了机器学习模型来分类肌肉蛋白受体亚型1 (M1) 相互作用. 这些模型有效地处理不平衡的数据,改善神经和呼吸系统疾病的药物发现.

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科学领域:

  • 药理学 药理学是指药理学的学科.
  • 计算化学的计算化学
  • 机器学习 机器学习

背景情况:

  • 肌肉蛋白受体亚型1 (M1) 是一种G蛋白结合受体 (GPCR),对于治疗外围神经病变,COPD和认知障碍至关重要.
  • 识别M1相互作用化合物对于合理的药物设计至关重要.
  • 公共可用的生物活性数据是药物发现的宝贵资源.

研究的目的:

  • 为M1受体开发基于机器学习的分类模型.
  • 为了应对生物活性数据中常见的不平衡数据集的挑战.
  • 加强化学数据库对潜在的M1向药物的选.

主要方法:

  • 利用M1受体的公开可用的生物活性数据.
  • 研究转移学习策略以提高模型性能.
  • 采用生成模型来过量抽样非活性化合物类.
  • 使用机器学习算法开发了M1分类模型.

主要成果:

  • 成功开发了M1受体分类的机器学习模型.
  • 证明转移学习和生成过量采样可以减少不活性化合物的错误分类.
  • 观察到M1和其他GPCR目标的分类性能有所改善.
  • 在不平衡的数据集上验证了开发模型的有效性.

结论:

  • 开发的M1分类模型可以快速选大型化学库.
  • 这些模型推进了针对M1相关疾病的合理药物设计过程.
  • 采用的策略适用于改善其他GPCR目标的模型.
  • 这项工作为重要的治疗领域提供了有效的药物发现.