Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Kidney Structure01:45

Kidney Structure

66.7K
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.
66.7K
Physiology of Urine Formation01:24

Physiology of Urine Formation

2.0K
Urine formation is an essential function of the human body. It plays a critical role in maintaining homeostasis by regulating the volume and composition of body fluids. The kidneys, the primary organs involved in this process, filter blood to remove waste products and excess substances, ultimately producing urine.
Glomerular Filtration
The first stage in urine formation is glomerular filtration. Each kidney contains approximately 1 million nephrons, the functional units of filtration, with a...
2.0K
Disorders of the Urinary System01:20

Disorders of the Urinary System

164
The urinary system is responsible for eliminating waste and excess fluids from the body. However, disorders of the urinary system can arise due to various reasons like infections, stress, age, congenital abnormalities, and lifestyle.
Urinary tract infections (UTIs) are one of the most common urinary system disorders. They are caused by bacteria that enter the urethra and can spread to the bladder resulting in cystitis. Pyelonephritis is the result of a UTI that has ascended to the level of the...
164
One-Compartment Open Model: Urinary Excretion Data and Determination of k01:11

One-Compartment Open Model: Urinary Excretion Data and Determination of k

66
The one-compartment open model leverages urinary excretion data to estimate renal clearance, which gauges the kidney's capacity to expel a drug. This method offers several benefits, including directly measuring drug elimination and assessing the kidney's contribution to overall drug clearance. However, this approach has limitations. It assumes sole renal excretion of the drug, which is not true for all drugs. Accurate urinary excretion and plasma drug concentration measurement can also...
66

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Targeting CD8<sup>+</sup> T cell-derived granzyme K alleviates radiation-induced pulmonary fibrosis by attenuating senescence and SASP.

Archives of biochemistry and biophysics·2026
Same author

Novel Thrombopoietin-Mimetic Peptide 2B9F: A Promising Therapeutic Candidate for Hematopoietic Acute Radiation Syndrome.

ACS omega·2026
Same author

Spatiotemporal Patterns of Suitable Wintering Habitats for the White-Naped Cranes Under Climate and Land-Use Change.

Animals : an open access journal from MDPI·2026
Same author

Synthesis and characterization of a π-extended nonbenzenoid perylene.

Chemical communications (Cambridge, England)·2026
Same author

Serum FAP⁺ exosome-based flow cytometric detection: potential utility in early diagnosis, disease progression assessment, and surgical response monitoring of lung adenocarcinoma.

Journal of nanobiotechnology·2026
Same author

Defect engineering boosts CC bond cleavage for highly efficient ethylene glycol electrooxidation on Pd<sub>2</sub>Pb<sub>3</sub>Zn<sub>4</sub> intermetallic compound.

Journal of colloid and interface science·2026

相关实验视频

Updated: May 8, 2025

Estimation of Urinary Nanocrystals in Humans using Calcium Fluorophore Labeling and Nanoparticle Tracking Analysis
07:45

Estimation of Urinary Nanocrystals in Humans using Calcium Fluorophore Labeling and Nanoparticle Tracking Analysis

Published on: February 9, 2021

3.5K

机器学习模型解读了尿路结石疾病与代谢性尿路概况之间的关联.

Lin Ma1, Yi Qiao1, Runqiu Wang2

  • 1Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.

Metabolites
|December 27, 2024
PubMed
概括

机器学习识别了尿路生物标志物,用于尿病. 尿液24小时内的升高与脏和多种结石有关,而肌素则具有保护作用.

关键词:
生物标志物 生物标志物机器学习是机器学习.代谢产物的代谢产物随机的森林随机的森林尿路结石疾病是什么

更多相关视频

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

8.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K

相关实验视频

Last Updated: May 8, 2025

Estimation of Urinary Nanocrystals in Humans using Calcium Fluorophore Labeling and Nanoparticle Tracking Analysis
07:45

Estimation of Urinary Nanocrystals in Humans using Calcium Fluorophore Labeling and Nanoparticle Tracking Analysis

Published on: February 9, 2021

3.5K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

8.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K

科学领域:

  • 泌尿器科 泌尿器科 泌尿器科 泌尿器科
  • 生物化学 生化学
  • 数据科学数据科学数据科学

背景情况:

  • 尿病的诊断和预防通常依赖于识别尿路代谢异常.
  • 先进的机器学习模型提供了新的方法来从复杂的尿道数据中发现生物标志物.

研究的目的:

  • 通过24小时的尿路代谢概况来识别尿病的生物标志物.
  • 调查已识别的生物标志物与不同类型的尿路结石疾病之间的关联.

主要方法:

  • 对468名被诊断患有尿路结石疾病 (脏,尿道,多处) 的患者进行了回顾性分析.
  • 机器学习算法的应用,包括随机森林和超级学习组合方法,用于尿路代谢物数据.
  • 多变量后勤回归用于确定每个石头类型的显著预测特征.

主要成果:

  • 随机森林获得了高的预测准确性 (脏AUC为0.809,尿管为0.99,多颗石头为0.775).
  • 24小时尿与脏和多种结石有积极的关联;24小时尿蛋白对脏和尿路结石有保护作用.
  • 估计的膜过率 (eGFR) 是尿道和多个位置石头的风险因素.

结论:

  • 机器学习有效地将24小时的泌尿代谢数据与泌尿石病联系起来.
  • 已识别的生物标志物可能会提高预测准确性,从而改善预防策略.
  • 根据这些发现,进一步的研究可以完善饮食和药理干预措施.