Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Renal Corpuscle01:20

Renal Corpuscle

3.1K
The glomerulus and Bowman's capsule are two essential components of the nephron, which is the functional unit of the kidney. These microscopic structures play a critical role in the process of blood filtration to produce urine.
Glomerulus: Structure and Function
The glomerulus is a tiny, intricate network of capillaries located at the beginning of the nephron. It's enveloped by the Bowman's capsule and receives its blood supply from an afferent arteriole, which divides into numerous...
3.1K
Chronic Kidney Disease III: Interprofessional Care01:28

Chronic Kidney Disease III: Interprofessional Care

70
Chronic kidney disease (CKD) requires collaborative and comprehensive management. CKD progresses through stages and can lead to end-stage kidney disease (ESKD) if untreated. Interprofessional collaboration and patient education are crucial, enabling patients to manage their health and improve their quality of life.Diagnostic approach for chronic kidney diseaseThe diagnosis of CKD primarily focuses on the glomerular filtration rate (GFR), which assesses kidney function by measuring how well...
70
Glomerular Filtration Rate and its Regulation01:28

Glomerular Filtration Rate and its Regulation

3.2K
The Glomerular Filtration Rate (GFR) is a measure of kidney function, reflecting the volume of filtrate formed per minute in the kidneys. On average, GFR is approximately 125 mL/min in males and 105 mL/min in females. Maintaining a relatively constant GFR is essential for the kidneys to effectively regulate body fluid homeostasis and maintain extracellular stability.
GFR regulation involves two primary intrinsic controls: the myogenic and tubuloglomerular feedback mechanisms.
The myogenic...
3.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Factors associated with loss of renal function in patients with polycystic kidney disease: a nationwide real-world data analysis.

Scientific reports·2026
Same author

Vascular RhoJ Is an Effective and Selective Target for Tumor Angiogenesis and Vascular Disruption.

Cancer cell·2026
Same author

Hydroxychloroquine-induced renal phospholipidosis manifesting as proximal tubulopathy in systemic lupus erythematosus.

BMC nephrology·2026
Same author

Attribute-based cross-classification reveals sex- and age-specific prognostic impact of anemia in ADPKD.

Clinical and experimental nephrology·2026
Same author

The Next-Generation CKD Heat Map: Hyperfiltration and eGFR Slope in the Cardiorenal Continuum.

Kidney international reports·2026
Same author

Circadian disruption and its clinical implications in Parkinson's disease: A Narrative review.

Sleep medicine·2026

Related Experiment Video

Updated: Aug 22, 2025

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

6.9K

Machine learning for morbid glomerular hypertrophy.

Yusuke Ushio1, Hiroshi Kataoka2,3, Kazuhiro Iwadoh1,4

  • 1Department of Nephrology, Tokyo Women's Medical University, 8-1 Kawada-Cho, Shinjuku-Ku, Tokyo, 162-8666, Japan.

Scientific Reports
|November 9, 2022
PubMed
Summary

This study introduces a novel machine learning approach to identify factors linked to significant glomerular hypertrophy, improving diagnostic accuracy in kidney disease research. The method effectively distinguishes key variables, offering a simpler, generalizable tool for medical data analysis.

More Related Videos

Author Spotlight: Aiding Research in Kidney Biology by Labeling Glomeruli in Cleared Tissues
09:50

Author Spotlight: Aiding Research in Kidney Biology by Labeling Glomeruli in Cleared Tissues

Published on: February 9, 2024

1.4K
Assessment of Kidney Function in Mouse Models of Glomerular Disease
09:16

Assessment of Kidney Function in Mouse Models of Glomerular Disease

Published on: June 30, 2018

17.8K

Related Experiment Videos

Last Updated: Aug 22, 2025

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

6.9K
Author Spotlight: Aiding Research in Kidney Biology by Labeling Glomeruli in Cleared Tissues
09:50

Author Spotlight: Aiding Research in Kidney Biology by Labeling Glomeruli in Cleared Tissues

Published on: February 9, 2024

1.4K
Assessment of Kidney Function in Mouse Models of Glomerular Disease
09:16

Assessment of Kidney Function in Mouse Models of Glomerular Disease

Published on: June 30, 2018

17.8K

Area of Science:

  • Nephrology
  • Medical Informatics
  • Biostatistics

Background:

  • Glomerular hypertrophy on renal biopsy has critical implications but is often misdiagnosed as adaptive hypertrophy.
  • A need exists for practical research methods combining data-driven machine learning with traditional statistics in medicine.

Purpose of the Study:

  • To explore factors associated with a maximal glomerular diameter ≥ 242.3 μm using a generative machine learning method.
  • To compare variable selection by symbolic regression via genetic programming (SR via GP) with traditional statistical methods.

Main Methods:

  • Employed generative machine learning and symbolic regression via genetic programming (SR via GP) for variable ranking and scoring.
  • Utilized multivariable logistic and linear regressions to compare SR via GP-selected variables against those selected by point-biserial correlation coefficients.
  • Assessed discriminatory ability, goodness-of-fit, and collinearity.

Main Results:

  • SR via GP identified key factors including body mass index, complement component C3, serum total protein, arteriolosclerosis, C-reactive protein, and Oxford E1 score.
  • Estimated glomerular filtration rate was ranked lower (46th) by SR via GP compared to other variables.
  • SR via GP demonstrated superior model fit (higher R², lower AICc) and reduced collinearity compared to point-biserial r selection.

Conclusions:

  • Data-driven machine learning, specifically SR via GP, effectively identifies significant factors related to glomerular hypertrophy.
  • The proposed method offers improved statistical performance and reduced collinearity over traditional approaches.
  • This procedural simplicity makes the machine learning method generalizable to diverse medical research areas.