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

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

4.7K
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,...
4.7K
Physiology of the Genitourinary System I: Renal Blood Flow and Glomerular Filtration01:29

Physiology of the Genitourinary System I: Renal Blood Flow and Glomerular Filtration

1.9K
The kidneys are vital organs responsible for regulating blood filtration, waste excretion, and fluid balance, all of which are crucial for maintaining homeostasis. Renal physiology examines renal blood flow, glomerular filtration, and urine formation, ensuring the body’s internal environment remains stable.Renal Blood FlowThe kidneys receive about 20-25% of the cardiac output, typically around 1200 mL of blood per minute in an average adult. Blood flows into the kidneys through the renal...
1.9K
Renal Drug Clearance: Overview01:06

Renal Drug Clearance: Overview

1.1K
Renal clearance is a crucial parameter in pharmacokinetics that quantifies the rate at which the kidneys excrete a drug. It represents a constant fraction of the central volume of distribution containing the drug that the kidney eliminates per unit of time.
Renal clearance can be calculated using different methods. One approach is to divide the urinary drug excretion rate by the plasma drug concentration. This method directly measures renal clearance, indicating the kidneys' efficiency in...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Transvenous and Subcutaneous Implantable Cardioverter Defibrillator in Arrhythmogenic Cardiomyopathy: Insights from a single-center experience.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology·2026
Same author

Early first-line systemic anticancer therapeutic attrition after metastatic recurrence in triple-negative breast cancer following neoadjuvant chemo-immunotherapy: a multicenter real-world study.

Breast cancer research and treatment·2026
Same author

The Impact of Relative Dose Intensity on pCR in Neoadjuvant Chemotherapy of Muscle-Invasive Urothelial Cancer: a Multicentre Retrospective Study.

The oncologist·2026
Same author

Predictive biomarkers of response in metastatic prostate cancer: paving the way for a new era of precision medicine.

Expert review of anticancer therapy·2026
Same author

Effectiveness and safety of avelumab maintenance in patients aged ≥75 years with advanced urothelial cancer: a sub-analysis of the meet-URO 25 (MALVA) study.

Frontiers in immunology·2026
Same author

Safety evaluation of TOPAZ-1 and KEYNOTE-966 regimens in metastatic biliary tract cancer: a systematic review and meta-analysis.

Clinical & experimental metastasis·2026

Related Experiment Video

Updated: May 2, 2026

Modeling Spontaneous Metastatic Renal Cell Carcinoma mRCC in Mice Following Nephrectomy
11:27

Modeling Spontaneous Metastatic Renal Cell Carcinoma mRCC in Mice Following Nephrectomy

Published on: April 29, 2014

16.3K

An agent-based learning model integrating sex differences in renal cell carcinoma.

Emanuela Merelli1, Tarek Taha2, Marco Caputo1

  • 1School of Sciences and Technology, University of Camerino, Camerino, MC, Italy.

Frontiers in Immunology
|May 1, 2026
PubMed
Summary

This study developed an agent-based learning model (ALM) to simulate renal cell carcinoma (RCC) progression, revealing sex-specific differences in treatment response influenced by hormones and tumor genetics.

Keywords:
Agent-Based Model (ABM)Agent-Learning Model (ALM)RCCcomputational simulationimmune system responseimmunotherapymachine learning

More Related Videos

A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies
06:38

A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies

Published on: April 12, 2017

13.0K
Microfluidic Co-culture of Renal Healthy and Tumor Epithelium to Model Kidney Cancer Progression
06:29

Microfluidic Co-culture of Renal Healthy and Tumor Epithelium to Model Kidney Cancer Progression

Published on: January 31, 2025

1.2K

Related Experiment Videos

Last Updated: May 2, 2026

Modeling Spontaneous Metastatic Renal Cell Carcinoma mRCC in Mice Following Nephrectomy
11:27

Modeling Spontaneous Metastatic Renal Cell Carcinoma mRCC in Mice Following Nephrectomy

Published on: April 29, 2014

16.3K
A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies
06:38

A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies

Published on: April 12, 2017

13.0K
Microfluidic Co-culture of Renal Healthy and Tumor Epithelium to Model Kidney Cancer Progression
06:29

Microfluidic Co-culture of Renal Healthy and Tumor Epithelium to Model Kidney Cancer Progression

Published on: January 31, 2025

1.2K

Area of Science:

  • Computational oncology
  • Immunogenomics
  • Systems biology

Background:

  • Sex-based differences significantly impact renal cell carcinoma (RCC) tumor biology, immune responses, and treatment outcomes.
  • Existing computational models often fail to integrate sex hormones, immune composition, and tumor genetic evolution.
  • Agent-based models (ABMs) are effective for simulating tumor-immune interactions but lack sex-specific modulation and machine learning optimization.

Purpose of the Study:

  • To enhance an agent-based learning model (ALM) for simulating RCC progression and treatment response.
  • To integrate hormonal effects, immune interactions, and tumor genetic adaptation into the ALM.
  • To optimize the ALM using data-driven tuning for improved accuracy.

Main Methods:

  • Developed an RCC-specific ALM incorporating immune agents (CD8+, NK, Treg, dendritic cells), hormone-sensitive mechanisms, and tumor genetic modules.
  • Modeled tumor evolution using a genetic algorithm simulating mutations, with fitness based on immune evasion and proliferation advantages.
  • Optimized model parameters using clinical outcomes from the ARON dataset via the Optuna framework, assessing performance with concordance index (CI) and mean squared error (MSE).

Main Results:

  • Simulations successfully reproduced sex-specific treatment responses in renal cell carcinoma (RCC).
  • Female models demonstrated delayed initial responses but stronger late immune activation and rapid tumor regression.
  • Male models showed more stable early responses but exhibited greater tumor resilience due to genetic adaptations.

Conclusions:

  • The enhanced ALM provides a framework for understanding the interplay of sex hormones, immune dynamics, and tumor genetics in RCC treatment outcomes.
  • The model's adaptive learning capability reduced prediction error, suggesting potential for patient stratification.
  • Further validation in larger cohorts is warranted to support the use of combined ABMs and data-driven optimization for patient prediction.