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

Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

400
Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
400
Mathematical Induction01:29

Mathematical Induction

297
Mathematical induction is a structured method of proof used to confirm the truth of statements involving natural numbers. Consider the sum of the first n natural numbers:This formula describes a pattern that appears to hold true as more terms are added. To verify that it is valid for all natural numbers, mathematical induction proceeds in two essential steps. The first is the base case, where the formula is tested for the initial value, typically n = 1. Substituting into both sides confirms the...
297
Fundamental Mathematical Principles in Pharmacokinetics: Mathematical Expressions and Units01:19

Fundamental Mathematical Principles in Pharmacokinetics: Mathematical Expressions and Units

1.6K
Mathematical principles play a crucial role in pharmacokinetics, providing a framework for understanding and quantifying drug distribution and elimination dynamics in the body. By utilizing mathematical expressions and units, pharmacologists can accurately characterize the behavior of drugs, optimize dosing regimens, and predict therapeutic outcomes.
One significant application of mathematics in pharmacokinetics is the characterization of drug distribution through the volume of distribution...
1.6K
Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs01:21

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

3.2K
The fundamental mathematical principles, such as calculus and graphs, play crucial roles in analyzing drug movement and determining pharmacokinetic parameters. Differential calculus examines rates of change and helps to determine the dissolution rate of drugs in biofluids, as well as how drug concentrations change over time. For instance, it can help calculate the rate of elimination of a drug from the body based on its concentration-time profile.
On the other hand, integral calculus focuses on...
3.2K
Personal Identity01:25

Personal Identity

473
Personal identity is the deeply felt sense of self that individuals cultivate over time, intricately woven from intrinsic qualities they consider essential to their existence—qualities such as morality, intelligence, and friendliness. These attributes serve as vital internal benchmarks, guiding individuals in evaluating whether their actions resonate with their true selves.When personal identity takes center stage in one's life, individuals often emphasize their distinctiveness,...
473
Psychodynamic Perspectives on Personality01:27

Psychodynamic Perspectives on Personality

1.6K
The psychodynamic perspective in psychology asserts that most personality functions operate unconsciously, outside of awareness. This means that the motives and emotions driving behavior often remain hidden, automatically buried in the unconscious mind as a defense mechanism to shield us from psychological distress. According to this theory, the unconscious mind contains thoughts, memories, and emotions that are too disturbing to face directly.
Psychodynamic theorists argue that unconscious...
1.6K

You might also read

Related Articles

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

Sort by
Same author

CauFinder: Steering Cell-State and Phenotype Transitions by Causal Disentanglement Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

A phase I/II trial of concurrent chemo-hormonal enzalutamide and cabazitaxel in patients with metastatic castration-resistant prostate cancer.

The oncologist·2026
Same author

Androgen deprivation therapy and kidney function in patients with prostate cancer: an analysis of the RADICAL-PC cohort.

International urology and nephrology·2026
Same author

Stratification of viral shedding patterns in saliva of COVID-19 patients.

eLife·2026
Same author

Small sample learning classifies Parkinson's disease patients based on their walking behavior.

Chaos (Woodbury, N.Y.)·2025
Same author

Carbon-Ion Radiotherapy for Prostate Cancer in Patients with a History of Surgery for Benign Prostatic Hyperplasia.

Cancers·2025

Related Experiment Video

Updated: Feb 14, 2026

A Bioluminescent and Fluorescent Orthotopic Syngeneic Murine Model of Androgen-dependent and Castration-resistant Prostate Cancer
07:25

A Bioluminescent and Fluorescent Orthotopic Syngeneic Murine Model of Androgen-dependent and Castration-resistant Prostate Cancer

Published on: March 6, 2018

13.9K

Personalizing Androgen Suppression for Prostate Cancer Using Mathematical Modeling.

Yoshito Hirata1,2, Kai Morino3,4, Koichiro Akakura5

  • 1Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan. yoshito@sat.t.u-tokyo.ac.jp.

Scientific Reports
|February 10, 2018
PubMed
Summary
This summary is machine-generated.

Personalized intermittent androgen suppression (IAS) can prevent or delay prostate specific antigen (PSA) relapse in some men. Mathematical modeling identified patient types benefiting most from IAS over continuous androgen suppression (CAS).

More Related Videos

An Orthotopic Murine Model of Human Prostate Cancer Metastasis
06:48

An Orthotopic Murine Model of Human Prostate Cancer Metastasis

Published on: September 18, 2013

35.9K
Generation of Prostate Cancer Patient Derived Xenograft Models from Circulating Tumor Cells
08:03

Generation of Prostate Cancer Patient Derived Xenograft Models from Circulating Tumor Cells

Published on: October 20, 2015

14.6K

Related Experiment Videos

Last Updated: Feb 14, 2026

A Bioluminescent and Fluorescent Orthotopic Syngeneic Murine Model of Androgen-dependent and Castration-resistant Prostate Cancer
07:25

A Bioluminescent and Fluorescent Orthotopic Syngeneic Murine Model of Androgen-dependent and Castration-resistant Prostate Cancer

Published on: March 6, 2018

13.9K
An Orthotopic Murine Model of Human Prostate Cancer Metastasis
06:48

An Orthotopic Murine Model of Human Prostate Cancer Metastasis

Published on: September 18, 2013

35.9K
Generation of Prostate Cancer Patient Derived Xenograft Models from Circulating Tumor Cells
08:03

Generation of Prostate Cancer Patient Derived Xenograft Models from Circulating Tumor Cells

Published on: October 20, 2015

14.6K

Area of Science:

  • Oncology
  • Mathematical Biology
  • Urology

Background:

  • Intermittent androgen suppression (IAS) is a treatment strategy for prostate cancer.
  • Fixed schedules for IAS may not be optimal for all patients.
  • Prostate-specific antigen (PSA) levels are a key indicator of treatment response and relapse.

Purpose of the Study:

  • To develop a mathematical model for personalizing intermittent androgen suppression (IAS) schedules.
  • To classify patients into distinct response types based on IAS effectiveness.
  • To compare the efficacy of personalized IAS with continuous androgen suppression (CAS).

Main Methods:

  • Retrospective analysis of 150 patients treated with IAS.
  • Mathematical modeling to estimate 100 parameter sets per patient, accounting for PSA observation uncertainty.
  • Exhaustive search for optimal treatment schedules minimizing PSA in worst-case scenarios.

Main Results:

  • Identified three patient types: relapse preventable by IAS (type i), relapse delayed by IAS (type ii), and IAS not beneficial (type iii).
  • Personalized IAS showed potential for keeping maximal PSA < 100 ng/ml longer than CAS when type (i) was most frequent.
  • No statistical difference in PSA control between IAS and CAS for other patient type distributions.

Conclusions:

  • Mathematically personalized IAS holds promise for optimizing prostate cancer treatment.
  • Patient-specific modeling can identify individuals who benefit most from IAS.
  • Prospective studies are warranted to validate the efficacy of personalized IAS strategies.