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Related Concept Videos

Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...

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Related Experiment Video

Updated: Jul 25, 2026

A Method for Murine Islet Isolation and Subcapsular Kidney Transplantation
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Comparative Analysis of Islet Auto-Transplantation Outcome Classification Systems: Evaluating Concordance,

Davide Catarinella1, Paola Magistretti2, Raffaella Melzi2

  • 1Clinic Unit of Regenerative Medicine and Organ Transplants, IRCCS Ospedale San Raffaele, Milan, Italy.

Transplant International : Official Journal of the European Society for Organ Transplantation
|August 4, 2025
PubMed
Summary

Comparing islet autotransplantation outcome classifications, a novel Data-Driven approach excelled. Fasting C-peptide is a reliable graft function predictor, guiding better patient monitoring for beta-cell replacement therapies.

Keywords:
C-peptideclassification systemsgraft functioninsulin secretionislet autotransplantation

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Area of Science:

  • Endocrinology
  • Transplantation Immunology
  • Metabolic Surgery

Background:

  • Standardized assessment of islet autotransplantation outcomes is vital for evaluating graft function and informing clinical management.
  • Existing classification systems (Milan, Minneapolis, Chicago, Leicester, Igls) vary in their criteria for assessing transplant success.

Purpose of the Study:

  • To compare the efficacy of established islet autotransplantation classification systems against a novel Data-Driven approach.
  • To determine which systems best differentiate transplant outcomes using metabolic and insulin secretion parameters.

Main Methods:

  • Comparative analysis of six islet autotransplantation classification systems: Milan, Minneapolis, Chicago, Leicester, Igls, and a Data-Driven approach.
  • Evaluation based on metabolic parameters and insulin secretion tests, including fasting C-peptide, arginine test, and Mixed Meal Tolerance Test (MMTT).

Main Results:

  • Strong concordance was observed among Milan, Minneapolis, Chicago, and Igls systems due to similar C-peptide thresholds.
  • The Leicester system simplified assessment by omitting severe hypoglycemia and HbA1c, while the Data-Driven approach offered a dynamic framework.
  • Fasting C-peptide levels proved highly reliable for predicting graft function; the arginine test was more effective than MMTT.
  • The Data-Driven approach demonstrated superior outcome stratification, emphasizing residual insulin secretion's role in metabolic control.

Conclusions:

  • Existing classification systems show significant overlap, necessitating refinement for improved clinical utility.
  • A Data-Driven approach offers enhanced stratification of islet autotransplantation outcomes, highlighting the importance of residual beta-cell function.
  • Further validation of refined classification systems, incorporating insulin sensitivity and residual secretion, is crucial for advancing beta-cell replacement therapies.