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Updated: Feb 24, 2026

Isolation and Culture of Primary Retinal Müller Cells from Sprague-Dawley (SD) Rats

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[The REDSIAM network].

M Goldberg¹, M Carton¹, A Doussin²

¹Unité cohortes épidémiologiques en population, UMS 11 Inserm, université René-Descartes, 94800 Villejuif, France.

Revue D'Epidemiologie Et De Sante Publique

|August 29, 2017

Summary

This summary is machine-generated.

The REDSIAM network facilitates collaboration to develop and validate methods for analyzing the French national health database (SNIIRAM). This promotes disease identification and public health research using SNIIRAM data.

Keywords:

Administrative databases Bases de données médico-administratives Network Réseau Sniiram

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Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy

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Area of Science:

Public Health
Health Informatics
Epidemiology

Background:

The French national health database (SNIIRAM) is valuable for public health but complex to utilize.
Significant resources and expertise are needed for effective SNIIRAM data analysis.

Purpose of the Study:

To establish the REDSIAM network for collaborative analysis of SNIIRAM data.
To develop and validate methods for disease identification and public health research using SNIIRAM.
To share knowledge and experience among specialized teams.

Main Methods:

Formation of the REDSIAM network with defined governance (Steering Committee, Bureau, Technical Committee).
Organization into thematic working groups focused on specific pathological domains.
Development and validation of algorithms for disease identification from SNIIRAM data.

Main Results:

The network has initiated the development and validation of algorithms for disease identification.
The first results from thematic working groups are presented in this special issue.
A charter has been established to govern network participation and data usage.

Conclusions:

The REDSIAM network is crucial for advancing the use of SNIIRAM data in public health and research.
Collaborative efforts enhance the development of robust analytical methods for health surveillance.
Sharing expertise within REDSIAM accelerates the validation of disease identification algorithms.