Related Experiment Video

Updated: Oct 17, 2025

Induction and Diverse Assessment Indicators of Experimental Autoimmune Encephalomyelitis
06:19

Induction and Diverse Assessment Indicators of Experimental Autoimmune Encephalomyelitis

Published on: September 9, 2022

3.9K

Reply to: Before attributing encephalomyelitis to SARS-CoV-2 vaccinations exclude differentials

Karolina Kania1, Wojciech Ambrosius1, Elzbieta Tokarz Kupczyk1

  • 1Department of Neurology, Poznan University of Medical Sciences, Poznan, Poland.

Annals of Clinical and Translational Neurology
|October 12, 2021
PubMed
Summary

No abstract available in PubMed .

More Related Videos

Simultaneous Isolation of Principal Central Nervous System-Resident Cell Types from Adult Autoimmune Encephalomyelitis Mice08:49

Simultaneous Isolation of Principal Central Nervous System-Resident Cell Types from Adult Autoimmune Encephalomyelitis Mice

Published on: October 6, 2023

2.0K
Detection of Polyfunctional T Cells in Children Vaccinated with Japanese Encephalitis Vaccine via the Flow Cytometry Technique09:37

Detection of Polyfunctional T Cells in Children Vaccinated with Japanese Encephalitis Vaccine via the Flow Cytometry Technique

Published on: September 23, 2022

2.0K

Related Concept Videos

Vaccinations01:51

Vaccinations

Overview
46.4K
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
305
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
806