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Related Experiment Video

Updated: Jan 27, 2026

Neural Tube Closure in Mouse Whole Embryo Culture

Neural Tube Closure in Mouse Whole Embryo Culture

Published on: October 21, 2011

Predicting Down syndrome and neural tube defects using basic risk factors.

Momina T Khattak¹, Eko Supriyanto², Muhammad N Aman³

¹School of Biomedical Engineering and Health Sciences, Universiti Teknologi Malaysia, Johor, Malaysia. momina.naveed1@gmail.com.

Medical & Biological Engineering & Computing

|March 17, 2019

Summary

This study developed logistic regression models using basic risk factors to assess prenatal risk for Down syndrome (trisomy 21) and neural tube defects. While not highly accurate, these models effectively screen out many low-risk cases in resource-limited settings.

Keywords:

Logistic regression Neural tube defects Prediction model Prenatal screening Trisomy 21

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Culturing Human Pluripotent and Neural Stem Cells in an Enclosed Cell Culture System for Basic and Preclinical Research

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Published on: June 10, 2016

Area of Science:

Medical Science
Public Health
Biostatistics

Background:

Congenital anomalies are leading causes of infant and under-5 mortality.
Down syndrome (trisomy 21) and neural tube defects are common congenital anomalies.
Limited access to advanced prenatal screening in developing countries necessitates simpler risk assessment tools.

Purpose of the Study:

To evaluate the practicality of using basic risk factors for initial prenatal risk assessment of trisomy 21 and neural tube defects.
To develop logistic regression models for early identification of pregnancies at risk.
To provide an accessible tool for risk stratification in resource-limited settings.

Main Methods:

Utilized logistic regression analysis.
Focused on basic, readily available risk factors for prediction.
Developed models for initial risk assessment of T-21 and NTDs.

Main Results:

Prediction models demonstrated a limited balanced classification rate.
Models successfully identified and could eliminate at least 50% of cases with no or low risk.
The approach offers a viable initial screening strategy.

Conclusions:

Despite moderate accuracy, logistic regression models using basic risk factors are effective for initial prenatal risk assessment of T-21 and NTDs.
These models can serve as a valuable preliminary screening tool, especially where advanced diagnostics are unavailable.
The study highlights the potential for simple risk factor-based models to improve prenatal care accessibility.