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Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

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Non-invasive Hemoglobin Measurement Predictive Analytics with Missing Data and Accuracy Improvement Using Gaussian

Jianing Man^1,2, Martin D Zielinski³, Devashish Das⁴

¹School of Mechanical Engineering, Institute of Industrial and Intelligent System Engineering, Beijing Institute of Technology, Beijing, China. jmbitie@bit.edu.cn.

Journal of Medical Systems

|September 26, 2022

Summary

Continuous hemoglobin (SpHb) monitoring offers real-time health insights but suffers from data gaps. A novel Gaussian process and functional regression model effectively imputes missing SpHb data, enhancing accuracy and reliability for clinical decision-making.

Keywords:

Functional principal component analysis Functional regression method Gaussian process Missing data Non-invasive hemoglobin measurement

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A Rapid and Chemical-free Hemoglobin Assay with Photothermal Angular Light Scattering

A Rapid and Chemical-free Hemoglobin Assay with Photothermal Angular Light Scattering

Published on: December 7, 2016

Area of Science:

Biomedical Engineering
Medical Informatics
Clinical Monitoring

Background:

Noninvasive continuous hemoglobin (SpHb) monitoring offers real-time data, unlike delayed laboratory hemoglobin (HgB) tests.
SpHb monitors aid in early detection of critical conditions like anemia and hemorrhagic shock.
Sensor detachment in SpHb monitoring leads to missing data, compromising accuracy and intervention effectiveness.

Purpose of the Study:

To develop and evaluate a model for imputing missing SpHb data and predicting laboratory-based HgB.
To address data gaps in continuous SpHb measurements and improve prediction accuracy.
To enhance the reliability of SpHb monitoring for clinical decision-making.

Main Methods:

Investigated a model incorporating imputation and prediction techniques for missing SpHb values.
Proposed Gaussian process and functional regression methods for SpHb data imputation and HgB prediction.
Considered multiple sub-model choices within the proposed framework and discussed recommendations.

Main Results:

The proposed method demonstrated significant improvements in accuracy based on a real-data study.
The modeling framework showed superior performance in handling missing SpHb data compared to existing methods.
Accuracy of HgB predictions was enhanced through effective imputation of missing SpHb measurements.

Conclusions:

The developed imputation and prediction model effectively addresses missing data challenges in SpHb monitoring.
The proposed framework offers a reliable solution for improving the accuracy and utility of continuous hemoglobin measurements.
The modeling approach is adaptable for other applications dealing with missing data scenarios.