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Related Concept Videos

What are Estimates?01:06

What are Estimates?

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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Postmortem proteomics to discover biomarkers for forensic PMI estimation.

Kyoung-Min Choi1, Angela Zissler2, Eunjung Kim3

  • 1Graduate School of Analytical Science and Technology (GRAST), Chungnam National University, Daejeon, South Korea.

International Journal of Legal Medicine
|March 14, 2019
PubMed
Summary

This study identifies new skeletal muscle protein markers, eEF1A2 and GAPDH, for estimating the postmortem interval (PMI) in humans. These proteins show consistent degradation patterns, offering a novel approach for time since death determination.

Keywords:
DegradationPostmortem interval (PMI)ProteinProteomicsSkeletal muscle

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Area of Science:

  • Forensic Science
  • Biochemistry
  • Proteomics

Background:

  • Estimating the time since death (postmortem interval, PMI) is crucial in forensic investigations.
  • Current protein degradation methods for PMI estimation are limited to a few skeletal muscle proteins.
  • Systemic protein analysis offers potential for identifying novel PMI markers.

Purpose of the Study:

  • To explore unbiased, system-wide proteomic analysis for identifying new postmortem skeletal muscle protein markers.
  • To investigate the degradation patterns of specific proteins (eEF1A2 and GAPDH) for PMI estimation.
  • To validate novel protein markers in both animal models and human autopsy cases.

Main Methods:

  • Quantitative proteomics using mass spectrometry to analyze skeletal muscle proteomes in rats and mice at various postmortem time points (0-96 hpm).
  • Hierarchical clustering analysis to identify differentially expressed proteins.
  • Western blot validation of selected protein markers (eEF1A2 and GAPDH) in rat models and human samples.

Main Results:

  • Proteomic profiling identified 579 (rat) and 896 (mouse) differentially expressed proteins postmortem.
  • Proteins eEF1A2 and GAPDH demonstrated consistent degradation in both species, indicating conserved behavior.
  • Western blot analysis confirmed eEF1A2 and GAPDH as reliable markers for PMI estimation in human autopsy cases.

Conclusions:

  • Mass spectrometry-based proteomics is feasible for discovering novel protein markers for PMI estimation.
  • The proteins eEF1A2 and GAPDH are identified as valuable and transferable biomarkers for estimating the postmortem interval in humans.