Quantitative Analysis of Nucleic Acids in Sweat with Advantage to Latent Finger Prints

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Rachadaporn Benchawattananonb

Abstract

Objectives: Fingerprints are one of the most important evidence of crime scene. In an attempt to recover DNA from latent fingerprint sweat components, DNA extraction is a crucial step in the recovery of DNA due to the fact that latent fingerprints contain low amounts of DNA[7,8] and simple, sensitive techniques are therefore required. Materials and Methods: This study was carried out to evaluate the accuracy of 3 different methods employed for DNA quantification, i.e., ultra violet (UV) spectrophotometry, NanoDrop, and Qubit Fluorometry, and to compare the effectiveness of 2 different methods used for DNA extraction, i.e., Proteinase K/phenol/glycogen (PPG) method (developed from the classical method proteinase K/phenol) and commercially available QIAmp DNA mini kit. Results: The results showed that Qubit® Fluorometer showed higher accuracy than other two methods and was observed to have the detection limit of 1 ng/μL and the sample volume of 1 μL was sufficient for the detection. Meanwhile, UV spectrophotometry and NanoDrop were found to have the detection limit of 60 and 10 ng/μL, respectively. The results indicated that Qubit® Fluorometer was suitable for detecting low amounts of DNA. The PPG method and QIAmp DNA mini kit were compared for their effectiveness in extracting DNA standards and DNA from fingerprint sweat on A4 papers and it was found that developed method could yield higher amount of DNA than QIAmp kit, which was about 4-fold with DNA standards and about 2-fold with fingerprint sweat. Conclusions: The findings indicated that the developed method was far more effective than QIAmp kit and should be considered for use in forensic aspects.

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How to Cite
Benchawattananonb, R. (2017). Quantitative Analysis of Nucleic Acids in Sweat with Advantage to Latent Finger Prints. Asian Journal of Pharmaceutics (AJP), 11(03). https://doi.org/10.22377/ajp.v11i03.1506
Section
ORIGINAL ARTICLES