Optimization of extraction of genomic DNA from archived dried blood spot (DBS): potential application in epidemiological research & bio banking
Bibliographic record
Abstract
Background: Limited infrastructure is available to collect, store and transport venous blood in field epidemiological studies. Dried blood spot (DBS) is a robust potential alternative sample source for epidemiological studies & bio banking. A stable source of genomic DNA (gDNA) is required for long term storage in bio bank for its downstream applications. Our objective is to optimize the methods of gDNA extraction from stored DBS and with the aim of revealing its utility in large scale epidemiological studies. Methods: The purpose of this study was to extract the maximum amount of gDNA from DBS on Whatman 903 protein saver card. gDNA was extracted through column (Qiagen) & magnetic bead based (Invitrogen) methods. Quantification of extracted gDNA was performed with a spectrophotometer, fluorometer, and integrity analyzed by agarose gel electrophoresis. Result: Large variation was observed in quantity & purity (260/280 ratio, 1.8-2.9) of the extracted gDNA. The intact gDNA bands on the electrophoresis gel reflect the robustness of DBS for gDNA even after prolonged storage time. The extracted gDNA amount 2.16 – 24 ng/µl is sufficient for its PCR based downstream application, but unfortunately it can’t be used for whole genome sequencing or genotyping from extracted gDNA. Sequencing or genotyping can be achieved by after increasing template copy number through whole genome amplification of extracted gDNA. The obtained results create a base for future research to develop high-throughput research and extraction methods from blood samples. Conclusion: The above results reveal, DBS can be utilized as a potential and robust sample source for bio banking in field epidemiological studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".