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Record W2032051433 · doi:10.1353/hub.2003.0050

Hypersensitive PCR, Ancient Human mtDNA, and Contamination

2003· article· en· W2032051433 on OpenAlexaff
Dongya Yang, Barry Eng, Shelley R. Saunders

Bibliographic record

VenueHuman Biology · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYersinia bacterium, plague, ectoparasites research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMitochondrial DNAAncient DNAPolymerase chain reactionBiologyDNAPolymeraseDNA polymeraseReal-time polymerase chain reactionMolecular biologyMultiple displacement amplificationGeneticsGeneDNA extractionPopulationMedicine

Abstract

fetched live from OpenAlex

When highly efficient polymerase was used with high cycle numbers (50-60), strong amplifications were observed, but negative controls were also unexpectedly amplified in a study of ancient human mtDNA from 2000-year-old skeletons. The results of a series of tests revealed that the hypersensitive polymerase chain reaction (PCR) generated by higher cycles and the presence of contaminant DNA (though at extremely low levels) should be responsible for the amplification of negative controls. We suggest that PCR sensitivity be optimized to take advantage of highly efficient polymerase and at the same time prevent "background DNA" from becoming "contaminant DNA" and obscuring the analysis of authentic ancient DNA. We propose the use of multiple positive controls when amplifying ancient human mtDNA samples to indicate the sensitivity of individual PCR amplifications and to monitor the contamination levels of modern human DNA. This study provides some suggestions as to how to amplify and analyze ancient human mtDNA when unavoidable and extremely tiny amounts of modern human DNA exist.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.023
GPT teacher head0.313
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations37
Published2003
Admission routes1
Has abstractyes

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