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Record W2050756738 · doi:10.1309/bkmrrnbfjpuy07c0

Mitochondrial DNA Analysis of Acellular Laboratory Samples

2007· article· en· W2050756738 on OpenAlexafffund
Victoria Snowdon, Robert W. Hay, Douglas J. Demetrick

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsCalgary Laboratory ServicesUniversity of Calgary
FundersCalgary Laboratory Services
KeywordsMitochondrial DNAAmpliconBiologyPolymerase chain reactiongenomic DNAMolecular biologymtDNA control regionGeneticsSingle-nucleotide polymorphismGenotypeDNAGeneHaplotype

Abstract

fetched live from OpenAlex

The source of acellular specimens is not infrequently challenged, especially for toxicology specimens, but such specimens may not be amenable to conventional genetic testing to confirm the source. Our evaluation of genomic and mitochondrial DNA (mtDNA) amplicons using polymerase chain reaction (PCR) from centrifuged, filtered, or whole urine specimens demonstrated higher sensitivity of detection of mtDNA than genomic DNA and a higher detection rate for the mtDNA markers than genomic markers in all sample sets. The mitochondrial amplicons were sequenced to identify specific single nucleotide polymorphisms (SNPs). Subsequently, a real-time PCR technique using fluorescence resonance energy transfer (FRET) probes designed to hybridize to the published mtDNA sequence over known SNP locations present within the mitochondrial control region was developed. Our results demonstrate the feasibility of using a FRET-based assay of mitochondrial genotypes with acellular laboratory specimens to screen for specimen mix-ups or to confirm sources of controversial toxicology specimens.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.452
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.376
Teacher spread0.356 · 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 teacher head, 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

Citations2
Published2007
Admission routes2
Has abstractyes

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