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An Efficient Method for the Assessment of DNA Quality of Archival Microdissected Specimens

2002· article· en· W1994447590 on OpenAlexafffund
Arek Siwoski, Adrian Ishkanian, Cathie Garnis, Lewei Zhang, Miriam P. Rosin, Wan L. Lam

Bibliographic record

VenueModern Pathology · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
FundersNational Institute of Dental and Craniofacial ResearchCanadian Institutes of Health Research
KeywordsRAPDPolymerase chain reactionDNABiologyMolecular biologyDNA extractionMultiple displacement amplificationGeneGeneticsMedicinePopulationGenetic diversity

Abstract

fetched live from OpenAlex

There will be an increasing need of methods for assessing the suitability of specimens for genetic-based assays as DNA markers become an integral part of molecular diagnosis. The targeting of specimens for specific analyses will require the ability to rapidly screen for DNA quality. Conventional methods such as Southern analysis and gene specific-polymerase chain reaction (PCR) often require quantities of material that represent a significant portion of the specimen, especially in microdissected samples. Here we describe a novel application of a commonly used PCR-based DNA-fingerprinting technology that requires minimal quantities of DNA to simultaneously assess multiple regions throughout the genome for DNA quality. Randomly amplified polymorphic DNA (RAPD) PCR generates DNA fragments of a broad size range with the product size reflecting the degree of sample fragmentation. Fourteen DNA samples extracted from cells microdissected from seven formalin-fixed, paraffin-embedded oral cancer biopsies were assessed for DNA quality using gene-specific PCR and RAPD-PCR. Although the more conventional assay required 2-ng DNA (or 300-cell equivalents) to examine DNA quality at a single locus, RAPD-PCR provided a more informative profile of DNA quality from the same microdissected archival 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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.453
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.033
GPT teacher head0.378
Teacher spread0.345 · 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

Citations36
Published2002
Admission routes2
Has abstractyes

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