MétaCan
Menu
Back to cohort
Record W2410582973 · doi:10.1385/1-59745-068-5:141

Dual-Color PRINS for <i>In Situ</i> Detection of Fetal Cells in Maternal Blood

2006· article· en· W2410582973 on OpenAlexafffund
Kada Krabchi, Macoura Gadji, Ju Yan, Régen Drouin

Bibliographic record

VenueHumana Press eBooks · 2006
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsUniversité de Sherbrooke
FundersMedical Research CouncilCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsFluorescence in situ hybridizationIn situIn situ hybridizationOligonucleotideMolecular biologyBiologyOligomer restrictionPeripheral bloodFetusFish <Actinopterygii>FluorescenceChromosomeDNAChemistryGeneticsImmunologyPregnancyGeneOpticsMessenger RNAPhysics

Abstract

fetched live from OpenAlex

Fetal nucleated cells circulating in the peripheral blood during pregnancy are potential targets for noninvasive genetic testing. Fluorescence in situ hybridization (FISH) frequently is used to quantify the total number of fetal cells in peripheral blood of pregnant women. We describe an alternative molecular cytogenetic procedure that is the primed in situ labeling (PRINS). This technique consists of annealing oligonucleotides specific to individual chromosome targets and in situ elongation using Taq DNA polymerase to incorporate labeled dUTPs. The sites of the newly synthesized DNA sequences were revealed as fluorescent signals using an immunochemical reaction. The dual-color PRINS was specifically performed for simultaneous detection of two chromosome targets, X and Y. The fluorescent signals corresponding to chromosomes X and Y were displayed as red and green color spots, respectively. The sensitivity and specificity of PRINS are similar to FISH and allow us to efficiently and reliably detect fetal cells in maternal blood. Moreover, dual-color PRINS is faster and more cost-effective than FISH.

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: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.648

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 head0.022
GPT teacher head0.248
Teacher spread0.226 · 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

Citations4
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueHumana Press eBooksSame topicPrenatal Screening and DiagnosticsFrench-language works237,207