De la conception du PRINS à son couronnement
Bibliographic record
Abstract
As a non-isotopic molecular cytogenetic technique, the primed in situ (PRINS) labelling reaction represents a major technological progress achieved in the past decade. It has become a routine technique for the microscopic visualization of specific DNA sequences in cells and nuclei and constitutes a good alternative to the fluorescence in situ hybridization (FISH) procedure. Among the multiple advantages that characterize the PRINS technique, specificity, rapidity, reliability, reproducibility, and cost-effectiveness can be mentioned. PRINS can be in addition associated with other techniques like FISH, indirect immunofluorescence, and nick translation. The most recent developments show the great potential of this technique. Now PRINS can be used to study single-copy genes and, consequently, can be routinely used to investigate deletions associated with microdeletion syndromes. Therefore, the PRINS technique has the potential to become a widely used molecular cytogenetic tool in clinics and research. This short review presents how the PRINS technique contributed to further the understanding of biological phenomena and describes the different possibilities and applications of the PRINS method in several biological and clinical fields (pre-implantation testing, prenatal, constitutional and oncologic genetic diagnosis).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".