Mutation analysis of Gaucher disease using dot-blood samples on FTA� filter paper
Bibliographic record
Abstract
FTA((R)) filter papers were used as an effective means of blood cell collection, genomic DNA processing, and delivery. Minute blood samples (<1 microL) were collected onto the filters via a simple lateral prick to the patient's finger, circumventing the need for intravenous blood puncture. Collected samples, which are stable at room temperature for several years, were subsequently sent through the postal system to the diagnostic laboratory, bypassing the stringent requirements of courier delivery. Using this method, we performed restriction fragment length polymorphism (RFLP) and nucleotide sequence analysis on prevalent mutations among Canadian and Chinese Gaucher disease patients. Of the 12 alleles (six patients) analyzed, 42% (5/12) have the N370S mutation and 58% (7/12) the L444P mutation, the two most common alleles found among Jewish and non-Jewish Gaucher disease patients. Uniquely, a Chinese Gaucher disease patient was found to have an N370S mutation. Although the presence of the N370S mutation is regarded as common in other ethnic groups, previous to this report it had not been noted in an individual of Asian descent. PvuII polymorphism analysis showed that the N370S mutation found in the Chinese patient was linked to the Pv1.1(-) polymorphism, as has been previously seen in the Jewish population. The use of FTA((R)) filter paper facilitates access of samples to diagnostic centers, and therefore provides an effective means of performing population-based mutational analysis of Gaucher disease internationally.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".