Quantitative measurement of FMRP in blood platelets as a new screening test for fragile X syndrome
Bibliographic record
Abstract
The fragile X syndrome usually results from CGG repeats expansion and methylation of the FMR1 gene leading to the absence of expression of its encoded protein, fragile X mental retardation protein (FMRP). Therefore, its diagnosis is traditionally based on the detection of these molecular alterations. As an alternative, FMRP-based screening methods have been proposed over the years. Most of them are based on immunohistochemistry analyses applied to a restricted number of lymphocytes (100) or hair roots (10-20) with limited diagnosis potential. In this study, we describe a truly quantitative approach using a new model, the blood platelet, which can be recovered easily with very high purity (99.9%). FMRP levels in platelets were first measured in a control population (n = 124) and reference values were established. FMRP measurements were also performed in confirmed fragile X subjects. Receiver operating characteristic curve analysis has shown that our test can easily discriminate fragile X males and females from controls (area under curve, AUC = 0.948). Cognitive functions were also assessed in these individuals using age-specific Wechsler Intelligence Scales for Children and the Vineland Adaptive Behavior Scales. A proportional relationship between FMRP levels, intelligence quotient and adaptive behavior was observed among fragile X individuals, suggesting that our test would be able to detect fragile X cases and may predict cognitive functions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".