Clinical significance of matrix metalloproteinase-9 in Fragile X Syndrome
Bibliographic record
Abstract
Abstract High plasma matrix metalloproteases-9 (MMP-9) levels have been reported in Fragile X Syndrome in a limited number of animal and human studies. Since the results obtained are method-dependent and not directly comparable, the clinical utility of MMP-9 measurement in FXS remains unclear. This study aimed to compare quantitative gel zymography and ELISA and to determine which method better discriminates abnormal MMP-9 levels of individuals with FXS from healthy controls and correlates with the clinical profile. The active and total forms of MMP-9 were quantified respectively, by gel zymography and ELISA in a cohort of FXS (n = 23) and healthy controls (n = 20). The clinical profile was assessed for the FXS group using the Aberrant Behavior Checklist FXS adapted version (ABC-CFX), Adaptive Behavior Assessment System (ABAS), Social Communication Questionnaire (SCQ), and Anxiety Depression and Mood Scale questionnaires. Method comparison showed a disagreement between gel zymography and ELISA with a constant error of − 0.18 [95% CI: − 0.35 to − 0.02] and a proportional error of 2.31 [95% CI: 1.53 to 3.24]. Plasma level of MMP-9 active form was significantly higher in FXS (n = 12) as compared to their age-sex and BMI matched controls (n = 12) (p = 0.039) and correlated with ABC-CFX (rs = 0.60; p = 0.039) and ADAMS (rs = 0.57; p = 0.043) scores. As compared to the plasma total form, the plasma MMP-9 active form better enables the discrimination of individuals with FXS from controls and correlates with the clinical profile. Our results highlight the importance of choosing the appropriate method to quantify plasma MMP-9 in future FXS clinical studies.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".