Depression in Patients with Mastocytosis: Prevalence, Features and Effects of Masitinib Therapy
Bibliographic record
Abstract
Depression in patients with mastocytosis is often reported but its prevalence and characteristics are not precisely described. In addition, the impact of therapies targeting mast cells proliferation, differentiation and degranulation on psychic symptoms of depression have never been investigated. Our objective was to determine the prevalence and to describe features of depression in a large cohort of mastocytosis patients (n = 288) and to investigate the therapeutic impact of the protein kinase inhibitor masitinib in depression symptoms. The description of depression was based on the analysis of a database with Hamilton scores using Principal Component Analysis (PCA). Efficacy of masitinib therapy was evaluated using non parametric Wilcoxon test for paired data within a three months period (n = 35). Our results show that patients with indolent mastocytosis present an elevated prevalence of depression (64%). Depression was moderate in 56% but severe in 8% of cases. Core symptoms (such as psychic anxiety, depressed mood, work and interests) characterized depression in mastocytosis patients. Masitinib therapy was associated with significant improvement (67% of the cases) of overall depression, with 75% of recovery cases. Global Quality of Life slightly improved after masitinib therapy and did not predicted depression improvement. In conclusion, depression is very frequent in mastocytosis patients and masitinib therapy is associated with the reduction its psychic experiences. We conclude that depression in mastocytosis may originate from processes related to mast cells activation. Masitinib could therefore be a useful treatment for mastocytosis patients with depression and anxiety symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".