Serotonin Transporter mRNA Expression Is Reduced in the Peripheral Blood Mononuclear Cells of Subjects with Major Depression but Normal in Fibromyalgia
Bibliographic record
Abstract
BACKGROUND: Fibromyalgia (FM) and major depression disorder (MDD) frequently co-occur. Both disorders may share common serotonergic alterations, although there is less evidence of such alterations in FM. It is also unclear as to whether these alterations are persistent over time or transient. The objectives of this study were to (i) examine the changes in mRNA expression of serotonin transporter (SERT) on the surface of peripheral blood mononuclear cells (PBMCs) in FM, MDD, and the FM + MDD subjects compared to healthy controls, and to (ii) evaluate the effect of drug treatment on SERT expression. METHODS: PBMCs were isolated from FM, MDD, FM + MDD, and control subjects. SERT expression was analyzed at the mRNA level via quantitative real-time polymerase chain reaction. Statistical analyses were performed using analyses of variance and linear mixed-effects models. RESULTS: < 0.001), but not in FM nor in FM + MDD subjects. Although the drug treatments improved symptoms in FM, MDD, and FM + MDD subjects, they had no significant effect on SERT mRNA expression. CONCLUSIONS: These results corroborate the role of the SERT in the pathophysiology of MDD, but not in FM, and show that the decreased mRNA expression of SERT is a persistent, rather than transient, phenomenon.
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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 | 0.000 | 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) | 0.002 | 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".