The Role of Immune and Oxidative Pathways in Menstrual-Cycle Associated Depressive, Physio-Somatic, Breast and Anxiety Symptoms: Modulation by Sex Hormones
Bibliographic record
Abstract
Objective: To examine whether 1) immune and nitro-oxidative stress (IO&NS) biomarkers are associated with premenstrual syndrome (PMS); and 2) changes in IO&NS biomarkers during the menstrual cycle (MC) are associated with PMS symptoms and plasma estradiol and progesterone. Methods: Forty-one women completed the Daily Record of Severity of Problems (DRSP) rating scale during 28 consecutive days and MC Associated Syndrome (MCAS) was diagnosed when the summed DRSP score during the MC is > 0.666 percentile. We assayed plasma levels of complement C3 and C4, highly sensitive C-reactive protein (hsCRP), haptoglobin (Hp), advanced oxidation protein products (AOPP), lipid hydroperoxides (LOOH), nitric oxide metabolites (NOx), total radical-trapping antioxidant parameter (TRAP), sulfhydryl (-SH) groups and the activity of paraoxonase (PON)1 at days 7 (D7), 14 (D14), 21 (D21) and 28 (D28) of the MC. Results: All biomarkers, except hsCRP, showed significant alterations during the MC. Arylesterase (AREase) was lowered at D28, while LOOH increased at D14 and C4 at D21 in women with MCAS. The total DRSP score was predicted by the combined effects of C4 (positively) and AREase and malondialdehyde (MDA) (both inversely associated). Progesterone lowered levels of LOOH, AOPP and C3 and estradiol lowered levels of Hp while both sex hormones increased 4-(chloromethyl)phenyl acetate (CMPA)ase and AREase activities and levels of -SH groups. Conclusion: PMS/MCAS is not accompanied by a peripheral inflammatory response. Lowered MDA and antioxidant defenses and increased C4 may play a role in MC-associated symptoms while sex hormones may have a protective effect against oxidative stress toxicity.
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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) | 0.001 | 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".