Melatonin inhibits apoptosis during early B‐cell development in mouse bone marrow
Bibliographic record
Abstract
The pineal secretory product, melatonin, exerts a variety of effects on the immune system. Administration of melatonin stimulates cell-mediated immunity, particularly by inhibiting apoptosis among T lymphocytes in the thymus and inducing production of T-cell-derived cytokines. However, its possible effects on the humoral immune system are unclear. In the present study, we have examined whether melatonin may influence the in vivo development of B lymphocytes in mouse bone marrow, a process in which apoptosis is normally a prominent feature. Double immunofluorescence labeling and flow cytometry were used to quantitate phenotypically defined precursor B-cell and mature B-cell populations and their apoptotic rates in bone marrow of mice fed either melatonin-containing or control diet for 16 days from 9 wk of age. In short-term bone marrow cultures, the incidence of apoptosis among large pre-B cells, including cells expressing the lambda5 component of pre-B-cell receptor, was markedly reduced in melatonin-treated mice, associated with an increase in the absolute number of large pre-B cells in bone marrow. In contrast, apoptosis of earlier precursor B cells and mature B lymphocytes did not differ from control values. The results indicate that orally administered melatonin can substantially promote the survival of precursor B cells in mouse bone marrow. Melatonin treatment may thus boost the survival of newly formed B cells mediating humoral immunity.
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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".