The First Trimester Gravid Serum Regulates Procalcitonin Expression in Human Macrophages Skewing Their Phenotype<i>In Vitro</i>
Bibliographic record
Abstract
Procalcitonin (PCT) is one of the best diagnostic and prognostic markers in clinical practice, widely used to evaluate the evolution of bacterial infections. Although it is mainly produced by thyroid, during sepsis almost all the peripheral tissues are involved in PCT production. Parenchymal cells have been suggested as the main source of PCT expression; however the contribution of macrophages is not clear yet. In response to environmental cues, tissue macrophages acquire distinct functional phenotypes, ranging from proinflammatory (M1) to anti-inflammatory (M2) phenotype. Macrophages at the fetal-maternal interface show immunosuppressive M2-like activities required for the maintenance of immunological homeostasis during pregnancy. This study aims to clarify the ability to synthesise PCT of fully differentiated (M0), polarized (M1/M2) macrophages and those cultured either in the presence of first trimester gravid serum (GS) or pregnancy hormones. We found out that M1 macrophages upregulate PCT expression following LPS stimulation compared to M0 and M2. The GS downregulates PCT expression in macrophages, skewing them towards an M2-like phenotype. This effect seems only partially mediated by the hormonal milieu. Our findings strengthen the key role of macrophages in counteracting inflammatory stimuli during pregnancy, suggesting PCT as a possible new marker of M1-like macrophages.
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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".