Bibliographic record
Abstract
Immune responses vary between men and women. The female sex is considered protective in combatting acute infections, but disadvantageous in chronic inflammatory diseases. As neutrophils play a central role in innate immunity, they may mediate the sexual dimorphism in immune responses. Neutrophils can combat invading microbes by forming neutrophil extracellular traps (NETs). We hypothesize that females release higher amounts of NETs, leading to more robust acute immune responses. To test our hypothesis, neutrophils isolated from healthy human males and females were induced to form NETs in response to various pharmacological and biological stimuli. Total DNA content was higher in neutrophils from females compared to males. Female neutrophils released decreased levels of DNA in response to the calcium ionophore A23187. PMA, LPS, ionomycin and bacteria stimulation did not result in significant sex differences. Correlating the serum levels of the sex hormones testosterone, estradiol and progesterone to NETosis levels in the above conditions revealed a negative correlation between testosterone and A23187-mediated DNA release. No correlation was detected in relation to endogenous estradiol and progesterone levels, but a combination effect has not been fully explored. Exogenous estradiol or progesterone treatment at physiological concentrations did not influence DNA release. Given the results thus far, a sex difference in NETosis was detected with A23187 treatment but not from biological stimuli in the context of healthy neutrophils.
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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) | 0.009 | 0.001 |
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".