Cord Blood Cytokine Levels Correlate With Types of Placental Pathology in Extremely Preterm Infants
Bibliographic record
Abstract
Background:Placental abnormalities are associated with inflammation and have been linked to brain injury in preterm infants. We studied the relationship between placental pathology and the temporal profiles of cytokine levels in extremely pre-term infants. Study Design:We prospectively enrolled 55 extremely preterm infants born between June 2017 and July 2018. Levels of 27 cytokines were measured in blood drawn from the umbilical artery at birth and from infants at 1–3 and 21–28 days of life. Placental pathology was grouped as normal (N), inflammation (I), vasculopathy (V), or combined vasculopathy and inflammation (V+I). Results:Complete data was available from 42 patients. Cord blood median levels of cytokines differed between groups with the highest levels observed in group V+I as compared to groups N, I and V for the following: Eotaxin (p= 0.038), G-CSF (p= 0.023), IFN-γ (p= 0.002), IL-1ra (p< 0.001), IL-4 (p= 0.005), IL-8 (p= 0.010), MCP-1 (p= 0.011), and TNFα (p= 0.002).Post-hocanalysis revealed sex differences between and within the placental pathology groups. Conclusion:Specific types of placental pathology may be associated with differential cytokine profiles in extremely pre-term infants. Sampling from cord blood may help assess the pathological status of the placenta and potentially infer outcome risks for the infant.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".