Thermal and cytokine responses to endotoxin challenge during early life
Bibliographic record
Abstract
Sudden infant death syndrome (SIDS) remains the leading cause of infant mortality beyond the neonatal period. An increase in body temperature as a result of high environmental temperature, overwrapping of infants, and (or) infection are associated with SIDS. Endotoxins such as lipopolysaccharide (LPS) and heat stress may perturb cardiorespiratory function and thermoregulation. Although LPS-mediated body temperature and cytokine responses are well documented in older animals, the capacity of LPS to induce fever and cytokine response in young rats remains unclear. Therefore, we sought to investigate the acute effects of LPS on body temperature and cytokine concentrations in rat pups. Postnatal day 7 rat pups were divided into 3 groups: Group 1, rats were administered LPS intraperitoneally (200 μg/kg); Group 2, rats received saline at volume equal to that administered in the LPS group; Group 3, rats received no treatment. Pups were placed in custom-made chambers maintained at ambient temperature of 33 °C. Body surface temperature was continuously monitored for 4 h. Thereafter, the rats were euthanized and serum was collected for cytokine analysis. We demonstrate that LPS treatment increased MIP-1α, IL-10, MCP-1, IP-10, fractalkine, and TNF-α with no concurrent rise in body surface temperature. Although neonatal rats produced an array of cytokines in response to LPS, there was no evidence of fever.
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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 | 0.001 |
| 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".