Splenic gene expression of cytokines at multiple time points following lipopolysaccharide challenge in layers
Bibliographic record
Abstract
OBJECTIVE: To investigate inflammatory responses to lipopolysaccharide (LPS) injection in layers. ANIMALS: 33 40-week-old laying hens were used. METHODS: 30 laying hens were divided into 2 groups: the first group was injected with 8 mg/kg LPS, while the second group was injected with sterile saline. At the start of the study, 3 birds served as a baseline and were used as the time 0 controls for both the saline and LPS-treated groups. Blood and spleen tissues were collected at 0 (before) and 1, 2, 3, 4, and 6 hours after injection. RESULTS: LPS administration increased splenic mRNA levels of IL-1β, IL-2, IL-6, IL-8, IL-10, interferon-γ, and tumor necrosis factor-α (P < .001) and serum IL-6 levels (P < .01) compared to saline injection. The mRNA expression of most cytokine genes increased rapidly toward peak values within 2 hours after the LPS injection, and then the difference between the saline and LPS treatments got smaller as time went on; serum IL-6 reached its highest concentration 2 hours after LPS administration. The magnitude of LPS-induced upregulation of gene expression was the highest for IL-6, followed by IL-1β and IL-8, and tumor necrosis factor-α was the least affected. CLINICAL RELEVANCE: The temporal and quantitative profile of these inflammatory mediators generated from this study provides valuable information in identifying the optimal time window and appropriate biomarkers for LPS-induced inflammation, which has significant implications in evaluating the effects of interventions on the immune system of chickens.
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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".