Modulation of functional activity of heat-stressed chicken macrophages by poultry-derived probiotic lactobacilli
Bibliographic record
Abstract
This study investigated the potential role of lactobacilli in mitigating the negative effects of heat stress on the functional activity of chicken macrophages. Macrophage-like MQ-NCSU cells were incubated at 40 and 44 °C in the presence or absence of a single or a mixture of different poultry-derived Lactobacillus spp., including Lactobacillus animalis, L. acidophilus, L. reuteri, and L. crispatus. Macrophage activation was evaluated by measuring nitric oxide (NO) production, phagocytic activity, and the transcription levels of cytokines, chemokine, and Toll-like receptors (TLRs). Macrophages exposed to heat stress exhibited increased production of NO, diminished expression of interleukin (IL)-1β and IL-12p40, and elevated expression of TLR2 and TLR4, whereas no significant alterations in the phagocytic activity of macrophages were observed. Conversely, treatment of macrophages with probiotic lactobacilli counteracted the effects associated with heat stress. This was evidenced by a notable enhancement in macrophage phagocytic activity, NO production and expression of IL-1β, IL-12p40, IL-18, and chemokine CXCL8, coupled with a reduction in TLR2 and TLR4 expression. These findings suggest that probiotic lactobacilli could be given to chickens to mitigate the negative effects of heat stress on their innate immune system. However, further studies are required to validate the observed effects in an in vivo model.
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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) | 0.000 | 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".