Effect of a Mushroom (Coriolus versicolor) Based Probiotic on the Expression of Toll-like Receptors and Signal Transduction in Goat Neutrophils
Bibliographic record
Abstract
Neutrophils recognize and destroy pathogens through activation of the Toll like receptor (TLR) system as part of the inflammatory response of innate immunity. The expression and modulation of genes in the TLR signaling pathway in caprine blood neutrophils was investigated. Following initial screening for infection, goats (N=15) were assigned to three groups of five (n=5) individuals. Goats were drenched daily with 10 mL of powdered CorPet (Mycology labs Inc) soaked in hot (treatment I) or cold (treatment II) sterile filtered endotoxin free water, for a 4-week period. A control group of five age-matched goats received sterile water (treatment III). Blood was collected weekly and analyzed for packed cell volume and white blood cell differential counts. At weeks 1 and 4 neutrophils were isolated, using differential centrifugation and hypotonic lysis of red blood cells. The concentration and purity of total RNA isolated using Trizol was determined on a Nanodrop spectrophotometer. The RETROscript kit was used to synthesize cDNA. The expression of 84 genes in the human TLR signaling pathway RT2 PCR Array was evaluated using real time PCR and the Livak method. The house keeping gene GAPDH was used to normalize the data. At week 1 untreated goats expressed 48 genes in the pathway. Goat neutrophils expressed 10 TLRs. Mushroom extracts modulated expression of and signaling by TLR. These results will help in the definition of the role of TLR expression in neutrophils and its contribution to goat innate immunity. Further this may aid in the design of therapeutics for goat health.
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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".