Galectin Secretion and Modulation in Sheep Blood
Bibliographic record
Abstract
Galectins (GAL) are β-galactoside binding proteins that can modulate both pregnancy and the response to pathogens. Increased susceptibility to infection by pathogens is linked to periparturient immune suppression and a periparturient rise (PPR) in parasite eggs on pasture. The possible role of GAL in periparturient immune relaxation and PRR needs definition. The objective of this study was to evaluate galectin secretion and its relationship to measures of the immune relaxation and PRR in periparturient sheep. Samples were collected from pregnant St. Croix sheep (n=6) weekly at days -21 to + 21 relative to lambing. Fecal samples were collected and evaluated for strongyle and coccidia parasite eggs. The concentration of IgA and IgE coproantibodies, total microbial DNA, Bifidobacteria and Lactobacillus levels in fecal samples were used as indicators of gut health. Blood samples were collected by jugular venipuncture and assessed for Packed Cell Volume (PCV), total and differential white blood cell counts. Total protein concentrations and protein profile were evaluated in serum. Secretion of GAL 1, 3, 9 and 14 were evaluated using ELISAs. Data were analyzed by one-way ANOVA and statistical significance was declared at P <0.05. Galectins tested were secreted in sheep blood. Differential modulation of GAL secretion and correlation with periparturient immune suppression and parasite infection was observed. Galectin secretion was modulated by the periparturient period, type and status of parasite infection. This first insight into a possible role of secreted galectins in periparturient immune relaxation and PRR improves the understanding of the immune response, informs development of management programs and therapeutics and presents Galectin profiles as biomarkers with diagnostic potential.
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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) | 0.001 | 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".