Differential Gene Expression of High and Low Immune Responder Canadian Holstein Dairy Cows
Bibliographic record
Abstract
Infectious diseases are an important cause of economic loss in the agri-food business. This study investigates indicators of bovine high (HR) and low (LR) immune response and their associated patterns of gene expression. Holstein cows were immunized to induce antibody (AMIR) and cell-mediated (CMIR) immune responses. Based on the results of enzyme-linked immunosorbent assay (ELISA) and delayed-type hypersensitivity (DTH), cows were ranked as HR, LR or average (AR) immune responders. For microarray analysis, phenotypic HR and LR status in both groups was confirmed and total RNA from blood mononuclear cells (BMCs) was obtained. RNA from a pool of AR cows was used as a common reference for hybridization to an in-house cDNAmicroarray. Results of microarray analysis showed transcriptional differences in several immune-related genes between the HR and LR groups. Genes identified as differentially expressed include transcription factors, cytokines, MHC, and TCR-related genes. These results can aid in the establishmentof selection programmes based on broad-based disease resistance, aimed at improving general health in cattle herds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".