Single Nucleotide Polymorphisms Alter the Promoter Activity of Bovine MIF
Bibliographic record
Abstract
Macrophage migration inhibitory factor (MIF) is a unique pro-inflammatory cytokine whose chief functions include modulating TLR4 expression, and suppressing the anti-inflammatory effects of glucocorticoids. Not surprisingly, MIF is involved in a number of inflammatory diseases and single nucleotide polymorphisms (SNPs) have been implicated in modulating disease severity. The objective of the present study was to determine if SNPs in 5' region of bovine MIF affects its promoter activity. Three SNPs were identified, -1078A>G, -395A>G, and -400G>C, all of which fall within predicted transcription factor binding regions. Reporter gene assays indicate that the identified SNPs have a significant effect of modulating MIF promoter activity. Finally, gene association analysis suggests a significant relationship of -395A>G with the susceptibility to Mycobacterium avium ssp. paratuberculosis infection, the causative agent of Johne's disease. Given the relationships revealed in the current study, it is clear that the role of MIF in bovine diseases such as Johne's disease merits further investigation.
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 | 0.001 |
| 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".