Bibliographic record
Abstract
Nuclear factor kappa B (NF-κB) is a ubiquitously expressed transcription factor comprised of various subunits (p50 (NF-κB1), p52 (NF-κB2), p65 (RelA), RelB, and c-Rel). Activation of certain NF-κB subunits appears to foster an inflammatory state that may promote the development of disease. Thus characterizing the specific NF-κB subunits may provide insight into the pathogenesis of certain diseases. The purpose of this study was to determine if 1 month of a diabetic state, induced by streptozotocin (STZ) treatment, alters the constitutive level of NF-κB activation, its subunit composition, or the content of NF-κB-related proteins in rodent liver, kidney, spleen, and heart. Diabetes was induced in male Sprague-Dawley rats by a single tail vein injection of STZ (55 mg·kg-1 body weight). After 30 days, the heart, liver, spleen, and kidney were assessed for NF-κB activation and subunit composition with electrophoretic mobility shift assay (EMSA), and p50 and p65 subunit content was assessed with Western blotting. In diabetic animals, the constitutive level of NF-κB activation was reduced in liver, but was unchanged in kidney, spleen, and heart. EMSA supershifts showed the predominant subunit in the activated NF-κB complexes from both diabetic and control animals to be p50, although the p65 subunit was detected in NF-κB complexes from diabetic hearts. The content of p50 was unaltered in all diabetic tissues examined, whereas the content of p65 was increased only in hearts from diabetic animals. These findings support the idea that a diabetic state may induce specific changes in NF-κB subunit composition in certain tissues.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".