Impact of Adiponectin Overexpression on Allergic Airways Responses in Mice
Bibliographic record
Abstract
Obesity is an important risk factor for asthma. Obese individuals have decreased circulating adiponectin, an adipose-derived hormone with anti-inflammatory properties. We hypothesized that transgenic overexpression of adiponectin would attenuate allergic airways inflammation and mucous hyperplasia in mice. To test this hypothesis, we used mice overexpressing adiponectin (Adipo Tg). Adipo Tg mice had marked increases in both serum adiponectin and bronchoalveolar lavage (BAL) fluid adiponectin. Both acute and chronic ovalbumin (OVA) sensitization and challenge protocols were used. In both protocols, OVA-induced increases in total BAL cells were attenuated in Adipo Tg versus WT mice. In the acute protocol, OVA-induced increases in several IL-13 dependent genes were attenuated in Adipo Tg versus WT mice, even though IL-13 per se was not affected. With chronic exposure, though OVA-induced increases in goblet cells numbers per millimeter of basement membrane were greater in Adipo Tg versus WT mice, mRNA abundance of mucous genes in lungs was not different. Also, adiponectin overexpression did not induce M2 polarization in alveolar macrophages. Our results indicate that adiponectin protects against allergen-induced inflammatory cell recruitment to the airspaces, but not development of goblet cell hyperplasia.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".