Effects of tumor necrosis factor α on leptin-sensitive intestinal vagal mechanoreceptors in the cat
Bibliographic record
Abstract
The involvement of tumour necrosis factor α (TNF-α) in inflammatory bowel disease (IBD) has been established, and anti-TNF-α has been suggested as a therapeutic approach for the treatment of these pathologies. We studied the effects of TNF-α on leptin-sensitive intestinal vagal units to determine whether TNF-α exerts its effects through the intestinal vagal mechanoreceptors and to investigate its interactions with substances regulating food intake. The activity of intestinal vagal mechanoreceptors was recorded via microelectrodes implanted into the nodose ganglion in anesthetized cats. TNF-α (1 μg, i.a.) increased the discharge frequency of leptin-activated units (type 1 units; P < 0.05) and had no effect on the discharge frequency of leptin-inhibited units (type 2 units). When TNF-α was administered 20 min after sulfated cholecystokinin-8 (CCK), its excitatory effects on type 1 units were significantly enhanced (P < 0.0001) and type 2 units were significantly (P < 0.05) activated. Pre-treatment with Il-1ra (250 μg, i.a.) blocked the excitatory effects of TNF-α on type 1 units whereas the excitatory effects of TNF-α administration after CCK treatment on type 2 units were not modified. The activation of leptin-sensitive units by TNF-α may explain, at least in part, the weight loss observed in IBD.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".