A293 SQUALAMINE INCREASES VAGAL AFFERENT FIRING FREQUENCY IN AGING MICE
Bibliographic record
Abstract
A large portion of the aging population exhibit comorbid gastrointestinal (GI) disorders and common age-related neurodegenerative conditions often including constipation, sleep disturbance, and depression. Luminal gut contents and microbiota have the ability to influence mood and behaviour through the vagal gut-brain axis. Squalamine, an aminosterol originally isolated from the dogfish shark, is a potent stimulator of the enteric nervous system and is currently in a Phase 2a study to evaluate its effect on constipation and other non-motor symptoms of Parkinson’s disease. We have recently shown that constipation in aged mice could be reversed by squalamine acting on the enteric nervous system. The purpose of this study is to explore the impact of aging on vagal afferent signaling in mice and evaluates the effects of squalamine on vagal outflow in this model. We hypothesize that squalamine will increase vagal afferent firing frequency in aged mice. Jejunal segments with attached mesentery from old (18–24 months) and young (3 months) male CD1 mice were excised, pinned out in a petri dish of Krebs and dissected to isolate the mesenteric nerve bundle. The jejunum was cannulated at both ends and luminally perfused with Krebs solution or Krebs with added squalamine (10µM). The mesenteric nerve bundle was sucked onto with a glass micropipette attached to a patch-clamp electrode and multi-unit electrical activity was recorded using an amplifier and signal converter. Single-unit firing was isolated using Dataview software and vagal fibres were identified by response to CCK. Basal afferent vagal firing frequency was measured in old and young mice before and after treatment with squalamine. Mean basal vagal afferent firing frequency was 0.58 ± 0.13 Hz (N=30[4]) in old mice compared to 1.1 ± 0.11 Hz (N=45[3]) for young mice representing a 57% reduction (p = 0.0016) in firing frequency in the elderly animals compared with the younger controls. Luminal application of squalamine increased vagal afferent firing frequency by 43% to 1 ± 0.097 Hz (p < 0.0001) in old mice compared to an increase of 28% to 1.4 ± 0.11 Hz (p = 0.001) in young mice. The onset latency to the peak of the squalamine response was 15 to 20 min. The results demonstrate that luminal exposure to squalamine: (1) stimulates vagal afferent signaling in both young and old mice, (2) the magnitude of the effect is larger in the older animals. The data suggest that diminished vagal afferent firing with aging is at least partially reversible with luminally-administered squalamine. NRC
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".