α-Motoneurons maintain biophysical heterogeneity in obesity and diabetes in Zucker rats
Bibliographic record
Abstract
Small-diameter sensory dysfunction resulting from diabetes has received much attention in the literature, whereas the impact of diabetes on α-motoneurons (MN) has not. In addition, the chance of developing insulin resistance and diabetes is increased in obesity. No study has examined the impact of obesity or diabetes on the biophysical properties of MN. Lean Zucker rats and Zucker diabetic fatty (ZDF) rats were separated into lean, obese (ZDF fed standard chow), and diabetic (ZDF fed high-fat diet that led to diabetes) groups. Glass micropipettes recorded hindlimb MN properties from identified flexor and extensor MN. MN were separated within their groups on the basis of input conductance, which created high- and low-input conductance subpopulations for each. A significant shorter (20%) afterhyperpolarization half-decay (AHP 1/2 ) was found in low-conductance MN for the diabetic group only, whereas AHP½ tended to be shorter in the obese group (19%). Significant positive correlations were found among rheobase and input conductance for both lean and obese animals. No differences were found between the groups for afterhyperpolarization amplitude (AHP amp ), input conductance, rheobase, or any of the rhythmic firing properties (frequency-current slope and spike-frequency adaptation index). MN properties continue to be heterogeneous in obese and diabetic animals. Obesity does not seem to influence lumbar MN. Despite the resistance of MN to the impact of diabetes, the reduced AHP 1/2 decay and the tendency for a reduction in AHP amp may be the first sign of change to MN function. NEW & NOTEWORTHY Knowledge about the impact of obesity and diabetes on the biophysical properties of motoneurons is lacking. We found that diabetes reduces the duration of the afterhyperpolarization and that motoneuron function is unchanged by obesity. A reduced afterhyperpolarization may impact discharge characteristics and may be the first sign of change to motoneuron function.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".