Suppression of leptin signaling reduces polyglucosan inclusions and seizure susceptibility in a mouse model for Lafora disease
Bibliographic record
Abstract
Lafora disease (LD) represents a fatal form of neurodegenerative disorder characterized by the presence of abnormally large number of polyglucosan bodies-called the Lafora bodies-in neurons and other tissues of the affected patients. The disease is caused by defects in the EPM2A gene coding for a protein phosphatase (laforin) or the NHLRC1 gene coding for an ubiquitin ligase (malin). Studies have shown that inhibition of glycogen synthesis in the brain could prevent the formation of Lafora bodies in the neurons and reduce seizure susceptibility in laforin-deficient mouse, an established animal model for LD. Since increased glucose uptake is thought to underlie increased glycogen in LD, and since the adipocyte hormone leptin is known to positively regulate the glucose uptake in neurons, we reasoned that blocking leptin signaling might reduce the neuronal glucose uptake and ameliorate the LD pathology. We demonstrate here that mice that were deficient for both laforin and leptin receptor showed a reduction in the glycogen level, Lafora bodies and gliosis in the brain, and displayed reduced susceptibility to induced seizures as compared to animals that were deficient only for laforin. Thus, blocking leptin signaling could be a one of the effective therapeutic strategies in LD.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".