Sensitive quantification of α-glucans in mouse tissues, cell cultures, and human cerebrospinal fluid
Bibliographic record
Abstract
The soluble α-polyglucan glycogen is a central metabolite enabling transient glucose storage to suit cellular energy needs. Glycogen storage diseases (GSDs) comprise over 15 entities caused by generalized or tissue-specific defects in enzymes of glycogen metabolism. In several, e.g. in Lafora disease caused by the absence of the glycogen phosphatase laforin or its interacting partner malin, degradation-resistant abnormally structured insoluble glycogen accumulates. Sensitive quantification methods for soluble and insoluble glycogen are critical to research, including therapeutic studies, in such diseases. This paper establishes methodological advancements relevant to glycogen metabolism investigations generally, and GSDs. Introducing a pre-extraction incubation method, we measure degradation-resistant glycogen in as little as 30 mg of skeletal muscle or a single hippocampus from Lafora disease mouse models. The digestion-resistant glycogen correlates with the disease-pathogenic insoluble glycogen and can readily be detected in very young mice where glycogen accumulation has just begun. Second, we establish a high-sensitivity glucose assay with detection of ATP depletion, enabling 1) quantification of α-glucans in cell culture using a medium-throughput assay suitable for assessment of candidate glycogen synthesis inhibitors, and 2) discovery of α-glucan material in healthy human cerebrospinal fluid, establishing a novel methodological platform for biomarker analyses in Lafora disease and other GSDs.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".