In silico reconstructions underpin aberrant trafficking dynamics in deficient axons of Dst knockout and Dst/Nefl double-knockout mice
Bibliographic record
Abstract
Aberrant neuronal trafficking is a significant hallmark of neurodegenerative pathology. Its real-time evolution remains elusive and poorly defined due to the lack of a predictive spatiotemporal framework. Building upon a general neurocytoskeletal-PDEs (iGCPs) model, we propose the concept of Virtual Cellular Dynamics for quantitative spatiotemporal simulations of mitochondrial dynamics within axons. The model integrates interactions of key cytoskeletal components such as dystonin, microtubule, neurofilament, and actin filament, providing a comprehensive framework for neuron-specific virtual cell modeling, enabling quantitative insight into axonal dysfunction and structural degradation across neurodegenerative disease. Not only does our model recapitulate the significant structural deformations and mitochondrial transport disruptions observed in Dst-deficient mice, but it further predicts that the ablation of Nefl alleviates severe neurodegenerative progression-a finding substantiated by multi-modal imaging and Dst/Nefl double-knockout murine models, which reveal phenotypic rescue and validate the potential of NF-L-targeted therapeutic strategies. Altogether, our work paves the way for next-generation virtual cell models tailored to neuron-specific disease states.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".