Morphological and functional phenotyping of skeletal muscle and bone in the zQ175 knock-in mouse model of Huntington's disease
Bibliographic record
Abstract
Background : Huntington's disease (HD) is a progressive neurodegenerative disorder primarily affecting the central nervous system (CNS). However, emerging evidence suggests that peripheral tissues, including skeletal muscle and bone, also undergo pathological changes contributing to disease burden. Objective : To characterize musculoskeletal impairments in the zQ175 knock-in (KI) mouse model of HD, through integrated behavioral, biomechanical, and imaging analyses. Methods : Motor function was assessed using grip strength, rotarod, and open field testing. Ex vivo contractility of the extensor digitorum longus (EDL) and Soleus (Sol) muscles was measured. Muscle fiber cross-sectional area (CSA) was quantified using semi-automated segmentation. Bone microarchitecture was analyzed using high-resolution micro-computed tomography (μCT). Results : Six-month-old homozygous zQ175 mice exhibited significantly reduced muscle strength and impaired contractile properties in both the EDL and Soleus muscles compared to wild-type (WT) controls. µCT analysis revealed decreased trabecular bone volume and alterations in bone structure. Conclusions : These findings provide a comprehensive musculoskeletal phenotyping of zQ175 mice, revealing early-onset muscle atrophy and skeletal fragility. Our study highlights the importance of targeting peripheral manifestations in HD and establishes zQ175 KI mice as a valuable additional model for investigating systemic disease mechanisms.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".