The Role of Aquaporin-4 in Freezing of Gait and Dynamic Balance Learning in Parkinson’s Disease
Bibliographic record
Abstract
Aquaporin-4 (AQP4), a modulator of motor symptoms and synaptic plasticity, may contribute to freezing of gait (FOG)-a significant gait disturbance in Parkinson's disease with an unclear pathophysiology-and its associated impairments in balance learning. However, this potential relationship has not been investigated until now. This preliminary study explores the potential role of AQP4 in FOG and its associated balance learning deficits. The study involved fifteen patients with FOG, fifteen patients without FOG, and fifteen healthy controls. Serum AQP4 levels were measured using enzyme-linked immunosorbent assay, and balance learning was assessed using a voluntary dynamic balance task performed on a stabilometer. Notably, patients who were FOG-positive exhibited significantly higher serum AQP4 levels compared to the other two groups (p < 0.001). These elevated levels showed a positive correlation with FOG severity (ρ = 0.51, p = 0.004). Furthermore, the serum AQP4 levels were inversely correlated with both the slope (ρ = -0.65, p < 0.001) and rate (ρ = -0.46, p = 0.01) of balance learning in PwPD. FOG-positive individuals exhibited impaired voluntary balance learning compared to both FOG-negative and healthy participants. FOG-negative participants showed initial improvement, while healthy individuals demonstrated continuous enhancement across consecutive learning blocks (the first significant main effect of time on balance performance, comparing Blocks 1 and 2, was p = 0.02 for FOG-negative and p = 0.03 for healthy groups), with both groups maintaining their acquired skills over time. These findings suggest that AQP4 may play a significant role in FOG and its associated learning impairments, warranting further investigation for potential treatments. Additionally, alternative balance learning protocols may be necessary for FOG-positive patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".