Intraocular Pressure and Retinal Nerve Fiber Layer Changes in a Microgravity Mouse Model and Relevance to Spaceflight-Associated Neuro-Ocular Syndrome
Bibliographic record
Abstract
Purpose: Microgravity-induced headward fluid shifts are one of the mechanisms implicated in spaceflight-associated eye conditions, including intraocular pressure (IOP) and retinal nerve fiber layer (RNFL) thickness changes. In this longitudinal study, we investigated IOP and RNFL thickness changes over time in a mouse model of microgravity-induced headward fluid shifts. Methods: /J mice, randomly assigned to two groups: the hindlimb unloading (HU) mice, unloaded for 21 days followed by 14 days of release, and control mice kept under the same conditions except HU for 35 days. IOP and RNFL thickness in peripapillary and peripheral rings of right and left eyes were measured before and once a week after HU. Our analysis utilized mixed linear models to compare the estimated marginal means of IOP and RNFL thickness on each day with baseline values for each eye. Post hoc splined mixed linear models with a knot at day 14 were employed to assess the rate of IOP change in each segment. Results: IOP was significantly elevated in both eyes of the HU mice on day 14 compared to baseline. The splined analysis revealed a bilateral positive rate of IOP change up to day 14, followed by a negative rate of change thereafter. In contrast, control mice displayed no significant differences in IOP at any timepoint. RNFL thicknesses of right eye peripapillary and peripheral rings were reduced after 1 week and 2 weeks, respectively. In contrast, left eye RNFL thickness measurements did not show any significant change compared to baseline. Conclusion: The HU mouse model displays a distinct ocular phenotype that may be useful for understanding IOP and RNFL changes in microgravity and their relevance to Spaceflight-Associated Neuro-ocular Syndrome.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".