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Record W4406107070 · doi:10.1016/j.nbd.2025.106793

Modeling sacsin depletion in Danio Rerio offers new insight on retinal defects in ARSACS

2025· article· en· W4406107070 on OpenAlexaboutno aff
Valentina Naef, Devid Damiani, Rosario Licitra, Maria Marchese, Stefania Della Vecchia, Matteo Baggiani, Letizia Brogi, Daniele Galatolo, Silvia Landi, Filippo M. Santorelli

Bibliographic record

VenueNeurobiology of Disease · 2025
Typearticle
Languageen
FieldMedicine
TopicRetinopathy of Prematurity Studies
Canadian institutionsnot available
FundersMinistero della SaluteFondazione Telethon
KeywordsDanioNeuroscienceZebrafishBiologyCognitive sciencePsychologyGeneticsGene

Abstract

fetched live from OpenAlex

Biallelic mutations in the SACS gene, encoding sacsin, cause early-onset autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS), a neurodegenerative disease also characterized by unique and poorly understood retinal abnormalities. While two murine models replicate the phenotypic and neuronal features observed in patients, no retinal phenotype has been described so far. In a zebrafish knock-out strain that faithfully mirrors the main aspects of ARSACS, we observed impaired visual function due to photoreceptor degeneration, likely caused by cell cycle defects in progenitor cells. RNA-seq analysis in embryos revealed dysfunction in proteins related to fat-soluble vitamins (e.g., TTPA, RDH5, VKORC) and suggested a key role of neuroinflammation in driving the retinal defects. Our findings indicate that studying retinal pathology in ARSACS could be crucial for understanding the impact of sacsin depletion and may offer insights into halting disease progression. • Zebrafish sacs mutants display visual impairment. • The study underscores importance of sacsin in normal retinal development in zebrafish. • Retinal studies in ARSACS zebrafish model may help to develop new therapies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.275
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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