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Record W2034017564 · doi:10.1371/journal.pone.0089296

An Assessment of the Long-Term Effects of Simulated Microgravity on Cranial Neural Crest Cells in Zebrafish Embryos with a Focus on the Adult Skeleton

2014· article· en· W2034017564 on OpenAlexafffund
Sara C. Edsall, Tamara A. Franz‐Odendaal

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsMount Saint Vincent UniversityDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyDalhousie UniversityMount Saint Vincent University
KeywordsJuvenileZebrafishCranial neural crestBiologyNeural crestSkullWeightlessnessSkeleton (computer programming)AnatomyAxial skeletonNeuroscienceEmbryoCell biologyEcology

Abstract

fetched live from OpenAlex

It is becoming increasingly important to address the long-term effects of exposure to simulated microgravity as the potential for space tourism and life in space become prominent topics amongst the World's governments. There are several studies examining the effects of exposure to simulated microgravity on various developmental systems and in various organisms; however, few examine the effects beyond the juvenile stages. In this study, we expose zebrafish embryos to simulated microgravity starting at key stages associated with cranial neural crest cell migration. We then analyzed the skeletons of adult fish. Gross observations and morphometric analyses show that exposure to simulated microgravity results in stunted growth, reduced ossification and severe distortion of some skeletal elements. Additionally, we investigated the effects on the juvenile skull and body pigmentation. This study determines for the first time the long-term effects of embryonic exposure to simulated microgravity on the developing skull and highlights the importance of studies investigating the effects of altered gravitational forces.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.265
Teacher spread0.254 · 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 designBench or experimental
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

Citations27
Published2014
Admission routes2
Has abstractyes

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