MétaCan
Menu
Back to cohort
Record W4313704018 · doi:10.1016/j.exger.2023.112086

Aging affects gene expression in spermatids of Brown Norway rats

2023· article· en· W4313704018 on OpenAlexafffund
Heather E. Fice, Bernard Robaire

Bibliographic record

VenueExperimental Gerontology · 2023
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsGene expressionGeneBiologyGeneticsExpression (computer science)Cell biologyAndrologyMedicine

Abstract

fetched live from OpenAlex

The effects of aging on the reproductive health of men and the consequences for their offspring are becoming more widely recognized. Correlative epidemiological studies examining paternal age and offspring health suggest there are more frequent occurrences of genetic disorders in the children of older fathers. Given the genetic basis for paternal age-related disorders, we aim to characterize gene expression in developing germ cells. Round spermatids (RS) were collected from young (mean = 5.3 months) and aged (mean = 19.5 months) Brown Norway rats, representative of humans aged 20-30 years and 55+ years, respectively. Gene expression data were obtained by mRNA sequencing (n = 5), and were analysed for differential expression. Sequencing data display 211 upregulated and 9 downregulated transcripts in RS of aged rats, compared to young (log2FC >1, p < 0.05). Transcripts with increased expression are involved in several processes including sperm motility/morphology, sperm-egg binding, capacitation, and epigenetic inheritance. In addition, there are numerous dysregulated transcripts that regulate germ cell epigenetic marks and Sertoli-germ cell binding and communication. These results show an overall increase in RS gene expression with age, with spermatogenic functions being perturbed. Taken together, these findings help identify the genetic origin of the fertility, germ cell niche, and epigenetic effects observed with advanced paternal aging.

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 score0.110
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.047
GPT teacher head0.355
Teacher spread0.308 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueExperimental GerontologySame topicBirth, Development, and HealthFrench-language works237,207