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Record W1056058979 · doi:10.1038/srep12895

Sex hormones have pervasive effects on thymic epithelial cells

2015· article· en· W1056058979 on OpenAlexafffund
Maude Dumont-Lagacé, Charles St-Pierre, Claude Perreault

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersCanadian Institutes of Health Research
KeywordsTranscriptomeSexual dimorphismBiologyHormoneSexual differentiationSex characteristicsGeneInternal medicineEndocrinologyGene expressionGeneticsMedicine

Abstract

fetched live from OpenAlex

The goal of our study was to evaluate at the systems-level, the effect of sex hormones on thymic epithelial cells (TECs). To this end, we sequenced the transcriptome of cortical and medullary TECs (cTECs and mTECs) from three groups of 6 month-old mice: males, females and males castrated at four weeks of age. In parallel, we analyzed variations in the size of TEC subsets in those three groups between 1 and 12 months of age. We report that sex hormones have pervasive effects on the transcriptome of TECs. These effects were exquisitely TEC-subset specific. Sexual dimorphism was particularly conspicuous in cTECs. Male cTECs displayed low proliferation rates that correlated with low expression of Foxn1 and its main targets. Furthermore, male cTECs expressed relatively low levels of genes instrumental in thymocyte expansion (e.g., Dll4) and positive selection (Psmb11 and Ctsl). Nevertheless, cTECs were more abundant in males than females. Accumulation of cTECs in males correlated with differential expression of genes regulating cell survival in cTECs and cell differentiation in mTECs. The sexual dimorphism of TECs highlighted here may be mechanistically linked to the well-recognized sex differences in susceptibility to infections and autoimmune diseases.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.001

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 head0.026
GPT teacher head0.252
Teacher spread0.226 · 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.

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

Citations60
Published2015
Admission routes2
Has abstractyes

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