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Record W3036066449 · doi:10.1242/dev.185884

Development and function of smooth muscle cells is modulated by <i>Hic1</i> in mouse testis

2020· article· en· W3036066449 on OpenAlexafffund
Aya Uchida, Sadman Sakib, Elodie Labit, Sepideh Abbasi, R. Wilder Scott, T. Michael Underhill, Jeff Biernaskie, Ina Dobrinski

Bibliographic record

VenueDevelopment · 2020
Typearticle
Languageen
FieldMedicine
TopicSperm and Testicular Function
Canadian institutionsUniversity of British ColumbiaUniversity of Calgary
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsBiologyFunction (biology)Cell biology

Abstract

fetched live from OpenAlex

In mammalian testis, contractile peritubular myoid cells (PMCs) regulate the transport of sperm and luminal fluid, while secreting growth factors and extra-cellular matrix (ECM) proteins to support the spermatogonial stem cell (SSC) niche. However, little is known about the role of testicular smooth muscle cells during the postnatal testicular development. Here we report age-dependent expression of Hypermethylated in cancer 1 (Hic1, ZBTB29) in testicular smooth muscle cells, including PMCs and vascular smooth muscle cells in mouse. Postnatal deletion of Hic1 in smooth muscle cells led to their increased proliferation and resulted in dilation of seminiferous tubules with increased numbers of PMC. These seminiferous tubules contained fewer Sertoli cells and more spermatogonia, and fibronectin was not detected in their basement membrane. The expression levels of genes encoding smooth muscle contractile proteins, Acta2 and Cnn1, were down regulated in the smooth muscle cells lacking Hic1, and the seminiferous tubules appeared to have reduced contractility. These data imply a role for Hic1 in determining the size of seminiferous tubules by regulating postnatal smooth muscle cell proliferation, subsequently affecting spermatogenesis at adulthood.

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 score0.546

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 head0.018
GPT teacher head0.207
Teacher spread0.189 · 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

Citations18
Published2020
Admission routes2
Has abstractyes

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