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Record W1502640053 · doi:10.1080/19491034.2014.1003745

Chromosome Conformation of Human Fibroblasts Grown in 3-Dimensional Spheroids

2015· article· en· W1502640053 on OpenAlexaff
Haiming Chen, Nicholas Comment, Jie Chen, Scott Ronquist, Alfred O. Hero, Thomas Ried, Indika Rajapakse

Bibliographic record

VenueNucleus · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsToronto Metropolitan University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesUniversity of Michigan
KeywordsSpheroidInterphaseChromosomeBiologyCell biologyBiophysicsMolecular biologyGeneticsIn vitroGene

Abstract

fetched live from OpenAlex

In the study of interphase chromosome organization, genome-wide chromosome conformation capture (Hi-C) maps are often generated using 2-dimensional (2D) monolayer cultures. These 2D cells have morphological deviations from cells that exist in 3-dimensional (3D) tissues in vivo, and may not maintain the same chromosome conformation. We used Hi-C maps to test the extent of differences in chromosome conformation between human fibroblasts grown in 2D cultures and those grown in 3D spheroids. Significant differences in chromosome conformation were found between 2D cells and those grown in spheroids. Intra-chromosomal interactions were generally increased in spheroid cells, with a few exceptions, while inter-chromosomal interactions were generally decreased. Overall, chromosomes located closer to the nuclear periphery had increased intra-chromosomal contacts in spheroid cells, while those located more centrally had decreased interactions. This study highlights the necessity to conduct studies on the topography of the interphase nucleus under conditions that mimic an in vivo environment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.234
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2015
Admission routes1
Has abstractyes

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