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The effect of <scp>Z</scp>hangfei on the unfolded protein response and growth of cells derived from canine and human osteosarcomas

2012· article· en· W1485276491 on OpenAlexaff
T. Bergeron, R. Zhang, Kirsty M Elliot, Noreen Rapin, Valerie MacDonald, Kathleen Linn, Elemir Simko, Vikram Misra

Bibliographic record

VenueVeterinary and Comparative Oncology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsUnfolded protein responseThapsigarginOsteosarcomaApoptosisGrowth inhibitionCell growthCell biologyMolecular biologyCell cultureTransfectionGeneChemistryBiologyCancer researchEndoplasmic reticulumBiochemistryGenetics

Abstract

fetched live from OpenAlex

The objective of this study was to determine whether the protein Zhangfei could suppress the unfolded protein response (UPR) and growth of osteosarcoma cells. Dog (D-17) and a human (Saos-2) osteosarcoma cells were infected with adenovirus vectors expressing either Zhangfei or the control protein beta- galactosidase. We monitored cell growth as well as levels of UPR gene transcripts and proteins. We found that Zhangfei suppressed the growth of both D-17 and Saos-2 cells. Zhangfei-expressing D-17 cells displayed large vacuoles containing culture medium and expressed phosphatidylserine on their external surface suggesting that Zhangfei induced macropinocytosis and apoptosis in these cells. While Zhangfei inhibited the growth of both D-17 and Saos-2 cells, it inhibited thapsigargin-induced UPR, as detected by a decrease in transcripts for UPR genes, and HERP and GRP78 proteins, only in D-17 cells, suggesting that the ability of Zhangfei to suppress the UPR and tumour cells growth may not be linked.

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 score0.001
Threshold uncertainty score0.003

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 head0.046
GPT teacher head0.336
Teacher spread0.290 · 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

Citations13
Published2012
Admission routes1
Has abstractyes

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