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Record W3012689698 · doi:10.1186/s13104-020-04971-0

Glycogen synthase kinase 3 (GSK-3) controls T-cell motility and interactions with antigen presenting cells

2020· article· en· W3012689698 on OpenAlex
Alison Taylor, Christopher E. Rudd

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBMC Research Notes · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and B-cell Immunology
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
FundersWellcome Trust
KeywordsMotilityGSK-3Cell biologyCytolysisCTL*Cytotoxic T cellT cellBiologyCellGSK3BAntigenChemistryKinaseCD8Immune systemBiochemistryImmunologyIn vitro

Abstract

fetched live from OpenAlex

OBJECTIVE: The threonine/serine kinase glycogen synthase kinase 3 (GSK-3) targets multiple substrates in T-cells, regulating the expression of Tbet and PD-1 on T-cells. However, it has been unclear whether GSK-3 can affect the motility of T-cells and their interactions with antigen presenting cells. RESULTS: Here, we show that GSK-3 controls T-cell motility and interactions with other cells. Inhibition of GSK-3, using structurally distinct inhibitors, reduced T-cell motility in terms of distance and displacement. While SB415286 reduced the number of cell-cell contacts, the dwell times of cells that established contacts with other cells did not differ for T-cells treated with SB415286. Further, the increase in cytolytic T-cell (CTL) function in killing tumor targets was not affected by the inhibition of motility. This data shows that the inhibition of GSK-3 has differential effects on T-cell motility and CTL function where the negative effects on cell-cell interactions is overridden by the increased cytolytic potential of CTLs.

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.

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 score0.029
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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.083
GPT teacher head0.326
Teacher spread0.243 · 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