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Record W7132926788

Examining The Roles of MICAL Family Monooxygenases In Breast Cancer Cells

2024· dissertation· W7132926788 on OpenAlexaff
Tony Liang

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCytokinesisBreast cancerCell growthCellCancerSuppressorActin cytoskeletonOncogene
DOInot available

Abstract

fetched live from OpenAlex

This thesis investigated the roles of the Molecule Interacting with CasL (MICAL) family in triple-negative breast cancer cells. MDA MB 231 cells’ migratory, proliferative, morphological and survival properties were examined. Scratch wound assay showed that MICAL KOs by CRISPR-Cas9 didn’t close the wounds efficiently compared to non-targeting control (NTC) cells, and single cell tracking revealed that NTC had more rapid random migration. The change in cells’ migratory abilities accompanied alterations in morphology, indicating changes in actin cytoskeleton. Additionally, MICAL1, -2, and -3 KOs also had reduced cytokinesis completion rate. Although serum starvation experiments revealed reduced cell proliferation in MICAL1 KOs, MICAL2 and MICAL3 KOs showed few differences, except that MICAL3 KOs achieved greater cell proliferation in zero serum compared to NTC. Lastly, MICAL1, -2, and -3 KOs behaved differently in hypoxic conditions, sometimes in opposition. The MICALs family is crucial in MDA MB 231’s migration, cell shape, cytokinesis, and proliferation.

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 head0.055
GPT teacher head0.342
Teacher spread0.287 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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