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Record W2068994140 · doi:10.1089/lrb.2009.0019

mTOR Signaling in Lymphangioleiomyomatosis

2010· review· en· W2068994140 on OpenAlexafffund
Arnold S. Kristof

Bibliographic record

VenueLymphatic Research and Biology · 2010
Typereview
Languageen
FieldMedicine
TopicTuberous Sclerosis Complex Research
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNational Cancer InstituteCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsLymphangioleiomyomatosisPI3K/AKT/mTOR pathwayDiscovery and development of mTOR inhibitorsSirolimusTuberous sclerosisCancer researchCell growthRPTORMedicineEverolimusBiologyBioinformaticsSignal transductionPathologyInternal medicineCell biologyGenetics

Abstract

fetched live from OpenAlex

The protein mammalian target of rapamycin (mTOR) plays a central role in cell growth and proliferation. Excessive mTOR activity is a prominent feature of many neoplasms and hamartoma syndromes, including lymphangioleiomyomatosis (LAM), a destructive lung disease that causes progressive respiratory failure in women. Although pharmacological inhibitors of mTOR should directly target the pathogenesis of these disorders, their clinical efficacy has been suboptimal. Recent scientific findings reviewed here have greatly improved our understanding of mTOR signaling mechanisms, provided new insights into the control of cell growth and proliferation, and facilitated the development of new therapeutic approaches in LAM, as well as other neoplastic disorders that exhibit excessive mTOR activity.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.405
GPT teacher head0.511
Teacher spread0.106 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations22
Published2010
Admission routes2
Has abstractyes

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