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Record W2906267672 · doi:10.1002/adma.201806214

Rationally Designed 3D Hydrogels Model Invasive Lung Diseases Enabling High‐Content Drug Screening

2018· article· en· W2906267672 on OpenAlex

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

VenueAdvanced Materials · 2018
Typearticle
Languageen
FieldMedicine
TopicTuberous Sclerosis Complex Research
Canadian institutionsUniversity of OttawaOttawa HospitalUniversity of Toronto
Fundersnot available
KeywordsLymphangioleiomyomatosismTORC1Self-healing hydrogelsCancer researchDrugLungBiologyCellTuberous sclerosisMaterials scienceCell biologySignal transductionPathologyPharmacologyMedicinePI3K/AKT/mTOR pathwayInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Cell behavior is highly dependent upon microenvironment. Thus, to identify drugs targeting metastatic cancer, screens need to be performed in tissue mimetic substrates that allow cell invasion and matrix remodeling. A novel biomimetic 3D hydrogel platform that enables quantitative analysis of cell invasion and viability at the individual cell level is developed using automated data acquisition methods with an invasive lung disease (lymphangioleiomyomatosis, LAM) characterized by hyperactive mammalian target of rapamycin complex 1 (mTORC1) signaling as a model. To test the lung-mimetic hydrogel platform, a kinase inhibitor screen is performed using tuberous sclerosis complex 2 (TSC2) hypomorphic cells, identifying Cdk2 inhibition as a putative LAM therapeutic. The 3D hydrogels mimic the native niche, enable multiple modes of invasion, and delineate phenotypic differences between healthy and diseased cells, all of which are critical to effective drug screens of highly invasive diseases including lung cancer.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.034
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.067
GPT teacher head0.313
Teacher spread0.246 · 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