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Record W2414234658 · doi:10.1385/0-89603-516-6:331

Methods for Microencapsulation with HEMA-MMA

2003· article· en· W2414234658 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTissue Engineering · 2003
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Toronto
FundersMedical Research Council Canada
KeywordsImmune systemMembraneExtracellular matrixCell biologyChemistryMaterials scienceBiologyImmunologyBiochemistry

Abstract

fetched live from OpenAlex

Encapsulation of cells in a membrane prior to implantation holds potential for controlling the adverse immune response that may be generated against the transplanted cells, by physically isolating the cells from the host's immune system. If successful, encapsulation eliminates or minimizes the adverse effects of immunosuppressive therapy and permits the use of xenogeneic cells. Ideally, the capsule membrane holds permselective properties, so that the passage of nutrients, growth factors, and the therapeutic product secreted by the cells occur readily across the membrane, but mediators of the immune system do not penetrate the membrane (Fig. 1). The major types of immunoisolation devices include intravascular arteriovenous shunts, diffusion chambers of tubular or planar geometry, and microcapsules (1-6). Fig. 1. Schematic drawing of a model microcapsule. Cells are encapsulated with or without an extracellular matrix in a permselective membrane, which provides isolation from the mediators of the immune system, but allows the passage of nutrients, growth factors, and the therapeutic product secreted by the cells.

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)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.240
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.015
GPT teacher head0.309
Teacher spread0.294 · 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