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Record W1993100501 · doi:10.1159/000014430

Artificial Cell Biotechnology for Medical Applications

2000· review· en· W1993100501 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

VenueBlood Purification · 2000
Typereview
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsMcGill University
Fundersnot available
KeywordsHemoperfusionArtificial cellUric acidMedicineGenetically modified organismBiotechnologyPharmacologyChemistryBiochemistryBiologyInternal medicineHemodialysisGene

Abstract

fetched live from OpenAlex

Artificial cells are prepared in the laboratory for medical and biotechnological applications. The earliest routine clinical use of artificial cells is in the form of coated activated charcoal for hemoperfusion. Implantation of encapsulated cells are being studied for the treatment of diabetes, liver failure and the use of encapsulated genetically engineered cells for gene therapy. We recently found that daily orally administered artificial cells containing a genetically engineered microorganism can lower the elevated urea level in uremic rats to normal levels and increase the survival of the animal. Furthermore, this can remove potassium, phosphate, uric acid and other waste metabolites from uremic plasma. Blood substitutes based on modified hemoglobin are already in phase-III clinical trials in patients with as much as 20 units infused into each patient during trauma surgery. Artificial cells containing enzymes are being developed for clinical trials in hereditary enzyme deficiency diseases and other diseases. Artificial cells are also being investigated for drug delivery and other uses in biotechnology, chemical engineering and medicine.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0010.000
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 head0.037
GPT teacher head0.324
Teacher spread0.286 · 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