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Record W3126122920 · doi:10.1126/scitranslmed.abd6726

Engineering biopharmaceutical formulations to improve diabetes management

2021· review· en· W3126122920 on OpenAlexfundno aff
Caitlin L. Maikawa, Andrea I. d’Aquino, Rayhan Lal, Bruce A. Buckingham, Eric A. Appel

Bibliographic record

VenueScience Translational Medicine · 2021
Typereview
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNatural Sciences and Engineering Research Council of CanadaAmerican Diabetes Association
KeywordsBiopharmaceuticalDiabetes managementDiabetes mellitusMedicineIntensive care medicineComputational biologyType 2 diabetesBiotechnologyBiologyEndocrinology

Abstract

fetched live from OpenAlex

Insulin was first isolated almost a century ago, yet commercial formulations of insulin and its analogs for hormone replacement therapy still fall short of appropriately mimicking endogenous glycemic control. Moreover, the controlled delivery of complementary hormones (such as amylin or glucagon) is complicated by instability of the pharmacologic agents and complexity of maintaining multiple infusions. In this review, we highlight the advantages and limitations of recent advances in drug formulation that improve protein stability and pharmacokinetics, prolong drug delivery, or enable alternative dosage forms for the management of diabetes. With controlled delivery, these formulations could improve closed-loop glycemic control.

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 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)0.001
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 score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.063
GPT teacher head0.388
Teacher spread0.325 · 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 teacher head, not a consensus.

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

Citations23
Published2021
Admission routes1
Has abstractyes

Explore more

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