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Record W4384067636 · doi:10.1002/exp.20210110

Glucose oxidase‐instructed biomineralization of calcium‐based biomaterials for biomedical applications

2023· review· en· W4384067636 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

VenueExploration · 2023
Typereview
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsGlucose oxidaseBiomineralizationNanotechnologyChemistryBiodegradationCalciumMaterials scienceChemical engineeringBiosensorEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In recent years, glucose oxidase (GOx) has aroused great research interest in the treatment of diseases related to abnormal glucose metabolisms like cancer and diabetes. However, as a kind of endogenous oxido‐reductase, GOx suffers from poor stability and system toxicity in vivo. In order to overcome this bottleneck, GOx is encapsulated in calcium‐based biomaterials (CaXs) such as calcium phosphate (CaP) and calcium carbonate (CaCO 3 ) by using it as a biotemplate to simulate the natural biomineralization process. The biomineralized GOx holds improved stability and reduced side effects, due to the excellent bioactivity, biocompatibitliy, and biodegradability of CaXs. In this review, the state‐of‐the‐art studies on GOx‐mineralized CaXs are introduced with an emphasis on their application in various biomedical fields including disease diagnosis, cancer treatment, and diabetes management. The current challenges and future perspectives of GOx‐mineralized CaXs are discussed, which is expected to promote further studies on these smart GOx‐mineralized CaXs biomaterials for practical applications.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.966
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
Bibliometrics0.0010.001
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 head0.107
GPT teacher head0.341
Teacher spread0.235 · 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