MétaCan
Menu
Back to cohort
Record W2999672373 · doi:10.1080/00914037.2020.1713783

Role of microsphere as drug carrier for osteogenic differentiation

2020· article· en· W2999672373 on OpenAlex
Yuju Jun, Hyunyoung Oh, Rajshekhar Karpoormath, Amitabh Jha, Rajkumar Patel

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

VenueInternational Journal of Polymeric Materials · 2020
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsAcadia University
Fundersnot available
KeywordsDrug deliveryMicrosphereBiocompatibilityDrugRegeneration (biology)Biomedical engineeringDrug carrierNanotechnologyTissue engineeringChemistryMaterials sciencePharmacologyCell biologyMedicineBiologyChemical engineeringEngineering

Abstract

fetched live from OpenAlex

Drug delivery system in sustainable manner has a great potential in biomedical applications. Along with its biocompatibility, microspheres ability to encapsulate and promote sustained release of drugs or growth factor makes them an ideal carrier for the transport of bioactive molecules for tissue regeneration and controlled drug delivery applications. Additionally, the injectable form of small spherical microsphere facilitates the accurate drug delivery. These drug carrier microspheres have significant role in osteogenic differentiation. The invasive delivery of bioactive molecules promotes bone regeneration. The drug stimulates the osteogenic differentiation of stem cell and promotes the damaged tissue for self-repair. This review elaborates the role of polymeric microspheres and the composite microspheres as a drug carrier for osteogenic differentiation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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