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 OpenAlexaff
Yuju Jun, Hyunyoung Oh, Rajshekhar Karpoormath, Amitabh Jha, Rajkumar Patel

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.001
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations6
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Polymeric MaterialsSame topicBone Tissue Engineering MaterialsFrench-language works237,207