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Record W3118289382 · doi:10.1186/s13578-020-00527-8

Extracellular vesicles in bone and periodontal regeneration: current and potential therapeutic applications

2021· review· en· W3118289382 on OpenAlexaff
Leila Gholami, Vajihe Taghdiri Nooshabadi, Shiva Shahabi, Marzieh Jazayeri, Rana Tarzemany, Zohreh Afsartala, Khatereh Khorsandi

Bibliographic record

VenueCell & Bioscience · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsUniversity of British Columbia
FundersHamadan University of Medical Sciences
KeywordsMesenchymal stem cellRegenerative medicineRegeneration (biology)Stem cellExtracellular vesiclesTissue engineeringCell biologyMedicineBioinformaticsBiomedical engineeringBiology

Abstract

fetched live from OpenAlex

Oral mesenchymal stem cells (MSCs) and their secretomes are considered important factors in the field of medical tissue engineering and cell free biotherapy due to their ease of access, differentiation potential, and successful therapeutic outcomes. Extracellular vesicles (EVs) and the conditioned medium (CM) from MSCs are gaining more attraction as an alternative to cell-based therapies due to the less ethical issues involved, and their easier acquisition, preservation, long term storage, sterilization, and packaging. Bone and periodontal regenerative ability of EVs and CM have been the focus of some recent studies. In this review, we looked through currently available literature regarding MSCs' EVs or conditioned medium and their general characteristics, function, and regenerative potentials. We will also review the novel applications in regenerating bone and periodontal defects.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.020
GPT teacher head0.296
Teacher spread0.276 · 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 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

Citations52
Published2021
Admission routes1
Has abstractyes

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