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Record W2120251683 · doi:10.3390/ijms150814348

Mesenchymal Stem Cell-Derived Microparticles: A Promising Therapeutic Strategy

2014· review· en· W2120251683 on OpenAlexaff
Xi Tan, Yong-Zhen Gong, Ping Wu, Duan‐Fang Liao, Xi‐Long Zheng

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsMesenchymal stem cellRegenerative medicineStem cellBiologyCellCell biologyCancer research

Abstract

fetched live from OpenAlex

Mesenchymal stem cells (MSCs) are multipotent stem cells that give rise to various cell types of the mesodermal germ layer. Because of their unique ability to home in on injured and cancerous tissues, MSCs are of great potential in regenerative medicine. MSCs also contribute to reparative processes in different pathological conditions, including cardiovascular diseases and cancer. However, many studies have shown that only a small proportion of transplanted MSCs can actually survive and be incorporated into host tissues. The effects of MSCs cannot be fully explained by their number. Recent discoveries suggest that microparticles (MPs) derived from MSCs may be important for the physiological functions of their parent. Though the physiological role of MSC-MPs is currently not well understood, inspiring results indicate that, in tissue repair and anti-cancer therapy, MSC-MPs have similar pro-regenerative and protective properties as their cellular counterparts. Thus, MSC-MPs represent a promising approach that may overcome the obstacles and risks associated with the use of native or engineered MSCs.

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 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 score0.798
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.035
GPT teacher head0.342
Teacher spread0.307 · 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.

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

Citations37
Published2014
Admission routes1
Has abstractyes

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