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Record W2766582147 · doi:10.21037/sci.2017.09.06

Clinical potential of mesenchymal stem/stromal cell-derived extracellular vesicles

2017· review· en· W2766582147 on OpenAlexfundno aff
Bernd Giebel, Lambros Kordelas, Verena Börger

Bibliographic record

VenueStem Cell Investigation · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsnot available
FundersEuropean Regional Development FundUniversität Duisburg-EssenEuropean CommissionVolkswagen FoundationStem Cell Network
KeywordsMesenchymal stem cellExtracellular vesiclesStromal cellVesicleStem cellMicrovesiclesCell biologyChemistryBiologyMedicinePathologymicroRNABiochemistryMembrane

Abstract

fetched live from OpenAlex

Within the last two decades mesenchymal stem/stromal cells (MSCs) emerged after hematopoietic stem cells as the second most investigated and applied somatic stem cell entity so far. MSCs mediate immunosuppressive as well as pro-regenerative activities. Against the initial assumption, MSCs may not primarily exert their therapeutic functions in a cellular but rather in a paracrine manner. Here, extracellular vesicles (EVs), such as exosomes and microvesicles, have been identified as major mediators of these paracrine effects. Meanwhile, MSC-EVs have been applied to an increasing amount of different animal models and were tested in a patient suffering from steroid-refractory acute graft-versus-host disease (acute GvHD) as well as in a patient cohort with chronic kidney disease. So far, the MSC-EV administration appears to be safe in humans and all tested animal models. Improvements were reported in all settings. Thus, MSC-EVs appear as promising novel therapeutic agents which might help to improve disease associated symptoms in millions of patients. Here, we review some of the milestones in the field, briefly discuss challenges and highlight clinical aspects of acute GvHD and its treatment with MSCs and MSC-EVs.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.792
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.073
GPT teacher head0.336
Teacher spread0.263 · 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.

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

Citations155
Published2017
Admission routes1
Has abstractyes

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