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Record W2969786311 · doi:10.1155/2019/8262849

Mesenchymal Stromal Cells Are More Effective Than Their Extracellular Vesicles at Reducing Lung Injury Regardless of Acute Respiratory Distress Syndrome Etiology

2019· article· en· W2969786311 on OpenAlexaff
Johnatas D. Silva, Lígia Lins de Castro, Cássia L. Braga, Gisele P. Oliveira, Stefano Trivelin, Carlos M. Barbosa-Junior, Marcelo M. Morales, Claúdia C. dos Santos, Daniel J. Weiss, Miquéias Lopes‐Pacheco, Fernanda Ferreira Cruz, Patrícia R. M. Rocco

Bibliographic record

VenueStem Cells International · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsUniversity of Toronto
FundersFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroConselho Nacional de Desenvolvimento Científico e TecnológicoMinistério da SaúdeCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMesenchymal stem cellARDSMedicineLungBronchoalveolar lavageVascular endothelial growth factorStromal cellTumor necrosis factor alphaImmunologyCancer researchPathologyInternal medicine

Abstract

fetched live from OpenAlex

Although mesenchymal stromal cells (MSCs) have demonstrated beneficial effects on experimental acute respiratory distress syndrome (ARDS), preconditioning may be required to potentiate their therapeutic effects. Additionally, administration of cell-free products, such as extracellular vesicles (EVs) obtained from MSC-conditioned media, might be as effective as MSCs. In this study, we comparatively evaluated the effects of MSCs, preconditioned or not with serum collected from mice with pulmonary or extrapulmonary ARDS (ARDSp and ARDSexp, respectively), and the EVs derived from these cells on lung inflammation and remodeling in ARDSp and ARDSexp mice. Administration of MSCs (preconditioned or not), but not their EVs, reduced static lung elastance, interstitial edema, and collagen fiber content in both ARDSp and ARDSexp. Although MSCs and EVs reduced alveolar collapse and neutrophil cell counts in lung tissue, therapeutic responses were superior in mice receiving MSCs, regardless of preconditioning. Despite higher total cell, macrophage, and neutrophil counts in bronchoalveolar lavage fluid in ARDSp than ARDSexp, MSCs and EVs (preconditioned or not) led to a similar decrease. In ARDSp, both MSCs and EVs, regardless of preconditioning, reduced levels of tumor necrosis factor- (TNF-) α, interleukin-6, keratinocyte chemoattractant (KC), vascular endothelial growth factor (VEGF), and transforming growth factor- (TGF-) β in lung homogenates. In ARDSexp, TNF-α, interleukin-6, and KC levels were reduced by MSCs and EVs, preconditioned or not; only MSCs reduced VEGF levels, while TGF-β levels were similarly increased in ARDSexp treated either with saline, MSCs, or EVs, regardless of preconditioning. In conclusion, MSCs yielded greater overall improvement in ARDS in comparison to EVs derived from the same number of cells and regardless of the preconditioning status. However, the effects of MSCs and EVs differed according to ARDS etiology.

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: Empirical
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 communication0.0000.000
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 head0.241
Teacher spread0.236 · 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

Citations59
Published2019
Admission routes1
Has abstractyes

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