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Record W3197527143 · doi:10.1002/mco2.74

Current therapeutic strategies for respiratory diseases using mesenchymal stem cells

2021· review· en· W3197527143 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedComm · 2021
Typereview
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsCAE (Canada)
FundersNational Natural Science Foundation of China
KeywordsMesenchymal stem cellMedicinePulmonary fibrosisAsthmaMechanism (biology)Stem cellRespiratory systemImmunologyStem-cell therapyFibrosisBioinformaticsIntensive care medicinePathologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

Mesenchymal stromal/stem cells (MSCs) have a great potential to proliferate, undergo multi-directional differentiation, and exert immunoregulatory effects. There is already much enthusiasm for their therapeutic potentials for respiratory inflammatory diseases. Although the mechanism of MSCs-based therapy has been well explored, only a few articles have summarized the key advances in this field. We hereby provide a review over the latest progresses made on the MSCs-based therapies for four types of inflammatory respiratory diseases, including idiopathic pulmonary fibrosis, acute respiratory distress syndrome, chronic obstructive pulmonary disease, and asthma, and the uncovery of their underlying mechanisms from the perspective of biological characteristics and functions. Furthermore, we have also discussed the advantages and disadvantages of the MSCs-based therapies and prospects for their optimization.

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.

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 score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.366
GPT teacher head0.485
Teacher spread0.119 · 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