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Record W3123114720 · doi:10.3389/fnins.2020.617377

Interaction Between Mesenchymal Stem Cells and Retinal Degenerative Microenvironment

2021· review· en· W3123114720 on OpenAlexaff
Lin Yu, Xiang Ren, Yongjiang Chen, Danian Chen

Bibliographic record

VenueFrontiers in Neuroscience · 2021
Typereview
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsMesenchymal stem cellStem cellRetinalNeuroscienceBiologyCell biology

Abstract

fetched live from OpenAlex

Retinal degenerative diseases (RDDs) are a group of diseases contributing to irreversible vision loss with yet limited therapies. Stem cell-based therapy is a promising novel therapeutic approach in RDD treatment. Mesenchymal stromal/stem cells (MSCs) have emerged as a leading cell source due to their neurotrophic and immunomodulatory capabilities, limited ethical concerns, and low risk of tumor formation. Several pre-clinical studies have shown that MSCs have the potential to delay retinal degeneration, and recent clinical trials have demonstrated promising safety profiles for the application of MSCs in retinal disease. However, some of the clinical-stage MSC therapies have been unable to meet primary efficacy end points, and severe side effects were reported in some retinal "stem cell" clinics. In this review, we provide an update of the interaction between MSCs and the RDD microenvironment and discuss how to balance the therapeutic potential and safety concerns of MSCs' ocular application.

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 score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.087
GPT teacher head0.357
Teacher spread0.270 · 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

Citations21
Published2021
Admission routes1
Has abstractyes

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