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Record W4408371696 · doi:10.7150/ijms.103806

The Role of Mesenchymal Stem Cells in Treating Diabetic Kidney Disease: Immunomodulatory Effects and Kidney Regeneration

2025· article· en· W4408371696 on OpenAlexaff
Po-Jen Hsiao, W.H. Linda Kao, Li‐Chin Sung, Chia-Yi Lin, Liam Li-An Tsou, Yung-Hsi Kao, Chu‐Lin Chou, Kung-Ta Lee

Bibliographic record

VenueInternational Journal of Medical Sciences · 2025
Typearticle
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsNational Defence Medical Centre
Fundersnot available
KeywordsMesenchymal stem cellRegeneration (biology)Renal stem cellKidneyKidney diseaseStem cellDiseaseMedicineBiologyPathologyCell biologyInternal medicineProgenitor cell

Abstract

fetched live from OpenAlex

hUC-MSCs treatment may present a potential avenue for reversing glomerulosclerosis and mitigating inflammation in DKD mice. The long-term therapeutic benefits of MSCs-based treatments in patients with DKD and other kidney diseases could be further investigated.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.323
Teacher spread0.311 · 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 designObservational
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

Citations7
Published2025
Admission routes1
Has abstractyes

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