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Record W2985470127 · doi:10.1097/mnh.0000000000000560

The emerging role of activins in renal disease

2019· review· en· W2985470127 on OpenAlexafffund
Nandita Mehta, Joan C. Krepinsky

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2019
Typereview
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsSt. Joseph’s Healthcare HamiltonSt. Joseph's HospitalMcMaster University
FundersCanadian Institutes of Health Research
KeywordsKidney diseaseDiseaseMedicineContext (archaeology)PathogenesisBioinformaticsKidneyFibrosisInflammationImmunologyInternal medicineBiology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review highlights recent discoveries and advances that have been made in understanding the role of the TGFβ superfamily members activins, and in particular, activin A (ActA), in renal disease. RECENT FINDINGS: A deleterious role for ActA in renal disease and its complications has begun to emerge. We summarize data supporting an important contribution of ActA to kidney fibrosis and inflammation of varying causes, as well as its role in the development of a particular bone mineral disorder seen in chronic kidney disease (CKD) called mineral bone disorder (MBD), including vascular calcification. Finally, we discuss ActA in the context of anemia associated with chronic kidney disease and review potential approaches to treatment based on ActA blockade. SUMMARY: ActA is an important contributor to the pathogenesis of acute and chronic kidney disease of varying causes. Preclinical studies show that ActA inhibition, through various approaches, is protective in rodent models of kidney disease. The potential adverse effects of some of these approaches can be attributed to their targeting of other TGFβ family ligands. Further preclinical and clinical investigations testing the therapeutic efficacy of more selective ActA inhibition on the progression of acute and chronic kidney disease and its impact on bone-mineral disorder would more definitively establish its role in renal disease.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.098
GPT teacher head0.391
Teacher spread0.293 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations27
Published2019
Admission routes2
Has abstractyes

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