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Record W2005372817 · doi:10.1155/2015/428508

Crosstalk between the Unfolded Protein Response and NF-<i>κ</i>B-Mediated Inflammation in the Progression of Chronic Kidney Disease

2015· review· en· W2005372817 on OpenAlexafffund
Zahraa Mohammed‐Ali, Gaile L. Cruz, Jeffrey G. Dickhout

Bibliographic record

VenueJournal of Immunology Research · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEndoplasmic Reticulum Stress and Disease
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
FundersCanadian Institutes of Health ResearchKidney Foundation of CanadaMcMaster University
KeywordsCrosstalkInflammationKidney diseaseDiseaseUnfolded protein responseNF-κBMedicineNFKB1Cancer researchBioinformaticsImmunologyBiologyCell biologyInternal medicineTranscription factorEndoplasmic reticulumGeneticsPhysicsGene

Abstract

fetched live from OpenAlex

The chronic inflammatory response is emerging as an important therapeutic target in progressive chronic kidney disease. A key transcription factor in the induction of chronic inflammation is NF-κB. Recent studies have demonstrated that sustained activation of the unfolded protein response (UPR) can initiate this NF-κB signaling phenomenon and thereby induce chronic kidney disease progression. A key factor influencing chronic kidney disease progression is proteinuria and this condition has now been demonstrated to induce sustained UPR activation. This review details the crosstalk between the UPR and NF-κB pathways as pertinent to chronic kidney disease. We present potential tools to study this phenomenon as well as potential therapeutics that are emerging to regulate the UPR. These therapeutics may prevent inflammation specifically induced in the kidney due to proteinuria-induced sustained UPR activation.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.049
GPT teacher head0.407
Teacher spread0.358 · 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 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

Citations40
Published2015
Admission routes2
Has abstractyes

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