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Record W4399406557 · doi:10.25040/lkv2024.01.064

The 14-3-3η Biomarker Platform for Diagnosis and Prognostic Monitoring of Patients with Rheumatoid Arthritis

2024· article· en· W4399406557 on OpenAlexaff
Walter P. Maksymowych

Bibliographic record

VenueLviv clinical bulletin · 2024
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRheumatoid arthritisBiomarkerMedicineOncologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Introduction. There are several gaps in the clinical evaluation and management of patients with rheumatoid arthritis (RA) that could be addressed through the development of new biomarkers. These include diagnostic biomarkers for primary care physicians that facilitate early referral to a rheumatologist and modifiable biomarkers that guide prognostic assessment and inform rheumatologists on the need for more intensive treatment. The aim of the study.To review the literature regarding the 14-3-3η biomarker platform for diagnosis and prognostic monitoring of patients with rheumatoid arthritis. Materials and methods. Content analysis, the method of systematic and comparative analysis, the bibliosemantic method of studying the current scientific research on 14-3-3η biomarker platform for diagnosis and prognostic monitoring of patients with RA were used. Results. The 14-3-3ηprotein is a new biomarker that is physiologically an intracellular chaperone but is detected extracellularly in joint fluid and peripheral blood specifically in patients with RA. Levels of this protein correlated with expression of metalloproteinases capable of degrading joint cartilage and with factors that enhance activation of osteoclasts. The mechanism of secretion into extracellular fluid may involve necrosis of synovial cells induced by tumor necrosis factor alpha (TNF-a).It enhances diagnostic accuracy of rheumatoid factor and anti-cyclic citrullinated peptide antibodies for detection of RA and is associated with more severe disease but correlates poorly with acute phase reactants such as C-reactive protein. Levels are reduced by several treatments, notably agents that target interleukin-6 and TNF-a. Prospective studies demonstrate that serial measures of 14-3-3η reflect prognostic risk for progression of joint damage on radiography, especially when used in combination with acute phase reactants. The extracellular appearance of 14-3-3η may induce antibodies to this protein which may themselves have diagnostic utility. Conclusions. The14-3-3η protein is selectively found in the joints and peripheral blood of patients with rheumatoid arthritis. It has properties of an inflammatory mediator in culture experiments involving monocytic and innate immune cells and levels in rheumatoid arthritis patients correlate with those of metalloproteinases associated with cartilage degradation. Longitudinal studies and serial assessment of 14-3-3η demonstrate that higher levels increase the risk for future joint damage in rheumatoid arthritis. These data should be replicated in additional cohorts.

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)0.013
metaresearch head score (Gemma)0.016
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: none
Teacher disagreement score0.013
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0100.007
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.054
GPT teacher head0.361
Teacher spread0.307 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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