Human and Viral microRNA Expression in Sjögren Syndrome
Bibliographic record
Abstract
Sjögren syndrome (SS) is one of the most common autoimmune diseases, and mainly affects women. It is characterized by features of systemic autoimmunity and dysfunction and inflammation in the exocrine glands. The dysfunction of the salivary glands often causes significant morbidity and has social implications. In a significant percentage of affected patients, extraglandular manifestations lead to systemic diseases with serious medical implications as more major organs are affected. Non-Hodgkin lymphoma is another major complication of SS, occurring in 5% of patients. The pathogenesis of SS has not been delineated, but it is believed that both immunologic and non-immune mechanisms are involved. In this issue of The Journal , Peng, et al , report their findings of microRNA profiling of peripheral mononuclear cells of primary SS (pSS) in a Chinese patient cohort1. Four patients with pSS and 3 healthy controls were profiled with microarrays and some of the most differentially expressed microRNA were validated with quantitative real-time PCR in a total of 33 pSS patient and 10 healthy control samples, including the samples used for … Address correspondence to Dr. Alevizos, SS and Salivary Gland Dysfunction Unit, Molecular Physiology and Therapeutics, National Institute of Dental and Craniofacial Research, 10 Center Drive, Bldg. 10, Rm 1N110, Bethesda, Maryland 20892, USA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".