The evaluation of cytokines to help establish diagnosis and guide treatment of autoinflammatory and autoimmune diseases
Bibliographic record
Abstract
Our knowledge of the role of cytokines in pathologic conditions has increased considerably with the emergence of molecular and genetic studies, particularly in the case of autoinflammatory monogenic diseases. Many rare disorders, considered orphan until recently, are directly related to abnormal gene regulation, and the treatment with biologic agents (biologics) targeting cytokine receptors, intracellular signaling or specific cytokines improve the symptoms of an increasing number of chronic inflammatory diseases. As it is currently impossible to systematically conduct genetic studies for all patients with autoinflammatory and autoimmune diseases, the evaluation of cytokines can be seen as a simple, less time consuming, and less expensive alternative. This approach could be especially useful when the diagnosis of syndromes of diseases of unknown etiology remains problematic. The evaluation of cytokines could also help avoid the current trial-and-error approach, which has the disadvantages of exposing patients to ineffective drugs with possible unnecessary side effects and permanent organ damages. In this review, we discuss the various possibilities, as well as the limitations of evaluating the cytokine profiles of patients suffering from autoinflammatory and autoimmune diseases, with methods such as direct detection of cytokines in the plasma/serum or following ex vivo stimulation of PBMCs leading to the production of their cytokine secretome. The patients' secretome, combined with biomarkers ranging from genetic and epigenetic analyses to immunologic biomarkers, may help not only the diagnosis but also guide the choice of biologics for more efficient and rapid treatments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".