Bibliographic record
Abstract
Interleukins, a family of proteins that regulate immunity and inflammation throughout the body, are also involved in autoimmune disease (rheumatoid arthritis, multiple sclerosis, and asthma); are implicated in such other conditions as cancer, myocardial infarction, and depression; and have value in the diagnosis and prognosis of many diseases. Luke O'Neill and Andrew Bowie bring together in Interleukin Protocols a collection of standard and advanced methods for measuring these powerful agents. Their readily reproducible techniques range from the assay of interleukin protein and mRNA-using ELISA, FACS, and RT-PCR-to the study of interleukin signal transduction. Newer techniques are also covered, including the analysis of interleukin gene polymorphisms and the use of cDNA microarrays. Many of the assays are geared to specific pathologies, including breast cancer, depression, psoriasis, Grave's disease, migraine, and myocardial infarction. There are also helpful discussions of the difficulties in measuring interleukins in different biological fluids, such as sputum from asthma patients, peritoneal fluids, synovial fluid from arthritic joints, and cerbrospinal fluid from patients with meningitis. Each step-by-step method is described by a hands-on expert and includes notes on how to avoid failure. Comprehensive and highly practical, Interleukin Protocols offers biomedical investigators a stellar collection of all the major techniques needed to analyze the role of interleukins in disease, to improve diagnosis, and to foster the rapid emergence of new and more powerful therapeutics
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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.
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; both teacher heads agree on what is shown here.
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".