Eosinophils—from cradle to grave
Bibliographic record
Abstract
Over the past years, eosinophils have become a focus of scientific interest, especially in the context of their recently uncovered functions (e.g. antiviral, anti-inflammatory, regulatory). These versatile cells display both beneficial and detrimental activities under various physiological and pathological conditions. Eosinophils are involved in the pathogenesis of many diseases which can be classified into primary (clonal) and secondary (reactive) disorders and idiopathic (hyper)eosinophilic syndromes. Depending on the biological specimen, the eosinophil count in different body compartments may serve as a biomarker reflecting the underlying pathophysiology and/or activity of distinct diseases and as a therapy-driving (predictive) and monitoring tool. Personalized selection of an appropriate therapeutic strategy directly or indirectly targeting the increased number and/or activity of eosinophils should be based on the understanding of eosinophil homeostasis including their interactions with other immune and non-immune cells within different body compartments. Hence, restoring as well as maintaining homeostasis within an individual's eosinophil pool is a goal of both specific and non-specific eosinophil-targeting therapies. Despite the overall favourable safety profile of the currently available anti-eosinophil biologics, the effect of eosinophil depletion should be monitored from the perspective of possible unwanted consequences.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".