Editorial: Streaming inflammation: From damage to healing and resilience–Volume II
Bibliographic record
Abstract
Streaming inflammation: From damage to healing and resilience-Volume II"It takes a very long time to become young."-Pablo Ruiz Picasso Age is a kaleidoscope of identity.It mirrors a number, a malady, a development, an insult and . . .even a compliment.In all dimensions of time, age reflects the fine balance between adaptability and integrity.Our first volume on Streaming Inflammation deemed every human as a multiplex of ecosystems (Devchand et al.).Here, we explore the impacts of damage, healing and resilience on the plasticity of identity as we age.Longitudinal studies emphasize that identity is fluid.Sayah et al. demonstrate how optical coherence tomography imaging coupled with an automated segmentation algorithm can be applied to study dynamic cellular responses during eye development.This noninvasive method coupled with selective-receptor modulation during oxygen-induced retinopathy provides a powerful approach to understanding retinopathy of prematurity in small rodents.In humans, using real-world dynamics of the JIR cohort of patients with pediatric inflammatory diseases, Hentgen et al. tackle the dosing regiment of off-label use of Interleukin-1 inhibitors.Interestingly, in patients with a monogenic auto-inflammatory disease, the actual-doses used in treat-to-target data present an adaptive comparison with that of recommended drug dosage of the medications.Although identity is personal, what we share in common also provides for targeted intervention in promoting healing.Hickey et al. take a computational approach to the chronic inflammation component of complex diseases.Innovatively applying GenePlexus supervised machine learning, they depict disease heterogeneity into gene clusters of diseasespecific chronic inflammation.This facilitates imputing drug priority per disease cluster and identifying potential novel therapeutics.Meanwhile, Skaria et al. toggle Wnt-5A signaling to evaluate pharmacokinetics mediated by damage from innate immune responses on primary human coronary artery endothelial cells.Using transcriptomics and gene ontology analysis,
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.032 | 0.025 |
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".