Neuro‐immune interactions in health and disease: Insights from FENS‐Hertie 2022 Winter School
Bibliographic record
Abstract
In a great partnership, the Federation of European Neuroscience Societies (FENS) and the Hertie Foundation organized the FENS-Hertie 2022 Winter School on 'Neuro-immune interactions in health and disease'. The school selected 27 PhD students and 13 postdoctoral fellows from 20 countries and involved 14 faculty members experts in the field. The Winter School focused on a rising field of research, the interactions between the nervous and both innate and adaptive immune systems under pathological and physiological conditions. A fine-tuned neuro-immune crosstalk is fundamental for healthy development, while disrupted neuro-immune communication might play a role in neurodegeneration, neuroinflammation and aging. However, much is yet to be understood about the underlying mechanisms of these neuro-immune interactions in the healthy brain and under pathological scenarios. In addition to new findings in this emerging field, novel methodologies and animal models were presented to foment research on neuro-immunology. The FENS-Hertie 2022 Winter School provided an insightful knowledge exchange between students and faculty focusing on the latest discoveries in the biology of neuro-immune interactions while fostering great academic and professional opportunities for early-career neuroscientists from around the world.
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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 | 0.010 | 0.017 |
| 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 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".