Trained immunity: A “new” weapon in the fight against infectious diseases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Innate immune cells can potentiate the response to reinfection through an innate form of immunological memory known as trained immunity. The potential of this fast-acting, nonspecific memory compared to traditional adaptive immunological memory in prophylaxis and therapy has been a topic of great interest in many fields, including infectious diseases. Amidst the rise of antimicrobial resistance and climate change-two major threats to global health-, harnessing the advantages of trained immunity compared to traditional forms of prophylaxis and therapy could be game-changing. Here, we present recent works bridging trained immunity and infectious disease that raise important discoveries, questions, concerns, and novel avenues for the modulation of trained immunity in practice. By exploring the progress in bacterial, viral, fungal, and parasitic diseases, we equally highlight future directions with a focus on particularly problematic and/or understudied pathogens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it