Training can’t always lead to Olympic macrophages
Bibliographic record
Abstract
Although the memory capacity of innate immune cells, termed trained immunity (TI), is a conserved evolutionary trait, the cellular and molecular mechanisms involved are incompletely understood. One fundamental question is whether the induction of TI generates a homogeneous or heterogeneous population of trained cells. In this issue of the JCI, Zhang, Moorlag, and colleagues tackle this question by combining an in vitro model system of TI with single-cell RNA sequencing. The induction of TI in human monocytes resulted in three populations with distinct transcriptomic profiles. Interestingly, the presence of lymphocytes in the microenvironment of monocytes substantially impacted TI. The authors also identified a similar population of monocytes in various human diseases or in individuals vaccinated with bacillus Calmette-Guérin. These insights warrant in-depth analysis of TI in responsive versus nonresponsive immune cells and suggest that modulating TI may provide a strategy for treating infections and inflammatory diseases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".