Unraveling the host's immune response to infection: Seeing is believing
Bibliographic record
Abstract
It has long been appreciated that understanding the interactions between the host and the pathogens that make us sick is critical for the prevention and treatment of disease. As antibiotics become increasingly ineffective, targeting the host and specific bacterial evasion mechanisms are becoming novel therapeutic approaches. The technology used to understand host-pathogen interactions has dramatically advanced over the last century. We have moved away from using simple in vitro assays focused on single-cell events to technologies that allow us to observe complex multicellular interactions in real time in live animals. Specifically, intravital microscopy (IVM) has improved our understanding of infection, from viral to bacterial to parasitic, and how the host immune system responds to these infections. Yet, at the same time it has allowed us to appreciate just how complex these interactions are and that current experimental models still have a number of limitations. In this review, we will discuss the advances in vivo IVM has brought to the study of host-pathogen interactions, focusing primarily on bacterial infections and innate immunity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".