From infection to repair: Understanding the workings of our innate immune cells
Bibliographic record
Abstract
In a world filled with microbes, some posing a threat to our body, our immune system is key to living a healthy life. The innate immune system is made of various cell types that act to guard our bodies. Unlike the adaptive immune system that has a specific response, our innate immune system encompasses cells that elicit unspecific immune responses, triggered whenever the right signals are detected. Our understanding of immunity started with the concept of our immune system only responding to "nonself" like the pathogens that invade our body. However, over the past few decades, we have learned that the immune system is more than an on/off switch that recognizes nonself. The innate immune system regularly patrols our bodies for pathogens and tissue damage. Our innate immune system not only seeks to resolve infection but also repair tissue injury, through phagocytosing debris and initiating the release of growth factors. Recently, we are starting to see that it is not just recognizing danger, our innate immune system plays a crucial role in repair. Innate immune cells phenotypically change during repair. In the context of severe injury or trauma, our innate immune system is modified quite drastically to help repair, resulting in reduced infection control. Moreover, these changes in immune cell function can be modified by sex as a biological variable. From past to present, in this overview, we provide a summary of the innate immune cells and pathways in infection and tissue repair. This article is categorized under: Immune System Diseases > Molecular and Cellular Physiology.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".