Involvement of Toso in activation of monocytes, macrophages, and granulocytes
Bibliographic record
Abstract
Rapid activation of immune responses is necessary for antibacterial defense, but excessive immune activation can result in life-threatening septic shock. Understanding how these processes are balanced may provide novel therapeutic potential in treating inflammatory disease. Fc receptors are crucial for innate immune activation. However, the role of the putative Fc receptor for IgM, known as Toso/Faim3, has to this point been unclear. In this study, we generated Toso-deficient mice and used them to uncover a critical regulatory function of Toso in innate immune activation. Development of innate immune cells was intact in the absence of Toso, but Toso-deficient neutrophils exhibited more reactive oxygen species production and reduced phagocytosis of pathogens compared with controls. Cytokine production was also decreased in Toso(-/-) mice compared with WT animals, rendering them resistant to septic shock induced by lipopolysaccharide. However, Toso(-/-) mice also displayed limited cytokine production after infection with the bacterium Listeria monocytogenes that was correlated with elevated presence of Listeria throughout the body. Accordingly, Toso(-/-) mice succumbed to infections of L. monocytogenes, whereas WT mice successfully eliminated the infection. Taken together, our data reveal Toso to be a unique regulator of innate immune responses during bacterial infection and septic shock.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".