The Functional Role of CD169+ Macrophages in Septic Shock
Bibliographic record
Abstract
Abstract Sepsis affects more than 30 million people and accounts for approximately 6 million deaths worldwide each year. Sepsis is often associated with gram-positive, gram-negative, or fungal infections. Currently, there are no known cures for sepsis; however, current therapeutic strategies consist of supportive care to keep vital organs functional and antibiotics to combat infections, with survival prognosis contingent on early detection. Our current knowledge of the cellular and molecular mechanisms that regulate the inflammatory pathways resulting in sepsis is limited. Notably, we know little about the precise subpopulations of immune cells that play a central role during sepsis. Here we examined the specific role of CD169+ macrophages and its immunological receptors in septic shock. We utilized the CD169-DTR mouse to selectively and temporally deplete CD169+ macrophages. Using this model, we examined survival, cytokine/chemokine milieu, and cellularity in response to gram-negative LPS. We report the critical role of CD169+ macrophages and the CD169 receptor in protection against LPS induced septic shock. We show that in the absence of these macrophages, mice stimulated with sublethal LPS fail to survive and exhibit increased inflammatory cytokines and impaired IL-10 production. Protection against LPS induced lethality is rescued by supplemental treatment with recombinant IL-10. Lastly, we show that following LPS treatment, CD169+ macrophages are the initial targeted cell. These findings not only reveal a pivotal role for CD169+ macrophages but also the role for the CD169 receptor as a critical mediator of protection from sepsis induced lethality, and provide new therapeutic targets against sepsis.
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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".