Natural Macrophage Membrane-Coated Nanoparticles as a Multifaceted Sepsis Therapeutic to Sequester Inflammatory and Toxic Mediators
Bibliographic record
Abstract
Bacterial sepsis is a life-threatening immune dysregulation triggered by bacterial infection and propagated by a dysfunctional host response, culminating in systemic tissue damage and multiorgan failure. In the United States, sepsis results in the hospitalization of more than one million patients annually and accounts for nearly one in three hospital deaths. Despite decades of efforts to develop immunoregulatory sepsis therapies, no clinically approved treatments exist. Recent advances in nanotechnology have introduced innovative approaches, including cellular nanodecoys synthesized from natural macrophage membranes coated onto polymeric nanoparticle cores. Here we introduce a human macrophage membrane-derived drug candidate, CTI-111, capable of sequestering soluble microbial toxins, drivers of inflammation, and pro-inflammatory cytokines from multiple sources. Therapeutic administration of CTI-111 reduces inflammation and improves survival in multiple murine sepsis models. We further demonstrate that CTI-111 can bind multiple sepsis-associated human cytokines in the complex environment of septic serum ex vivo. Together, these findings highlight the potential of CTI-111 as a multifaceted therapy for 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) | 0.000 | 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".