A Peptide-Based Checkpoint Immunomodulator Alleviates Immune Dysfunction in Murine Polymicrobial Sepsis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT: Sepsis-induced immunosuppression involves both innate and adaptive immunity and is associated with the increased expression of checkpoint inhibitors, such as programmed cell-death protein 1 (PD-1). The expression of PD-1 is associated with poor outcomes in septic patients, and in models of sepsis, blocking PD-1 or its ligands with antibodies increased survival and alleviated immune suppression. While inhibitory antibodies are effective, they can lead to immune-related adverse events (irAEs), in part due to continual blockade of the PD-1 pathway, resulting in hyperactivation of the immune response. Peptide-based therapeutics are an alternative drug modality that provide a rapid pharmacokinetic profile, reducing the incidence of precipitating irAEs. We recently reported that the potent, peptide-based PD-1 checkpoint antagonist, LD01, improves T-cell responses. The goal of the current study was to determine whether LD01 treatment improved survival, bacterial clearance, and host immunity in the cecal-ligation and puncture (CLP)-induced murine polymicrobial sepsis model. LD01 treatment of CLP-induced sepsis significantly enhanced survival and decreased bacterial burden. Altered survival was associated with improved macrophage phagocytic activity and T-cell production of interferon-γ. Further, myeloperoxidase levels and esterase-positive cells were significantly reduced in LD01-treated mice. Taken together, these data establish that LD01 modulates host immunity and is a viable therapeutic candidate for alleviating immunosuppression that characterizes sepsis and other infectious diseases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it