IFN-γ signature in the plasma proteome distinguishes pediatric hemophagocytic lymphohistiocytosis from sepsis and SIRS
Why this work is in the frame
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Bibliographic record
Abstract
Hemophagocytic lymphohistiocytosis (HLH) is a syndrome characterized by pathologic immune activation in which prompt recognition and initiation of immune suppression is essential for survival. Children with HLH have many overlapping clinical features with critically ill children with sepsis and systemic inflammatory response syndrome (SIRS) in whom alternative therapies are indicated. To determine whether plasma biomarkers could differentiate HLH from other inflammatory conditions and to better define a core inflammatory signature of HLH, concentrations of inflammatory plasma proteins were compared in 40 patients with HLH to 47 pediatric patients with severe sepsis or SIRS. Fifteen of 135 analytes were significantly different in HLH plasma compared with SIRS/sepsis, including increased interferon-γ (IFN-γ)-regulated chemokines CXCL9, CXCL10, and CXCL11. Furthermore, a 2-analyte plasma protein classifier including CXCL9 and interleukin-6 was able to differentiate HLH from SIRS/sepsis. Gene expression in CD8+ T cells and activated monocytes from blood were also enriched for IFN-γ pathway signatures in peripheral blood cells from patients with HLH compared with SIRS/sepsis. This study identifies differential expression of inflammatory proteins as a diagnostic strategy to identify critically ill children with HLH, and comprehensive unbiased analysis of inflammatory plasma proteins and global gene expression demonstrates that IFN-γ signaling is uniquely elevated in HLH. In addition to demonstrating the ability of diagnostic criteria for HLH and sepsis or SIRS to identify groups with distinct inflammatory patterns, results from this study support the potential for prospective evaluation of inflammatory biomarkers to aid in diagnosis of and optimizing therapeutic strategies for children with distinctive hyperinflammatory syndromes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it