T-cell activation profiles distinguish hemophagocytic lymphohistiocytosis and early sepsis
Bibliographic record
Abstract
Hemophagocytic lymphohistiocytosis (HLH) is a fatal disorder of immune hyperactivation that has been described as a cytokine storm. Sepsis due to known or suspected infection has also been viewed as a cytokine storm. Although clinical similarities between these syndromes suggest similar immunopathology and may create diagnostic uncertainty, distinguishing them is critical as treatments are widely divergent. We examined T-cell profiles from children with either HLH or sepsis and found that HLH is characterized by acute T-cell activation, in clear contrast to sepsis. Activated T cells in patients with HLH were characterized as CD38high/HLA-DR+ effector cells, with activation of CD8+ T cells being most pronounced. Activated T cells were type 1 polarized, proliferative, and displayed evidence of recent and persistent activation. Circulating activated T cells appeared to be broadly characteristic of HLH, as they were seen in children with and without genetic lesions or identifiable infections and resolved with conventional treatment of HLH. Furthermore, we observed even greater activation and type 1 polarization in tissue-infiltrating T cells, described here for the first time in a series of patients with HLH. Finally, we observed that a threshold of >7% CD38high/HLA-DR+ cells among CD8+ T cells had strong positive and negative predictive value for distinguishing HLH from early sepsis or healthy controls. We conclude that the cytokine storm of HLH is marked by distinctive T-cell activation whereas early sepsis is not, and that these 2 syndromes can be readily distinguished by T-cell phenotypes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".