Impaired T Cell Responsiveness to Interleukin-6 in Hematological Patients with Invasive Aspergillosis
Bibliographic record
Abstract
Invasive mold infections (IMI) are among the most devastating complications following chemotherapy and hematopoietic stem cell transplantation (HSCT), with high mortality rates. Yet, the molecular basis for human susceptibility to invasive aspergillosis (IA) and mucormycosis remain poorly understood. Herein, we aimed to characterize the immune profile of individuals with hematological malignancies (n = 18) who developed IMI during the course of chemotherapy or HSCT, and compared it to that of hematological patients who had no evidence of invasive fungal infection (n = 16). First, we measured the expression of the pattern recognition receptors pentraxin 3, dectin-1, and Toll-like receptors (TLR) 2 and 4 in peripheral blood of chemotherapy and HSCT recipients with IMI. Compared to hematological controls, individuals with IA and mucormycosis had defective expression of dectin-1; in addition, patients with mucormycosis had decreased TLR2 and increased TLR4 expression. Since fungal recognition via dectin-1 favors T helper 17 responses and the latter are highly dependent on activation of the signal transducer and activator of transcription (STAT) 3, we next used phospho-flow cytometry to measure the phosphorylation of the transcription factors STAT1 and STAT3 in response to interferon-gamma (IFN-γ) and interleukin (IL)-6, respectively. While IFN-γ/STAT1 signaling was similar between groups, naïve T cells from patients with IA, but not those with mucormycosis, exhibited reduced responsiveness to IL-6 as measured by STAT3 phosphorylation. Furthermore, IL-6 increased Aspergillus-induced IL-17 production in culture supernatants from healthy and hematological controls but not in patients with IA. Altogether, these observations suggest an important role for dectin-1 and the IL-6/STAT3 pathway in protective immunity against Aspergillus.
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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 | 0.000 | 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".