Interferon gamma as an immune modulating adjunct therapy for invasive mucormycosis after severe burn – A case report
Bibliographic record
Abstract
Background: Mucormycosis is a deadly fungal infection that mainly affects severely immunocompromised patients. We report herein the case of a previously immunocompetent adult woman who developed invasive cutaneous mucormycosis after severe burn injuries. Interferon-gamma (IFN-γ) treatment was added after failure of conventional treatment and confirmation of a sustained profound immunodepression. The diagnosis was based on a reduced expression of HLA-DR on monocytes (mHLA-DR), NK lymphopenia and a high proportion of immature neutrophils. The immune-related alterations were longitudinally monitored using panels of immune-related biomarkers. Results: Initiation of IFN-γ was associated with a rapid clinical improvement and a subsequent healing of mucormycosis infection, with no residual fungi at the surgical wound repair. The serial immunological assessment showed sharp improvements of immune parameters: a rapid recovery of mHLA-DR and of transcriptomic markers for T-cell proliferation. The patient survived and was later discharged from the ICU. Conclusion: The treatment with recombinant IFN-γ participated to the resolution of a progressively invasive mucormycosis infection, with rapid improvement in immune parameters. In the era of precision medicine in the ICU, availability of comprehensive immune monitoring tools could help guiding management of refractory infections and provide rationale for immune stimulation strategies in these high risk patients.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| 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".