Fc functional antibodies in humans with severe H7N9 and seasonal influenza
Bibliographic record
Abstract
BACKGROUND: Both seasonal and novel avian influenza viruses can result in severe infections requiring hospitalization. Anti-influenza antibodies (Abs) with Fc-mediated effector functions, such as Ab-dependent cellular cytotoxicity (ADCC), are of growing interest in control of influenza but have not previously been studied during severe human infections. As such, the objective of this study was to examine Fc-mediated Ab functions in humans hospitalized with influenza infection. METHODS: Serum Ab response was studied in subjects hospitalized with either pandemic H7N9 avian influenza virus in China (n = 18) or circulating seasonal influenza viruses in Melbourne, Australia (n = 16). Recombinant soluble Fc receptor dimer ELISAs, natural killer (NK) cell activation assays, and Ab-dependent killing assays with influenza-infected target cells were used to assess the Fc functionality of anti-influenza hemagglutinin (HA) Abs during severe human influenza infection. RESULTS: We found that the peak generation of Fc functional HA Abs preceded that of neutralizing Abs for both severe H7N9 and seasonal influenza infections. Subjects who succumbed to complications of H7N9 infection demonstrated reduced HA-specific Fc receptor-binding Abs (in magnitude and breadth) immediately prior to death compared with those who survived. Subjects who recovered from H7N9 and severe seasonal influenza infections demonstrated increased Fc receptor-binding Abs not only against the homologous infecting strain but against HAs from different influenza A subtypes. CONCLUSION: Collectively, survivors of severe influenza infection rapidly generate a functional Ab response capable of mediating ADCC against divergent influenza viruses. Broadly binding HA Abs with Fc-mediated functions may be a useful component of protective immunity to severe influenza infection. FUNDING: The National Health and Medical Research Council ([NHMRC] grants 1023294, 1041832, and 1071916), the Australian Department of Health, and the joint University of Melbourne/Fudan University International Research and Research Training Fund provided funding for this study.
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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 | 0.000 |
| 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 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".