Longitudinal characterization of dysfunctional T cell-activation during human acute Ebola infection
Bibliographic record
Abstract
Data on immune responses during human Ebola virus disease (EVD) are scanty, due to limitations imposed by biosafety requirements and logistics. A sustained activation of T-cells was recently described but functional studies during the acute phase of human EVD are still missing. Aim of this work was to evaluate the kinetics and functionality of T-cell subsets, as well as the expression of activation, autophagy, apoptosis and exhaustion markers during the acute phase of EVD until recovery. Two EVD patients admitted to the Italian National Institute for Infectious Diseases, Lazzaro Spallanzani, were sampled sequentially from soon after symptom onset until recovery and analyzed by flow cytometry and ELISpot assay. An early and sustained decrease of CD4 T-cells was seen in both patients, with an inversion of the CD4/CD8 ratio that was reverted during the recovery period. In parallel with the CD4 T-cell depletion, a massive T-cell activation occurred and was associated with autophagic/apoptotic phenotype, enhanced expression of the exhaustion marker PD-1 and impaired IFN-gamma production. The immunological impairment was accompanied by EBV reactivation. The association of an early and sustained dysfunctional T-cell activation in parallel to an overall CD4 T-cell decline may represent a previously unknown critical point of Ebola virus (EBOV)-induced immune subversion. The recent observation of late occurrence of EBOV-associated neurological disease highlights the importance to monitor the immuno-competence recovery at discharge as a tool to evaluate the risk of late sequelae associated with resumption of EBOV replication. Further studies are required to define the molecular mechanisms of EVD-driven activation/exhaustion and depletion of T-cells.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".