Sequence‐Based Human Leukocyte Antigen–B Typing of Patients Infected with Ebola Virus in Uganda in 2000: Identification of Alleles Associated with Fatal and Nonfatal Disease Outcomes
Bibliographic record
Abstract
The Sudan species of Ebola virus (SEBOV) causes severe, often fatal infection in approximately 50% of infected humans. We sought to determine whether the human leukocyte antigen-B (HLA-B) locus has a role in the outcome of SEBOV disease by typing 77 cases from an outbreak in northern Uganda in 2000-2001. Sequence-based HLA-B typing was performed using leukocytes isolated from 77 patients. Statistical analysis and a predictive discriminant analysis (PDA) were applied to typing data. Epitope prediction software was also applied to SEBOV sequences. Statistically significant associations were found between certain sets of alleles and either fatal or nonfatal disease outcomes. Alleles B*67 and B*15 were associated with fatal outcomes, whereas B*07 and B*14 were associated with nonfatal outcomes. The PDA-derived functions that were produced were 81.8% accurate in classifying patients into their correct outcome group. Several epitopes predicted to bind strongly to HLA-B*07 molecules were identified in the viral polymerase, nucleoprotein, and VP35 protein. HLA-B alleles associated with either fatal or nonfatal outcomes of SEBOV disease were identified and can be used in a predictive model. Studies of HLA-B-restricted epitopes could contribute to characterization of protective host responses and to vaccine development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".