Association of Human Leukocyte Antigen with Outcomes of Infectious Diseases: The Streptococcal Experience
Bibliographic record
Abstract
The role of host genetic factors in determining susceptibility to infections has become more evident. Certain individuals appear to be predisposed to certain infections, whereas others are protected. By studying the immune response and the genetic makeup of susceptible and resistant individuals a better understanding of the disease process can be achieved. Infections caused by group A streptococci offer an excellent model to study host-pathogen interactions and how the host genetic variation can influence the infection outcome. These studies showed that the same clone of these bacteria can cause severe or non-severe invasive disease. This difference was largely related to the human leukocyte antigen class 11 type of the patient. Certain class II haplotypes present the streptococcal superantigens in a way that results in responses, whereas others present the same superantigens in a way that elicits very potent inflammatory responses that can lead to organ failure and shock. These findings underscore the role of host genetic factors in determining the outcome of serious infections and warrants further investigations into how the same or different genetic factors affect susceptibility to other emerging and re-emerging pathogens.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".