Genetic control of susceptibility to bacterial infections in mouse models
Bibliographic record
Abstract
Historically, the laboratory mouse (Mus musculus) has been the experimental model of choice to study pathophysiology of infection with bacterial pathogens, including natural and acquired host defence mechanisms. Inbred mouse strains differ significantly in their degree of susceptibility to infection with various human pathogens such as Mycobacterium, Salmonella, Legionella and many others. Segregation analyses and linkage studies have indicated that some of these differences are under simple genetic control whereas others behave as complex traits. Major advances in genome technologies have greatly facilitated positional cloning of single gene effects. Thus, a number of genes playing a key role in initial susceptibility, progression and outcome of infection have been uncovered and the functional characterization of the encoded proteins has provided new insight into the molecular basis of antimicrobial defences of polymorphonuclear leukocytes, macrophages, as well as T and B lymphocytes. The multigenic control of susceptibility to infection with certain human pathogens is beginning to be characterized by quantitative trait locus mapping in genome wide scans. This review summarizes recent progress on the mapping, cloning and characterization of genes and proteins that affect susceptibility to infection with major intracellular bacterial 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".