© 2004 Canadian Medical Association or its licensors
Bibliographic record
Abstract
Genetic profiling may help to define different clinical forms of leprosy. The severity of the dis-ease depends partly on how an individual’s immune system re-sponds to the causative organ-ism, Mycobacterium leprae. Tu-berculoid leprosy is typically a self-limited disease with a low bacterial load. In contrast, pa-tients who have lepromatous leprosy present with dissemi-nated lesions and high bacterial loads, which reflects the sup-pression of cell-mediated immu-nity (see pages 55 and 71 in this issue). To determine the reason for this difference in immune re-sponse, Bleharski and colleagues compared the patterns of ex-pression of about 12 000 genes between patients with either form of leprosy. They demon-strated that there were clear dif-ferences in the expression of certain genes within the skin le-sions between the tuberculoid and lepromatous groups. Specif-ically, they found an increase in the expression of type 2 cyto-kines in the lepromatous sam-ples, which had been previously associated with immune sup-pression. However, they also discovered an increase in the ex-pression of receptors in the leukocyte immunoglobulin-like receptor family. Hypothesizing that these receptors may also in-hibit the immune response, they found that manipulating them with antibodies indeed resulted in an imbalance between the cytokines necessary for mount-ing an inflammatory response. Within a larger context, the authors state that the impor-tance of such findings indicates that genes involved in bene-ficial or maladaptive immune responses can be identified, which may lead to a greater un-derstanding of disease progres-sion in general, and perhaps therapy. (Bleharski et al. Science
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.916 | 0.869 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".