Anti-IL-4 antibody therapy causes regression of chronic lesions caused by medium-dose Leishmania major infection in BALB/c mice
Bibliographic record
Abstract
Experimental infection of BALB/c mice with a high number of Leishmania major parasites results in a predominant Th2 response and rapidly progressing, non-healing lesions. Disease can be prevented in such mice by simple therapies, such as administering neutralizing anti-IL-4 or anti-CD4 antibody prior to or around the time of infection, but not once the infection is well established. Established infections can be resolved by combined therapies, such as intralesional administration of massive doses of IL-12 and of anti-leishmanial drugs. We explored the possibility of using simple therapies to cure mice with stable, chronic and large lesions, a state that corresponds more closely to human cutaneous leishmaniasis than does the rapidly progressing model. The anti-parasite immune responses of mice bearing such chronic lesions have a mixed Th1/Th2 phenotype. Administration of either anti-IL-4 or anti-CD4 antibody alone results in the reliable regression of such lesions even when large. Cured mice display a dominant Th1 response with increased L. major-specific IgG2a antibody, increased production of IL-12p40 and of nitric oxide by macrophages, indicating increased parasiticidal activity. Cured mice resist a normally pathogenic L. major challenge. These findings may have implications for treatment of human cutaneous leishmaniasis and other chronic infectious diseases caused by intracellular 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".