Dysregulated lymphatic remodeling promotes immunopathology during non-healing cutaneous leishmaniasis
Bibliographic record
Abstract
Abstract Cutaneous leishmaniasis (CL) is a vector borne disease that is endemic to tropical and sub-tropical regions of the world disproportionately affecting those of low socioeconomic status. The combined role of the parasite and the host’s immune response in determining disease severity has made it challenging to discover new anti-leishmanial treatments. Previous work from our lab has established that the dermal lymphatic network is necessary for wound resolution in a model of healing CL with Leishmania major parasites. In CL, lymphatic remodeling allows for accumulated fluid to drain from the lesional site, thereby reducing disease severity. In this report, we present a new mechanism of immunopathology during non-healing CL brought about by L. amazonensis infection. We show non-healing CL develops alongside an accumulation of cells and fluid in the skin, resulting in chronic inflammation. Lymphatic remodeling is attenuated during the chronic phase of L. amazonensis infection. Moreover, the percentage of proliferating lymphatic endothelial cells (LECs) decreases from 6 to 12 weeks post infection (wpi), leading to a decrease over time in lymphatic vessel (LV) density. To induce lymphangiogenesis, exogenous vascular endothelial growth factor-C (VEGF-C) was administered by adenoviral delivery. VEGF-C increased LV dilation leading to reduced lesion sizes without altering parasite burdens, arguing targeting the lymphatics can alleviate immunopathology. Taken together, these results show impaired lymphatic function contributes to non-healing disease due to L. amazonensis infection and the lymphatics can be targeted to decrease inflammation in the skin during infection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".