Doxycycline and Benznidazole Reduce the Profile of Th1, Th2, and Th17 Chemokines and Chemokine Receptors in Cardiac Tissue from Chronic <i>Trypanosoma cruzi</i> -Infected Dogs
Bibliographic record
Abstract
Chemokines (CKs) and chemokine receptors (CKR) promote leukocyte recruitment into cardiac tissue infected by the Trypanosoma cruzi . This study investigated the long-term treatment with subantimicrobial doses of doxycycline (Dox) in association, or not, with benznidazole (Bz) on the expression of CK and CKR in cardiac tissue. Thirty mongrel dogs were infected, or not, with the Berenice-78 strain of T. cruzi and grouped according their treatments: (i) two months after infection, Dox (50 mg/kg) 2x/day for 12 months; (ii) nine months after infection, Bz (3,5 mg/kg) 2x/day for 60 days; (iii) Dox + Bz; and (iv) vehicle. After 14 months of infection, hearts were excised and processed for qPCR analysis of Th1 (CCL2, CCL3, CCL4, CCL5, CXCL9, and CXCL11), Th2 (CCL1, CCL17, CCL24, and CCL26), Th17 (CCL20) CKs, Th1 (CCR5, CCR6, and CXCR3), and Th2/Th17 (CCR3, CCR4, and CCR8) CKR, as well as IL-17. T. cruzi infection increases CCL1, CCL2, CCL4, CCL5, CCL17, CXCL10, and CCR5 expression in the heart. Dox, Bz, or Dox + Bz treatments cause a reversal of CK and CKR and reduce the expression of CCL20, IL-17, CCR6, and CXCR3. Our data reveal an immune modulatory effect of Dox with Bz, during the chronic phase of infection suggesting a promising therapy for cardiac protection.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".