Curcumin Can Decrease Tissue Inflammation and the Severity of HSV-2 Infection in the Female Reproductive Mucosa
Bibliographic record
Abstract
Herpes Simplex Virus Type 2 (HSV-2) is one of the most prevalent sexually transmitted viruses and is a known risk factor for HIV acquisition in the Female Genital Tract (FGT). Previously, we found that curcumin can block HSV-2 infection and abrogate the production of inflammatory cytokines and chemokines by genital epithelial cells in vitro. In this study, we investigated whether curcumin, encapsulated in nanoparticles and delivered by various in vivo routes, could minimize inflammation and prevent or reduce HSV-2 infection in the FGT. Female mice were pre-treated with curcumin nanoparticles through oral, intraperitoneal and intravaginal routes, and then exposed intravaginally to the tissue inflammation stimulant CpG-oligodeoxynucleotide (ODN). Local intravaginal delivery of curcumin nanoparticles, but not intraperitoneal or oral delivery, reduced CpG-mediated inflammatory histopathology and decreased production of pro-inflammatory cytokines Interleukin (IL)-6, Tumor Necrosis Factor Alpha (TNF-α) and Monocyte Chemoattractant Protein-1 (MCP-1) in the FGT. However, curcumin nanoparticles did not demonstrate anti-viral activity nor reduce tissue pathology when administered prior to intravaginal HSV-2 infection. In an alternative approach, intravaginal pre-treatment with crude curcumin or solid dispersion formulations of curcumin demonstrated increased survival and delayed pathology following HSV-2 infection. Our results suggest that curcumin nanoparticle delivery in the vaginal tract could reduce local tissue inflammation. The anti-inflammatory properties of curcumin delivered to the vaginal tract could potentially reduce the severity of HSV-2 infection and decrease the risk of HIV acquisition in the FGT of women.
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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".