Modified host defence peptide GF19 slows TNT-mediated spread of corneal herpes simplex virus serotype I infection
Bibliographic record
Abstract
Corneal HSV-1 infections are a leading cause of infectious blindness globally by triggering tissue damage due to the intense inflammation. HSV-1 infections are treated mainly with antiviral drugs that clear the infections but are inefficient as prophylactics. The body produces innate cationic host defence peptides (cHDP), such as the cathelicidin LL37. Various epithelia, including the corneal epithelium, express LL37. cHDPs can cause disintegration of pathogen membranes, stimulate chemokine production, and attract immune cells. Here, we selected GF17, a peptide containing the LL37 fragment with bioactivity but with minimal cytotoxicity, and added two cell-penetrating amino acids to enhance its activity. The resulting GF19 was relatively cell-friendly, inducing only partial activation of antigen presenting immune cells in vitro. We showed that HSV-1 spreads by tunneling nanotubes in cultured human corneal epithelial cells. GF19 given before infection was able to block infection, most likely by blocking viral entry. When cells were sequentially exposed to viruses and GF19, the infection was attenuated but not arrested, supporting the contention that the GF19 mode of action was to block viral entry. Encapsulation into silica nanoparticles allowed a more sustained release of GF19, enhancing its activity. GF19 is most likely suitable as a prevention rather than a virucidal treatment.
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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".