Sonoporation Delivery of Monoclonal Antibodies against Human Papillomavirus 16 E6 Restores p53 Expression in Transformed Cervical Keratinocytes
Bibliographic record
Abstract
High-risk types of human papillomavirus (HPV), such as HPV16, have been found in nearly all cases of cervical cancer. Therapies targeted at blocking the HPV16 E6 protein and its deleterious effects on the tumour suppressor pathways of the cell can reverse the malignant phenotype of affected keratinocytes while sparing uninfected cells. Through a strong interdisciplinary collaboration between engineering and biology, a novel, non-invasive intracellular delivery method for the HPV16 E6 antibody, F127-6G6, was developed. The method employs high intensity focused ultrasound (HIFU) in combination with microbubbles, in a process known as sonoporation. In this proof of principle study, it was first demonstrated that sonoporation antibody delivery into the HPV16 positive cervical carcinoma derived cell lines CaSki and SiHa was possible, using chemical transfection as a baseline for comparison. Delivery of the E6 antibody using sonoporation significantly restored p53 expression in these cells, indicating the antibody is able to enter the cells and remains active. This delivery method is targeted, non-cytotoxic, and non-invasive, making it more easily translatable for in vivo experiments than other transfection methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".