Differential Behaviour of Fluid Liposomes Toward Mammalian Epithelial Cells and Bacteria: Restriction of Fusion to Bacteria
Bibliographic record
Abstract
Previous work demonstrated that fluid liposomes developed in our laboratory are able to fuse with bacterial outer membranes. This fusion improved the penetration and activity of liposome-encapsulated antibiotics and antisense oligonucleotides into the bacterial cells. Because it is anticipated that fluid liposome encapsulated antibiotics will be administered by aerosols to patients with chronic pulmonary infections or cystic fibrosis (CF), we conducted comparative studies in E. coli, P. aeruginosa and human lung epithelial cells using lipid-mixing assays to investigate the possibility that fluid liposomes might fuse with surrounding epithelial cells. After a 2 h incubation at 4 and 37 degrees C, no fusion between fluid liposomes and human lung epithelial cells was observed, whereas mean levels of 71 and 37% of fusion were observed at 37 degrees C with E. coli and P. aeruginosa cells, respectively. No fusion was observed at 4 degrees C in any cells. A kinetic study where temperature was gradually increased from 7 to 37 degrees C indicated that the fusion process in the two bacteria starts between 28 and 31 degrees C with a mean fusion rate of 0.60%/min at 31 degrees C to reach 1.18%/min at 37 degrees C. The present work suggests that it is unlikely that fluid liposomes fuse with host cells lining the human respiratory tract and further elucidates the fusogenic properties of fluid liposomes with respect to prokaryotes and eukaryotes.
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 | 0.001 |
| 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) | 0.000 | 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".