Redox Triggered Vesicles a Promising Approach for Drug Delivery
Bibliographic record
Abstract
Drug delivery systems are one of the biggest challenges and emerging fields in the world nowadays. There is a wide interest in developing an efficient method that will be able to transport a biologically active material to a desired location, and then releasing it using a simple process. From the different approaches that tried to overcome the different developing challenges only four nanoparticle-based drug delivery platforms were approved by the Food and Drug Administration (FDA). We present here a novel design of a smart drug delivery liposomes based on the use of redox active phospholipids. The redox triggering is very sensitive to small and local changes; therefore it can be applied without affecting other species in the environment as opposed to pH, temperature, ultrasound and photochemistry changes. The system was characterized using advanced methods such as SECM, TEM, DLS and immunoarray fluorescent imaging. Furthermore, when loading the vesicles with anti-cancer medicine and exposing them to live cell we show that the redox induced payload mechanism is fully functional making it a promising candidate for a fully functional drug delivery system Figure 1
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".