Perspective on the Application of Erythrocyte Liposome-Based Drug Delivery for Infectious Diseases
Bibliographic record
Abstract
Nanoparticles are explored as drug carriers with the promise for the treatment of diseases to increase the efficacy and also reduce side effects sometimes seen with conventional drugs. To accomplish this goal, drugs are encapsulated in or conjugated to the nanocarriers and selectively delivered to their targets. Potential applications include immunization, the delivery of anti-cancer drugs to tumours, antibiotics to infections, targeting resistant bacteria, and delivery of therapeutic agents to the brain. Despite this great promise and potential, drug delivery systems have yet to be established, mainly due to their limitations in physical instability and rapid clearance by the host's immune response. Recent interest has been taken in using red blood cells (RBC) as drug carriers due to their naturally long circulation time, flexible structure, and direct access to many target sites. This includes coating of nanoparticles with the membrane of red blood cells, and the fabrication and manipulation of liposomes made of the red blood cells' cytoplasmic membrane. The properties of these erythrocyte liposomes, such as charge and elastic properties, can be tuned through the incorporation of synthetic lipids to optimize physical properties and the loading efficiency and retention of different drugs. Specificity can be established through the anchorage of antigens and antibodies in the liposomal membrane to achieve targeted delivery. Although still at an early stage, this erythrocyte-based platform shows first promising results in vitro and in animal studies. However, their full potential in terms of increased efficacy and side effect minimization still needs to be explored in vivo.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 teacher head, 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".