Exploring protein candidates with enhanced cargo loading capabilities into Saccharomyces cerevisiae extracellular vesicles
Bibliographic record
Abstract
Extracellular vesicles (EVs) are a promising drug delivery platform as they compartmentalize bioactive payloads prior to delivery, have low immunogenicity, and are capable of tissue-specific targeting. EV-based therapeutics in most research pipelines are produced using cultured human mesenchymal stem cells. This approach is limiting as they are not amenable to complex genetic engineering, are expensive to grow and maintain, produce heterogenous EVs that are challenging to purify, and cannot be easily upscaled. Baker’s yeast (Saccharomyces cerevisiae) is an excellent candidate for EV research, as it exhibits similar fundamental EV biology and may overcome these and other limitations. Using S. cerevisiae as a model, this study aims to determine if yeast strains expressing EV localization peptides can produce engineered EVs loaded with bioactive cargo. To achieve this, a panel of genetically modified yeast strains was generated to express fluorescently tagged versions of known EV markers and bioproduction was quantified with single particle analyses.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".