pH controlled release of extracellular vesicles from a hydrogel scaffold for therapeutic applications
Bibliographic record
Abstract
Cell-based therapies are gaining attention as a promising approach for repairing damaged tissues and organs, offering alternatives to invasive treatments like organ transplants and powerful medications. Recent research has shifted towards extracellular vesicles (EVs), membrane-bound particles that can carry therapeutic compounds like DNA, RNA, and proteins, which may offer advantages over cell-based therapies, such as higher potency and reduced immune reactions. A key challenge in EV therapy is ensuring that the vesicles reach their intended target tissues. While EVs are often delivered via injection, systemic administration can result in off-target effects. To address this, we highlight the microfluidic encapsulation of EVs in hydrogel microcapsules that include a CD9 binding peptide (CD9BP), allowing for controlled EV release in response to a shift in environmental pH. By encapsulating CD9+ EVs in CD9BP hydrogel capsules, we demonstrate the release of their contents in acidified environments typical of damaged tissues. This method allows for targeted, localized EV delivery. The approach promises more effective tissue regeneration while reducing the need for broad, non-specific drug delivery.
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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".