Outer membrane vesicles as novel therapeutics for heart repair
Bibliographic record
Abstract
Ischemic heart disease is the leading cause of global morbidity and mortality. Amending an injured heart remains a major challenge in both clinics and basic research. Cardiac regenerative medicine that utilizes stem cells for heart repair and regeneration has transitioned into extracellular vesicles. Despite advancements in extracellular vesicle treatment for heart disease, the selection of parental cells and the transplantation pattern—whether autologous or allogeneic—remains a topic of ongoing debate due to immunological and therapeutic variability. Outer membrane vesicles (OMVs) offer an alternative option due to their unique properties, including large-scale production, and highly efficient drug loading/eluting, as well as proven modulation of pathological conditions in various diseases. Additionally, engineering strategies, including surface modification, cargo encapsulation, and microbiome modulation, enhance the specificity and safety of OMVs. At the joint of microbial engineering and cardiac regenerative medicine, OMVs represent a novel platform to develop precise therapeutics for heart disease treatment. This review underscores OMVs as innovative nanotherapeutic tools, bridging microbial-host interactions and cardiovascular health, with transformative potential for patient care.
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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".