Bacterial Extracellular Vesicles: Bridging Pathogen Biology and Therapeutic Innovation
Bibliographic record
Abstract
The main role of bacterial extracellular vesicles (BEVs) has been associated with various processes such as intercellular communication and host-pathogen interactions. This comprehensive review explores the multifaceted functions of BEVs across different biological domains, emphasizing their dual nature as contributors to disease and potential vehicles for therapeutic intervention. We examine the intricate interactions of BEVs within bacterial communities and between bacteria and hosts, their involvement in disease development through cargo delivery mechanisms, and their beneficial impact on microbial ecology. The review also highlights BEVs' applications in biomedical field, where they are revolutionizing vaccine development, targeted drug delivery, and cancer therapy. By utilizing the inherent properties of BEVs for controlled drug release, targeted antigen delivery, and immune modulation, they offer a promising frontier in precision medicine. In addition, the diagnostic potential of BEVs is explored through their utility as biomarkers, providing valuable insights into disease states and treatment efficacy. Looking forward, this review underscores the challenges and opportunities in translating BEV research to clinical practice, promoting the need of standardized methods in BEV characterization and scaling up production. The diverse abilities of BEVs, ranging from contributing to pathogen virulence to driving therapeutic innovation, highlight their potential as a cornerstone in the future of biomedical advancements. STATEMENT OF SIGNIFICANCE: Bacterial extracellular vesicles (BEVs) are emerging as pivotal players in both pathogenesis and therapeutic innovation. This review explores their dual nature as agents of disease and as promising biomaterials for biomedical applications, and provides a comprehensive survey on their involvement in disease mechanisms and microbial ecology, and their potential in biomedical applications such as vaccine development, targeted drug delivery, cancer therapy, and diagnosis. It highlights the complex interactions of BEVs within bacterial communities and between bacteria and hosts. This review also addresses current advancements, challenges, and opportunities in translating BEV research into clinical practice. The insights presented here position BEVs as a cornerstone in the future of biomedical advancements, advocating for standardized methods in BEV characterization and scalable production techniques.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".