Bibliographic record
Abstract
Both, prokaryotic and eukaryotic cells contain the ability to communicate with cells in their environment. Classically, intercellular cell communication was thought to be mainly mediated by direct cell-cell contacts and by soluble factors including cytokines, growth factors and hormones. In 1996 Raposo and colleagues showed that intercellular communication can also be mediated by vesicles; specifically they reported that B lymphoblastoid cells release MHC class II carrying vesicles which are able to induce antigen-specific MHC class II-restricted T cell responses (1). Since then, an exponentially increasing amount of studies demonstrates the importance of extracellular vesicle (EV) mediated intercellular signaling in a huge variety of different cellular systems and organisms ranging from bacteria and yeast to plants and humans. It became evident that EV-mediated intercellular communication is essentially involved in many physiological and pathophysiological processes (2). Thus, EV-mediated intercellular communication provides a third, very complex mode of intercellular communication. Unraveling the basic principles of this novel intercellular communication system and gaining insight in its complexity will for sure have large impacts on the overall understanding of biological processes and certainly will pave the way for novel diagnostic and therapeutic approaches (2-4).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| 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".