Biochemical and functional characterization of intercellular adhesion and gap junctions in fibroblasts
Bibliographic record
Abstract
Despite their significance in wound healing, little is known about the molecular determinants of cell-to-cell adhesion and gap junctional communication in fibroblasts. We characterized intercellular adherens junctions and gap junctions in human gingival fibroblasts (HGFs) using a novel model. Calcein-labeled donor cells in suspension were added onto an established, Texas red dextran (10 kDa)-labeled acceptor cell monolayer. Cell-to-cell adhesion required Ca(2+) and was >30-fold stronger than cell-to-fibronectin adhesion at 15 min. Electron micrographs showed rapid formation of adherens junction-like structures at approximately 15 min that matured by approximately 2-3 h; distinct gap junctional complexes were evident by approximately 3 h. Immunoblotting showed that HGF expressed beta-catenin and that cadherins and connexin43 were recruited to the Triton-insoluble cytoskeletal fraction in confluent cultures. Confocal microscopy localized the same molecules to intercellular contacts of acceptor and donor cells. There was extensive calcein dye transfer in a cohort of Texas red dextran-labeled cells, but this was almost completely abolished by the gap junction inhibitor beta-glycyrrhetinic acid and the connexin43 mimetic peptide GAP 27. This donor-acceptor cell model allows large numbers (>10(5)) of cells to form synchronous cell-to-cell contacts, thereby enabling the simultaneous functional and molecular studies of adherens junctions and gap junctions.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".