Bibliographic record
Abstract
Articular cartilage is unique and distinguishable amongst other tissue types due to its limited self-healing capacity, driving the need to investigate novel methods capable of replacing or repairing damaged or diseased tissue. In order to create tissue engineered constructs to repair cartilage defects, large populations of chondrocytes are needed, thereby requiring the expansion of primary cells. However, routine methods of cell expansion (e.g. monolayer expansion) tend to result in the loss of chondrogenic phenotype and insensitivity to mechanical stimuli. It has been suggested that the chondrocyte’s primary cilium is one factor responsible in transducing mechanical signals between the cell and its surrounding environment. Thus, this work focused on restoring primary cilia (length and incidence) and mechanoreceptive characteristics using lithium chloride (LiCl). Overall, results revealed that primary cilia length and incidence increased with LiCl concentration, pre-culture duration, and passage number. However, the mechanosensitivity of chondrocytes, as determined by changes in cartilaginous matrix synthesis after exposure to mechanical stimuli, appeared to be dependent on the degree of prior cell passaging. Thus, these results suggest that although the primary cilium acts as a transducer of mechanical signals, the effects of passaging might impact its functionality.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".