Salivary gland bioengineering - yesterday, today, tomorrow!
Bibliographic record
Abstract
Salivary glands are specialized structures developed as an extensively compact, arborized design through classical embryogenesis, accompanied by a cascade of events channelized by numerous growth factors and genetic regulatory pathways. Salivary secretions maintain oral homeostasis and, when diminished in certain conditions, present as xerostomia or salivary hypofunction, adversely impacting the patient's quality of life. The current available treatments primarily aim at tackling the immediate symptoms providing temporary relief to the patient. Despite scientific efforts to develop permanent and effective solutions to restore salivation, a significant permanent treatment is yet to be established. Tissue engineering has proven as a promising remedial tool in several diseases, as well as in xerostomia, and aims to restore partial loss of organ function. Recapitulating the physiological cellular microenvironment to in vitro culture conditions is constantly evolving. Replicating the dynamic multicellular interactions, genetic pathways, and cytomorphogenic forces, as displayed during salivary gland development have experienced considerable barriers. Through this review, we endeavour to provide an outlook on the evolution of in vitro salivary gland research, highlighting the key bioengineering advances and the challenges faced with the current therapeutic strategies for salivary hypofunction, with an insight into our team's scientific contributions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".