Expansion of functional human salivary acinar cell spheroids with reversible thermo-ionically crosslinked 3D hydrogels
Bibliographic record
Abstract
Xerostomia (dry mouth) is frequently experienced by patients treated with radiotherapy for head and neck cancers or with Sjögren's syndrome, with no permanent cure existing for this debilitating condition. To this end, in vitro platforms are needed to test therapies directed at salivary (fluid-secreting) cells. However, since these are highly differentiated secretory cells, the maintenance of their differentiated state while expanding in numbers is challenging. In this study, the efficiency of three reversible thermo-ionically crosslinked gels: (1) alginate-gelatin (AG), (2) collagen-containing AG (AGC), and (3) hyaluronic acid-containing AG (AGHA), to recapitulate a native-like environment for human salivary gland (SG) cell expansion and 3D spheroid formation was compared. Although all gels were of mechanical properties comparable to human SG tissue (~11 kPa) and promoted the formation of 3D spheroids, AGHA gels produced larger (>100 cells/spheroid), viable (>93%), proliferative, and well-organized 3D SG spheroids while spatially and temporally maintaining the high expression of key SG proteins (aquaporin-5, NKCC1, ZO-1, α-amylase) for 14 days in culture. Moreover, the spheroids responded to agonist-induced stimulation by increasing α-amylase secretory granules. Here, we propose alternative low-cost, reproducible, and reversible AG-based 3D hydrogels that allow the facile and rapid retrieval of intact, highly viable 3D-SG spheroids.
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) | 0.001 | 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".