The effect of <i>Aloe vera</i> gel on viability of dental pulp stem cells
Bibliographic record
Abstract
BACKGROUND: Dental pulp stem cells (DPSCs) can play a prominent role in tissue regeneration. Aloe vera L. (Liliaceae) contains the polysaccharide of acemannan that was shown to be a trigger factor for cell proliferation, differentiation, mineralization, and dentin formation. AIM: This study sought to determine the viability of DPSCs in Aloe vera in comparison with Hank's balanced salt solution (HBSS). MATERIALS AND METHOD: Twelve rabbits underwent anesthesia, and their incisor teeth were extracted; the pulp tissue was removed, chopped, treated with collagenase and plated in culture flasks. DPSCs from passage 3 were cultured in 24-well plates, and after 3 days, the culture media changed to 10, 25, 50, and 100% concentrations of Aloe vera at intervals of 45 and 90 min and 3 and 6 h. Distilled water was used as negative and HBSS as positive control for comparison. The cell morphology, viability, population doubling time (PDT), and growth kinetics were evaluated. RT-PCR was carried out for characterization and karyotyping for chromosomal stability. RESULTS: Aloe vera showed a significant higher viability than HBSS (74.74%). The 50% Aloe vera showed higher viability (97.73%) than other concentrations. PDT in 50% concentration was 35.1 h and for HBSS was 49.5 h. DPSCs were spindle shaped and were positive for CD73 and negative for CD34 and CD45. Karyotyping was normal. CONCLUSIONS: Aloe vera as an inexpensive and available herb can improve survival of avulsed or broken teeth in emergency cases as a transfer media.
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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 | 0.000 |
| 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".