Short‐period Effects of Zirconia and Titanium on Osteoblast MicroRNAs
Bibliographic record
Abstract
BACKGROUND: MicroRNAs (miRNAs) are a class of small, functional, noncoding RNAs of 19 to 23 nucleotides which induce degradation of specific messenger RNAs (mRNAs), thus controlling the translational process (ie, synthesis of proteins from mRNAs). In addition, mRNAs regulate the promoter of specific miRNAs activating an autoregulatory feedback loop. PURPOSE: Titanium and zirconium dioxide ceramics (ZDCs) are used to make dental implants. Because the molecular mechanism by which ZDC and Ti act on osteoblasts is incompletely understood, we attempted to get more information by comparing the effect of ZDC and Ti on osteoblast miRNAs. MATERIALS AND METHODS: By using miRNA microarray technique, we identified in osteoblast-like cell line (MG63) grown on grade 3 Ti and ZDC disks several miRNAs whose expression was modified. We collected mRNAs after 24 hours of cell culturing to better understand molecular events related to early bone healing around inserted implants. An mRNA microarray technique was then performed as a control. RESULTS: There were six up- and four down-regulated miRNAs. Because every miRNA regulates hundreds of genes, we focused only on those related to bone formation. Among them, the most notable are BMP4 and 7, which are both up-regulated in osteoblasts cultured on Ti disks. CONCLUSION: The detected miRNAs differentially expressed in osteoblast-like cells grown on ZDC versus Ti act on a limited number of miRNAs and bone-related genes. The most notable are BMP4 and 7, which are more expressed in osteoblasts exposed to Ti surface. Consequently, we suggest that Ti surfaces could provide some advantages to immediate load implantology.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".