Profiling Osteogenic microRNAs For RNAi-Functionalization Of Scaffolds In Bone Tissue Engineering
Bibliographic record
Abstract
Bone remodeling and bone repair are natural processes occurring in the body throughout life. This metabolic process ensures that microfractures and microinjuries are repaired and thus maintaining healthy bones. However, when the damage is too large, such as from trauma injury or bone tumors, repair is limited and grafts are required to assist in bone repair. The use of allografts can cause immunological complications, whilst autografts subject the patient to two surgeries. Bone tissue engineering is a multidisciplinary field encompassing material science, medicine, chemistry and molecular biology aimed to produce a functional graft in vitro as an alternative to allografts and autografts. We explored the microRNAs (miRNAs) that aid in the bone formation process. MiRNAs are small non-coding RNAs of about 17-22 nucleotides in length that target the 3’UTR of mRNAs and represses their expression. MiRNAs have been found to facilitate many processes in the body including development, metabolism, and are implicated in many diseases. Many miRNAs have been identified with roles in osteogenesis, however a large systematic view at miRNA expression throughout osteogenesis that includes early, intermediate and late time points has yet to be done. We aimed to identify the expression profiles of miRNAs that as mesenchymal stem cells underwent osteogenesis with microRNA-sequencing. Most miRNAs were downregulated during osteogenesis and only few were upregulated. With the use of weighted gene correlated gene analysis we identified several expression profiles. Several miRNAs were validated in their osteogenic capabilities by overexpressing and knocking down the miRNAs, then assessing their ALP activities, Alizarin Red and ALP stainings and the expression of osteoblastic markers. In our screen we have identified both miRNAs that have been reported previously and many novel miRNAs with potent osteogenic capabilities. For tissue engineering applications, we then functionalized scaffolds with the miRNAs we identified and observed an increase in osteogenic capabilities in our 3D cultures. Our findings depicted the miRNA expression landscape as mesenchymal stem cells underwent osteogenic differentiation. We also highlight the potency of miRNAs as biological therapeutics in bone tissue engineering.
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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 | 0.001 |
| 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".