Single or Mixed Tethered Peptides To Promote hMSC Differentiation toward Osteoblastic Lineage
Bibliographic record
Abstract
The commitment and differentiation of human mesenchymal stem cells (hMSCs) are guided by bioactive molecules within the extracellular matrix. Among the various approaches to design biomaterials, the functionalization of biomaterial surfaces with peptides from the sequence of proteins from the extracellular matrix is quite common. The purpose of this functionalization is to recruit hMSCs and promote their differentiation into the appropriate lineage. The aim of this work was to investigate the influence of RGD and FHRRIKA peptides and peptide sequences taken from bone morphogenic protein (BMP-2) and histone H4 (osteogenic growth peptide; OGP) either tethered alone or as a mixture on the surface of a model material and to also examine the level of hMSC osteogenic commitment without using a differentiation medium. Grafting of the different peptides was assessed by X-ray photoelectron spectroscopy (XPS), while their surface density was quantified by fluorescence microscopy, and their surface properties were assessed by atomic force microscopy (AFM) and contact angle (CA). The osteogenic commitment of hMSCs cultured on the different surfaces was characterized by immunohistochemistry using Runx-2 as an earlier osteogenic marker and OPN, a late osteogenic marker, and by RT-qPCR through the expression of ColI-a1, Runx-2, and ALP. Biological results show that the osteogenic commitment of the hMSCs was increased on surfaces tethered with a mixture of peptides. Results indicate that tethered peptides in the range of pmol mm –2 were indeed effective in inducing a cellular response after 2 weeks of cell culture without using an osteogenic media. These findings contribute to the research efforts to design biomimetic materials able to induce a response in human stem cells through tethered bioactive molecules for 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 | 0.000 |
| 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".