Using Biomimetic Polymers in Place of Noncollagenous Proteins to Achieve Functional Remineralization of Dentin Tissues
Bibliographic record
Abstract
In calcified tissues such as bones and teeth, mineralization is regulated by an extracellular matrix that includes noncollagenous proteins (NCP). This natural process has been adapted or mimicked to restore tissues following physical damage or demineralization by using polyanionic acids in place of NCPs, but the remineralized tissues fail to fully recover their mechanical properties. Here, we show that pretreatment with certain amphiphilic peptoids, a class of peptide-like polymers consisting of N-substituted glycines that have defined monomer sequences, enhances ordering and mineralization of collagen and induces functional remineralization of dentin lesions in vitro. In the vicinity of dentin tubules, the newly formed apatite nanocrystals are coaligned with the c -axis parallel to the tubular periphery, and recovery of tissue ultrastructure is accompanied by development of high mechanical strength. The observed effects are highly sequence-dependent with alternating polar and nonpolar groups leading to positive outcomes, whereas diblock sequences have no effect. The observations suggest aromatic groups interact with the collagen while the hydrophilic side chains bind the mineralizing constituents and highlight the potential of synthetic sequence-defined biomimetic polymers to serve as NCP mimics in tissue remineralization.
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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) | 0.000 | 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 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".