Helical Rosette Nanotubes: Topographical and Chemical Roles in Osteoblast Attachment
Bibliographic record
Abstract
Success in surgical joint replacements has resulted in a huge demand amongst patients. Coupled with the lowered average age of patients requiring hip replacements, younger patients are demanding longer life expectancy from such devices. The increasing need and demand for more durable implants have led to new formulations of high performance nanomaterials (materials with basic structural units of 1-100 nm). Nanotubes in particular have shown great promise because they: 1) have sizes that approach biological structures and 2) possess efficient channels for displaying chemistries relevant to living systems at high densities and well-controlled spatial distribution. Helical rosette nanotubes (HRN) are a new class of soft organic nanomaterials composed of a guanine-cytosine building block that self-assembles in aqueous environments into stable nanotubular structures with an inner diameter of ~1.1 nm. HRN can be decorated with biologically active chemical functionalities such as cell attracting peptide fragments. Previously, we have showed that HRN coated Ti can enhance OB attachment. In addition, proteins were seen to interact favorably with HRN networks in a manner favorable toward OB attachment. Furthermore, in the absence of proteins, HRN were seen to play the role of proteins in promoting OB attachment. The studies herein, attempt to understand the role of the lysine clusters on HRN toward OB attachment. Results show that OB do respond to lysine and molecular orientation considerations were shown to be important. Detailed structural considerations from molecular modeling further present the possibility of topographical influences (nanotube network architecture) towards OB attachment.
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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".