Protein Nanotubes: From Bionanotech towards Medical Applications
Bibliographic record
Abstract
Nanobiotechnology involves the study of structures found in nature to construct nanodevices for biological and medical applications with the ultimate goal of commercialization. Within a cell most biochemical processes are driven by proteins and associated macromolecular complexes. Evolution has optimized these protein-based nanosystems within living organisms over millions of years. Among these are flagellin and pilin-based systems from bacteria, viral-based capsids, and eukaryotic microtubules and amyloids. While carbon nanotubes (CNTs), and protein/peptide-CNT composites, remain one of the most researched nanosystems due to their electrical and mechanical properties, there are many concerns regarding CNT toxicity and biodegradability. Therefore, proteins have emerged as useful biotemplates for nanomaterials due to their assembly under physiologically relevant conditions and ease of manipulation via protein engineering. This review aims to highlight some of the current research employing protein nanotubes (PNTs) for the development of molecular imaging biosensors, conducting wires for microelectronics, fuel cells, and drug delivery systems. The translational potential of PNTs is highlighted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".