Multi-Cantilevered Tetrahedral DNA Spider (TDSpider): An Efficient Molecular Machine for Biometrics and Disease Diagnosis
Bibliographic record
Abstract
Low-dimensional machines frequently encounter difficulties in terms of limited walking efficiency, largely due to the scarcity of binding arms and inaccurate biorecognition process. Fabricating molecule machines possessing flexible effector that facilitates the conversion of target content into a signal, and entitle machines with stereoscopic structure, could largely enhance the operational efficiency of conventional molecule machines through recognition and transferring process. Here a multicantilevered TDN was synthesized, comprising extended arms with allosteric nucleic acid enzyme (ANAzyme), named "DNA spider" (TDSpider, TDS). In specific, upon introduction of the target, the extended arms bind with targets and fold to active ANAzyme, which serves as a highly sensitive regulator, thereby initiating the TDS. It crawls on the surface of nanomaterials in a more predictable and precise manner and exhibits bimodal operable property with liberating fluorescence signals on the surface of gold nanoparticles (AuNPs) orbitals and elemental signals on the surface of magnetic beads (MBs) orbitals. TDS demonstrates improved walking efficiency compared to 1D DNA machines and promises to provide novel tools for biometrics and disease diagnostic processes.
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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 | 0.001 | 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".