Synergetic Gating of Metal-Latching Ligands and Metal-Chelating Proteins for Mesoporous Silica Nanovehicles to Enhance Delivery Efficiency
Bibliographic record
Abstract
Stimuli-responsive drug delivery systems are highly desirable for improved therapeutic efficacy and minimized adverse effects of drugs. Mesoporous silica nanoparticles (MSNs) functionalized with pentadentate ligands, N-(3-trimethoxysilylpropyl)ethylenediamine triacetate (TSP-DATA), in the presence of metal ions with and without myoglobin (Mb)-containing surface-accessible histidine residues, were constructed for pH-triggered controlled release. The DATA ligands immobilized on the MSN pore outlets could encapsulate cargo within the pores by metal latching across pore openings, and release efficiency increased with the increase of surface density of the DATA ligands. The release efficiencies for the metal-chelating protein nanogates, through multiple-site binding of Mb with the metal-chelating ligands, were higher than those for the metal-latching ligand nanogates but were almost independent of surface density of the ligands investigated. Both the metal-latching ligands and the metal-chelating proteins played a synergetic role in gating MSNs for high-loading drug delivery and stimuli-responsive controlled release. The constructed Mb-Cu(2+)-gated MSN delivery system has promising applications in targeted drug therapy of tumors.
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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".