Lewis Acid-Boosted Nanozyme for In Situ Metallization Electrochemical Sensing of Extracellular Vesicles
Bibliographic record
Abstract
Extracellular vesicles (EVs) mediate tumor progression by facilitating signal transduction between cancer cells and the microenvironment, positioning them as critical biomarkers for diagnosis. CD20 is a key surface antigen of B cells, and its detection is of great significance for lymphoma detection and therapy monitoring. However, conventional EVs detection methods suffer from high costs, operational complexity, and limited sensitivity. Here, we developed an electrochemical immunosensor utilizing a Lewis acid-boosted nanozyme, Ce/UiO-67, which was synthesized through a one-step hydrothermal method. Ce 4+ doping enhances its phosphatase-like activity by mimicking Zn 2+ –O–Zn 2+ active sites, catalyzing the hydrolysis of l -ascorbic acid-2-phosphate (SAP) to ascorbic acid (AA). The resultant AA reduces Ag + to Ag 0 for in situ deposition on the electrode, with the Ag 0 oxidation current quantified by differential pulse voltammetry (DPV) for specific CD20 detection on EVs. This sensor achieves a detection range of 5.12 × 10 2 to 1.6 × 10 7 particles/μL and a limit of detection (LOD) of 280 particles/μL, offering superior sensitivity and simplicity. Applied to clinical plasma, it distinguishes lymphoma patients from healthy individuals, demonstrating the potential for diagnosis and treatment evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".