Optimal Biofunctionalization of Gold Nanoislands for Electrochemical Detection of Soluble Programmed Death Ligand 1
Bibliographic record
Abstract
Soluble programmed death ligand-1 (sPD-L1), a pivotal immune checkpoint protein, serves as a biomarker for evaluating the efficacy of cancer therapies. Aptamers, as highly stable and specific recognition elements, play an essential role in emerging point-of-care diagnostic technologies. Yet, crucial advancements rely on engineering the intricate interaction between aptamers and sensor substrates to achieve specificity and signal enhancement. Here, a comprehensive physicochemical characterization and performance optimization of a sPD-L1 aptamer-based biosensor by a complementary set of state-of-the-art methodologies is presented, including atomic force microscopy-based infrared spectroscopy and high-resolution transmission electron microscopy, providing critical insights on the surface coverage and binding mechanism. The optimal nanoaptasensors detect sPD-L1 across a wide concentration range (from am to μm) with a detection limit of 0.76 am in both buffer and mouse serum samples. These findings, demonstrating superior selectivity, reproducibility, and stability, pave the way for engineering miniaturized point-of-care and portable biosensors for cancer diagnostics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.000 | 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".