Optimal Biofunctionalization of Gold Nanoislands for Electrochemical Detection of Soluble Programmed Death Ligand 1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it