From Lab to Clinical Application: Establishing a “Gold” Touchstone for Lung Cancer Biomarker-CEA to Advance Diagnostic
Bibliographic record
Abstract
Colloidal gold immunochromatography has emerged as a pivotal platform for point-of-care diagnostics, yet challenges persist in stabilizing nanoparticle-antibody interactions and ensuring batch-to-batch reproducibility. To address these limitations, we engineered a nanogold-affinity peptide probe for the rapid detection of the lung cancer biomarker carcinoembryonic antigen (CEA), leveraging phage-display-derived peptides as biorecognition elements. The probe was synthesized by conjugating CEA-specific affinity peptides to polyethylene glycol (PEG)-functionalized gold nanoparticles (AuNPs) via covalent amide bonding, ensuring precise orientation and enhanced colloidal stability. Systematic optimization of reaction parameters, including the PEGylation time, peptide-to-nanoparticle ratios, and centrifugation conditions, yielded a robust preparation protocol. The resulting immunochromatographic test strip demonstrated a detection limit of 2.5 ng/mL for CEA, surpassing the clinical threshold of 5 ng/mL, and exhibited 91.7% accuracy in clinical serum samples. Notably, the substitution of antibodies with synthetic affinity peptides reduced costs by approximately 8-fold while maintaining high specificity and resistance to nonspecific binding. This work not only advances the integration of biomolecular engineering and nanotechnology for diagnostic applications but also establishes a scalable framework for developing stable, low-cost biosensors targeting macromolecular biomarkers.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".