Genetically encoded fluorescent probes to assist in the diagnosis of small cell lung cancer
Bibliographic record
Abstract
Small cell lung cancer (SCLC) is one of the most aggressive malignancies, with early detection being crucial for improving patient outcomes. Serum biomarkers, neuron-specific enolase (NSE), and pro-gastrin releasing peptide (ProGRP), play significant roles in the early screening and pathological classification of SCLC. In the study, the affinity peptides of NSE and ProGRP were screened by phage display technology, which were then assessed for binding affinity using enzyme-linked immunosorbent assay (ELISA) and biolayer interferometry (BLI). Circularly permuted fluorescent protein (cpFP) probes were constructed by genetically encoding the selected peptides as binding domains. The E1 probe for NSE and the P10 probe for ProGRP demonstrated high sensitivity and specificity in detecting their respective targets. The E1 probe with a concentration of 4 μg/mL reacted well with NSE (1-16,000 ng/mL), and the reaction exhibited a good linear relationship when the NSE concentration was between 1 and 100 ng/mL. The 4 μg/mL P10 probe reacted well with ProGRP (0.01-2000 ng/mL) and showed good linear relationship between 0.01 and 50 ng/mL. Clinical validation revealed that adjusting the upper limit of normal concentrations significantly improved the probes' diagnostic sensitivity and specificity for SCLC. These probes offer a high-sensitivity, specific, rapid, and cost-effective approach to SCLC detection, holding promise for early diagnosis and improved patient management . KEY POINTS: In this study, peptides targeting NSE and ProGRP were selected by phage display technology, and the peptides obtained have good affinity with the corresponding proteins. Based on R-GECO1, cpFP probes were constructed using peptides as binding domains, and E1 probe for NSE and P10 probe for ProGRP were obtained. E1 probe and P10 probe have good sensitivity and specificity for the diagnosis of SCLC.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".