Stable Hydrogen‐Bonded Cobalt‐porphyrin Framework for High‐Performance Electrochemical Detection of Carcinoembryonic Antigen
Bibliographic record
Abstract
ABSTRACT The accurate and sensitive detection of low‐abundance cancer‐related biomarkers in blood remains a key technical challenge in clinical applications. Herein, a simple and accurate sandwich‐type electrochemical immunosensor based on a stable hydrogen‐bonded cobalt‐porphyrin framework (Co‐HOF) was successfully developed for the ultrasensitive detection of the cancer‐related biomarker, carcinoembryonic antigen (CEA). The antibody‐modified Co‐HOF forms a sandwich structure with the CEA aptamer electrode exclusively in the presence of CEA, enabling the specific electrochemical detection of CEA. The electrochemical signal increased linearly with the concentration of CEA, demonstrating a wide linear range (0.001–50 ng mL−1) and a low detection limit (0.22 pg mL−1), surpassing the performance of commercial ELISA kits and most reported detection methods. The sensor was successfully employed for CEA detection in spiked human serum, with recoveries ranging from 85.04% to 105.20%. Additionally, we collected blood samples from colorectal cancer patients and healthy individuals to clinically validate the sensor, observing that CEA levels increased with cancer progression. The sensor detection results showed strong consistency (R2 = 0.995) with those obtained from commercial ELISA kits, demonstrating the proposed sensor's practicality for clinical detection of CEA and related cancer biomarkers.
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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 | 0.000 |
| 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".