Low-Cost Leukemic Serum Marker Screening Using Large Area Nanohole Arrays on Plastic Substrates
Bibliographic record
Abstract
Plasmonic biosensors, particularly arrays of nanoholes on thin gold films, have been widely explored in recent years as possible platforms for fast medical diagnostic. In this work, we present a screening method for leukemia cancer markers that uses a plasmonic biosensor based on nanohole arrays fabricated on plastic substrates. The low-cost, scalable, and reproducible nanohole array structures were fabricated by UV nanoimprinting technique. The relative concentration of human immunoglobulin kappa and lambda light chains in blood serum was employed as a screening method. The kappa/lambda concentration ratio was used to determine an unbalance in the immunoglobulin production due to leukemia. The platform was tested using serum samples from patients with known leukemia diagnoses. The results indicated that this inexpensive and flexible plasmonic platform is a promising tool for routine screening in clinical settings.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".