Detection of leukemia markers using long-range surface plasmon waveguides functionalized with Protein G
Bibliographic record
Abstract
A novel optical biosensor based on long-range surface plasmon-polariton (LRSPP) waveguides is demonstrated for the detection of leukemia markers in patient serum using a functionalization strategy based on Protein G. The sensor consists of thin straight Au waveguides (5 μm × 35 nm × 3.2 mm) embedded in fluoropolymer CYTOP™ with a fluidic channel etched into the top cladding. B-cell leukemia is characterized by a high B-cell count and abnormal distribution of immunoglobulin G kappa (IgGκ) and lambda (IgGλ) light chains in serum. The detection of leukemic abnormalities in serum was performed based on determining IgGκ-to-IgGλ ratios (κ : λ). Three patient sera were tested: high kappa (HKS, κ : λ ~12.7 : 1), high lambda (HLS, λ : κ ~6.9 : 1) and normal (control) sera (NS, κ : λ ~1.7 : 1). Au waveguides were functionalized with Protein G and two complementary immobilization approaches were investigated: a) the reverse approach, where the Protein G surface is functionalized with patient serum and then tested against goat anti-human IgG light chains in buffer, and b) the direct approach, where the Protein G surface is functionalized with goat anti-human IgGs first and then tested against patient serum. The reverse approach was found to be more effective and robust because Protein G-functionalized surface performs as an "immunological filter" by capturing primarily IgGs out of the pool of serum proteins. For the reverse approach, the ratios measured were 3.7 : 1(κ : λ), 9.7 : 1(λ : κ) and 1.9 : 1(κ : λ) for HKS, HLS and NS, respectively, which compare favorably with corresponding protein densitometry measurements. The respective ratios for the direct approach were 2.6 : 1(κ : λ), 2.6 : 1(λ : κ) and 1.7 : 1(κ : λ). The binding strength and cross-reactivity of goat anti-human IgGs light chains were also determined using pure solutions. The LRSPP biosensor along with the innovative "reverse approach" can provide a low-cost and compact solution to B-cell leukemia screening.
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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) | 0.001 | 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 | 0.000 |
| 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".