Response Monitoring of Acute Lymphoblastic Leukemia Patients Undergoing <scp>l</scp>-Asparaginase Therapy: Successes and Challenges Associated with Clinical Sample Analysis in Plasmonic Sensing
Bibliographic record
Abstract
Abstract Monitoring the response of patients undergoing chemotherapeutic treatments is of great importance to predict remission success, avoid adverse effects and thus, maximize the patients’ quality of life. In the case of leukemia patients treated with E. colil-asparaginase, monitoring the immune response by the detection of specific antibodies to l-asparaginase in the serum of patients can prevent extended immune response to the drug. Here, we developed a surface plasmon resonance (SPR) biosensor to rapidly detect anti-asparaginase antibodies directly in patients’ sera, without requiring sample pretreatment or dilution. A direct assay with SPR sensing to detect anti-asparaginase antibodies exhibited a limit of detection of 500 pM and a high sensitivity range between 100 nM and 1 μM in pooled and undiluted human serum from a commercial source. While the SPR assay showed excellent reproducibility (12% RSD) in pooled serum, challenges were encountered upon analyzing clinical samples due to high sample-to-sample variability in color and turbidity and, in all likelihood, in composition. As a result, direct detection in clinical samples was unreliable due to factors that may generally affect assays based on plasmonic detection. Addition of a secondary detection step overcame sample variability due to bulk differences in patients’ sera. By those means, the SPR biosensor was successfully applied to the analysis of clinical samples from leukemia patients undergoing asparaginase treatments and the results agreed well with the standard ELISA assay. Monitoring antibodies against drugs is common such that this type of sensing scheme could serve to monitor a plethora of immune responses in sera of patients undergoing treatment.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| 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".