Exploiting second-order advantage for simultaneous biosensing of romidepsin and vorinostat in the presence of belinostat as uncalibrated interference in human serum samples
Bibliographic record
Abstract
In this work, a novel, ultra-sensitive, selective, and multi-electrochemical techniques biosensor was fabricated for simultaneous determination of romidepsin (RD) and vorinostat (VN) in the presence of belinostat (BS) as uncalibrated interference in human serum samples. A glassy carbon electrode (GCE) was modified with chitosan-ionic liquid (CS-IL) which was used as a platform to immobilize histone deacetylase (HDAC) by the use of glutaraldehyde. The RD and VN as inhibitors of the HDAC were trapped by the HDAC onto the biosensor surface which didn’t show any response at the surface of the CS-IL/GCE. In order to simultaneous biosensing of RD and VN, the biosensor was immersed into a probe solution to get a response from the biosensor. By individual immersion of the biosensor into RD and VN solutions different steric hindrances were occurred at the biosensor surface which caused generation of two different responses from the biosensor. The biosensor responses were individually calibrated and used to develop second-order calibration models to support the biosensor for simultaneous determination of RD and VN. Second-order hydrodynamic square wave voltammetric (HSWV) data were generated and modeled by PARAFAC2, MCR-ALS, PARASIAS, and U-PCA/RBL to find the best method to couple with outputs of the biosensor. The results confirmed the best performance for the biosensor- U-PCA/RBL for simultaneous determination of RD and VN in the presence of BS in artificial human serum samples. The biosensor-U-PCA/RBL combinatorial method was also successful in simultaneous determination of RD and VN in the presence of BS in real human serum samples with a comparable performance with HPLC as the reference method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".