A novel and intelligent chemometric-electrochemical-enzymatic biosensing procedure and mimicking a clinical condition environment to trick the red blood cells for counting them under physiological conditions: A new connection among chemometry, electrochemistry and hematology
Bibliographic record
Abstract
Here, a novel electrochemical biosensing procedure has been developed for determination of the number of red blood cells (RBCs) under physiochemical conditions based on chemometric modeling of hydrodynamic differential pulse voltammetric (HDPV), and amperometric data as responses of a modified edge plane pyrolytic graphite electrode (EPPGE). In order to obtain a good sensitivity from the EPPGE, its surface was modified with a thin layer of multiwalled carbon nanotubes-ionic liquid (MWCNTs-IL). Catalase (CAT) was immobilized onto the surface of MWCNTs-IL/EPPGE with help of nafion. The response of the biosensor was based on electrochemical reduction of oxygen of the blood samples which was enhanced by a trick based on addition of hydrogen peroxide (H2O2) to blood samples which can be reduced by the CAT to produce extra oxygen. Prior to experiments, the solution in electrochemical cell was bubbled with pure N2 to purge the oxygen in the solution, but in order to increase the selectivity of the biosensor towards detection of the oxygen obtained from the red blood cells, voltammetric responses of the biosensor were modeled by multivariate chemometric calibration methods with the help of radial basis function-partial least squares (RBF-PLS), least squares-support vector machines (LS-SVM), recursive weighted partial least squares (rPLS), ant colony optimization-mathematical pre-processing selection by genetic algorithm-sample selection through a distance-based procedure-partial least squares-1 (ACO-GA-SS-PLS1), and radial basis function-artificial neural networks (RBF-ANN) to select the best method for determination of the number of the RBCs. The results confirmed the amperometric methods modeled by RBF-ANN showed the best performance for supporting the biosensor in determination of the number of the RBCs with a performance which had an excellent compatibility with the results of a hemocytometer. The results of this study as the newest application of chemometric-electrochemical methods can make a strong connection among electrochemists, chemometricians and hematologists to expand their collaborations on determination of blood factors.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".