Analysis of Normal and Infected Bio-Cell Using Dual Nanoprobe
Bibliographic record
Abstract
The objective of this paper is to analyze the yeast cell, liver cell and blood cell under both infected and healthy conditions by applying electrical signal through dual nanoprobe from source to cell. Knowledge of nanoprobe based bio-cell analysis can be used to differentiate infected cells from healthy ones, since their electrical behaviour is different. The voltage can be applied either inside the cytoplasm penetrating the cell wall, or at the outer surface of cell membrane. In this paper, the simulation has been carried out by applying voltage inside the cytoplasm using ABAQUS 6.10 CAE, powerful finite element software and the results obtained from simulation shows current flow healthy yeast and dead yeast cells are 1·9nA and 34pA respectively whereas the value of the current measured for the leukaemia affected blood cell is 21·2nA, being 2% less than the White Blood Cell (WBC). Since the conductivity of the cytoplasm of the healthy cell is theoretically higher than that of dead or infected cell, which is verified by simulation. The results from simulation show that the measured cell current from liver tumour cell is 2-7 times larger than healthy liver cell including membrane as it is supposed to be since the conductivity of cell including cell membrane is theoretically greater for dead or infected cell than healthy cell.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".