Combination of the insulator‐based dielectrophoresis and hydrodynamic methods for separating bacteria smaller than 3 μm in bloodstream infection: Numerical simulation approach
Bibliographic record
Abstract
Abstract Bloodstream infections have a high mortality rate with >80,000 deaths per year in North America. The inability to detect pathogens quickly in the early stages of the infection causes high mortality. Such inability has led to a growing interest in developing a rapid, sensitive, and specific method for identifying these pathogens. The rapid detection of bloodstream infections requires the rapid and efficient separation of bacteria from the blood. But the problem is that the number of bacteria is much lower than other blood components. The blood culture step needs to be accomplished first for bacteria identification and antibiotic susceptibility testing. As the blood culture is time‐consuming, a method based on the insulator‐based has been presented that increases the number of bacteria by combining the blood culture method and increasing the concentration. In this model, the dielectrophoresis technique was utilized in a curved microchannel with a constriction for sorting three particle sizes including 9, 7–4 μm, as well as smaller than 3 μm. The results showed that the applied voltage and the channel dimensions affect separation efficiency. Suppose these values are properly selected (for example, a voltage of 110 V that was causing the maximum electric field of 200 V/cm). The proposed model can completely (100%) separate larger than 9 μm and smaller than 3 μm particles. The proposed model has simple geometry and is considered an appropriate technique for sorting all bacteria separation in bloodstream infection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".