Compression-dependency of soft tissue bioimpedance for <i>in-vivo</i> and <i>in-vitro</i> tissue testing
Bibliographic record
Abstract
Abstract The present study determines the effect of compression over bioimpedance of healthy soft tissue ( in-vitro and in-vivo) . Electrical impedance spectroscopy (EIS) is a promising tissue characterization and tumor detection technique that uses tissue impedance or admittance to characterize tissue and identify tissue properties as well as cell structure. Variation in EIS measurements while applying pressure suggests that compression tends to affect soft tissue bioimpedance. Moreover, the displacements in tissue caused by applied compression may provide useful information about the structure and state of the tissue. Thus combining the changes to the electrical properties of tissue resulted by applied compression, with the changes in tissue displacements caused by applied compression, and consequently measuring the effect that electrical and mechanical properties have on each other, can be useful to identify tissue structure. In this study, multifrequency bioimpedance measurements were performed on in-vitro and in-vivo soft tissue at different pressure levels. Increasing compression on the in-vitro tissue results in an increase in both extracellular resistance and membrane capacitance while it causes a reduction in the intracellular resistance. However, as the compression over the in-vivo samples increases, the intracellular and extracellular resistance increase and the membrane capacitance decreases. The in-vivo measurements on human body are also tested on contra-lateral tissue sites and similar tissue impedance variation trends are observed in the contra-lateral sites of human body. The evidence from these tests suggests the possibility of using this EIS-Pressure combined measurement method to improve tumor detection in soft tissue. Based upon the observations, the authors envision developing an advanced model based upon the Cole model, which is dependent on tissue displacements.
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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) | 0.000 | 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 | 0.000 |
| 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".