Electrical Stimulation in Tissue Regeneration
Bibliographic record
Abstract
That human body generates biological electric field and current is a well-known natural phenomenon. In 1983, electrical potentials ranging between 10 and 60 mV at various locations of the human body were measured by Barker (Foulds & Barker, 1983), who also located the so-called epidermal or skin “battery” inside the living layer of the epidermis. Naturally occurred electrical field in human body was also reviewed in 1993 (Zipse, 1993). Bioelectricity is inherent in wound healing. An injury potential occurs in the form of a steady direct current (DC) electric field (EF) when a wound is created. This endogenous EF has been shown to guide cell migration to sprout directly toward the wound edge. On the other hand, wound healing is compromised when the EF is inhibited. McCaig et al. (McCaig et al., 2005) revealed that electrical events induced by injury potential could persist for a long time and present across hundreds of microns rather than be confined to the immediate vicinity of the cell membrane. Moreover, a voltage gradient called “action potential” across cell membrane is known to trigger cells to transmit signals and secrete hormones. The electrical resistivity of biological tissues obviously varies due to the variation in tissue composition, such as tissue type and density, cell membrane permeability, and electrolyte content. The resistivity of these biological tissues has been measured by means of bioelectrical impedance analysis (BIA). When nutritional and metabolic disorders occur, the electrical properties of certain tissues become abnormal. BIA has therefore been used to diagnose human organ malfunctions. However, it remains difficult to delineate living tissue, such as bone tissue, because this tissue is a composite material that is anisotropic in structure and inhomogeneous in composition. For example, in 1975, Liboff (Liboff et al., 1975) reported a resistance ranging widely from 0.7 to 1 × 105 ohm/cm in human tibia. Recent advances in computed three-dimensional microtomography (microCT) now enable us to clarify the interrelationships between the electrical properties and the microstructures of human bone. The electrical property of bones varies widely caused by many factors such as the unevenly distributed and electrolyte-filled pores, moisture content, pH and conductivity of the immersing fluid. Nevertheless, it remains both essential and possible to normalize the resistance and the capacitance of different bone types. Electrical measurements provide a tool for the rapid quantitative diagnosis of bone
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".