MétaCan
Menu
Back to cohort
Record W1511389583 · doi:10.5772/18874

Electrical Stimulation in Tissue Regeneration

2011· book-chapter· en· W1511389583 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInTech eBooks · 2011
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlanarian Biology and Electrostimulation
Canadian institutionsUniversité LavalHôpital Saint-François d'Assise
Fundersnot available
KeywordsRegeneration (biology)StimulationBiomedical engineeringNeuroscienceBiologyMedicineCell biology

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.659
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.247
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it