MétaCan
Menu
Back to cohort
Record W2792139365

Effects of Electrical Stimulation on Wound Closure in Mice with Experimental Diabetes Mellitus

2000· article· en· W2792139365 on OpenAlexfundno aff
Habiba A. Thawer, Pamela E. Houghton, Luther C. Kloth, Anna Butryn

Bibliographic record

Venuee-Publications@Marquette (Marquette University) · 2000
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsnot available
FundersPhysiotherapy Foundation of Canada
KeywordsDiabetes mellitusStimulationMedicineWound closureSurgeryInternal medicineWound healingEndocrinology
DOInot available

Abstract

fetched live from OpenAlex

The purpose of the present study was to examine the effect of electrical stimulation (ES) on the closure of full-thickness excisional wounds in mice with type-1 experimental diabetes mellitus (DM). Alloxon monohydrate (100mg/kg) was used to induce experimental DM in mole CD-1 mice (n = 88). Full-thickness skin excisions (1cm2) in diabetic (urine glucose > 0) and non-diabetic (urine glucose = 0) mice were administered 1, 3, or 5 treatments of ES (200μs, 200 Hz) for 15 minutes, at 0 (sham), 5, 10, or 12.5 volts. Alloxon injection resulted in a positive urine glucose test in 48 mice yielding an induction rate for DM of 54.5 percent. All groups exhibited decreases in wound length, perimeter, and surface area between days 2 and 16 following the creation of wounds. Non-diabetic wounds treated with ES hod the greatest percentage (60%) of closure. Diabetic wounds treated with ES hod a greater percentage of clo­sure (36%) compared with sham-treated diabetic animals (12.5%). Treatment of wounds with the highest voltage of ES (12.5V) produced significant (P < 0.01) decreases in the surface area, and significant (P < 0.01) changes in the shapes of wounds in both diabetic and non-diabetic animals compared with sham-treated animals. These results support the clinical use of this adjunctive therapy to accelerate the closure of ulcers due to OM.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.174
Teacher spread0.170 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2000
Admission routes1
Has abstractyes

Explore more

Same venuee-Publications@Marquette (Marquette University)Same topicWireless Body Area NetworksFrench-language works237,207