Intermittent Electrical Stimulation‐Induced Contractions Accelerate Pro‐Regenerative Processes in Muscles With Deep Tissue Injury in Rats
Bibliographic record
Abstract
The goal of this study was to investigate the effects of an intermittent electrical stimulation (IES) paradigm on the healing of deep tissue pressure injury (DTPI). Electrical stimulation has been extensively studied for the treatment of open wounds, often with amplitudes lower than motor threshold. The effect of electrical stimulation in producing palpable muscle contractions on the treatment of DTPI has not been explored. IES was tested in rats with complete spinal cord injury in which a DTPI was subsequently induced. The goals of the study were to investigate: (i) the natural progression of a DTPI; and (ii) the role of IES in expediting the rate of healing. Three groups of rats were used: (a) a control group in which DTPI is induced, (b) a control group in which stimulation only is applied without the induction of DTPI and (c) an intervention group in which DTPI is induced and IES is applied. Magnetic resonance imaging monitored the extent and progression of injury 1, 3, 5 and 7 days post-induction of injury and/or initiation of IES. Immunohistochemistry assessed the severity of injury and rate of healing by evaluating the presence of inflammatory cells and embryonic myofibres. When applied on day 1 after the induction of DTPI, IES significantly reduced the edema associated with the injury, increased pro- and anti-inflammatory macrophages and increased cell proliferation. It also decreased the overall size of injury. These results suggest that when applied early after the initiation of a DTPI, IES can expedite anti-inflammatory and pro-regenerative events and may be an effective means for treating the injury.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".