ArticleThe Journal of surgical research2016
Severe burn increased skeletal muscle loss in mdx mutant mice.
Article in The Journal of surgical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- MyoV: a deep learning-based tool for the automated quantification of muscle fibers.Briefings in bioinformatics · 2024Article
- Mild burn amplifies the locomotive depression in demyelinated mice without muscle pathophysiological changes.PloS one · 2024Article
- Acute muscle mass loss was alleviated with HMGB1 neutralizing antibody treatment in severe burned rats.Scientific reports · 2023Article
- Skeletal muscle transcriptome is affected by age in severely burned mice.Scientific reports · 2022Article
- [Effects of wedge-shaped heel pad in the treatment of extremely severe burn patients with foot drop deformity].Zhonghua shao shang za zhi = Zhonghua shaoshang zazhi = Chinese journal of burns · 2021Article
- ER stress and subsequent activated calpain play a pivotal role in skeletal muscle wasting after severe burn injury.PloS one · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundSevere burn causes muscle mass loss and atrophy. The balance between muscle cell death and growth maintains tissue homeostasis. We hypothesize that preexisting cellular structural defects will exacerbate skeletal muscle mass loss after burn. Using a Duchenne muscular dystrophy (mdx) mutant mouse, we investigated whether severe burn caused more damage in skeletal muscle with preexisting muscle disease.
methodsThe mdx mice and wild-type (WT) mice received 25% total body surface area scald burn. Gastrocnemius (GM), tibialis anterior, and gluteus muscles were obtained at days 1 and 3 after burn. GM muscle function was measured on day 3. Animals without burn served as controls.
resultsWet tissue weight significantly decreased in tibialis anterior and gluteus in both mdx and WT mice after burn (P < 0.05). The ratio of muscle to body weight decreased in mdx mutant mice (P < 0.05) but not WT. Isometric force was significantly lower in mdx GM, and this difference persisted after burn (P < 0.05). Caspase-3 activity increased significantly after burn in both the groups, whereas HMGB1 expression was higher in burn mdx mice (P < 0.05). Proliferating cell nuclear antigen decreased significantly in mdx mice (P < 0.05). Myogenic markers pax7, myoD, and myogenin increased after burn in both the groups and were higher in mdx mice (P < 0.05).
conclusionsMore muscle loss occurred in response to severe burn in mdx mutant mice. Cell turnover in mdx mice after burn is differed from WT. Although markers of myogenic activation are elevated in mdx mutant mice, the underlying muscle pathophysiology is less tolerant of traumatic injury.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.