The role of inflammatory factors in skeletal muscle injury

Biotarget ◽  
2018 ◽  
Vol 2 ◽  
pp. 7-7 ◽  
Author(s):  
Wenjing Ma ◽  
Tongtong Xu ◽  
Ye Wang ◽  
Changyue Wu ◽  
Lingbin Wang ◽  
...  
2015 ◽  
Vol 36 (6) ◽  
pp. 377-393 ◽  
Author(s):  
Magdalena Kozakowska ◽  
Katarzyna Pietraszek-Gremplewicz ◽  
Alicja Jozkowicz ◽  
Jozef Dulak

2006 ◽  
Vol 38 (Supplement) ◽  
pp. S388
Author(s):  
Christopher Black ◽  
Christopher Elder ◽  
Gary Dudley

2019 ◽  
Vol 295 ◽  
pp. 25-28 ◽  
Author(s):  
Tuomas Paana ◽  
Samuli Jaakkola ◽  
Katriina Bamberg ◽  
Antti Saraste ◽  
Emilia Tuunainen ◽  
...  

2009 ◽  
Vol 28 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Andres J. Quintero ◽  
Vonda J. Wright ◽  
Freddie H. Fu ◽  
Johnny Huard

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Bruno Paun ◽  
Daniel García Leon ◽  
Alex Claveria Cabello ◽  
Roso Mares Pages ◽  
Elena de la Calle Vargas ◽  
...  

Abstract Background Skeletal muscle injury characterisation during healing supports trauma prognosis. Given the potential interest of computed tomography (CT) in muscle diseases and lack of in vivo CT methodology to image skeletal muscle wound healing, we tracked skeletal muscle injury recovery using in vivo micro-CT in a rat model to obtain a predictive model. Methods Skeletal muscle injury was performed in 23 rats. Twenty animals were sorted into five groups to image lesion recovery at 2, 4, 7, 10, or 14 days after injury using contrast-enhanced micro-CT. Injury volumes were quantified using a semiautomatic image processing, and these values were used to build a prediction model. The remaining 3 rats were imaged at all monitoring time points as validation. Predictions were compared with Bland-Altman analysis. Results Optimal contrast agent dose was found to be 20 mL/kg injected at 400 μL/min. Injury volumes showed a decreasing tendency from day 0 (32.3 ± 12.0mm3, mean ± standard deviation) to day 2, 4, 7, 10, and 14 after injury (19.6 ± 12.6, 11.0 ± 6.7, 8.2 ± 7.7, 5.7 ± 3.9, and 4.5 ± 4.8 mm3, respectively). Groups with single monitoring time point did not yield significant differences with the validation group lesions. Further exponential model training with single follow-up data (R2 = 0.968) to predict injury recovery in the validation cohort gave a predictions root mean squared error of 6.8 ± 5.4 mm3. Further prediction analysis yielded a bias of 2.327. Conclusion Contrast-enhanced CT allowed in vivo tracking of skeletal muscle injury recovery in rat.


2016 ◽  
Vol 42 (3) ◽  
pp. 343-349 ◽  
Author(s):  
O. E. Zinovyeva ◽  
A. Yu. Emelyanova ◽  
N. D. Samkhaeva ◽  
N. S. Shcheglova ◽  
B. S. Shenkman ◽  
...  

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