Purpose To assess the biologic basis of massage therapies, we developed an experimental approach to mimic Swedish massage and evaluate this approach on recovery from eccentric exercise-induced muscle damage using a well-controlled animal model. Methods Tibialis anterior muscles of six New Zealand White rabbits were subjected to one bout of damaging, eccentric contractions. One muscle was immediately subjected to cyclic compressive loads, and the contralateral served as the exercised control. Results We found that commencing 30 min of cyclic compressive loading to the muscle, immediately after a bout of eccentric exercise, facilitated recovery of function and attenuated leukocyte infiltration. In addition, fiber necrosis and wet weight of the tissue were also reduced by compressive loading. Conclusion We conclude that subjecting muscle to compressive loads immediately after exercise leads to an enhanced recovery of muscle function and attenuation of the damaging effects of inflammation in the rabbit model. Although these observations suggest that skeletal muscle responds to cyclic compressive forces similar to those generated by clinical approaches, such as therapeutic massage, further research is needed to assess the translational efficacy of these findings.
Case series provide little support for the use of massage to aid muscle recovery or performance after intense exercise. In contrast, RCTs provide moderate data supporting its use to facilitate recovery from repetitive muscular contractions. Further investigation using standardized protocols measuring similar outcome variables is necessary to more conclusively determine the efficacy of sport massage and the optimal strategy for its implementation to enhance recovery following intense exercise.
Objective The goal of this study was to develop an algorithm to semi-automatically segment the meniscus in a series of magnetic resonance (MR) images to use for normal knees and those with moderate osteoarthritis (OA). Method The segmentation method was developed then evaluated on 10 baseline magnetic resonance images obtained from subjects with no evidence, symptoms, or risk factors of knee (OA), and 14 from subjects with established knee OA enrolled in the Osteoarthritis Initiative (OAI). After manually choosing a seed point within the meniscus, a threshold level was calculated through a Gaussian fit model. Under anatomical, intensity, and range constraints, a threshold operation was completed followed by conditional dilation and post-processing. The post-processing operation reevaluates the pixels included and excluded in the area surrounding the meniscus to improve accuracy. The developed method was evaluated for both normal and degenerative menisci by comparing the segmentation algorithm results with manual segmentations from five human readers. Results The semi-automated segmentation method produces results similar to those of trained observers, with an average similarity index over 0.80 for normal participants and 0.75, 0.67, and 0.64 for participants with established knee osteoarthritis with Osteoarthritis Research International Society International (OARSI) joint space narrowing scores of 0, 1, and 2 respectively. Conclusion The semi-automatic segmentation method produced accurate and consistent segmentations of the meniscus when compared to manual segmentations in the assessment of normal menisci in mild to moderate OA. Future studies will examine the change in volume, thickness, and intensity characteristics at different stages of OA.
In this paper, we present a semi-automated segmentation method for magnetic resonance images of the quadriceps muscles. Our method uses an anatomically anchored, template-based initialization of the level set-based segmentation approach. The method only requires the input of a single point from the user inside the rectus femoris. The templates are quantitatively selected from a set of images based on modes in the patient population, namely, sex and body type. For a given image to be segmented, a template is selected based on the smallest Kullback–Leibler divergence between the histograms of that image and the set of templates. The chosen template is then employed as an initialization for a level set segmentation, which captures individual anatomical variations in the image to be segmented. Images from 103 subjects were analyzed using the developed method. The algorithm was trained on a randomly selected subset of 50 subjects (25 men and 25 women) and tested on the remaining 53 subjects. The performance of the algorithm on the test set was compared against the ground truth using the Zijdenbos similarity index (ZSI). The average ZSI means and standard deviations against two different manual readers were as follows: rectus femoris, 0.78±0.12; vastus intermedius, 0.79±0.10; vastus lateralis, 0.82±0.08; and vastus medialis, 0.69±0.16.
Background: Uterine cancer (UC) is one of the leading gynecologic neoplastic disorders in the United States (US), of which over 80% are endometrioid adenocarcinomas (EA). In contrast to EA, carcinosarcoma (CS) of the uterus is a sporadic and highly malignant tumor, phylogenetically containing both epithelial and mesenchymal histologic elements. This study sought to analyze demographic, pathological retrospectively, and survival characteristics of a large cohort of CS patients compared to EA patients to identify prognostic factors and treatment approaches that may improve the current clinical management of CS patients. Methods: Demographic and clinical data were abstracted from 88,530 patients diagnosed with uterine malignancy from the Surveillance, Epidemiology, and End Results (SEER) database for 38 years (1973-2010). Extracted variables were analyzed using the Chi-square test, paired t-test, and multivariate analysis, while Kaplan-Meier functions were used to compare survival between groups. Statistical analyses were performed with IBM Statistical Product and Service Solutions (SPSS ©), version 20.2 (IBM Corp., Armonk, NY). Results: A total of 3,706 cases of CS comprised 38.2% of uterine sarcomas (n=9,702), and 4.1% of uterine cancers overall (n=88,530). EA made up 88.6% (n=78,481) of all uterine cancers. CS patients presented later in life (68.3±11.5 years) than EA (61.9±12.5 years). 65.2% of CS and 77.8% of EA occurred in Caucasians. The incidence (per million) of EA was higher in Caucasians compared to African-Americans (AA) (41% vs. 26.8%), while the incidence of CS was higher among AA than Caucasians (4% vs. 1.9%, p<0.001). 33.4% of CS was poorly differentiated at presentation, compared to 13.1% of EA. 27.8% of CS patients presented with a distant disease compared to only 4.7% of EA patients. 29.9% of AA patients with CS presented with metastatic disease, compared to 28.2% of Caucasian patients (p<0.001). Mean survival for CS patients (6.6±0.2 years) was significantly lower than that of EA patients (17.7±0.7 years, p<0.001), and AA CS patients had significantly lower survival than Caucasians CS patients (4.5±0.4 years vs. 7.1±0.3 years, p<0.001). CS patients treated with combined surgery and radiotherapy had the highest survival (9.4±0.5 years, p<0.001), while EA patients treated with surgery alone had the highest survival (20.4±1.2 years, p<0.001). Survival among AA CS patients treated with
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