Mature Models Girdle

Mature Models Girdle

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Spirella certainly had their favourite models and the very shapely woman on the left (by no means in her first flush of youth) was often to be see strutting the cat-walks and corridors of Spirella HQ in her 305's and corselettes.

Duchenne muscular dystrophies (DMD) and Limb-girdle muscular dystrophies (LGMD) are common inherited degenerative muscle diseases caused by mutations in genes coding for memberance associated proteins in muscle cells. DMD and LGMD often manifest themselves in young age and lead to severe morbidity and fatality, with no currently available effective treatment. In addition, the diseases are usually genotypically recessive, which makes them suitable for gene replacement therapy with vectors. Recombinant adeno-associated virus (rAAV) is one such vector based on defective human parvoviruses. rAAV system has attracted attention due to its non-pathogenicity, genomic integration, transduction of quiescent cells, and apparent lack of cellular immune reactions. In contrast to other viral vectors, rAAV is capable of efficiently bypassing the myofiber basal lamina and tranducing mature muscle cells. We have demonstrated that rAAV vectors harboring a foreign gene can achieve highly efficient and sustained gene transfer in mature muscle of immunocompetent animals for more than 1.5 years without detectable toxicity. Recently, significant improvement in vector production methodology has made it possible to generate high titer and high quality rAAV vectors completely free of helper adenovirus contamination. However, no experiments using rAAV vectors to restore the functional deficits in muscle tissue itself have been reported to date. In this proposal, we will use delta-SG as the target disease gene, the delta-SG deficient hamster as the LGDM animal model, and rAAV as the gene delivery vector to test our general hypothesis that safe, efficient and sustained functional rescue of muscle deficiency can be achieved by genetic complementation of inherited muscular dystrophies with rAAV vectors. Specifically, we would like to achieve the following aims: 1) To study gene transfer efficiency and functional rescue in the LGMD hamster model by local intramuscular delivery of delta-SG-rAAV vectors and examine their short term ability to correlate the genetic defect in both skeletal and cardiac muscle. 2) To evaluate the gene therapy efficacy after systemic delivery of rAAV vectors through intra-artery or intra-ventricle injection. 3) To investigate the molecular kinetics and fate of rAAV vectors, especially after systemic vector delivery. 4) To develop new generation, high titer, helper-virus-free rAAV producer cells, which not only harbor vector and packaging genes, but also contain the necessary helper genes from adenovirus in a highly regulated and inducible manner.

The mouse is one of the most widely used animal models to study neuromuscular diseases and test new therapeutic strategies. However, findings from successful pre-clinical studies using mouse models frequently ...

Limb-girdle muscular dystrophies (LGMDs) are a heterogeneous group of inherited autosomal myopathies that preferentially affect voluntary muscles of the shoulders and hips. LGMD has been clinically described i...

hTERT/cdk4 immortalized myogenic human cell lines represent an important tool for skeletal muscle research, being used as therapeutically pertinent models of various neuromuscular disorders and in numerous fun...

DMD is one of the most common hereditary (X-linked recessive) neuromuscular disorders affecting the pediatric population and also represents a prototypical muscle disorder with proximal limb girdle weakness that results in a wide spectrum of physical impairments. Its prevalence is approximately 1 per 3500 to 5000 boys, making it the most common and severe form of childhood muscular dystrophy. Boys with DMD are usually confined to a wheelchair by 10 years of age and have a median life expectancy of 30 years [7]. Muscle weakness, followed by muscle and tendon retractions and joint deformities, causes gait impairment in patients with DMD, leading to compensatory movements and gait deformation. The compensatory movements occur because of the selection of possible synergic movements on hip, knees, and ankles and the development of new motor strategies used to allow the maintenance of ambulation [8].

The aim of this work is to test the efficiency of existing regression models (originally built based on data from healthy population samples) on children with disabilities. Since boys with muscular disability (and DMD in particular) perform compensatory movements to walk and have a different body mass composition, it is possible that this population requires a specific model rather than reusing normal models. Existing works have targeted studying resting energy expenditure (REE) in DMD patients and report it to be significantly lower than controls of similar population [9]. Elliott et al [10] predicted REE using existing equations based on anthropomorphic features and fat-free mass. Souza et al [11] estimated EE during ambulatory activities for a study of 3 patients using a linear formula based on heart rate.

Results from the performance of the DMD-ENS and DMD-NOR models compared with models built over normal adults are shown in Table 3. It can be seen that existing adult models give a very poor performance (only 40% correlation) and a root mean square error (RMSE) of 0.05 to 0.75. Figure 3 gives a snapshot of EE values obtained from our ensemble model versus the actual reference values.

In this work, we explored using machine-learning techniques over data from accelerometer and heart rate sensors to obtain an accurate EE model for children with disabilities. Compared to the EE data obtained from the COSMED K4b2, EE estimation based on our proposed model (DMD-ENS) has high correlation and can be obtained by simple body-worn accelerometer and heart rate sensors, which are becoming more and more popular with new emerging wearable devices such as Fitbit, Apple Watch, and Microsoft Band. Although these devices use proprietary algorithms, the algorithms are based on machine-learning models built for different activities of daily living [19]. In our prior work, we have shown that the machine-learning models developed in the lab can outperform these algorithms for specific ambulatory movements [20]. The poor performance of algorithms for the healthy population (only 40% correlation) indicates that these devices are not ready to use for measuring physical activity in populations with muscular dystrophy. The high correlation of a custom machine-learning model built over a dataset from children with disabilities, however, shows feasibility of developing population-specific models for EE estimation. In our future work, we would like to conduct trials over a large sample size with a larger set of ambulatory activities.

While this single model appears to work across a range of activities in a clinical setting, further investigation into the validity of this EE estimation model for daily activities outside of the clinic is needed. We observed that the existing models, developed based on adult populations, do not provide accurate levels of EE estimates. When we built regression models on healthy children (controls), we realized that these models do not extend to children with disabilities. It is not merely the age of subjects but also their gait and other aberrations which affect EE for populations with muscular dystrophy. This confirms our assertion that population-specific models are required for EE estimation and a generic framework will not work. We also need to expand our population base to include children with other forms of muscular dystrophy to see if our proposed model scales well to those populations.

Further investigation into the bodily placement of multiple sensors will add to the information gained by sensors in specific bodily locations. Boys with DMD perform a high number of compensatory movements to walk and cover shorter distances; it would be possible to infer that using multiple accelerometers would detect such movements and this could be a confounding factor. In this study, we placed a single accelerometer sensor at the waist of the boys with DMD and found that waist acceleration is not a good predictor for EE. It is conceivable that information from multiple sensors will increase accuracy of this EE model for disabled populations depending on the particular conditions of the disability and impairment. Sensors placed on multiple body locations may be able to capture all dimensions of body motion and energy expenditure. Recent work [8] uses videotape analysis of DMD patients to develop a functional evaluation scale of gait for DMD. Sensor-based models can be used to augment functional evaluation scales in understanding progression of the disease.

The experiments show that machine-learning models developed for healthy populations are inaccurate for children with disabilities. An ensemble machine learning technique (bagging) based on combined accelerometer and heart rate sensor readings gave high accuracy (91.21%) to actual EE. The results are encouraging and will be useful to track energy expenditure of large patient populations in field activities.

Dystroglycan is a transmembrane glycoprotein that links the extracellular basement membrane to cytoplasmic dystrophin. Disruption of the extensive carbohydrate structure normally present on α-dystroglycan causes an array of congenital and limb girdle muscular dystrophies known as dystroglycanopathies. The essential role of dystroglycan in development has hampered elucidation of the mechanisms underlying dystroglycanopathies. Here, we developed a dystroglycanopathy mouse model using inducible or muscle-specific promoters to conditionally disrupt fukutin (Fktn), a gene required for dystroglycan processing. In conditional Fktn-KO mice, we observed a near absence of functionally glycosylated dystroglycan within 18 days of gene deletion. Twenty-week-old KO mice showed clear dystrophic histopathology and a defect in glycosylation near the dystroglycan O-mannose phosphate, whether onset of Fktn excision driven by muscle-specific promoters occurred at E8 or E17. However, the earlier gene deletion resulted in more severe phenotypes, with a faster onset of damage and weakness, reduced weight and viability, and regenerating fibers of smaller size. The dependence of phenotype severity on the developmental timing of muscle Fktn deletion supports a role for dystroglycan in muscle development or differentiation. Moreover, given that this conditional Fktn-KO mouse allows the generation of tissue- and timing-specific defects in dystroglycan glycosylation, avoids embryonic lethality, and produces a phenotype resembling patient pathology, it is a promising new model for the study of secondary dystroglycanopathy. 59ce067264

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