Computer-based assessment for facioscapulohumeral dystrophy diagnosis

Chambers, Olga; Milenković, Jana; Pražnikar, Aleš; Tasič, J.F.

doi:10.1016/j.cmpb.2015.03.006

Cited by 10 publications

(4 citation statements)

References 31 publications

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“…The cleavage and polyadenylation reactions are governed by more than 80 RNA-binding proteins but less than 20 factors compose the core of the processing complex and are necessary and sufficient to mediate cleavage and polyadenylation in vitro [ 23 , 24 ]. These 20 factors are distributed in eight complexes ( Figure 1 ) [ 23 , 25 ]: Cleavage and polyadenylation specificity factor (CPSF): is a multiprotein complex implicated in the PAS recognition and the cleavage of the pre-mRNA [ 26 , 27 , 28 ]. The core of CPSF complex is composed of CPSF100 and CPSF73 which form a heterodimer and recruit the other CPSF subunits and symplekin [ 29 , 30 ].…”

Section: Polyadenylation Mechanismsmentioning

confidence: 99%

Targeting the Polyadenylation Signal of Pre-mRNA: A New Gene Silencing Approach for Facioscapulohumeral Dystrophy

Marsollier

Joubert

Mariot

et al. 2018

IJMS

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Facioscapulohumeral dystrophy (FSHD) is characterized by the contraction of the D4Z4 array located in the sub-telomeric region of the chromosome 4, leading to the aberrant expression of the DUX4 transcription factor and the mis-regulation of hundreds of genes. Several therapeutic strategies have been proposed among which the possibility to target the polyadenylation signal to silence the causative gene of the disease. Indeed, defects in mRNA polyadenylation leads to an alteration of the transcription termination, a disruption of mRNA transport from the nucleus to the cytoplasm decreasing the mRNA stability and translation efficiency. This review discusses the polyadenylation mechanisms, why alternative polyadenylation impacts gene expression, and how targeting polyadenylation signal may be a potential therapeutic approach for FSHD.

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Section: Polyadenylation Mechanismsmentioning

confidence: 99%

Targeting the Polyadenylation Signal of Pre-mRNA: A New Gene Silencing Approach for Facioscapulohumeral Dystrophy

Marsollier

Joubert

Mariot

et al. 2018

IJMS

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“…The popular fuzzy c-means method was used to initially separate the muscle and fat tissue, followed by a method to delineate a boundary between the types of fat, such as using a snake or random forests 10, 11, 12 . However, there are few papers that attempt to quantify the levels of edema with these other two groups. Chambers et al initially separated the muscle and fat by using FCM, refined by an algorithm using principles from the live-wire technique and an edge-enhancement algorithm initially used for fingerprints 13 . The edema was calculated afterward using the results of the T1 FCM results.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of fat, muscle, and edema in thigh MRIs using random forest classification

et al. 2016

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There are many diseases that affect the distribution of muscles, including Duchenne and fascioscapulohumeral dystrophy among other myopathies. In these disease cases, it is important to quantify both the muscle and fat volumes to track the disease progression. There has also been evidence that abnormal signal intensity on the MR images, which often is an indication of edema or inflammation can be a good predictor for muscle deterioration.We present a fully-automated method that examines magnetic resonance (MR) images of the thigh and identifies the fat, muscle, and edema using a random forest classifier. First the thigh regions are automatically segmented using the T1 sequence. Then, inhomogeneity artifacts were corrected using the N3 technique. The T1 and STIR (short tau inverse recovery) images are then aligned using landmark based registration with the bone marrow. The normalized T1 and STIR intensity values are used to train the random forest. Once trained, the random forest can accurately classify the aforementioned classes.This method was evaluated on MR images of 9 patients. The precision values are 0.91±0.06, 0.98±0.01 and 0.50±0.29 for muscle, fat, and edema, respectively. The recall values are 0.95±0.02, 0.96±0.03 and 0.43±0.09 for muscle, fat, and edema, respectively. This demonstrates the feasibility of utilizing information from multiple MR sequences for the accurate quantification of fat, muscle and edema.

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“…The main problem facing these approaches is that segmentation using active contour-based methods yields unreliable results when the “fascia lata” is obscured, which usually is the case in moderate to severe disease diagnoses. Other works introduced a muscle region segmentation method by detecting the facia lata contour [ 12 , 13 , 14 ]. Advancements have also been shown using deep learning convolutional neural networks ( CNNs ), used for automatic segmentation of IMAT in thigh and calf MR images.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Intra-Muscular Adipose Infiltration in Calf/Thigh MRI Using Fully and Weakly Supervised Semantic Segmentation

et al. 2022

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Infiltration of fat into lower limb muscles is one of the key markers for the severity of muscle pathologies. The level of fat infiltration varies in its severity across and within patients, and it is traditionally estimated using visual radiologic inspection. Precise quantification of the severity and spatial distribution of this pathological process requires accurate segmentation of lower limb anatomy into muscle and fat. Methods: Quantitative magnetic resonance imaging (qMRI) of the calf and thigh muscles is one of the most effective techniques for estimating pathological accumulation of intra-muscular adipose tissue (IMAT) in muscular dystrophies. In this work, we present a new deep learning (DL) network tool for automated and robust segmentation of lower limb anatomy that is based on the quantification of MRI’s transverse (T2) relaxation time. The network was used to segment calf and thigh anatomies into viable muscle areas and IMAT using a weakly supervised learning process. A new disease biomarker was calculated, reflecting the level of abnormal fat infiltration and disease state. A biomarker was then applied on two patient populations suffering from dysferlinopathy and Charcot–Marie–Tooth (CMT) diseases. Results: Comparison of manual vs. automated segmentation of muscle anatomy, viable muscle areas, and intermuscular adipose tissue (IMAT) produced high Dice similarity coefficients (DSCs) of 96.4%, 91.7%, and 93.3%, respectively. Linear regression between the biomarker value calculated based on the ground truth segmentation and based on automatic segmentation produced high correlation coefficients of 97.7% and 95.9% for the dysferlinopathy and CMT patients, respectively. Conclusions: Using a combination of qMRI and DL-based segmentation, we present a new quantitative biomarker of disease severity. This biomarker is automatically calculated and, most importantly, provides a spatially global indication for the state of the disease across the entire thigh or calf.

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Computer-based assessment for facioscapulohumeral dystrophy diagnosis

Cited by 10 publications

References 31 publications

Targeting the Polyadenylation Signal of Pre-mRNA: A New Gene Silencing Approach for Facioscapulohumeral Dystrophy

Targeting the Polyadenylation Signal of Pre-mRNA: A New Gene Silencing Approach for Facioscapulohumeral Dystrophy

Differentiation of fat, muscle, and edema in thigh MRIs using random forest classification

Quantification of Intra-Muscular Adipose Infiltration in Calf/Thigh MRI Using Fully and Weakly Supervised Semantic Segmentation

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