Instantaneous frequency as a new approach for evaluating the clinical severity of Duchenne muscular dystrophy through ultrasound imaging

Ultrasound imaging allows detection of pathologic change in muscle on the basis of increased strength of echoes. With current commercial equipment, however, there is no method of quantitation of the echoes representing muscle, and there is lack of uniformity in scanning methodology. We describe a specially constructed scanning system, designed to access the raw echo data directly from the ultrasound transducer, and allow display and measurement of the echo signals on a computer. In a study of 38 boys with Duchenne muscular dystrophy, aged 1 to 11 years, who had an ultrasound scan of the thigh muscle, 32 (84%) had abnormality on quantitation of the ultrasound echoes. The quantitative techniques we describe could easily be incorporated into the design of ultrasound scanners. (J Child Neurol 1989;4:S101-S106).

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Growth hormone inhibition causes increased selenium levels in Duchenne muscular dystrophy: a possible new approach to therapy.

Journal of Medical Genetics ◽

10.1136/jmg.21.4.254 ◽

1984 ◽

Vol 21 (4) ◽

pp. 254-256 ◽

Cited By ~ 21

Author(s):

P J Collipp ◽

J Kelemen ◽

S Y Chen ◽

M Castro-Magana ◽

M Angulo ◽

...

Keyword(s):

Growth Hormone ◽

Duchenne Muscular Dystrophy ◽

Muscular Dystrophy ◽

New Approach

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Serum biomarkers associated with baseline clinical severity in young steroid-naïve Duchenne muscular dystrophy boys

Human Molecular Genetics ◽

10.1093/hmg/ddaa132 ◽

2020 ◽

Vol 29 (15) ◽

pp. 2481-2495 ◽

Cited By ~ 1

Author(s):

Utkarsh J Dang ◽

Michael Ziemba ◽

Paula R Clemens ◽

Yetrib Hathout ◽

Laurie S Conklin ◽

...

Keyword(s):

Duchenne Muscular Dystrophy ◽

Muscular Dystrophy ◽

Serum Proteins ◽

Clinical Symptoms ◽

Clinical Severity ◽

Multivariate Models ◽

Protein Biomarkers ◽

Serum Samples ◽

Functional Outcome Measures ◽

Weighted Correlation

Abstract Duchenne muscular dystrophy (DMD) is caused by loss of dystrophin in muscle, and while all patients share the primary gene and biochemical defect, there is considerable patient–patient variability in clinical symptoms. We sought to develop multivariate models of serum protein biomarkers that explained observed variation, using functional outcome measures as proxies for severity. Serum samples from 39 steroid-naïve DMD boys 4 to <7 years enrolled into a clinical trial of vamorolone were studied (NCT02760264). Four assessments of gross motor function were carried out for each participant over a 6-week interval, and their mean was used as response for biomarker models. Weighted correlation network analysis was used for unsupervised clustering of 1305 proteins quantified using SOMAscan® aptamer profiling to define highly representative and connected proteins. Multivariate models of biomarkers were obtained for time to stand performance (strength phenotype; 17 proteins) and 6 min walk performance (endurance phenotype; 17 proteins) including some shared proteins. Identified proteins were tested with associations of mRNA expression with histological severity of muscle from dystrophinopathy patients (n = 28) and normal controls (n = 6). Strong associations predictive of both clinical and histological severity were found for ERBB4 (reductions in both blood and muscle with increasing severity), SOD1 (reductions in muscle and increases in blood with increasing severity) and CNTF (decreased levels in blood and muscle with increasing severity). We show that performance of DMD boys was effectively modeled with serum proteins, proximal strength associated with growth and remodeling pathways and muscle endurance centered on TGFβ and fibrosis pathways in muscle.

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Deep Learning of Ultrasound Imaging for Evaluating Ambulatory Function of Individuals with Duchenne Muscular Dystrophy

Diagnostics ◽

10.3390/diagnostics11060963 ◽

2021 ◽

Vol 11 (6) ◽

pp. 963

Author(s):

Ai-Ho Liao ◽

Jheng-Ru Chen ◽

Shi-Hong Liu ◽

Chun-Hao Lu ◽

Chia-Wei Lin ◽

...

Keyword(s):

Deep Learning ◽

Duchenne Muscular Dystrophy ◽

Muscular Dystrophy ◽

Ultrasound Imaging ◽

Roc Curve ◽

Confusion Matrix ◽

Automatic Generation ◽

Classification Performance ◽

Fine Tuning ◽

Muscle Ultrasound

Duchenne muscular dystrophy (DMD) results in loss of ambulation and premature death. Ultrasound provides real-time, safe, and cost-effective routine examinations. Deep learning allows the automatic generation of useful features for classification. This study utilized deep learning of ultrasound imaging for classifying patients with DMD based on their ambulatory function. A total of 85 individuals (including ambulatory and nonambulatory subjects) underwent ultrasound examinations of the gastrocnemius for deep learning of image data using LeNet, AlexNet, VGG-16, VGG-16TL, VGG-19, and VGG-19TL models (the notation TL indicates fine-tuning pretrained models). Gradient-weighted class activation mapping (Grad-CAM) was used to visualize features recognized by the models. The classification performance was evaluated using the confusion matrix and receiver operating characteristic (ROC) curve analysis. The results show that each deep learning model endows muscle ultrasound imaging with the ability to enable DMD evaluations. The Grad-CAMs indicated that boundary visibility, muscular texture clarity, and posterior shadowing are relevant sonographic features recognized by the models for evaluating ambulatory function. Of the proposed models, VGG-19 provided satisfying classification performance (the area under the ROC curve: 0.98; accuracy: 94.18%) and feature recognition in terms of physical characteristics. Deep learning of muscle ultrasound is a potential strategy for DMD characterization.

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New Approach for Antisense Oligonucleotide-Mediated Exon Skipping in Duchenne Muscular Dystrophy

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2012.p0521 ◽

2012 ◽

Vol 16 (4) ◽

pp. 521-526

Author(s):

Yoshitsugu Aoki ◽

◽

Tetsuya Nagata ◽

Shin’ichi Takeda

Keyword(s):

Duchenne Muscular Dystrophy ◽

Muscular Dystrophy ◽

Antisense Oligonucleotide ◽

Antisense Oligonucleotides ◽

Exon Skipping ◽

New Approach ◽

Reading Frame ◽

Promising Therapeutic Approach ◽

Dmd Gene ◽

Dystrophin Protein

Duchenne Muscular Dystrophy (DMD) is a lethalmuscle disorder characterized by mutations in the DMD gene. These mutations primarily disrupt the reading frame, resulting in the absence of functional dystrophin protein. Exon skipping, which involves the use of antisense oligonucleotides is a promising therapeutic approach for DMD, and clinical trials on exon skipping are currently underway in DMD patients. Recently, stable and less-toxic antisense oligonucleotides with higher efficacy have been developed in mouse and dog models of DMD. This review highlights a new approach for antisense oligonucleotide-based therapeutics for DMD, particularly for exon skipping-based methods.

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A new approach for carrier detection in Duchenne muscular dystrophy: Protein synthesis of niuscle polyribosomes in vitro

Neurology ◽

10.1212/wnl.21.7.703 ◽

1971 ◽

Vol 21 (7) ◽

pp. 703-703 ◽

Cited By ~ 24

Author(s):

V. Ionasescu ◽

H. Zellweger ◽

T. W. Conway

Keyword(s):

Protein Synthesis ◽

Duchenne Muscular Dystrophy ◽

Muscular Dystrophy ◽

Carrier Detection ◽

New Approach

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Clinical Evaluation of Duchenne Muscular Dystrophy Severity Using Ultrasound Small-Window Entropy Imaging

Entropy ◽

10.3390/e22070715 ◽

2020 ◽

Vol 22 (7) ◽

pp. 715 ◽

Cited By ~ 2

Author(s):

Dong Yan ◽

Qiang Li ◽

Chia-Wei Lin ◽

Jeng-Yi Shieh ◽

Wen-Chin Weng ◽

...

Keyword(s):

Duchenne Muscular Dystrophy ◽

Muscular Dystrophy ◽

Ultrasound Imaging ◽

Characteristic Curve ◽

Image Data ◽

Control Group ◽

Information Uncertainty ◽

Small Window ◽

Stage 1

Information entropy of ultrasound imaging recently receives much attention in the diagnosis of Duchenne muscular dystrophy (DMD). DMD is the most common muscular disorder; patients lose their ambulation in the later stages of the disease. Ultrasound imaging enables routine examinations and the follow-up of patients with DMD. Conventionally, the probability distribution of the received backscattered echo signals can be described using statistical models for ultrasound parametric imaging to characterize muscle tissue. Small-window entropy imaging is an efficient nonmodel-based approach to analyzing the backscattered statistical properties. This study explored the feasibility of using ultrasound small-window entropy imaging in evaluating the severity of DMD. A total of 85 participants were recruited. For each patient, ultrasound scans of the gastrocnemius were performed to acquire raw image data for B-mode and small-window entropy imaging, which were compared with clinical diagnoses of DMD by using the receiver operating characteristic curve. The results indicated that entropy imaging can visualize changes in the information uncertainty of ultrasound backscattered signals. The median with interquartile range (IQR) of the entropy value was 4.99 (IQR: 4.98–5.00) for the control group, 5.04 (IQR: 5.01–5.05) for stage 1 patients, 5.07 (IQR: 5.06–5.07) for stage 2 patients, and 5.07 (IQR: 5.06–5.07) for stage 3 patients. The diagnostic accuracies were 89.41%, 87.06%, and 72.94% for ≥stage 1, ≥stage 2, and ≥stage 3, respectively. Comparisons with previous studies revealed that the small-window entropy imaging technique exhibits higher diagnostic performance than conventional methods. Its further development is recommended for potential use in clinical evaluations and the follow-up of patients with DMD.

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