scholarly journals Efficacy of Quantitative Muscle Ultrasound Using Texture-Feature Parametric Imaging in Detecting Pompe Disease in Children

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 714
Author(s):  
Hong-Jen Chiou ◽  
Chih-Kuang Yeh ◽  
Hsuen-En Hwang ◽  
Yin-Yin Liao
Keyword(s):  
Pompe Disease ◽  
Late Onset ◽  
Characteristic Curve ◽  
Texture Feature ◽  
Rectus Femoris ◽  
Parametric Imaging ◽  
Imaging Method ◽  
Healthy Children ◽  
Linear Discriminant ◽  
Optimal Feature

Pompe disease is a hereditary neuromuscular disorder attributed to acid α-glucosidase deficiency, and accurately identifying this disease is essential. Our aim was to discriminate normal muscles from neuropathic muscles in children affected by Pompe disease using a texture-feature parametric imaging method that simultaneously considers microstructure and macrostructure. The study included 22 children aged 0.02–54 months with Pompe disease and six healthy children aged 2–12 months with normal muscles. For each subject, transverse ultrasound images of the bilateral rectus femoris and sartorius muscles were obtained. Gray-level co-occurrence matrix-based Haralick’s features were used for constructing parametric images and identifying neuropathic muscles: autocorrelation (AUT), contrast, energy (ENE), entropy (ENT), maximum probability (MAXP), variance (VAR), and cluster prominence (CPR). Stepwise regression was used in feature selection. The Fisher linear discriminant analysis was used for combination of the selected features to distinguish between normal and pathological muscles. The VAR and CPR were the optimal feature set for classifying normal and pathological rectus femoris muscles, whereas the ENE, VAR, and CPR were the optimal feature set for distinguishing between normal and pathological sartorius muscles. The two feature sets were combined to discriminate between children with and without neuropathic muscles affected by Pompe disease, achieving an accuracy of 94.6%, a specificity of 100%, a sensitivity of 93.2%, and an area under the receiver operating characteristic curve of 0.98 ± 0.02. The CPR for the rectus femoris muscles and the AUT, ENT, MAXP, and VAR for the sartorius muscles exhibited statistically significant differences in distinguishing between the infantile-onset Pompe disease and late-onset Pompe disease groups (p < 0.05). Texture-feature parametric imaging can be used to quantify and map tissue structures in skeletal muscles and distinguish between pathological and normal muscles in children or newborns.

Automatic radiomic feature extraction using deep learning for angiographic parametric imaging of intracranial aneurysms

2019 ◽  
Vol 12 (4) ◽  
pp. 417-421 ◽  
Author(s):  
Alexander R Podgorsak ◽  
Ryan A Rava ◽  
Mohammad Mahdi Shiraz Bhurwani ◽  
Anusha R Chandra ◽  
Jason M Davies ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
Region Of Interest ◽  
Semantic Segmentation ◽  
Parametric Imaging ◽  
Dice Similarity Coefficient ◽  
Imaging Method ◽  
Digital Subtraction ◽  
Imaging Procedures ◽  
The Mean

BackgroundAngiographic parametric imaging (API) is an imaging method that uses digital subtraction angiography (DSA) to characterize contrast media dynamics throughout the vasculature. This requires manual placement of a region of interest over a lesion (eg, an aneurysm sac) by an operator.ObjectiveThe purpose of our work was to determine if a convolutional neural network (CNN) was able to identify and segment the intracranial aneurysm (IA) sac in a DSA and extract API radiomic features with minimal errors compared with human user results.MethodsThree hundred and fifty angiographic images of IAs were retrospectively collected. The IAs and surrounding vasculature were manually contoured and the masks put to a CNN tasked with semantic segmentation. The CNN segmentations were assessed for accuracy using the Dice similarity coefficient (DSC) and Jaccard index (JI). Area under the receiver operating characteristic curve (AUROC) was computed. API features based on the CNN segmentation were compared with the human user results.ResultsThe mean JI was 0.823 (95% CI 0.783 to 0.863) for the IA and 0.737 (95% CI 0.682 to 0.792) for the vasculature. The mean DSC was 0.903 (95% CI 0.867 to 0.937) for the IA and 0.849 (95% CI 0.811 to 0.887) for the vasculature. The mean AUROC was 0.791 (95% CI 0.740 to 0.817) for the IA and 0.715 (95% CI 0.678 to 0.733) for the vasculature. All five API features measured inside the predicted masks were within 18% of those measured inside manually contoured masks.ConclusionsCNN segmentation of IAs and surrounding vasculature from DSA images is non-inferior to manual contours of aneurysms and can be used in parametric imaging procedures.

Late onset Pompe Disease in India – Beyond the Caucasian phenotype

2021 ◽  
Author(s):  
Ratna Dua Puri ◽  
Nitika Setia ◽  
Vinu N ◽  
Sujatha Jagadeesh ◽  
Sheela Nampoothiri ◽  
...  
Keyword(s):  
Pompe Disease ◽  
Late Onset

NEO1/NEO-EXT studies: Safety and exploratory efficacy of repeat avalglucosidase alfa dosing after up to 6 years in participants with late-onset pompe disease (LOPD)

2021 ◽  
Vol 132 (2) ◽  
pp. S34
Author(s):  
Mazen M. Dimachkie ◽  
Richard J. Barohn ◽  
Barry Byrne ◽  
Ozlem Goker-Alpan ◽  
Priya S. Kishnani ◽  
...  
Keyword(s):  
Pompe Disease ◽  
Late Onset

scholarly journals Application of portrait recognition system for emergency evacuation in mass emergencies

2021 ◽  
Vol 30 (1) ◽  
pp. 893-902
Author(s):  
Ke Xu
Keyword(s):  
Detection Rate ◽  
Characteristic Curve ◽  
Recognition Rate ◽  
Recognition System ◽  
Emergency Evacuation ◽  
Shopping Mall ◽  
Single Shot ◽  
Data Set ◽  
Linear Discriminant ◽  
Adaboost Algorithm

Abstract A portrait recognition system can play an important role in emergency evacuation in mass emergencies. This paper designed a portrait recognition system, analyzed the overall structure of the system and the method of image preprocessing, and used the Single Shot MultiBox Detector (SSD) algorithm for portrait detection. It also designed an improved algorithm combining principal component analysis (PCA) with linear discriminant analysis (LDA) for portrait recognition and tested the system by applying it in a shopping mall to collect and monitor the portrait and establish a data set. The results showed that the missing detection rate and false detection rate of the SSD algorithm were 0.78 and 2.89%, respectively, which were lower than those of the AdaBoost algorithm. Comparisons with PCA, LDA, and PCA + LDA algorithms demonstrated that the recognition rate of the improved PCA + LDA algorithm was the highest, which was 95.8%, the area under the receiver operating characteristic curve was the largest, and the recognition time was the shortest, which was 465 ms. The experimental results show that the improved PCA + LDA algorithm is reliable in portrait recognition and can be used for emergency evacuation in mass emergencies.

scholarly journals Rare Variants in Autophagy and Non-Autophagy Genes in Late-Onset Pompe Disease: Suggestions of Their Disease-Modifying Role in Two Italian Families

2021 ◽  
Vol 22 (7) ◽  
pp. 3625
Author(s):  
Filomena Napolitano ◽  
Giorgia Bruno ◽  
Chiara Terracciano ◽  
Giuseppina Franzese ◽  
Nicole Piera Palomba ◽  
...  
Keyword(s):  
Pompe Disease ◽  
Rare Variants ◽  
Clinical Symptoms ◽  
Late Onset ◽  
Respiratory Muscles ◽  
Autosomal Recessive Disorder ◽  
Compound Heterozygous ◽  
Enzyme Replacement ◽  
Whole Exome ◽  
Clinical Pictures

Pompe disease is an autosomal recessive disorder caused by a deficiency in the enzyme acid alpha-glucosidase. The late-onset form of Pompe disease (LOPD) is characterized by a slowly progressing proximal muscle weakness, often involving respiratory muscles. In LOPD, the levels of GAA enzyme activity and the severity of the clinical pictures may be highly variable among individuals, even in those who harbour the same combination of GAA mutations. The result is an unpredictable genotype–phenotype correlation. The purpose of this study was to identify the genetic factors responsible for the progression, severity and drug response in LOPD. We report here on a detailed clinical, morphological and genetic study, including a whole exome sequencing (WES) analysis of 11 adult LOPD siblings belonging to two Italian families carrying compound heterozygous GAA mutations. We disclosed a heterogeneous pattern of myopathic impairment, associated, among others, with cardiac defects, intracranial vessels abnormality, osteoporosis, vitamin D deficiency, obesity and adverse response to enzyme replacement therapy (ERT). We identified deleterious variants in the genes involved in autophagy, immunity and bone metabolism, which contributed to the severity of the clinical symptoms observed in the LOPD patients. This study emphasizes the multisystem nature of LOPD and highlights the polygenic nature of the complex phenotype disclosed in these patients.

scholarly journals Imaging Mass Spectrometry-Based Proteomic Analysis to Differentiate Melanocytic Nevi and Malignant Melanoma

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3197
Author(s):  
Rita Casadonte ◽  
Mark Kriegsmann ◽  
Katharina Kriegsmann ◽  
Isabella Hauk ◽  
Rolf R. Meliß ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Malignant Melanoma ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Imaging Mass Spectrometry ◽  
Independent Set ◽  
Molecular Techniques ◽  
Charge Ratio ◽  
Linear Discriminant ◽  
Melanocytic Nevi

The discrimination of malignant melanoma from benign nevi may be difficult in some cases. For this reason, immunohistological and molecular techniques are included in the differential diagnostic toolbox for these lesions. These methods are time consuming when applied subsequently and, in some cases, no definitive diagnosis can be made. We studied both lesions by imaging mass spectrometry (IMS) in a large cohort (n = 203) to determine a different proteomic profile between cutaneous melanomas and melanocytic nevi. Sample preparation and instrument setting were tested to obtain optimal results in term of data quality and reproducibility. A proteomic signature was found by linear discriminant analysis to discern malignant melanoma from benign nevus (n = 113) with an overall accuracy of >98%. The prediction model was tested in an independent set (n = 90) reaching an overall accuracy of 93% in classifying melanoma from nevi. Statistical analysis of the IMS data revealed mass-to-charge ratio (m/z) peaks which varied significantly (Area under the receiver operating characteristic curve > 0.7) between the two tissue types. To our knowledge, this is the largest IMS study of cutaneous melanoma and nevi performed up to now. Our findings clearly show that discrimination of melanocytic nevi from melanoma is possible by IMS.

Expanding the phenotype of late-onset pompe disease: Tongue weakness: A new clinical observation

2011 ◽  
Vol 44 (6) ◽  
pp. 897-901 ◽  
Author(s):  
Alberto Dubrovsky ◽  
Jose Corderi ◽  
Min Lin ◽  
Priya S. Kishnani ◽  
Harrison N. Jones
Keyword(s):  
Pompe Disease ◽  
Clinical Observation ◽  
Late Onset
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