Obesity: new insight into the anthropometric classification of fat distribution shown by computed tomography.

Purpose To better enable communication among researchers, clinicians, and caregivers, we aimed to assess how untrained listeners classify early infant vocalization types in comparison to terms currently used by researchers and clinicians. Method Listeners were caregivers with no prior formal education in speech and language development. A 1st group of listeners reported on clinician/researcher-classified vowel, squeal, growl, raspberry, whisper, laugh, and cry vocalizations obtained from archived video/audio recordings of 10 infants from 4 through 12 months of age. A list of commonly used terms was generated based on listener responses and the standard research terminology. A 2nd group of listeners was presented with the same vocalizations and asked to select terms from the list that they thought best described the sounds. Results Classifications of the vocalizations by listeners largely overlapped with published categorical descriptors and yielded additional insight into alternate terms commonly used. The biggest discrepancies were found for the vowel category. Conclusion Prior research has shown that caregivers are accurate in identifying canonical babbling, a major prelinguistic vocalization milestone occurring at about 6–7 months of age. This indicates that caregivers are also well attuned to even earlier emerging vocalization types. This supports the value of continuing basic and clinical research on the vocal types infants produce in the 1st months of life and on their potential diagnostic utility, and may also help improve communication between speech-language pathologists and families.

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Classification of Temporal Bone Pneumatization on Computed Tomography

Skull Base ◽

10.1055/s-2006-958565 ◽

2007 ◽

Vol 16 (S 2) ◽

Author(s):

Su-Jin Han ◽

Sang-Woo Moon ◽

Mee-Hyun Song ◽

Ho-Ki Lee

Keyword(s):

Computed Tomography ◽

Temporal Bone ◽

Temporal Bone Pneumatization

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Perspective approaches to classification of radiation accidents in radiology on the example of computed tomography

10.1063/5.0053135 ◽

2021 ◽

Author(s):

A. V. Vodovatov ◽

S. A. Ryzhov ◽

L. A. Chipiga ◽

A. M. Biblin ◽

P. S. Druzhinina

Keyword(s):

Computed Tomography ◽

Radiation Accidents

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Classification of acute appendicitis (CAA): treatment directed new classification based on imaging (ultrasound, computed tomography) and pathology

International Journal of Colorectal Disease ◽

10.1007/s00384-021-03940-8 ◽

2021 ◽

Author(s):

Jörg C. Hoffmann ◽

Claus-Peter Trimborn ◽

Michael Hoffmann ◽

Ralf Schröder ◽

Sarah Förster ◽

...

Keyword(s):

Computed Tomography ◽

Acute Appendicitis ◽

New Classification ◽

Ultrasound Computed Tomography

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Ecotoxicological Assessment of Microplastics in Freshwater Sources—A Review

Water ◽

10.3390/w13010056 ◽

2020 ◽

Vol 13 (1) ◽

pp. 56

Author(s):

Martina Miloloža ◽

Dajana Kučić Grgić ◽

Tomislav Bolanča ◽

Šime Ukić ◽

Matija Cvetnić ◽

...

Keyword(s):

Complex Problem ◽

Living Standards ◽

Way Of Life ◽

Inorganic Pollutants ◽

Ecotoxicological Assessment ◽

Characterization Methods ◽

Special Concern ◽

Micro Organisms ◽

Insight Into

High living standards and a comfortable modern way of life are related to an increased usage of various plastic products, yielding eventually the generation of an increased amount of plastic debris in the environment. A special concern is on microplastics (MPs), recently classified as contaminants of emerging concern (CECs). This review focuses on MPs’ adverse effects on the environment based on their bioactivity. Hence, the main topic covered is MPs’ ecotoxicity on various aquatic (micro)organisms such as bacteria, algae, daphnids, and fish. The cumulative toxic effects caused by MPs and adsorbed organic/inorganic pollutants are presented and critically discussed. Since MPs’ bioactivity, including ecotoxicity, is strongly influenced by their properties (e.g., types, size, shapes), the most common classification of MPs types present in freshwater are provided, along with their main characteristics. The review includes also the sources of MPs discharge in the environment and the currently available characterization methods for monitoring MPs, including identification and quantification, to obtain a broader insight into the complex problem caused by the presence of MPs in the environment.

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Role of smoking on body fat distribution and coronary artery disease

European Heart Journal ◽

10.1093/ehjci/ehaa946.1420 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

M Sabet ◽

S Elkaffas ◽

S.W.G Bakhoum ◽

H Kandil

Keyword(s):

Computed Tomography ◽

Coronary Artery Disease ◽

Adipose Tissue ◽

Coronary Artery ◽

Body Fat ◽

Fat Distribution ◽

Relative Distribution ◽

Coronary Artery Atherosclerosis ◽

Total Fat ◽

Artery Disease

Abstract Introduction Smoking and obesity are recognized as important modifiable risk factors for coronary artery disease (CAD). However, the general perception that smoking protects against obesity is a common reason for starting, and/or not quitting smoking. Purpose To detect the quantity, quality and relative distribution of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) estimated by abdominal computed tomography in smokers versus non- smokers. Methods The abdominal muscular wall was traced manually to calculate SAT and VAT areas (cm2) (outside and inside abdominal muscular wall respectively) as well as SAT density [Hounsfield units (HU)] at L4-L5 in 409 consecutive patients referred for evaluation of chest pain by multi-slice computed tomography coronary angiography (MSCT-CA). Results 26% of the studied patients (n=107) were current smokers, while the remaining 74% (n=302) never smoked. Coronary artery atherosclerosis was more prevalent in smokers compared to non-smokers (64.5% vs 55.0%; p=0.09). Smokers had statistically significantly lower body mass index (BMI) (31.2±4.3 vs. 32.5±4.7 kg/m2; p=0.015), hip circumference (HC) (98.6±22.5 vs. 103.9±20.9 cm; p=0.031), total fat area (441.62±166.34 vs. 517.95±169.51cm2; p<0.001), and SAT area (313.07±125.54 vs. 390.93±143.28 cm2; p<0.001) as compared to non-smokers. However, smokers had statistically significantly greater waist-to-hip ratio (0.98±0.08 vs. 0.96±0.08; p=0.010), VAT/SAT area ratio (0.41±0.23 vs. 0.35±0.20; p=0.013), and denser SAT depot (−98.91±7.71 vs. −102.08±6.44 HU; p<0.001). Conclusion Smoking contributes to CAD and to the pathogenic redistribution of body fat towards VAT, through limiting SAT potential to expand. Funding Acknowledgement Type of funding source: None

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Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

Diagnostics ◽

10.3390/diagnostics11050893 ◽

2021 ◽

Vol 11 (5) ◽

pp. 893

Author(s):

Yazan Qiblawey ◽

Anas Tahir ◽

Muhammad E. H. Chowdhury ◽

Amith Khandakar ◽

Serkan Kiranyaz ◽

...

Keyword(s):

Computed Tomography ◽

Neural Networks ◽

Early Stage ◽

Similarity Coefficient ◽

Dice Similarity Coefficient ◽

Lung Region ◽

Computed Tomography Images ◽

Feature Pyramid ◽

Severity Levels

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

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