scholarly journals Imaging features and differential diagnoses of non-neoplastic diffuse mediastinal diseases

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Flavian Tabotta ◽  
Gilbert R. Ferretti ◽  
Helmut Prosch ◽  
Samia Boussouar ◽  
Anne-Laure Brun ◽  
...  
Keyword(s):  
Early Stage ◽  
Specific Treatment ◽  
Imaging Features ◽  
Mediastinal Diseases ◽  
Pictorial Review ◽  
Wide Range ◽  
Chest X Ray ◽  
Delayed Recognition ◽  
Patient’S History ◽  
Contiguous Spread

Abstract Acute or chronic non-neoplastic diffuse mediastinal diseases have multiple causes, degrees of severity, and a wide range of management. Some situations require emergency care while others do not need specific treatment. Although the diagnosis may be suspected on chest X-ray, it is mainly based on CT. A delayed recognition is not uncommonly observed. Some findings may prompt the radiologist to look for specific associated injuries or lesions. This pictorial review will successively describe the various non-neoplastic causes of diffuse mediastinal diseases with their typical findings and major differentials. First, pneumomediastinum that can be provoked by extra- or intra-thoracic triggers requires the knowledge of patient’s history or recent occurrences. Absence of any usual etiological factor should raise suspicion of cocaine inhalation in young individuals. Next, acute mediastinitis may be related to post-operative complications, esophageal perforation, or contiguous spread of odontogenic or retropharyngeal infections. The former diagnosis is not an easy task in the early stage, owing to the similarities of imaging findings with those of normal post-operative appearance during the first 2–3 weeks. Finally, fibrosing mediastinitis that is linked to an excessive fibrotic reaction in the mediastinum with variable compromise of mediastinal structures, in particular vascular and airway ones. Differential diagnosis includes tumoral and inflammatory infiltrations of the mediastinum.

scholarly journals DEEP LEARNING (CNN) MODEL FOR COVID-19 DETECTION FROM CHEST XRAY IMAGES

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Ahmed Mohamed ◽  
Ahmed Abdelhady
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Stage ◽  
Open Data ◽  
Disease Outbreak ◽  
Imaging Features ◽  
X Ray ◽  
X Ray Imaging ◽  
Chest X Ray ◽  
Data Source

The Coronavirus disease outbreak result in many people to have severe respira- tory problems and it was recognized as a global health threat. Since the virus is targeting the lungs in the human body initially, chest x-ray imaging features were considered to be useful for the detection of the infection in the early stage. In this study, the chest x-ray data of 130 infected patients from an open data source that referenced Cohen J. Morrison P. Dao L., 2020 was used to build a CNN( Convolutional Neural-Network) model for the early detection of the disease. The model was trained with both infected and not-infected peoples’ chest x-ray images with 100 epochs which led to 0.98 accuracy finally. In order to use this model as a professional diagnosis element, it is highly recommended it be improved with more images and the model can be restructured to get a better accuracy.

scholarly journals Current Status of Treatment Options, Clinical Trials, and Vaccine Development for SARS-CoV-2 Infection

2020 ◽  
Vol 14 (suppl 1) ◽  
pp. 733-740
Author(s):  
Ran Jing ◽  
Rama Rao Vunnam ◽  
Yuhong Yang ◽  
Adam Karevoll ◽  
Srinivas Rao Vunnam
Keyword(s):  
Clinical Trials ◽  
Supportive Care ◽  
Vaccine Development ◽  
Clinical Symptoms ◽  
Early Stage ◽  
Specific Treatment ◽  
World Health ◽  
Current Status ◽  
Respiratory Syndrome Virus ◽  
Wide Range

The severe acute respiratory syndrome virus (SARS-CoV-2), a novel coronavirus first discovered in Wuhan, China in December 2019 causes the Coronavirus Disease 19 (COVID-19), which presents with a wide range of clinical symptoms from mild or moderate to severe and critical illnesses. With the continuing transmission of the virus worldwide and the rapidly evolving situation globally, the World Health Organization (WHO) declared the COVID-19 outbreak a pandemic in March. Currently, there is no proven specific treatment for this potentially deadly disease beyond supportive care. However, a massive effort has been put globally into the investigation of medications and other interventional measures to fight COVID-19. Convalescent plasma therapy from recovered patients has recently drawn considerable interest. Several alternative medical treatments, although evidence of their efficacy still lacking, have also gained popularity, especially in countries with such traditions such as India and China. Rapid repurposing of drugs for COVID-19 has revealed a few promising candidate antiviral agents, but further research, especially high quality randomized controlled trials, will be needed to prove their efficacy and safety in the clinical use to treat COVID-19. Vaccine development has been the imperative task in the battle against SARS-CoV-2. While clinical trials have been launched for several candidate vaccines, research on COVID-19 vaccines is still at an early stage. So far, optimized supportive care remains the best practice against COVID-19.

scholarly journals Photonic Crystal Nanobeam Cavities for Nanoscale Optical Sensing: A Review

Micromachines ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 72 ◽  
Author(s):  
Da-Quan Yang ◽  
Bing Duan ◽  
Xiao Liu ◽  
Ai-Qiang Wang ◽  
Xiao-Gang Li ◽  
...  
Keyword(s):  
Photonic Crystal ◽  
Optical Waveguides ◽  
Optical Sensing ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Label Free ◽  
Quality Factors ◽  
Integrated Sensor ◽  
Early Stage Disease ◽  
Wide Range

The ability to detect nanoscale objects is particular crucial for a wide range of applications, such as environmental protection, early-stage disease diagnosis and drug discovery. Photonic crystal nanobeam cavity (PCNC) sensors have attracted great attention due to high-quality factors and small-mode volumes (Q/V) and good on-chip integrability with optical waveguides/circuits. In this review, we focus on nanoscale optical sensing based on PCNC sensors, including ultrahigh figure of merit (FOM) sensing, single nanoparticle trapping, label-free molecule detection and an integrated sensor array for multiplexed sensing. We believe that the PCNC sensors featuring ultracompact footprint, high monolithic integration capability, fast response and ultrahigh sensitivity sensing ability, etc., will provide a promising platform for further developing lab-on-a-chip devices for biosensing and other functionalities.

scholarly journals Non-Albumin Proteinuria (NAP) as a Complementary Marker for Diabetic Kidney Disease (DKD)

Life ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 224
Author(s):  
Jaehyun Bae ◽  
Young Jun Won ◽  
Byung-Wan Lee
Keyword(s):  
Kidney Disease ◽  
Filtration Rate ◽  
Diabetic Kidney Disease ◽  
Estimated Glomerular Filtration Rate ◽  
Early Stage ◽  
Screening Tools ◽  
Tubular Injury ◽  
Current Standard ◽  
Diabetic Kidney ◽  
Wide Range

Diabetic kidney disease (DKD) is one of the most common forms of chronic kidney disease. Its pathogenic mechanism is complex, and it can affect entire structures of the kidney. However, conventional approaches to early stage DKD have focused on changes to the glomerulus. Current standard screening tools for DKD, albuminuria, and estimated glomerular filtration rate are insufficient to reflect early tubular injury. Therefore, many tubular biomarkers have been suggested. Non-albumin proteinuria (NAP) contains a wide range of tubular biomarkers and is convenient to measure. We reviewed the clinical meanings of NAP and its significance as a marker for early stage DKD.

scholarly journals Contagion Management at the Méditerranée Infection University Hospital Institute

2021 ◽  
Vol 10 (12) ◽  
pp. 2627
Author(s):  
Pierre-Edouard Fournier ◽  
Sophie Edouard ◽  
Nathalie Wurtz ◽  
Justine Raclot ◽  
Marion Bechet ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Early Diagnosis ◽  
Personal Protective Equipment ◽  
Early Stage ◽  
Point Of Care ◽  
University Hospital ◽  
Protective Equipment ◽  
Monitoring Point ◽  
Epidemiological Monitoring ◽  
Wide Range

The Méditerranée Infection University Hospital Institute (IHU) is located in a recent building, which includes experts on a wide range of infectious disease. The IHU strategy is to develop innovative tools, including epidemiological monitoring, point-of-care laboratories, and the ability to mass screen the population. In this study, we review the strategy and guidelines proposed by the IHU and its application to the COVID-19 pandemic and summarise the various challenges it raises. Early diagnosis enables contagious patients to be isolated and treatment to be initiated at an early stage to reduce the microbial load and contagiousness. In the context of the COVID-19 pandemic, we had to deal with a shortage of personal protective equipment and reagents and a massive influx of patients. Between 27 January 2020 and 5 January 2021, 434,925 nasopharyngeal samples were tested for the presence of SARS-CoV-2. Of them, 12,055 patients with COVID-19 were followed up in our out-patient clinic, and 1888 patients were hospitalised in the Institute. By constantly adapting our strategy to the ongoing situation, the IHU has succeeded in expanding and upgrading its equipment and improving circuits and flows to better manage infected patients.

scholarly journals Design of Personalized Devices—The Tradeoff between Individual Value and Personalization Workload

2020 ◽  
Vol 11 (1) ◽  
pp. 241
Author(s):  
Juliane Kuhl ◽  
Andreas Ding ◽  
Ngoc Tuan Ngo ◽  
Andres Braschkat ◽  
Jens Fiehler ◽  
...  
Keyword(s):  
Customer Value ◽  
Early Stage ◽  
Treatment Success ◽  
Product Family ◽  
Flow Diverter ◽  
New Method ◽  
Product Families ◽  
Wide Range ◽  
The Individual ◽  
Individual Value

Personalized medical devices adapted to the anatomy of the individual promise greater treatment success for patients, thus increasing the individual value of the product. In order to cater to individual adaptations, however, medical device companies need to be able to handle a wide range of internal processes and components. These are here referred to collectively as the personalization workload. Consequently, support is required in order to evaluate how best to target product personalization. Since the approaches presented in the literature are not able to sufficiently meet this demand, this paper introduces a new method that can be used to define an appropriate variety level for a product family taking into account standardized, variant, and personalized attributes. The new method enables the identification and evaluation of personalizable attributes within an existing product family. The method is based on established steps and tools from the field of variant-oriented product design, and is applied using a flow diverter—an implant for the treatment of aneurysm diseases—as an example product. The personalization relevance and adaptation workload for the product characteristics that constitute the differentiating product properties were analyzed and compared in order to determine a tradeoff between customer value and personalization workload. This will consequently help companies to employ targeted, deliberate personalization when designing their product families by enabling them to factor variety-induced complexity and customer value into their thinking at an early stage, thus allowing them to critically evaluate a personalization project.

scholarly journals Cortical Excitability across the ALS Clinical Motor Phenotypes

2021 ◽  
Vol 11 (6) ◽  
pp. 715
Author(s):  
Thanuja Dharmadasa
Keyword(s):  
Motor Cortex ◽  
Cortical Excitability ◽  
Phenotypic Variability ◽  
Specific Treatment ◽  
Underlying Disease ◽  
Selective Vulnerability ◽  
Clinical Feature ◽  
Cortical Hyperexcitability ◽  
Wide Range

Amyotrophic lateral sclerosis (ALS) is characterized by its marked clinical heterogeneity. Although the coexistence of upper and lower motor neuron signs is a common clinical feature for most patients, there is a wide range of atypical motor presentations and clinical trajectories, implying a heterogeneity of underlying pathogenic mechanisms. Corticomotoneuronal dysfunction is increasingly postulated as the harbinger of clinical disease, and neurophysiological exploration of the motor cortex in vivo using transcranial magnetic stimulation (TMS) has suggested that motor cortical hyperexcitability may be a critical pathogenic factor linked to clinical features and survival. Region-specific selective vulnerability at the level of the motor cortex may drive the observed differences of clinical presentation across the ALS motor phenotypes, and thus, further understanding of phenotypic variability in relation to cortical dysfunction may serve as an important guide to underlying disease mechanisms. This review article analyses the cortical excitability profiles across the clinical motor phenotypes, as assessed using TMS, and explores this relationship to clinical patterns and survival. This understanding will remain essential to unravelling central disease pathophysiology and for the development of specific treatment targets across the ALS clinical motor phenotypes.

scholarly journals Chest computed tomography imaging features in patients with coronavirus disease 2019 (COVID-19)

2021 ◽  
Vol 49 (5) ◽  
pp. 030006052110106
Author(s):  
Hoda Salah Darwish ◽  
Mohamed Yasser Habash ◽  
Waleed Yasser Habash
Keyword(s):  
Computed Tomography ◽  
Intermediate Phase ◽  
Early Stage ◽  
Lower Lobe ◽  
Ground Glass Opacity ◽  
Intermediate Stage ◽  
Mediastinal Lymphadenopathy ◽  
Imaging Features ◽  
Stage Disease ◽  
Bilateral Lesions

Objective To analyze computed tomography (CT) features of symptomatic patients with coronavirus disease 2019 (COVID-19). Methods Ninety-five symptomatic patients with COVID-19 confirmed by reverse-transcription polymerase chain reaction from 1 May to 14 July 2020 were retrospectively enrolled. Follow-up CT findings and their distributions were analyzed and compared from symptom onset to late-stage disease. Results Among all patients, 15.8% had unilateral lung disease and 84.2% had bilateral disease with slight right lower lobe predilection (47.4%). Regarding lesion density, 49.4% of patients had pure ground glass opacity (GGO) and 50.5% had GGO with consolidation. Typical early-stage patterns were bilateral lesions in 73.6% of patients, diffuse lesions (41.0%), and GGO (65.2%). Pleural effusion occurred in 13.6% and mediastinal lymphadenopathy in 11.5%. During intermediate-stage disease, 47.4% of patients showed GGO as the disease progressed; however, consolidation was the predominant finding (52.6%). Conclusion COVID-19 pneumonia manifested on lung CT scans with bilateral, peripheral, and right lower lobe predominance and was characterized by diffuse bilateral GGO progressing to or coexisting with consolidation within 1 to 3 weeks. The most frequent CT lesion in the early, intermediate, and late phases was GGO. Consolidation appeared in the intermediate phase and gradually increased, ending with reticular and lung fibrosis-like patterns.

scholarly journals Integration of early disease-resistance phenotyping, histological characterization, and transcriptome sequencing reveals insights into downy mildew resistance in impatiens

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ze Peng ◽  
Yanhong He ◽  
Saroj Parajuli ◽  
Qian You ◽  
Weining Wang ◽  
...  
Keyword(s):  
Disease Resistance ◽  
Downy Mildew ◽  
Transcriptome Sequencing ◽  
Early Stage ◽  
Economic Losses ◽  
Potential Candidate ◽  
Early Disease ◽  
True Leaf ◽  
Cotyledon Stage ◽  
Wide Range

AbstractDowny mildew (DM), caused by obligate parasitic oomycetes, is a destructive disease for a wide range of crops worldwide. Recent outbreaks of impatiens downy mildew (IDM) in many countries have caused huge economic losses. A system to reveal plant–pathogen interactions in the early stage of infection and quickly assess resistance/susceptibility of plants to DM is desired. In this study, we established an early and rapid system to achieve these goals using impatiens as a model. Thirty-two cultivars of Impatiens walleriana and I. hawkeri were evaluated for their responses to IDM at cotyledon, first/second pair of true leaf, and mature plant stages. All I. walleriana cultivars were highly susceptible to IDM. While all I. hawkeri cultivars were resistant to IDM starting at the first true leaf stage, many (14/16) were susceptible to IDM at the cotyledon stage. Two cultivars showed resistance even at the cotyledon stage. Histological characterization showed that the resistance mechanism of the I. hawkeri cultivars resembles that in grapevine and type II resistance in sunflower. By integrating full-length transcriptome sequencing (Iso-Seq) and RNA-Seq, we constructed the first reference transcriptome for Impatiens comprised of 48,758 sequences with an N50 length of 2060 bp. Comparative transcriptome and qRT-PCR analyses revealed strong candidate genes for IDM resistance, including three resistance genes orthologous to the sunflower gene RGC203, a potential candidate associated with DM resistance. Our approach of integrating early disease-resistance phenotyping, histological characterization, and transcriptome analysis lay a solid foundation to improve DM resistance in impatiens and may provide a model for other crops.

scholarly journals Early Dropout Prediction in MOOCs through Supervised Learning and Hyperparameter Optimization

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1701
Author(s):  
Theodor Panagiotakopoulos ◽  
Sotiris Kotsiantis ◽  
Georgios Kostopoulos ◽  
Omiros Iatrellis ◽  
Achilles Kameas
Keyword(s):  
Online Education ◽  
Online Courses ◽  
Early Stage ◽  
Activity Patterns ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Educational Institutions ◽  
Student Dropout ◽  
Wide Range

Over recent years, massive open online courses (MOOCs) have gained increasing popularity in the field of online education. Students with different needs and learning specificities are able to attend a wide range of specialized online courses offered by universities and educational institutions. As a result, large amounts of data regarding students’ demographic characteristics, activity patterns, and learning performances are generated and stored in institutional repositories on a daily basis. Unfortunately, a key issue in MOOCs is low completion rates, which directly affect student success. Therefore, it is of utmost importance for educational institutions and faculty members to find more effective practices and reduce non-completer ratios. In this context, the main purpose of the present study is to employ a plethora of state-of-the-art supervised machine learning algorithms for predicting student dropout in a MOOC for smart city professionals at an early stage. The experimental results show that accuracy exceeds 96% based on data collected during the first week of the course, thus enabling effective intervention strategies and support actions.

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