scholarly journals COVID-19 and Nanoscience in the Developing World: Rapid Detection and Remediation in Wastewater

Nanomaterials ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 991
Author(s):  
Muhammad Adeel ◽  
Tahir Farooq ◽  
Noman Shakoor ◽  
Sunny Ahmar ◽  
Sajid Fiaz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Developing Countries ◽  
Rapid Detection ◽  
Virus Detection ◽  
Disease Spread ◽  
Effective Alternative ◽  
Invasive Technique ◽  
Early Prediction ◽  
Non Invasive ◽  
Targeted Detection

Given the known presence of SARS-Cov-2 in wastewater, stemming disease spread in global regions where untreated effluent in the environment is common will experience additional pressure. Though development and preliminary trials of a vaccine against SARS-CoV-2 have been launched in several countries, rapid and effective alternative tools for the timely detection and remediation of SARS-CoV-2 in wastewater, especially in the developing countries, is of paramount importance. Here, we propose a promising, non-invasive technique for early prediction and targeted detection of SARS-CoV-2 to prevent current and future outbreaks. Thus, a combination of nanotechnology with wastewater-based epidemiology and artificial intelligence could be deployed for community-level wastewater virus detection and remediation.

scholarly journals Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2032
Author(s):  
Ahmad Chaddad ◽  
Jiali Li ◽  
Qizong Lu ◽  
Yujie Li ◽  
Idowu Paul Okuwobi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Texture Analysis ◽  
Molecular Data ◽  
Autism Spectrum ◽  
Invasive Technique ◽  
Non Invasive ◽  
Learning Techniques ◽  
Healthy Control ◽  
Aided Diagnosis

Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks such as autism spectrum disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to the variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and healthy control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.

scholarly journals Early Prediction of Non-Cardiac disorders From ECG Using Lab view

2019 ◽  
Vol 8 (2S5) ◽  
pp. 13-17
Keyword(s):  
Reference Signal ◽  
Invasive Technique ◽  
Cardiac Diseases ◽  
Ecg Signal ◽  
Early Prediction ◽  
Non Invasive ◽  
Cardiac Disorders ◽  
Electrocardiogram Ecg ◽  
Tolerance Method ◽  
Time Acquisition

The Electrocardiogram (ECG) is one of the most basic cardiological test done for any suspected diseases related to cardiological system. Abnormalities in any other system can also be detected with change in morphology of ECG. In this paper we note the changes in morphology of ECG for prediction of non-cardiac diseases like Emphysema, CNS haemorrhage, Thyroidism, Hypokalemia and Hyperkalemia. ECG is used to predict these diseases as it is a non-invasive technique and also the morphology of ECG wave is repetitive until any abnormality manifests itself through ECG. If any of the above mentioned non-cardiac diseases occur, significant changes appear in ECG signal and with the knowledge of these changes, early clues are provided regarding the diseases which are lifesaving. This paper works on acquisition and segmentation of ECG for extraction of features that are inevitable for the prediction of above mentioned diseases. The extracted features are classified as normal or abnormal based on the comparison with the reference signal. The reference signal contains information about the normal and abnormal morphological conditions of ECG which are segmented, extracted and stored prior in the LabVIEW. The automatic prediction of non-cardiac diseases is carried out with LabVIEW through which a tolerance method is used to correctly compare and predict that particular kind of disease. This will be later extended to real-time acquisition, processing and classification. The basic motive behind this project is to create an awareness and alert the patient before the fatal stage.

USING DOPPLER TO ASSESS THE FETAL HEALTH FOR THE PREDICTION OF GESTATIONAL OUTCOME

2014 ◽  
pp. 9-18
Author(s):  
Thi Linh Giang Truong ◽  
Vu Quoc Huy Nguyen
Keyword(s):  
Middle Cerebral Artery ◽  
Cerebral Artery ◽  
Umbilical Artery ◽  
Functional Method ◽  
Invasive Technique ◽  
Routine Practice ◽  
Fetal Health ◽  
Uterine Arteries ◽  
Non Invasive ◽  
Key Words Doppler

Background: Assessment of fetal health plays the most important role in prenatal care because of influence of the prediction of gestational outcome. One of the main aims of routine antenatal care is to identify the ‘ at risk ‘ fetus in order to apply clinical interventions which could results in reduced perinatal morbidity and mortality. Doppler ultrasound is a non invasive technique whereby the movement of blood is studied by detecting the change in frequence of reflected sound, Doppler blood flow velocity waves form of fetal side (umbilical artery, middle cerebral artery ...) and maternal side ( uterine arteries) are discussed and monograms for routine practice are presented. Recently this method is important tool for qualifying high risk pregnancies and help early forecasts the health of the babies and mothers disorder. Doppler sonography in obstetrics is a widely accepted functional method of examining the prediction of gestational outcome. Key words: Doppler, umbilical artery, middle cerebral artery, uterine arteries

scholarly journals Thermal Liquid Biopsy (TLB) Focused on Benign and Premalignant Pancreatic Cyst Diagnosis

2020 ◽  
Vol 11 (1) ◽  
pp. 25
Author(s):  
Sonia Hermoso-Durán ◽  
Guillermo García-Rayado ◽  
Laura Ceballos-Laita ◽  
Carlos Sostres ◽  
Sonia Vega ◽  
...  
Keyword(s):  
Liquid Biopsy ◽  
Pancreatic Cyst ◽  
Point Of View ◽  
Invasive Technique ◽  
Background Current ◽  
Interesting Point ◽  
Pancreatic Lesion ◽  
Non Invasive ◽  
Patient Diagnosis ◽  
New Biomarkers

Background: Current efforts in the identification of new biomarkers are directed towards an accurate differentiation between benign and premalignant cysts. Thermal Liquid Biopsy (TLB) has been previously applied to inflammatory and tumor diseases and could offer an interesting point of view in this type of pathology. Methods: In this work, twenty patients (12 males and 8 females, average ages 62) diagnosed with a pancreatic cyst benign (10) and premalignant (10) cyst lesions were recruited, and biological samples were obtained during the endoscopic ultrasonography procedure. Results: Proteomic content of cyst liquid samples was studied and several common proteins in the different groups were identified. TLB cyst liquid profiles reflected protein content. Also, TLB serum score was able to discriminate between healthy and cysts patients (71% sensitivity and 98% specificity) and between benign and premalignant cysts (75% sensitivity and 67% specificity). Conclusions: TLB analysis of plasmatic serum sample, a quick, simple and non-invasive technique that can be easily implemented, reports valuable information on the observed pancreatic lesion. These preliminary results set the basis for a larger study to refine TLB serum score and move closer to the clinical application of TLB providing useful information to the gastroenterologist during patient diagnosis.

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