Development of a novel computational method using computed tomography images for the early detection and severity classification of COVID-19 cases.

BACKGROUND: Recent occurrence of the 2019 coronavirus disease (COVID-19) outbreak, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has highlighted the need for fast, accurate, and simple strategies to identify cases on a large scale. OBJECTIVE: This study aims to develop and test an accurate detection and severity classification methodology that may help medical professionals and non-radiologists recognize the behavior and propagation mechanisms of the virus by viewing computed tomography (CT) images of the lungs with implicit materials. METHODS: In this study, the process of detecting the virus began with the deployment of a virtual material inside CT images of the lungs of 128 patients. Virtual material is a hypothetical material that can penetrate the healthy regions in the image by performing sequential numerical measurements to interpret images with high data accuracy. The proposed method also provides a segmented image of only the healthy parts of the lung. RESULTS: The resulting segmented images, which represent healthy parts of the lung, are classified into six levels of severity. These levels are classified according to physical symptoms. The results of the proposed methodology are compared with those of the radiologists' reports. This comparison revealed that the gold-standard reports correlated with the results of the proposed methodology with a high accuracy rate of 93%. CONCLUSION: The study results indicate the possibility of relying on the proposed methodology for discovering the effects of the SARS-CoV-2 virus in the lungs through CT imaging analysis with limited dependency on radiologists.

An innovative approach in titanium determination based on incorporating 2-amino-4-((4-nitrophenyl)diazenyl)pyridine-3-ol in a PVC membrane.

A pioneering optical sensor has been effectively developed to achieve precise and reliable detection of titanium ions. The sensor employs an optode membrane composed of 2-amino-4-((4-nitrophenyl)diazenyl)pyridine-3-ol (ANPDP) and sodium tetraphenylborate (NaTPB) incorporated into a plasticized PVC matrix, with dioctyl sebacate (DOS) acting as the plasticizer. When exposed to Ti4+ ions at pH 8.25, the color of the sensing membrane undergoes a distinctive transformation from yellow-orange to violet. Extensive investigations were carried out to assess and optimize various factors influencing the efficiency of ion uptake. Through careful experimentation, the optimum conditions were determined to be 60.0% DOS, 6.0% ANPDP, 30% PVC, and 4.0% NaTPB, with a rapid response time of 5.0 min. Within these conditions, the developed optode demonstrates an impressive linear range of 3.0-225 ng mL-1, boasting detection (LOD) and quantification (LOQ) limits of 0.91 and 2.95 ng mL-1, respectively. Moreover, the precision of the sensor, as indicated by the relative standard deviation (RSD%), remained consistently below 1.55% in six replicate determinations of 100 ng mL-1 Ti4+ across diverse membranes. The selectivity of the sensor was rigorously examined for a range of cations and anions, successfully establishing the tolerance limits for interfering species. Notably, the presence of EDTA as a masking agent did not compromise the high selectivity of the sensor. Consequently, the innovative probe holds significant potential as a reliable analytical tool for quantifying titanium content in various samples, including water, geological materials, soil, plants, paints, cosmetics, and plastics.