ABSTRACT
In this study, n-type MoS2 monolayer flakes are grown through chemical vapor deposition (CVD), and a p-type Cu2O thin film is grown via electrochemical deposition. The crystal structure of the grown MoS2 flakes is analyzed through transmission electron microscopy. The monolayer structure of the MoS2 flakes is verified with Raman spectroscopy, multiphoton excitation microscopy, atomic force microscopy, and photoluminescence (PL) measurements. After the preliminary processing of the grown MoS2 flakes, the sample is then transferred onto a Cu2O thin film to complete a p-n heterogeneous structure. Data are confirmed via scanning electron microscopy, SHG, and Raman mapping measurements. The luminous energy gap between the two materials is examined through PL measurements. Results reveal that the thickness of the single-layer MoS2 film is 0.7 nm. PL mapping shows a micro signal generated at the 627 nm wavelength, which belongs to the B2 excitons of MoS2 and tends to increase gradually when it approaches 670 nm. Finally, the biosensor is used to detect lung cancer cell types in hydroplegia significantly reducing the current busy procedures and longer waiting time for detection. The results suggest that the fabricated sensor is highly sensitive to the change in the photocurrent with the number of each cell, the linear regression of the three cell types is as high as 99%. By measuring the slope of the photocurrent, we can identify the type of cells and the number of cells.
Subject(s)
Biosensing Techniques , Lung Neoplasms , Biosensing Techniques/methods , Humans , Lung Neoplasms/diagnosis , Microscopy, Electron, Transmission , Molybdenum/chemistry , Spectrum Analysis, RamanABSTRACT
ABSTRACT: The aim of this investigation was to compare the diagnostic performance of radiographers and deep learning algorithms in pulmonary nodule/mass detection on chest radiograph.A test set of 100 chest radiographs containing 53 cases with no pathology (normal) and 47 abnormal cases (pulmonary nodules/masses) independently interpreted by 6 trained radiographers and deep learning algorithems in a random order. The diagnostic performances of both deep learning algorithms and trained radiographers for pulmonary nodules/masses detection were compared.QUIBIM Chest X-ray Classifier, a deep learning through mass algorithm that performs superiorly to practicing radiographers in the detection of pulmonary nodules/masses (AUCMass: 0.916 vs AUCTrained radiographer: 0.778, Pâ<â.001). In addition, heat-map algorithm could automatically detect and localize pulmonary nodules/masses in chest radiographs with high specificity.In conclusion, the deep-learning based computer-aided diagnosis system through 4 algorithms could potentially assist trained radiographers by increasing the confidence and access to chest radiograph interpretation in the age of digital age with the growing demand of medical imaging usage and radiologist burnout.
Subject(s)
Burnout, Professional/prevention & control , Clinical Competence , Deep Learning , Lung/diagnostic imaging , Multiple Pulmonary Nodules/diagnosis , Radiologists , Solitary Pulmonary Nodule/diagnosis , Algorithms , Burnout, Professional/etiology , Female , Humans , Male , Middle Aged , Radiography, Thoracic/methods , Radiography, Thoracic/standards , Radiologists/education , Radiologists/psychology , Radiologists/standards , Sensitivity and Specificity , TaiwanABSTRACT
The p-n heterojunction photoelectrochemical biosensor, which comprises a p-type Cu2O film formed by electrochemical deposition and n-type ZnO nanorods formed by the hydrothermal method, is prone to photoelectrochemical reactions and self-powered. Four types of human esophageal cancer cells (ECCs) were detected by this biosensor without requiring an extra bias voltage. The measured photocurrent values of high invasion capacity cancer cells was consistently 2 times higher than those measured by a slight invasion capacity cancer cells. The response time, which was about 0.5 s, allowed repeated measurement.
