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The fate of prazosin and levonorgestrel after electrochemical degradation process: Monitoring by-products using LC-TOF/MS

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Journal of Environmental Sciences 2018年第12期30卷 134-146页

作者： Fouad Fadhil Al-Qaim zainab haider mussa Ali Yuzir Jalifah Latip Mohamed Rozali Othman Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Department of Chemistry Faculty of Science for Women University of Babylon School of Chemical Sciences and Food Technology Faculty of Science and Technology Universiti Kebangsaan Malaysia (UKM) Centre for Water Research and Analysis (ALIR) Faculty of Science and Technology Universiti Kebangsaan Malaysia (UKM)

Prazosin（PRZ） and levonorgestrel（LNG） are widely used as an anti-disease drugs due to their biological activity in the human body. The frequent detection of these compounds in water samples requires alternative technologies for the removal of both compounds. After electrochemical degradation of PRZ and LNG, the parent compounds could be completely removed after treatment, but the identification and characterization of by-products are necessary as well. In this study, the effects of NaCl concentration and applied voltage were investigated during the electrochemical degradation process. The results revealed that the increase of NaCl concentration and applied voltage could promote the generation of hypochlorite OCl-and then enhance the degradation of PRZ and LNG. After initial study, 6 V and 0.2 g NaCl were selected for further experiments（96% and 99% removal of PRZ and LNG after 40 min, respectively）. Energy consumption was also evaluated and calculated for PRZ and LNG at 3, 6 and 8 V. Solid phase extraction（SPE） method plays an important role in enhancing the detection limit of by-products. Furthermore, characterization and identification of chlorinated and non-chlorinated by-products were conducted using an accurate liquid chromatography-time of flight/mass spectrometry LC-TOF/MS instrument. The monitoring of products during the electrochemical degradation process was performed at6 V and 0.2 g NaCl in a 50 m L solution. The results indicated that two chlorinated products were formed during the electrochemical process. The toxicity of by-products toward E. coli bacteria was investigated at 37°C and 20 hr incubation time.

关键词： Prazosin Levonorgestrel Electrochemical degradation Solid phase extraction Chlorinated by-products Toxicity

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Gastric Tract Disease Recognition Using Optimized Deep Learning Features

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Computers, Materials & Continua 2021年第8期68卷 2041-2056页

作者： zainab Nayyar Muhammad Attique Khan Musaed Alhussein Muhammad Nazir Khursheed Aurangzeb Yunyoung Nam Seifedine Kadry Syed Irtaza haider Department of Computer Science HITEC UniversityTaxila47040Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab UniversityBeirutLebanon

Artificial intelligence aids for healthcare have received a great deal of *** one million patients with gastrointestinal diseases have been diagnosed via wireless capsule endoscopy(WCE).Early diagnosis facilitates appropriate treatment and saves *** learning-based techniques have been used to identify gastrointestinal ulcers,bleeding sites,and ***,small lesions may be *** developed a deep learning-based best-feature method to classify various stomach diseases evident in WCE ***,we use hybrid contrast enhancement to distinguish diseased from normal ***,a pretrained model is fine-tuned,and further training is done via transfer *** features are extracted from the last two layers and fused using a vector length-based *** improve the genetic algorithm using a fitness function and kurtosis to select optimal features that are graded by a *** evaluate a database containing 24,000 WCE images of ulcers,bleeding sites,polyps,and healthy *** cubic support vector machine classifier was optimal;the average accuracy was 99%.

关键词： Stomach cancer contrast enhancement deep learning optimization features fusion

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