BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

To enable magnetic resonance (MR)-only radiotherapy and facilitate modelling of radiation attenuation in humans, synthetic-CT (sCT) images need to be generated. Considering the application of MR-guided radiotherapy and online adaptive replanning, sCT generation should occur within minutes. This work aims at assessing whether an existing deep learning network can rapidly generate sCT images to be used for accurate MR-based dose calculations in the entire pelvis. A study was conducted on data of 91 patients with prostate, rectal and cervical cancer who underwent external beam radiotherapy acquiring both CT and MRI for patients' simulation. Dixon reconstructed water, fat and in-phase images obtained from a conventional dual gradient-recalled echo sequence were used to generate sCT images. A conditional generative adversarial network (cGAN) was trained in a paired fashion on 2D transverse slices of 32 prostate cancer patients. The trained network was tested on the remaining patients to generate sCT images. For 30 patients in the test set, dose recalculations of the clinical plan were performed on sCT images. Dose distributions were evaluated comparing voxel-based dose differences, gamma and dose-volume histogram (DVH) analysis. The sCT generation required 5.6 s and 21 s for a single patient volume on a GPU and CPU, respectively. On average, sCT images resulted in a higher dose to the target of maximum 0.3%. Results suggest that accurate MR-based dose calculation using sCT images generated with a cGAN trained on prostate cancer patients is feasible for the entire pelvis. The sCT generation was sufficiently fast to be integrated into an MR-guided radiotherapy workflow.

Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy

Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review.

Considering the coronavirus disease 2019 (COVID-19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision-making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID-19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID-19 by presenting a systematic literature review of the state-of-the-art COVID-19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical (n = 30), social (n = 4), economic (n = 13) and technological (n = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID-19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.22699

Rise of multiattribute decision-making in combating COVID-19: A systematic review of the state-of-the-art literature

Generative adversarial networks (GANs) are unsupervised deep learning approach in the computer vision community which has gained significant attention from the last few years in identifying the internal structure of multimodal medical imaging data The adversarial network simultaneously generates realistic medical images and corresponding annotations, which proven to be useful in many cases such as image augmentation, image registration, medical image generation, image reconstruction, and image-to-image translation These properties bring the attention of the researcher in the field of medical image analysis and we are witness of rapid adaption in many novel and traditional applications This chapter provides state-of-the-art progress in GANs based clinical application in medical image generation and cross-modality synthesis The various framework of GANs which gained popularity in the interpretation of medical images, such as deep convolutional GAN (DCGAN), Laplacian GAN (LAPGAN), pix2pix, CycleGAN, and unsupervised image-to-image translation model (UNIT),continue to improve their performance by incorporating additional hybrid architecture, has been discussed Further, some of the recent applications of these frameworks for image reconstruction, and synthesis, and future research directions in the area have been covered

Medical Image Generation Using Generative Adversarial Networks: A Review

The epidemic of coronavirus disease 2019 (COVID-19) has posed an unprecedented challenge before humankind, with more than 3.2 million confirmed cases and death toll to more than 225 thousand till the end of April, 2020 globally. Currently, one-half of the world is under lockdown and complying with social distancing to arrest its spread. Does the COVID-19 crisis illustrate the necessity for solid artificial intelligence (AI) and machine learning (ML) strategy? AI and ML research groups across the world are extensively working to tackle various aspects of the COVID-19 crisis including epidemiological (e.g. prediction, controlling and forecasting viral dynamics), molecular studies and drug development (e.g. molecular modeling and drug targets identification), medical (e.g. AI-enable diagnostic and treatment), and socio-economical applications (e.g. economical impact forecasting and mitigation). In the last few months, several research papers have been published with AI applications against COVID-19. In this chapter, we performed a meta-analysis of the current state-of-the-art of AI against COVID-19 by using various publication repositories (PubMed, PubMed Central, Scopus, Google Scholar) and preprint servers (bioRxiv, medRxiv, arXiv). This meta-analysis helps the researcher to understand the broad spectrum of AI to combat COVID-19.

Artificial Intelligence Against COVID-19: A Meta-analysis of Current Research

With the current pandemic of the novel coronavirus disease 2019 (COVID-19) in hand, researchers around the globe are dexterously working to find the best suitable drug candidates and overcome vacci...

https://journals.sagepub.com/doi/pdf/10.1177/1934578X211031707

In Silico Molecular Docking and Molecular Dynamic Simulation Analysis of Phytochemicals From Indian Foods as Potential Inhibitors of SARS-CoV-2 RdRp and 3CLpro

The Severe Acute Respiratory Syndrome 2 (SARS-CoV-2) is an infectious virus that causes mild to severe life-threatening upper respiratory tract infection. The virus emerged in Wuhan, China in 2019, and later spread across the globe. Its genome has been completely sequenced and based on the genomic information, the virus possessed 3C-Like Main Protease (3CLpro), an essential multifunctional enzyme that plays a vital role in the replication and transcription of the virus by cleaving polyprotein at eleven various sites to produce different non-structural proteins. This makes the protein an important target for drug design and discovery. Herein, we analyzed the interaction between the 3CLpro and potential inhibitory compounds identified from the extracts of Zingiber offinale and Anacardium occidentale using in silico docking and Molecular Dynamics (MD) Simulation. The crystal structure of SARS-CoV-2 main protease in complex with 02J (5-Methylisoxazole-3-carboxylic acid) and PEJ (composite ligand) (PDB Code: 6LU7, 2.16 A) retrieved from Protein Data Bank (PDB) and subject to structure optimization and energy minimization. A total of twenty-nine compounds were obtained from the extracts of Z. offinale and the leaves of A. occidentale. These compounds were screened for physicochemical (Lipinski rule of five, Veber rule, and Egan filter), Pan-Assay Interference Structure, and pharmacokinetic properties to determine the Pharmaceutical Active Ingredients. Of the 29 compounds, only nineteen (19) possessed drug-likeness properties with efficient oral bioavailability and less toxicity. These compounds subjected to molecular docking analysis to determine their binding energies with the 3CLpro. The result of the analysis indicated that the free binding energies of the compounds ranged between − 5.08 and − 10.24 kcal/mol, better than the binding energies of 02j (− 4.10 kcal/mol) and PJE (− 5.07 kcal/mol). Six compounds (CID_99615 = − 10.24 kcal/mol, CID_3981360 = 9.75 kcal/mol, CID_9910474 = − 9.14 kcal/mol, CID_11697907 = − 9.10 kcal/mol, CID_10503282 = − 9.09 kcal/mol and CID_620012 = − 8.53 kcal/mol) with good binding energies further selected and subjected to MD Simulation to determine the stability of the protein–ligand complex. The results of the analysis indicated that all the ligands form stable complexes with the protein, although, CID_9910474 and CID_10503282 had a better stability when compared to other selected phytochemicals (CID_99615, CID_3981360, CID_620012, and CID_11697907).

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In Silico Molecular Docking and Molecular Dynamic Simulation of Potential Inhibitors of 3C-like Main Proteinase (3CLpro) from Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) Using Selected African Medicinal Plants

Even after one and a half years since the outbreak of COVID-19, its complete and effective control is still far from being achieved despite vaccination drives, symptomatic management with available...

Khalid Raza

Papers

Medical Image Generation Using Generative Adversarial Networks: A Review

Artificial Intelligence Against COVID-19: A Meta-analysis of Current Research

In Silico Molecular Docking and Molecular Dynamic Simulation Analysis of Phytochemicals From Indian Foods as Potential Inhibitors of SARS-CoV-2 RdRp and 3CLpro

In Silico Molecular Docking and Molecular Dynamic Simulation of Potential Inhibitors of 3C-like Main Proteinase (3CLpro) from Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) Using Selected African Medicinal Plants

Ayurvedic Medicinal Plants Against COVID-19: An In Silico Analysis: