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  • 1
    Keywords: Virology. ; Epidemiology. ; Artificial intelligence. ; Virology. ; Epidemiology. ; Artificial Intelligence.
    Description / Table of Contents: Chapter 1.Comprehensive Claims of AI for Healthcare Applications-Coherence towards COVID-19 -- Chapter 2. Artificial Intelligence based systems for combating COVID-19 -- Chapter 3. Artificial intelligence mediated medical diagnosis of COVID-19 -- Chapter 4. AI combined with medical imaging enables rapid diagnosis for COVID-19 -- Chapter 5. Role of Artificial Intelligence in COVID-19 prediction based on Statistical Methods -- Chapter 6. Data Driven symptom Analysis and Location Prediction Model for Clinical Health Data Processing and Knowledgebase Development for COVID 19 -- Chapter 7. A decision support System using Rule based Expert System For COVID -19 Prediction and Diagnosis -- Chapter 8. A Predictive Mechanism to Intimate the Danger of Infection via nCOVID-19 through Unsupervised Learning -- Chapter 9. AI-enabled prognosis technologies for SARS Co-2. Chapter 10. Intelligent agent Based Case Base Reasoning Systems Build Knowledge Representation in COVID-19 analysis of Recovery, Infectious Patients -- Chapter 11. Epidemic Analysis of COVID 19 Using Machine Learning -- Chapter 12. Machine learning application in COVID-19 drug development -- Chapter 13. COVID 19 Epidemic Analysis Using Linear and Polynomial Regression Approach -- Chapter 14. Prediction & Analysis of outbreak of COVID-19 Pandemic Using Machine Learning -- Chapter 15. Predictive Risk Analysis by using Machine Learning during Covid-19 -- Chapter 16. Analysis and Validation of Risk Prediction by Stochastic Gradient Boosting Along With Recursive Feature Elimination for COVID-19 -- Chapter 17. Artificial intelligence in mental healthcare during COVID-19 pandemic -- Chapter 18. Effect of Covid-19 on Autism Spectrum Disorder: Prognosis, diagnosis and therapeutics based On AI -- Chapter 19. Use of mobile phone apps for contact tracing to control the COVID-19 pandemic: A Literature Review -- Chapter 20. Role of IoT and Social Networking in Mental Healthcare of Transgender Community in Covid-19 Pandemic -- Chapter 21. TECHNOLOGY ACCEPTANCE AND USE OF IOT DURING COVID 19 PANDEMIC-CASE STUDY OF HEALTH SECTOR IN INDIA. Chapter 22. Artificial Intelligence – The Strategies used in COVID-19 for Diagnosis -- Chapter 23. Impact of Isolation and Quarantine on Covid-19 Patients and Potential Role of Technology in Mitigation -- Chapter 24. Impact of loneliness and Quarantine on COVID-19 patients with artificial intelligence applications -- Chapter 25. Can Technology fight the loneliness Lockdown: A study of factors Affecting Loneliness in NCR during COVID 19 -- Chapter 26. Psycho-economic Impact of Obligatory Job Switching during Covid-19 Pandemic: A Study of Hawkers in Bhubaneswar (India) -- Chapter 27. AI’s Role in Essential Commodities during a Pandemic Situation -- Chapter 28.Impact of COVID-19 on Manufacturing and Operational Ecosystem in India -- Chapter 29. Impact of Repatriated Migrants on the Production Possibility of Agricultural Sector owing to Covid: A Study on the basis of Inferential Statistics -- Chapter 30. Nicotine in Covid-19: Friend or Foe”;? -- Chapter 31. Artificial Intelligence in Covid’19: Application and Legal Conundrums.
    Abstract: The book examines the role of artificial intelligence during the COVID-19 pandemic, including its application in i) early warnings and alerts, ii) tracking and prediction, iii) data dashboards, iv) diagnosis and prognosis, v) treatments, and cures, and vi) social control. It explores the use of artificial intelligence in the context of population screening and assessing infection risks, and presents mathematical models for epidemic prediction of COVID-19. Furthermore, the book discusses artificial intelligence-mediated diagnosis, and how machine learning can help in the development of drugs to treat the disease. Lastly, it analyzes various artificial intelligence-based models to improve the critical care of COVID-19 patients.
    Type of Medium: Online Resource
    Pages: XX, 595 p. 216 illus., 147 illus. in color. , online resource.
    Edition: 1st ed. 2021.
    ISBN: 9789811573170
    Series Statement: Medical Virology: From Pathogenesis to Disease Control,
    DDC: 579.2
    Language: English
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  • 2
    Online Resource
    Online Resource
    Singapore :Springer Nature Singapore :
    Keywords: Virology. ; Diseases Causes and theories of causation. ; Artificial intelligence. ; Virology. ; Pathogenesis. ; Artificial Intelligence.
    Description / Table of Contents: Chapter 1. Artificial Intelligence for Global Healthcare -- Chapter 2. Artificial Intelligence for Epidemiology COVID-19: Quick Assessment -- Chapter 3. Artificial Intelligence in rural health in developing countries -- Chapter 4. THE ROLE OF ARTIFICIAL INTELLIGENCE TO TRACK COVID-19 DISEASE -- Chapter 5. Artificial Intelligence Techniques based on K-Means two way Clustering and Greedy Triclustering approach for 3D Gene Expression Data (GED) -- Chapter 6. Detection of COVID-19 cases from X-Ray and CT Images using Transfer Learning & Deep Convolution Neural Networks -- Chapter 7. Computer Vision: Augmented Reality (AR), Virtual Reality (VR), Telehealth and Digital Radiology -- Chapter 8. STROKE DISEASE PREDICTION MODEL USING ANOVA WITH CLASSIFICATION ALGORITHMS -- Chapter 9. A CONCISE REVIEW ON DEVELOPMENTAL AND EVALUATION METHODS OF ARTIFICIAL INTELLIGENCE ON COVID 19 DETECTION -- Chapter 10. Artificial Intelligence based Healthcare Industry 4.0 for Disease Detection using Machine Learning Techniques -- Chapter 11. Deep Autoencoder Neural Networks for Heart Sound Classification.
    Abstract: This book comprehensively reviews the potential of Artificial Intelligence (AI) in biomedical research and healthcare, with a major emphasis on virology. The initial chapter presents the applications of machine learning methods for structured data, such as the classical support vector machine and neural network, modern deep learning, and natural language processing for unstructured data in biomedical research and healthcare. The subsequent chapters explore the applications of AI in tackling COVID-19, analysis of the pandemic, viral infection, disease spread, and control. The book further identifies the potential applications of machine learning in the field of virology with a focus on the key aspects of infection: diagnosis, transmission, response to treatment, and resistance. The book also discusses progress and challenges in developing viral vaccines and examines the application of viruses in translational research and human healthcare. Furthermore, the book covers the applications of artificial intelligence-mediated diagnosis and the development of drugs to treat the disease. Towards the end, the book summarizes the ethical and legal challenges posed by AI in healthcare and biomedical research. This book is an invaluable source for researchers, medical and industry practitioners, academicians, and students exploring the applications of AI in biomedical research and healthcare.
    Type of Medium: Online Resource
    Pages: XVI, 189 p. 1 illus. , online resource.
    Edition: 1st ed. 2023.
    ISBN: 9789819903696
    Series Statement: Medical Virology: From Pathogenesis to Disease Control,
    DDC: 579.2
    Language: English
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  • 3
    Publication Date: 2002-06-01
    Description: The high degree of similarity between the mouse and human genomes is demonstrated through analysis of the sequence of mouse chromosome 16 (Mmu 16), which was obtained as part of a whole-genome shotgun assembly of the mouse genome. The mouse genome is about 10% smaller than the human genome, owing to a lower repetitive DNA content. Comparison of the structure and protein-coding potential of Mmu 16 with that of the homologous segments of the human genome identifies regions of conserved synteny with human chromosomes (Hsa) 3, 8, 12, 16, 21, and 22. Gene content and order are highly conserved between Mmu 16 and the syntenic blocks of the human genome. Of the 731 predicted genes on Mmu 16, 509 align with orthologs on the corresponding portions of the human genome, 44 are likely paralogous to these genes, and 164 genes have homologs elsewhere in the human genome; there are 14 genes for which we could find no human counterpart.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Mural, Richard J -- Adams, Mark D -- Myers, Eugene W -- Smith, Hamilton O -- Miklos, George L Gabor -- Wides, Ron -- Halpern, Aaron -- Li, Peter W -- Sutton, Granger G -- Nadeau, Joe -- Salzberg, Steven L -- Holt, Robert A -- Kodira, Chinnappa D -- Lu, Fu -- Chen, Lin -- Deng, Zuoming -- Evangelista, Carlos C -- Gan, Weiniu -- Heiman, Thomas J -- Li, Jiayin -- Li, Zhenya -- Merkulov, Gennady V -- Milshina, Natalia V -- Naik, Ashwinikumar K -- Qi, Rong -- Shue, Bixiong Chris -- Wang, Aihui -- Wang, Jian -- Wang, Xin -- Yan, Xianghe -- Ye, Jane -- Yooseph, Shibu -- Zhao, Qi -- Zheng, Liansheng -- Zhu, Shiaoping C -- Biddick, Kendra -- Bolanos, Randall -- Delcher, Arthur L -- Dew, Ian M -- Fasulo, Daniel -- Flanigan, Michael J -- Huson, Daniel H -- Kravitz, Saul A -- Miller, Jason R -- Mobarry, Clark M -- Reinert, Knut -- Remington, Karin A -- Zhang, Qing -- Zheng, Xiangqun H -- Nusskern, Deborah R -- Lai, Zhongwu -- Lei, Yiding -- Zhong, Wenyan -- Yao, Alison -- Guan, Ping -- Ji, Rui-Ru -- Gu, Zhiping -- Wang, Zhen-Yuan -- Zhong, Fei -- Xiao, Chunlin -- Chiang, Chia-Chien -- Yandell, Mark -- Wortman, Jennifer R -- Amanatides, Peter G -- Hladun, Suzanne L -- Pratts, Eric C -- Johnson, Jeffery E -- Dodson, Kristina L -- Woodford, Kerry J -- Evans, Cheryl A -- Gropman, Barry -- Rusch, Douglas B -- Venter, Eli -- Wang, Mei -- Smith, Thomas J -- Houck, Jarrett T -- Tompkins, Donald E -- Haynes, Charles -- Jacob, Debbie -- Chin, Soo H -- Allen, David R -- Dahlke, Carl E -- Sanders, Robert -- Li, Kelvin -- Liu, Xiangjun -- Levitsky, Alexander A -- Majoros, William H -- Chen, Quan -- Xia, Ashley C -- Lopez, John R -- Donnelly, Michael T -- Newman, Matthew H -- Glodek, Anna -- Kraft, Cheryl L -- Nodell, Marc -- Ali, Feroze -- An, Hui-Jin -- Baldwin-Pitts, Danita -- Beeson, Karen Y -- Cai, Shuang -- Carnes, Mark -- Carver, Amy -- Caulk, Parris M -- Center, Angela -- Chen, Yen-Hui -- Cheng, Ming-Lai -- Coyne, My D -- Crowder, Michelle -- Danaher, Steven -- Davenport, Lionel B -- Desilets, Raymond -- Dietz, Susanne M -- Doup, Lisa -- Dullaghan, Patrick -- Ferriera, Steven -- Fosler, Carl R -- Gire, Harold C -- Gluecksmann, Andres -- Gocayne, Jeannine D -- Gray, Jonathan -- Hart, Brit -- Haynes, Jason -- Hoover, Jeffery -- Howland, Tim -- Ibegwam, Chinyere -- Jalali, Mena -- Johns, David -- Kline, Leslie -- Ma, Daniel S -- MacCawley, Steven -- Magoon, Anand -- Mann, Felecia -- May, David -- McIntosh, Tina C -- Mehta, Somil -- Moy, Linda -- Moy, Mee C -- Murphy, Brian J -- Murphy, Sean D -- Nelson, Keith A -- Nuri, Zubeda -- Parker, Kimberly A -- Prudhomme, Alexandre C -- Puri, Vinita N -- Qureshi, Hina -- Raley, John C -- Reardon, Matthew S -- Regier, Megan A -- Rogers, Yu-Hui C -- Romblad, Deanna L -- Schutz, Jakob -- Scott, John L -- Scott, Richard -- Sitter, Cynthia D -- Smallwood, Michella -- Sprague, Arlan C -- Stewart, Erin -- Strong, Renee V -- Suh, Ellen -- Sylvester, Karena -- Thomas, Reginald -- Tint, Ni Ni -- Tsonis, Christopher -- Wang, Gary -- Wang, George -- Williams, Monica S -- Williams, Sherita M -- Windsor, Sandra M -- Wolfe, Keriellen -- Wu, Mitchell M -- Zaveri, Jayshree -- Chaturvedi, Kabir -- Gabrielian, Andrei E -- Ke, Zhaoxi -- Sun, Jingtao -- Subramanian, Gangadharan -- Venter, J Craig -- Pfannkoch, Cynthia M -- Barnstead, Mary -- Stephenson, Lisa D -- New York, N.Y. -- Science. 2002 May 31;296(5573):1661-71.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Celera Genomics, 45 West Gude Drive, Rockville, MD 20850, USA. richard.mural@celera.com〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/12040188" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Base Composition ; Chromosomes/*genetics ; Chromosomes, Human/genetics ; Computational Biology ; Conserved Sequence ; Databases, Nucleic Acid ; Evolution, Molecular ; Genes ; Genetic Markers ; *Genome ; *Genome, Human ; Genomics ; Humans ; Mice ; Mice, Inbred A/genetics ; Mice, Inbred DBA/genetics ; Mice, Inbred Strains/*genetics ; Molecular Sequence Data ; Physical Chromosome Mapping ; Proteins/chemistry/genetics ; Sequence Alignment ; *Sequence Analysis, DNA ; Species Specificity ; *Synteny
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 4
    Publication Date: 2001-02-22
    Description: A 2.91-billion base pair (bp) consensus sequence of the euchromatic portion of the human genome was generated by the whole-genome shotgun sequencing method. The 14.8-billion bp DNA sequence was generated over 9 months from 27,271,853 high-quality sequence reads (5.11-fold coverage of the genome) from both ends of plasmid clones made from the DNA of five individuals. Two assembly strategies-a whole-genome assembly and a regional chromosome assembly-were used, each combining sequence data from Celera and the publicly funded genome effort. The public data were shredded into 550-bp segments to create a 2.9-fold coverage of those genome regions that had been sequenced, without including biases inherent in the cloning and assembly procedure used by the publicly funded group. This brought the effective coverage in the assemblies to eightfold, reducing the number and size of gaps in the final assembly over what would be obtained with 5.11-fold coverage. The two assembly strategies yielded very similar results that largely agree with independent mapping data. The assemblies effectively cover the euchromatic regions of the human chromosomes. More than 90% of the genome is in scaffold assemblies of 100,000 bp or more, and 25% of the genome is in scaffolds of 10 million bp or larger. Analysis of the genome sequence revealed 26,588 protein-encoding transcripts for which there was strong corroborating evidence and an additional approximately 12,000 computationally derived genes with mouse matches or other weak supporting evidence. Although gene-dense clusters are obvious, almost half the genes are dispersed in low G+C sequence separated by large tracts of apparently noncoding sequence. Only 1.1% of the genome is spanned by exons, whereas 24% is in introns, with 75% of the genome being intergenic DNA. Duplications of segmental blocks, ranging in size up to chromosomal lengths, are abundant throughout the genome and reveal a complex evolutionary history. Comparative genomic analysis indicates vertebrate expansions of genes associated with neuronal function, with tissue-specific developmental regulation, and with the hemostasis and immune systems. DNA sequence comparisons between the consensus sequence and publicly funded genome data provided locations of 2.1 million single-nucleotide polymorphisms (SNPs). A random pair of human haploid genomes differed at a rate of 1 bp per 1250 on average, but there was marked heterogeneity in the level of polymorphism across the genome. Less than 1% of all SNPs resulted in variation in proteins, but the task of determining which SNPs have functional consequences remains an open challenge.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Venter, J C -- Adams, M D -- Myers, E W -- Li, P W -- Mural, R J -- Sutton, G G -- Smith, H O -- Yandell, M -- Evans, C A -- Holt, R A -- Gocayne, J D -- Amanatides, P -- Ballew, R M -- Huson, D H -- Wortman, J R -- Zhang, Q -- Kodira, C D -- Zheng, X H -- Chen, L -- Skupski, M -- Subramanian, G -- Thomas, P D -- Zhang, J -- Gabor Miklos, G L -- Nelson, C -- Broder, S -- Clark, A G -- Nadeau, J -- McKusick, V A -- Zinder, N -- Levine, A J -- Roberts, R J -- Simon, M -- Slayman, C -- Hunkapiller, M -- Bolanos, R -- Delcher, A -- Dew, I -- Fasulo, D -- Flanigan, M -- Florea, L -- Halpern, A -- Hannenhalli, S -- Kravitz, S -- Levy, S -- Mobarry, C -- Reinert, K -- Remington, K -- Abu-Threideh, J -- Beasley, E -- Biddick, K -- Bonazzi, V -- Brandon, R -- Cargill, M -- Chandramouliswaran, I -- Charlab, R -- Chaturvedi, K -- Deng, Z -- Di Francesco, V -- Dunn, P -- Eilbeck, K -- Evangelista, C -- Gabrielian, A E -- Gan, W -- Ge, W -- Gong, F -- Gu, Z -- Guan, P -- Heiman, T J -- Higgins, M E -- Ji, R R -- Ke, Z -- Ketchum, K A -- Lai, Z -- Lei, Y -- Li, Z -- Li, J -- Liang, Y -- Lin, X -- Lu, F -- Merkulov, G V -- Milshina, N -- Moore, H M -- Naik, A K -- Narayan, V A -- Neelam, B -- Nusskern, D -- Rusch, D B -- Salzberg, S -- Shao, W -- Shue, B -- Sun, J -- Wang, Z -- Wang, A -- Wang, X -- Wang, J -- Wei, M -- Wides, R -- Xiao, C -- Yan, C -- Yao, A -- Ye, J -- Zhan, M -- Zhang, W -- Zhang, H -- Zhao, Q -- Zheng, L -- Zhong, F -- Zhong, W -- Zhu, S -- Zhao, S -- Gilbert, D -- Baumhueter, S -- Spier, G -- Carter, C -- Cravchik, A -- Woodage, T -- Ali, F -- An, H -- Awe, A -- Baldwin, D -- Baden, H -- Barnstead, M -- Barrow, I -- Beeson, K -- Busam, D -- Carver, A -- Center, A -- Cheng, M L -- Curry, L -- Danaher, S -- Davenport, L -- Desilets, R -- Dietz, S -- Dodson, K -- Doup, L -- Ferriera, S -- Garg, N -- Gluecksmann, A -- Hart, B -- Haynes, J -- Haynes, C -- Heiner, C -- Hladun, S -- Hostin, D -- Houck, J -- Howland, T -- Ibegwam, C -- Johnson, J -- Kalush, F -- Kline, L -- Koduru, S -- Love, A -- Mann, F -- May, D -- McCawley, S -- McIntosh, T -- McMullen, I -- Moy, M -- Moy, L -- Murphy, B -- Nelson, K -- Pfannkoch, C -- Pratts, E -- Puri, V -- Qureshi, H -- Reardon, M -- Rodriguez, R -- Rogers, Y H -- Romblad, D -- Ruhfel, B -- Scott, R -- Sitter, C -- Smallwood, M -- Stewart, E -- Strong, R -- Suh, E -- Thomas, R -- Tint, N N -- Tse, S -- Vech, C -- Wang, G -- Wetter, J -- Williams, S -- Williams, M -- Windsor, S -- Winn-Deen, E -- Wolfe, K -- Zaveri, J -- Zaveri, K -- Abril, J F -- Guigo, R -- Campbell, M J -- Sjolander, K V -- Karlak, B -- Kejariwal, A -- Mi, H -- Lazareva, B -- Hatton, T -- Narechania, A -- Diemer, K -- Muruganujan, A -- Guo, N -- Sato, S -- Bafna, V -- Istrail, S -- Lippert, R -- Schwartz, R -- Walenz, B -- Yooseph, S -- Allen, D -- Basu, A -- Baxendale, J -- Blick, L -- Caminha, M -- Carnes-Stine, J -- Caulk, P -- Chiang, Y H -- Coyne, M -- Dahlke, C -- Mays, A -- Dombroski, M -- Donnelly, M -- Ely, D -- Esparham, S -- Fosler, C -- Gire, H -- Glanowski, S -- Glasser, K -- Glodek, A -- Gorokhov, M -- Graham, K -- Gropman, B -- Harris, M -- Heil, J -- Henderson, S -- Hoover, J -- Jennings, D -- Jordan, C -- Jordan, J -- Kasha, J -- Kagan, L -- Kraft, C -- Levitsky, A -- Lewis, M -- Liu, X -- Lopez, J -- Ma, D -- Majoros, W -- McDaniel, J -- Murphy, S -- Newman, M -- Nguyen, T -- Nguyen, N -- Nodell, M -- Pan, S -- Peck, J -- Peterson, M -- Rowe, W -- Sanders, R -- Scott, J -- Simpson, M -- Smith, T -- Sprague, A -- Stockwell, T -- Turner, R -- Venter, E -- Wang, M -- Wen, M -- Wu, D -- Wu, M -- Xia, A -- Zandieh, A -- Zhu, X -- New York, N.Y. -- Science. 2001 Feb 16;291(5507):1304-51.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Celera Genomics, 45 West Gude Drive, Rockville, MD 20850, USA. humangenome@celera.com〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/11181995" target="_blank"〉PubMed〈/a〉
    Keywords: Algorithms ; Animals ; Chromosome Banding ; Chromosome Mapping ; Chromosomes, Artificial, Bacterial ; Computational Biology ; Consensus Sequence ; CpG Islands ; DNA, Intergenic ; Databases, Factual ; Evolution, Molecular ; Exons ; Female ; Gene Duplication ; Genes ; Genetic Variation ; *Genome, Human ; *Human Genome Project ; Humans ; Introns ; Male ; Phenotype ; Physical Chromosome Mapping ; Polymorphism, Single Nucleotide ; Proteins/genetics/physiology ; Pseudogenes ; Repetitive Sequences, Nucleic Acid ; Retroelements ; *Sequence Analysis, DNA/methods ; Species Specificity
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 5
    Publication Date: 2002-05-31
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 6
    Publication Date: 2001-02-16
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 7
    ISSN: 1520-510X
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 8
    ISSN: 1520-4995
    Source: ACS Legacy Archives
    Topics: Biology , Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 9
    ISSN: 1520-4995
    Source: ACS Legacy Archives
    Topics: Biology , Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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  • 10
    ISSN: 1520-4995
    Source: ACS Legacy Archives
    Topics: Biology , Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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