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Application of Omic Techniques to Identify New Biomarkers and Drug Targets for COVID-19 [[electronic resource] /] / edited by Paul C. Guest
| Application of Omic Techniques to Identify New Biomarkers and Drug Targets for COVID-19 [[electronic resource] /] / edited by Paul C. Guest |
| Autore | Guest Paul C |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (503 pages) |
| Disciplina | 614.5924144 |
| Collana | Proteomics, Metabolomics, Interactomics and Systems Biology |
| Soggetto topico |
Biochemical markers
Proteins Metabolism Genomics Biomarkers |
| ISBN | 3-031-28012-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. The COVID-19 Pandemic: SARS-CoV-2 Structure, Infection, Transmission, Symptomology and Variants of Concern -- Chapter 2. Long-term Vaccination and Treatment Strategies for COVID-19 Disease and Future Coronavirus Pandemics -- Chapter 3. Consequences of the Lockdown - Domestic Violence during the COVID-19 Pandemic -- Chapter 4. Psychological distress impact of Coronavirus disease (COVID-19) outbreak on three continents: A systematic review and meta-analysis -- Chapter 5. A Molecular Biomarker-based Triage Approach for Targeted Treatment of Post-COVID-19 Syndrome Patients with Persistent Neurological or Neuropsychiatric Symptoms -- Chapter 6. Genetic Associations with Coronavirus Susceptibility and Disease Severity -- Chapter 7. COVID Diagnostics: from Molecules to Omics -- Chapter 8. Assessing Biomarkers in Viral Infection -- Chapter 9. Proteomic Investigation of COVID-19 Severity During the Tsunamis Second Wave in Mumbai -- Chapter 10. NMR-Metabolomics in COVID-19 Research -- Chapter 11. Potential Biomarkers of Mitochondrial Dysfunction Associated with COVID-19 Infection -- Chapter 12. Red Cell Distribution Width as a Prognostic Indicator for Mortality and ICU Admission in Patients with COVID-19 -- Chapter 13. Predicting the COVID-19 Patients Status using Chest CT Scan Findings: A Risk Assessment Model Based on Decision Tree Analysis -- Chapter 14. Inferring Recombination Events in SARS-CoV-2 Variants In Silico -- Chapter 15. Amplicon-based Nanopore Sequencing of Patients Infected by the SARS-CoV-2 Omicron (B.1.1.529) Variant in India -- Chapter 16. Perspectives on Rapid Antigen Tests for Downstream Validation and Development of Theranostics -- Chapter 17. Machine Learning and COVID-19: Lessons from SARS-CoV-2 -- Chapter 18. The Relationship between Psoriasis, COVID-19 Infection and Vaccination during Treatment of Patients -- Chapter 19. Immunogenicity of Inactivated SARS-CoV-2 Vaccine (BBIBP-CorV; Sinopharm) and Short-term Clinical Outcomes in Vaccinated Solid Organ Transplant Recipients: A Prospective Cohort Study -- Chapter 20. Spices and Biomarkers of COVID-19: A Mechanistic and Therapeutic Perspective -- Chapter 21. Antiviral Mechanisms of Curcumin and its Derivatives in Prevention and Treatment of COVID-19: A review -- Chapter 22. Evaluation of Curcumin-Piperine Supplementation in COVID-19 Patients Admitted to the Intensive Care: A Double-Blind, Randomized Controlled Trial -- Chapter 23. Chronobiological Efficacy of Combined Therapy of Pelargonium Sidoides and Melatonin in Acute and Persistent Cases of COVID-19: A Hypothetical Approach -- Chapter 24. The Potential Effect of Royal Jelly on Biomarkers Related to COVID-19 Infection and Severe Progression -- Chapter 25. Statins: Beneficial Effects in Treatment of COVID-19 -- Chapter 26. Multiplex Immunoassay Approaches Using Luminex® xMAP® Technology for the Study of COVID-19 Disease -- Chapter 27. Rapid Detection of SARS-CoV-2 Variants of Concern by Genomic Surveillance Techniques. |
| Record Nr. | UNINA-9910736994103321 |
Guest Paul C
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Covid-19 Metabolomics and Diagnosis [[electronic resource] ] : Volume 2 / / edited by Frank N. Crespilho
| Covid-19 Metabolomics and Diagnosis [[electronic resource] ] : Volume 2 / / edited by Frank N. Crespilho |
| Autore | Crespilho Frank N |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (159 pages) |
| Disciplina | 614.5924144 |
| Soggetto topico |
Chemistry
Biochemistry Medicine Clinical Medicine |
| Soggetto non controllato |
Public Health
Medical |
| ISBN |
9783031279225
9783031279218 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Fourier-Transform Infrared Spectroscopy and Spectromicroscopy Applied to Covid-19 Studies -- Point-of-Care Devices with Electrochemical Detection For Covid-19 Diagnosis -- Carbon Nanomaterials for Covid-19 Electrochemical Sensors -- Use of Metallic Nanostructures In Electrochemical Biosensing of Sars-Cov-2 -- 3d Printing For Virus Diagnosis -- Post-Covid-19 Metabolomics: Pursuing the Sequels af a Pandemic -- Metabolic Behavior of Covid-19 Infection Severity -- “Pandemics-On-A-Chip”: Organ-On-A-Chip Models for Studying Viral Infections. |
| Record Nr. | UNINA-9910728382303321 |
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Crespilho Frank N
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Ethnopharmacology and Drug Discovery for COVID-19: Anti-SARS-CoV-2 Agents from Herbal Medicines and Natural Products [[electronic resource] /] / edited by Jen-Tsung Chen
| Ethnopharmacology and Drug Discovery for COVID-19: Anti-SARS-CoV-2 Agents from Herbal Medicines and Natural Products [[electronic resource] /] / edited by Jen-Tsung Chen |
| Autore | Chen Jen-Tsung |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (591 pages) |
| Disciplina | 614.5924144 |
| Soggetto topico |
Pharmacology
Natural products Nanobiotechnology Bioinformatics Diseases - Causes and theories of causation Molecular biology Natural Products Pathogenesis Molecular Biology |
| ISBN | 981-9936-64-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. COVID-19: An overview of virology, mutations, pathology, epidemiology, diagnosis, preventions and treatments -- 2. The recent development of therapeutic strategies against COVID-19 -- 3. Plant immunoenhancers: Promising ethnopharmacological candidates for anti-SARS-CoV-2 activity -- 4. Herbal formulations in fighting against the SARS-CoV-2 infection -- 5. Rejuvenation of traditional medicine in the 21st century against SARS-CoV-2 -- 6. Traditional herbal medicines and their active constituents in combating SARS-CoV-2 infection -- 7. Antiviral phytocompounds against animal-to-human transmittable SARS-CoV-2 -- 8. Plants-derived bioactive compounds as potential ACE-2 inhibitors against SARS-CoV-2 infection -- 9. Insights into in silico methods to explore plant bioactive substances in combating SARS-CoV-2 -- 10. Dietary plants, spices and fruits in curbing SARS-CoV-2 virulence -- 11. Therapeutic potential of selected medicinal plants for neurological disorders after the infection of COVID-19 -- 12. Glycyrrhizae Radix et Rhizoma (Gan Cao) for the management of COVID-19 -- 13. COVID-19-induced kidney disease: Ethnopharmacological intervention to ameliorate kidney damage and improve kidney function -- 14. Phytochemicals and nutraceuticals targeting SARS-CoV-2: An in silico analysis -- 15. Therapeutic and prophylactic effects of plant derivatives against SARS-CoV-2 -- 16. Therapeutic potential of essential oils against SARS-CoV-2 infection -- 17. Antiviral properties of South Indian plants against SARS-CoV-2 -- 18. Immune-boosting plants used in Turkish folk medicine and their potential against COVID-19 -- 19. A comparison study of medicinal plants used against SARS-CoV-2 and those recommended against malaria in Africa -- 20. Exploring the potential antiviral properties of Nigella sativa L. against SARS-CoV-2: Mechanisms and prospects -- . |
| Record Nr. | UNINA-9910746084103321 |
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Chen Jen-Tsung
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Global COVID-19 Research and Modeling : A Historical Record
| Global COVID-19 Research and Modeling : A Historical Record |
| Autore | Cao Longbing |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore Pte. Limited, , 2024 |
| Descrizione fisica | 1 online resource (409 pages) |
| Disciplina | 614.5924144 |
| Collana | Data Analytics Series |
| ISBN | 981-9999-15-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- Acronyms -- Notations -- List of Figures -- List of Tables -- 1 COVID-19 Characteristics and Complexities -- 1.1 COVID-19 Pandemic -- 1.2 Coronavirus and COVID-19 Complexities -- 1.3 COVID-19 Data Complexities -- 1.4 COVID-19 Modeling Complexities -- 1.5 Concluding Remarks -- 2 Review Objectives, Questions and Methods -- 2.1 Motivation and Objectives -- 2.1.1 Objectives for Overall Research Profiling -- 2.1.2 Objectives for Modeling Research Profiling -- 2.2 Review Questions -- 2.2.1 Review Questions for Overall Research -- 2.2.2 Review Questions for Modeling Research -- 2.3 Review Methods -- 2.3.1 Review Methods for Overall Research -- 2.3.2 Review Methods for Modeling Research -- 2.3.2.1 Review Scope -- 2.3.2.2 Modeling Methods -- 2.4 Publication Analysis Methods -- 2.4.1 Publication Analysis for Overall Research -- 2.4.2 Publication Analysis for Modeling Research -- 3 Highlights of the Findings -- 3.1 Highlights of the Findings of the Overall Research -- 3.2 Highlights of the Findings on the Modeling Research -- Part I Overall Research Profile -- 4 Overall Publication Collection and Processing -- 4.1 Publication Collection -- 4.1.1 COVID-19 Open Research Data Acquisition -- 4.1.2 Supplementary Information Collection -- 4.1.3 Collection of Publication Impact Metrics -- 4.1.3.1 The Literature Selection on COVID-19 Modeling -- 4.2 Publication Processing -- 4.2.1 Extracting Modeling Keywords -- 4.2.2 Categorizing Publication Disciplines -- 4.2.3 Extracting Modeling Publications -- 4.2.4 Extracting Global GDP and Population Data -- 4.2.5 Extracting COVID-19 Case Data -- 4.3 Evaluation Measures -- 4.3.1 Publication Impact Metrics -- 4.3.2 Composite Indicator (CI) -- 4.3.3 GDP per Capita -- 4.3.4 Publication-GDP Correlation Coefficient -- 4.3.5 Publication-COVID-19 Infection Correlation Coefficient.
5 COVID-19 Research Profile and Impact -- 5.1 Global Research Publication Profile -- 5.1.1 Overview of Research Publications -- 5.1.1.1 Total Publications -- 5.1.1.2 Publications by Discipline -- 5.1.1.3 Publications with Publication Date and First-author's Affiliated Country -- 5.1.1.4 Statistics on Publication Impacts -- 5.1.1.5 Mean Publication Impact in Major Disciplines -- 5.1.2 COVID-19 Research Publication Distribution -- 5.1.3 Word Cloud of COVID-19 Research Publications -- 5.2 COVID-19 Research Publication Impact -- 5.2.1 COVID-19 Research Publication Impact by Country's Composite Indicator -- 5.2.2 COVID-19 Research Publication Impact by H5-Index -- 5.2.3 Overall Research Publication Impact Per Impact Factor -- 5.2.4 COVID-19 Research Publication Impact Per CiteScore -- 5.2.5 COVID-19 Research Publication Impact by SNIP -- 5.2.6 COVID-19 Research Publication Impact by SJR -- 5.2.7 Top-10 Most Published Countries' Research Impact -- 5.2.8 Top-10 Most Published Countries' Research Impact by Discipline -- 5.3 Research Collaborations -- 5.4 Global research Co-authorship -- 5.4.1 Most Published Authors -- 5.4.2 Most Published Institutions with Identifiable Author Information -- 5.4.3 Co-authorship by Discipline -- 6 G20 and OECD Research Profile and Impact -- 6.1 G20 Publication Profile -- 6.1.1 G20 Countries/Regions' Publication Impact -- 6.1.2 G20 Countries/Regions' Publication Impact in Computer Science -- 6.1.3 G20 Countries/Regions' Publication Impact in Medical Science -- 6.1.4 G20 Countries/Regions' Publication Impact in Social Science -- 6.1.5 G20 Countries/Regions' Mean Publication Impact -- 6.2 OECD Publication Profile -- 6.2.1 OECD Countries' Mean Publication Impact -- 6.3 Box Plots of G20 and OECD Countries/Regions' Composite Indicator -- 6.3.1 Box Plots of G20 and OECD Countries/Regions' Paper-Averaged Composite Indicator. 6.3.2 Box Plots of G20 and OECD Countries/Regions' Daily-Averaged Cumulative Composite Indicator -- 6.3.3 Box Plots of G20 and OECD Countries/Regions' Monthly Paper-Averaged Composite Indicator -- 6.4 Box Plots of US and China's Monthly Paper-Averaged Composite Indicator -- 7 Correlations Between Research, the Economy and Infection -- 7.1 Correlation Between Research and the Economy -- 7.1.1 Global Correlation Between Publications and GDP Per Capita -- 7.1.2 Correlation Between G20 Publications and GDP Per Capita -- 7.1.3 Correlation Between OECD Publications and GDP Per Capita -- 7.2 Correlation Between Number of Publications and Infections -- 7.2.1 Global Correlation Between Number of Publications and Infections -- 7.2.2 Global Correlation Between Number of Publications and Deaths -- 7.2.3 Correlation Between Monthly Publications by Discipline and Number of Infections -- 7.2.4 Correlation Between Number of G20 Publications and Infections -- 7.2.5 Correlation Between Number of G20 Publications and Deaths -- 7.2.6 Correlation Between Number of Publications and Infections in OECD Countries -- 7.2.7 Correlation Between Number of OECD Publications and Deaths -- Part II Modeling Research Profile -- 8 Modeling Publication Collection and Processing -- 8.1 Meta-Synthetic and Meta-Analytical Review -- 8.2 Objectives of COVID-19 Modeling -- 8.3 Categorization of COVID-19 Modeling -- 9 Modeling Research Profile and Impact -- 9.1 Keyword Word Cloud Distributions -- 9.1.1 Keyword Word Cloud of Modeling Publications -- 9.1.2 Keyword Word Cloud of Modeling Publications in Computer Science -- 9.1.3 Keyword Word Cloud of Modeling Publications in Medical Science -- 9.1.4 Keyword Word Cloud of Modeling Publications in Social Science -- 9.1.5 Keyword Word Cloud of Modeling Publications from the US -- 9.1.6 Keyword Word Cloud of Modeling Publications from China. 9.1.7 Keyword Word Cloud of Modeling Publications from the EU -- 9.2 Top-k Research Trends -- 9.2.1 Top-50 Modeling Problems and Their Publication Impact -- 9.2.2 Top-50 Problems and Their Modeling Methods -- 9.2.3 Top-50 Modeling Methods and Their Publication Impact -- 9.2.4 Top-10 Monthly Problems of Concern in Modeling COVID-19 -- 9.2.5 Top-10 Problems of Concern in the Top-10 Most Published Countries -- 9.2.6 Top-10 Modeling Methods Applied by the Top-10 Most Published Countries -- 9.2.7 Top-10 Problems and Top-10 Modeling Methods by the Top-10 Most Published Countries -- 10 Modeling Methods -- 10.1 Mathematical Modeling -- 10.1.1 Time-series Analysis -- 10.1.1.1 Time-Series Models -- 10.1.1.2 Time-series Modeling -- 10.1.2 Statistical Modeling -- 10.1.2.1 Statistical Models -- 10.1.2.2 Statistical Analysis -- 10.2 Data-Driven Learning -- 10.2.1 Shallow and Deep Learning -- 10.2.2 Shallow Learning -- 10.2.3 Deep Learning -- 10.3 Domain-driven Modeling -- 10.3.1 Epidemic Modeling -- 10.3.1.1 Epidemiological Compartmental Models -- 10.3.1.2 Epidemiological Modeling -- 10.3.2 Medical and Biomedical Analyses -- 10.3.2.1 COVID-19 Infection Diagnosis, Test and Case Identification -- 10.3.2.2 Patient Risk and Prognosis Analyses -- 10.3.2.3 Medical Imaging Analyses -- 10.3.2.4 Pathological and Treatment Analyses and Drug Development -- 10.4 Influence and Impact Modeling -- 10.4.1 Modeling Intervention and Policy Effects -- 10.4.2 Modeling Psychological and Mental Impact -- 10.4.3 Modeling Economic Impact -- 10.4.4 Modeling Social Impact -- 10.5 Simulation Modeling -- 10.6 Hybrid Modeling -- Part III Examples: Modeling Techniques -- 11 Modeling Intervention, Vaccination, Mutation and Ethnic Condition Influence on Resurgence -- 11.1 Introduction -- 11.2 Data and Processing -- 11.3 Interaction and Simulation Models -- 11.4 Simulation and Forecasting Results. 11.4.1 Main Results -- 11.4.2 Additional Results -- 11.4.3 Findings and Insights -- 11.4.4 Discussion -- 11.5 Conclusions and Future Work -- 11.5.1 Concluding Remarks -- 11.5.2 Gaps and Opportunities -- 12 AISDR: AI and Data Science for Crisis and Disaster Resilience -- 12.1 Emergencies, Crises and Disasters -- 12.2 The ECD Landscape -- 12.3 From ECD Management to Resilience -- 12.3.1 Classic ECD Management -- 12.3.2 Smart ECD Resilience: The SDR Landscape -- 12.4 ECD Data and Complexities -- 12.4.1 ECD Data Sources -- 12.4.2 ECD Data Characteristics and Complexities -- 12.5 AISDR Research Landscape -- 12.5.1 AISDR Research Map -- 12.5.2 AISDR Research Tasks -- 13 Making Science Ready for Future Emergencies, Crises and Disasters -- 13.1 COVID-19 Modeling Gap Analyses -- 13.1.1 Limitations in COVID-19 Modeling Research -- 13.1.2 Gaps in Understanding the Nature of the COVID-19 Problem -- 13.1.3 Gaps in Modeling COVID-19 System Complexities -- 13.1.4 Gaps in Actionable COVID-19 Modeling and Validation -- 13.2 ECD Research Gap Analyses -- 13.2.1 ECD Research Review -- 13.2.2 Gap Analyses of ECD Research -- 13.3 Preparing for Future Emergencies, Crises and Disasters -- 13.3.1 Characterizing ECD Ecosystem Complexities -- 13.3.2 Enhancing Deep ECD Analytics and Learning -- 13.3.3 Exploring New Epidemic and ECD Modeling Opportunities -- 13.4 Future of AISDR -- 13.5 Concluding Remarks -- A List of Disciplinary Categorization -- B List of Predefined Modeling Keywords -- C List of COVID-19 Publication Metadata -- D List of Publication Impact Metrics -- E List of COVID-19 Publication Analysis Results -- F List of COVID-19 Public Data -- References -- Index. |
| Record Nr. | UNINA-9910847087103321 |
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Cao Longbing
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| Singapore : , : Springer Singapore Pte. Limited, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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The Probabilistic SIR Model (PSIR) in the Pandemic Process : Project Management in Prevention and Support / / by Marcus Hellwig
| The Probabilistic SIR Model (PSIR) in the Pandemic Process : Project Management in Prevention and Support / / by Marcus Hellwig |
| Autore | Hellwig Marcus |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer Vieweg, , 2023 |
| Descrizione fisica | 1 online resource (78 pages) |
| Disciplina | 614.5924144 |
| Collana | Springer essentials |
| Soggetto topico |
Statistics
Public health Probabilities Applied Statistics Public Health Applied Probability COVID-19 Epidemiologia Estadística matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-31190-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Occasion, derived from a letter to the editor -- Objectives -- SIR model as a basis for a probabilistic model -- Introduction: Consideration of an infection interval for a federal state -- The “infection curve” I(t) is replaced by the skewed, steep Eqb density function -- Random ranges of NV and Eqb -- Presentation of the equibalance distribution, Eqb -- Infection management in connection with the course of the incidence -- Infection, avoidance and healing process, feedback -- Representation of a process management -- Pre-phase planning supported by network planning technology -- Summary. |
| Record Nr. | UNINA-9910734840103321 |
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Hellwig Marcus
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer Vieweg, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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