LEADER 01623nam 2200397 a 450 001 9910696439803321 005 20080331135442.0 035 $a(CKB)5470000002378445 035 $a(OCoLC)213811170 035 $a(EXLCZ)995470000002378445 100 $a20080324d2008 ua 0 101 0 $aeng 135 $aurbn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aDescription and analysis of alternative wealth transfer tax systems$b[electronic resource] $escheduled for a public hearing before the Senate Committee on Finance on March 12, 2008 /$fprepared by the staff of the Joint Committee on Taxation 215 $ai, 24 pages $cdigital, PDF file 300 $aTitle from title screen (viewed on Mar. 24, 2009). 300 $a"March 10, 2008." 300 $a"JCX-22-08." 320 $aIncludes bibliographical references. 517 $aDescription and analysis of alternative wealth transfer tax systems 606 $aInheritance and transfer tax$xLaw and legislation$zUnited States 606 $aGifts$xTaxation$xLaw and legislation$zUnited States 606 $aWealth tax$xLaw and legislation$zUnited States 615 0$aInheritance and transfer tax$xLaw and legislation 615 0$aGifts$xTaxation$xLaw and legislation 615 0$aWealth tax$xLaw and legislation 712 02$aUnited States.$bCongress.$bJoint Committee on Taxation. 801 0$bGPO 801 1$bGPO 906 $aBOOK 912 $a9910696439803321 996 $aDescription and analysis of alternative wealth transfer tax systems$93534564 997 $aUNINA LEADER 04491nam 22006855 450 001 9910999674003321 005 20250426130200.0 010 $a3-031-89474-X 024 7 $a10.1007/978-3-031-89474-9 035 $a(CKB)38641730800041 035 $a(DE-He213)978-3-031-89474-9 035 $a(MiAaPQ)EBC32029798 035 $a(Au-PeEL)EBL32029798 035 $a(EXLCZ)9938641730800041 100 $a20250426d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLong COVID Fatigue $eClinical Sciences, Artificial Intelligence and the Future of Brain Health /$fby Thorsten Rudroff 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (XXI, 181 p. 31 illus., 26 illus. in color.) 311 08$a3-031-89473-1 327 $a1. A Mysterious Malaise -- 2. Understanding Fatigue -- 3. Factors contributing to Long COVID Fatigue -- 4. Sex-Based Differences -- 5. Age-Related Considerations -- 6. Children and Long COVID Fatigue -- 7. The Science of Long COVID Fatigue -- 8. Cellular and Neurological Mechanisms in Long COVID Fatigue -- 9. Integration and Clinical Applications -- 10. Neuroimaging as a Window into Long COVID Fatigue -- 11. Neuroimaging Biomarkers in Long COVID Fatigue: Advanced Techniques and Clinical Applications -- 12. Risk Factors and Predictors of Long COVID Fatigue -- 13. Vaccination Status and Long COVID Fatigue -- 14. Diagnosis of Long COVID Fatigue -- 15. Core Treatment Approaches for Long COVID Fatigue -- 16. Pharmacological and Non-Pharmacological Interventions -- 17. Revealing the Complexity of Long COVID Fatigue: Challenges and Promises of Artificial Intelligence -- 18. Final Remarks: Long COVID Fatigue in Brain Health Research: A Call to Action. 330 $aThis book offers the first comprehensive analysis of long COVID fatigue using advanced neuroimaging and artificial intelligence (AI). It bridges the gap between basic science and patient care in post-viral syndromes. The volume guides readers from fundamental concepts to future innovations, making complex neurobiological mechanisms accessible to researchers and clinicians. Each chapter builds on the previous, connecting molecular mechanisms to clinical manifestations. The integration of AI in diagnosis and treatment is a pioneering approach in long COVID literature. The book provides detailed analysis of brain metabolic patterns in long COVID fatigue, insights into protective mechanisms like metabolic heterogeneity in the basal ganglia, practical guidelines for AI-enhanced diagnostic and treatment approaches, and pathways for translating research into clinical practice. It combines rigorous scientific analysis with practical applications, serving as both a reference and a roadmap for future developments in long COVID research and treatment. The main objectives are to provide a comprehensive understanding of long COVID fatigue mechanisms, present evidence-based approaches for diagnosis and treatment, showcase innovative AI applications in medical imaging, establish a framework for future research, and offer practical clinical management guidelines. 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