LEADER 00867nam0-22003011i-450- 001 990006856720403321 005 20001010 035 $a000685672 035 $aFED01000685672 035 $a(Aleph)000685672FED01 035 $a000685672 100 $a20001010d--------km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $aPolitica tra le nazioni$ela lotta per il potere e la pace$fHans J. Morgenthau. 210 $aBologna$cIl Mulino$d1997. 215 $aXXX, 558 p.$d21 cm 225 1 $aCollezione di testi e di studi$iScienza politica 676 $a327 700 1$aMorgenthau,$bHans J.$f<1904-1985>$0212702 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990006856720403321 952 $aXIV B 1615$b31536$fFSPBC 959 $aFSPBC 996 $aPolitica tra le nazioni$9624593 997 $aUNINA DB $aGEN01 LEADER 07029nam 2201945z- 450 001 9910557367503321 005 20220111 035 $a(CKB)5400000000042216 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76447 035 $a(oapen)doab76447 035 $a(EXLCZ)995400000000042216 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aClinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (374 p.) 311 08$a3-0365-1134-2 311 08$a3-0365-1135-0 330 $aIn recent years, artificial intelligence has increasingly been playing an essential role in diverse areas in medicine, assisting clinicians in patient management. In nephrology and transplantation, artificial intelligence can be utilized to enhance clinical care, such as through hemodialysis prescriptions and the follow-up of kidney transplant patients. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions. For this Special Issue, we made a call to action to stimulate researchers and clinicians to submit their invaluable works and present, here, a collection of articles covering original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in nephrology including acute kidney injury, electrolytes and acid-base, chronic kidney disease, glomerular disease, dialysis, and transplantation that will provide additional knowledge and skills in the field of nephrology and transplantation toward improving patient outcomes. 606 $aMedicine and Nursing$2bicssc 610 $a(cardiac) surgery 610 $a16S rRNA sequencing 610 $aacute kidney injury 610 $aacute rejection 610 $aallograft 610 $aallograft steatosis 610 $aanti-GBM disease 610 $aarea under the precision-recall curve (AUCPR) 610 $aarea under the receiver operating characteristic (AUROC) 610 $aartificial intelligence 610 $aartificial neural network (ANN), clinical natural language processing (clinical NLP) 610 $abig data 610 $ablood pressure 610 $abone mineral density 610 $abutyrate-producing bacteria 610 $aC/D ratio 610 $acardiovascular mortality 610 $acellular crescent 610 $achronic kidney disease 610 $achronic kidney disease (CKD) 610 $aCKD-MBD 610 $aCKD-Mineral and Bone Disorder 610 $aclinical studies 610 $acollagen type VI 610 $acomorbidity 610 $aconservative management 610 $aconversion 610 $acystatin C 610 $adeath 610 $adeceased donor 610 $adialysis 610 $adiarrhea 610 $adischarge summaries 610 $adual-energy X-ray absorptiometry 610 $aelderly 610 $aelectronic health record (EHR) 610 $aepidemiology 610 $aeradication 610 $aerythropoietin 610 $aestimated glomerular filtration rate (eGFR) 610 $aethnic disparity 610 $aEurotransplant Senior Program 610 $aeverolimus 610 $aextracellular matrix 610 $aFGF-23 610 $afibroblast growth factor 23 610 $afibrosis 610 $ageneralized linear model network (GLMnet) 610 $agenetic polymorphisms 610 $aglobal sclerosis 610 $aglomerulosclerosis 610 $aGoodpasture syndrome 610 $aGoogle TrendsTM 610 $agraft failure 610 $agraft survival 610 $agut microbiome 610 $agut microbiota 610 $ahemodialysis 610 $ahepatitis C infection 610 $ahospitalization 610 $ahyperfiltration 610 $aICD-10 billing codes 610 $aimmunosuppressed host 610 $aimmunosuppression 610 $aimmunosuppressive medication 610 $ain-hospital mortality 610 $ainflammation 610 $ainflammatory cytokines 610 $aintensive care 610 $ainterferon-free regimen 610 $akidney donor 610 $akidney injury molecule (KIM)-1 610 $akidney transplant 610 $akidney transplant recipients 610 $akidney transplantation 610 $aklotho 610 $alaboratory values 610 $alaparoscopic surgery 610 $aLCPT 610 $alifestyle 610 $alipopeliosis 610 $alive donors 610 $aliver transplantation 610 $aliving donation 610 $aliving kidney donation 610 $aliving-donor kidney transplantation 610 $along-term outcomes 610 $amachine learning 610 $amachine learning (ML) 610 $aMeltDoseŽ 610 $ametabolism 610 $ametabolomics 610 $aminimally-invasive donor nephrectomy 610 $anephrology 610 $aNephrology 610 $aNLR 610 $ano known kidney disease (NKD) 610 $aorgan donation 610 $aoutcome 610 $aoutcomes 610 $apatent ductus arteriosus 610 $apatient survival 610 $aphenotyping 610 $aPLR 610 $apre-emptive transplantation 610 $apredictive value 610 $aprocurement kidney biopsy 610 $aprolonged ischaemic time 610 $aProteobacteria 610 $apublic awareness 610 $arandom forest (RF) 610 $arenal function 610 $arenal transplant recipient 610 $arenal transplantation 610 $arepeated kidney transplantation 610 $arisk prediction 610 $arisk stratification 610 $arobot-assisted surgery 610 $aRPGN 610 $atacrolimus 610 $atacrolimus metabolism 610 $atorquetenovirus 610 $atransplant numbers 610 $atransplantation 610 $atubular damage 610 $aurine 610 $avascular calcification 610 $aweekend effect 610 $awithdrawal 610 $a?-Klotho 615 7$aMedicine and Nursing 700 $aCheungpasitporn$b Wisit$4edt$01277893 702 $aThongprayoon$b Charat$4edt 702 $aKaewput$b Wisit$4edt 702 $aCheungpasitporn$b Wisit$4oth 702 $aThongprayoon$b Charat$4oth 702 $aKaewput$b Wisit$4oth 906 $aBOOK 912 $a9910557367503321 996 $aClinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation$93026967 997 $aUNINA