SwePub
Tyck till om SwePub Sök här!
Sök i SwePub databas

  Utökad sökning

Träfflista för sökning "WFRF:(Bencivenga Filippo) "

Sökning: WFRF:(Bencivenga Filippo)

  • Resultat 1-4 av 4
Sortera/gruppera träfflistan
   
NumreringReferensOmslagsbildHitta
1.
  • Ksenzov, Dmitriy, et al. (författare)
  • Nanoscale Transient Magnetization Gratings Created and Probed by Femtosecond Extreme Ultraviolet Pulses
  • 2021
  • Ingår i: Nano Letters. - : American Chemical Society (ACS). - 1530-6984 .- 1530-6992. ; 21:7, s. 2905-2911
  • Tidskriftsartikel (refereegranskat)abstract
    • We utilize coherent femtosecond extreme ultraviolet (EUV) pulses from a free electron laser (FEL) to generate transient periodic magnetization patterns with periods as short as 44 nm. Combining spatially periodic excitation with resonant probing at the M-edge of cobalt allows us to create and probe transient gratings of electronic and magnetic excitations in a CoGd alloy. In a demagnetized sample, we observe an electronic excitation with a rise time close to the FEL pulse duration and similar to 0.5 ps decay time indicative of electron-phonon relaxation. When the sample is magnetized to saturation in an external field, we observe a magnetization grating, which appears on a subpicosecond time scale as the sample is demagnetized at the maxima of the EUV intensity and then decays on the time scale of tens of picoseconds via thermal diffusion. The described approach opens multiple avenues for studying dynamics of ultrafast magnetic phenomena on nanometer length scales.
  •  
2.
  •  
3.
  • Okoye, Chukwuma, et al. (författare)
  • Predicting mortality and re-hospitalization for heart failure : a machine-learning and cluster analysis on frailty and comorbidity
  • 2023
  • Ingår i: Aging Clinical and Experimental Research. - 1594-0667 .- 1720-8319. ; 35, s. 2919-2928
  • Tidskriftsartikel (refereegranskat)abstract
    • BackgroundMachine-learning techniques have been recently utilized to predict the probability of unfavorable outcomes among elderly patients suffering from heart failure (HF); yet none has integrated an assessment for frailty and comorbidity. This research seeks to determine which machine-learning-based phenogroups that incorporate frailty and comorbidity are most strongly correlated with death or readmission at hospital for HF within six months following discharge from hospital.MethodsIn this single-center, prospective study of a tertiary care center, we included all patients aged 65 and older discharged for acute decompensated heart failure. Random forest analysis and a Cox multivariable regression were performed to determine the predictors of the composite endpoint. By k-means and hierarchical clustering, those predictors were utilized to phenomapping the cohort in four different clusters.ResultsA total of 571 patients were included in the study. Cluster analysis identified four different clusters according to frailty, burden of comorbidities and BNP. As compared with Cluster 4, we found an increased 6-month risk of poor outcomes patients in Cluster 1 (very frail and comorbid; HR 3.53 [95% CI 2.30-5.39]), Cluster 2 (pre-frail with low levels of BNP; HR 2.59 [95% CI 1.66-4.07], and in Cluster 3 (pre-frail and comorbid with high levels of BNP; HR 3.75 [95% CI 2.25-6.27])).ConclusionsIn older patients discharged for ADHF, the cluster analysis identified four distinct phenotypes according to frailty degree, comorbidity, and BNP levels. Further studies are warranted to validate these phenogroups and to guide an appropriate selection of personalized, model of care.
  •  
4.
  •  
Skapa referenser, mejla, bekava och länka
  • Resultat 1-4 av 4

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy