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  1. AU="Lara-Hernandez, A"
  2. AU=Turck J
  3. AU="Aali, Ghazaleh"
  4. AU="Catapano, Joshua S"
  5. AU=Zoloth Laurie
  6. AU="Scholtz, Clarke H"
  7. AU="Meirelles, Gustavo de Souza Portes"
  8. AU=Demirbilek Nevzat
  9. AU="Larrosa-Escartín, Nieves"
  10. AU=Crago Aimee M.
  11. AU="Mármora, Lelio"
  12. AU="Asbell, Madison"
  13. AU="Yuka Ikeda"
  14. AU="Oppenheimer, Federic"
  15. AU=Guillevin Loic
  16. AU="Sabiha Alam"
  17. AU="Taher, Bianca Petra"
  18. AU="Obier, Frank"
  19. AU=Davila Eduardo AU=Davila Eduardo
  20. AU="Albizu, Constanza Lopez"
  21. AU="Antonova, Anastasiia"
  22. AU=Crowther L. M.
  23. AU=Zhan Xiping
  24. AU="Xuhui Bao"
  25. AU="Zuman Dou"

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  1. Artikel ; Online: Deep Learning-Based Image Registration in Dynamic Myocardial Perfusion CT Imaging.

    Lara-Hernandez, A / Rienmuller, T / Juarez, I / Perez, M / Reyna, F / Baumgartner, D / Makarenko, V N / Bockeria, O L / Maksudov, M / Rienmuller, R / Baumgartner, C

    IEEE transactions on medical imaging

    2023  Band 42, Heft 3, Seite(n) 684–696

    Abstract: Registration of dynamic CT image sequences is a crucial preprocessing step for clinical evaluation of multiple physiological determinants in the heart such as global and regional myocardial perfusion. In this work, we present a deformable deep learning- ... ...

    Abstract Registration of dynamic CT image sequences is a crucial preprocessing step for clinical evaluation of multiple physiological determinants in the heart such as global and regional myocardial perfusion. In this work, we present a deformable deep learning-based image registration method for quantitative myocardial perfusion CT examinations, which in contrast to previous approaches, takes into account some unique challenges such as low image quality with less accurate anatomical landmarks, dynamic changes of contrast agent concentration in the heart chambers and tissue, and misalignment caused by cardiac stress, respiration, and patient motion. The introduced method uses a recursive cascade network with a ventricle segmentation module, and a novel loss function that accounts for local contrast changes over time. It was trained and validated on a dataset of n = 118 patients with known or suspected coronary artery disease and/or aortic valve insufficiency. Our results demonstrate that the proposed method is capable of registering dynamic cardiac perfusion sequences by reducing local tissue displacements of the left ventricle (LV), whereas contrast changes do not affect the registration and image quality, in particular the absolute CT (HU) values of the entire CT sequence. In addition, the deep learning-based approach presented reveals a short processing time of a few seconds compared to conventional image registration methods, demonstrating its application potential for quantitative CT myocardial perfusion measurements in daily clinical routine.
    Mesh-Begriff(e) Humans ; Deep Learning ; Tomography, X-Ray Computed ; Myocardium ; Heart/diagnostic imaging ; Perfusion ; Image Processing, Computer-Assisted ; Myocardial Perfusion Imaging
    Sprache Englisch
    Erscheinungsdatum 2023-03-02
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 622531-7
    ISSN 1558-254X ; 0278-0062
    ISSN (online) 1558-254X
    ISSN 0278-0062
    DOI 10.1109/TMI.2022.3214380
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel: Molecular Characterization of

    Vazquez-Rosas, Guillermo Jose / Merida-Vieyra, Jocelin / Aparicio-Ozores, Gerardo / Lara-Hernandez, Antonino / De Colsa, Agustin / Aquino-Andrade, Alejandra

    Infection and drug resistance

    2021  Band 14, Seite(n) 1545–1556

    Abstract: Purpose: Staphylococcus aureus: Materials and methods: We analysed 249 : Results: Thirty-eight percent of the isolates were MRSA and showed an expanded profile of resistance to other non-beta-lactam antibiotics, while MSSA strains presented a ... ...

    Abstract Purpose: Staphylococcus aureus
    Materials and methods: We analysed 249
    Results: Thirty-eight percent of the isolates were MRSA and showed an expanded profile of resistance to other non-beta-lactam antibiotics, while MSSA strains presented a reduced resistance profile. SCC
    Conclusion: MSSA isolates had more virulence factors. MRSA isolates were resistant to more non-beta-lactam antibiotics, and those with SCC
    Sprache Englisch
    Erscheinungsdatum 2021-04-21
    Erscheinungsland New Zealand
    Dokumenttyp Journal Article
    ZDB-ID 2494856-1
    ISSN 1178-6973
    ISSN 1178-6973
    DOI 10.2147/IDR.S302416
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: Nosocomial, Multidrug-Resistant Klebsiella pneumoniae Strains Isolated from Mexico City Produce Robust Biofilms on Abiotic Surfaces but Not on Human Lung Cells.

    Ostria-Hernandez, Martha Lorena / Juárez-de la Rosa, Karla Cecilia / Arzate-Barbosa, Patricia / Lara-Hernández, Antonino / Sakai, Fuminori / Ibarra, J Antonio / Castro-Escarpulli, Graciela / Vidal, Jorge E

    Microbial drug resistance (Larchmont, N.Y.)

    2017  Band 24, Heft 4, Seite(n) 422–433

    Abstract: Background: Klebsiella pneumoniae (Kpn) strains are a leading cause of hospital-acquired infections, including ventilator-associated pneumonia. Resistance to antibiotics, biofilm formation, and the production of certain fimbriae play an important role ... ...

    Abstract Background: Klebsiella pneumoniae (Kpn) strains are a leading cause of hospital-acquired infections, including ventilator-associated pneumonia. Resistance to antibiotics, biofilm formation, and the production of certain fimbriae play an important role in the pathogenesis.
    Aim: We investigated the genetic relatedness, antibiotic resistance, virulence potential, and ability to form biofilms of Kpn strains isolated from hospital-acquired infections (n = 76). Strains were isolated at three major hospitals serving the largest metropolitan urban area in Mexico City, Mexico.
    Results: Enterobacterial repetitive intergenic consensus (ERIC)-PCR demonstrated that clonal groups predominate in each hospital. Selected strains chosen from clonal groups (n = 47) were multidrug resistant (MDR, 83%), although the majority (∼70%) were susceptible to carbapenems. All strains produced robust biofilms on abiotic surfaces, and ∼90% harbored adhesin genes fimH, mrkA, and ecpA. The ultrastructure of biofilms was further studied by high-resolution confocal microscopy. The average height of Kpn biofilms on abiotic surfaces was ∼40 μm. We then assessed formation of biofilms on human lung cells, as a surrogate of lung infection. While Kpn strains formed robust biofilms on abiotic surfaces, studies on lung cells revealed attachment to human cells but scarce formation of biofilms. Gene expression studies revealed a differential temporal expression of an adhesin (ecpA) and a capsule (galF) gene when biofilms were formed on different substrates.
    Conclusions: Kpn strains isolated from nosocomial infections in Mexico City are MDR, although the majority are still susceptible to carbapenems and form more robust biofilms on polystyrene in comparison to those formed on human cells.
    Mesh-Begriff(e) Adhesins, Bacterial/genetics ; Anti-Bacterial Agents/pharmacology ; Bacterial Proteins/genetics ; Biofilms/drug effects ; Biofilms/growth & development ; Carbapenems/pharmacology ; Cells, Cultured ; Cross Infection/drug therapy ; Cross Infection/microbiology ; Drug Resistance, Multiple, Bacterial/drug effects ; Drug Resistance, Multiple, Bacterial/genetics ; Enterobacteriaceae/drug effects ; Enterobacteriaceae/genetics ; Fimbriae, Bacterial/genetics ; Hospitals ; Humans ; Klebsiella Infections/drug therapy ; Klebsiella Infections/microbiology ; Klebsiella pneumoniae/genetics ; Klebsiella pneumoniae/isolation & purification ; Mexico ; Virulence/genetics
    Chemische Substanzen Adhesins, Bacterial ; Anti-Bacterial Agents ; Bacterial Proteins ; Carbapenems
    Sprache Englisch
    Erscheinungsdatum 2017-09-15
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 1290490-9
    ISSN 1931-8448 ; 1076-6294
    ISSN (online) 1931-8448
    ISSN 1076-6294
    DOI 10.1089/mdr.2017.0073
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Verfügbar in ZB MED Köln/Königswinter

    Zs.A 4435: Hefte anzeigen Standort:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
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