University of Nebraska Omaha

About: University of Nebraska Omaha is a education organization based out in Omaha, Nebraska, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 4526 authors who have published 8905 publications receiving 213914 citations. The organization is also known as: UNO & University of Omaha.

An immune reconstitution syndrome-like illness associated with Cryptococcus neoformans infection in organ transplant recipients

Abstract: Background. We describe an immune reconstitution syndrome (IRS)-like entity in the course of evolution of Cryptococcus neoformans infection in organ transplant recipients. Methods. The study population comprised a cohort of 83 consecutive organ transplant recipients with cryptococcosis who were observed for a median of 2 years in an international, multicenter study. Results. In 4 (4.8%) of the 83 patients, an IRS-like entity was observed a median of 5.5 weeks after the initiation of appropriate antifungal therapy. Worsening of clinical manifestations was documented, despite cultures being negative for C. neoformans. These patients were significantly more likely to have received tacrolimus, mycophenolate mofetil, and prednisone as the regimen of immunosuppressive therapy than were all other patients (P = .007). The proposed basis of this phenomenon is reversal of a predominantly Th2 response at the onset of infection to a Thi proinflammatory response as a result of receipt of effective antifungal therapy and a reduction in or cessation of immunosuppressive therapy. Conclusions. This study demonstrated that an IRS-like entity occurs in organ transplant recipients with C. neoformans infection. Furthermore, this entity may be misconstrued as a failure of therapy. Immunomodulatory agents may have a role as adjunctive therapy in such cases.

Low-dimensional representation of faces in higher dimensions of the face space

Abstract: Faces can be represented efficiently as a weighted linear combination of the eigenvectors of a covariance matrix of face images. It has also been shown [ J. Opt. Soc. Am.4, 519– 524 ( 1987)] that identifiable faces can be made by using only a subset of the eigenvectors, i.e., those with the largest eigenvalues. This low-dimensional representation is optimal in that it minimizes the squared error between the representation of the face image and the original face image. The present study demonstrates that, whereas this low-dimensional representation is optimal for identifying the physical categories of face, like sex, it is not optimal for recognizing the faces (i.e., discriminating known from unknown faces). Various low-dimensional representations of the faces in the higher dimensions of the face space (i.e., the eigenvectors with smaller eigenvalues) provide better information for face recognition.