Search

It is unclear how the brain network changed after kidney transplantation (KT). We explored the patterns of large‐scale complex network after KT in end‐stage renal disease (ESRD) patients with resting‐state functional MRI (rs‐fMRI) and diffusion tensor imaging (DTI). Twenty‐one ESRD patients (14 men; mean age, 31.5 ± 9.9 years) scheduled for KT and 17 age‐ and gender‐matched healthy controls (HC) (8 men; mean age, 28.9 ± 7.2 years) were enrolled in this study. Each participant underwent rs‐fMRI and DTI scans in three time points (pre‐KT, 1 and 6 months after KT [for ESRD]). Graph theory analysis was used to characterize the topological properties by using functional and structural network connectivities intergroup correlation analysis was performed between functional/structural MR indexes and clinical markers. Compared with HC, pre‐KT ESRD patients showed an altered topological organization in both functional and structural networks. Compared with pre‐KT, increased node degree and node efficiency were observed for both functional and structural networks at 1 month after KT (all p <.05), which were further increased at 6 months after KT (p <.05). Both functional and structural networks did not recover completely at 6 months after KT (all p <.05). The patients showed an increased functional–structural connectivity coupling at 1 month after KT compared with HC (p =.041). A trend of progressive recovery of functional and structural connectivity networks was observed in ERSD patients after KT, which did not recover to the normal levels even in 6 months after KT. The study results underlie cognitive function recovery in ESRD patients following KT in the neuropathophysiological perspective. [ABSTRACT FROM AUTHOR]

CONTEXT: Few studies have used classification tree analysis to produce empirically driven decision tools that identify subgroups of women at risk of STDs during pregnancy. Such tools can guide care, treatment and prevention efforts in clinical settings. METHODS: A sample of 647 women aged 14–25 attending two urban obstetrics and gynecology clinics in 2001–2004 were surveyed in their second and third trimesters. Baseline predictors at the individual, dyad, and family and community levels were used to develop a classification tree that differentiated subgroups of women by STD incidence at 35 weeks’ gestation. Logistic regression analyses were conducted to assess whether the classification tree groups or commonly used risk factors better predicted STD incidence. RESULTS: Nineteen percent of women had an incident STD during pregnancy. Classification tree analysis identified three subgroups with a high STD incidence (33–61%), one with a moderate incidence (16%) and three with a low incidence (6–11%). Women in subgroups with high STD incidence included those not living with the partner with whom they conceived and those who had a moderate or a high level of depression, a history of STDs and a low level of social support. A logistic regression model using groups defined by the classification tree analysis had better predictive ability than one using common demographic and sexual risk predictors. CONCLUSION: This classification tree identified risk factors not captured by traditional risk screenings, and could be used to guide STD treatment, care and prevention within the prenatal care setting. [ABSTRACT FROM AUTHOR]