HIV Distal Neuropathic Pain Is Associated with Smaller Ventral Posterior Cingulate Cortex.

Objective: . Despite modern antiretroviral therapy, HIV-associated neuropathy is one of the most prevalent, disabling and treatment-resistant complications of HIV disease. The presence and intensity of distal neuropathic pain is not fully explained by the degree of peripheral nerve damage. A better understanding of brain structure in HIV distal neuropathic pain may help explain why some patients with HIV neuropathy report pain while the majority does not. Previously, we reported that more intense distal neuropathic pain was associated with smaller total cerebral cortical gray matter volumes. The objective of this study was to determine which parts of the cortex are smaller. Methods: . HIV positive individuals with and without distal neuropathic pain enrolled in the multisite (N = 233) CNS HIV Antiretroviral Treatment Effects (CHARTER) study underwent structural brain magnetic resonance imaging. Voxel-based morphometry was used to investigate regional brain volumes in these structural brain images. Results: . Left ventral posterior cingulate cortex was smaller for HIV positive individuals with versus without distal neuropathic pain (peak P = 0.017; peak t = 5.15; MNI coordinates x = -6, y = -54, z = 20). Regional brain volumes within cortical gray matter structures typically associated with pain processing were also smaller for HIV positive individuals having higher intensity ratings of distal neuropathic pain. Conclusions: . The posterior cingulate is thought to be involved in inhibiting the perception of painful stimuli. Mechanistically a smaller posterior cingulate cortex structure may be related to reduced anti-nociception contributing to increased distal neuropathic pain.

An assessment of the predictors of the dynamics in arable production per capita index, arable production and permanent cropland and forest area based on structural equation models.

This study sets out to verify the key predictors of the dynamics of the arable production per capita index, the arable production and permanent crop land and forest area at a national scale in Cameroon. To achieve this objective, data for twelve time series data variables spanning the period 1961-2000 were collected from Oxford University, the United Nations Development program, the World Bank, FAOSTAT and the World Resource Institute. The data were analysed using structural equation models (SEM) based on the two stage least square approach (2SLS). To optimize the results, variables that showed high correlations were dropped because they will not add any new information into the models. The results show that the arable production per capita index is impacted more by population while the influence of rainfall on the arable production per capita index is weak. Arable production and permanent cropland on its part has as the main predictor arable production per capita index. Forest area is seen to be more vulnerable to trade in forest products and logging than any other variable. The models presented in this study are quite reliable because the p and t values are consistent and overall, these results are consistent with previous studies.