RESUMO
Premenstrual Dysphoric Disorder (PMDD) is related to an abrupt drop in progesterone and impairments in the HPA axis that cause anxiety. Suffering persons report higher daily-life stress and anxiety proneness that may contribute to developing PMDD, considered a chronic stress-related disorder. Here, we explored the effect of chronic unpredictable stress (CUS) in rats subjected to progesterone withdrawal (PW) and evaluated gene expression of HPA axis activation in the stress-vulnerable Wistar-Kyoto (WKY) rat strain that is prone to anxiety. Ovariectomized WKY rats were randomly assigned to CUS or Standard-housed conditions (SHC) for 30 days. To induce PW, animals received 2 mg/kg of progesterone on day 25th for 5 days; 24 h later, they were tested using the anxiety-like burying behavior test (BBT). After behavioral completion, rats were euthanized, and brains were extracted to measure Crh (PVN) and Nr3c1 (hippocampus) mRNA. Blood corticosterone and vasopressin levels were determined. Results showed that PW exacerbated anxiety-like behaviors through passive coping in CUS-WKY. PW decreased Crh-PVN mRNA and the Nr3c1-hippocampal mRNA expression in SHC. CUS decreased Crh-PVN mRNA compared to SHC, and no further changes were observed by PW or BBT exposure. CUS reduced Nr3c1-hippocampal gene expression compared to SHC animals, and lower Nr3c1 mRNA was detected due to BBT. The PW increased corticosterone in SHC and CUS rats; however, CUS blunted corticosterone when combined with PW+BBT and similarly occurred in vasopressin concentrations. Chronic stress blunts the response of components of the HPA axis regulation when PW and BBT (systemic and psychogenic stressors, respectively) are presented. This response may facilitate less adaptive behaviors through passive coping in stress-vulnerable subjects in a preclinical model of premenstrual anxiety.
Assuntos
Transtorno Disfórico Pré-Menstrual , Progesterona , Humanos , Ratos , Feminino , Animais , Ratos Endogâmicos WKY , Progesterona/metabolismo , Corticosterona , Transtorno Disfórico Pré-Menstrual/metabolismo , Sistema Hipotálamo-Hipofisário/metabolismo , Neurobiologia , Sistema Hipófise-Suprarrenal/metabolismo , Estresse Psicológico/etiologia , Vasopressinas/metabolismo , RNA Mensageiro/metabolismoRESUMO
Caborca is one of the most productive asparagus-growing regions in the state of Sonora in northwest Mexico, an area where some fresh fruits and vegetables are sold at unregulated open-air street markets. This is a cross-sectional study in which fifty bundles of asparagus for exportation, 50 bundles of sub-standard asparagus, and 50 bundles of asparagus from open-air markets were selected randomly and then subjected to Faust, Kinyoun and ELISA testing to detect intestinal parasites. Pearson's chi-square (χ2) and Student-NewmanKeuls tests were used to estimate differences among the sampling site groups (P < 0.05). The pathogens Cryptosporidium spp. (29%) G. intestinalis (5%) and Cyclospora spp. (3%) were found in the asparagus sold in the region. The prevalence of Cryptosporidium spp. was higher in both the sub-standard asparagus and the product sampled from the open-air markets than in the samples for exportation (P < 0.05). This is the first study to demonstrate contamination by intestinal parasites in asparagus sold in different markets in northwest Mexico.
Assuntos
Asparagus/parasitologia , Contaminação de Alimentos/análise , Verduras/parasitologia , Estudos Transversais , Cryptosporidium/isolamento & purificação , Cyclospora/isolamento & purificação , MéxicoRESUMO
The segmentation of head images is useful to detect neuroanatomical structures and to follow and quantify the evolution of several brain lesions. 2D images correspond to brain slices. The more images are used the higher the resolution obtained is, but more processing power is required and parallelism becomes desirable. We present a new approach to segmentation of brain image volumes using DLML (Data List Management Library), a tool developed by our team. We organise the integer numbers identifying images into a list, and our DLML version process them both in parallel and with dynamic load balancing transparently to the programmer. We compare the performance of our DLML version to other typical parallel approaches developed with MPI (master-slave and static data distribution), using cluster configurations with 4-32 processors.