Effects of early life permethrin exposure on spatial working memory and on monoamine levels in different brain areas of pre-senescent rats

Pesticide exposure during brain development could represent an important risk factor for the onset of neurodegenerative diseases. Previous studies investigated the effect of permethrin (PERM) administered at 34 mg/kg, a dose close to the no observable adverse effect level (NOAEL) from post natal day (PND) 6 to PND 21 in rats. Despite the PERM dose did not elicited overt signs of toxicity (i.e. normal body weight gain curve), it was able to induce striatal neurodegeneration (dopamine and Nurr1 reduction, and lipid peroxidation increase). The present study was designed to characterize the cognitive deficits in the current animal model. When during late adulthood PERM treated rats were tested for spatial working memory performances in a T-maze-rewarded alternation task they took longer to choose for the correct arm in comparison to age matched controls. No differences between groups were found in anxiety-like state, locomotor activity, feeding behavior and spatial orientation task. Our findings showing a selective effect of PERM treatment on the T-maze task point to an involvement of frontal cortico-striatal circuitry rather than to a role for the hippocampus. The predominant disturbances concern the dopamine (DA) depletion in the striatum and, the serotonin (5-HT) and noradrenaline (NE) unbalance together with a hypometabolic state in the medial prefrontal cortex area. In the hippocampus, an increase of NE and a decrease of DA were observed in PERM treated rats as compared to controls. The concentration of the most representative marker for pyrethroid exposure (3-phenoxybenzoic acid) measured in the urine of rodents 12 h after the last treatment was 41.50 µ/L and it was completely eliminated after 96 h.

Quality of Life Predictors and Normative Data

This study identifies predictors and normative data for quality of life (QOL) in a sample of Portuguese adults from general population. A cross-sectional correlational study was undertaken with two hundred and fifty-five (N = 255) individuals from Portuguese general population (mean age 43 years, range 25–84 years; 148 females, 107 males). Participants completed the European Portuguese version of the World Health Organization Quality of Life short-form instrument and the European Portuguese version of the Center for Epidemiologic Studies Depression Scale. Demographic information was also collected. Portuguese adults reported their QOL as good. The physical, psychological and environmental domains predicted 44 % of the variance of QOL. The strongest predictor was the physical domain and the weakest was social relationships. Age, educational level, socioeconomic status and emotional status were significantly correlated with QOL and explained 25 % of the variance of QOL. The strongest predictor of QOL was emotional status followed by education and age. QOL was significantly different according to: marital status; living place (mainland or islands); type of cohabitants; occupation; health. The sample of adults from general Portuguese population reported high levels of QOL. The life domain that better explained QOL was the physical domain. Among other variables, emotional status best predicted QOL. Further variables influenced overall QOL. These findings inform our understanding on adults from Portuguese general population QOL and can be helpful for researchers and practitioners using this assessment tool to compare their results with normative data

A Clinical Support System Based on Quality of Life Estimation

Quality of life is a concept influenced by social, economic, psychological, spiritual or medical state factors. More specifically, the perceived quality of an individual's daily life is an assessment of their well-being or lack of it. In this context, information technologies may help on the management of services for healthcare of chronic patients such as estimating the patient quality of life and helping the medical staff to take appropriate measures to increase each patient quality of life. This paper describes a Quality of Life estimation system developed using information technologies and the application of data mining algorithms to access the information of clinical data of patients with cancer from Otorhinolaryngology and Head and Neck services of an oncology institution. The system was evaluated with a sample composed of 3013 patients. The results achieved show that there are variables that may be significant predictors for the Quality of Life of the patient: years of smoking (p value 0.049) and size of the tumor (p value < 0.001). In order to assign the variables to the classification of the quality of life the best accuracy was obtained by applying the John Platt's sequential minimal optimization algorithm for training a support vector classifier. In conclusion data mining techniques allow having access to patients additional information helping the physicians to be able to know the quality of life and produce a well-informed clinical decision.