Background: Geranium bellumRose, locally known as “Pata de león”, is a perennial plant distributed in the mountains of Hidalgo, Mexico. It is widely used in Mexican traditional medicine to treat fever, pain, and gastrointestinal disorders. To date, there are not published studies regarding thein vivoantinociceptive and anti-inflammatory potential of the acetone-aqueous extract from the aerial parts ofG. bellum.Methods: Antinociceptive effects of the acetone-aqueousG. bellum(AGB) extract and the isolated compounds were assessed using experimental pain models, including thermal nociception like hot plate test, and chemical nociception induced by intraperitoneal acetic acid or subplantar formalin injectionin vivo. The anti-inflammatory properties of the extract were studied using systemic administration in carrageenan-induced paw edema.Results: Intra-gastric administration of AGB (75, 150, and 300 mg/kg) showed a dose-dependent antinociceptive effect in intraperitoneal acetic acid (writhing), thermal nociception in CD1 mice, and subplantar formalin models, as well as anti-inflammatory effect in carrageenan- induced paw edema in Wistar rats. Geraniin and quercetin showed the highest antinociceptive activity in writhing test, whereas ellagic acid was the most active compound in the hot plate model.Conclusion: These studies provide evidences thatG. bellumshows antinociceptive and anti- inflammatory effects, and gives support to its use in treating pain in Mexican traditional medicine.

Alzheimer's disease is a multifactorial neurodegenerative disorder preceded by a prodromal stage called mild cognitive impairment (MCI). Early diagnosis of MCI is crucial for delaying the progression and optimizing the treatment. In this study we propose a random forest (RF) classifier to distinguish between MCI and healthy control subjects (HC), identifying the most relevant features computed from structural T1-weighted and diffusion-weighted magnetic resonance images (sMRI and DWI), combined with neuro-psychological scores. To train the RF we used a set of 60 subjects (HC = 30, MCI = 30) drawn from the Alzheimer's disease neuroimaging initiative database, while testing with unseen data was carried out on a 23-subjects Mexican cohort (HC = 12, MCI = 11). Features from hippocampus, thalamus and amygdala, for left and right hemispheres were fed to the RF, with the most relevant being previously selected by applying extra trees classifier and the mean decrease in impurity index. All the analyzed brain structures presented changes in sMRI and DWI features for MCI, but those computed from sMRI contribute the most to distinguish from HC. However, sMRI+DWI improves classification performance in training area under the receiver operating characteristic curve (AUROC = 93.5 ± 8%, accuracy = 88.8 ± 9%) and testing with unseen data (AUROC = 93.79%, accuracy = 91.3%), having a better performance when neuro-psychological scores were included. Compared to other classifiers the proposed RF provide the best performance for HC/MCI discrimination and the application of a feature selection step improves its performance. These findings imply that multimodal analysis gives better results than unimodal analysis and hence may be a useful tool to assist in early MCI diagnosis.

Emotional processing has an important role in social interaction. We report the findings about the Independent Component Analysis carried out on a fMRI set obtained with a paradigm of face emotional processing. The results showed that an independent component, mainly cerebellar-medial-frontal, had a positive modulation associated with fear processing. Also, another independent component, mainly parahippocampal-prefrontal, showed a negative modulation that could be associated with implicit reappraisal of emotional stimuli. Independent Component Analysis could serve as a method to understand complex cognitive processes and their underlying neural dynamics.

Cardiac resynchronization therapy (CRT) improves functional classification among patients with left ventricle malfunction and ventricular electric conduction disorders. However, a high percentage of subjects under CRT (20%–30%) do not show any improvement. Nonetheless the presence of mechanical contraction dyssynchrony in ventricles has been proposed as an indicator of CRT response. This work proposes an automated classification model of severity in ventricular contraction dyssynchrony. The model includes clinical data such as left ventricular ejection fraction (LVEF), QRS and P-R intervals, and the 3 most significant factors extracted from the factor analysis of dynamic structures applied to a set of equilibrium radionuclide angiography images representing the mechanical behavior of cardiac contraction. A control group of 33 normal volunteers (28±5years, LVEF of59.7%±5.8%) and a HF group of 42 subjects (53.12±15.05years, LVEF < 35%) were studied. The proposed classifiers had hit rates of 90%, 50%, and 80% to distinguish between absent, mild, and moderate-severe interventricular dyssynchrony, respectively. For intraventricular dyssynchrony, hit rates of 100%, 50%, and 90% were observed distinguishing between absent, mild, and moderate-severe, respectively. These results seem promising in using this automated method for clinical follow-up of patients undergoing CRT.