Insulin-like Growth Factor 1, Growth Hormone, and Anti-Müllerian Hormone Receptors Are Differentially Expressed during GnRH Neuron Development.

Gonadotropin-releasing hormone (GnRH) neurons are key neuroendocrine cells in the brain as they control reproduction by regulating hypothalamic-pituitary-gonadal axis function. In this context, anti-Müllerian hormone (AMH), growth hormone (GH), and insulin-like growth factor 1 (IGF1) were shown to improve GnRH neuron migration and function in vitro. Whether AMH, GH, and IGF1 signaling pathways participate in the development and function of GnRH neurons in vivo is, however, currently still unknown. To assess the role of AMH, GH, and IGF1 systems in the development of GnRH neuron, we evaluated the expression of AMH receptors (AMHR2), GH (GHR), and IGF1 (IGF1R) on sections of ex vivo mice at different development stages. The expression of AMHR2, GHR, and IGF1R was assessed by immunofluorescence using established protocols and commercial antibodies. The head sections of mice were analyzed at E12.5, E14.5, and E18.5. In particular, at E12.5, we focused on the neurogenic epithelium of the vomeronasal organ (VNO), where GnRH neurons, migratory mass cells, and the pioneering vomeronasal axon give rise. At E14.5, we focused on the VNO and nasal forebrain junction (NFJ), the two regions where GnRH neurons originate and migrate to the hypothalamus, respectively. At E18.5, the median eminence, which is the hypothalamic area where GnRH is released, was analyzed. At E12.5, double staining for the neuronal marker ß-tubulin III and AMHR2, GHR, or IGF1R revealed a signal in the neurogenic niches of the olfactory and VNO during early embryo development. Furthermore, IGF1R and GHR were expressed by VNO-emerging GnRH neurons. At E14.5, a similar expression pattern was found for the neuronal marker ß-tubulin III, while the expression of IGF1R and GHR began to decline, as also observed at E18.5. Of note, hypothalamic GnRH neurons labeled for PLXND1 tested positive for AMHR2 expression. Ex vivo experiments on mouse sections revealed differential protein expression patterns for AMHR2, GHR, and IGF1R at any time point in development between neurogenic areas and hypothalamic compartments. These findings suggest a differential functional role of related systems in the development of GnRH neurons.

Cameroonian Spice Extracts Modulate Molecular Mechanisms Relevant to Cardiometabolic Diseases in SW 872 Human Liposarcoma Cells.

The molecular pathophysiology of cardiometabolic diseases is known to be influenced by dysfunctional ectopic adipose tissue. In addition to lifestyle improvements, these conditions may be managed by novel nutraceutical products. This study evaluatedthe effects of 11 Cameroonian medicinal spice extracts on triglyceride accumulation, glucose uptake, reactive oxygen species (ROS) production and interleukin secretion in SW 872 human adipocytes after differentiation with 100 µM oleic acid. Triglyceride content was significantly reduced by all spice extracts. Glucose uptake was significantly increased by Tetrapleura tetraptera, Aframomum melegueta and Zanthoxylum leprieurii. Moreover, Xylopia parviflora, Echinops giganteus and Dichrostachys glomerata significantly reduced the production of ROS. Concerning pro-inflammatory cytokine secretion, we observed that Tetrapleura tetraptera, Echinops giganteus, Dichrostachys glomerata and Aframomum melegueta reduced IL-6 secretion. In addition, Xylopia parviflora, Monodora myristica, Zanthoxylum leprieurii, and Xylopia aethiopica reduced IL-8 secretion, while Dichrostachys glomerata and Aframomum citratum increased it. These findings highlight some interesting properties of these Cameroonian spice extracts in the modulation of cellular parameters relevant to cardiometabolic diseases, which may be further exploited, aiming to develop novel treatment options for these conditions based on nutraceutical products.

Cortical response to levodopa in Parkinson's disease patients with dyskinesias.

Levodopa-induced dyskinesias are a common and disabling side effect of dopaminergic therapy in Parkinson's disease, but their neural mechanisms in vivo are still poorly understood. Besides striatal pathology, the importance of cortical dysfunction has been increasingly recognized. The supplementary motor area in particular, may have a relevant role in dyskinesias onset given its involvement in endogenously generated actions. The aim of the present study was to investigate the levodopa-related cortical excitability changes along with the emergence of levodopa-induced peak-of-dose dyskinesias in subjects with Parkinson's disease. Thirteen patients without dyskinesias and ten with dyskinesias received 200/50 mg fast-acting oral levodopa/benserazide following overnight withdrawal (12 hr) from their dopaminergic medication. We targeted transcranial magnetic stimulation to the supplementary motor area, ipsilateral to the most dopamine-depleted striatum defined with single-photon emission computed tomography with [123 I]N-ω-fluoropropyl-2ß-carbomethoxy-3ß-(4-iodophenyl)nortropane, and recorded transcranial magnetic stimulation-evoked potentials with high-density electroencephalography before and at 30, 60, and 180 min after levodopa/benserazide intake. Clinical improvement from levodopa/benserazide paralleled the increase in cortical excitability in both groups. Subjects with dyskinesias showed higher fluctuation of cortical excitability in comparison to non-dyskinetic patients, possibly reflecting dyskinetic movements. Together with endogenous brain oscillation, levodopa-related dynamics of brain state could influence the therapeutic response of neuromodulatory interventions.

Diagnosis of autism through EEG processed by advanced computational algorithms: A pilot study.

BACKGROUND: Multi-Scale Ranked Organizing Map coupled with Implicit Function as Squashing Time algorithm(MS-ROM/I-FAST) is a new, complex system based on Artificial Neural networks (ANNs) able to extract features of interest in computerized EEG through the analysis of few minutes of their EEG without any preliminary pre-processing. A proof of concept study previously published showed accuracy values ranging from 94%-98% in discerning subjects with Mild Cognitive Impairment and/or Alzheimer's Disease from healthy elderly people. The presence of deviant patterns in simple resting state EEG recordings in autism, consistent with the atypical organization of the cerebral cortex present, prompted us in applying this potent analytical systems in search of a EEG signature of the disease. AIM OF THE STUDY: The aim of the study is to assess how effectively this methodology distinguishes subjects with autism from typically developing ones. METHODS: Fifteen definite ASD subjects (13 males; 2 females; age range 7-14; mean value = 10.4) and ten typically developing subjects (4 males; 6 females; age range 7-12; mean value 9.2) were included in the study. Patients received Autism diagnoses according to DSM-V criteria, subsequently confirmed by the ADOS scale. A segment of artefact-free EEG lasting 60 seconds was used to compute input values for subsequent analyses. MS-ROM/I-FAST coupled with a well-documented evolutionary system able to select predictive features (TWIST) created an invariant features vector input of EEG on which supervised machine learning systems acted as blind classifiers. RESULTS: The overall predictive capability of machine learning system in sorting out autistic cases from normal control amounted consistently to 100% with all kind of systems employed using training-testing protocol and to 84% - 92.8% using Leave One Out protocol. The similarities among the ANN weight matrixes measured with apposite algorithms were not affected by the age of the subjects. This suggests that the ANNs do not read age-related EEG patterns, but rather invariant features related to the brain's underlying disconnection signature. CONCLUSION: This pilot study seems to open up new avenues for the development of non-invasive diagnostic testing for the early detection of ASD.

Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer's disease.

BACKGROUND: Medial temporal lobe (MTL) atrophy is one of the key biomarkers to detect early neurodegenerative changes in the course of Alzheimer's disease (AD). There is active research aimed at identifying automated methodologies able to extract accurate classification indexes from T1-weighted magnetic resonance images (MRI). Such indexes should be fit for identifying AD patients as early as possible. SUBJECTS: A reference group composed of 144AD patients and 189 age-matched controls was used to train and test the procedure. It was then applied on a study group composed of 302 MCI subjects, 136 having progressed to clinically probable AD (MCI-converters) and 166 having remained stable or recovered to normal condition after a 24month follow-up (MCI-non converters). All subjects came from the ADNI database. METHODS: We sampled the brain with 7 relatively small volumes, mainly centered on the MTL, and 2 control regions. These volumes were filtered to give intensity and textural MRI-based features. Each filtered region was analyzed with a Random Forest (RF) classifier to extract relevant features, which were subsequently processed with a Support Vector Machine (SVM) classifier. Once a prediction model was trained and tested on the reference group, it was used to compute a classification index (CI) on the MCI cohort and to assess its accuracy in predicting AD conversion in MCI patients. The performance of the classification based on the features extracted by the whole 9 volumes is compared with that derived from each single volume. All experiments were performed using a bootstrap sampling estimation, and classifier performance was cross-validated with a 20-fold paradigm. RESULTS: We identified a restricted set of image features correlated with the conversion to AD. It is shown that most information originate from a small subset of the total available features, and that it is enough to give a reliable assessment. We found multiple, highly localized image-based features which alone are responsible for the overall clinical diagnosis and prognosis. The classification index is able to discriminate Controls from AD with an Area Under Curve (AUC)=0.97 (sensitivity ≃89% at specificity ≃94%) and Controls from MCI-converters with an AUC=0.92 (sensitivity ≃89% at specificity ≃80%). MCI-converters are separated from MCI-non converters with AUC=0.74(sensitivity ≃72% at specificity ≃65%). FINDINGS: The present automated MRI-based technique revealed a strong relationship between highly localized baseline-MRI features and the baseline clinical assessment. In addition, the classification index was also used to predict the probability of AD conversion within a time frame of two years. The definition of a single index combining local analysis of several regions can be useful to detect AD neurodegeneration in a typical MCI population.