RESUMEN
Background: Recent studies suggested that MPTP could cause gastrointestinal motility deficits additionally to its nonconclusive and controverted effects on the CNS (behavior and brain oxidative stress) in rats. A possible interaction between MPTP typical impairments and magnesium modulatory potential was previously suggested, as magnesium role was described in neuroprotection, gastrointestinal function, and oxidative stress. Aim: To investigate the possible modulatory effect of several magnesium intake formulations (via drinking water) in MPTP neurotoxicity and functional gastrointestinal impairment induction. Materials and Methods: Adult male Wistar rats were subjected to 3-week magnesium intake-controlled diets (magnesium depleted food and magnesium enriched drinking water) previously to acute subcutaneous MPTP treatment (30 mg/ kg body weight). Gastrointestinal motility (one hour stool collection test), and behavioral patterns (Y maze task, elevated plus maze test, open field test, forced swim test) were evaluated. Followingly, brain and bowel samples were collected, and oxidative stress was evaluated (glutathione peroxidase activity, malondial-dehyde concentrations). Results: MPTP could lead to magnesium intake-dependent constipation-like gastrointestinal motility impairments, anxiety- and depressive-like affective behavior changes, and mild pain tolerance defects. Also, we found similar brain and intestinal patterns in magnesium-dependent oxidative stress. Conclusion: While the MPTP effects in normal magnesium intake could be regarded as not fully relevant in rat models and limited to the current experimental conditions, the abnormalities observed in the affective behavior, gastrointestinal status, pain tolerance, peripheric and central oxidative status could be indicative of the extent of the systemic effects of MPTP that are not restricted to the CNS level, but also to gastro-intestinal system.
RESUMEN
Scanning precession electron diffraction (SPED) enables the local crystallography of materials to be probed on the nanoscale by recording a two-dimensional precession electron diffraction (PED) pattern at every probe position as a dynamically rocking electron beam is scanned across the specimen. SPED data from nanocrystalline materials commonly contain some PED patterns in which diffraction is measured from multiple crystals. To analyse such data, it is important to perform nanocrystal segmentation to isolate both the location of each crystal and a corresponding representative diffraction signal. This also reduces data dimensionality significantly. Here, two approaches to nanocrystal segmentation are presented, the first based on virtual dark-field imaging and the second on non-negative matrix factorization. Relative merits and limitations are compared in application to SPED data obtained from partly overlapping nanoparticles, and particular challenges are highlighted associated with crystals exciting the same diffraction conditions. It is demonstrated that both strategies can be used for nanocrystal segmentation without prior knowledge of the crystal structures present, but also that segmentation artefacts can arise and must be considered carefully. The analysis workflows associated with this work are provided open-source. LAY DESCRIPTION: Scanning precession electron diffraction is an electron microscopy technique that enables studies of the local crystallography of a broad selection of materials on the nanoscale. The technique involves the acquisition of a two-dimensional diffraction pattern for every probe position in an area of the sample. The four-dimensional dataset collected by this technique can typically comprise up to 500 000 diffraction patterns. For nanocrystalline materials, it is common that single diffraction patterns contain signals from overlapping crystals. To process such data, we use nanocrystal segmentation, where a representative diffraction pattern is constructed for each individual crystal, together with a real space image showing its morphology and location in the data. This reduces the dimensionality of the data and allows unmixing of signals from overlapping crystals. In this work, we demonstrate two methods for nanocrystal segmentation, one based on creating virtual dark-field images, and one based on unsupervised machine learning. A model system of partly overlapping nanoparticles is used to demonstrate the segmentation, and a demanding case for segmentation is highlighted, where some crystals are not discernible based on their diffraction patterns. To obtain a more complete nanocrystal segmentation, we add an image segmentation routine to both methods, and we discuss benefits and limitations of the two methods. The demonstration data and the used code are provided open-source, so that it can be used by everyone for analysis of nanocrystalline materials or as a starting point for further development of nanocrystal segmentation in scanning precession electron diffraction data.
