Markers of Mitochondrial Mitophagy and Fusion - to - Fission Ratio are Greater in Older vs. Young Rats

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Evaluation of Cytokine Pattern in Perivascular and Intramuscular Fat During Heart Failure

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Humic Acids of the Amazonian Dark Earth Soils: Terra Preta De Índio.

The humic acid fraction (HA) of some Amazonian dark earth soils (Terra Preta de Índio) from Brazil were characterized using ultraviolet-visible (UV-Vis), Fourier transform diffuse reflectance infrared (DRIFT), fluorescence excitation and emission, electron paramagnetic resonance (EPR), and nuclear magnetic resonance (NMR) spectroscopy, thermogravimetric analysis, elemental composition, and measurement of acidity (total, carboxylic, phenolic). The HA fraction was extracted using the method recommended by the International Humic Substances Society (IHSS). The HA samples were separated in 3 groups based on the corresponding land use of the area of its origin: anthropogenic soils under forest (SAF), anthropogenic soils under agricultural use (SAC), non-anthropogenic soils under forest (SNAF). The SNAF soils were representative of Amazonian soils. They were collected in adjacent areas to the anthropogenic soil profiles. This way the SNAF group was a reference group for comparison purposes to the anthropogenic soil groups (SAF and SAC). Comparative (test t) and multivariate statistical analyses (factor analysis, cluster analysis, and discriminant analysis) were applied in the study. The anthropogenic soil groups (SAF and SAC) showed better fertility characteristics than the non-anthropogenic soils (SNAF) (pH: SAF = 5.1, SAC = 5.4, SNAF = 4.4; base saturation [V%]: SAF =59, SAC = 51, SNAF = 18; calculated cation exchange capacity [CEC]: SAF = 17.5, SAC = 17.2, SNAF = 9.5 cmolc/kg; available P: SAF = 116, SAC = 291, SNAF = 5 mg/kg). In the SAF and SAC soil groups ~44% of the total carbon was found in the humic fraction, ~32% in the humic acid fraction, and ~13% in the fulvic acid fraction. These values for the SNAF soils were 49, 19, 16%, respectively. The most relevant characteristics of the HA of anthropogenic soils, when compared to the non-anthropogenic ones were their superior reactivity, stability, and humification degree. The HA of the SAF and SAC groups featured higher total acidity (SAF = 612, SAC = 712, SNAF = 575 cmolkg) and carboxylic acidity (SAF = 435, SAC = 454, SNAF = 320 cmol/kg), higher concentration of organic free radicals (SAF = 4.07, SAC = 6.59, SNAF = 2.11 spin g-1 1017), higher thermogravimetric index (ITG) (SAF = 3.0, SAC = 3.3, SNAF = 2.3), lower E4/E6 ratio (SAF = 4.2, SAC = 4.2, SNAF = 6.0), higher aromaticity index (IADRIFT: SAF = 0.87, SAC = 0.85, SNAF = 0.77; NMR(%): SAF = 36, SAC = 39, SNAF = 25), higher hidrophobicity index (SAF = 0.37, SAC = 0.48, SNAF = 0.35), higher humification degree (A4/A1: SAF = 2.574, SAC = 3.313, SNAF = 1.713; I485/I400: SAF = 2.004, SAC = 2.161, SNAF = 1.510), and were more recalcitrant (recalcitrant C/labile C: SAF = 2.0, SAC = 2.0, SNAF = 1.0) than the HA of the SNAF group. Data also showed that there was difference between the HA of the SAF and SAC soil groups

Terra preta de indio "Dark Earth Soils": chemical and spectroscopic characterization of humic acids.

The HA of Amazonian dark earth soils (Terra Preta de Índio) from Brazilian territory were characterized using ultraviolet-visible, Fourier transform diffuse reflectance infrared, fluorescence excitation and emission, electron paramagnetic resonance, and nuclear magnetic resonance spectroscopy, thermogravimetric analysis, elemental composition, and measurement of acidity (total, carboxylic, phenolic)

Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering

Background and ObjectivesRecent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed, and the question arises of whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see whether data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers.MethodsWe used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups.ResultsWe included 1,078 unmedicated adolescents and adults. Seven clusters were identified, of which 4 clusters included predominantly individuals with cataplexy. The 2 most distinct clusters consisted of 158 and 157 patients, were dominated by those without cataplexy, and among other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening, and weekend-week sleep length difference. Patients formally diagnosed as having narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these 2 clusters.DiscussionUsing a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset REM periods in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features