Postmenopausal osteoporosis (PMO) is a risk factor for periodontitis, and current therapeutics against PMO prevent the aggravated alveolar bone loss of periodontitis in estrogen-deficient women. Gut microbiota is recognized as a promising therapeutic target for PMO. Berberine extracted from Chinese medicinal plants has shown its effectiveness in the treatment of metabolic diseases such as obesity and diabetes via regulating gut microbiota. Here, we hypothesize that berberine ameliorates periodontal bone loss by improving the intestinal barriers by regulating gut microbiota under an estrogen-deficient condition. Experimental periodontitis was established in ovariectomized (OVX) rats, and the OVX-periodontitis rats were treated with berberine for 7 wk before sacrifice for analyses. Micro-computed tomography and histologic analyses showed that berberine treatment significantly reduced alveolar bone loss and improved bone metabolism of OVX-periodontitis rats as compared with the vehicle-treated OVX-periodontitis rats. In parallel, berberine-treated OVX-periodontitis rats harbored a higher abundance of butyrate-producing gut microbiota with elevated butyrate generation, as demonstrated by 16S rRNA sequencing and high-performance liquid chromatography analysis. Berberine-treated OVX-periodontitis rats consistently showed improved intestinal barrier integrity and decreased intestinal paracellular permeability with a lower level of serum endotoxin. In parallel, IL-17A-related immune responses were attenuated in berberine-treated OVX-periodontitis rats with a lower serum level of proinflammatory cytokines and reduced IL-17A cells in alveolar bone as compared with vehicle-treated OVX-periodontitis rats. Our data indicate that gut microbiota is a potential target for the treatment of estrogen deficiency-aggravated periodontal bone loss, and berberine represents a promising adjuvant therapeutic by modulating gut microbiota.
BackgroundThe degree of polymerization of amylose starch in potato was so large that the gel was hardness after gelatinization. Therefore, it is one of the most important ways that the microwave treatment was used to change the physicochemical properties of starch gel to make it suitable for the preparation of instant food.ResultsThe effect of microwave treatment on the physicochemical properties including morphology, crystalline structure, molecular weight distribution and rheological properties of potato starch granules was evaluated by treating time of varying duration (0, 5, 10, 15, 20 s) at 2450 MHz and 750 W. Scanning electron micrographs (SEM) of potato starch granules showed flaws or fractures on the surface after 5 to 10s of microwaving and collapse after 15 to 20 s. Polarized light microscopy (PLM) indicated that microwave treating damaged the crystalline structure of potato starch, such that the birefringence of starch granules gradually decreased after 5 to 10s and even disappeared after microwaving from 15 to 20 s. The molecular weight (Mw) values of potato starch and the proportion of large MW fraction were considerably reduced with increasing the microwave treating time from 0 to 20s. The molecular weight slowly decreased over 5 ~ 15 s microwave treating but decreased abruptly at the time of 20s microwave treating. The apparent viscosity decreased as shear rate increased and presented shear-thinning behavior. The magnitudes of the storage modulus (G’) and loss modulus (G”) obtained at each shear rate increased with duration of microwave treating from 0 to 15 s but decreased from 15 to 20 s.ConclusionsThese results demonstrated that the morphology and crystalline structure was damaged by microwave treatment. The high molecular weight of potato starch above 2 × 108 Da was so sensitive to the vibrational motion of the polar molecules due to the application microwave energy and broke easily for longer dextran chains. The fracture of starch granules, molecular chains leached from the starch granules and degradation of dextran chains contributing to the development of rheological properties.
Automatic seizure detection technology is of great significance for long-term electroencephalogram (EEG) monitoring of epilepsy patients. The aim of this work is to develop a seizure detection system with high accuracy. The proposed system was mainly based on multifractal analysis, which describes the local singular behavior of fractal objects and characterizes the multifractal structure using a continuous spectrum. Compared with computing the single fractal dimension, multifractal analysis can provide a better description on the transient behavior of EEG fractal time series during the evolvement from interictal stage to seizures. Thus both interictal EEG and ictal EEG were analyzed by multifractal formalism and their differences in the multifractal features were used to distinguish the two class of EEG and detect seizures. In the proposed detection system, eight features (α0, α(min), α(max), Δα, f(α(min)), f(α(max)), Δf and R) were extracted from the multifractal spectrums of the preprocessed EEG to construct feature vectors. Subsequently, relevance vector machine (RVM) was applied for EEG patterns classification, and a series of post-processing operations were used to increase the accuracy and reduce false detections. Both epoch-based and event-based evaluation methods were performed to appraise the system's performance on the EEG recordings of 21 patients in the Freiburg database. The epoch-based sensitivity of 92.94% and specificity of 97.47% were achieved, and the proposed system obtained a sensitivity of 92.06% with a false detection rate of 0.34/h in event-based performance assessment.
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