Effects of Baicalein on Cortical Proinflammatory Cytokines and the Intestinal Microbiome in Senescence Accelerated Mouse Prone 8

Baicalein, a flavonoid derived from the roots of <i>Scutellariae baicalensis</i> Georgi, has shown health benefits for an array of human diseases including dementia. The senescence-accelerated mouse prone 8 (SAMP8) strain is extensively used as a senile dementia model. To further investigate the effects of baicalein in SAMP8 mice, behavioral testing, biochemical detection, and gut microbiota analysis were performed. The results demonstrated that treatment with baicalein ameliorated the senescence status of the SAMP8 mice, as manifested by reducing the grading score of senescence. Additionally, baicalein improved the cognitive functions of the SAMP8 mice, including spatial learning and memory abilities, object recognition memory, and olfactory memory. Furthermore, baicalein significantly inhibited the release of proinflammatory cytokines such as interleukin-6 (IL-6), interleukin-1 beta (IL-1β), and tumor necrosis factor-α (TNF-α) in the brain cortex of SAMP8 mice. Gut microbiota analysis revealed that treatment with baicalein markedly altered the abundance of six genera in SAMP8 mice. Correlation analysis indicated that the abundances of <i>Mucispirillum</i>, <i>Bacteroides</i>, and <i>Sutterella</i> were negatively correlated with cognitive abilities and that <i>Christensenellaceae</i> was positively correlated with cognition. Furthermore, the abundance of <i>Christensenellaceae</i> was negatively correlated with the levels of IL-6 and TNF-α, while [<i>Prevotella</i>] was positively correlated with the levels of IL-1β and IL-6. In addition, <i>Mucispirillum</i> and <i>Bacteroides</i> were positively correlated with the level of IL-6 in the brain cortex. These data indicated that baicalein ameliorates senescence status and improves cognitive function in SAMP8 mice and that this effect might be attributable to suppression of cortical proinflammatory cytokines and modulation of the intestinal microbiome

Conditional probability distributions (TM), (Non-TM) (on the left), and (IU), (Non-IU) (on the right), where is, from top to bottom, polarity, as determined by the Grantham and Zimmerman-Eleizer-Simha scales, bulkiness, and flexibility

TM = transmembrane, IU = intrinsically unstructured. The plots on the left were reproduced with permission from [].<p><b>Copyright information:</b></p><p>Taken from "Investigation of transmembrane proteins using a computational approach"</p><p>http://www.biomedcentral.com/1471-2164/9/S1/S7</p><p>BMC Genomics 2008;9(Suppl 1):S7-S7.</p><p>Published online 20 Mar 2008</p><p>PMCID:PMC2386072.</p><p></p

Average EFSMO prediction performance by using MAQC-II multiple myeloma dataset with the measurements testing accuracy (left column), MCC values (middle column), and AUC errors (right column), respectively.

<p>Classification models are setup based on the best training. In each column, the best combination of gene selection and classifier is highlighted by a dash circle.</p

Conditional probability distributions (TM), (Non-TM) (on the left), and (IU), (Non-IU) (on the right), where is, from top to bottom, van der Waals volume, polarizability, elec-tronic effects, and helicity

TM = transmembrane, IU = intrinsically unstructured. The plots on the left were reproduced with permission from [].<p><b>Copyright information:</b></p><p>Taken from "Investigation of transmembrane proteins using a computational approach"</p><p>http://www.biomedcentral.com/1471-2164/9/S1/S7</p><p>BMC Genomics 2008;9(Suppl 1):S7-S7.</p><p>Published online 20 Mar 2008</p><p>PMCID:PMC2386072.</p><p></p

The submarine slope in the transition between the Intermediate Domain and the External Subbetic during the Jurassic (La Mola of Novelda, prov. of Alicante)

The Mola Unit has a Jurassic stratigraphic section with features that are otherwise characteristic of the Intermediate Domain (a dolomitization with rising Middle Jurassic materials and calcareous turbidites in the Upper Jurassic) and the External Subbetic (pelagic and condensed facies of Ammonitico Rosso facies). The composition of the calcareous turbidites, along with the present position of the tectonic unit, leads us to think that pelagic Subbetics wells as the source areas of the turbidites. The Jurassic materials of the La Mola unit would have been deposited in the northern part of the External Subbetic swell, making the transition with the Intermediate Domain throug

The 25 cancer related genes of the 100 features selected by LOOCSFS on original training group of MAQC-II breast cancer data for pCR prediction.

<p>The 25 cancer related genes of the 100 features selected by LOOCSFS on original training group of MAQC-II breast cancer data for pCR prediction.</p

Conditional probability distributions (TM), (Non-TM) (on the left), and (IU), (Non-IU) (on the right), where is hydropathy, as determined by the Kyte-Doolittle, Eisenberg-Schwarz- Komaromy-Wall, Engelman-Steitz-Goldman, and Liu-Deber scales

TM = transmembrane, IU = intrinsi-cally unstructured. The plots on the left were reproduced with permission from [].<p><b>Copyright information:</b></p><p>Taken from "Investigation of transmembrane proteins using a computational approach"</p><p>http://www.biomedcentral.com/1471-2164/9/S1/S7</p><p>BMC Genomics 2008;9(Suppl 1):S7-S7.</p><p>Published online 20 Mar 2008</p><p>PMCID:PMC2386072.</p><p></p

The 40 cancer related genes of the 100 features selected by SVMRFE on original training group of MAQC-II breast cancer data for erpos prediction.

<p>The 40 cancer related genes of the 100 features selected by SVMRFE on original training group of MAQC-II breast cancer data for erpos prediction.</p

Average OSMO prediction performance by using MAQC-II multiple myeloma dataset with the measurements testing accuracy (left column), MCC values (middle column), and AUC errors (right column), respectively.

<p>Classification models are setup based on the best training. In each column, the best combination of gene selection and classifier is highlighted by a dash circle. If there are multiple best combinations, or the difference of these combinations is not conspicuous, multiple circles are placed.</p

Comparison of different gene selection methods for the training of pCR endpoint of MAQC-II breast cancer dataset using the four classifiers.

<p>X-axis shows the number of used features and Y-axis shows average values of the training accuracy (left column), MCC values (middle column), and AUC errors (right column) of twenty-time experiments, respectively.</p