Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)

Molecular Basis of NDM-1, a New Antibiotic Resistance Determinant

The New Delhi Metallo-β-lactamase (NDM-1) was first reported in 2009 in a Swedish patient. A recent study reported that Klebsiella pneumonia NDM-1 positive strain or Escherichia coli NDM-1 positive strain was highly resistant to all antibiotics tested except tigecycline and colistin. These can no longer be relied on to treat infections and therefore, NDM-1 now becomes potentially a major global health threat

Exercise Prevents Weight Gain and Alters the Gut Microbiota in a Mouse Model of High Fat Diet-Induced Obesity

Background: Diet-induced obesity (DIO) is a significant health concern which has been linked to structural and functional changes in the gut microbiota. Exercise (Ex) is effective in preventing obesity, but whether Ex alters the gut microbiota during development with high fat (HF) feeding is unknown.Objective: Determine the effects of voluntary Ex on the gastrointestinal microbiota in LF-fed mice and in HF-DIO.Methods: Male C57BL/6 littermates (5 weeks) were distributed equally into 4 groups: low fat (LF) sedentary (Sed) LF/Sed, LF/Ex, HF/Sed and HF/Ex. Mice were individually housed and LF/Ex and HF/Ex cages were equipped with a wheel and odometer to record Ex. Fecal samples were collected at baseline, 6 weeks and 12 weeks and used for bacterial DNA isolation. DNA was subjected both to quantitative PCR using primers specific to the 16S rRNA encoding genes for Bacteroidetes and Firmicutes and to sequencing for lower taxonomic identification using the Illumina MiSeq platform. Data were analyzed using a one or two-way ANOVA or Pearson correlation.Results: HF diet resulted in significantly greater body weight and adiposity as well as decreased glucose tolerance that were prevented by voluntary Ex (p2 = 0.35, p = 0.043).Conclusion: Ex induces a unique shift in the gut microbiota that is different from dietary effects. Microbiota changes may play a role in Ex prevention of HF-DIO.</p

Exercise Prevents Weight Gain and Alters the Gut Microbiota in a Mouse Model of High Fat Diet-Induced Obesity

<div><p>Background</p><p>Diet-induced obesity (DIO) is a significant health concern which has been linked to structural and functional changes in the gut microbiota. Exercise (Ex) is effective in preventing obesity, but whether Ex alters the gut microbiota during development with high fat (HF) feeding is unknown.</p><p>Objective</p><p>Determine the effects of voluntary Ex on the gastrointestinal microbiota in LF-fed mice and in HF-DIO.</p><p>Methods</p><p>Male C57BL/6 littermates (5 weeks) were distributed equally into 4 groups: low fat (LF) sedentary (Sed) LF/Sed, LF/Ex, HF/Sed and HF/Ex. Mice were individually housed and LF/Ex and HF/Ex cages were equipped with a wheel and odometer to record Ex. Fecal samples were collected at baseline, 6 weeks and 12 weeks and used for bacterial DNA isolation. DNA was subjected both to quantitative PCR using primers specific to the 16S rRNA encoding genes for Bacteroidetes and Firmicutes and to sequencing for lower taxonomic identification using the Illumina MiSeq platform. Data were analyzed using a one or two-way ANOVA or Pearson correlation.</p><p>Results</p><p>HF diet resulted in significantly greater body weight and adiposity as well as decreased glucose tolerance that were prevented by voluntary Ex (p<0.05). Visualization of Unifrac distance data with principal coordinates analysis indicated clustering by both diet and Ex at week 12. Sequencing demonstrated Ex-induced changes in the percentage of major bacterial phyla at 12 weeks. A correlation between total Ex distance and the ΔCt Bacteroidetes: ΔCt Firmicutes ratio from qPCR demonstrated a significant inverse correlation (r² = 0.35, p = 0.043).</p><p>Conclusion</p><p>Ex induces a unique shift in the gut microbiota that is different from dietary effects. Microbiota changes may play a role in Ex prevention of HF-DIO.</p></div

ΔCt Bacteroidetes: ΔCt Firmicutes Ratio Correlates with Exercise Distance.

<p>There was a significant, but modest inverse relationship between the ΔCt Bacteroidetes: ΔCt Firmicutes ratio and the distance recorded for the combined LF/Ex and HF/Ex mice. Data analyzed by Pearson product-moment correlation coefficient with an alpha level of p<0.05. n = 6 all groups.</p

Clustering of Samples Based on Litter, Diet and Activity.

<p>Principal coordinate analysis (PCA) was performed based on the weighted UniFrac distance matrix generated from sequencing fecal 16S rRNA gene in samples from mice at week 0 and 12 of the diet and activity protocol. A. Clustering demonstrated by litter at week 0. B. No clustering demonstrated by litter at week 12. C. No clustering demonstrated by diet and activity at week 0. D. Clustering demonstrated by diet and activity at week 12. The top panels show the PCA keyed by litter (6 liters, 1–6, of 4 mice each) and the bottom panels show the PCA keyed by diet and activity group. The X-axis represents the primary coordinate, the Y-axis represents the secondary coordinate. Axis numbering represents the relative distance between samples based on the weighted UniFrac distance matrix.</p

Beta Diversity is Elevated by HF Diet and Exercise.

<p>Significant differences indicated as follows: “*” p<0.05 for diet effect, “†” p<0.05 activity effect and “‡” p<0.05 diet and activity interaction. n = 6 mice/group.</p

Diet and Activity Altered the Relative Level of Bacteroidetes and Firmicutes.

<p>A. The fold change in Bacteroidetes and Firmicutes was determined from the 2^−ΔΔCt values calculated from the ΔCt values generated by quantitative polymerase chain reaction (qPCR) using primers specific to each phyla (one-way ANOVA). B. Criterion validity of qPCR was examined by correlating the ΔCt-Bacteroidetes: ΔCt-Firmicutes ratio with the %-Bacteroidetes: %- Firmicutes ratios from sequencing. Data was analyzed by Pearson product-moment correlation coefficient and alpha level of p<0.05.</p

Phylum Level Changes with Diet and Activity.

<p>At week 12, diet and activity changed the levels of two major and two minor phyla of bacteria. Data were analyzed by 2-way ANOVA with a Sidak post hoc test. Significant differences indicated as follows: “*” p<0.05 for diet effect, “†” p<0.05 activity effect and “‡” p<0.05 diet and activity interaction. n = 6 mice/group.</p