Effects of Once-Weekly Exenatide on Cardiovascular Outcomes in Type 2 Diabetes.

Abstract BACKGROUND: The cardiovascular effects of adding once-weekly treatment with exenatide to usual care in patients with type 2 diabetes are unknown. METHODS: We randomly assigned patients with type 2 diabetes, with or without previous cardiovascular disease, to receive subcutaneous injections of extended-release exenatide at a dose of 2 mg or matching placebo once weekly. The primary composite outcome was the first occurrence of death from cardiovascular causes, nonfatal myocardial infarction, or nonfatal stroke. The coprimary hypotheses were that exenatide, administered once weekly, would be noninferior to placebo with respect to safety and superior to placebo with respect to efficacy. RESULTS: In all, 14,752 patients (of whom 10,782 [73.1%] had previous cardiovascular disease) were followed for a median of 3.2 years (interquartile range, 2.2 to 4.4). A primary composite outcome event occurred in 839 of 7356 patients (11.4%; 3.7 events per 100 person-years) in the exenatide group and in 905 of 7396 patients (12.2%; 4.0 events per 100 person-years) in the placebo group (hazard ratio, 0.91; 95% confidence interval [CI], 0.83 to 1.00), with the intention-to-treat analysis indicating that exenatide, administered once weekly, was noninferior to placebo with respect to safety (P<0.001 for noninferiority) but was not superior to placebo with respect to efficacy (P=0.06 for superiority). The rates of death from cardiovascular causes, fatal or nonfatal myocardial infarction, fatal or nonfatal stroke, hospitalization for heart failure, and hospitalization for acute coronary syndrome, and the incidence of acute pancreatitis, pancreatic cancer, medullary thyroid carcinoma, and serious adverse events did not differ significantly between the two groups. CONCLUSIONS: Among patients with type 2 diabetes with or without previous cardiovascular disease, the incidence of major adverse cardiovascular events did not differ significantly between patients who received exenatide and those who received placebo. (Funded by Amylin Pharmaceuticals; EXSCEL ClinicalTrials.gov number, NCT01144338 .)

Variability of dense water formation in the Ross Sea

The paper presents results from a model study of the interannual variability of High Salinity Shelf Water (HSSW) properties in the Ross Sea.Salinity, potential temperature and volume of HSSW formed in the western Ross Sea show oscillatory behaviour at periods of 5-6 and 9 years superimposed on long-term fluctuations.While the shorter oscillations are induced by wind variability, variability on the scale of decades appears to be related to air temperature fluctuations.At least part of the strong decrease of HSSW salinities deduced from observations for the period 1963-2000 is shown to be an aliasing artefact due to an undersampling of the periodic signal.While sea ice formation is responsible for the yearly salinity increase that triggers the formation of High Salinity Shelf Water, interannual variability of net freezing rates hardly affects changes in the properties of the resulting water mass.Instead, results from model experiments indicate that the interannual variability of dense water characteristics is predominantly controlled by variations in the shelf inflow through a sub-surface salinity and a deep temperature signal.The origin of the variability of inflow characteristics to the Ross Sea continental shelf can be traced into the Amundsen and Bellingshausen Seas.The temperature anomalies are induced at the continental shelf break in the western Bellingshausen Sea by fluctuations of the meridional transport of Circumpolar Deep Water with the eastern cell of the Ross Gyre.Upwelling in the centre of this gyre carries the signal into the surface layer where it causes anomalies of brine release near the sea ice edge in the Amundsen Sea, which results in a sub-surface salinity anomaly.With the westward flowing coastal current, both the sub-surface salinity and deep temperature signals are advected onto the Ross Sea continental shelf.Convection carries the signal of salinity variability into the deep ocean, where it interacts with Modified Circumpolar Deep Water upwelled onto the continental shelf as the second source water mass of HSSW.Sea ice formation on the Ross Sea continental shelf thus drives the vertical propagation of the signal rather than determining the signal itself

MOESM1 of Lignolytic-consortium omics analyses reveal novel genomes and pathways involved in lignin modification and valorization

Additional file 1: Table S1. Compounds identified by gas chromatography–mass spectrometry (GC-MS) in the lignin-waste stream used for establishment of the lignin-degrading microbial community (LigMet). Table S2. Sequencing statistics and data processing of amplicon libraries constructed for profiling LigMet and soil samples analyzed. Table S3. Diversity and richness indices of the LigMet and soil samples based on 16S rRNA and ITS2 region sequences. Table S4. Protozoa identified in LigMet based on 18S rRNA sequencing. Table S5. Assembly statistics from draft genomes recovered from LigMet (all assemblies). Table S6. Genome statistics of Paenarthrobacter sp. str. HW13. Figure S1. Microbial growth was monitored by OD 600 nm, observing exponential growing during the first 40 hours of consortium growth. The consumption of reducing sugars over time as monitored by DNS, the exponential phase was completed after the first 40 hours of growth when monitoring sugar consumption. Figure S2. Rarefaction curves based on targeted sequencing of 16S rRNA gene amplicons derived from the LigMet (A) and sugarcane soil (B) samples. The rarefaction curves of each biological replicate are shown in different colors. Figure S3. Rarefaction curves based on targeted sequencing of the ITS2 region derived from the LigMet sample. The rarefaction curves of each biological replicate are shown in different colors. Figure S4. The taxonomic profiles from LigMet and sugarcane soil samples at the class level based on 16S rRNA gene amplicon. The respective relative abundances of each biological replicate for LigMet and sugarcane soil are shown. Figure S5. The archaeal phylum abundance in LigMet and sugarcane soil sample. The relative abundance is shown in percentage for each biological replicate of the LigMet and sugacarcane soil. Figure S6. Metabolic pathways related to aromatic compound degradation identified in LigMet according to KEGG automatic annotation. Figure S7. Classification of the predicted proteins from the LigMet according to the dbCAN database. Figure S8. Predicted auxiliary activity (AA) and carbohydrate esterase (CE) families from LigMet and draft genomes, based on the dbCAN database. AA and CE families are related to peroxidase activity and break down of lignin ester cross links, respectively. Figure S9. Phylogenetic relationship of the strain HW13 relative to the most closely related strains of the genus Paenarthrobacter. EzBioCloud webserver was used to perform a similarity-based search of HW13 16S rRNA to retrieve the most closely related sequences. The resulting 16S rRNA sequences were aligned using the MAFFT v7.299b software. A phylogenetic tree was inferred using the maximum likelihood method implemented in RAxML v8.2.0, evolutionary distances were based on the GTRGAMMAI model, inferred as the best model by jModelTest2. Numbers at the nodes are percentages of bootstrap values obtained by repeating the analysis 1,000 times. The type strains are marked with a superscript ‘T’. Accession numbers are shown in parentheses. Figure S10. Phylogenetic relationships among feruloyl-CoA synthetase (upper) and Enoyl-CoA hydratase/aldolase. The phylogenetic tree was generated using amino acid sequences retrieved from NCBI and Uniprot database. The sequences were aligned using MAFFT v7.299b software [5]. The phylogenetic tree was reconstructed using maximum likelihood method implemented in RAxML v8.2.0 [6], evolutionary distances were based on the GTRGAMMAI model, inferred as the best model by jModelTest2 [7]. The bootstrap values (1,000 replicate runs, shown as %) higher than 70 % are represented. Accession numbers are listed in parentheses. The FerA_B3 and FerB_B11 amino acid sequence retrieved from LigMet data set is printed in bold