Rapid whole-brain quantitative MT imaging.

PURPOSE: To provide a robust whole-brain quantitative magnetization transfer (MT) imaging method that is not limited by long acquisition times. METHODS: Two variants of a spiral 2D interleaved multi-slice spoiled gradient echo (SPGR) sequence are used for rapid quantitative MT imaging of the brain at 3 T. A dual flip angle, steady-state prepared, double-contrast method is used for combined B1 and-T1 mapping in combination with a single-contrast MT-prepared acquisition over a range of different saturation flip angles (50 deg to 850 deg) and offset frequencies (1 kHz and 10 kHz). Five sets (containing minimum 6 to maximum 18 scans) with different MT-weightings were acquired. In addition, main magnetic field inhomogeneities (ΔB0) were measured from two Cartesian low-resolution 2D SPGR scans with different echo times. Quantitative MT model parameters were derived from all sets using a two-pool continuous-wave model analysis, yielding the pool-size ratio, F, their exchange rate, kf, and their transverse relaxation time, T2r. RESULTS: Whole-brain quantitative MT imaging was feasible for all sets with total acquisition times ranging from 7:15 min down to 3:15 min. For accurate modeling, B1-correction was essential for all investigated sets, whereas ΔB0-correction showed limited bias for the observed maximum off-resonances at 3 T. CONCLUSION: The combination of rapid B1-T1 mapping and MT-weighted imaging using a 2D multi-slice spiral SPGR research sequence offers excellent prospects for rapid whole-brain quantitative MT imaging in the clinical setting.

In vitro inhibitory activities of sugarcane extract on avian Eimeria sporozoites.

The current in vitro study aimed to investigate the effects of a processed sugarcane extract on the viability of avian Eimeria sporozoites. Treatments were applied to hatched sporozoites: 1) without additives (no-treatment control); 2) with ethanol; 3) with salinomycin; 4) with Polygain™. All treatments were incubated in RPMI media containing live sporozoites at 37 °C for 14 h and then the number of viable sporozoites were counted. Compared to the no-treatment control, Polygain™ decreased (P < 0.001) the counts of E. maxima, E. acervulina, E. bruneti, and E. mitis sporozoites to a level similar to salinomycin (P > 0.05). In conclusion, Polygain™ could be a potential candidate as an anticoccidial agent.

One-minute whole-brain magnetization transfer ratio imaging with intrinsic B1 -correction.

PURPOSE: Magnetization transfer ratio (MTR) histograms are used widely for the assessment of diffuse pathological changes in the brain. For broad clinical application, MTR scans should not only be fast, but confounding factors should also be minimized for high reproducibility. To this end, a 1-minute whole-brain spiral MTR method with intrinsic B1 -field correction is introduced. METHODS: A spiral multislice spoiled gradient-echo sequence with adaptable magnetization-transfer saturation pulses (angle ß) is proposed. After a low-resolution single-shot spiral readout and a dummy preparation period, high-resolution images are acquired using an interleaved spiral readout. For whole-brain MTR imaging, 50 interleaved slices with three different magnetization-transfer contrasts (ß = 0°, 350°, and 550°) together with an intrinsic B1 -field map are recorded in 58.5 seconds on a clinical 3T system. From the three contrasts, two sets of MTR images are derived and used for subsequent B1 correction, assuming a linear dependency on ß. For validation, a binary spin bath model is used. RESULTS: For the proposed B1 -correction scheme, numerical simulations indicate for brain tissue a decrease of about a factor of 10 for the B1 -related bias on MTR. As a result, following B1 correction, MTR differences in gray and white matter become markedly accentuated, and the reproducibility of MTR histograms from scan-rescan experiments is improved. Furthermore, B1 -corrected MTR histograms show a lower variability for age-matched normal-appearing brain tissue. CONCLUSION: From its speed and offering intrinsic B1 correction, the proposed method shows excellent prospects for clinical studies that explore magnetization-transfer effects based on MTR histogram analysis.

Microbiota and Metabolite Profiling Combined With Integrative Analysis for Differentiating Cheeses of Varying Ripening Ages.

Cheese maturation and flavor development results from complex interactions between milk substrates, cheese microbiota and their metabolites. In this study, bacterial 16S rRNA-gene sequencing, untargeted metabolomics (gas chromatography-mass spectrometry) and data integration analyses were used to characterize and differentiate commercial Cheddar cheeses of varying maturity made by the same and different manufacturers. Microbiota and metabolite compositions varied between cheeses of different ages and brands, and could be used to distinguish the cheeses. Individual amino acids and carboxylic acids were positively correlated with the ripening age for some brands. Integration and Random Forest analyses revealed numerous associations between specific bacteria and metabolites including a previously undescribed positive correlation between Thermus and phenylalanine and a negative correlation between Streptococcus and cholesterol. Together these results suggest that multi-omics analyses has the potential to be used for better understanding the relationships between cheese microbiota and metabolites during ripening and for discovering biomarkers for validating cheese age and brand authenticity.

New insights into cheddar cheese microbiota-metabolome relationships revealed by integrative analysis of multi-omics data.

Cheese microbiota and metabolites and their inter-relationships that underpin specific cheese quality attributes remain poorly understood. Here we report that multi-omics and integrative data analysis (multiple co-inertia analysis, MCIA) can be used to gain deeper insights into these relationships and identify microbiota and metabolite fingerprints that could be used to monitor product quality and authenticity. Our study into different brands of artisanal and industrial cheddar cheeses showed that Streptococcus, Lactococcus and Lactobacillus were the dominant taxa with overall microbial community structures differing not only between industrial and artisanal cheeses but also among different cheese brands. Metabolome analysis also revealed qualitative and semi-quantitative differences in metabolites between different cheeses. This also included the presence of two compounds (3-hydroxy propanoic acid and O-methoxycatechol-O-sulphate) in artisanal cheese that have not been previously reported in any type of cheese. Integrative analysis of multi-omics datasets revealed that highly similar cheeses, identical in age and appearance, could be distinctively clustered according to cheese type and brand. Furthermore, the analysis detected strong relationships, some previously unknown, which existed between the cheese microbiota and metabolome, and uncovered specific taxa and metabolites that contributed to these relationships. These results highlight the potential of this approach for identifying product specific microbe/metabolite signatures that could be used to monitor and control cheese quality and product authenticity.

Cheesomics: the future pathway to understanding cheese flavour and quality.

Cheese is a fermented dairy product, harboring diverse microbial communities (microbiota) that change over time and vary depending on the type of cheese and their respective starter and adjunct cultures. These microorganisms play a crucial role in determining the flavor, quality and safety of the final product. Exploring the composition of cheese microbiota and the underlying molecular mechanisms involved in cheese ripening has been the subject of many studies. Recent advances in next generation sequencing (NGS) methods and the development of sophisticated bioinformatics tools have provided deeper insights into the composition and potential functionality of cheese microbiota far beyond the information provided by culture-dependent methods. These advances, which include rRNA gene amplicon sequencing and metagenomics, have been complemented and expanded in recent years by the application of metatranscriptomics, metaproteomics and metabolomics. This paper reviews studies in which application of these meta-omics technologies has led to a better understanding of the microbial composition and functionality of cheese and highlights opportunities by which the integration of outputs from diverse multi-omics analytical platforms (cheesomics) could be used in the future to advance our knowledge of the cheese ripening process and identify biomarkers for predicting cheese flavor, quality, texture and safety, and bioactive metabolites with potential to influence human health.

Investigation of the Effects of Inulin and ß-Glucan on the Physical and Sensory Properties of Low-Fat Beef Burgers Containing Vegetable Oils: Optimisation of the Formulation Using D-Optimal Mixture Design.

In this study, the D-optimal mixture design methodology was applied to determine the optimised proportions of inulin, ß-glucan and breadcrumbs in formulation of low-fat beef burgers containing pre-emulsified canola and olive oil blend. Also, the effect of each of the ingredients individually as well as their interactions on cooking characteristics, texture, colour and sensory properties of low-fat beef burgers were investigated. The results of this study revealed that the increase of inulin content in the formulations of burgers led to lower cooking yield, moisture retention and increased lightness, overall acceptability, mouldability and desired textural parameters. In contrast, incorporation of ß-glucan increased the cooking yield, moisture retention and decreased lightness, overall acceptability, mouldability and desired textural parameters of burger patties. The interaction between inulin and ß-glucan improved the cooking characteristics of the burgers without significantly negative effect on the colour or sensory properties. The results of the study clearly stated that the optimum mixture for the burger formulation consisted of (in g per 100 g): inulin 3.1, ß-glucan 2.2 and breadcrumbs 2.7. The texture parameters and cooking characteristics were improved by using the mixture of inulin, ß-glucan and breadcrumbs, without any negative effects on the sensory properties of the burgers.