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We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development.

The cattle rumen has a diverse microbial ecosystem that is essential for the host to digest plant material. Extremes in body weight (BW) gain in mice and humans have been associated with different intestinal microbial populations. The objective of this study was to characterize the microbiome of the cattle rumen among steers differing in feed efficiency. Two contemporary groups of steers (n=148 and n=197) were fed a ration (dry matter basis) of 57.35% dry-rolled corn, 30% wet distillers grain with solubles, 8% alfalfa hay, 4.25% supplement, and 0.4% urea for 63 days. Individual feed intake (FI) and BW gain were determined. Within contemporary group, the four steers within each Cartesian quadrant were sampled (n=16/group) from the bivariate distribution of average daily BW gain and average daily FI. Bacterial 16S rRNA gene amplicons were sequenced from the harvested bovine rumen fluid samples using next-generation sequencing technology. No significant changes in diversity or richness were indicated, and UniFrac principal coordinate analysis did not show any separation of microbial communities within the rumen. However, the abundances of relative microbial populations and operational taxonomic units did reveal significant differences with reference to feed efficiency groups. Bacteroidetes and Firmicutes were the dominant phyla in all ruminal groups, with significant population shifts in relevant ruminal taxa, including phyla Firmicutes and Lentisphaerae, as well as genera Succiniclasticum, Lactobacillus, Ruminococcus, and Prevotella. This study suggests the involvement of the rumen microbiome as a component influencing the efficiency of weight gain at the 16S level, which can be utilized to better understand variations in microbial ecology as well as host factors that will improve feed efficiency.

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The objective of this study is to investigate individual animal variation of bovine fecal microbiota including as affected by diets. Fecal samples were collected from 426 cattle fed 1 of 3 diets typically fed to feedlot cattle: 1) 143 steers fed finishing diet (83% dry-rolled corn, 13% corn silage, and 4% supplement), 2) 147 steers fed late growing diet (66% dry-rolled corn, 26% corn silage, and 8% supplement), and 3) 136 heifers fed early growing diet (70% corn silage and 30% alfalfa haylage). Bacterial 16S rRNA gene amplicons were determined from individual fecal samples using next-generation pyrosequencing technology. A total of 2,149,008 16S rRNA gene sequences from 333 cattle with at least 2,000 sequences were analyzed. Firmicutes and Bacteroidetes were dominant phyla in all fecal samples. At the genus level, Oscillibacter, Turicibacter, Roseburia, Fecalibacterium, Coprococcus, Clostridium, Prevotella, and Succinivibrio were represented by more than 1% of total sequences. However, numerous sequences could not be assigned to a known genus. Dominant unclassified groups were unclassified Ruminococcaceae and unclassified Lachnospiraceae that could be classified to a family but not to a genus. These dominant genera and unclassified groups differed (P < 0.001) with diets. A total of 176,692 operational taxonomic units (OTU) were identified in combination across all the 333 cattle. Only 2,359 OTU were shared across 3 diet groups. UniFrac analysis showed that bacterial communities in cattle feces were greatly affected by dietary differences. This study indicates that the community structure of fecal microbiota in cattle is greatly affected by diet, particularly between forage- and concentrate-based diets.