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This study evaluated the supplementation of a precision biotic (PB) on the enterohepatic health markers and growth performance of broiler chickens undergoing an enteric challenge. In the first study, three treatments were used: Unchallenged Control (UC); Challenged Control (CC; dietary challenge and 10× dose of coccidia vaccine); and a challenged group supplemented with PB (1.3 kg/ton). In the second study, three treatments were used: control diet, diet supplemented with Avilamycin (10 ppm), and a diet supplemented with PB (0.9 kg/ton). All the birds were exposed to natural challenge composed by dietary formulation and reused litter from a coccidiosis positive flock. In Trial 1, PB decreased ileal histological damage, increased villi length, and the expression of SLC5A8 in ileal tissue versus CC; it reduced ileal expression of IL-1β compared to both UC and CC treatments. PB increased the expression of cell cycling gene markers CCNA2 and CDK2 in the ileum compared to CC. In Trial 2, PB improved the growth performance, intestinal lesion scores and intestinal morphology of broiler chickens. These results indicate that birds supplemented with PB are more resilient to enteric challenges, probably by its action in modulating microbiome metabolic pathways related to nitrogen metabolism and protein utilization.

A vast majority of mass spectrometry data remains uncharacterized, leaving much of its biological and chemical information untapped. Recent advances in machine learning have begun to address this gap, particularly for tasks such as spectral identification in tandem mass spectrometry data. Here, we present the latest generation of LSM-MS2, a large-scale deep learning foundation model trained on millions of spectra to learn a semantic chemical space. LSM-MS2 achieves state-of-the-art performance in spectral identification, improving on existing methods by 30% in accuracy of identifying challenging isomeric compounds, yielding 42% more correct identifications in complex biological samples, and maintaining robustness under low-concentration conditions. Furthermore, LSM-MS2 produces rich spectral embeddings that enable direct biological interpretation from minimal downstream data, successfully differentiating disease states and predicting clinical outcomes across diverse translational applications.

Mass spectrometry-based metabolomics allows for the quantitation of metabolite levels in diverse biological samples. The traditional method of converting peak areas to absolute concentrations involves the use of matched heavy isotopologues. However, this approach is laborious and limited to a small number of metabolites. We addressed these limitations by developing PyxisTM, a machine learning-based technology which converts raw mass spectrometry data to absolute concentration measurements without the need for per-analyte standards. Here, we demonstrate Pyxis performance by quantifying metabolome concentration dynamics in murine blood plasma. Pyxis performed equivalently to traditional quantitation workflows used by research institutions, with a fraction of the time needed for analysis. We show that absolute quantitation by Pyxis can be expanded to include concentrations for additional metabolites, without the need to acquire new data. Furthermore, Pyxis allows for absolute quantitation as part of an untargeted metabolomics workflow. By removing the bottleneck of per-analyte standards, Pyxis allows for absolute quantitation in metabolomics that is scalable to large numbers of metabolites. The ability of Pyxis to make concentration-based measurements across the metabolome has the potential to deepen our understanding of diverse metabolic perturbations.