Publications by authors named "M D McBride"

Unlabelled: causes bacterial cold-water disease (BCWD) in salmonids and other fish, resulting in substantial economic losses in aquaculture worldwide. The mechanisms uses to cause disease are poorly understood. Despite considerable effort, most strains of have resisted attempts at genetic manipulation.

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Background: All for Them is a theory-based and evidence-informed multilevel, multicomponent program delivered through schools to increase HPV vaccination among medically underserved youth across Texas. Given the potential logistical challenges of program implementation, understanding how to best support the implementation and sustainment of the program is critical. The overall goals of this study are twofold: 1) develop a multifaceted implementation strategy, Implementing All for Them (IM-AFT); and 2) evaluate the impact of IM-AFT on implementation outcomes for schools and healthcare providers to successfully implement All for Them in their respective settings.

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Aging significantly increases the incidence and severity of infections, with individuals aged 65 and above accounting for 65% of sepsis cases. Innate immune training, known as "trained immunity" or "innate immune memory", has emerged as a potential strategy to enhance infection resistance by modulating the aging immune system. We investigated the impact of β-glucan-induced trained immunity on aged mice (18-20 months old) compared to young adult mice (10-12 weeks old).

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Bacteria communicate through the accumulation of autoinducer (AI) molecules that regulate gene expression at critical densities in a process called quorum sensing (QS). Extensive work using simple systems and single strains of bacteria have revealed a role for QS in the regulation of virulence factors and biofilm formation; however, less is known about QS dynamics among communities, especially in vivo. In this review, we summarize the diversity of QS signals as well as their ability to influence "non-target" behaviors among species that have receptors but not synthases for those signals.

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Though somatic mutations play a critical role in driving cancer initiation and progression, the systems-level functional impacts of these mutations-particularly, how they alter expression across the genome and give rise to cancer hallmarks-are not yet well-understood, even for well-studied cancer driver genes. To address this, we designed an integrative machine learning model, Dyscovr, that leverages mutation, gene expression, copy number alteration (CNA), methylation, and clinical data to uncover putative relationships between nonsynonymous mutations in key cancer driver genes and transcriptional changes across the genome. We applied Dyscovr pan-cancer and within 19 individual cancer types, finding both broadly relevant and cancer type-specific links between driver genes and putative targets, including a subset we further identify as exhibiting negative genetic relationships.

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