Publications by authors named "A C Kidd"

Chlamydiosis is a common infectious disease impacting koalas and is a major cause of population decline due to resulting mortality and infertility. Polymorphisms of major histocompatibility complex (MHC) genes influence chlamydial disease outcomes in several species but koala studies have produced variable results. We aimed to identify the MHC II DAB and DBB repertoire of koalas from Liverpool Plains, NSW, a population heavily impacted by chlamydiosis.

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Introduction: Altered body composition is associated with adverse survival in multiple cancers. We determined the prevalence, prognostic significance and clinicopathological correlates of sarcopenia and adipopenia in Pleural Mesothelioma (PM) patients receiving chemotherapy.

Methods: We performed a multi-centre retrospective cohort study.

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Article Synopsis
  • Targeting tumor cell metabolism has emerged as a promising cancer therapy approach, but the complex nature of cancer metabolism presents challenges in finding effective treatments.
  • We used advanced modeling techniques to analyze metabolic states across various cancer cell lines, revealing connections to tumor types and identifying vulnerabilities in different genetic and tissue contexts.
  • Our research highlights specific metabolic states linked to genetic mutations and potential treatment targets, which could lead to more precise and tailored cancer therapies.
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Our skin is home to a diverse community of commensal microorganisms integral to cutaneous function. However, microbial dysbiosis and barrier perturbation increase the risk of local and systemic infection. Staphylococcus aureus is a particularly problematic bacterial pathogen, with high levels of antimicrobial resistance and direct association with poor healing outcome.

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Targeting the distinct metabolic needs of tumor cells has recently emerged as a promising strategy for cancer therapy. The heterogeneous, context-dependent nature of cancer cell metabolism, however, poses challenges in identifying effective therapeutic interventions. Here, we utilize various unsupervised and supervised multivariate modeling approaches to systematically pinpoint recurrent metabolic states within hundreds of cancer cell lines, elucidate their association with tumor lineage and growth environments, and uncover vulnerabilities linked to their metabolic states across diverse genetic and tissue contexts.

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