Although aspirin is recommended for the prevention of colorectal cancer, the specific individuals for whom the benefits outweigh the risks are not clearly defined. Moreover, the precise mechanisms by ...which aspirin reduces the risk of cancer are unclear. We recently launched the ASPirin Intervention for the REDuction of colorectal cancer risk (ASPIRED) trial to address these uncertainties.
ASPIRED is a prospective, double-blind, multidose, placebo-controlled, biomarker clinical trial of aspirin use in individuals previously diagnosed with colorectal adenoma. Individuals (n = 180) will be randomized in a 1:1:1 ratio to low-dose (81 mg/day) or standard-dose (325 mg/day) aspirin or placebo. At two study visits, participants will provide lifestyle, dietary and biometric data in addition to urine, saliva and blood specimens. Stool, grossly normal colorectal mucosal biopsies and cytology brushings will be collected during a flexible sigmoidoscopy without bowel preparation. The study will examine the effect of aspirin on urinary prostaglandin metabolites (PGE-M; primary endpoint), plasma inflammatory markers (macrophage inhibitory cytokine-1 (MIC-1)), colonic expression of transcription factor binding (transcription factor 7-like 2 (TCF7L2)), colonocyte gene expression, including hydroxyprostaglandin dehydrogenase 15-(NAD) (HPGD) and those that encode Wnt signaling proteins, colonic cellular nanocytology and oral and gut microbial composition and function.
Aspirin may prevent colorectal cancer through multiple, interrelated mechanisms. The ASPIRED trial will scrutinize these pathways and investigate putative mechanistically based risk-stratification biomarkers.
This protocol is registered with the U.S. National Institutes of Health trial registry, ClinicalTrials.gov, under the identifier NCT02394769 . Registered on 16 March 2015.
Early life exposures may modify risk of inflammatory bowel diseases (IBD; Crohn's disease (CD), ulcerative colitis (UC)). However, the relationship between early life exposures and natural history of ...IBD has not been previously examined.
This single center study included patients with CD or UC recruited in a prospective IBD registry. Enrolled patients completed a detailed environmental questionnaire that assessed various early life environmental exposures. Our primary outcome was requirement for disease-related surgery in CD and UC. Logistic regression models defined independent effect of early life exposures, adjusting for potential confounders.
Our study included 333 CD and 270 UC patients. Just over half were female with a median age at diagnosis of 25 years. One-third of the cohort had history of bowel surgery (31%) and nearly half had used at least one biologic agent (47%). Among those with CD, being breastfed was associated with reduced risk of CD-related surgery (34% vs. 55%), while childhood cigarette smoke exposure was associated with increased risk. On multivariate analysis, history of being breastfed (odds ratio (OR) 0.21, 95% confidence interval CI 0.09-0.46) and cigarette smoke exposure as a child (OR 2.17, 95% CI 1.10-4.29) remained independently associated with surgery. None of the early life variables influenced disease phenotype or outcome in UC.
A history of being breastfed was associated with a decreased risk while childhood cigarette smoke exposure was associated with an increased risk of surgery in patients with CD. Further investigation to examine biological mechanisms is warranted.
By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control of the spread of the SARS-CoV-2 virus. Along with this, ...epidemiological machine learning studies of SARS-CoV-2 have been frequently published. While these models can be perceived as precise and policy-relevant to guide governments towards optimal containment policies, their black box nature can hamper building trust and relying confidently on the prescriptions proposed. This paper focuses on interpretable AI-based epidemiological models in the context of the recent SARS-CoV-2 pandemic. We systematically review existing studies, which jointly incorporate AI, SARS-CoV-2 epidemiology, and explainable AI approaches (XAI). First, we propose a conceptual framework by synthesizing the main methodological features of the existing AI pipelines of SARS-CoV-2. Upon the proposed conceptual framework and by analyzing the selected epidemiological studies, we reflect on current research gaps in epidemiological AI toolboxes and how to fill these gaps to generate enhanced policy support in the next potential pandemic.
LINKED CONTENT
This article is linked to Deng et al papers. To view these articles, visit
https://doi.org/10.1111/apt.17649
and
https://doi.org/10.1111/apt.17730
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