Publications by authors named "A Zaikin"

: Respiratory viral infections (RVIs) exhibit seasonal patterns influenced by biological, ecological, and climatic factors. Weather variables such as temperature, humidity, and wind impact the transmission of droplet-borne viruses, potentially affecting disease severity. However, the role of climate in predicting complications in pediatric RVIs remains unclear, particularly in the context of climate-change-driven extreme weather events.

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  • The study examines the reliability of the Illumina Methylation array platform in measuring DNA methylation by evaluating the consistency of repeated measures using both type I and type II Infinium probes.
  • A new method is proposed to identify unreliable probes by using dynamic thresholds for mean intensity (MI) and unreliability scores based on simulations that factor in technical noise.
  • Validation across multiple datasets indicates that probes with low MI tend to show higher variability in β values, and an R package is introduced to help researchers calculate MI and unreliability scores for better data analysis.
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Background: The grim (<10% 5-year) survival rates for pancreatic ductal adenocarcinoma (PDAC) are attributed to its complex intrinsic biology and most often late-stage detection. The overlap of symptoms with benign gastrointestinal conditions in early stage further complicates timely detection. The suboptimal diagnostic performance of carbohydrate antigen (CA) 19-9 and elevation in benign hyperbilirubinaemia undermine its reliability, leaving a notable absence of accurate diagnostic biomarkers.

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Biological computing is a promising field with potential applications in biosafety, environmental monitoring, and personalized medicine. Here we present work on the design of bacterial computers using spatial patterning to process information in the form of diffusible morphogen-like signals. We demonstrate, mathematically and experimentally, that single, modular, colonies can perform simple digital logic, and that complex functions can be built by combining multiple colonies, removing the need for further genetic engineering.

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  • Ovarian cancer is the most deadly gynecological cancer, with CA125 being the leading biomarker; however, it’s not effective for general population screening.
  • Recent studies suggest that incorporating additional biomarkers in combined models could enhance early detection.
  • Our research, utilizing data from the UK Collaborative Trial of Ovarian Cancer Screening, found that a CA125-HE4 model significantly outperformed CA125 alone in detecting ovarian cancer, especially one year prior to diagnosis.
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