Examinando por Autor "Zaikin, Alexey"

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Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers
(Wiley, 2024-04-10) Abrego, Luis; Zaikin, Alexey; Marino, Ines P.; Krivonosov, Mikhail I.; Jacobs, Ian; Menon, Usha; Gentry-Maharaj, Aleksandra; Blyuss, Oleg
Background: Ovarian cancer is the most lethal of all gynecological cancers. Cancer Antigen 125 (CA125) is the best-performing ovarian cancer biomarker which however is still not effective as a screening test in the general population. Recent literature reports additional biomarkers with the potential to improve on CA125 for early detection when using longitudinal multimarker models. Methods: Our data comprised 180 controls and 44 cases with serum samples sourced from the multimodal arm of UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Our models were based on Bayesian change-point detection and recurrent neural networks. Results: We obtained a significantly higher performance for CA125–HE4 model using both methodologies (AUC 0.971, sensitivity 96.7% and AUC 0.987, sensitiv- ity 96.7%) with respect to CA125 (AUC 0.949, sensitivity 90.8% and AUC 0.953, sensitivity 92.1%) for Bayesian change-point model (BCP) and recurrent neural networks (RNN) approaches, respectively. One year before diagnosis, the CA125– HE4 model also ranked as the best, whereas at 2 years before diagnosis no multi- marker model outperformed CA125. Conclusions: Our study identified and tested different combination of biomark- ers using longitudinal multivariable models that outperformed CA125 alone. We showed the potential of multivariable models and candidate biomarkers to in- crease the detection rate of ovarian cancer.
Multi-Marker Longitudinal Algorithms Incorporating HE4 and CA125 in Ovarian Cancer Screening of Postmenopausal Women
(MDPI, 2020-07-17) Gentry-Maharaj, Aleksandra; Blyuss, Oleg; Ryan, Andy; Burnell, Matthew; Karpinskyj, Chloe; Gunu, Richard; Kalsi, Jatinderpal K.; Dawnay, Anne; Marino, Ines P.; Manchanda, Ranjit; Lu, Karen; Yang, Wei-Lei; Timms, John F.; Parmar, Max; Skates, Steven J.; Bast, Robert C. Jr.; Jacobs, Ian J.; Zaikin, Alexey; Menon, Usha
Longitudinal CA125 algorithms are the current basis of ovarian cancer screening. We report on longitudinal algorithms incorporating multiple markers. In the multimodal arm of United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), 50,640 postmenopausal women underwent annual screening using a serum CA125 longitudinal algorithm. Women (cases) with invasive tubo-ovarian cancer (WHO 2014) following outcome review with stored annual serum samples donated in the 5 years preceding diagnosis were matched 1:1 to controls (no invasive tubo-ovarian cancer) in terms of the number of annual samples and age at randomisation. Blinded samples were assayed for serum human epididymis protein 4 (HE4), CA72-4 and anti-TP53 autoantibodies. Multimarker method of mean trends (MMT) longitudinal algorithms were developed using the assay results and trial CA125 values on the training set and evaluated in the blinded validation set. The study set comprised of 1363 (2–5 per woman) serial samples from 179 cases and 181 controls. In the validation set, area under the curve (AUC) and sensitivity of longitudinal CA125-MMT algorithm were 0.911 (0.871–0.952) and 90.5% (82.5–98.6%). None of the longitudinal multi-marker algorithms (CA125-HE4, CA125-HE4-CA72-4, CA125-HE4-CA72-4-anti-TP53) performed better or improved on lead-time. Our population study suggests that longitudinal HE4, CA72-4, anti-TP53 autoantibodies adds little value to longitudinal serum CA125 as a first-line test in ovarian cancer screening of postmenopausal women.
The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging
(Frontiers Media, 2020-05-25) Whitwell, Harry J.; Bacalini, Maria Giulia; Blyuss, Oleg; Chen, Shangbin; Garagnani, Paolo; Gordleeva, Susan Yu; Jalan, Sarika; Ivanchenko, Mikhail; Kanakov, Oleg; Kustikova, Valentina; Mariño, Inés P.; Meyerov, Iosif; Ullner, Ekkehard; Franceschi, Claudio; Zaikin, Alexey
Biological aging is a complex process involving multiple biological processes. These can be understood theoretically though considering them as individual networks—e.g., epigenetic networks, cell-cell networks (such as astroglial networks), and population genetics. Mathematical modeling allows the combination of such networks so that they may be studied in unison, to better understand how the so-called “seven pillars of aging” combine and to generate hypothesis for treating aging as a condition at relatively early biological ages. In this review, we consider how recent progression in mathematical modeling can be utilized to investigate aging, particularly in, but not exclusive to, the context of degenerative neuronal disease. We also consider how the latest techniques for generating biomarker models for disease prediction, such as longitudinal analysis and parenclitic analysis can be applied to as both biomarker platforms for aging, as well as to better understand the inescapable condition. This review is written by a highly diverse and multi-disciplinary team of scientists from across the globe and calls for greater collaboration between diverse fields of research.