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Intensive epigenome and transcriptome analyses have unveiled numerous biological mechanisms, including the regulation of cell differentiation, proliferation, and induced apoptosis in neoplastic cells, as well as the modulation of the antineoplastic action of the immune system, which plausibly explains the observed population-based relationship between low vitamin D status and increased cancer risk. However, large randomized clinical trials involving cholecalciferol supplementation have so far failed to show the potential of such interventions in cancer prevention. In this article, we attempt to reconcile the supposed contradiction of these findings by undertaking a thorough review of the literature, including an assessment of the limitations in the design, conduct, and analysis of the studies conducted thus far. We examine the long-standing dilemma of whether the beneficial effects of vitamin D levels increase significantly above a critical threshold or if the conjecture is valid that an increase in available cholecalciferol translates directly into an increase in calcitriol activity. In addition, we try to shed light on the high interindividual epigenetic and transcriptomic variability in response to cholecalciferol supplementation. Moreover, we critically review the standards of interpretation of the available study results and propose criteria that could allow us to reach sound conclusions in this field. Finally, we advocate for options tailored to individual vitamin D needs, combined with a comprehensive intervention that favors prevention through a healthy environment and responsible health behaviors.

Purpose Minimally invasive surgery (MIS) of the spine prevents the collapse of osteoporotic vertebral fractures (OVF) with lower complication and bleeding rates than open surgery. However, the possibility of hidden blood loss (HBL) has been recently described. This study aimed to estimate the postoperative impact of HBL in patients undergoing MIS for OVF. Materials and methods This was a retrospective study of a series of patients. Those with pathological fractures, using anticoagulant or antiplatelet therapy, with severe anemia (hemoglobin [Hb]

Este artículo se encuentra disponible en la siguiente URL: https://www.mdpi.com/1660-4601/19/9/5546 Este artículo pertenece la número especial "Exclusive Papers Collection of Editorial Board Members (and Invited Scholars) in Section Public Health Statistics and Risk Assessment". Background: Forecasting the behavior of epidemic outbreaks is vital in public health. This makes it possible to anticipate the planning and organization of the health system, as well as possible restrictive or preventive measures. During the COVID-19 pandemic, this need for prediction has been crucial. This paper attempts to characterize the alternative models that were applied in the first wave of this pandemic context, trying to shed light that could help to understand them for future practical applications. Methods: A systematic literature search was performed in standardized bibliographic repertoires, using keywords and Boolean operators to refine the findings, and selecting articles according to the main PRISMA 2020 statement recommendations. Results: After identifying models used throughout the first wave of this pandemic (between March and June 2020), we begin by examining standard data-driven epidemiological models, including studies applying models such as SIR (Susceptible-Infected-Recovered), SQUIDER, SEIR, time-dependent SIR, and other alternatives. For data-driven methods, we identify experiences using autoregressive integrated moving average (ARIMA), evolutionary genetic programming machine learning, short-term memory (LSTM), and global epidemic and mobility models. Conclusions: The COVID-19 pandemic has led to intensive and evolving use of alternative infectious disease prediction models. At this point it is not easy to decide which prediction method is the best in a generic way. Moreover, although models such as the LSTM emerge as remarkably versatile and useful, the practical applicability of the alternatives depends on the specific context of the underlying variable and on the information of the target to be prioritized. In addition, the robustness of the assessment is conditioned by heterogeneity in the quality of information sources and differences in the characteristics of disease control interventions. Further comprehensive comparison of the performance of models in comparable situations, assessing their predictive validity, is needed. This will help determine the most reliable and practical methods for application in future outbreaks and eventual pandemics.