In Cuba, health research is based on the priorities of national scientific policy, derived from the health status of the population. The objective of this article is to describe the characteristics of the System of Science and Technological Innovation and how the results of its research benefit the health of the population groups. To this end, research related to the generation of products and technologies, diabetes, dengue and disability was selected. This system follows a methodology outlined by the Ministry of Science, Technology and Environment and has 37 research entities. It is organized into programs and projects that favor basic and applied research, with a multidisciplinary and intersectoral approach; these programs and projects are funded mostly by the State and are organized in self-contained cycles, i.e., the same entity is responsible for the entire process, from research to marketing, including market studies and post-marketing surveillance. The selected research shows an alignment between the research, the generalization of the results and its effect in improving health and universal access to health in the population. Positive results were obtained in diagnostic methods, preventive and therapeutic vaccines, warning signs for the prognosis and treatment of dengue, prevention of congenital malformations, and policies and programs that have benefited people with disabilities and their families. The will of the State to develop and fund scientific research, intersectoral action, the definition of research priorities, and the systematic training and attention to human resources have been key factors for the fulfillment of the objectives of the system.
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http://dx.doi.org/10.26633/RPSP.2018.32 | DOI Listing |
Sci Data
January 2025
Federal University of Bahia, Institute of Computing, Salvador, 40170-110, Brazil.
Multiple Myeloma (MM) is a cytogenetically heterogeneous clonal plasma cell proliferative disease whose diagnosis is supported by analyses on histological slides of bone marrow aspirate. In summary, experts use a labor-intensive methodology to compute the ratio between plasma cells and non-plasma cells. Therefore, the key aspect of the methodology is identifying these cells, which relies on the experts' attention and experience.
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January 2025
Mechanisms, Biomarkers and Models Section - Genome Stability Group, Department of Environment and Health, Istituto Superiore di Sanità , Viale Regina Elena, 299 - 00161, Rome, Italy.
The WRN protein is vital for managing perturbed replication forks. Replication Protein A strongly enhances WRN helicase activity in specific in vitro assays. However, the in vivo significance of RPA binding to WRN has largely remained unexplored.
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January 2025
School of Humanities and Social Sciences, Anhui University of Science and Technology, Huainan, 232001, China.
In this paper, the Hefei metropolitan area is selected as the research object to measure industrial carbon emissions in this area during 2010-2022. The main contribution is to deeply analyze the characteristics of the spatial correlation network of industrial carbon emissions in the Hefei metropolitan area with the modified gravity model and social network analysis(SNA), and to explore the driving factors of its formation with quadratic assignment procedure(QAP). It establishes the foundation for the Hefei metropolitan area to differentiated green city development policies.
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January 2025
School of Finance and Accounting, Chengdu Jincheng College, Chengdu, 610097, Sichuan, China.
China's digital economy is currently thriving, with the "dual carbon" targets representing a significant pursuit of economic development. The role of the digital economy in achieving these targets warrants detailed discussion. Using urban panel data from China spanning 2011 to 2021, this paper empirically examines the impact of the digital economy on urban carbon emissions.
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January 2025
Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, Liaoning, China.
The problem of ground-level ozone (O) pollution has become a global environmental challenge with far-reaching impacts on public health and ecosystems. Effective control of ozone pollution still faces complex challenges from factors such as complex precursor interactions, variable meteorological conditions and atmospheric chemical processes. To address this problem, a convolutional neural network (CNN) model combining the improved particle swarm optimization (IPSO) algorithm and SHAP analysis, called SHAP-IPSO-CNN, is developed in this study, aiming to reveal the key factors affecting ground-level ozone pollution and their interaction mechanisms.
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