Zobrazeno 1 - 10
of 1 416
pro vyhledávání: '"Risk indicator"'
Autor:
Maritess Oliveros-Villarico, Patimaporn Pungchanchaikul, Supatra Watthanasaen, Rajda Chaichit, Waranuch Pitiphat
Publikováno v:
BMC Oral Health, Vol 24, Iss 1, Pp 1-10 (2024)
Abstract Background Limited published data exist on early childhood caries (ECC) among children 2 years old and below. The study aimed to determine ECC prevalence and its association with socio-demographic indicators, feeding practices and oral healt
Externí odkaz:
https://doaj.org/article/0f8b143416a14c22af5c34a25f2119ac
Publikováno v:
BMC Infectious Diseases, Vol 24, Iss 1, Pp 1-7 (2024)
Abstract Background Necrotising fasciitis (NF) is a life-threatening soft-tissue infection that rapidly destroys the epidermis, subcutaneous tissue, and fascia. Despite their low virulence, Lactobacillus spp. can cause NF, and because of its rare inc
Externí odkaz:
https://doaj.org/article/2a3f4c7fe7f64957bf5b479c423ba748
Autor:
Jun-Xu Gu, Juan Huang, Kun Wang, Yue Yin, Jun-Ling Fang, Ai-Min Zhang, Shan-Shan Li, Xiao-Qin Yao, Ming Yang, Na Zhang, Mei Jia, Ming Su
Publikováno v:
Heliyon, Vol 10, Iss 17, Pp e37415- (2024)
Background: High circulatory lipoprotein(a) [Lp(a)] concentration promotes atherosclerosis; however, its efficacy in predicting the extent of atherosclerotic coronary heart disease (CHD) with coronary artery obstruction and major adverse cardiovascul
Externí odkaz:
https://doaj.org/article/8232cc780dd5431eaf36d5ff39b596e5
Publikováno v:
Multimodal Transportation, Vol 3, Iss 3, Pp 100139- (2024)
The main aim of this study was to propose a comprehensive risk indicator to identify the potential driving risk caused by the changing environment at tunnel entrance. Driving decisions are affected by many external factors, especially at the entrance
Externí odkaz:
https://doaj.org/article/cc2a5b35a7fe47688aa0b7b57cbe1d3e
Publikováno v:
Technologies, Vol 12, Iss 7, p 103 (2024)
Artificial intelligence has brought many innovations to our lives. At the same time, it is worth designing robust safety machine learning (ML) algorithms to obtain more benefits from technology. Reinforcement learning (RL) being an important ML metho
Externí odkaz:
https://doaj.org/article/c7156fcdd1dc419ab1d2e96c554864a4
Akademický článek
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Autor:
Ziyue Wang, Zixin Zeng, Feng Gao, Ziwei Gui, Juan Du, Ningning Shen, Yangwei Shang, Zhiqing Yang, Lifang Shang, Rong Wei, Wenxia Ma, Chen Wang
Publikováno v:
BMC Medical Genomics, Vol 16, Iss 1, Pp 1-17 (2023)
Abstract Background Osteosarcoma has been the most common primary bone malignant tumor in children and adolescents. Despite the considerable improvement in the understanding of genetic events attributing to the rapid development of molecular patholog
Externí odkaz:
https://doaj.org/article/99bd500d9b404467a638466e256e8c11
Autor:
Eduarda Besen, Karina Mary Paiva, Luciana Berwanger Cigana, Marcos José Machado, Alessandra Giannella Samelli, Patrícia Haas
Publikováno v:
Audiology Research, Vol 13, Iss 1, Pp 107-115 (2023)
Objective: to verify the frequency of congenital infections in newborns and their possible associations with the universal-neonatal-hearing-screening (UNHS) results, and evaluate a reference UNHS service in the Unified Health System (Sistema Único d
Externí odkaz:
https://doaj.org/article/f0f5e4b6e405400db0a38b2dd0bc360b
Publikováno v:
IEEE Access, Vol 11, Pp 15096-15108 (2023)
Understanding how vulnerable road users (cyclists and pedestrians) behave enables the construction of better roadways with adapted geometric and surface design which leads to improve cycling safety and comfort. This study examines the behavior of cyc
Externí odkaz:
https://doaj.org/article/90decc88d9684994b63de6bee292bf2a
Publikováno v:
Psychosocial Intervention, Vol 32, Iss 1, Pp 43-53 (2023)
Intimate partner violence against women (IPVAW) and intimate partner homicide against women (IPHAW) are multidimensional phenomena. The aim of this study was to identify typologies of Spanish IPHAW and IPVAW victims, based on the differences between
Externí odkaz:
https://doaj.org/article/6c9bf64ef3f54f2fa7857123ab135c71