Global

Elsevier,

Handbook on the Toxicology of Metals (Fifth Edition), Volume I: General Considerations, 2022, Pages 137-182

In this chapter, we review the relevant scientific literature providing insights on health-related effects caused by inhalation of particulate metals, and their potential causal pathways.
The high rate of SARS-CoV-2 infection poses a serious threat to public health. Previous studies have suggested that SARS-CoV-2 can infect human ovary, the core organ of the female reproductive system. However, it remains unclear which type of ovarian cells are easily infected by SARS-CoV-2 and whether ovarian infectivity differs from puberty to menopause.
For International Day of Persons with Disabilities 2021, Stacy Masucci, publisher for bioscience and translational medicine at Elsevier speaks to Richard Mankin and Kate Nash about the challenges, barriers and opportunities for people who live with disabilities in the context of the global pandemic.
This chapter aligns with the SDG goal 3 of good health and wellbeing by showing the use of senolytic therapies for liver disease and inflammation.
Elsevier,

Child and Adolescent Online Risk Exposure An Ecological Perspective 2021, Pages 255-281

This book chapter advances SDG3 Good Health and Wellbeing and SDG 10 Reducing Inequalities by reviewing existing literature examining youth with disabilities involved in cyberbullying and/or cybervictimization.
Elsevier,

Measuring Sustainable Development Goals Performance, 2022, Pages 139-219

This chapter advances SDGs by explaining how the economist takes part in bridging the gap between science and policy.
Elsevier,

Polycystic Ovary Syndrome, Challenging Issues in the Modern Era of Individualized Medicine, 2022, Pages 23-38

Focuses on the evidence for PCOS pathogenesis in women and underlying molecular gateways enabling its development during hyperandrogenic gestations in animal models. Support the SDG target 3.7.1 Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methods.
In this article, we pursue the automatic detection of fake news reporting on the Syrian war using machine learning and meta-learning. The proposed approach is based on a suite of features that include a given article's linguistic style; its level of subjectivity, sensationalism, and sectarianism; the strength of its attribution; and its consistency with other news articles from the same “media camp”. To train our models, we use FA-KES, a fake news dataset about the Syrian war.
COVID-19 is disrupting and transforming the world. We argue that transformations catalysed by this pandemic should be used to improve human and planetary health and wellbeing. This paradigm shift requires decision makers and policy makers to go beyond building back better, by nesting the economic domain of sustainable development within social and environmental domains.

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