To review the scientific literature related to the safe handling of hazardous drugs (HDs). Critical analysis of works retrieved from MEDLINE, the Cochrane Library, Scopus, CINHAL, Web of Science and LILACS using the terms "Hazardous Substances", "Antineoplastic Agents" and "Cytostatic Agents", applying "Humans" and "Guidelines" as filters. Date of search: January 2017. In total, 1100 references were retrieved, and from those, 61 documents were selected based on the inclusion and exclusion criteria: 24 (39.3%) documents related to recommendations about HDs; 27 (44.3%) about antineoplastic agents, and 10 (33.3%) about other types of substances (monoclonal antibodies, gene medicine and other chemical and biological agents). In 14 (23.3%) guides, all the stages in the manipulation process involving a risk due to exposure were considered. Only one guide addressed all stages of the handling process of HDs (including stages with and without the risk of exposure). The most described stages were drug preparation (41 guides, 67.2%), staff training and/or patient education (38 guides, 62.3%), and administration (37 guides, 60.7%). No standardized informatics system was found that ensured quality management, traceability and minimization of the risks associated with these drugs. Most of the analysed guidelines limit their recommendations to the manipulation of antineoplastics. The most frequently described activities were preparation, training, and administration. It would be convenient to apply ICTs (Information and Communications Technologies) to manage processes involving HDs in a more complete and simpler fashion.

Wrist wearables are becoming more and more popular, and its use is widespread in sports, both professional and amateur. However, at present, they do not seem to exploit all their potential. The objective of this study is to explore innovative proposals for the use of wearable wrist technology in the field of sports, to understand its potential and identify new challenges and lines of future research related to this technology. A systematic review of the scientific literature, collected in 4 major repositories, was carried out to locate research initiatives where wrist wearables were introduced to address some sports-related challenges. Those works that were limited to evaluating sensor performance in sports activities and those in which wrist wearable devices did not play a significant role were excluded. 26 articles were eventually selected for full-text analysis that discuss the introduction of wrist-worn wearables to address some innovative use in the sports field. This study showcases relevant proposals in 10 different sports. The research initiatives identified are oriented to the use of wearable wrist technology (i) for the comprehensive monitoring of sportspeople’s behavior in activities not supported by the vendors, (ii) to identify specific types of movements or actions in specific sports, and (iii) to prevent injuries. There are, however, open issues that should be tackled in the future, such as the incorporation of these devices in sports activities not currently addressed, or the provision of specific recommendation services for sport practitioners.

(1) Background: Increasing life expectancy allows for more age-related health issues. Enhancing physical, cognitive, mental, and social health is crucial. Promoting healthy habits combats stress and diseases. Recommendation systems, like collaborative filtering, tailor suggestions but face challenges. Techniques such as artificial intelligence and machine learning are vital. Personalized health recommendations improve lifestyles and mitigate issues. (2) Methods: A systematic review adhering to the general principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses was conducted with the aim of identifying articles on innovative research about using recommendation algorithms, machine learning, or artificial intelligence to promote healthy habits and active aging. (3) Results: A total of 34 articles were included in this work. They address the topic of recommendation systems that use machine learning or artificial intelligence in the promotion of healthy habits. (4) Conclusions: This article reviews health-related activity recommendation techniques for the general population. With rising life expectancy and common health issues, effective recommendations are crucial for future public health. Limitations include excluding simpler models. Despite many proposals, systematic adherence mechanisms are lacking. Developing traceable, verifiable systems for healthy activity recommendations is vital for aging populations in developed countries.

The classification and identification of individual tree species in forest environments are critical for biodiversity conservation, sustainable forestry management, and ecological monitoring. Recent advances in drone technology and artificial intelligence have enabled new methodologies for detecting and classifying trees at an individual level. However, significant challenges persist, particularly in heterogeneous forest environments with high species diversity and complex canopy structures. This systematic review explores the latest research on drone-based data collection and AI-driven classification techniques, focusing on studies that classify specific tree species rather than generic tree detection. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, peer review studies from the last decade were analyzed to identify trends in data acquisition instruments (e.g., RGB, multispectral, hyperspectral, LiDAR), preprocessing techniques, segmentation approaches, and machine learning (ML) algorithms used for classification. Findings of this study reveal that deep learning (DL) models, particularly convolutional neural networks (CNN), are increasingly replacing traditional ML methods such as random forest (RF) or support vector machines (SVMs) because there is no need for a feature extraction phase, as this is implicit in the DL models. The integration of LiDAR with hyperspectral imaging further enhances classification accuracy but remains limited due to cost constraints. Additionally, we discuss the challenges of model generalization across different forest ecosystems and propose future research directions, including the development of standardized datasets and improved model architectures for robust tree species classification. This review provides a comprehensive synthesis of existing methodologies, highlighting both advancements and persistent gaps in AI-driven forest monitoring.