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- Evaluation of the responsiveness pattern to caffeine through a smart data-driven ECG non-linear multi-band analysisPublication . Domingues, Rita; Batista, Patrícia; Pintado, Manuela; Oliveira-Silva, Patrícia; Rodrigues, Pedro MiguelThis study aimed to explore more efficient ways of administering caffeine to the body by investigating the impact of caffeine on the modulation of the nervous system's activity through the analysis of electrocardiographic signals (ECG). An ECG non-linear multi-band analysis using Discrete Wavelet Transform (DWT) was employed to extract various features from healthy individuals exposed to different caffeine consumption methods: expresso coffee (EC), decaffeinated coffee (ED), Caffeine Oral Films (OF_caffeine), and placebo OF. Non-linear feature distributions representing every ECG minute time series have been selected by PCA with different variance percentages to serve as inputs for 23 machine learning models in a leave-one-out cross-validation process for analyzing the behavior differences between ED/EC and OF_placebo/OF_caffeine groups, respectively, over time. The study generated 50-point accuracy curves per model, representing the discrimination power between groups throughout the 50 minutes. The best model accuracies for DC/EC varied between 30-70%, (using the decision tree classifier) and OF_placebo/OF_caffeine ranged from 62-84% (using Fine Gaussian). Notably, caffeine delivery through OFs demonstrated effective capacity compared to its placebo counterpart, as evidenced by significant differences in accuracy curves between OF_placebo/OF_caffeine. Caffeine delivery via OFs also exhibited rapid dissolution efficiency and controlled release rate over time, distinguishing it from EC. The study supports the potential of caffeine delivery through Caffeine OFs as a superior technology compared to traditional methods by means of ECG analysis. It highlights the efficiency of OFs in controlling the release of caffeine and underscores their promise for future caffeine delivery systems.
- Making wavesPublication . Alygizakis, Nikiforos; Ng, Kelsey; Čirka, Ľuboš; Berendonk, Thomas; Cerqueira, Francisco; Cytryn, Eddie; Deviller, Geneviève; Fortunato, Gianuario; Iakovides, Iakovos C.; Kampouris, Ioannis; Michael-Kordatou, Irene; Lai, Foon Yin; Lundy, Lian; Manaia, Celia M.; Marano, Roberto B. M.; Paulus, Gabriela K.; Piña, Benjamin; Radu, Elena; Rizzo, Luigi; Ślipko, Katarzyna; Kreuzinger, Norbert; Thomaidis, Nikolaos S.; Ugolini, Valentina; Vaz-Moreira, Ivone; Slobodnik, Jaroslav; Fatta-Kassinos, DespoWith the global concerns on antibiotic resistance (AR) as a public health issue, it is pivotal to have data exchange platforms for studies on antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in the environment. For this purpose, the NORMAN Association is hosting the NORMAN ARB&ARG database, which was developed within the European project ANSWER. The present article provides an overview on the database functionalities, the extraction and the contribution of data to the database. In this study, AR data from three studies from China and Nepal were extracted and imported into the NORMAN ARB&ARG in addition to the existing AR data from 11 studies (mainly European studies) on the database. This feasibility study demonstrates how the scientific community can share their data on AR to generate an international evidence base to inform AR mitigation strategies. The open and FAIR data are of high potential relevance for regulatory applications, including the development of emission limit values / environmental quality standards in relation to AR. The growth in sharing of data and analytical methods will foster collaboration on risk management of AR worldwide, and facilitate the harmonization in the effort for identification and surveillance of critical hotspots of AR. The NORMAN ARB&ARG database is publicly available at: https://www.norman-network.com/nds/bacteria/.