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Paramedics work under physically and cognitively demanding conditions to provide emergency care. As physical fitness could positively impact the quality of patient care, we investigated within a pilot study whether concurrent training (CT) affects work-related performance parameters in paramedics. At baseline (T1), 16 paramedics performed cardiopulmonary resuscitation whereby resuscitation performance (RP; total resuscitation quality, compressions with correct frequency, and correct ventilation), cognitive performance (CP; reaction time, divided attention, and working memory), and heart rate variability (HRV) were assessed (pre-exertion). Then, participants climbed seven floors carrying 20 kg of gear before completing the same assessments again (post-exertion). The baseline testing was followed by a CT intervention (12 weeks, three sessions/week). After the intervention (T2), the two-stage testing was repeated. We analyzed whether the pre-exertion and post-exertion values, as well as the difference between the pre-exertion and post-exertion values, changed from T1 to T2. Nine paramedics (male: N = 7; age = 26.3 (SD = 8.17) years) took part in the study. The comparison of the pre-exertion values showed significantly better reaction times (p = 0.001) and divided attention (p = 0.02) and a trend toward greater working memory and RP parameters at T2. Regarding the post-exertion values, significant improvements in working memory (p = 0.03) and a trend toward improved reaction time, divided attention, and RP occurred at T2. The difference between the pre- and post-exertion values did not change for any parameter from T1 to T2. HRV decreased significantly from pre- to post-exertion (T1: p = 0.01, T2: p = 0.01). These results indicate that CT is a promising training concept to improve RP and CP in paramedics and should therefore be investigated further to increase patient care quality.

Elastin, a key structural protein essential for the elasticity of the skin and elastogenic tissues, degrades with age. Replenishing elastin holds promise for anti-aging cosmetics and the supplementation of elastic activities of the cardiovascular system. We employed RiboScreenTM, a technology for identifying molecules that enhance the production of specific proteins, to target the production of tropoelastin. We make use of RiboScreenTM in two crucial steps: first, to pinpoint a target ribosomal protein (TRP), which acts as a switch to increase the production of the protein of interest (POI), and second, to identify small molecules that activate this ribosomal protein switch. Using RiboScreenTM, we identified ribosomal protein L40, henceforth eL40, as a TRP switch to boost tropoelastin production. Drug discovery identified a small-molecule hit that binds to eL40. In-cell treatment demonstrated activity of the eL40 ligand and delivered increased tropoelastin production levels in a dose-dependent manner. Thus, we demonstrate that RiboScreenTM can successfully identify a small-molecule hit capable of selectively enhancing tropoelastin production. This compound has the potential to be developed for topical or systemic applications to promote skin rejuvenation and to supplement elastic functionality within the cardiovascular system.

Abstract Decision tree ensembles are among the most robust, high-performing and computationally efficient machine learning approaches for quantitative structure–activity relationship (QSAR) modeling. Among them, gradient boosting has recently garnered particular attention, for its performance in data science competitions, virtual screening campaigns, and bioactivity prediction. However, different variants of gradient boosting exist, the most popular being XGBoost, LightGBM and CatBoost. Our study provides the first comprehensive comparison of these approaches for QSAR. To this end, we trained 157,590 gradient boosting models, which were evaluated on 16 datasets and 94 endpoints, comprising 1.4 million compounds in total. Our results show that XGBoost generally achieves the best predictive performance, while LightGBM requires the least training time, especially for larger datasets. In terms of feature importance, the models surprisingly rank molecular features differently, reflecting differences in regularization techniques and decision tree structures. Thus, expert knowledge must always be employed when evaluating data-driven explanations of bioactivity. Furthermore, our results show that the relevance of each hyperparameter varies greatly across datasets and that it is crucial to optimize as many hyperparameters as possible to maximize the predictive performance. In conclusion, our study provides the first set of guidelines for cheminformatics practitioners to effectively train, optimize and evaluate gradient boosting models for virtual screening and QSAR applications. Graphical abstract

Abstract While in the last years there has been a dramatic increase in the number of available bioassay datasets, many of them suffer from extremely imbalanced distribution between active and inactive compounds. Thus, there is an urgent need for novel approaches to tackle class imbalance in drug discovery. Inspired by recent advances in computer vision, we investigated a panel of alternative loss functions for imbalanced classification in the context of Gradient Boosting and benchmarked them on six datasets from public and proprietary sources, for a total of 42 tasks and 2 million compounds. Our findings show that with these modifications, we achieve statistically significant improvements over the conventional cross-entropy loss function on five out of six datasets. Furthermore, by employing these bespoke loss functions we are able to push Gradient Boosting to match or outperform a wide variety of previously reported classifiers and neural networks. We also investigate the impact of changing the loss function on training time and find that it increases convergence speed up to 8 times faster. As such, these results show that tuning the loss function for Gradient Boosting is a straightforward and computationally efficient method to achieve state-of-the-art performance on imbalanced bioassay datasets without compromising on interpretability and scalability. Graphical Abstract

Cephalexin (CEX) is an antibiotic commonly used to treat bacterial infections in humans and animals. However, it is also a micropollutant. Thus, this study evaluated the degradation of CEX using ultraviolet irradiation (UV-C) and analyzed the by-products as well as their residual antimicrobial activity. A reactor with a mercury vapor lamp was used for the degradation. Irradiated CEX solutions were collected over a period of 4 hours and analyzed using high-performance liquid chromatography coupled with mass spectrometry. For the residual antimicrobial activity the susceptibility test was performed using Staphylococcus aureus and Escherichia coli microorganisms by broth microdilution. It was found that CEX, after treatment, generated a metabolite with a mass of 150 m/z in 15 min. A four- and eightfold increase in the minimum inhibitory concentration of the drug against S. aureus and E. coli could be observed, respectively, after 20 min. Therefore, this treatment proved to be effective in the degradation of CEX, being able to degrade 81% of the initial molecule of the drug in 20 min. Furthermore, the antimicrobial activity of the CEX solution decreased as the irradiation time increased, indicating loss of antimicrobial function of the initial CEX molecule and the resulting by-products. HIGHLIGHTS Degradation of micropollutant cephalexin.; Metabolites analyzed and residual antimicrobial activity.; Water treatment with irradiation.; Antimicrobial activity using susceptibility test.;

Abstract Cheese whey is the main by-product obtained in the production of cheese. Despite its high nutritional value, approximately half of the whey volume generated is still disposed incorrectly, which causes damage to the ecosystem due to the high cheese whey pollutant load. Therefore, it is important to use this by-product and its components in an increasing number of applications, especially as food ingredient. This review aimed to show the technology of production of butter from whey cream, as well as showing the physico-chemical, sensory, and nutritional characteristics of the product. There were no significant variations in the physico-chemical composition of milk cream butter and whey cream butter in the literature available. As the technology to produce whey butter is quite simple, this by-product has potential to be exploited by the dairy industry. Additionally, further studies on production process, characterization, and sensory analysis are required to enable its large-scale production.