Asthma in children is a heterogeneous disease manifested by various phenotypes and endotypes. The level of disease control, as well as the effectiveness of anti-inflammatory treatment, is variable ...and inadequate in a significant portion of patients. By applying machine learning algorithms, we aimed to predict the treatment success in a pediatric asthma cohort and to identify the key variables for understanding the underlying mechanisms. We predicted the treatment outcomes in children with mild to severe asthma (N = 365), according to changes in asthma control, lung function (FEV1 and MEF50) and FENO values after 6 months of controller medication use, using Random Forest and AdaBoost classifiers. The highest prediction power is achieved for control- and, to a lower extent, for FENO-related treatment outcomes, especially in younger children. The most predictive variables for asthma control are related to asthma severity and the total IgE, which were also predictive for FENO-based outcomes. MEF50-related treatment outcomes were better predicted than the FEV1-based response, and one of the best predictive variables for this response was hsCRP, emphasizing the involvement of the distal airways in childhood asthma. Our results suggest that asthma control- and FENO-based outcomes can be more accurately predicted using machine learning than the outcomes according to FEV1 and MEF50. This supports the symptom control-based asthma management approach and its complementary FENO-guided tool in children. T2-high asthma seemed to respond best to the anti-inflammatory treatment. The results of this study in predicting the treatment success will help to enable treatment optimization and to implement the concept of precision medicine in pediatric asthma treatment.
During March 2020, most European countries implemented lockdowns to restrict the transmission of SARS-CoV-2, the virus which causes COVID-19 through their populations. These restrictions had positive ...impacts for air quality due to a dramatic reduction of economic activity and atmospheric emissions. In this work, a machine learning approach was designed and implemented to analyze local air quality improvements during the COVID-19 lockdown in Graz, Austria. The machine learning approach was used as a robust alternative to simple, historical measurement comparisons for various individual pollutants. Concentrations of NO2 (nitrogen dioxide), PM10 (particulate matter), O3 (ozone) and Ox (total oxidant) were selected from five measurement sites in Graz and were set as target variables for random forest regression models to predict their expected values during the city’s lockdown period. The true vs. expected difference is presented here as an indicator of true pollution during the lockdown. The machine learning models showed a high level of generalization for predicting the concentrations. Therefore, the approach was suitable for analyzing reductions in pollution concentrations. The analysis indicated that the city’s average concentration reductions for the lockdown period were: -36.9 to −41.6%, and −6.6 to −14.2% for NO2 and PM10, respectively. However, an increase of 11.6–33.8% for O3 was estimated. The reduction in pollutant concentration, especially NO2 can be explained by significant drops in traffic-flows during the lockdown period (−51.6 to −43.9%). The results presented give a real-world example of what pollutant concentration reductions can be achieved by reducing traffic-flows and other economic activities.
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•The European COVID-19 lockdowns had effects on air quality.•Quantifying the effects was achieved with machine learning techniques.•All pollutants which were analyzed decreased, with the exception of ozone.•The results have implications for air quality management.•A decrease in NO2 can be associated with a reduction in traffic density.
U organskoj kemiji sve se više pažnje posvećuje sintezi u mikro- i mezo- protočnim sustavima (engl. flow chemistry), koja ima brojne prednosti nad šaržnom sintezom. Glavne prednosti provedbe organske ...sinteze u takvim protočnim reaktorima su veća učinkovitost, ekološka prihvatljivost i sigurnost. Unatoč tome sinteza u protočnim sustavima ne može se primjenjivati kao univerzalni pristup za sve probleme koji mogu zateći organske sintetske kemičare te prije provedbe odabranih reakcija treba razmotriti isplativost s obzirom na šaržnu sintezu. Sigurnosti i ekološkoj prihvatljivosti sinteza u protočnim reaktorima značajno doprinosi upotreba malog volumena kemikalija i otapala budući da se reakcije provode u mikro- ili mezo- reaktorima napravljenima u pravilu od inertnih materijala. Zbog brojnih prednosti, organske reakcije u protočnim sustavima predmet su kontinuiranog istraživanja, pri čemu se uvjeti provedbe reakcija optimiraju u svrhu povećanja učinkovitosti i sigurnosti procesa te njegova uvećanja.
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We present a collection of publicly available intrinsic aqueous solubility data of 829 drug‐like compounds. Four different machine learning algorithms (random forests RF, LightGBM, partial least ...squares, and least absolute shrinkage and selection operator LASSO) coupled with multistage permutation importance for feature selection and Bayesian hyperparameter optimization were used for the prediction of solubility based on chemical structural information. Our results show that LASSO yielded the best predictive ability on an external test set with a root mean square error (RMSE) (test) of 0.70 log points, an R2(test) of 0.80, and 105 features. Taking into account the number of descriptors as well, an RF model achieves the best balance between complexity and predictive ability with an RMSE(test) of 0.72 log points, an R2(test) of 0.78, and with only 17 features. On a more aggressive test set (principal component analysis PCA‐based split), better generalization was observed for the RF model. We propose a ranking score for choosing the best model, as test set performance is only one of the factors in creating an applicable model. The ranking score is a weighted combination of generalization, number of features, and test performance. Out of the two best learners, a consensus model was built exhibiting the best predictive ability and generalization with RMSE(test) of 0.67 log points and a R2(test) of 0.81.
This work focuses on balancing the trade‐offs in machine learning that one has to deal with in QSAR, such as limiting the number of the utilized features, which can affect the models' complexity, the choice of algorithm, and how these factors affect their predictive ability. The challenges were evaluated on a created collection of intrinsic solubility data.
Promising test results on biological activity of our previously described benzobicyclo3.2.1octadienes, synthetically obtained by intramolecular photochemical cycloaddition reaction, prompted us to ...pursue with the further functionalization of this basic skeleton toward oxime derivatives. The free energies of formation of complexes between planned new oximes showing promising ADME properties and the active sites of cholinesterases were calculated using docking and density functional theory, showing that thermodynamical stability of some of the examined complexes is the same as the stability of the complex formed with well known and efficient cholinesterase inhibitor huperzine A. On the basis of calculated stabilities of complexes, the synthesis of several representative new compounds was succesfully performed. In some cases, the furan ring opened on two different ways so different opened oximes and oxime ethers were also formed. All new prepared oximes and oxime ethers present good material for further experimental investigation as inhibitors/reactivators of cholinesterases.
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•The stabilities of complexes between oximes and cholinesterases were calculated by DFT.•On the basis of calculations synthesis of new compounds was performed.•Oximes and oxime ethers will be explored as potential inhibitors of cholinesterases.
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