Dental prosthesis supports Candida species growth and may predispose the oral cavity to lesions. C. tropicalis has emerged as a colonizer of prosthesis and has shown resistance to clinically used antifungal agents, which has increased the search for new antifungals. This work describes the effectiveness of fifteen essential oils (EOs) against C. tropicalis. The EOs were obtained by hydrodistillation and were chemically characterized by gas chromatography-mass spectrometry. The antifungal activities of the EOs were evaluated by the microdilution method and showed that Pelargonium graveolens (Geraniaceae) (PG-EO) was the most effective oil. Geraniol and linalool were the major constituents of PG-EO. The 2,3-Bis-(2-Methoxy-4-Nitro-5-Sulfophenyl)-2H-Tetrazolium-5-Carboxanilide (XTT) assay showed that all the clinical C. tropicalis strains formed viable biofilms. Scanning electron microscopy examination of the biofilms revealed a complex architecture with basal layer of yeast cells and an upper layer of filamentous cells. Treatments with PG-EO, linalool, and geraniol significantly reduced the number of viable biofilm cells and inhibited biofilm formation after exposure for 48 h. PG-EO, geraniol, and linalool were not toxic to normal human lung fibroblasts (GM07492A) at the concentrations they were active against C. tropicalis. Together, our results indicated that C. tropicalis is susceptible to treatment with PG-EO, geraniol, and linalool, which could become options to prevent or treat this infection.

The Asteraceae, one of the largest families among angiosperms, is chemically characterised by the production of sesquiterpene lactones (SLs). A total of 1,111 SLs, which were extracted from 658 species, 161 genera, 63 subtribes and 15 tribes of Asteraceae, were represented and registered in two dimensions in the SISTEMATX, an in-house software system, and were associated with their botanical sources. The respective 11 block of descriptors: Constitutional, Functional groups, BCUT, Atom-centred, 2D autocorrelations, Topological, Geometrical, RDF, 3D-MoRSE, GETAWAY and WHIM were used as input data to separate the botanical occurrences through self-organising maps. Maps that were generated with each descriptor divided the Asteraceae tribes, with total index values between 66.7% and 83.6%. The analysis of the results shows evident similarities among the Heliantheae, Helenieae and Eupatorieae tribes as well as between the Anthemideae and Inuleae tribes. Those observations are in agreement with systematic classifications that were proposed by Bremer, which use mainly morphological and molecular data, therefore chemical markers partially corroborate with these classifications. The results demonstrate that the atom-centred and RDF descriptors can be used as a tool for taxonomic classification in low hierarchical levels, such as tribes. Descriptors obtained through fragments or by the two-dimensional representation of the SL structures were sufficient to obtain significant results, and better results were not achieved by using descriptors derived from three-dimensional representations of SLs. Such models based on physico-chemical properties can project new design SLs, similar structures from literature or even unreported structures in two-dimensional chemical space. Therefore, the generated SOMs can predict the most probable tribe where a biologically active molecule can be found according Bremer classification.

This work aimed to investigate and compare the performance of different machine learning models in predicting the compressive strength of concrete using a data set of 1234 compressive strength values. The predictive variables were selected based on their relevance using the SelectKBest method, resulting in an analysis of eight and six predictive variables. The evaluation was conducted through linear correlation studies via simple linear regression and non-linear correlation studies using support vector regression (SVR), random forest (RF), gradient boosting (GB), and artificial neural networks (ANN). The results showed a coefficient of determination (R²) = 0.897 and a root mean square error (RMSE) = 6.535 MPa for SVR, R² = 0.885 and RMSE = 5.437 MPa for GB, R² = 0.868 and RMSE = 5.859 MPa for GB and R² = 0.894 and RMSE = 5.192 MPa for ANN, all for test set and eight predictor variables. The comparison between the machine learning methods revealed significant differences. For instance, ANN stood out with a higher R² value, demonstrating its remarkable ability to explain the variability in the data. ANN also showed the lowest RMSE value, indicating notable accuracy in the predictions. Although ANN has demonstrated higher performance, GB shows a closer performance, which no differences from a practical application. The choice between these approaches depends on considerations regarding the balance between explainability and accuracy. While GB provides a more in-depth understanding of the relationship between variables, ANN stands out for the accuracy of its predictions.