Over the past few years, software applications for medical assistance, including tele-rehabilitation, have known an increasing presence in the health arena. Despite the several therapeutic and economic advantages of this new paradigm, it is important to follow certain guidelines, in order to build a safe, useful, scalable, and ergonomic tool. This work proposes to address all these points, through the case study of a physical tele-rehabilitation platform for patients after hip replacement surgery. The scalability and versatility of the system is handled by the implementation of a modular architecture. The safeness and effectiveness of the tool is ensured by an artificial intelligence module that assesses the quality of the movements performed by the user. The usability of the application is evaluated by a cognitive walkthrough method. Results show that the system (i) is able to properly assess the correctness of the human’s motion through two possible methods (Dynamic Time Warping and Hidden Markov Model), and (ii) provides a good user experience. The discussion addresses (i) the advantages and disadvantages of the main approaches for a gesture recognition of therapeutic movements, and (ii) critical aspects to provide the patient with the best usability of a tele-rehabilitation platform.

For understanding a chemical compound’s mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 developed target-centric models (TCM) employing different molecular descriptions and machine learning algorithms. They were contrasted with 17 third-party models implemented as web tools (WTCM). In both sets of models, consensus strategies were implemented as potential improvement over individual predictions. The findings indicate that TCM reach f1-score values greater than 0.8. Comparing both approaches, the best TCM achieves values of 0.75, 0.61, 0.25 and 0.38 for true positive/negative rates (TPR, TNR) and false negative/positive rates (FNR, FPR); outperforming the best WTCM. Moreover, the consensus strategy proves to have the most relevant results in the top $$20\%$$20 % of target profiles. TCM consensus reach TPR and FNR values of 0.98 and 0; while on WTCM reach values of 0.75 and 0.24. The implemented computational tool with the TCM and their consensus strategy at: https://bioquimio.udla.edu.ec/tidentification01/. Scientific Contribution: We compare and discuss the performances of 17 public compound-target interaction prediction models and 15 new constructions. We also explore a compound-target interaction prioritization strategy using a consensus approach, and we analyzed the challenging involved in interactions modeling. Graphical Abstract

Rice grain production is important for the world economy. Determining the moisture content of the grains, at several stages of production, is crucial for controlling the quality, safety, and storage of the grain. This work inspects how well rice images from global and local descriptors work for determining the moisture content of the grains using artificial vision and intelligence techniques. Three sets of images of rice grains from the INIAP 12 variety (National Institute of Agricultural Research of Ecuador) were captured with a mobile camera. The first one with natural light and the other ones with a truncated pyramid-shaped structure. Then, a set of global descriptors (color, texture) and a set of local descriptors (AZAKE, BRISK, ORB, and SIFT) in conjunction with the dominate technique bag of visual words (BoVW) were used to analyze the content of the image with classification and regression algorithms. The results show that detecting humidity through images with classification and regression algorithms is possible. Finally, f1-score values of at least 0.9 were accomplished for global color descriptors and of 0.8 for texture descriptors, in contrast to the local descriptors (AKAZE, BRISK, and SIFT) that reached up to an f1-score of 0.96.

The Pelargonium genus, encompassing over 280 species, remains markedly underexplored despite extensive traditional use for respiratory, gastrointestinal, and dermatological disorders. This review of aqueous, alcoholic, and hydroalcoholic extracts reveals critical research gaps: only 10 species have undergone chemical characterization, while 17 have been evaluated for biological activities. Phytochemical analysis identified 252 unique molecules across all studies, with flavonoids emerging as the predominant class (n = 108). Glycosylated derivatives demonstrated superior bioactivity profiles compared to non-glycosylated analogs. Phenolic acids (n = 43) and coumarins (n = 31) represented additional major classes. Experimental studies primarily documented antioxidant, antibacterial, and anti-inflammatory effects, with emerging evidence for antidiabetic, anticancer, and hepatoprotective activities. However, methodological heterogeneity across studies limits comparative analysis and comprehensive understanding. In silico target prediction analysis was performed on 197 high-confidence molecular structures. Glycosylated flavonols, anthocyanidins, flavones, and coumarins showed strong predicted interactions with key inflammatory targets (ALOX15, ALOX5, PTGER4, and NOS2) and metabolic regulators (GSK3A and PI4KB), providing mechanistic support for observed therapeutic effects and suggesting potential applications in chronic inflammatory and metabolic diseases. These findings underscore the substantial therapeutic potential of underexplored Pelargonium species and advocate for systematic research employing untargeted metabolomics, standardized bioassays, and compound-specific mechanistic validation to fully unlock the pharmacological potential of this diverse genus.