Multidrug resistance (MDR) proteins related to the ATP-binding cassette family are found in a very wide range of human tumors and result in therapeutic failure. The overexpression of efflux pumps such as ABCB1 is one of the mechanisms of MDR. This paper aims to develop a reliable quantitative structure-activity relationship (QSAR) model that best describes the correlation between the activity and the molecular structures in order to predict the inhibitory biological activity towards ABCB1. In this regard, a series of quinoline derivatives of 18 compounds were analyzed using different linear and non-linear machine learning (ML) regression methods including k-nearest neighbors (KNN), decision tree (DT), back propagation neural networks (BPNN) and gradient boosting-based (GB) methods. Their aim is to explain the origin of the activity of these investigated compounds and therefore, design new quinoline derivatives with higher effect on ABCB1. A total of 16 ML predictive models were developed on different number of 2D and 3D descriptors and were evaluated using the coefficient of determination (R²) and the root mean squared error (RMSE) statistical metrics. Among all developed models, A GB-based model in particular catboost achieved the highest predictive quality, with one descriptor, expressed by R² and RMSE of 95% and 0.283 respectively. Molecular docking studies against the target crystal structure of the outward-facing p-glycoprotein (6C0V) revealed significant binding affinities via both hydrophobic and H-bond interactions with the relevant compounds. The 17 has shown the highest binding energy of -9.22 kcal/mol. Therefore, it can suggest that 17 may prove to be a valuable potential lead structure for the design and synthesis of more potent P-glycoprotein inhibitors for combination used with anti-cancer drugs for cancer multidrug resistance management.

Alternative and non-destructive analytical methods that predict analyte concentration accurately and immediately in a specific matrix are becoming vital in the analytical chemistry domain. Here, a new innovative and rapid method of predicting mass loss of cement samples based on a combination of Machine Learning (ML) and the emerging technique called Hyperspectral Imaging (HSI) is presented. The method has proved its reliability and accuracy by providing a predictive ML model, with satisfactory best validation scores recorded using partial least squared regression, with a reported ratio of performance to inter-quartile distance and root mean squared error of 12,89 and 0.337, respectively. Moreover, the possibility of optimizing and boosting the performance of the method by optimizing the predictive model performance has been suggested. Therefore, a features selection approach was conducted to disqualify non-relevant wavelengths and stress only relevant ones in order to make them the only contributors to a final optimized model. The best selected features subset was composed of 28 wavelengths out of 121, found by applying genetic algorithm combined to partial least squares regression as a feature selection method, on spectra preprocessed consecutively by the first-order savitzky-golay derivative calculated with 7-point quadratic SG filter, and multiplicative scatter correction method. The overall results show the possibility of combining HSI and ML for fast monitoring of water content in cement samples.

Background/Objectives: The rise in antibiotic resistance presents a serious and urgent global health challenge, emphasizing the need to develop new therapeutic compounds. This study focuses on the design and evaluation of a novel series of hybrid molecules that combine the 2-quinolone and 1,2,3-triazole pharmacophores, both recognized for their broad-spectrum antimicrobial properties. Methods: A library of 29 candidate molecules was first designed using in silico techniques, including QSAR modeling, ADMET prediction, molecular docking, and molecular dynamics simulations, to optimize antibacterial activity and drug-like properties. The most promising compounds were then synthesized and characterized by ¹H and ¹³C NMR APT, mass spectrometry (MS), Fourier-transform infrared (FT-IR) spectroscopy, and UV-Vis spectroscopy. Results: Antibacterial evaluation revealed potent activity against both Gram-positive and Gram-negative bacterial strains, with minimum inhibitory concentration (MIC) values ranging from 0.019 to 1.25 mg/mL. Conclusions: These findings demonstrate the strong potential of 2-quinolone–triazole hybrids as effective antibacterial agents and provide a solid foundation for the development of next-generation antibiotics to combat the growing threat of bacterial resistance.

Human african trypanosomiasis (HAT), commonly referred to as sleeping sickness, is a disease that originates from protozoan parasites of the Trypanosoma genus, which might be fatal if left untreated, or treated inadequately. Tsetse fly, a tropical blood-feeding insect, is generally the biological vector for this disease. Initially, the disease was neglected, however, when faced with the exploding number of cases, the urgent human needs for safe and cost-effective drugs combating the disease have increased significantly. The current study aims to computationally help advance the clinical care of HAT disease by leveraging computational tools to analyze the potential of quinolone amide derivatives to inhibit the enzyme responsible for this disease. The results demonstrate that jointly considering molecular affinity, polarity, and volume enables accurate inference of the bioactivity of quinolone derivatives, facilitating the efficient screening of large compound libraries. An effective QSAR model was developed using a dataset of 31 molecules, allowing reliable prediction of antitrypanosomal activity. Three promising compounds were identified based on their favorable physicochemical and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiles, as well as their strong binding interactions with ornithine decarboxylase (ODC) complexed with putrescine (PDB: 1F3T). Finally, a straightforward synthetic route was proposed for a novel compound with high biological potential. Overall, the study shows that an in-silico study can be used to efficiently drive a Quinolone-based drug design to target the HAT disease.

Forest ecosystem productivity is affected by tree species richness and may be further modulated by different mycorrhizal fungal types, for example, arbuscular mycorrhizal (AM) and ectomycorrhizal (EM) fungi, which strongly affect soil processes. Productivity and soil processes are linked through leaf litterfall, a major pathway for carbon and nutrient input to soils. Higher tree diversity and contrasting mycorrhizal strategies may enhance the variability and heterogeneity of litterfall quantity and quality, yet the combined effects of tree species richness and mycorrhizal type mixtures on the temporal dynamics and spatial distribution of litterfall remain underexplored. We investigated leaf litterfall dynamics over 1 year in the 8‐year‐old MyDiv tree diversity experiment, spanning a gradient of tree species richness (one, two, four species) and mycorrhizal treatments (AM, EM, AM+EM). Monthly litterfall production and nutrient concentrations were measured, and annual wood production (i.e. wood biomass increment) was assessed to quantify aboveground biomass allocation to wood and litter. Tree species richness did not affect annual production of wood or leaf litter. In contrast, annual wood production varied with mycorrhizal types, being highest in EM tree communities, whereas mycorrhizal type did not affect annual leaf litterfall. However, litterfall dynamics were influenced by both tree species richness and mycorrhizal type. Specifically, higher tree diversity extended the litterfall period and increased spatial variability (CV) of litterfall at the meter scale, while temporal variability remained unaffected. Phosphorus concentration in leaf litter differed among mycorrhizal types, with the highest values observed in AM communities. Species‐rich stands exhibited lower C:P and N:P ratios in leaf litter and greater phosphorus fluxes. Contrary to expectations, mixed mycorrhizal communities did not produce more leaf litter than monotypic combinations (AM or EM tree species). Synthesis: Our findings show that leaf litterfall dynamics are modified by tree species richness and mycorrhizal type. The increased spatial variability of litterfall at the meter scale and the extended litterfall period associated with higher tree species richness could create a wider range of resource niches for soil organisms in time and space. This may promote the diversity and activity of the decomposer community, ultimately strengthening overall forest nutrient cycling.