Jose Antonio

Pseudomonas aeruginosa frequently develops resistance to fluoroquinolones, limiting the usefulness of these agents for the treatment of healthcare-associated infections. This study evaluated the in vitro activity of delafloxacin in comparison with the older-generation fluoroquinolones against clinical P. aeruginosa isolates, and explored its potential to suppress the selection of resistant subpopulations. One hundred clinical isolates were tested against ciprofloxacin, levofloxacin, and delafloxacin by broth microdilution method. Susceptibility was interpreted according to EUCAST criteria for ciprofloxacin and levofloxacin and FDA criteria for delafloxacin. Mutant prevention concentrations (MPCs) were determined for the three fluoroquinolones in 12 selected isolates using a high-inoculum agar-based method. Resistance was observed in 29% of isolates for both ciprofloxacin and levofloxacin, compared with 25% for delafloxacin. Among isolates resistant to both ciprofloxacin and levofloxacin, 32.0% were susceptible or intermediate to delafloxacin. The median MPC/MIC ratio was 4 (IQR, 4-8) for delafloxacin, compared with 8 (IQR, 7-16) for ciprofloxacin and 16 (IQR, 7-64) for levofloxacin. Overall, delafloxacin retained in vitro activity against a subset of fluoroquinolone-resistant P. aeruginosa isolates and showed a favorable mutant prevention profile in the isolates tested, with a lower median MPC/MIC ratio than ciprofloxacin and levofloxacin. These findings provide preliminary evidence supporting further investigation of delafloxacin against fluoroquinolone-resistant P. aeruginosa.

Abstract Reducing energy consumption is a very important topic for engineers and researchers.

Centenarians — individuals aged ≥100 years — provide a unique human model for understanding resilience to cardiovascular ageing.

Gamma-aminobutyric acid (GABA) has been associated with health properties and has therefore attracted the attention of the food industry for the development of functional foods.

Abstract The fermentation of cow's milk and soy beverages, a plant-based alternative, by lactic acid bacteria (LAB) and bifidobacteria

Summary Background Colorectal cancer (CRC) in older adults represents an increasing clinical challenge.

This study presents a benchmark analysis of deep convolutional neural network (CNN) architectures for rock type classification using high-resolution two-dimensional grayscale slices extracted from 3D micro-computed tomography (µCT) datasets. The main contribution of this work is the development of a controlled and systematic benchmarking framework for CNN architectures under standardized µCT imaging conditions, rather than expanding the number of lithological classes or facies. A dataset comprising 52,706 two-dimensional grayscale µCT slices derived from 11 sandstone samples, 16 limestone samples, and eight shale plugs extracted from a single core was used as a three-class geological classification problem. Seventeen pretrained CNN architectures were evaluated under a standardized workflow incorporating data augmentation, area-like image downsampling, and specimen-level grouped three-fold cross-validation to reduce data leakage and account for the physical sampling structure. Model performance was assessed using accuracy, loss, weighted precision and weighted recall, weighted F1-score, mean per-class accuracy (MPCA), confusion matrices, specimen-level majority-vote metrics, and learning curves. Several architectures, including DenseNet169, DenseNet121, DenseNet201, and InceptionV3, achieved reliable classification performance across all metrics with stable convergence behavior. Sandstone and limestone samples were the most frequently confused classes, whereas shale samples were classified more consistently within the available dataset. Comparison with previous studies highlights recurring challenges such as intra-class variability, overfitting in small datasets, and the influence of image modality on model performance. Overall, the results provide a reproducible Earth science informatics framework for evaluating deep learning models in micro-CT-based rock image classification and digital rock analysis.

Abstract The maintenance of industrial buildings is essential to ensure the operability, safety, and efficiency of these facilities, which are key pillars of the industrial sector.

Abstract NPK fertilization can modify crop yield and contribute to the production of foods with greater nutritional value.

PHERGain was a randomized, non-comparative phase 2 trial evaluating a positron F-fluorodeoxyglucose-positron emission tomography ( F-FDG-PET)-guided, pathological