Role of parC Mutations at Position 84 on High-Level Delafloxacin Resistance in Methicillin-Resistant Staphylococcus aureus.

High-level delafloxacin-resistant (H-L DLX-R) Staphylococcus aureus isolates (minimum inhibitory concentration ≥1 mg/L) associated with mutations affecting position 84 of ParC have emerged. We aimed to elucidate the role of these mutations as a mechanism of H-L DLX resistance in methicillin-resistant S. aureus (MRSA) isolates recovered from blood cultures. Susceptibility to DLX was determined in 75 MRSA isolates by E-test, and an rt-PCR was developed to detect mutations affecting position 84 of ParC to screen a further 185 MRSA isolates. The genomes of 48 isolates, including all DLX-R isolates or with alterations at position 84, and also a subset of DLX-susceptible isolates were analyzed. Among the 75 isolates studied, 77.34% were DLX-susceptible and only 4 H-L DLX-R isolates were found. Seven (3.8%) isolates with alterations at position 84 of ParC were detected by rt-PCR. Genomic analysis showed that 89.9% (8/9) of isolates with the substitution E84K/G in ParC, together with other mutations in gyrA and parC, were H-L DLX-R. However, the E84K substitution in ParC alone or with other alterations was found in two isolates without H-L DLX-R. Alterations at position 84 of ParC are rare but play a key role in H-L DLX resistance in MRSA but only when other alterations in GyrA are present.

Deep Learning Framework for Real-Time Estimation of in-silico Thrombotic Risk Indices in the Left Atrial Appendage.

Patient-specific computational fluid dynamics (CFD) simulations can provide invaluable insight into the interaction of left atrial appendage (LAA) morphology, hemodynamics, and the formation of thrombi in atrial fibrillation (AF) patients. Nonetheless, CFD solvers are notoriously time-consuming and computationally demanding, which has sparked an ever-growing body of literature aiming to develop surrogate models of fluid simulations based on neural networks. The present study aims at developing a deep learning (DL) framework capable of predicting the endothelial cell activation potential (ECAP), an in-silico index linked to the risk of thrombosis, typically derived from CFD simulations, solely from the patient-specific LAA morphology. To this end, a set of popular DL approaches were evaluated, including fully connected networks (FCN), convolutional neural networks (CNN), and geometric deep learning. While the latter directly operated over non-Euclidean domains, the FCN and CNN approaches required previous registration or 2D mapping of the input LAA mesh. First, the superior performance of the graph-based DL model was demonstrated in a dataset consisting of 256 synthetic and real LAA, where CFD simulations with simplified boundary conditions were run. Subsequently, the adaptability of the geometric DL model was further proven in a more realistic dataset of 114 cases, which included the complete patient-specific LA and CFD simulations with more complex boundary conditions. The resulting DL framework successfully predicted the overall distribution of the ECAP in both datasets, based solely on anatomical features, while reducing computational times by orders of magnitude compared to conventional CFD solvers.