Hypericin enhances β-lactam antibiotics activity by inhibiting sarA expression in methicillin-resistant Staphylococcus aureus.

Bacteremia is a life-threating syndrome often caused by methicillin-resistant Staphylococcus aureus (MRSA). Thus, there is an urgent need to develop novel approaches to successfully treat this infection. Staphylococcal accessory regulator A (SarA), a global virulence regulator, plays a critical role in pathogenesis and β-lactam antibiotic resistance in Staphylococcus aureus. Hypericin is believed to act as an antibiotic, antidepressant, antiviral and non-specific kinase inhibitor. In the current study, we investigated the impact of hypericin on β-lactam antibiotics susceptibility and mechanism(s) of its activity. We demonstrated that hypericin significantly decreased the minimum inhibitory concentrations of β-lactam antibiotics (e.g., oxacillin, cefazolin and nafcillin), biofilm formation and fibronectin binding in MRSA strain JE2. In addition, hypericin significantly reduced sarA expression, and subsequently decreased mecA, and virulence-related regulators (e.g., agr RNAⅢ) and genes (e.g., fnbA and hla) expression in the studied MRSA strain. Importantly, the in vitro synergistic effect of hypericin with β-lactam antibiotic (e.g., oxacillin) translated into in vivo therapeutic outcome in a murine MRSA bacteremia model. These findings suggest that hypericin plays an important role in abrogation of β-lactam resistance against MRSA through sarA inhibition, and may allow us to repurpose the use of β-lactam antibiotics, which are normally ineffective in the treatment of MRSA infections (e.g., oxacillin)

Self-assembly kinetics of amphiphilic dendritic copolymers

The self-assembly process of amphiphilic dendritic copolymers(ADPs) with a hydrophilic core and a hydrophobic shell was investigated via laser light scattering. The self-assembly occurs via a fast step and a slow step with different relaxation times. At the critical micelle concentration (CMC), the fusion of small micelles results in the rapid increase of the micelle size in the fast step. The slow step is associated with equilibrium through the fission and fusion of the micelles. The micelle size increases with the unimer concentration, which leads to a lower micelle concentration. The lower micelle concentration makes the relaxation time of the fast step increase with increasing unimer concentration. However, the fusion of larger micelles at higher concentration is more efficient to increase micelle size. The fusion of small micelles with large micelles at higher concentration accelerates the approaching equilibrium of the micelles except for the fission and fusion of micelles. With the increasing degree of amidation (DA), the relaxation time in the fast step increases and in the slow step it decreases

Cone Identification in Choroideremia: Repeatability, Reliability, and Automation Through Use of a Convolutional Neural Network

Purpose: Adaptive optics imaging has enabled the visualization of photoreceptors both in health and disease. However, there remains a need for automated accurate cone photoreceptor identification in images of disease. Here, we apply an open-source convolutional neural network (CNN) to automatically identify cones in images of choroideremia (CHM). We further compare the results to the repeatability and reliability of manual cone identifications in CHM. Methods: We used split-detection adaptive optics scanning laser ophthalmoscopy to image the inner segment cone mosaic of 17 patients with CHM. Cones were manually identified twice by one experienced grader and once by two additional experienced graders in 204 regions of interest (ROIs). An open-source CNN either pre-trained on normal images or trained on CHM images automatically identified cones in the ROIs. True and false positive rates and Dice\u27s coefficient were used to determine the agreement in cone locations between data sets. Interclass correlation coefficient was used to assess agreement in bound cone density. Results: Intra- and intergrader agreement for cone density is high in CHM. CNN performance increased when it was trained on CHM images in comparison to normal, but had lower agreement than manual grading. Conclusions: Manual cone identifications and cone density measurements are repeatable and reliable for images of CHM. CNNs show promise for automated cone selections, although additional improvements are needed to equal the accuracy of manual measurements. Translational Relevance: These results are important for designing and interpreting longitudinal studies of cone mosaic metrics in disease progression or treatment intervention in CHM