The macrophage response to planktonic Staphylococcus aureus involves the induction of proinflammatory microbicidal activity. However, S. aureus biofilms can interfere with these responses in part by polarizing macrophages toward an anti-inflammatory profibrotic phenotype. Here we demonstrate that conditioned medium from mature S. aureus biofilms inhibited macrophage phagocytosis and induced cytotoxicity, suggesting the involvement of a secreted factor(s). Iterative testing found the active factor(s) to be proteinaceous and partially agr-dependent. Quantitative mass spectrometry identified alpha-toxin (Hla) and leukocidin AB (LukAB) as critical molecules secreted by S. aureus biofilms that inhibit murine macrophage phagocytosis and promote cytotoxicity. A role for Hla and LukAB was confirmed by using hla and lukAB mutants, and synergy between the two toxins was demonstrated with a lukAB hla double mutant and verified by complementation. Independent confirmation of the effects of Hla and LukAB on macrophage dysfunction was demonstrated by using an isogenic strain in which Hla was constitutively expressed, an Hla antibody to block toxin activity, and purified LukAB peptide. The importance of Hla and LukAB during S. aureus biofilm formation in vivo was assessed by using a murine orthopedic implant biofilm infection model in which the lukAB hla double mutant displayed significantly lower bacterial burdens and more macrophage infiltrates than each single mutant. Collectively, these findings reveal a critical synergistic role for Hla and LukAB in promoting macrophage dysfunction and facilitating S. aureus biofilm development in vivo.

Amelogenin is the most abundant matrix protein in enamel. Proper amelogenin processing by proteinases is necessary for its biological functions during amelogenesis. Matrix metalloproteinase 9 (MMP-9) is responsible for the turnover of matrix components. The relationship between MMP-9 and amelogenin during tooth development remains unknown. We tested the hypothesis that MMP-9 binds to amelogenin and they are co-expressed in ameloblasts during amelogenesis. We evaluated the distribution of both proteins in the mouse teeth using immunohistochemistry and confocal microscopy. At postnatal day 2, the spatial distribution of amelogenin and MMP-9 was co-localized in preameloblasts, secretory ameloblasts, enamel matrix and odontoblasts. At the late stages of mouse tooth development, expression patterns of amelogenin and MMP-9 were similar to that seen in postnatal day 2. Their co-expression was further confirmed by RT-PCR, Western blot and enzymatic zymography analyses in enamel organ epithelial and odontoblast-like cells. Immunoprecipitation assay revealed that MMP-9 binds to amelogenin. The MMP-9 cleavage sites in amelogenin proteins across species were found using bio-informative software program. Analyses of these data suggest that MMP-9 may be involved in controlling amelogenin processing and enamel formation.

Image post-processing is used in clinical-grade ultrasound scanners to improve image quality (e.g., reduce speckle noise and enhance contrast). These post-processing techniques vary across manufacturers and are generally kept proprietary, which presents a challenge for researchers looking to match current clinical-grade workflows. We introduce a deep learning framework, MimickNet, that transforms conventional delay-and-summed (DAS) beams into the approximate Dynamic Tissue Contrast Enhanced (DTCE™) post-processed images found on Siemens clinicalgrade scanners. Training MimickNet only requires post-processed image samples from a scanner of interest without the need for explicit pairing to DAS data. This flexibility allows MimickNet to hypothetically approximate any manufacturer's post-processing without access to the preprocessed data. MimickNet post-processing achieves a 0.940±0.018 structural similarity index measurement (SSIM) compared to clinical-grade post-processing on a 400 cine-loop test set, 0.937±0.025 SSIM on a prospectively acquired dataset, and 0.928±0.003 SSIM on an out-ofdistribution cardiac cine-loop after gain adjustment. To our knowledge, this is the first work to establish deep learning models that closely approximate ultrasound post-processing found in current medical practice. MimickNet serves as a clinical post-processing baseline for future works in ultrasound image formation to compare against. Additionally, it can be used as a pretrained model for fine-tuning towards different post-processing techniques. To this end, we have made the MimickNet software, phantom data, and permitted in vivo data open-source at https://github.com/ ouwen/MimickNet.

No abstract

Image post-processing is used in clinical-grade ultrasound scanners to improve image quality (e.g., reduce speckle noise and enhance contrast). These post-processing techniques vary across manufacturers and are generally kept proprietary, which presents a challenge for researchers looking to match current clinical-grade workflows. We introduce a deep learning framework, MimickNet, that transforms raw conventional delayand-summed (DAS) beams into the approximate post-processed images found on clinical-grade scanners. Training MimickNet only requires post-processed image samples from a scanner of interest without the need for explicit pairing to raw DAS data. This flexibility allows it to hypothetically approximate any manufacturer's post-processing without access to the preprocessed data. MimickNet generates images with an average similarity index measurement (SSIM) of 0.930±0.0892 on a 300 cineloop test set, and it generalizes to cardiac cineloops outside of our train-test distribution achieving an SSIM of 0.967±0.002. We also explore the theoretical SSIM achievable by evaluating MimickNet performance when trained under graybox constraints (i.e., when both pre-processed and post-processed images are available). To our knowledge, this is the first work to establish deep learning models that closely approximate current clinical-grade ultrasound post-processing under realistic blackbox constraints where before and after post-processing data is unavailable. MimickNet serves as a clinical post-processing baseline for future works in ultrasound image formation to compare against. To this end, we have made the MimickNet software open source.