Classification of breast microcalcifications with GaAs photon-counting spectral mammography using an inverse problem approach.

The purpose of this study was to investigate the use of a Gallium Arsenide (GaAs) photon-counting spectral mammography system to differentiate between Type I and Type II calcifications. Type I calcifications, consisting of calcium oxalate dihydrate (CO) or weddellite compounds are more often associated with benign lesions in the breast, and Type II calcifications containing hydroxyapatite (HA) are associated with both benign and malignant lesions in the breast. To be able to differentiate between these two calcification types, it is necessary to be able to estimate the full spectrum of the x-ray beam transmitted through the breast. We propose a novel method for estimating the energy-dependent x-ray transmission fraction of a beam using a photon counting detector with a limited number of energy bins. Using the estimated x-ray transmission through microcalcifications, it was observed that calcification type can be accurately estimated with machine learning. The study was carried out on a custom-built laboratory benchtop system using the SANTIS 0804 GaAs detector prototype system from DECTRIS Ltd with two energy thresholds enabled. Four energy thresholds detector was simulated by taking two separate acquisitions in which two energy thresholds were enabled for each acquisition and set at (12 keV, 21 keV) and then (29 keV, 36 keV). Measurements were performed using BR3D (CIRS, Norfolk, VA) breast imaging phantoms mimicking 100% adipose and 100% glandular tissues swirled together in an approximate 50/50 ratio by weight with the addition of in-house-developed synthetic microcalcifications. First, an inverse problem-based approach was used to estimate the full energy x-ray transmission fraction factor using known basis transmission factors from varying thicknesses of aluminum and polymethyl methacrylate (PMMA). Second, the classification of Type I and Type II calcifications was performed using the estimated energy-dependent transmission fraction factors for the pixels containing calcifications. The results were analyzed using receiver operating characteristic (ROC) analysis and demonstrated good discrimination performance with the area under the ROC curve greater than 84%. They indicated that GaAs photon-counting spectral mammography has potential use as a non-invasive method for discrimination between Type I and Type II calcifications. Results from this study suggested that GaAs-based spectral mammography could serve as a non-invasive measure for ruling out malignancy of calcifications found in the breast. Additional studies in more clinically realistic conditions involving breast tissues samples with smaller microcalcification specks should be performed to further explore the feasibility of this approach.

Influenza A virus diffusion through mucus gel networks.

Mucus in the lung plays an essential role as a barrier to infection by viral pathogens such as influenza A virus (IAV). Previous work determined mucin-associated sialic acid acts as a decoy receptor for IAV hemagglutinin (HA) binding and the sialic-acid cleaving enzyme, neuraminidase (NA), facilitates virus passage through mucus. However, it has yet to be fully addressed how the physical structure of the mucus gel influences its barrier function and its ability to trap viruses via glycan mediated interactions to prevent infection. To address this, IAV and nanoparticle diffusion in human airway mucus and mucin-based hydrogels is quantified using fluorescence video microscopy. We find the mobility of IAV in mucus is significantly influenced by the mesh structure of the gel and in contrast to prior reports, these effects likely influence virus passage through mucus gels to a greater extent than HA and NA activity. In addition, an analytical approach is developed to estimate the binding affinity of IAV to the mucus meshwork, yielding dissociation constants in the mM range, indicative of weak IAV-mucus binding. Our results provide important insights on how the adhesive and physical barrier properties of mucus influence the dissemination of IAV within the lung microenvironment.

Modeling Airway Dysfunction in Asthma Using Synthetic Mucus Biomaterials.

As asthma worsens, occlusion of airways with mucus significantly contributes to airflow obstruction and reduced lung function. Recent evidence from clinical studies has shown mucus obtained from adults and children with asthma possesses altered mucin composition. However, how these changes alter the functional properties of the mucus gel is not yet fully understood. To study this, we have engineered a synthetic mucus biomaterial to closely mimic the properties of native mucus in health and disease. We demonstrate that this model possesses comparable biophysical and transport properties to native mucus ex vivo collected from human subjects and in vitro isolated from human airway epithelial (HAE) tissue cultures. We found by systematically varying mucin composition that mucus gel viscoelasticity is enhanced when predominantly composed of mucin 5AC (MUC5AC), as is observed in asthma. As a result, asthma-like synthetic mucus gels are more slowly transported on the surface of HAE tissue cultures and at a similar rate to native mucus produced by HAE cultures stimulated with type 2 cytokine IL-13, known to contribute to airway inflammation and MUC5AC hypersecretion in asthma. We also discovered that the barrier function of asthma-like synthetic mucus toward influenza A virus was impaired as evidenced by the increased frequency of infection in MUC5AC-rich hydrogel-coated HAE cultures. Together, this work establishes a biomaterial-based approach to understand airway dysfunction in asthma and related muco-obstructive lung diseases.