Search

The purpose of this study was to develop, validate and test a prediction model for discriminating malignant from benign breast lesions using conventional ultrasound (US), US elastography of strain elastography and contrast-enhanced ultrasound (CEUS). The study included 454 patients with breast imaging-reporting and data system (BI-RADS) category 4 breast lesions identified on histologic examinations. Firstly, 228 breast lesions (cohort 1) were analyzed by logistic regression analysis to identify the risk factors, and a breast malignancy prediction model was created. Secondly, the prediction model was validated in cohort 2 (84 patients) and tested in cohort 3 (142 patients) by using analysis of the area under the receiver operating characteristic curve (AUC). Univariate regression indicated that age ≥40 y, taller than wide shape on US, early hyperenhancement on CEUS and enlargement of enhancement area on CEUS were independent risk factors for breast malignancy (all p0.05). The logistic regression equation was established as follows: p = 1/1+Exp∑[-5.066 + 3.125 x (if age ≥40 y) + 1.943 x (if taller than wide shape) + 1.479 x (if early hyperenhancement) + 4.167 x (if enlargement of enhancement area). The prediction model showed good discrimination performance with an AUC of 0.967 in cohort 1, 0.948 in cohort 2 and 0.920 in cohort 3. By using the prediction model to selectively downgrade category 4a lesions, the re-rated BI-RADS yield an AUC of 0.880 (95% confidence interval [CI], 0.794-0.965) in cohort 2 and 0.870 (95% CI, 0.801-0.939) in cohort 3. The specificity increased from 0.0% (0/35) to 80.0% (28/35) without loss of sensitivity (from 100.0% to 95.9%, p = 0.153) in cohort 2. Similarly, the specificity increased from 0.0% (0/58) to 77.6% (45/58) without loss of sensitivity (from 100.0% to 96.4%, p = 0.081) in cohort 3. Multimodal US showed good diagnostic performance in predicting breast malignancy of BI-RADS category 4 lesions. Although the loss of sensitivity was existing, the addition of multimodal US to US BI-RADS could improve the specificity in BI-RADS category 4 lesions, which reduced unnecessary biopsies.

Density functional theory calculations (DFT), coupled with microkinetic modelling, have been used to simulate Temperature Programmed Desorption (TPD) experiments for calcined ceria nanopowders with the aim to gain insight into the chemistry governing their high redox activity. Our simulations consider two main nanoparticle models. One is a perfect ceria octahedron supercharged with adsorbed oxygen molecules turned into superoxide ions, as has previously been used to explain the enhanced oxygen storage capacity (OSC) in nanoceria. The other model is a variant where we have introduced oxygen vacancies under ridge Ce ions, thereby reducing their coordination numbers to five. The results from our microkinetic modelling suggest that including such five-coordinated Ce adsorption sites results in a TPD spectrum that better matches the experimental counterpart in terms of both peak position and width. In addition, this new structural model allows for the co-existence of Ce 3 + ions, superoxide ions and O2 molecules, as seen in experiments in the literature.

Cloud platforms allow users to execute tasks directly from their web browser and are a key enabling technology not only for commerce but also for computational science. Research software is often developed by scientists with limited experience in (and time for) user interface design, which can make research software difficult to install and use for novices. When combined with the increasing complexity of scientific workflows (involving many steps and software packages), setting up a computational research environment becomes a major entry barrier. AiiDAlab is a web platform that enables computational scientists to package scientific workflows and computational environments and share them with their collaborators and peers. By leveraging the AiiDA workflow manager and its plugin ecosystem, developers get access to a growing range of simulation codes through a python API, coupled with automatic provenance tracking of simulations for full reproducibility. Computational workflows can be bundled together with user-friendly graphical interfaces and made available through the AiiDAlab app store. Being fully compatible with open-science principles, AiiDAlab provides a complete infrastructure for automated workflows and provenance tracking, where incorporating new capabilities becomes intuitive, requiring only Python knowledge., Comment: Manuscript: 25 pages, 6 figures. Supplementary information: 15 pages, 10 figures

Studies have shown the potential of nanomaterials for the accurate and early detection of cancer. The aim of the present study was to design and evaluate the value of prostate-specific membrane antigen (PSA)-targeted manganese oxide–mesoporous silica nanoparticles (Mn–Msns) for the detection of prostate cancer. Mn–Msns were prepared, and then conjugated with the PSA antibody and Cy7 to create the multimodality PSA-Mn-Msn-Cy7. Their particle size, zeta potential, stability and magnetic resonance imaging (MRI) features of the nanoparticles were characterized. Optical and MR imaging were evaluated in cell and tumor-bearing mouse models. The Mn in tissues was measured by inductively coupled plasma mass spectrometry. The fabricated nanoparticles were stable and showed good T1relaxivity. The targeted nanoparticles accumulated to a great extent in prostate cancer cells in vitro but not in noncancerous cells. In vivo studies further demonstrated a targeted distribution of PSA-Mn-Msn-Cy7 to cancer tissues as shown by high optical and T1 signals. The targeted distribution was also confirmed by determining the Mn content in the cancer tissues. Our data demonstrate that PSA targeted fluorescence and MR dual-functional nanoparticle can visualize prostate cancer and can be used as NIRF/MR contrast agents.