Betaxolol as a Potent Inhibitor of NDM-1-Positive E. coli That Synergistically Enhances the Anti-Inflammatory Effect in Combination with Meropenem.

With significant human and economic losses, increasing bacterial resistance is a serious global threat to human life. Due to their high efficacy, broad spectrum, and cost-effectiveness, beta-lactams are widely used in the clinical management of bacterial infection. The emergence and wide spread of New Delhi metallo-ß-lactamase (NDM-1), which can effectively inactivate ß-lactams, has posed a challenge in the design of effective new antimicrobial treatments. Medicine repurposing is now an important tool in the development of new alternative medicines. We present a known glaucoma therapeutic, betaxolol (BET), which with a 50% inhibitory concentration (IC50) of 19.3 ± 0.9 µM significantly inhibits the hydrolytic activity of the NDM-1 enzyme and may represent a potential NDM-1 enzyme inhibitor. BET combined with meropenem (MEM) showed bactericidal synergism in vitro. The efficacy of BET was further evaluated against systemic bacterial infections in BALB/c mice. The results showed that BET+MEM decreased the numbers of leukocytes and inflammatory factors in peripheral blood, as well as the organ bacterial load and pathological damage. Molecular docking and kinetic simulations showed that BET can form hydrogen bonds and hydrophobic interactions directly with key amino acid residues in the NDM-1 active site. Thus, we demonstrated that BET inhibited NDM-1 by competitively binding to it and that it can be developed in combination with MEM as a new therapy for the management of infections caused by medicine-resistant bacteria.

Case-Specific Focal Sensor Design for Cardiac Electrical Impedance Tomography.

Electrical impedance tomography (EIT) is a non-invasive detection technology that uses the electrical response value at the boundary of an observation field to image the conductivity changes in an area. When EIT is applied to the thoracic cavity of the human body, the conductivity change caused by the heartbeat will be concentrated in a sub-region of the thoracic cavity, that is, the heart region. In order to improve the spatial resolution of the target region, two sensor optimization methods based on conformal mapping theory were proposed in this study. The effectiveness of the proposed method was verified by simulation and phantom experiment. The qualitative analysis and quantitative index evaluation of the reconstructed image showed that the optimized model could achieve higher imaging accuracy of the heart region compared with the standard sensor. The reconstruction results could effectively reflect the periodic diastolic and systolic movements of the heart and had a better ability to recognize the position of the heart in the thoracic cavity.

Spatially adaptive blind deconvolution methods for optical coherence tomography.

Optical coherence tomography (OCT) is a powerful noninvasive imaging technique for detecting microvascular abnormalities. Following optical imaging principles, an OCT image will be blurred in the out-of-focus domain. Digital deconvolution is a commonly used method for image deblurring. However, the accuracy of traditional digital deconvolution methods, e.g., the Richardson-Lucy method, depends on the prior knowledge of the point spread function (PSF), which varies with the imaging depth and is difficult to determine. In this paper, a spatially adaptive blind deconvolution framework is proposed for recovering clear OCT images from blurred images without a known PSF. First, a depth-dependent PSF is derived from the Gaussian beam model. Second, the blind deconvolution problem is formalized as a regularized energy minimization problem using the least squares method. Third, the clear image and imaging depth are simultaneously recovered from blurry images using an alternating optimization method. To improve the computational efficiency of the proposed method, an accelerated alternating optimization method is proposed based on the convolution theorem and Fourier transform. The proposed method is numerically implemented with various regularization terms, including total variation, Tikhonov, and l1 norm terms. The proposed method is used to deblur synthetic and experimental OCT images. The influence of the regularization term on the deblurring performance is discussed. The results show that the proposed method can accurately deblur OCT images. The proposed acceleration method can significantly improve the computational efficiency of blind demodulation methods.

Self-attentional microvessel segmentation via squeeze-excitation transformer Unet.

Automatic vessel segmentation is a key step of clinical or pre-clinical vessel bio-markers for clinical diagnosis. In previous research, the segmentation architectures are mainly based on Convolutional Neural Networks (CNN). However, due to the limitation of the receipt of field (ROF) of convolution operation, it is difficult to further improve the accuracy of the CNN-based methods. To solve this problem, a Squeeze-Excitation Transformer U-net (SETUnet) is proposed to break the ROF limitation of CNN. The proposed squeeze-excitation Transformer can introduce the self attention mechanism into the vessel segmentation task by generating a global attention mapping according to the entire vessel image. To test the performance of the proposed SETUnet, the SETUnet is trained and tested on several public vessel data-sets. The results show that the SETUnet outperforms several state-of-the-art vessel segmentation neural networks, especially on the connectivity of the segmented vessels.

Imaging depth adaptive resolution enhancement for optical coherence tomography via deep neural network with external attention.

Optical coherence tomography (OCT) is a promising non-invasive imaging technique that owns many biomedical applications. In this paper, a deep neural network is proposed for enhancing the spatial resolution of OCTen faceimages. Different from the previous reports, the proposed can recover high-resolutionen faceimages from low-resolutionen faceimages at arbitrary imaging depth. This kind of imaging depth adaptive resolution enhancement is achieved through an external attention mechanism, which takes advantage of morphological similarity between the arbitrary-depth and full-depthen faceimages. Firstly, the deep feature maps are extracted by a feature extraction network from the arbitrary-depth and full-depthen faceimages. Secondly, the morphological similarity between the deep feature maps is extracted and utilized to emphasize the features strongly correlated to the vessel structures by using the external attention network. Finally, the SR image is recovered from the enhanced feature map through an up-sampling network. The proposed network is tested on a clinical skin OCT data set and an open-access retinal OCT dataset. The results show that the proposed external attention mechanism can suppress invalid features and enhance significant features in our tasks. For all tests, the proposed SR network outperformed the traditional image interpolation method, e.g. bi-cubic method, and the state-of-the-art image super-resolution networks, e.g. enhanced deep super-resolution network, residual channel attention network, and second-order attention network. The proposed method may increase the quantitative clinical assessment of micro-vascular diseases which is limited by OCT imaging device resolution.

Optical design and fabrication of an all-aluminum unobscured two-mirror freeform imaging telescope.

Freeform optics offers more degrees of freedom to optical design that can benefit from a compact package size and a large field of view for imaging systems. Motivated by the advances in modern optical fabrication and metrology, freeform optics has been found in many applications. In this paper, we will describe the challenging optical design, fabrication, metrology, and assembly of an all-aluminum unobscured two-mirror freeform imaging telescope. The telescope has a large field of view of 20∘×15∘. The freeform aluminum mirrors are manufactured by diamond turning based on a feedback modification strategy. The freeform mirrors are measured by a computer-generated hologram-based interferometric null test method. All-aluminum configuration has the advantages of being athermal and cost-effective.

A Shape-Based Statistical Inversion Method for EIT/URT Dual-Modality Imaging.

A shape-based statistical inversion method is proposed for Electrical Impedance Tomography (EIT) and Ultrasound Reflection Tomography (URT) dual-modality imaging. It is promising to improve the imaging accuracy in inclusion detection problems. The proposed image reconstruction method is based on the statistical shape inversion framework. The likelihood function is derived from EIT and URT forward models. The prior distribution is constructed using the Markov random field (MRF) prior. The measurement uncertainty is modeled by conditional error model method. The statistical shape inversion problem is solved by the Maximum a posterior (MAP) method with conventional error model. A set of numerical and experimental tests are carried out to evaluate the performance of the proposed method. The results show that the proposed EIT/URT dual-modality imaging method has obvious improvement in imaging accuracy compared to the traditional single-modality EIT and URT methods.

Focusing Sensor Design for Open Electrical Impedance Tomography Based on Shape Conformal Transformation.

Electrical Impedance Tomography (EIT) is a non-invasive detection method to image the conductivity changes inside an observation region by using the electrical measurements at the boundary of this region. In some applications of EIT, the observation domain is infinite and is only accessible from one side, which leads to the so-called open EIT (OEIT) problem. Compared with conventional EIT problems, the observation region in OEIT can only be measured from limited projection directions, which makes high resolution imaging much more challenging. To improve the imaging quality of OEIT, a focusing sensor design strategy is proposed based on shape conformal theory. The conformal bijection is used to map a standard EIT sensor defined at a unit circle to a focusing OEIT sensor defined at an upper half plane. A series of numerical and experimental testes are conducted. Compared with the traditional sensor structure, the proposed focusing sensor has higher spatial resolution at the near-electrode region and is good at distinguishing multi-inclusions which are close to each other.

A Lagrange-Newton Method for EIT/UT Dual-Modality Image Reconstruction.

An image reconstruction method is proposed based on Lagrange-Newton method for electrical impedance tomography (EIT) and ultrasound tomography (UT) dual-modality imaging. Since the change in conductivity distribution is usually accompanied with the change in acoustic impedance distribution, the reconstruction targets of EIT and UT are unified to the conductivity difference using the same mesh model. Some background medium distribution information obtained from ultrasound transmission and reflection measurements can be used to construct a hard constraint about the conductivity difference distribution. Then, the EIT/UT dual-modality inverse problem is constructed by an equality constraint equation, and the Lagrange multiplier method combining Newton-Raphson iteration is used to solve the EIT/UT dual-modality inverse problem. The numerical and experimental results show that the proposed dual-modality image reconstruction method has a better performance than the single-modality EIT method and is more robust to the measurement noise.

A Statistical Shape-Constrained Reconstruction Framework for Electrical Impedance Tomography.

A statistical shape-constrained reconstruction (SSCR) framework is presented to incorporate the statistical prior information of human lung shapes for lung electrical impedance tomography. The prior information is extracted from 8000 chest-computed tomography scans across 800 patients. The reconstruction framework is implemented with two approaches-a one-step SSCR and an iterative SSCR in lung imaging. The one-step SSCR provides fast and high accurate reconstructions of healthy lungs, whereas the iterative SSCR allows to simultaneously estimate the pre-injured lung and the injury lung part. The approaches are evaluated with the simulated examples of thorax imaging and also with the experimental data from a laboratory setting, with difference imaging considered in both the approaches. It is demonstrated that the accuracy of lung shape reconstruction is significantly improved. In addition, the proposed approaches are proved to be robust against measurement noise, modeling error caused by inaccurately known domain boundary, and the selection of the regularization parameters.

Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach.

PURPOSE: To develop a reliable method to estimate electron density based on anatomic magnetic resonance imaging (MRI) of the brain. METHODS AND MATERIALS: We proposed a unifying multi-atlas approach for electron density estimation based on standard T1- and T2-weighted MRI. First, a composite atlas was constructed through a voxelwise matching process using multiple atlases, with the goal of mitigating effects of inherent anatomic variations between patients. Next we computed for each voxel 2 kinds of conditional probabilities: (1) electron density given its image intensity on T1- and T2-weighted MR images; and (2) electron density given its spatial location in a reference anatomy, obtained by deformable image registration. These were combined into a unifying posterior probability density function using the Bayesian formalism, which provided the optimal estimates for electron density. We evaluated the method on 10 patients using leave-one-patient-out cross-validation. Receiver operating characteristic analyses for detecting different tissue types were performed. RESULTS: The proposed method significantly reduced the errors in electron density estimation, with a mean absolute Hounsfield unit error of 119, compared with 140 and 144 (P<.0001) using conventional T1-weighted intensity and geometry-based approaches, respectively. For detection of bony anatomy, the proposed method achieved an 89% area under the curve, 86% sensitivity, 88% specificity, and 90% accuracy, which improved upon intensity and geometry-based approaches (area under the curve: 79% and 80%, respectively). CONCLUSION: The proposed multi-atlas approach provides robust electron density estimation and bone detection based on anatomic MRI. If validated on a larger population, our work could enable the use of MRI as a primary modality for radiation treatment planning.

Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma.

OBJECTIVE: To develop and validate a volume-based, quantitative imaging marker by integrating multi-parametric MR images for predicting glioblastoma survival, and to investigate its relationship and synergy with molecular characteristics. METHODS: We retrospectively analysed 108 patients with primary glioblastoma. The discovery cohort consisted of 62 patients from the cancer genome atlas (TCGA). Another 46 patients comprising 30 from TCGA and 16 internally were used for independent validation. Based on integrated analyses of T1-weighted contrast-enhanced (T1-c) and diffusion-weighted MR images, we identified an intratumoral subregion with both high T1-c and low ADC, and accordingly defined a high-risk volume (HRV). We evaluated its prognostic value and biological significance with genomic data. RESULTS: On both discovery and validation cohorts, HRV predicted overall survival (OS) (concordance index: 0.642 and 0.653, P < 0.001 and P = 0.038, respectively). HRV stratified patients within the proneural molecular subtype (log-rank P = 0.040, hazard ratio = 2.787). We observed different OS among patients depending on their MGMT methylation status and HRV (log-rank P = 0.011). Patients with unmethylated MGMT and high HRV had significantly shorter survival (median survival: 9.3 vs. 18.4 months, log-rank P = 0.002). CONCLUSION: Volume of the high-risk intratumoral subregion identified on multi-parametric MRI predicts glioblastoma survival, and may provide complementary value to genomic information. KEY POINTS: • High-risk volume (HRV) defined on multi-parametric MRI predicted GBM survival. • The proneural molecular subtype tended to harbour smaller HRV than other subtypes. • Patients with unmethylated MGMT and high HRV had significantly shorter survival. • HRV complements genomic information in predicting GBM survival.

Interface and permittivity simultaneous reconstruction in electrical capacitance tomography based on boundary and finite-elements coupling method.

Electrical capacitance tomography (ECT) is a non-destructive detection technique for imaging the permittivity distributions inside an observed domain from the capacitances measurements on its boundary. Owing to its advantages of non-contact, non-radiation, high speed and low cost, ECT is promising in the measurements of many industrial or biological processes. However, in the practical industrial or biological systems, a deposit is normally seen in the inner wall of its pipe or vessel. As the actual region of interest (ROI) of ECT is surrounded by the deposit layer, the capacitance measurements become weakly sensitive to the permittivity perturbation occurring at the ROI. When there is a major permittivity difference between the deposit and the ROI, this kind of shielding effect is significant, and the permittivity reconstruction becomes challenging. To deal with the issue, an interface and permittivity simultaneous reconstruction approach is proposed. Both the permittivity at the ROI and the geometry of the deposit layer are recovered using the block coordinate descent method. The boundary and finite-elements coupling method is employed to improve the computational efficiency. The performance of the proposed method is evaluated with the simulation tests. This article is part of the themed issue 'Supersensing through industrial process tomography'.