Comparison of consistency in acute stroke: Automated ASPECT score software tools and manual consensus scores.

PURPOSE: The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a standardized semi-quantitative scoring system. It is widely used to assess the extent of acute ischemic stroke (AIS). We evaluated the consistency of three automatic software packages with the overall and region-specific manual ASPECTS scores of AIS patients. METHODS: Retrospectively, we gathered patients who presented with stroke symptoms between February 2019 and June 2022, and 174 cases were eventually included in the trial. Two radiologists reviewed the NCCT images independently; After four weeks, the same two radiologists began randomly reviewing the DWI images, discussed different scores and give consistent results as ground truth. RESULTS: Median ASPECTS of the expert consensus reading was 7 (5-9). Good to excellent correlation of ASPECTS total scores among the three software tools (0.70, 0.74 and 0.83). Correlation among ground truth and Rapid-ASPECTS, RealNow-ASPECTS, ShuKun-ASPECTS (ICC = 0.51, Cronbach's α = 0.53), (ICC = 0.60, Cronbach's α = 0.70) and (ICC = 0.52, Cronbach's α = 0.64) respectively. The AUCs for Rapid, RealNow and ShuKun were 0.61, 0.67, and 0.62 respectively. The region-specific results showed a poor to good correlation. The correlations between the non-dominant and dominant cerebral hemispheres and the ground truth were statistically different (P < 0.05). CONCLUSIONS: Overall, the scoring consistency between the three automated scoring software and the ground truth is comparable, with RealNow-ASPECTS being no less consistent and effective than Rapid-ASPECTS and ShuKun-ASPECTS, and even better than both. But the consistency grade that still is developable.

Photoelectrocatalytic oxidation of Cu(II)-EDTA at the TiO2 electrode and simultaneous recovery of Cu(II) by electrodeposition.

The simultaneous decomplexation of Cu-EDTA and electrodeposition recovery of Cu(2+) ions was attempted in a photoelectrocatalytic (PEC) system using TiO2/Ti as the anode and stainless steel as the cathode. At a current density of 0.5 mA/cm(2), removal efficiencies of 0.05 mM Cu-EDTA by photocatalysis, electrooxidation, and PEC processes were determined to be 15, 43, and 72% at 3 h, respectively. Recovery percentages of Cu(2+) ions were determined to be 10, 33, and 67%, respectively. These results indicated that a synergetic effect in the decomplexation of Cu-EDTA and recovery of Cu(2+) ions occurred in the PEC process, which favored acid conditions and increased with the current densities. The removal of Cu-EDTA and Cu(2+) ions can be described by a pseudo-first-order kinetics model. Ca(2+) ions significantly increase the removal of Cu-EDTA and recovery of Cu(2+) ions. Intermediates, including Cu-NTA, Cu-EDDA, acetic acid, formic acid, and oxalic acid, were identified, and a decomplexation pathway of Cu-EDTA was proposed. The Cu-EDTA decomplexation at the anode via oxidation of hydroxyl radicals was revealed. On the basis of X-ray photoelectron spectra analysis, a reduction pathway of Cu(2+) ions at the cathode was discussed. The present study may provide a promising alternative for destruction of the metal complex and recovery of metal ions.

Dual attention enhancement feature fusion network for segmentation and quantitative analysis of paediatric echocardiography.

Paediatric echocardiography is a standard method for screening congenital heart disease (CHD). The segmentation of paediatric echocardiography is essential for subsequent extraction of clinical parameters and interventional planning. However, it remains a challenging task due to (1) the considerable variation of key anatomic structures, (2) the poor lateral resolution affecting accurate boundary definition, (3) the existence of speckle noise and artefacts in echocardiographic images. In this paper, we propose a novel deep network to address these challenges comprehensively. We first present a dual-path feature extraction module (DP-FEM) to extract rich features via a channel attention mechanism. A high- and low-level feature fusion module (HL-FFM) is devised based on spatial attention, which selectively fuses rich semantic information from high-level features with spatial cues from low-level features. In addition, a hybrid loss is designed to deal with pixel-level misalignment and boundary ambiguities. Based on the segmentation results, we derive key clinical parameters for diagnosis and treatment planning. We extensively evaluate the proposed method on 4,485 two-dimensional (2D) paediatric echocardiograms from 127 echocardiographic videos. The proposed method consistently achieves better segmentation performance than other state-of-the-art methods, whichdemonstratesfeasibility for automatic segmentation and quantitative analysis of paediatric echocardiography. Our code is publicly available at https://github.com/end-of-the-century/Cardiac.

Fully Automatic Pediatric Echocardiography Segmentation Using Deep Convolutional Networks Based on BiSeNet.

Accurate segmentation of pediatric echocardiography is an essential preprocessing step for a wide range of analysis tasks. Currently, it highly relies on sonographer's manual segmentation, which is time-consuming and redundant, and therefore might lead to mistakes. In this paper, we present a deep learning method based on Bilateral Segmentation Network (BiSeNet) to fully automatic segment pediatric echocardiography images in 4 chamber view. BiSeNet consists of two paths, a spatial path for capturing low-level spatial features, and a context path for exploiting high-level context semantic features. In addition, a feature fusion module is used to fuse features learned by both the two paths. Experiments based on our selfcollected dataset shows that our method achieves 0.932, and 0.908 in term of Dice index in the left ventricle and left atrium segmentation task, which outperforms different state-of-the-art U-Net architectures.

[Flocculation characteristics and mechanism of a conical fluidized-bed reactor].

Controlling parameters, flocculation efficiencies and flocs characteristics in a conical fluidized bed were studied, and the flocculation mechanism of this reactor was discussed. The results showed that the changes of the properties of packed particles, bed height and liquid superficial velocity would cause the changes of velocity gradient (G) and reaction time (T). The removal fraction (RF) of kaolin clay suspension increased with increasing of the value of GT, when the sediment time was 20 min. The RF was greatly affected by T, when G was within 169.2-189.7 s(-1). However, when G was less than 169 s(-1), it was so low that limited the collision among particles, which flattened increase of RF by increasing the T. It is the vortex caused by random movement of particles and vertically gradually decreased liquid superficial velocity in the conical fluidized bed that makes it applicable on flocculation. The flocs formed in the conical fluidized bed, were slightly larger in size and smaller in density than those in the mechanical mixing device, when close G and T were applied in two reactors. Better flocculation and sediment performance could be expected when the reactor and the reaction condition was improved.