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Resonant nanoelectromechanical systems (NEMS) based on two-dimensional (2D) materials such as molybdenum disulfide (MoS

OBJECTIVE To provide an automatic method for segmentation and diameter measurement of type B aortic dissection (TBAD). MATERIALS AND METHODS Aortic computed tomography angiographic images from 139 patients with TBAD were consecutively collected. We implemented a deep learning method based on a three-dimensional (3D) deep convolutional neural (CNN) network, which realizes automatic segmentation and measurement of the entire aorta (EA), true lumen (TL), and false lumen (FL). The accuracy, stability, and measurement time were compared between deep learning and manual methods. The intra- and inter-observer reproducibility of the manual method was also evaluated. RESULTS The mean dice coefficient scores were 0.958, 0.961, and 0.932 for EA, TL, and FL, respectively. There was a linear relationship between the reference standard and measurement by the manual and deep learning method (r = 0.964 and 0.991, respectively). The average measurement error of the deep learning method was less than that of the manual method (EA, 1.64% vs. 4.13%; TL, 2.46% vs. 11.67%; FL, 2.50% vs. 8.02%). Bland-Altman plots revealed that the deviations of the diameters between the deep learning method and the reference standard were -0.042 mm (-3.412 to 3.330 mm), -0.376 mm (-3.328 to 2.577 mm), and 0.026 mm (-3.040 to 3.092 mm) for EA, TL, and FL, respectively. For the manual method, the corresponding deviations were -0.166 mm (-1.419 to 1.086 mm), -0.050 mm (-0.970 to 1.070 mm), and -0.085 mm (-1.010 to 0.084 mm). Intra- and inter-observer differences were found in measurements with the manual method, but not with the deep learning method. The measurement time with the deep learning method was markedly shorter than with the manual method (21.7 ± 1.1 vs. 82.5 ± 16.1 minutes, p < 0.001). CONCLUSION The performance of efficient segmentation and diameter measurement of TBADs based on the 3D deep CNN was both accurate and stable. This method is promising for evaluating aortic morphology automatically and alleviating the workload of radiologists in the near future.

To develop a clot-based radiomics model using CT imaging radiomic features and machine learning to identify cardioembolic (CE) stroke before mechanical thrombectomy (MTB) in patients with acute ischemic stroke (AIS).This retrospective four-center study consecutively included 403 patients with AIS who sequentially underwent CT and MTB between April 2016 and July 2021. These were grouped into training, testing, and external validation cohorts. Thrombus-extracted radiomic features and basic information were gathered to construct a machine learning model to predict CE stroke. The radiological characteristics and basic information were used to build a routine radiological model. A combined radiomics and radiological features model was also developed. The performances of all models were evaluated and compared in the validation cohort. A histological analysis helped further assess the proposed model in all patients.The radiomics model yielded an area under the curve (AUC) of 0.838 (95% confidence interval [CI], 0.771-0.891) for predicting CE stroke in the validation cohort, significantly higher than the radiological model (AUC, 0.713; 95% CI, 0.636-0.781; p = 0.007) but similar to the combined model (AUC, 0.855; 95% CI, 0.791-0.906; p = 0.14). The thrombus radiomic features achieved stronger correlations with red blood cells (|rThe proposed CT-based radiomics model could reliably predict CE stroke in AIS, performing better than the routine radiological method.• Admission CT imaging could offer valuable information to identify the acute ischemic stroke source by radiomics analysis. • The proposed CT imaging-based radiomics model yielded a higher area under the curve (0.838) than the routine radiological method (0.713; p = 0.007). • Several radiomic features showed significantly stronger correlations with two main thrombus constituents (red blood cells, |r

Based on the advantages of azole molecules and fluoroquinolone drugs, we designed and synthesized 34 clinafloxacin-azole conjugates using fragment-based drug design and drug combination principles. The in vitro activities of the synthesized conjugates against Mycobacterium tuberculosis (H37Rv), Hela cell as well as Gram-positive and Gram-negative bacteria were assayed. The bioassay results revealed that most of the target molecules had anti-tuberculosis (anti-TB) activity, of which 14 compounds had very strong anti-TB activity [minimum inhibitory concentration (MIC) 2 μM]. In addition, the compounds with strong activity towards H37Rv had weak activity towards Gram-negative and Gram-positive bacteria, showing obvious selectivity towards H37Rv. Predicted toxicity data indicated that 27 molecules were less toxic or equivalent to that of the original drug (clinafloxacin). Especially, it is demonstrated that compound TM2l exhibited the strongest anti-TB activity (MIC = 0.29 μM), low antibacterial activity, negligible toxicity, and good drug-likeness values, which can be considered as an ideal lead molecule for future optimization.

The computed tomography angiography (CTA) postprocessing manually recognized by technologists is extremely labor intensive and error prone. We propose an artificial intelligence reconstruction system supported by an optimized physiological anatomical-based 3D convolutional neural network that can automatically achieve CTA reconstruction in healthcare services. This system is trained and tested with 18,766 head and neck CTA scans from 5 tertiary hospitals in China collected between June 2017 and November 2018. The overall reconstruction accuracy of the independent testing dataset is 0.931. It is clinically applicable due to its consistency with manually processed images, which achieves a qualification rate of 92.1%. This system reduces the time consumed from 14.22 ± 3.64 min to 4.94 ± 0.36 min, the number of clicks from 115.87 ± 25.9 to 4 and the labor force from 3 to 1 technologist after five months application. Thus, the system facilitates clinical workflows and provides an opportunity for clinical technologists to improve humanistic patient care., Manual postprocessing of computed tomography angiography (CTA) images is extremely labor intensive and error prone. Here, the authors propose an artificial intelligence reconstruction system that can automatically achieve CTA reconstruction in healthcare services.

Background: We pioneered a Deep Learning-based Coronary computed tomography–angiography Automatic Reconstruction and Diagnosis System (D-CARDS) for optimizing the workflow of diagnosis in coronary artery disease. Its efficiency and performance in clinical settings were verified. Method: In the model establishment stage, D-CARDS was trained with coronary computed tomography–angiography (CCTA) imaging data from 10,410 patients divided into training, tuning, and external validation test sets in a ratio of 7:2:1. A total of 685 patients were included in the study of clinical validation. 350 CCTA cases were collected for comparison of time efficiency. Another 335 CCTA cases were selected to reveal the diagnostic performance of D-CARDS with paired invasive coronary angiography (ICA) as the reference standard. Stenosis of 50% was considered to be obstructive and 70% or more to be significantly obstructive. The diagnostic performance of D-CARDS was evaluated as the receiver operating characteristic curve (ROC) and the corresponding area under the curve (AUC) at patient, vascular and segmental bases compared to both unilateral expert and arbitrated expert results. Findings: The average time taken of CCTA procedure after scanning (post-processing and diagnostic reporting) by D-CARDS was decreased by 73.3% from an average of 16.1 min with the conventional approach to 4.3 min (p=0.000). A total of 335 patients with 1,222 vessels and 3,559 segments were included in the final comparison of diagnostic performance. D-CARDS showed greater sensitivity (89.3% and 72.4%) than arbitrated expert results(82.6% and 62.0%)for detecting stenosis at both the 50% and 70% thresholds on patient-base, whereas its specificities were lower on every bases. The AUC showed that its diagnostic performance was equivalent to that of either of a unilateral expert on patient-based analysis, although slightly inferior to the arbitrated expert results. Interpretation: D-CARDS greatly improves the efficiency of CCTA procedure. Its diagnostic performance in detecting coronary stenosis is closer to that of an attending radiologist on patient-based analysis. The system can be used to optimize CCTA workflow. Funding Statement: This study was funded by Beijing science and technology committee. (grant reference number, Z201100005620009). Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: The study was approved by the Institutional Review Board (IRB)/Ethics Committee. The work was conducted in a manner compliant with the Measures for the Ethical Review of Biomedical Research Involving Humans and was adherent to the tenets of the Declaration of Helsinki.

The cerebral artery network is a complex system. Each cerebral hemisphere has three main arteries: anterior/middle/posterior cerebral artery (ACA/MCA/PCA). In the middle of the brain, these arteries form the Willis circle. The vessel diseases around the Willis circle are generally a warning signal of stroke. Computed tomography angiography (CTA) is an important tool to visualize the cerebral artery system, but due to the complexity of the artery network, it is difficult to generate straightened lumen of the arteries. Hence, radiologists need to spend a long time inspecting every corner on the original CTA image and detecting the stenosis. In this paper, we propose an automatic pipeline that can identify the main arteries, dissect them into segments, and generate their straightened lumen. The main idea is to utilize the registration to identify key points of the cerebral artery system and apply the shortest path finding algorithm to extract the main routes of the artery system. Our approach is robust to all kinds of brain/neck scanning protocols and most topological variations of the artery system. Furthermore, our approach is very fast (15.7 s per case on average). Consequently, our method is more useful in real scenarios compared with previous methods.

In aortic endovascular repair, the prediction of stented vessel remodeling informs treatment plans and risk evaluation; however, there are no highly accurate and efficient methods to quantitatively simulate stented vessels. This study developed a fast virtual stenting algorithm to simulate stent-induced aortic remodeling to assist in real-time thoracic endovascular aortic repair planning. Methods: The virtual stenting algorithm was established based on simplex deformable mesh and mechanical contact analysis. The key parameters of the mechanical contact analysis were derived from mechanical tests on aortic tissue (n=40) and commonly used stent-grafts (n=6). Genetic algorithm was applied to select weighting parameters. Testing and validation of the algorithm were performed using pre- and post-treatment computed tomography angiography datasets of type-B aortic dissection cases (n=66). Results: The algorithm was efficient in simulating stent-induced aortic deformation (mean computing time on a single processor: 13.78±2.80s) and accurate at the morphological (curvature difference: 1.57±0.57%; cross-sectional area difference: 4.11±0.85%) and hemodynamic (similarity of wall shear stress-derived parameters: 90.16-90.94%) levels. Stent-induced wall deformation was higher (p

Endovascular repair with parallel stent-grafts (SG) is a challenging technique that reconstructs the luminal flow pathways by implanting parallel-placed SGs into the vessel. After treatment, occlusion and shifting of the parallel SGs are sometimes reported, which could be fatal and difficult to be re-operated. These issues are highly related to the local hemodynamic conditions in the stented region. In this study, a patient case treated by the octopus endograft technique (a head-SG with three limb-SGs) and experienced limb-SG occlusion is studied. 3-D models are established based on computed tomography (CT) angiography datasets pretreatment and posttreatment as well as during follow-ups. Hemodynamic quantities such as pressure drop, wall shear stress-related parameters, and flow division in limb-SGs and visceral arteries are quantitatively investigated. Optimizations on the length of the head-SG and diameter of the limb-SGs are analyzed based on various scenarios. The results indicate that when reconstructing the flow pathways via octopus stenting, it is important to ensure the flow distribution as physiologically required with this new morphology. Position (or length) of the head-SG and diameter of the limb-SGs play an important role in controlling flow division, and high time average wall shear stress (TAWSS) around the head-SG acts as a main factor for graft immigration. This study, by proposing optimization suggestions with hemodynamic analyses for a specific case, implicates that pretreatment SG scenarios may assist in wise selection and placement of the device and thus may improve long-term effectiveness of this kind of challenging endovascular repair techniques.

In this study, two generic haptens of phthalate esters (PAEs) were synthesized by introducing spacer arms with different lengths at the meta-position of an ester group of dimethyl phthalate (DMP). Immune and coating antigens were prepared by coupling the haptens with bovine serum albumin (BSA) and ovalbumin (OVA), respectively. Polyclonal antibodies against two immunogens were obtained, and their sensitivities and specificities were examined by enzyme-linked immunosorbent assay (ELISA). Under the optimal experimental conditions, the IC50 values of eight PAEs were obtained in the range of 17.12–102.57 ng mL−1 with the limits of detection (LODs) around 0.012–0.042 ng mL−1. The prepared polyclonal antibody with a higher titer displayed a broad cross-reactivity in the range of 16.69–83.84% for the other seven PAEs (except DMP). The average recoveries of PAEs at the spiked levels of 0.5, 1.0 and 10.0 ng mL−1 ranged from 63.9% to 103.6% with relative standard deviations (RSDs) below 11.32%, which was consistent with gas chromatography-mass spectrometry (GC-MS). The developed ELISA method was successfully applied to determine multiple PAEs in greenhouse soils.

Thoracic endovascular aortic repair (TEVAR) is commonly applied in type-B aortic dissection. For patients with dissection affects descending aorta and extends downward to involve abdominal aorta and possibly iliac arteries, false lumen (FL) expansion might occur post-TEVAR. Predictions of dissection development may assist in medical decision on re-intervention or surgery. In this study, two patients are selected with similar morphological features at initial presentation but with different long-term FL development post-TEVAR (stable and enlarged FL). Patient-specific models are established for each of the follow-ups. Flow boundaries and computational validations are obtained from Doppler ultrasound velocimetry. By analyzing the hemodynamic parameters, the false-to-true luminal pressure difference (PDiff) and particle relative residence time (RRT) are found related to FL remodeling. It is found that (i) the position of the first FL flow entry is the watershed of negative-and-positive PDiff and, in long-term follow-ups, and the position of largest PDiff is consistent with that of the greatest increase of FL width; (ii) high RRT occurs at the FL proximal tip and similar magnitude of RRT is found in both stable and enlarged cases; (iii) comparing to the RRT at 7days post-TEVAR, an increase of RRT afterwards in short-term is found in the stable case while a slight decrease of this parameter is found in the enlarged case, indicating that the variation of RRT in short-term post-TEVAR might be potential to predict long-term FL remodeling.