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Excessive inflammation is the primary cause of mortality in patients with severe COVID-19, yet the underlying mechanisms remain poorly understood. Our study reveals that ACE2-dependent and -independent entries of SARS-CoV-2 in epithelial cells versus myeloid cells dictate viral replication and inflammatory responses. Mechanistically, SARS-CoV-2 NSP14 potently enhances NF-κB signalling by promoting IKK phosphorylation, while SARS-CoV-2 ORF6 exerts an opposing effect. In epithelial cells, ACE2-dependent SARS-CoV-2 entry enables viral replication, with translated ORF6 suppressing NF-κB signalling. In contrast, in myeloid cells, ACE2-independent entry blocks the translation of ORF6 and other viral structural proteins due to inefficient subgenomic RNA transcription, but NSP14 could be directly translated from genomic RNA, resulting in an abortive replication but hyperactivation of the NF-κB signalling pathway for proinflammatory cytokine production. Importantly, we identified TLR1 as a critical factor responsible for viral entry and subsequent inflammatory response through interaction with E and M proteins, which could be blocked by the small-molecule inhibitor Cu-CPT22. Collectively, our findings provide molecular insights into the mechanisms by which strong viral replication but scarce inflammatory response during the early (ACE2-dependent) infection stage, followed by low viral replication and potent inflammatory response in the late (ACE2-independent) infection stage, may contribute to COVID-19 progression.

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COVID-19 , SARS-CoV-2 , Humanos , Enzima Convertidora de Angiotensina 2 , COVID-19/metabolismo , COVID-19/virología , FN-kappa B/metabolismo , SARS-CoV-2/fisiología , Replicación Viral , Interacciones Huésped-Parásitos

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Cesium lead halide (CsPbX3, X = Cl, Br and I) perovskite nanocrystals embedded glasses exhibit good optical properties and have potential as gain media. However, origins of the amplified spontaneous emission (ASE) from CsPbX3 nanocrystals are controversial. Here, it is found that ASE is from CsPbX3 nanocrystals in inclusions instead of CsPbX3 nanocrystals dispersed in the glass matrix. Inclusions with various sizes are capable of generating ASE, and ASE of the inclusions can sustain at energy densities as high as several tens of mJ/cm2. Thresholds of the fs laser energy densities increase with the increase in fs laser wavelength, and high net optical gain coefficient is obtained.

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Organic electronic-based gas sensors hold great potential for portable healthcare- and environment-monitoring applications. It has recently been shown that introducing a porous structure into an organic semiconductor (OSC) film is an efficient way to improve the gas-sensing performance because it facilitates the interaction between the gaseous analyte and the active layer. Although several methods have been used to generate porous structures, the development of a robust approach that can facilely engineer the porous OSC film with a uniform pore pattern remains a challenge. Here, we demonstrate a robust approach to fabricate porous OSC films by using a femtosecond laser-processed porous dielectric layer template. With this laser-assisted strategy, various polymeric OSC layers with controllable pore size and well-defined pore patterns were achieved. The consequent porous p-type polymer-based device exhibits enhanced sensitivity to the ammonia analyte in the range from 100 ppb to 10 ppm with remarkable reproducibility and selectivity. The micropattern of the active layer was precisely controlled by generating various pore densities in the predecorated templates, which results in modulated ammonia sensitivities ranging from 30 to 65% ppm-1. Furthermore, we show that this approach can be used to fabricate flexible gas sensors with enhanced sensing performance and mechanical durability, which indicate that this femtosecond laser-assisted approach is very promising for the fabrication of next-generation wearable electronics.

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Ultrashort pulse laser shows good potential for heat control improvement in metal additive manufacturing. The challenge of applying ultrashort pulse laser as the heat source is to form a fully melted and dense microstructure. In this study, a picosecond pulse laser is introduced for fabricating single layer Ti6Al4V samples. The results, by examining through X-ray computed tomography (X-CT), scanning electron microscopy (SEM), show that highly dense Ti6Al4V samples were fabricated with optimized process parameters. The analysis of the cross section presents a three-zones structure from top to bottom in the sequence of the fully melted zone, the partially melted zone, and the heat-affected zone. A semi-quantitative study is performed to estimate the thermal efficiency of melted pool formation. The mechanical properties of the samples are tested using nano-indentation, showing an elastic modulus of 89.74 ± 0.74 GPa. The evidence of dense melted pool with good mechanical properties indicates that the picosecond laser can be integrated as the heat source with the current metal additive manufacturing to fabricate parts with accuracy control for the smaller size of thermal filed.

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PURPOSE: Due to the low contrast, blurry boundaries, and large amount of shadows in breast ultrasound (BUS) images, automatic tumor segmentation remains a challenging task. Deep learning provides a solution to this problem, since it can effectively extract representative features from lesions and the background in BUS images. METHODS: A novel automatic tumor segmentation method is proposed by combining a dilated fully convolutional network (DFCN) with a phase-based active contour (PBAC) model. The DFCN is an improved fully convolutional neural network with dilated convolution in deeper layers, fewer parameters, and batch normalization techniques; and has a large receptive field that can separate tumors from background. The predictions made by the DFCN are relatively rough due to blurry boundaries and variations in tumor sizes; thus, the PBAC model, which adds both region-based and phase-based energy functions, is applied to further improve segmentation results. The DFCN model is trained and tested in dataset 1 which contains 570 BUS images from 89 patients. In dataset 2, a 10-fold support vector machine (SVM) classifier is employed to verify the diagnostic ability using 460 features extracted from the segmentation results of the proposed method. RESULTS: Advantages of the present method were compared with three state-of-the-art networks; the FCN-8s, U-net, and dilated residual network (DRN). Experimental results from 170 BUS images show that the proposed method had a Dice Similarity coefficient of 88.97 ± 10.01%, a Hausdorff distance (HD) of 35.54 ± 29.70 pixels, and a mean absolute deviation (MAD) of 7.67 ± 6.67 pixels, which showed the best segmentation performance. In dataset 2, the area under curve (AUC) of the 10-fold SVM classifier was 0.795 which is similar to the classification using the manual segmentation results. CONCLUSIONS: The proposed automatic method may be sufficiently accurate, robust, and efficient for medical ultrasound applications.

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Neoplasias de la Mama/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador/métodos , Redes Neurales de la Computación , Ultrasonografía Mamaria , Automatización , Humanos , Modelos Teóricos

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OBJECTIVES: This work focused on extracting novel and validated digital high-throughput features to present a detailed and comprehensive description of the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) with the goal of improving the accuracy of ultrasound breast cancer diagnosis. METHODS: First, the phase congruency approach was used to segment the tumors automatically. Second, high-throughput features were designed and extracted on the basis of each BI-RADS category. Then features were selected based on the basis of a Student t test and genetic algorithm. Finally, the AdaBoost classifier was used to differentiate benign tumors from malignant ones. RESULTS: Experiments were conducted on a database of 138 pathologically proven breast tumors. The system was compared with 6 state-of-art BI-RADS feature extraction methods. By using leave-one-out cross-validation, our system achieved a highest overall accuracy of 93.48%, a sensitivity of 94.20%, a specificity of 92.75%, and an area under the receiver operating characteristic curve of 95.67%, respectively, which were superior to those of other methods. CONCLUSIONS: The experiments demonstrated that our computerized BI-RADS feature system was capable of helping radiologists detect breast cancers more accurately and provided more guidance for final decisions.

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Neoplasias de la Mama/diagnóstico por imagen , Sistemas de Información Radiológica/estadística & datos numéricos , Ultrasonografía Mamaria/estadística & datos numéricos , Mama/diagnóstico por imagen , Diagnóstico Diferencial , Femenino , Humanos , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Ultrasonografía Mamaria/métodos

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INTRODUCTION: In current clinical practice, invasive ductal carcinoma is always screened using medical imaging techniques and diagnosed using immunohistochemistry. Recent studies have illustrated that radiomics approaches provide a comprehensive characterization of entire tumors and can reveal predictive or prognostic associations between the images and medical outcomes. To better reveal the underlying biology, an improved understanding between objective image features and biologic characteristics is urgently required. PATIENTS AND METHODS: A total of 215 patients with definite histologic results were enrolled in our study. The tumors were automatically segmented using our phase-based active contour model. The high-throughput radiomics features were designed and extracted using a breast imaging reporting and data system and further selected using Student's t test, interfeature coefficients and a lasso regression model. The support vector machine classifier with threefold cross-validation was used to evaluate the relationship. RESULTS: The radiomics approach demonstrated a strong correlation between receptor status and subtypes (P < .05; area under the curve, 0.760). The appearance of hormone receptor-positive cancer and human epidermal growth factor receptor 2-negative cancer on ultrasound scans differs from that of triple-negative cancer. CONCLUSION: Our approach could assist clinicians with the accurate prediction of prognosis using ultrasound findings, allowing for early medical management and treatment.

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Neoplasias de la Mama/diagnóstico por imagen , Carcinoma Ductal de Mama/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador/métodos , Ultrasonografía Mamaria/métodos , Adulto , Biopsia , Mama/diagnóstico por imagen , Mama/patología , Mama/cirugía , Neoplasias de la Mama/patología , Neoplasias de la Mama/cirugía , Carcinoma Ductal de Mama/patología , Carcinoma Ductal de Mama/cirugía , Estudios de Factibilidad , Femenino , Humanos , Persona de Mediana Edad , Clasificación del Tumor , Pronóstico , Receptor ErbB-2/metabolismo , Receptores de Estrógenos/metabolismo , Receptores de Progesterona/metabolismo , Estudios Retrospectivos , Máquina de Vectores de Soporte

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PURPOSE: Digital Breast Imaging Reporting and Data System (BI-RADS) features extracted from ultrasound images are essential in computer-aided diagnosis, prediction, and prognosis of breast cancer. This study focuses on the reproducibility of quantitative high-throughput BI-RADS features in the presence of variations due to different segmentation results, various ultrasound machine models, and multiple ultrasound machine settings. METHODS: Dataset 1 consists of 399 patients with invasive breast cancer and is used as the training set to measure the reproducibility of features, while dataset 2 consists of 138 other patients and is a validation set used to evaluate the diagnosis performances of the final reproducible features. Four hundred and sixty high-throughput BI-RADS features are designed and quantized according to BI-RADS lexicon. Concordance Correlation Coefficient (CCC) and Deviation (Dev) are used to assess the effect of the segmentation methods and Between-class Distance (BD) is used to study the influences of the machine models. In addition, the features jointly shared by two methodologies are further investigated on their effects with multiple machine settings. Subsequently, the absolute value of Pearson Correlation Coefficient (Rabs ) is applied for redundancy elimination. Finally, the features that are reproducible and not redundant are preserved as the stable feature set. A 10-fold Support Vector Machine (SVM) classifier is employed to verify the diagnostic ability. RESULTS: One hundred and fifty-three features were found to have high reproducibility (CCC > 0.9 & Dev < 0.1) within the manual and automatic segmentation. Three hundred and thirty-nine features were stable (BD < 0.2) at different machine models. Two feature sets shared the same 102 features, in which nine features were highly sensitive to the machine settings. Forty-six features were finally preserved after redundancy elimination. For the validation in dataset 2, the area under curve (AUC) of the 10-fold SVM classifier was 0.915. CONCLUSIONS: Three factors, segmentation results, machine models, and machine settings may affect the reproducibility of high-throughput BI-RADS features to various degrees. Our 46 reproducible features were robust to these factors and were capable of distinguishing benign and malignant breast tumors.

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Neoplasias de la Mama/diagnóstico por imagen , Ultrasonografía Mamaria , Femenino , Humanos , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Ultrasonografía

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This study aims to predict expression of estrogen receptor (ER) in breast cancer by radiomics. Firstly, breast cancer images are segmented automatically by phase-based active contour (PBAC) method. Secondly, high-throughput features of ultrasound images are extracted and quantized. A total of 404 high-throughput features are divided into three categories, such as morphology, texture and wavelet. Then, the features are selected by R language and genetic algorithm combining minimum-redundancy-maximum-relevance (mRMR) criterion. Finally, support vector machine (SVM) and AdaBoost are used as classifiers, achieving the goal of predicting ER by breast ultrasound image. One hundred and four cases of breast cancer patients were conducted in the experiment and optimal indicator was obtained using AdaBoost. The prediction accuracy of molecular marker ER could achieve 75.96% and the highest area under the receiver operating characteristic curve (AUC) was 79.39%. According to the results of experiment, the feasibility of predicting expression of ER in breast cancer using radiomics was verified.