Deep Learning Models for Automatic Upper Airway Segmentation and Minimum Cross-Sectional Area Localisation in Two-Dimensional Images.

OBJECTIVE: To develop and validate convolutional neural network algorithms for automatic upper airway segmentation and minimum cross-sectional area (CSAmin) localisation in two-dimensional (2D) radiographic airway images. MATERIALS AND METHODS: Two hundred and one 2D airway images acquired using cone-beam computed tomography (CBCT) scanning were randomly assigned to a test group (n = 161) to train artificial intelligence (AI) models and a validation group (n = 40) to evaluate the accuracy of AI processing. Four AI models, UNet18, UNet36, DeepLab50 and DeepLab101, were trained to automatically segment the upper airway 2D images in the test group. Precision, recall, Intersection over Union, the dice similarity coefficient and size difference were used to evaluate the performance of the AI-driven segmentation models. The CSAmin height in each image was manually determined using three-dimensional CBCT data. The nonlinear mathematical morphology technique was used to calculate the CSAmin level. Height errors were assessed to evaluate the CSAmin localisation accuracy in the validation group. The time consumed for airway segmentation and CSAmin localisation was compared between manual and AI processing methods. RESULTS: The precision of all four segmentation models exceeded 90.0%. No significant differences were found in the accuracy of any AI models. The consistency of CSAmin localisation in specific segments between manual and AI processing was 0.944. AI processing was much more efficient than manual processing in terms of airway segmentation and CSAmin localisation. CONCLUSIONS: We successfully developed and validated a fully automatic AI-driven system for upper airway segmentation and CSAmin localisation using 2D radiographic airway images.

NLRC4 promotes the cGAS-STING signaling pathway by facilitating CBL-mediated K63-linked polyubiquitination of TBK1.

TANK-binding kinase 1 (TBK1) is crucial in producing type Ⅰ interferons (IFN-Ⅰ) that play critical functions in antiviral innate immunity. The tight regulation of TBK1, especially its activation, is very important. Here we identify NLRC4 as a positive regulator of TBK1. Ectopic expression of NLRC4 facilitates the activation of the IFN-ß promoter, the mRNA levels of IFN-ß, ISG54, and ISG56, and the nuclear translocation of interferon regulatory factor 3 induced by cGAS and STING. Consistently, under herpes simplex virus-1 (HSV-1) infection, knockdown or knockout of NLRC4 in BJ cells and primary peritoneal macrophages from Nlrc4-deficient (Nlrc4-/- ) mice show attenuated Ifn-ß, Isg54, and Isg56 mRNA transcription, TBK1 phosphorylation, and augmented viral replications. Moreover, Nlrc4-/- mice show higher mortality upon HSV-1 infection. Mechanistically, NLRC4 facilitates the interaction between TBK1 and the E3 ubiquitin ligase CBL to enhance the K63-linked polyubiquitination of TBK1. Our study elucidates a previously uncharacterized function for NLRC4 in upregulating the cGAS-STING signaling pathway and antiviral innate immunity.

UNC93B1 attenuates the cGAS-STING signaling pathway by targeting STING for autophagy-lysosome degradation.

Stimulator of interferon genes (STING) is a pivotal innate immune adaptor, and its functions during DNA virus infections have been extensively documented. However, its homeostatic regulation is not well understood. Our study demonstrates that Unc-93 homolog B1 (UNC93B1) is a crucial checker for STING to prevent hyperactivation. Ectopic expression of UNC93B1 attenuates IFN-ß promoter activity and the transcriptions of IFN-ß, ISG54, and ISG56 genes. Moreover, UNC93B1 also blocks the IRF3 nuclear translocation induced by ectopic expression of both cyclic GMP-AMP synthase (cGAS) and STING and reduces the stability of STING by facilitating its autophagy-lysosome degradation, which can be reversed by lysosome inhibitors. Mechanistically, UNC93B1 interacts with STING and suppresses STING-activated downstream signaling by delivering STING to the lysosomes for degradation, depending on its trafficking capability. UNC93B1 knockout in human embryonic kidney 293T cells facilitates IFN-ß promoter activity, IFN-ß, ISG54, and ISG56 transcriptions, and IRF3 nuclear translocation induced by ectopic expression of cGAS and STING. Infected with herpes simplex virus-1 (HSV-1), UNC93B1 knockdown BJ cells or primary peritoneal macrophages from Unc93b1-deficient (Unc93b1-/- ) mice show enhanced IFN-ß, ISG54, and ISG56 transcriptions, TBK1 phosphorylation, and reduced STING degradation and viral replication. In addition, Unc93b1-/-  mice exhibit higher IFN-ß, ISG54, and ISG56 transcriptions and lower mortality upon HSV-1 infection in vivo. Collectively, these findings demonstrate that UNC93B1 attenuates the cGAS-STING signaling pathway by targeting STING for autophagy-lysosome degradation and provide novel insights into the function of UNC93B1 in antiviral innate immunity.

MedQ: Lossless ultra-low-bit neural network quantization for medical image segmentation.

Implementing deep convolutional neural networks (CNNs) with boolean arithmetic is ideal for eliminating the notoriously high computational expense of deep learning models. However, although lossless model compression via weight-only quantization has been achieved in previous works, it is still an open problem about how to reduce the computation precision of CNNs without losing performance, especially for medical image segmentation tasks where data dimension is high and annotation is scarce. This paper presents a novel CNN quantization framework that can squeeze a deep model (both parameters and activation) to extremely low bitwidth, e.g., 1∼2 bits, while maintaining its high performance. In the new method, we first design a strong baseline quantizer with an optimizable quantization range. Then, to relieve the back-propagation difficulty caused by the discontinuous quantization function, we design a radical residual connection scheme that allows gradients to flow through every quantized layer freely. Moreover, a tanh-based derivative function is used to further boost gradient flow and a distributional loss is employed to regularize the model output. Extensive experiments and ablation studies are conducted on two well-established public 3D segmentation datasets, i.e., BRATS2020 and LiTS. Experimental results evidence that our framework not only outperforms state-of-the-art quantization approaches significantly, but also achieves lossless performance on both datasets with ternary (2-bit) quantization.

When human guanylate-binding proteins meet viral infections.

Innate immunity is the first line of host defense against viral infection. After invading into the cells, pathogen-associated-molecular-patterns derived from viruses are recognized by pattern recognition receptors to activate the downstream signaling pathways to induce the production of type I interferons (IFN-I) and inflammatory cytokines, which play critical functions in the host antiviral innate immune responses. Guanylate-binding proteins (GBPs) are IFN-inducible antiviral effectors belonging to the guanosine triphosphatases family. In addition to exerting direct antiviral functions against certain viruses, a few GBPs also exhibit regulatory roles on the host antiviral innate immunity. However, our understanding of the underlying molecular mechanisms of GBPs' roles in viral infection and host antiviral innate immune signaling is still very limited. Therefore, here we present an updated overview of the functions of GBPs during viral infection and in antiviral innate immunity, and highlight discrepancies in reported findings and current challenges for future studies, which will advance our understanding of the functions of GBPs and provide a scientific and theoretical basis for the regulation of antiviral innate immunity.

ß-Catenin Is Required for the cGAS/STING Signaling Pathway but Antagonized by the Herpes Simplex Virus 1 US3 Protein.

The cGAS/STING-mediated DNA-sensing signaling pathway is crucial for interferon (IFN) production and host antiviral responses. Herpes simplex virus I (HSV-1) is a DNA virus that has evolved multiple strategies to evade host immune responses. Here, we demonstrate that the highly conserved ß-catenin protein in the Wnt signaling pathway is an important factor to enhance the transcription of type I interferon (IFN-I) in the cGAS/STING signaling pathway, and the production of IFN-I mediated by ß-catenin was antagonized by HSV-1 US3 protein via its kinase activity. Infection by US3-deficienct HSV-1 and its kinase-dead variants failed to downregulate IFN-I and IFN-stimulated gene (ISG) production induced by ß-catenin. Consistent with this, absence of ß-catenin enhanced the replication of US3-deficienct HSV-1, but not wild-type HSV-1. The underlying mechanism was the interaction of US3 with ß-catenin and its hyperphosphorylation of ß-catenin at Thr556 to block its nuclear translocation. For the first time, HSV-1 US3 has been shown to inhibit IFN-I production through hyperphosphorylation of ß-catenin and to subvert host antiviral innate immunity.IMPORTANCE Although increasing evidence has demonstrated that HSV-1 subverts host immune responses and establishes lifelong latent infection, the molecular mechanisms by which HSV-1 interrupts antiviral innate immunity, especially the cGAS/STING-mediated cellular DNA-sensing signaling pathway, have not been fully explored. Here, we show that ß-catenin promotes cGAS/STING-mediated activation of the IFN pathway, which is important for cellular innate immune responses and intrinsic resistance to DNA virus infection. The protein kinase US3 antagonizes the production of IFN by targeting ß-catenin via its kinase activity. The findings in this study reveal a novel mechanism for HSV-1 to evade host antiviral immunity and add new knowledge to help in understanding the interaction between the host and HSV-1 infection.

Antibacterial Properties of Graphene-Based Nanomaterials.

Bacteria mediated infections may cause various acute or chronic illnesses and antibiotic resistance in pathogenic bacteria has become a serious health problem around the world due to their excessive use or misuse. Replacement of existing antibacterial agents with a novel and efficient alternative is the immediate demand to alleviate this problem. Graphene-based materials have been exquisitely studied because of their remarkable bactericidal activity on a wide range of bacteria. Graphene-based materials provide advantages of easy preparation, renewable, unique catalytic properties, and exceptional physical properties such as a large specific surface area and mechanical strength. However, several queries related to the mechanism of action, significance of size and composition toward bacterial activity, toxicity criteria, and other issues are needed to be addressed. This review summarizes the recent efforts that have been made so far toward the development of graphene-based antibacterial materials to face current challenges to combat against the bacterial targets. This review describes the inherent antibacterial activity of graphene-family and recent advances that have been made on graphene-based antibacterial materials covering the functionalization with silver nanoparticles, other metal ions/oxides nanoparticles, polymers, antibiotics, and enzymes along with their multicomponent functionalization. Furthermore, the review describes the biosafety of the graphene-based antibacterial materials. It is hoped that this review will provide valuable current insight and excite new ideas for the further development of safe and efficient graphene-based antibacterial materials.

Automatic Segmentation of Acute Ischemic Stroke From DWI Using 3-D Fully Convolutional DenseNets.

Acute ischemic stroke is recognized as a common cerebral vascular disease in aging people. Accurate diagnosis and timely treatment can effectively improve the blood supply of the ischemic area and reduce the risk of disability or even death. Understanding the location and size of infarcts plays a critical role in the diagnosis decision. However, manual localization and quantification of stroke lesions are laborious and time-consuming. In this paper, we propose a novel automatic method to segment acute ischemic stroke from diffusion weighted images (DWIs) using deep 3-D convolutional neural networks (CNNs). Our method can efficiently utilize 3-D contextual information and automatically learn very discriminative features in an end-to-end and data-driven way. To relieve the difficulty of training very deep 3-D CNN, we equip our network with dense connectivity to enable the unimpeded propagation of information and gradients throughout the network. We train our model with Dice objective function to combat the severe class imbalance problem in data. A DWI data set containing 242 subjects (90 for training, 62 for validation, and 90 for testing) with various types of acute ischemic stroke was constructed to evaluate our method. Our model achieved high performance on various metrics (Dice similarity coefficient: 79.13%, lesionwise precision: 92.67%, and lesionwise F1 score: 89.25%), outperforming the other state-of-the-art CNN methods by a large margin. We also evaluated the model on ISLES2015-SSIS data set and achieved very competitive performance, which further demonstrated its generalization capacity. The proposed method is fast and accurate, demonstrating a good potential in clinical routines.

Prospective study on factors affecting the prognosis of oral cancer in a Chinese population.

This study was performed to identify the factors affecting prognosis of oral cancer patients. 1240 pathologically confirmed oral cancer patients were included. The sociodemographic and clinical characteristics of all patients were collected. Univariate and multivariate Cox proportional hazards models were used to assess potential prognostic factors for survival. 1240 oral cancer patients were followed up for 49235.00 person months, and the 5-year overall survival rate was 64.38%. Both univariate and multivariate Cox regression analysis indicated that Body Mass Index < 18.5 kg/m2 (vs 18.5-23.9 kg/m2), age ≥ 55 years (vs < 55 years), clinical stages of II-IV (vs stage I), and poor differentiation (vs well differentiation) were associated with worse survival of oral cancer patients. While surgery (vs non-surgery) and origin of urban area (vs rural area) were protective factors. However, no significant association was found between adjuvant therapy and survival in oral cancer patients.

A general and efficient copper catalyst for the double carbonylation reaction.

The use of (NHC)CuI complex in combination with a N-heterocyclic carbene precursor as catalyst for the double carbonylation of aryl Iodides and secondary amines solves the problem of using the precious metal Pd and phosphine ligands. The new protocol requires a nonprecious metal catalyst and has greater generality than those previously reported.