Radiomics model based on intratumoral and peritumoral features for predicting major pathological response in non-small cell lung cancer receiving neoadjuvant immunochemotherapy.

Objective: To establish a radiomics model based on intratumoral and peritumoral features extracted from pre-treatment CT to predict the major pathological response (MPR) in patients with non-small cell lung cancer (NSCLC) receiving neoadjuvant immunochemotherapy. Methods: A total of 148 NSCLC patients who underwent neoadjuvant immunochemotherapy from two centers (SRRSH and ZCH) were retrospectively included. The SRRSH dataset (n=105) was used as the training and internal validation cohort. Radiomics features of intratumoral (T) and peritumoral regions (P1 = 0-5mm, P2 = 5-10mm, and P3 = 10-15mm) were extracted from pre-treatment CT. Intra- and inter- class correlation coefficients and least absolute shrinkage and selection operator were used to feature selection. Four single ROI models mentioned above and a combined radiomics (CR: T+P1+P2+P3) model were established by using machine learning algorithms. Clinical factors were selected to construct the combined radiomics-clinical (CRC) model, which was validated in the external center ZCH (n=43). The performance of the models was assessed by DeLong test, calibration curve and decision curve analysis. Results: Histopathological type was the only independent clinical risk factor. The model CR with eight selected radiomics features demonstrated a good predictive performance in the internal validation (AUC=0.810) and significantly improved than the model T (AUC=0.810 vs 0.619, p<0.05). The model CRC yielded the best predictive capability (AUC=0.814) and obtained satisfactory performance in the independent external test set (AUC=0.768, 95% CI: 0.62-0.91). Conclusion: We established a CRC model that incorporates intratumoral and peritumoral features and histopathological type, providing an effective approach for selecting NSCLC patients suitable for neoadjuvant immunochemotherapy.

Deep learning and big data mining for Metal-Organic frameworks with high performance for simultaneous desulfurization and carbon capture.

Carbon capture and desulfurization of flue gases are crucial for the achievement of carbon neutrality and sustainable development. In this work, the "one-step" adsorption technology with high-performance metal-organic frameworks (MOFs) was proposed to simultaneously capture the SO2 and CO2. Four machine learning algorithms were used to predict the performance indicators (NCO2+SO2, SCO2+SO2/N2, and TSN) of MOFs, with Multi-Layer Perceptron Regression (MLPR) showing better performance (R2 = 0.93). To address sparse data of MOF chemical descriptors, we introduced the Deep Factorization Machines (DeepFM) model, outperforming MLPR with a higher R2 of 0.95. Then, sensitivity analysis was employed to find that the adsorption heat and porosity were the key factors for SO2 and CO2 capture performance of MOF, while the influence of open alkali metal sites also stood out. Furthermore, we established a kinetic model to batch simulate the breakthrough curves of TOP 1000 MOFs to investigate their dynamic adsorption separation performance for SO2/CO2/N2. The TOP 20 MOFs screened by the dynamic performance highly overlap with those screened by the static performance, with 76 % containing open alkali metal sites. This integrated approach of computational screening, machine learning, and dynamic analysis significantly advances the development of efficient MOF adsorbents for flue gas treatment.

CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD.

BACKGROUND: Computed tomography (CT) plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease (COPD). This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients. METHODS: This retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group. The radiomic features of the whole lung volume were extracted. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection and radiomic signature construction. A radiomic nomogram was established by combining the radiomic score and clinical factors. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomic nomogram in the training, internal validation, and independent external validation cohorts. RESULTS: Eighteen radiomic features were collected from the whole lung volume to construct a radiomic model. The area under the curve (AUC) of the radiomic model in the training, internal, and independent external validation cohorts were 0.888 [95% confidence interval (CI) 0.869-0.906], 0.874 (95%CI 0.844-0.904) and 0.846 (95%CI 0.822-0.870), respectively. All were higher than the clinical model (AUC were 0.732, 0.714, and 0.777, respectively, P < 0.001). DCA demonstrated that the nomogram constructed by combining radiomic score, age, sex, height, and smoking status was superior to the clinical factor model. CONCLUSIONS: The intuitive nomogram constructed by CT-based whole-lung radiomic has shown good performance and high accuracy in identifying COPD in this multicenter study.

Outer Membrane Vesicles Released from Garlic Exosome-like Nanoparticles (GaELNs) Train Gut Bacteria that Reverses Type 2 Diabetes via the Gut-Brain Axis.

Gut microbiota function has numerous effects on humans and the diet humans consume has emerged as a pivotal determinant of gut microbiota function. Here, a new concept that gut microbiota can be trained by diet-derived exosome-like nanoparticles (ELNs) to release healthy outer membrane vesicles (OMVs) is introduced. Specifically, OMVs released from garlic ELN (GaELNs) trained human gut Akkermansia muciniphila (A. muciniphila) can reverse high-fat diet-induced type 2 diabetes (T2DM) in mice. Oral administration of OMVs released from GaELNs trained A. muciniphila can traffick to the brain where they are taken up by microglial cells, resulting in inhibition of high-fat diet-induced brain inflammation. GaELNs treatment increases the levels of OMV Amuc-1100, P9, and phosphatidylcholines. Increasing the levels of Amuc-1100 and P9 leads to increasing the GLP-1 plasma level. Increasing the levels of phosphatidylcholines is required for inhibition of cGas and STING-mediated inflammation and GLP-1R crosstalk with the insulin pathway that leads to increasing expression of Insulin Receptor Substrate (IRS1 and IRS2) on OMV targeted cells. These findings reveal a molecular mechanism whereby OMVs from plant nanoparticle-trained gut bacteria regulate genes expressed in the brain, and have implications for the treatment of brain dysfunction caused by a metabolic syndrome.

CT-based whole lung radiomics nomogram: a tool for identifying the risk of cardiovascular disease in patients with chronic obstructive pulmonary disease.

OBJECTIVES: To evaluate the value of CT-based whole lung radiomics nomogram for identifying the risk of cardiovascular disease (CVD) in patients with chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS: A total of 974 patients with COPD were divided into a training cohort (n = 402), an internal validation cohort (n = 172), and an external validation cohort (n = 400) from three hospitals. Clinical data and CT findings were analyzed. Radiomics features of whole lung were extracted from the non-contrast chest CT images. A radiomics signature was constructed with algorithms. Combined with the radiomics score and independent clinical factors, multivariate logistic regression analysis was used to establish a radiomics nomogram. ROC curve was used to analyze the prediction performance of the model. RESULTS: Age, weight, and GOLD were the independent clinical factors. A total of 1218 features were extracted and reduced to 15 features to build the radiomics signature. In the training cohort, the combined model (area under the curve [AUC], 0.731) showed better discrimination capability (p < 0.001) than the clinical factors model (AUC, 0.605). In the internal validation cohort, the combined model (AUC, 0.727) performed better (p = 0.032) than the clinical factors model (AUC, 0.629). In the external validation cohort, the combined model (AUC, 0.725) performed better (p < 0.001) than the clinical factors model (AUC, 0.690). Decision curve analysis demonstrated the radiomics nomogram outperformed the clinical factors model. CONCLUSION: The CT-based whole lung radiomics nomogram has the potential to identify the risk of CVD in patients with COPD. CLINICAL RELEVANCE STATEMENT: This study helps to identify cardiovascular disease risk in patients with chronic obstructive pulmonary disease on chest CT scans. KEY POINTS: • To investigate the value of CT-based whole lung radiomics features in identifying the risk of cardiovascular disease in chronic obstructive pulmonary disease patients. • The radiomics nomogram showed better performance than the clinical factors model to identify the risk of cardiovascular disease in patients with chronic obstructive pulmonary disease. • The radiomics nomogram demonstrated excellent performance in the training, internal validation, and external validation cohort (AUC, 0.731; AUC, 0.727; AUC, 0.725).

Genomic Features and Comparative Genomic Analysis of Streptococcus sp. v1. nov., Isolated from an Endophthalmitis Patient.

Endophthalmitis is an acute inflammatory intraocular condition that can cause permanent vision loss. The treatment strategy and visual outcome partly depend on the identification of the agents of pathogens. In this study, metagenomic sequencing was conducted to investigate the microbial and antibiotic resistance genes (ARGs) composition in the vitreous (intraocular body fluid) of an endophthalmitis patient, who progressed rapidly and accompanied by severe pain. Metagenomic sequencing data revealed that the vitreous sample was predominated by Streptococcus, with a low-diversity microbiome in the vitreous. This strain harbor's the ARGs mainly against beta-lactam, macrolide-lincosamide-streptogramin, and multidrug. Additionally, metagenome-assembled genome sequence of Streptococcus sp. v1. nov. was identified. The Tetra Correlation Search (TCS) analysis uncovered that the closest relative of the Streptococcus sp. v1. nov. was Streptococcus mitis SK321. Pan/core genome analysis for Streptococcus sp. v1. nov. and TCS top 25 hits strains revealed that most unique genes of Streptococcus sp. v1. nov. were linked to ATP-binding cassette transport system, which could indicate unique virulence and pathogenic potentials of Streptococcus sp. v1. nov. In addition, a total of 7 virulence factors were identified, and the overwhelming of them were classified into "offensive virulence factors". The high pathogenicity of Streptococcus sp. v1. nov. could be a reason for the patient's rapid disease progression. Our study was first isolated an ocular pathogen with highly virulent based on metagenomic sequencing and bioinformatics analysis, which has important reference value for revealing the composition and genome characteristics of pathogens in endophthalmitis patient in the future.

Low-Temperature Transient Liquid Phase Bonding Technology via Cu Porous-Sn58Bi Solid-Liquid System under Formic Acid Atmosphere.

This study proposes a low-temperature transient liquid phase bonding (TLPB) method using Sn58Bi/porous Cu/Sn58Bi to enable efficient power-device packaging at high temperatures. The bonding mechanism is attributed to the rapid reaction between porous Cu and Sn58Bi solder, leading to the formation of intermetallic compounds with high melting point at low temperatures. The present paper investigates the effects of bonding atmosphere, bonding time, and external pressure on the shear strength of metal joints. Under formic acid (FA) atmosphere, Cu6Sn5 forms at the porous Cu foil/Sn58Bi interface, and some of it transforms into Cu3Sn. External pressure significantly reduces the micropores and thickness of the joint interconnection layer, resulting in a ductile fracture failure mode. The metal joint obtained under a pressure of 10 MPa at 250 °C for 5 min exhibits outstanding bonding mechanical performance with a shear strength of 62.2 MPa.

An extracellular vesicular mutant KRAS-associated protein complex promotes lung inflammation and tumor growth.

Extracellular vesicles (EVs) contain more than 100 proteins. Whether there are EVs proteins that act as an 'organiser' of protein networks to generate a new or different biological effect from that identified in EV-producing cells has never been demonstrated. Here, as a proof-of-concept, we demonstrate that EV-G12D-mutant KRAS serves as a leader that forms a protein complex and promotes lung inflammation and tumour growth via the Fn1/IL-17A/FGF21 axis. Mechanistically, in contrast to cytosol derived G12D-mutant KRAS complex from EVs-producing cells, EV-G12D-mutant KRAS interacts with a group of extracellular vesicular factors via fibronectin-1 (Fn1), which drives the activation of the IL-17A/FGF21 inflammation pathway in EV recipient cells. We show that: (i), depletion of EV-Fn1 leads to a reduction of a number of inflammatory cytokines including IL-17A; (ii) induction of IL-17A promotes lung inflammation, which in turn leads to IL-17A mediated induction of FGF21 in the lung; and (iii) EV-G12D-mutant KRAS complex mediated lung inflammation is abrogated in IL-17 receptor KO mice. These findings establish a new concept in EV function with potential implications for novel therapeutic interventions in EV-mediated disease processes.

Delta radiomics model for the prediction of progression-free survival time in advanced non-small-cell lung cancer patients after immunotherapy.

Objective: To assess the validity of pre- and posttreatment computed tomography (CT)-based radiomics signatures and delta radiomics signatures for predicting progression-free survival (PFS) in stage III-IV non-small-cell lung cancer (NSCLC) patients after immune checkpoint inhibitor (ICI) therapy. Methods: Quantitative image features of the largest primary lung tumours were extracted on CT-enhanced imaging at baseline (time point 0, TP0) and after the 2nd-3rd immunotherapy cycles (time point 1, TP1). The critical features were selected to construct TP0, TP1 and delta radiomics signatures for the risk stratification of patient survival after ICI treatment. In addition, a prediction model integrating the clinicopathologic risk characteristics and phenotypic signature was developed for the prediction of PFS. Results: The C-index of TP0, TP1 and delta radiomics models in the training and validation cohort were 0.64, 0.75, 0.80, and 0.61, 0.68, 0.78, respectively. The delta radiomics score exhibited good accuracy for distinguishing patients with slow and rapid progression to ICI treatment. The predictive accuracy of the combined prediction model was higher than that of the clinical prediction model in both training and validation sets (P<0.05), with a C-index of 0.83 and 0.70, respectively. Additionally, the delta radiomics model (C-index of 0.86) had a higher predictive accuracy compared to PD-L1 expression (C-index of 0.50) (P<0.0001). Conclusions: The combined prediction model including clinicopathologic characteristics (tumour anatomical classification and brain metastasis) and the delta radiomics signature could achieve the individualized prediction of PFS in ICIs-treated NSCLC patients.

The value of CT-based radiomics nomogram in differential diagnosis of different histological types of gastric cancer.

To establish and verify a nomogram based on computed tomography (CT) radiomics analysis to predict the histological types of gastric cancer preoperatively for patients with surgical indications. A sum of 171 patients with gastric cancer were included into this retrospective study. The least absolute shrinkage and selection operator (LASSO) was used for feature selection while the multivariate Logistic regression method was used for radiomics model and nomogram building. The area under curve (AUC) was used for performance evaluation in this study. The radiomics model got AUCs of 0.755 (95% CI 0.650-0.859), 0.71 (95% CI 0.543-0.875) and 0.712 (95% CI 0.500-0.923) for histological prediction in the training, the internal and external verification cohorts. The radiomics nomogram based on radiomics features and Carbohydrate antigen 125 (CA125) showed good discriminant performance in the training cohort (AUC: 0.777; 95% CI 0.679-0.875), the internal (AUC: 0.726; 95% CI 0.5591-0.8933) and external verification cohort (AUC: 0.720; 95% CI 0.5036-0.9358). The calibration curve of the radiomics nomogram also showed good results. The decision curve analysis (DCA) shows that the radiomics nomogram is clinically practical. The radiomics nomogram established and verified in this study showed good performance for the preoperative histological prediction of gastric cancer, which might contribute to the formulation of a better clinical treatment plan.

Gut bacterial isoamylamine promotes age-related cognitive dysfunction by promoting microglial cell death.

The intestinal microbiome releases a plethora of small molecules. Here, we show that the Ruminococcaceae metabolite isoamylamine (IAA) is enriched in aged mice and elderly people, whereas Ruminococcaceae phages, belonging to the Myoviridae family, are reduced. Young mice orally administered IAA show cognitive decline, whereas Myoviridae phage administration reduces IAA levels. Mechanistically, IAA promotes apoptosis of microglial cells by recruiting the transcriptional regulator p53 to the S100A8 promoter region. Specifically, IAA recognizes and binds the S100A8 promoter region to facilitate the unwinding of its self-complementary hairpin structure, thereby subsequently enabling p53 to access the S100A8 promoter and enhance S100A8 expression. Thus, our findings provide evidence that small molecules released from the gut microbiome can directly bind genomic DNA and act as transcriptional coregulators by recruiting transcription factors. These findings further unveil a molecular mechanism that connects gut metabolism to gene expression in the brain with implications for disease development.

An original deep learning model using limited data for COVID-19 discrimination: A multicenter study.

OBJECTIVES: Artificial intelligence (AI) has been proved to be a highly efficient tool for COVID-19 diagnosis, but the large data size and heavy label force required for algorithm development and the poor generalizability of AI algorithms, to some extent, limit the application of AI technology in clinical practice. The aim of this study is to develop an AI algorithm with high robustness using limited chest CT data for COVID-19 discrimination. METHODS: A three dimensional algorithm that combined multi-instance learning with the LSTM architecture (3DMTM) was developed for differentiating COVID-19 from community acquired pneumonia (CAP) while logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and a three dimensional convolutional neural network set for comparison. Totally, 515 patients with or without COVID-19 between December 2019 and March 2020 from five different hospitals were recruited and divided into relatively large (150 COVID-19 and 183 CAP cases) and relatively small datasets (17 COVID-19 and 35 CAP cases) for either training or validation and another independent dataset (37 COVID-19 and 93 CAP cases) for external test. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, F1 score, and G-mean were utilized for performance evaluation. RESULTS: In the external test cohort, the relatively large data-based 3DMTM-LD achieved an AUC of 0.956 (95% confidence interval, 95% CI, 0.929∼0.982) with 86.2% and 98.0% for its sensitivity and specificity. 3DMTM-SD got an AUC of 0.937 (95% CI, 0.909∼0.965), while the AUC of 3DCM-SD decreased dramatically to 0.714 (95% CI, 0.649∼0.780) with training data reduction. KNN-MMSD, LR-MMSD, SVM-MMSD, and 3DCM-MMSD benefited significantly from the inclusion of clinical information while models trained with relatively large dataset got slight performance improvement in COVID-19 discrimination. 3DMTM, trained with either CT or multi-modal data, presented comparably excellent performance in COVID-19 discrimination. CONCLUSIONS: The 3DMTM algorithm presented excellent robustness for COVID-19 discrimination with limited CT data. 3DMTM based on CT data performed comparably in COVID-19 discrimination with that trained with multi-modal information. Clinical information could improve the performance of KNN, LR, SVM, and 3DCM in COVID-19 discrimination, especially in the scenario with limited data for training.

Garlic exosome-like nanoparticles reverse high-fat diet induced obesity via the gut/brain axis.

Background: Obesity is becoming a global epidemic and reversing the pathological processes underlying obesity and metabolic co-morbidities is challenging. Obesity induced chronic inflammation including brain inflammation is a hallmark of obesity via the gut-brain axis. The objective of this study was to develop garlic exosome-like nanoparticles (GaELNs) that inhibit systemic as well as brain inflammatory activity and reverse a HFD induced obesity in mice. Methods: GELNs were isolated and administrated orally into HFD fed mice. GaELNs were fluorescent labeled for monitoring their in vivo trafficking route after oral administration and quantified the number particles in several tissues. The brain inflammation was determined by measuring inflammatory cytokines by ELISA and real-time PCR. Mitochondrial membrane permeability of microglial cells was determined using JC-10 fluorescence dye. The in vivo apoptotic cell death was quantified by TUNEL assay. The brain metabolites were identified and quantified by LC-MS analysis. Memory function of the mice was determined by several memory functional analysis. The effect of GaELNs on glucose and insulin response of the mice was determined by glucose and insulin tolerance tests. c-Myc localization and interaction with BASP1 and calmodulin was determined by confocal microscopy. Results: Our results show that GaELNs is preferentially taken up microglial cells and inhibits the brain inflammation in HFD mice. GaELN phosphatidic acid (PA) (36:4) is required for the uptake of GaELNs via interaction with microglial BASP1. Formation of the GaELNs/BASP1 complex is required for inhibition of c-Myc mediated expression of STING. GaELN PA binds to BASP1, leading to inhibition of c-Myc expression and activity through competitively binding to CaM with c-Myc transcription factor. Inhibition of STING activity leads to reducing the expression of an array of inflammatory cytokines including IFN-γ and TNF-α. IFN-γ induces the expression of IDO1, which in turn the metabolites generated as IDO1 dependent manner activate the AHR pathway that contributes to developing obesity. The metabolites derived from the GaELNs treated microglial cells promote neuronal differentiation and inhibit mitochondrial mediated neuronal cell death. GaELNs treated HFD mice showed improved memory function and increased glucose tolerance and insulin sensitivity in these mice. Conclusion: Collectively, these results demonstrate how nanoparticles from a healthy diet can inhibit unhealthy high-fat diet induced brain inflammation and reveal a link between brain microglia/diet to brain inflammatory disease outcomes via diet-derived exosome-like nanoparticles.

Exosome-like nanoparticles from Mulberry bark prevent DSS-induced colitis via the AhR/COPS8 pathway.

Bark protects the tree against environmental insults. Here, we analyzed whether this defensive strategy could be utilized to broadly enhance protection against colitis. As a proof of concept, we show that exosome-like nanoparticles (MBELNs) derived from edible mulberry bark confer protection against colitis in a mouse model by promoting heat shock protein family A (Hsp70) member 8 (HSPA8)-mediated activation of the AhR signaling pathway. Activation of this pathway in intestinal epithelial cells leads to the induction of COP9 Constitutive Photomorphogenic Homolog Subunit 8 (COPS8). Utilizing a gut epithelium-specific knockout of COPS8, we demonstrate that COPS8 acts downstream of the AhR pathway and is required for the protective effect of MBELNs by inducing an array of anti-microbial peptides. Our results indicate that MBELNs represent an undescribed mode of inter-kingdom communication in the mammalian intestine through an AhR-COPS8-mediated anti-inflammatory pathway. These data suggest that inflammatory pathways in a microbiota-enriched intestinal environment are regulated by COPS8 and that edible plant-derived ELNs may hold the potential as new agents for the prevention and treatment of gut-related inflammatory disease.

Restoring Oat Nanoparticles Mediated Brain Memory Function of Mice Fed Alcohol by Sorting Inflammatory Dectin-1 Complex Into Microglial Exosomes.

Microglia modulate pro-inflammatory and neurotoxic activities. Edible plant-derived factors improve brain function. Current knowledge of the molecular interactions between edible plant-derived factors and the microglial cell is limited. Here an alcohol-induced chronic brain inflammation model is used to identify that the microglial cell is the novel target of oat nanoparticles (oatN). Oral administration of oatN inhibits brain inflammation and improves brain memory function of mice that are fed alcohol. Mechanistically, ethanol activates dectin-1 mediated inflammatory pathway. OatN is taken up by microglial cells via ß-glucan mediated binding to microglial hippocalcin (HPCA) whereas oatN digalactosyldiacylglycerol (DGDG) prevents assess of oatN ß-glucan to dectin-1. Subsequently endocytosed ß-glucan/HPCA is recruited in an endosomal recycling compartment (ERC) via interaction with Rab11a. This complex then sequesters the dectin-1 in the ERC in an oatN ß-glucan dependent manner and alters the location of dectin-1 from Golgi to early endosomes and lysosomes and increases exportation of dectin-1 into exosomes in an Rab11a dependent manner. Collectively, these cascading actions lead to preventing the activation of the alcoholic induced brain inflammation signing pathway(s). This coordinated assembling of the HPCA/Rab11a/dectin-1 complex by oral administration of oatN may contribute to the prevention of brain inflammation.

Lemon exosome-like nanoparticles enhance stress survival of gut bacteria by RNase P-mediated specific tRNA decay.

Diet and bile play critical roles in shaping gut microbiota, but the molecular mechanism underlying interplay with intestinal microbiota is unclear. Here, we showed that lemon-derived exosome-like nanoparticles (LELNs) enhance lactobacilli toleration to bile. To decipher the mechanism, we used Lactobacillus rhamnosus GG (LGG) as proof of concept to show that LELNs enhance LGG bile resistance via limiting production of Msp1 and Msp3, resulting in decrease of bile accessibility to cell membrane. Furthermore, we found that decline of Msps protein levels was regulated through specific tRNAser UCC and tRNAser UCG decay. We identified RNase P, an essential housekeeping endonuclease, being responsible for LELNs-induced tRNAser UCC and tRNAser UCG decay. We further identified galacturonic acid-enriched pectin-type polysaccharide as the active factor in LELNs to increase bile resistance and downregulate tRNAser UCC and tRNAser UCG level in the LGG. Our study demonstrates a tRNA-based gene expression regulation mechanism among lactobacilli to increase bile resistance.

Plant-derived exosomal microRNAs inhibit lung inflammation induced by exosomes SARS-CoV-2 Nsp12.

Lung inflammation is a hallmark of coronavirus disease 2019 (COVID-19). In this study, we show that mice develop inflamed lung tissue after being administered exosomes released from the lung epithelial cells exposed to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Nsp12 and Nsp13 (exosomesNsp12Nsp13). Mechanistically, we show that exosomesNsp12Nsp13 are taken up by lung macrophages, leading to activation of nuclear factor κB (NF-κB) and the subsequent induction of an array of inflammatory cytokines. Induction of tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-1ß from exosomesNsp12Nsp13-activated lung macrophages contributes to inducing apoptosis in lung epithelial cells. Induction of exosomesNsp12Nsp13-mediated lung inflammation was abolished with ginger exosome-like nanoparticle (GELN) microRNA (miRNA aly-miR396a-5p. The role of GELNs in inhibition of the SARS-CoV-2-induced cytopathic effect (CPE) was further demonstrated via GELN aly-miR396a-5p- and rlcv-miR-rL1-28-3p-mediated inhibition of expression of Nsp12 and spike genes, respectively. Taken together, our results reveal exosomesNsp12Nsp13 as potentially important contributors to the development of lung inflammation, and GELNs are a potential therapeutic agent to treat COVID-19.

Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19.

COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful information for COVID-19 diagnosis. Although several deep learning-based segmentation methods have been proposed for COVID-19 segmentation and have achieved state-of-the-art results, the segmentation accuracy is still not high enough (approximately 85%) due to the variations of COVID-19 infected areas (such as shape and size variations) and the similarities between COVID-19 and non-COVID-infected areas. To improve the segmentation accuracy of COVID-19 infected areas, we propose an interactive attention refinement network (Attention RefNet). The interactive attention refinement network can be connected with any segmentation network and trained with the segmentation network in an end-to-end fashion. We propose a skip connection attention module to improve the important features in both segmentation and refinement networks and a seed point module to enhance the important seeds (positions) for interactive refinement. The effectiveness of the proposed method was demonstrated on public datasets (COVID-19CTSeg and MICCAI) and our private multicenter dataset. The segmentation accuracy was improved to more than 90%. We also confirmed the generalizability of the proposed network on our multicenter dataset. The proposed method can still achieve high segmentation accuracy.

Radiological insights of ectopic thyroid in the porta hepatis: A case report and review of the literature.

BACKGROUND: Ectopic thyroid is defined as a rare developmental anomaly where thyroid tissues are atypically found in locations other than its normal anatomical position: Anterolateral to the second, third, and fourth tracheal cartilages. An intemperate descent or a migration failure of the thyroid anlage results in sub-diaphragmatic thyroid ectopia, a sparse clinical entity. CASE SUMMARY: This case portrays a 63-year-old female patient presenting with chronic abdominal discomfort at a local hospital whereby a computed tomography (CT) scan revealed a well-defined mass in the hepatic entrance. For further examination, the patient underwent a CT scan with contrast, magnetic resonance imaging (MRI), and CT-angiography (CTA) at our department. The CT scan showed a well-defined and high attenuated mass measuring 43 mm × 38 mm in the hepatic entrance with calcification. The CTA revealed an additional finding: Blood supply to the mass from the right hepatic artery. MRI of the upper abdomen demonstrated a mass with mixed signal intensity on T1 and T2 weighted images in the hepatic entrance. The patient underwent surgery with resection of the mass which was sent for histopathology. Ectopic thyroid at the level of porta hepatis with nodules was the definitive diagnosis since histopathological report revealed presence of thyroid tissue in the resected liver mass. CONCLUSION: This case delivers a rare insight of pre-operative radiological imaging of an ectopic thyroid located in the liver. These findings can aid in narrowing down potential differential diagnosis when managing a patient with those subsequent findings.

Pulmonary artery trunk enlargement on admission as a predictor of mortality in in-hospital patients with COVID-19.

PURPOSE: To describe the prognostic value of pulmonary artery (PA) trunk enlargement on the admission of in-hospital patients with severe COVID-19 infection by unenhanced CT image. MATERIALS AND METHODS: In-hospital patients confirmed COVID-19 from January 18, 2020, to March 7, 2020, were retrospectively enrolled. PA trunk diameters on admission and death events were collected to calculate the optimum cutoff using a receiver operating characteristic curve. According to the cutoff, the subjects on admission were divided into two groups. Then the in-hospital various parameters were compared between the two groups to assess the predictive value of PA trunk diameter. RESULTS: In the 180 enrolled in-hospital patients (46.99 ± 14.95 years; 93 (51.7%) female, 14 patients (7.8%) died during their hospitalization. The optimum cutoff PA trunk diameter to predict in-hospital mortality was > 29 mm with a sensitivity of 92.59% and a specificity of 91.11%. Kaplan-Meier survival curves for PA trunk diameter on admission showed that a PA trunk diameter > 29 mm was a significant predictor of subsequent death (log-rank < 0.001, median survival time of PA > 29 mm was 28 days). CONCLUSION: PA trunk enlargement can be a useful predictive factor for distinguishing between mild and severe COVID-19 disease progression.