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Influenza A virus (IAV) can cause high morbidity and mortality globally every year. Myriad host kinases and their related signaling pathways are involved in IAV infection, and the important role of the c-Jun N-terminal kinase signaling pathway during infection has been demonstrated. SP600125, an inhibitor of c-Jun N-terminal kinase, was found in our previous study to suppress IAV replication in vitro. In this study, we established a mouse model of H1N1 IAV infection and treated the mice with SP600125 to study its protective effect. The results showed that SP600125 treatment reduced the pulmonary inflammatory response, lung injury, and pulmonary viral load and increased the survival rate of H1N1-infected mice. Our data confirm the crucial role of c-Jun N terminal kinase in H1N1 virus replication and inflammatory responses in vivo. Hence, we speculate that SP600125 has a potential antiviral therapeutic benefit against IAV infection. Supplementary Information The online version contains supplementary material available at 10.1007/s00705-021-05103-0.

Objective To develop a fully automated AI system to quantitatively assess the disease severity and disease progression of COVID-19 using thick-section chest CT images. Methods In this retrospective study, an AI system was developed to automatically segment and quantify the COVID-19-infected lung regions on thick-section chest CT images. Five hundred thirty-one CT scans from 204 COVID-19 patients were collected from one appointed COVID-19 hospital. The automatically segmented lung abnormalities were compared with manual segmentation of two experienced radiologists using the Dice coefficient on a randomly selected subset (30 CT scans). Two imaging biomarkers were automatically computed, i.e., the portion of infection (POI) and the average infection HU (iHU), to assess disease severity and disease progression. The assessments were compared with patient status of diagnosis reports and key phrases extracted from radiology reports using the area under the receiver operating characteristic curve (AUC) and Cohen’s kappa, respectively. Results The dice coefficient between the segmentation of the AI system and two experienced radiologists for the COVID-19-infected lung abnormalities was 0.74 ± 0.28 and 0.76 ± 0.29, respectively, which were close to the inter-observer agreement (0.79 ± 0.25). The computed two imaging biomarkers can distinguish between the severe and non-severe stages with an AUC of 0.97 (p value < 0.001). Very good agreement (κ = 0.8220) between the AI system and the radiologists was achieved on evaluating the changes in infection volumes. Conclusions A deep learning–based AI system built on the thick-section CT imaging can accurately quantify the COVID-19-associated lung abnormalities and assess the disease severity and its progressions. Key Points • A deep learning–based AI system was able to accurately segment the infected lung regions by COVID-19 using the thick-section CT scans (Dice coefficient ≥ 0.74). • The computed imaging biomarkers were able to distinguish between the non-severe and severe COVID-19 stages (area under the receiver operating characteristic curve 0.97). • The infection volume changes computed by the AI system were able to assess the COVID-19 progression (Cohen’s kappa 0.8220). Electronic supplementary material The online version of this article (10.1007/s00330-020-07042-x) contains supplementary material, which is available to authorized users.

Lung squamous cell carcinoma (SCC) is a common type of lung cancer. There is limited information on the genes and pathways that initiate lung SCC. Here, we report that loss of TGFβ type II receptor (Tgfbr2), frequently deleted in human lung cancer, led to predominant lung SCC development in KrasG12D mice with a short latency, high penetrance, and extensive metastases. Tgfbr2-loss–driven lung SCCs resembled the salient features of human lung SCC, including histopathology, inflammatory microenvironment, and biomarker expression. Surprisingly, loss of Smad4, a key mediator of Tgfbr2, failed to drive lung SCC; instead, low levels of phosphorylated ERK1/2, a Smad-independent downstream effector of Tgfbr2, were tightly associated with lung SCC in both mouse and human. Mechanistically, inhibition of phosphorylated ERK1/2 significantly upregulated the expression of SOX2, an oncogenic driver of lung SCC, and cooperated with SMAD4 repression to elevate SOX2. Inhibition of ERK1/2 in Smad4fl/fl;KrasG12D mice led to extensive lung SCC formation that resembled the SCC phenotype of Tgfbr2-deficient mice. Overall, we reveal a key role of ERK1/2 in suppressing SCC formation and demonstrate that dysregulated Tgfbr2/ERK-Smad4/SOX2 signaling drives lung SCC formation. We also present a mouse model of metastatic lung SCC that may be valuable for screening therapeutic targets. Significance: This study sheds new light on the mechanisms underlying lung SCC formation driven by mutated Kras.

Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. All methods were evaluated using metrics using the precision, accuracy, sensitivity, specificity, and DICE coefficient (DC). The DC ranged from 0.7354 $\pm$ 0.1149 to 0.8372 $\pm$ 0.0858. The DC of the best method was close to the inter-observer agreement (0.8398 $\pm$ 0.0890). All methods were based on deep learning and categorized into two groups: multi-model method and single model method. In general, multi-model methods were significantly better ( p $ 0.01) than single model methods, with mean DC of 0.7966 and 0.7544, respectively. Deep learning based methods could potentially help pathologists find suspicious regions for further analysis of lung cancer in WSI.

BackgroundThick-section CT scanners are more affordable for the developing countries. Considering the widely spread COVID-19, it is of great benefit to develop an automated and accurate system for quantification of COVID-19 associated lung abnormalities using thick-section chest CT images.PurposeTo develop a fully automated AI system to quantitatively assess the disease severity and disease progression using thick-section chest CT images.Materials and MethodsIn this retrospective study, a deep learning based system was developed to automatically segment and quantify the COVID-19 infected lung regions on thick-section chest CT images. 531 thick-section CT scans from 204 patients diagnosed with COVID-19 were collected from one appointed COVID-19 hospital from 23 January 2020 to 12 February 2020. The lung abnormalities were first segmented by a deep learning model. To assess the disease severity (non-severe or severe) and the progression, two imaging bio-markers were automatically computed, i.e., the portion of infection (POI) and the average infection HU (iHU). The performance of lung abnormality segmentation was examined using Dice coefficient, while the assessment of disease severity and the disease progression were evaluated using the area under the receiver operating characteristic curve (AUC) and the Cohen’s kappa statistic, respectively.ResultsDice coefficient between the segmentation of the AI system and the manual delineations of two experienced radiologists for the COVID-19 infected lung abnormalities were 0.74±0.28 and 0.76±0.29, respectively, which were close to the inter-observer agreement, i.e., 0.79±0.25. The computed two imaging bio-markers can distinguish between the severe and non-severe stages with an AUC of 0.9680 (p-value< 0.001). Very good agreement (κ = 0.8220) between the AI system and the radiologists were achieved on evaluating the changes of infection volumes.ConclusionsA deep learning based AI system built on the thick-section CT imaging can accurately quantify the COVID-19 associated lung abnormalities, assess the disease severity and its progressions.Key ResultsA deep learning based AI system was able to accurately segment the infected lung regions by COVID-19 using the thick-section CT scans (Dice coefficient ≥ 0.74).The computed imaging bio-markers were able to distinguish between the non-severe and severe COVID-19 stages (area under the receiver operating characteristic curve 0.968).The infection volume changes computed by the AI system was able to assess the COVID-19 progression (Cohen’s kappa 0.8220).Summary StatementA deep learning based AI system built on the thick-section CT imaging can accurately quantify the COVID-19 infected lung regions, assess patients disease severity and their disease progressions.

This retrospective study aims to illustrate the radiographic characteristics of Coronavirus Disease 2019 and the correlation with the clinical course.195 hospitalized patients confirmed as Coronavirus Disease 2019 at First Hospital of Changsha, Hunan Province from December 31, 2019 to February 20, 2020 were enrolled. Chest computed tomography scan, clinical data and laboratory tests results were collected accordingly. Variable characteristics were recorded, radiographic evolution and outcome were analyzed along with the time course. Representative laboratory tests results were analyzed based on the image findings.Majority of the patients showed bilateral (73.8%), multiple lobes involvements (75.9%), peripheral distribution (83.1%), ground-glass opacification (41.0%), increased vascular margins (63.1%), long axis parallelism (55.9%), patchy ground-glass opacities beneath the pleura (51.3%) and consolidation (45.6%). According to the repeated radiology analysis, patients of improving/stable group tended to have younger age compared with worsening group (45.3 ± 15.0 VS. 59.3 ± 13.5, P = 0.001). Based on the laboratory test results, patients with positive image findings shared elder age, 46.0 (35.0-60.0)VS.31.0 (12.0-37.0) P 0.001, and higher chance developing fever(P 0.05); higher level of lymphocytes, C-reactive protein, erythrocyte sedimentation rate and lactate dehydrogenase; lower level of white blood cells, neutrophil and albumin(P 0.001).There are several specific image changes along with the disease progression may be helpful in early recognition and differential diagnosis of Coronavirus Disease 2019. Comprehensive assessments of both imaging feature and laboratory test results may offer an intact knowledge of Coronavirus Disease 2019.

Bronchoscopy inspection, as a follow-up procedure next to the radiological imaging, plays a key role in the diagnosis and treatment design for lung disease patients. When performing bronchoscopy, doctors have to make a decision immediately whether to perform a biopsy. Because biopsies may cause uncontrollable and life-threatening bleeding of the lung tissue, thus doctors need to be selective with biopsies. In this paper, to help doctors to be more selective on biopsies and provide a second opinion on diagnosis, we propose a computer-aided diagnosis (CAD) system for lung diseases, including cancers and tuberculosis (TB). Based on transfer learning (TL), we propose a novel TL method on the top of DenseNet: sequential fine-tuning (SFT). Compared with traditional fine-tuning (FT) methods, our method achieves the best performance. In a data set of recruited 81 normal cases, 76 TB cases and 277 lung cancer cases, SFT provided an overall accuracy of 82% while other traditional TL methods achieved an accuracy from 70% to 74%. The detection accuracy of SFT for cancers, TB, and normal cases are 87%, 54%, and 91%, respectively. This indicates that the CAD system has the potential to improve lung disease diagnosis accuracy in bronchoscopy and it may be used to be more selective with biopsies., This paper presents the development and clinical trial of a novel approach to bronchoscopy inspection following radiological imaging. The computer-aided diagnosis system for lung diseases, including cancers and tuberculosis, is a novel transfer learning (TL) method on top of DenseNet: sequential fine-tuning. The new method demonstrated an overall accuracy of 82%, compared to traditional TL methods that are 70% to 74% accurate.

Carbonaceous materials have attracted considerable attention as promising catalysts of persulfate (PS) activation for antibiotic degradation. In this work, an environment-friendly and low-cost carbonaceous material (Cr2O3/BC) was prepared by pyrolyzing chrome shavings, and the catalytic ability of Cr2O3/BC for PS activation was evaluated using tetracycline (TC) as the model antibiotic. The Cr2O3/BC/PS system exhibited ultrafast TC removal efficiency (99.9% removal rate within 5 min), which was owing to persistent free radicals, defective sites, and dispersed Cr2O3 particles, which were beneficial for the generation of free radicals, such as OH·, SO4·-, ·O2– and 1O2. Coexisting substances, such as inorganic ions and humic acid, exerted little effect on TC removal. In addition, 99.9% of TC removal rates were achieved in TC contaminated tap water. Cr2O3/BC/PS system also exhibited the high removal rates towards ciprofloxacin (98% removal rate within 5 min) and sulfadiazine (99.8% removal rate within 5 min). Surprisingly, no chromium was detected after TC degradation, indicating that no secondary pollution was generated. This work provides a feasible strategy for the high-value use of chrome shavings and a practical method for antibiotic removal.

Cisplatin is one of the most effective chemotherapeutic agents; however, its clinical use is limited by serious side effects of which nephrotoxicity is the most important. Nephrotoxicity induced by cisplatin is closely associated with autophagy reduction and caspase activation. In this study, we investigated whether neferine, an autophagy inducer, had a protective effect against cisplatin-induced nephrotoxicity. In an in vitro cisplatin-induced nephrotoxicity model, we determined that neferine was able to induce autophagy and that pretreatment with neferine not only attenuated cisplatin-induced cell apoptosis but further activated cell autophagy. This pro-survival effect was abolished by the autophagic flux inhibitor chloroquine. Furthermore, neferine pretreatment activated the AMPK/mTOR pathway; however, pharmacological inhibition of AMPK abolished neferine-mediated autophagy and nephroprotection against cisplatin-induced apoptosis. Collectively, our findings suggest for the first time the possible protective mechanism of neferine, which is crucial for its further development as a potential therapeutic agent for cisplatin-induced nephrotoxicity.

Ovarian cancer has the highest mortality rate among all gynecological cancers with its pathogenic mechanisms largely unknown. Here, we uncovered that ovarian cancer tissues exhibit higher heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1) expression than normal ovarian epithelium tissues. Increased hnRNPA2B1 level matches along with poor prognosis of ovarian cancer patients. Importantly, hnRNPA2B1 inhibition hampers growth, reduces mobility of ovarian cancer cells in vitro and hinders xenograft tumor formation in vivo. Transcriptome profiling analysis reveals that hnRNPA2B1 dictates the expression of various important genes involved in tumorigenesis and Lin-28 Homolog B (Lin28B) is down-regulated upon hnRNPA2B1 loss. hnRNPA2B1 regulates expression of Lin28B via binding to Lin28B mRNA and enhancing its stability. Furthermore, knockdown of Lin28B reduces proliferation and mobility of ovarian cancer cells and impairs tumorigenesis in vivo, whereas Lin28B overexpression promotes xenograft tumor formation. Finally, re-expression of Lin28B in hnRNPA2B1 knockdown cells results in rescued phenotypes. Collectively, our results demonstrate that hnRNPA2B1 facilitates the malignant phenotype of ovarian cancer through activating Lin28B expression.

The inflammatory response and apoptosis have been proved to have a crucial role in the pathogenesis of the influenza A virus (IAV). Previous studies indicated that while IAV commonly causes pancreatitis and pancreatic damage in naturally and experimentally infected animals, the molecular mechanisms of the pathogenesis of IAV infection are less reported. In the present study, we showed for the first time that both avian-like (α-2,3-linked) and human-like (α-2,6-linked) sialic acid (SA) receptors were expressed by the mouse pancreatic cancer cell line PAN02 and the human pancreatic cancer cell line PANC-1. Using growth kinetics experiments, we also showed that PAN02 and PANC-1 cells supported the productive replication of the H5N1 highly pathogenic avian influenza while exhibited the limited replication of IAV subtypes H1N1 and H7N2 in vitro. The in vivo infection of H5N1 in pancreatic cells was confirmed by the histopathological and immunohistochemical staining of pancreas tissue from mice. Other than H1N1 and H7N2, severe damage and extensive positive signals were observed in pancreas of H5N1 infected mice. All three virus subtypes induced apoptosis but also triggered the infected PAN02 and PANC-1 cells to release pro-inflammatory cytokines and chemokines including interferon (IFN)-α, IFN-β, IFN-γ, chemokine (C-C motif) ligand 2 (CCL2), tumor necrosis factor (TNF)-α, and interleukin (IL)-6. Notably, the subtypes of H5N1 could significantly upregulate these cytokines and chemokines in both two cells when compared with H1N1 and H7N2. The present data provide further understanding of the pathogenesis of H5N1 IAV in pancreatic cells derived from humans and mammals and may also benefit the development of new treatment against H5N1 influenza virus infection.

The current study was to identify a protective role of Shuanghuanglian (SHL) injection powder in vitro and in vivo after H5N1 viral infection. Immunofluorescent staining was used to determine the susceptibility of rat intestinal mucosa microvascular endothelial cells (RIM-MVECs) to the H5N1 virus. Viral replication of RIM-MVECs was measured by transmission electron microscopy (TEM) a hemagglutination assay and real-time quantitative PCR. H5N1 virally infected RIM-MVECs, and BALB/c mice were treated with SHL to investigate its therapeutic effect. Animal survival and the weight of H5N1 virally infected BALB/c mice after SHL treatment was noted, and histology and real-time PCR applied to mouse lungs were used to confirm the anti-H5N1 viral effects of SHL. RIM-MVECs supported replication of the H5N1 virus in vitro. SHL treatment reduced viral titers in H5N1 virally infected RIM-MVECs and mouse lungs. SHL -treated mice survived compared to controls. Mild pathological changes, reduced inflammatory cell infiltration and fewer viral antigens were observed in the lungs of SHL-treated mice at days 3 and 6 post-infection. In conclusion, SHL may have the antiviral activity against the H5N1 virus infection by inhibiting viral replication and alleviating lung injury.

Background: Idiopathic pulmonary fibrosis (IPF) is an incurable, progressive, and fatal interstitial lung disease with the characteristics of lung tissue damage and an enhancement in extracellular matrix (ECM). Alveolar epithelial cells (AMs) are major target cells that can directly promote to occurring of pulmonary fibrosis by acquisition of a mesenchymal phenotype through epithelial- mesenchymal transition (EMT). Neferine, a component of Chinese herbs, has been thought to be involved in anti-fibrotic activity in experimental lung fibrosis. However, its mechanism is not clear. In this study, we explore the regulation of neferine in TGF-β-induced EMT in lung fibrosis model and illustrate its mechanism of action. Methods: The alveolar epithelial cell line A549 was stimulated with TGF-β1 with or without Neferine pretreatment in advance. Morphologic variations and expression of EMT-related markers, including E-cadherin, β-catenina-SMA and Vimentin were detected. Expressions of Smad2, p- Smad 2, Smad3 and p-Smad3 were measured. Results: TGF-β1–treated A549 cells were transformed into the mesenchymal morphology with less E-cadherin, β-catenin and more a-SMA, Vimentin expression. The addition of Neferine inhibited the TGF-β1–induced change of the mesenchymal phenotype. Furthermore, Neferine inhibited the TGF-β1–induced increase in the expression of p- Smad2 and p-Smad3. Conclusions: Our study illustrate that Neferine inhibits TGF-β1–induced EMT in lung fibrosis model via TGF-β signaling pathway.