Jian Lou

Bio: Jian Lou is an academic researcher from Zhejiang University. The author has contributed to research in topics: Lung injury & Autophagy. The author has an hindex of 5, co-authored 5 publications receiving 180 citations.

Activation of MTOR in pulmonary epithelium promotes LPS-induced acute lung injury.

Abstract: MTOR (mechanistic target of rapamycin [serine/threonine kinase]) plays a crucial role in many major cellular processes including metabolism, proliferation and macroautophagy/autophagy induction, and is also implicated in a growing number of proliferative and metabolic diseases. Both MTOR and autophagy have been suggested to be involved in lung disorders, however, little is known about the role of MTOR and autophagy in pulmonary epithelium in the context of acute lung injury (ALI). In the present study, we observed that lipopolysaccharide (LPS) stimulation induced MTOR phosphorylation and decreased the expression of MAP1LC3B/LC3B (microtubule-associated protein 1 light chain 3 β)-II, a hallmark of autophagy, in mouse lung epithelium and in human bronchial epithelial (HBE) cells. The activation of MTOR in HBE cells was mediated by TLR4 (toll-like receptor 4) signaling. Genetic knockdown of MTOR or overexpression of autophagy-related proteins significantly attenuated, whereas inhibition of autophagy ...

Spread and control of COVID-19 in China and their associations with population movement, public health emergency measures, and medical resources

Abstract: BACKGROUND The COVID-19 epidemic, first emerged in Wuhan during December 2019, has spread globally. While the mass population movement for Chinese New Year has significantly influenced spreading the disease, little direct evidence exists about the relevance to epidemic and its control of population movement from Wuhan, local emergency response, and medical resources in China. METHODS Spearman’s correlation analysis was performed between official data of confirmed COVID-19 cases from Jan 20th to Feb 19th, 2020 and real-time travel data and health resources data. RESULTS There were 74,675 confirmed COVID-19 cases in China by Feb 19th, 2020. The overall fatality rate was 2.84%, much higher in Hubei than in other regions (3.27% vs 0.73%). The index of population inflow from Hubei was positively correlated with total (Provincial r=0.9159, p CONCLUSIONS The spread of the COVID-19 in China in its early phase was attributed primarily to population movement from Hubei, and effective governmental health emergency measures and adequate medical resources played important roles in subsequent control of epidemic and improved prognosis of affected individuals.

A rapid screening classifier for diagnosing COVID-19.

Abstract: Rationale: Coronavirus disease 2019 (COVID-19) has caused a global pandemic A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach Methods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia A deep neural network (DNN) was applied, and deep features derived from CXR and clinical findings formed fused features for diagnosis prediction Results: The clinical features of COVID-19 and influenza showed different patterns Patients with COVID-19 experienced less fever, more diarrhea, and more salient hypercoagulability Classifiers constructed using the clinical features or CXR had an area under the receiver operating curve (AUC) of 0909 and 0919, respectively The diagnostic efficacy of the classifier combining the clinical features and CXR was dramatically improved and the AUC was 0952 with 915% sensitivity and 812% specificity Moreover, combined classifier was functional in both severe and non-serve COVID-19, with an AUC of 0971 with 969% sensitivity in non-severe cases, which was on par with the computed tomography (CT)-based classifier, but had relatively inferior efficacy in severe cases compared to CT In extension, we performed a reader study involving three experienced pulmonary physicians, artificial intelligence (AI) system demonstrated superiority in turn-around time and diagnostic accuracy compared with experienced pulmonary physicians Conclusions: The classifier constructed using clinical and CXR features is efficient, economical, and radiation safe for distinguishing COVID-19 from influenza A/B pneumonia, serving as an ideal rapid screening tool during the COVID-19 pandemic

Endothelial cell-specific anticoagulation reduces inflammation in a mouse model of acute lung injury.

Abstract: Tissue factor (TF)-dependent coagulation contributes to lung inflammation and the pathogenesis of acute lung injury (ALI). In this study, we explored the roles of targeted endothelial anticoagulation in ALI using two strains of transgenic mice expressing either a membrane-tethered human tissue factor pathway inhibitor (hTFPI) or hirudin fusion protein on CD31+ cells, including vascular endothelial cells (ECs). ALI was induced by intratracheal injection of LPS, and after 24 h the expression of TF and protease-activated receptors (PARs) on EC in lungs were assessed, alongside the extent of inflammation and injury. The expression of TF and PARs on the EC in lungs was upregulated after ALI. In the two strains of transgenic mice, expression of either of hTFPI or hirudin by EC was associated with significant reduction of inflammation, as assessed by the extent of leukocyte infiltration or the levels of proinflammatory cytokines, and promoted survival after LPS-induced ALI. The beneficial outcomes were associated with inhibition of the expression of chemokine CCL2 in lung tissues. The protection observed in the CD31-TFPI-transgenic strain was abolished by injection of an anti-hTFPI antibody, but not by prior engraftment of the transgenic strains with WT bone marrow, confirming that the changes observed were a specific transgenic expression of anticoagulants by EC. These results demonstrate that the inflammation in ALI is TF and thrombin dependent, and that expression of anticoagulants by EC significantly inhibits the development of ALI via repression of leukocyte infiltration, most likely via inhibition of chemokine gradients. These data enhance our understanding of the pathology of ALI and suggest a novel therapeutic strategy for treatment.

Pharmacological modulation of autophagy: therapeutic potential and persisting obstacles.

Abstract: Autophagy is central to the maintenance of organismal homeostasis in both physiological and pathological situations Accordingly, alterations in autophagy have been linked to clinically relevant conditions as diverse as cancer, neurodegeneration and cardiac disorders Throughout the past decade, autophagy has attracted considerable attention as a target for the development of novel therapeutics However, such efforts have not yet generated clinically viable interventions In this Review, we discuss the therapeutic potential of autophagy modulators, analyse the obstacles that have limited their development and propose strategies that may unlock the full therapeutic potential of autophagy modulation in the clinic

Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak: an observational and modelling study

Abstract: Background: The COVID-19 outbreak containment strategies in China based on non-pharmaceutical interventions (NPIs) appear to be effective. Quantitative research is still needed however to assess the efficacy of different candidate NPIs and their timings to guide ongoing and future responses to epidemics of this emerging disease across the World. Methods: We built a travel network-based susceptible-exposed-infectious-removed (SEIR) model to simulate the outbreak across cities in mainland China. We used epidemiological parameters estimated for the early stage of outbreak in Wuhan to parameterise the transmission before NPIs were implemented. To quantify the relative effect of various NPIs, daily changes of delay from illness onset to the first reported case in each county were used as a proxy for the improvement of case identification and isolation across the outbreak. Historical and near-real time human movement data, obtained from Baidu location-based service, were used to derive the intensity of travel restrictions and contact reductions across China. The model and outputs were validated using daily reported case numbers, with a series of sensitivity analyses conducted. Results: We estimated that there were a total of 114,325 COVID-19 cases (interquartile range [IQR] 76,776 - 164,576) in mainland China as of February 29, 2020, and these were highly correlated (p<0.001, R2=0.86) with reported incidence. Without NPIs, the number of COVID-19 cases would likely have shown a 67-fold increase (IQR: 44 - 94), with the effectiveness of different interventions varying. The early detection and isolation of cases was estimated to prevent more infections than travel restrictions and contact reductions, but integrated NPIs would achieve the strongest and most rapid effect. If NPIs could have been conducted one week, two weeks, or three weeks earlier in China, cases could have been reduced by 66%, 86%, and 95%, respectively, together with significantly reducing the number of affected areas. However, if NPIs were conducted one week, two weeks, or three weeks later, the number of cases could have shown a 3-fold, 7-fold, and 18-fold increase across China, respectively. Results also suggest that the social distancing intervention should be continued for the next few months in China to prevent case numbers increasing again after travel restrictions were lifted on February 17, 2020. Conclusion: The NPIs deployed in China appear to be effectively containing the COVID-19 outbreak, but the efficacy of the different interventions varied, with the early case detection and contact reduction being the most effective. Moreover, deploying the NPIs early is also important to prevent further spread. Early and integrated NPI strategies should be prepared, adopted and adjusted to minimize health, social and economic impacts in affected regions around the World.

Predicting COVID-19 in China Using Hybrid AI Model

Abstract: The coronavirus disease 2019 (COVID-19) breaking out in late December 2019 is gradually being controlled in China, but it is still spreading rapidly in many other countries and regions worldwide. It is urgent to conduct prediction research on the development and spread of the epidemic. In this article, a hybrid artificial-intelligence (AI) model is proposed for COVID-19 prediction. First, as traditional epidemic models treat all individuals with coronavirus as having the same infection rate, an improved susceptible–infected (ISI) model is proposed to estimate the variety of the infection rates for analyzing the transmission laws and development trend. Second, considering the effects of prevention and control measures and the increase of the public’s prevention awareness, the natural language processing (NLP) module and the long short-term memory (LSTM) network are embedded into the ISI model to build the hybrid AI model for COVID-19 prediction. The experimental results on the epidemic data of several typical provinces and cities in China show that individuals with coronavirus have a higher infection rate within the third to eighth days after they were infected, which is more in line with the actual transmission laws of the epidemic. Moreover, compared with the traditional epidemic models, the proposed hybrid AI model can significantly reduce the errors of the prediction results and obtain the mean absolute percentage errors (MAPEs) with 0.52%, 0.38%, 0.05%, and 0.86% for the next six days in Wuhan, Beijing, Shanghai, and countrywide, respectively.

Autophagy is essential for ultrafine particle-induced inflammation and mucus hyperproduction in airway epithelium

Abstract: Environmental ultrafine particulate matter (PM) is capable of inducing airway injury, while the detailed molecular mechanisms remain largely unclear. Here, we demonstrate pivotal roles of autophagy in regulation of inflammation and mucus hyperproduction induced by PM containing environmentally persistent free radicals in human bronchial epithelial (HBE) cells and in mouse airways. PM was endocytosed by HBE cells and simultaneously triggered autophagosomes, which then engulfed the invading particles to form amphisomes and subsequent autolysosomes. Genetic blockage of autophagy markedly reduced PM-induced expression of inflammatory cytokines, e.g. IL8 and IL6, and MUC5AC in HBE cells. Mice with impaired autophagy due to knockdown of autophagy-related gene Becn1 or Lc3b displayed significantly reduced airway inflammation and mucus hyperproduction in response to PM exposure in vivo. Interference of the autophagic flux by lysosomal inhibition resulted in accumulated autophagosomes/amphisomes, and intriguingly, this process significantly aggravated the IL8 production through NFKB1, and markedly attenuated MUC5AC expression via activator protein 1. These data indicate that autophagy is required for PM-induced airway epithelial injury, and that inhibition of autophagy exerts therapeutic benefits for PM-induced airway inflammation and mucus hyperproduction, although they are differentially orchestrated by the autophagic flux.

MicroRNA-377-3p released by mesenchymal stem cell exosomes ameliorates lipopolysaccharide-induced acute lung injury by targeting RPTOR to induce autophagy

Abstract: Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are the severe lung damage and respiratory failure without effective therapy. However, there was a lack of understanding of the mechanism by which exosomes regulate autophagy during ALI/ARDS. Here, we found lipopolysaccharide (LPS) significantly increased inflammatory factors, administration of exosomes released by human umbilical cord mesenchymal stem cells (hucMSCs) successfully improved lung morphometry. Further studies showed that miR-377-3p in the exosomes played a pivotal role in regulating autophagy, leading to protect LPS induced ALI. Compared to exosomes released by human fetal lung fibroblast cells (HFL-1), hucMSCs-exosomes overexpressing miR-377-3p more effectively suppressed the bronchoalveolar lavage (BALF) and inflammatory factors and induced autophagy, causing recoveration of ALI. Administration of miR-377-3p expressing hucMSCs-exosomes or its target regulatory-associated protein of mTOR (RPTOR) knockdown significantly reduced ALI. In summary, miR-377-3p released by hucMSCs-exosomes ameliorated Lipopolysaccharide-induced acute lung injury by targeting RPTOR to induce autophagy in vivo and in vitro.