Electronic health  and mobile health  approaches to address low physical activity levels, sedentary behavior, and unhealthy diets have received significant research attention. However, attempts to systematically map the entirety of the research field are lacking. This gap can be filled with a bibliometric study, where publication-specific data such as citations, journals, authors, and keywords are used to provide a systematic overview of a specific field. Such analyses will help researchers better position their work.The objective of this review was to use bibliometric data to provide an overview of the eHealth and mHealth research field related to physical activity, sedentary behavior, and diet.The Web of Science (WoS) Core Collection was searched to retrieve all existing and highly cited (as defined by WoS) physical activity, sedentary behavior, and diet related eHealth and mHealth research papers published in English between January 1, 2000 and December 31, 2016. Retrieved titles were screened for eligibility, using the abstract and full-text where needed. We described publication trends over time, which included journals, authors, and countries of eligible papers, as well as their keywords and subject categories. Citations of eligible papers were compared with those expected based on published data. Additionally, we described highly-cited papers of the field .The search identified 4805 hits, of which 1712 (including 42 highly-cited papers) were included in the analyses. Publication output increased on an average of 26% per year since 2000, with 49.00% (839/1712) of papers being published between 2014 and 2016. Overall and throughout the years, eHealth and mHealth papers related to physical activity, sedentary behavior, and diet received more citations than expected compared with papers in the same WoS subject categories. The Journal of Medical Internet Research published most papers in the field . Most papers originated from high-income countries , in particular the United States . Most papers were trials and studied physical activity. Beginning in 2013, research on Generation 2 technologies  sharply increased, while research on Generation 1  technologies increased at a reduced pace. Reviews accounted for 20 of the 42 highly-cited papers (n=19 systematic reviews). Social media, smartphone apps, and wearable activity trackers used to encourage physical activity, less sedentary behavior, and/or healthy eating were the focus of 14 highly-cited papers.This study highlighted the rapid growth of the eHealth and mHealth physical activity, sedentary behavior, and diet research field, emphasized the sizeable contribution of research from high-income countries, and pointed to the increased research interest in Generation 2 technologies. It is expected that the field will grow and diversify further and that reviews and research on most recent technologies will continue to strongly impact the field. Being regularly active, having a less sedentary lifestyle, and consuming a healthy diet has many benefits for physical health, mental health, and well-being -3. This New technologies have been put forward as a cost-effective means to deliver behavioral health interventions and, as a result, prevent noncommunicable diseases (NCD) -12. ThisThus far, eHealth and mHealth research related to physical activity, sedentary behavior, and diet has been summarized in several studies that focused on use and effectiveness of different technologies such as mobile phone and/or SMS (short message service) text messaging -18, digiAlthough these studies summarized important aspects of the eHealth and mHealth research field related to physical activity, sedentary behavior, and diet, no attempt has been made to map out the entire field in a systematic manner. A bibliometric study uses publication-related information such as citations, journals, authors, and keywords to gain a bird's eye view of a field . BibliomThe purpose of this study is to examine the eHealth and mHealth research field related to physical activity, sedentary behavior and diet from its infancy until the end of 2016, and provide an overview of highly-cited papers which have considerably contributed to the maturation of the field. This will help researchers better position their work.We opted for using the Web of Science (WoS) Core Collection  because it provides many bibliometric indicators and includes literature from most disciplines. We developed a search strategy in an iterative manner starting from search terms used in published reviews and literature already known to us. We refined the search strategy by screening the titles of the most accessed papers listed at websites of journals that publish in the research field, and the titles of all publications of 6 researchers from different countries that are highly active in the field see .The final search was conducted on April 26, 2017 to ensure all relevant papers that were published between January 1, 2000 and December 31, 2016 were registered in the WoS Core Collection. We used 146 search terms related to (1) physical activity, sedentary behavior, and diet and (2) use of technology . Terms were combined with Boolean Operators . We restricted the search to publications in English and did not search for book chapters, conference proceedings, book citation indexes, and chemical indices . They comprisedeHealth and mHealth intervention studies; papers on the components or characteristics of eHealth and mHealth ; papers on the relationship between technology use and the health behaviors, validation studies of consumer-based assessment tools ; and papers on the development of eHealth and mHealth interventions targeting physical activity, sedentary behavior, and/or diet. Reviews, protocols, editorials, commentaries, and original research papers were eligible to gain a comprehensive picture of the field. We excluded papers that were not related to the field ; reported that technology was only used for data collection  or the delivery of education without trying to change behavior ; or were related to validation of research-grade assessment technologies . The detailed screening guide is presented in We had earlier piloted the screening procedure. Coauthors screened the same set of 20 papers (selected at random from preliminary searches) using a protocol that described the inclusion and exclusion criteria and a tutorial video. The video introduced the overall concept of a bibliometric study compared with a systematic review and detailed the inclusion and exclusion criteria with examples to illustrate how they should be applied . CoauthoSeven trained coauthors  each received a unique set of papers for title screening with optional screening of the abstract and full-text. As in the pilot phase, they chose “include,” “exclude,” or the option “unsure.” Papers marked as unsure were screened by four of the authors  and discussed until consensus was reached.We computed the (compound) growth rate of publications over time. This was done by raising the ratio of the number of publications in 2016 over those in 2000 to the power of 1/16, after which we subtracted one and multiplied by 100:We calculated the citation rate by dividing the number of citations per publication by the time since publication until December 2016, and expressed this per year. The citation rate does not depend on time since publication and is therefore a more precise measure of a paper’s research impact than raw citation counts ,51. BecaCitation trends for physical activity, sedentary behavior, and diet related eHealth and mHealth research were studied between 2007 and 2016 because our analyses made use of published citation rates  that areh-index is the number of eligible papers of an author that were cited at least h times each  [g-index is the unique largest number of top cited eligible papers of an author that together received at least g2 citations  [We explored the journals and authors who published most papers on eHealth and mHealth related to physical activity, sedentary behavior, and diet, along with the publication output of countries. We used WoS subject categories to count subject fields of papers. For the author analysis, we calculated 2 metrics using data within our dataset only: The es each) . The g-ijointly) . Countrijointly) .Generation 1, whereas papers on apps, wearable trackers, exergames, and social media were classified as Generation 2. Papers including both technologies were classified into Generation 2.To analyze the content of our dataset in more detail, we classified eligible papers into categories representing the studied exposure, technology, study population, setting, and methodology used. We did so by searching the title words and keywords identified by the author or by WoS editorial staff for occurrences of relevant terms. We defined the classification search terms using the agreed literature search strategy and the identified keywords in the eligible papers as starting point. The titles and keywords of the papers classified into each category were then double-checked by hand, as were those of all papers that were not classified into any or only a single category. This resulted in a refinement of the classification terms. This process was repeated until no inconsistencies were found. Using the final categorization reported in For the highly-cited papers, we also analyzed the papers based on their core content. These analyses were conducted independently by 2 coauthors (AMM and AM). They agreed on the descriptions of the paper’s core content by also using NVivo 11 .We conducted descriptive analyses using Microsoft Excel version 2016 and the Bibliometrix package version 1.7  for R veThe number of papers on eHealth and mHealth related to physical activity, sedentary behavior, and diet increased steeply over the 17-year period (mean increase: 26% per annum). The period between 2014 and 2016 accounted for 49.00% (839/1712) of all papers .The 1712 papers received 31,505 citations . Of the 1712 papers, 266 were not cited (15.54%), while 715 (41.67%) received 1 to 9 citations, 692 received 10 to 99 citations (40.42%), and 39 received 100 or more citations (2.28%). Overall, each paper received a median number of 2.0 citations per year (interquartile range 4.0).Compared with all papers from the same WoS subject categories, the absolute number of citations was higher than expected for included papers see .Journal of Medical Internet Research published the most papers  followed by BMC Public Health  and the Games for Health Journal . The Journal of Medical Internet Research was also the highest cited journal and accounted for 13.48% of all citations in the field . The American Journal of Preventive Medicine  and Annals of Behavioral Medicine  received the second and third highest number of citations, respectively.Overall, the papers were published by 471 different journals. As In WoS, papers can be assigned to multiple subject categories. The papers included in this study were assigned to a total of 2797 WoS subject categories, of which 104 subject categories were unique. In total, 5654 authors contributed to the 1712 papers . The top 10 authors  contribuAs the country of the corresponding author usually indicates where the research originated, we could also analyze the origin of the research that was published in the field. Corresponding authors were from 46 countries see . Most paAuthors specified a total of 2448 different keywords across papers. After also adding the keywords specified by WoS editors, this resulted in 4283 unique keywords. The number of keywords per paper varied widely . A total of 43 papers lacked specification of any keywords. The keywords that were most used reflected the exposure , the general topic , the study design , or the population . Several of the keywords, including commonly used ones, were uninformative by themselves . We were able to use the keywords in combination with title words to classify the content of the paper into categories.Generation 1 technologies and 742 (43.34%) papers as studying Generation 2 technologies, with 82 (4.79%) papers being still unclassified. Before 2014, studies on Generation 1 technologies were most common. From 2014 onwards, studies on Generation 2 technologies were most common; their number steeply increased between 2013 and 2016. Within this period, the number of studies on Generation 1 technologies increased less markedly (Generation 1 technologies (n=11), followed by Harvey-Berino J (n=6). Gao Z was the most common first author of papers on Generation 2 technologies (n=7), followed by Baranowski T (n=6).We classified 888 (51.87%) papers as studying markedly . VandelaA table with all highly-cited papers can be found in American Journal of Preventive Medicine (n=13) and the Journal of Medical Internet Research (n=6) published most highly-cited papers. Corresponding authors were from nine countries, with US-based authors being the most common (n=20), followed by authors from Australia (n=8) and the United Kingdom (n=5). Overall, 226 authors contributed to the 42 highly-cited papers (mean 5.2 authors per paper) with Vandelanotte C (n=5) and Brug J (n=4) contributing to most highly-cited papers.The 42 highly-cited papers received a total of 4883 citations  and were published between 2006 and 2016 across 19 journals. The A systematic review and meta-analysis that reported on the link between intervention characteristics and intervention effectiveness on health behaviors  in Internet interventions had the highest citation rate (79.5 citations per year) . The autGeneration 2 technologies, the majority of which related to social media, apps, or trackers (n=11). Most of the highly-cited papers were published in 2013 or later.Of the 42 highly-cited papers, 20 were reviews of the literature, of which 19 used a systematic review approach, . A total of 13 papers reported data from primary studies and most of these were experimental trials such as randomized controlled trials (n=10); 8 studies reported content analyses of various smartphone apps, and 1 study introduced an eHealth and mHealth intervention development methodology. In terms of the technology used, 14 of the highly-cited papers studied The purpose of this paper was to examine the entirety of the eHealth and mHealth research field related to physical activity, sedentary behavior, and diet using bibliometric data. We observed a substantial growth of research output in the field with most papers being published in recent years.An exponential growth pattern has been observed across all research disciplines. For example, there is a 2.3% increase in scientific publications per annum leading to a doubling of the publication volume every 24 years . If the Journal of Medical Internet Research and BMC Public Health are the most popular outlets for researchers in this field. The Journal of Medical Internet Research was also the leading journal in an earlier bibliometric study that examined the overall mHealth literature [Journal of Medical Internet Research receiving most and the American Journal of Preventive Medicine receiving the second most citations, respectively. Journals that publish open access enjoy a citation advantage in terms of speed of building up citations and overall citation count [The open access journals terature . The leaon count ,66. Howeon count ,68. The While eHealth and mHealth research related to physical activity, sedentary behavior, and diet is maturing rapidly in many high-income countries, the research output of non-high-income countries is still meager. Only 3% of all papers were from upper-middle or lower-middle income countries. None of these countries published many papers in the English language journals included in WoS. It is possible that more papers are published in a local language or in journals not included in WoS. Nonetheless, this observation is unsatisfying, considering that (1) fast globalization and urbanization in many upper-middle and lower-middle income countries is related to reduced physical activity and unhealthy diets, which is associated with an unprecedented rise in NCDs seen in many of these countries ,69,70 anGeneration 1 eHealth and mHealth technologies  accounted for most papers in the field, its annual growth rate was 20.0% compared with 40.4% for research using Generation 2 technologies such as smartphone apps and wearables. This might indicate that the interest in Generation 1 technologies in the research field is slowly declining as researchers and funding agencies prioritize Generation 2 technologies. The trend of using the newest technologies to address health behaviors is expected to continue, but whether these technologies have a meaningful and long-lasting impact on people’s physical activity, sedentary behavior, and dietary habits needs to be seen [Although research related to relatively older  be seen . AlthougWe also found that most papers in the field were on physical activity compared with sedentary behavior and/or diet. That physical activity research is more common than research on dietary behaviors has been seen in the literature before ,75. ReseWe identified 42 highly-cited papers, which exert a strong influence on the field. These papers may be a resource for those less familiar with the field. The largest proportion of the highly-cited papers employed a systematic review approach to provide an overview of certain subfields. Systematic reviews and meta-analyses attract a high number of citations  as they The strength of this paper is that it provides a comprehensive overview of the eHealth and mHealth research field related to physical activity, sedentary behavior, and diet. To capture all relevant papers, we consulted the literature, discussed search terms, executed pilot searches, and refined our search before conducting the final search . We were similarly systematic in our paper-screening procedure. With this, we are confident that we have identified most eHealth and mHealth research papers related to physical activity, sedentary behavior, and diet.We conducted a keyword analysis in combination with a title search to gain an insight into the studied exposures, technologies, populations, settings, and used methodologies. Because we did not confirm the obtained classification by retrieving the full-text, the number of papers for each category and subcategory we reported remains an estimate. However, this method allowed us to form an impression of the type of eHealth and mHealth research on physical activity, sedentary behavior, and diet that has been conducted between 2000 and 2016. Using keywords that are often used in a field is beneficial because others who consult databases to identify relevant research are likely to use these terms . If authDespite these strengths, limitations of our work need to be acknowledged. First, we only obtained papers from journals indexed by a single database—WoS. However, WoS is a large database that offers a wide variety of publication metrics that were vital for our analyses. Additionally, WoS only includes journals that meet certain criteria  to ensure high quality . Despite likely having excluded eligible papers in journals not included in WoS, we obtained papers from high-quality international journals that are the most influential source of scientific communication . Using oFinally, we have exclusively evaluated the scientific research literature and have identified trends that mainly concern scientific discovery, which will likely impact new research efforts. Therefore, the broader impact of the research outside of academia  cannot be deduced. Measuring and proving the societal impact of research is essential but difficult, mainly because this impact becomes usually only apparent in the far future and there are no agreed-upon measures to capture impact . HoweverJournal of Medical Internet Research was highlighted as the primary outlet for research in the field. Despite the many promising developments, research in upper-middle, lower-middle, and low-income countries is still scant. More research in such settings is needed to examine the public health impact of eHealth and mHealth interventions on physical activity, sedentary behavior, and diet where needed the most. Systematic reviews and papers that report on recent technologies (mainly smartphone apps) exert a strong impact on the field and their influence will likely remain high in the future.In this paper, we provided a bird's eye view of the research on eHealth and mHealth related to physical activity, sedentary behavior, and diet. Our analysis of 1712 papers published between January 2000 and December 2016 showed that research output is increasing rapidly; a trend that appears likely to continue. The


Pancreatic ductal adenocarcinoma (PDAC) is one of the most common cancer death reasons. Anti-tumor necrosis factor-alpha (TNF-α) antibodies have shown promising effects in PDAC pre-clinical models. However, the prognostic values of TNF-α, underlying mechanisms by which anti-TNF-α treatments inhibit PDAC, and potential synergistic effects of anti-TNF-α treatments with chemotherapy are still unclear.in vitro. In vivo, anti-TNF-α treatments showed effects in reducing desmoplasia and the tumor promoting inflammatory microenvironment in PDAC. Combination of anti-TNF-α treatments with chemotherapy partly overcame chemoresistance of PDAC tumor cells and prolonged the survival of PDAC mouse model.To identify the targeting values of TNF-α in PDAC, we measured TNF-α expression in different stages of PDAC initiation and evaluated its prognostic significance in a pancreatic cancer cohort. We found that TNF-α expression elevated in PDAC initiation process, and high expression of TNF-α was an independent prognostic marker of poor survival. We further evaluated anti-tumor effects of anti-TNF-α treatments in PDAC. Anti-TNF-α treatments resulted in decreased cell viability in both PDAC tumor cells and pancreatic satellite cells in similar dose In conclusion, our findings indicated that TNF-α in PDAC can be a prognostic and therapeutic target. Inhibition of TNF-α synergized with chemotherapy in PDAC resulted in better pre-clinical responses via killing tumor cells as well as diminishing desmoplasia and inflammation in PDAC tumor stroma. Pancreatic adenocarcinoma (PDAC) is a lethal disease, evidenced by the around five months' average survival time. , 2 The bTumor necrosis factor-alpha (TNF-α) is a master regulator of inflammation and a key player in the cytokine network. , 8. TNF-The presence of heavy desmoplasia is a hallmark of PDAC forming a unique microenvironment that comprises various cell types including pancreatic stellate cells (PSCs), inflammatory and immune cells, endothelial cells and extracellular matrix (ECM).  During tThe previous study showed that anti-TNF-α antibodies demonstrated promising anti-cancer effects in pre-clinical models of PDAC.  However,To understand roles of TNF-α in PDAC initiation, we sought to determine whether TNF-α expression level is aberrant in pancreatic intraepithelial neoplasia (PanIN) lesions, PDAC lesions, PDAC tumor cells, and PSCs cell lines. We found that high expression of TNF-α was mainly shown in PanIN and PDAC lesions, but not in normal pancreas Figure . The hisBecause our data showed that expression of TNF-α is associated with PDAC initiation, we sought to determine whether TNF-α expression level could serve as an independent prognostic marker of PDAC patients. To that end, the association between TNF-α expression and overall survival (OS) in pancreatic cancer was evaluated using a retrospective cohort including samples of 100 pancreatic cancer patients . Ninety-P = 0.0061 and 0.013, respectively). In all pancreatic cancer cases, median OS of high TNF-α expression subgroup was 10 months , while the low TNF-α expression subgroup had a median OS of 12 months . In univariate analysis, TNM stage, pathological grade, lymph node status, and TNF-α level were found to be significantly associated with the OS of all pancreatic cancer patients  dose was used as the treatment dose of each drug in cell lines . PDAC cein vitro (data not shown). However, in the presence of complement or immune effector cells, both infliximab and etanercept reduced viability of PDAC tumor cells via inducing ADCC and CDC effects  with 10% fetal bovine serum (FBS) and 1% Pen-strap (Giboco). Primary cell lines hPSCs and mPSCs were isolated from surgical resected human PDAC tissues and KrasLSL.G12D/+; p53R172H/+; PdxCretg/+ (KPC) mice PDAC tumor tissues respectively by following procedure described by Sharon et al.  hPSCs anCell viability was determined using Dojindo Cell Counting Kit-8 (CCK8). After treatment with cytotoxic drugs in different concentrations, 10 μl of CCK-8 was added for 1 h incubation at 37°C. Then absorbance at 450nm was measured. Cell viability was calculated by: (OD450nm (Treatment)-OD450nm (Blank))/(OD450nm (Control)-OD450nm (Blank)).Two Food and Drug Administration approved drugs infliximab and etanercept were used for anti-TNF-α purposes in current study. Both of these two drugs were able to react with mouse TNF-α and were used in previous studies. , 32 For Expression of TNF-α was detected with fluorescence activated cell sorter analysis . Cells were collected from fresh cell culture, washed and suspended to single cell suspension. Primary antibody TNF-α  was incubated on ice for 1 hour followed by washing and fluorescence labeled secondary antibody for 15 minutes at room temperature. Apoptosis analysis by FACS was conducted using DAPI and Annexin-V  according to the manufacturer's instructions. Only transmembrane TNF-α was measured as cells were not permeabilized before TNF-α staining. Data were visualized by FlowJo software.6 MiaPaca-2 cells were inoculated subcutaneously into the flanks of hind legs. Seven days after inoculation, mice were randomized into four groups (10 mice/group): saline, gemcitabine (100 mg/kg/week) + paclitaxel (10 mg/kg/week), gemcitabine (100 mg/kg/week) + paclitaxel (10 mg/kg/week) + etanercept (5 mg/kg/3days) and gemcitabine (100 mg/kg/week) + paclitaxel (10 mg/kg/week) + infliximab (10 mg/kg/week). Treatment started from day 8 after inoculation and tumors were monitored every 7 days with microcalipers. Tumor volume was calculated by length × width2 × π/6. For the survival study, excessive ascites, more than 20% body weight loss or other signs of distress were considered as end point.All animal studies were approved by the Animal Care and Use Committee of Wuhan University. Six week old female BALB/c nude mice  were housed in specific pathogen-free environment with a 12-hour light-dark cycle. A total of 5 × 103, tumors were dissected and cut into 10 mm3 pieces by biopsy instrument. Tumor chunks were then implanted into pancreas of additional BALB/c nude mice. Animals were randomized and tagged prior to treatment . Treatment with saline, recombinant human TNF-α protein , etanercept (5 mg/kg/3days), and infliximab (10 mg/kg/week) started 7 days after implantation. Animals were treated for 28 days prior to tissue collection.To evaluate expression of TNF-α during PDAC development, KPC mice (originated from Dr. Tuveson lab), constructed based on previous report.  were useThe cytokine quantification analysis was performed by beads based assay. Human and mouse T helper cytokine panel  was purchased from Biolegend . PDX mice were treated with saline, TNF-α (10 μg/mouse/week), TNF-α (10 μg/mouse/week) + etanercept (5 mg/kg/3days) or TNF-α (10 μg/mouse/week) + infliximab (10 mg/kg/week). Two mice were in each group. After 28 days' treatment, tumors were resected and cut into pieces. Small tissue pieces were then minced and filtered to get cell precipitation. Proteins were extracted by RIPA buffer with protease inhibitor and quantified by BCA protein assay. Protein concentrations from different samples were normalized to the same before measurement. For the serum tests, same volume was taken from each sample. Remaining steps were performed according to the manufacturer's recommendations. Each sample was measured in duplicate and mean values were used in analysis.For histology analyses, 4 μm paraffin embedded tissue sections were deparaffinized in xylene and hydrated through gradient ethanol. Hematoxylin and eosin (H&E) staining were performed to confirm histological features. To quantify collagen amount, tissue sections were stained using picrosirius red staining solution  and imaged with Texas Red filter.Using of human specimens in this study was approved by the Medical Ethics Committee of Wuhan University. All PDAC specimens were obtained from PDAC patients by surgery with informed consent. Tissue microarrays containing a total of 100 patients' specimens were constructed by Shanghai Outdo Biotech . All these 100 patients were treated with Whipple procedure or cytoreductive surgery of PDAC, prior to administration of chemotherapy or radiotherapy. Histological diagnosis and grade of differentiation were reconfirmed by two board certified pathologists (Z.X. and H.C.) before staining. All of the PDAC samples were classified based on the AJCC TNM classification (7th Edition). Clinicopathological factors of PDAC patients were listed in ® conjugated secondary antibodies were used after primary antibody staining. Slides were counterstained with mounting DAPI media.Slides were first deparaffinized and hydrated, then were steamed with Reveal Decloaker  for 30 minutes for antigen retrieval. To minimize background staining, slides were incubated with 5% BSA buffer for 20 minutes at room temperature. Primary antibodies for TNF-α (Abcam), Vimentin (Abcam), cleaved-Caspase3 (Abcam), Ki67 (Abcam), CD11b (Abcam) and F4/80 (Abcam) were diluted according to vendor's instruction and were incubated overnight at 4°C. Alexa FluorTo correlate TNF-α expression with clinical significances of PDAC, PDAC tissue microarrays were stained by immunohistochemistry. Slides were graded by two pathologists (Z.X. and H.C.) simultaneously in a same displayer. Both the two pathologists were independent and blinded to the clinical features of patients. The scores of the two pathologists were compared and any discrepant scores were reassessed by reevaluation of the stained tissue specimen to achieve a consensus score. The area of positive (AP) were graded as follows: 0 (no positive area or positive area < 5%), 1 (positive area 5–25%), 2 (positive area 26–50%), 3 (positive area > 51%). Intensity of staining (IS) by the numerical value was resulted in scores: 0 , 1  2  and 3 . Expression levels of TNF-α were calculated based on the total score as the following equation: ID = AP × IS. The ID cutoff value for high and low expression was determined by the receiver operating characteristic (ROC) curve analysis with the respect to OS.t-test. Multigroup data were analyzed by one-way ANOVA following Bonferroni's pairwise comparisons. ROC curve analysis was conducted to determine the cutoff of ID and evaluate the predictive value of TNF-α expression for OS of pancreatic cancer patients. To analyze the correlation between the clinicopathological parameters and TNF-α expression, the χ2 test or Fisher's exact test was used. The OS was estimated using the Kaplan Meier method and was compared using the long-rank test. Multivariate analysis using Cox proportional hazard regression model was performed to test independent prognosis values. A two tailed p-value < 0.05 was considered statistically significant.Values were presented as mean ± SD. Two group quantification data were analyzed using


Although anti-tumor necrosis factor (TNF) agents are highly effective in the treatment of psoriasis, 2–5% of treated patients develop psoriasis-like skin lesions called paradoxical psoriasis. The pathogenesis of this side effect and its distinction from classical psoriasis remain unknown. Here we show that skin lesions from patients with paradoxical psoriasis are characterized by a selective overexpression of type I interferons, dermal accumulation of plasmacytoid dendritic cells (pDC), and reduced T-cell numbers, when compared to classical psoriasis. Anti-TNF treatment prolongs type I interferon production by pDCs through inhibition of their maturation. The resulting type I interferon overexpression is responsible for the skin phenotype of paradoxical psoriasis, which, unlike classical psoriasis, is independent of T cells. These findings indicate that paradoxical psoriasis represents an ongoing overactive innate inflammatory process, driven by pDC-derived type I interferon that does not lead to T-cell autoimmunity. The pathogenesis of paradoxical psoriasis in patients receiving anti-TNF treatments for classical psoriasis is unclear. Here, the authors show that anti-TNF drugs enhance the production of type I interferon by plasmacytoid dendritic cells, causing skin lesions that, unlike classical psoriasis, lack T- cell autoimmunity. TNF exerts potent pro-inflammatory functions via activation of immune cells and vascular endothelial cells4. Increased TNF expression levels can be found at sites of inflammation in many autoimmune diseases, such as rheumatoid arthritis, Crohn’s disease, or psoriasis7. TNF blockade is highly efficacious and has become the benchmark in management of these diseases11. As such, more than two million patients have been treated with TNF blockers.Tumor necrosis factor (TNF) is a homotrimeric cytokine produced by immune and epithelial cells in response to infection or tissue injury13. Another common side effect of TNF blockade is the development of inflammatory skin lesions, which resemble psoriasis and are observed in 2–5% of patients receiving anti-TNF therapy18. These skin manifestations are called “paradoxical psoriasis”, as TNF blockade is usually highly efficacious in psoriasis treatment. Notably, this side effect even occurs in patients undergoing successful psoriasis treatment with anti-TNFs. More severe cases necessitate interruption or complete cessation of anti-TNF therapy and, for several diseases, no equivalent alternative treatments exist. Therefore, understanding the pathogenic mechanism underlying paradoxical psoriasis, and its distinctions from classical psoriasis, remains a critical issue for the future design of successful therapeutic and preventive measures.Nevertheless, TNF blockade as a therapeutic option has its limitations. Long-term TNF neutralization increases susceptibility to infections and skin cancer21. Evidence for a pathogenic role of T cells stems from the following observations: first, T-cell-targeted therapies, including cyclosporine , DAB-IL-2 (interleukin-2 receptor-specific fusion toxin)22, and inhibitors of T-cell costimulation, including alefacept23, efalizumab24, and CTLA-4-Ig25, are efficacious in psoriasis treatment; second, HLA-Cw6 represents the strongest genetic risk variant associated with psoriasis26; third, clinically relevant xenotransplant models of psoriasis are dependent on T cells29; and, finally, lesional T cells are oligoclonal and recognize epidermal autoantigens34. These pathogenic T cells mediate the chronic and relapsing course of psoriasis and define it as an autoimmune disease.Classical psoriasis is a chronic, autoimmune skin disease mediated by T cells35. pDC-derived type I IFNs unleash the autoimmune response by promoting activation and maturation of conventional DCs (cDCs) that stimulate expansion of autoreactive T cells. These autoreactive T cells—in particular CD8+ T cells—migrate into the epidermis, where they recognize keratinocyte autoantigens and induce keratinocyte hyperproliferation36. Whether paradoxical psoriasis follows a similar pathomechanism remains unknown.Autoimmune T-cell responses in psoriasis are initiated by a subset of dendritic cells called plasmacytoid dendritic cells (pDCs), which infiltrate pre-psoriatic skin and are activated to produce type I interferons (IFNs)Here we show that paradoxical psoriasis induced by anti-TNF is characterized by an exaggerated type I IFN response, which does not lead to T-cell autoimmunity. Anti-TNF antibodies directly increase the capacity of pDCs to produce type I IFNs, by inhibiting their maturation. The exaggerated type I IFN response induced by anti-TNF treatments is sufficient to trigger a psoriatic skin phenotype. However, in contrast to classical psoriasis, type I IFN fails to induce cDC maturation and the subsequent activation of autoimmune T cells that is required for a chronic-relapsing disease course. Thus, paradoxical psoriasis is a side effect of an anti-TNF treatment stemming from an overactive, but self-limiting innate inflammation driven by pDC-derived type I IFN.n = 6), psoriasis and/or psoriatic arthritis (n = 8), ankylosing spondylitis (n = 8), rheumatoid arthritis (n = 1), as well as SAPHO (n = 1) and juvenile rheumatoid arthritis (n = 1). Patients were treated with the anti-TNF antibodies infliximab (n = 10), adalimumab (n = 10), certolizumab (n = 1), and golimumab (n = 2), and the TNF-receptor fusion protein etanercept (n = 2). Anti-TNF-induced paradoxical psoriasis appeared independent of the underlying diseases or the type of anti-TNF agent used (Supplementary Table 38) and severe noncicatricial alopecia, in numerous cases with scalp involvement , IL6, and IL1B when comparing skin lesions from paradoxical psoriasis with classical psoriasis .We analyzed mRNA expression levels of selected innate cytokines involved in the pathogenesis of psoriasis to identify expression patterns unique to paradoxical psoriasis. We observed no significant difference in the expression levels of sis Fig. . In contsis Fig. . Importasis Fig. . However+ lymphoid cells in skin represent bona-fide pDCs35, as demonstrated by co-staining with BDCA2 (CLEC4C) in selected cryo-samples of paradoxical psoriasis . CD123sis Fig. . pDCs weons Fig. . This inons Fig. . ExpressNA2 Fig.  and IFNBIFNA2 expression in anti-TNF-induced paradoxical psoriasis, we investigated whether TNF blockade would enhance IFN-α production by pDCs directly. As LL37 complexed with DNA has been shown to activate pDCs in psoriasis41, and because CAMP mRNA expression (corresponding to LL37) in paradoxical psoriasis was comparable to psoriasis  and coincided with the decrease of IFNA2 expression  48 h after activation , a significantly lower number of CD8+ T cells was present in the epidermis of paradoxical psoriasis . Because mature cDCs in psoriatic skin represent the key stimulators of pathogenic CD8+ T cells to migrate into the epidermis, we next quantified mature cDCs in skin samples using the maturation marker LAMP3. We found a significantly increased number of LAMP3+ cDCs in the skin of classical psoriasis as compared to skin from healthy donors , natural killer cells57, mast cells58, and neutrophils59, which have all been reported to express IL22.The mechanisms by which type I IFNs promote the psoriatic skin phenotype are currently unclear. Type I IFN itself does not induce keratinocyte proliferation nor is it responsible for the altered differentiation44.In addition to increased type I IFN expression, higher numbers of skin pDCs are observed in paradoxical psoriasis as compared to classical psoriasis. The increased pDC numbers is not a direct anti-TNF effect, but rather dependent on the type I IFN overexpression induced by TNF blockade. Although the exact mechanisms by which type I IFN drives pDC infiltration in paradoxical psoriasis remains to be elucidated, CXCR3 ligands induced by type I IFNs may prolong the recruitment of pDCs into the skin in a self-amplifying loopOur study also identifies a new mouse model for the induction of a psoriasiform skin phenotype with acanthosis, basal keratinocyte hyperproliferation, and altered epidermal differentiation. This model displays the following three key features of human paradoxical psoriasis, which are clearly distinct from classical psoriasis. First, the psoriatic phenotype in this model is induced and not blocked by anti-TNFs; second, like in paradoxical psoriasis, the phenotype in this model is T-cell-independent, whereas classical psoriasis is a T-cell-mediated disease; and finally, the model shows sustained type I IFN expression, which is in line with the persistent type I IFN expression in the skin of paradoxical psoriasis but not classical psoriasis. An intriguing question is why anti-TNF is able to induce a psoriatic phenotype in wild-type mice and enhance type I IFN production by pDCs from blood of healthy donors, but only 2–5% of anti-TNF-treated patients develop paradoxical psoriasis. The future identification of specific genetic variants potentially involving pDC activation and/or type I IFN signaling may provide an explanation for an increased susceptibility of these individuals to develop paradoxical psoriasis in the context of anti-TNF treatment.In conclusion, this study identifies the relevance of the temporal equilibrium of TNF and type I IFN (TNF-IFN yin-yang) in the pathogenesis of paradoxical psoriasis. While TNF controls type I IFN under steady-state conditions, anti-TNF treatment may tip the balance toward type I IFN ultimately driving the psoriatic phenotype in paradoxical psoriasis. In contrast to classical psoriasis, paradoxical psoriasis fails to turn into a T-cell-mediated autoimmune disease with a relapsing course but remains a drug-related side effect in which inflammation perpetuates in self-amplifying innate immune response. These findings provide the basis for the design of new strategies targeting pDCs and type I IFN for the treatment and prevention of paradoxical psoriasis.n = 16) and the Department of Dermatology, University Hospital of Zurich (n = 9) between 2011 and 2013. Paradoxical psoriasis was defined as newly appearing psoriasiform skin lesions under anti-TNF therapy despite response to treatment.This study was performed in accordance with the guidelines of the Declaration of Helsinki and was approved by the local ethics committee . Clinical data of 25 patients with paradoxical psoriasis were collected at the Department of Dermatology, University Hospital CHUV, Lausanne  or snap-frozen and stored at −80 °C for reverse transcription-PCR (RT-PCR) . Paraffin-embedded skin sections were deparaffinized, stained with anti-CD123 (BD Pharmingen), anti-LAMP3 , or anti-CD8 , and visualized using standard horseradish peroxidase technique. Frozen skin sections were fixed with 4% paraformaldehyde and stained for BDCA2  and for CD123 . For quantitative RT-PCR, cDNA was synthesized using Superscript II reverse transcriptase (Invitrogen) and relative Gene expression was quantified using specific Taqman probes  or anti-Ki-67  followed by labeled secondary antibody. For flow cytometry analysis, mouse skin was digested with Dispase (Sigma-Aldrich) and collagenase (Invitrogen), and stained with anti-B220 FITC , anti-CD45 PerCp-Cy5.5 , anti-CD11c PE , and anti-PDCA1 APC , anti-CD80 PE , or anti-CD86 PE . For flow cytometry analyses of human pDCs, antibodies used include anti-CD123 APC , anti-BDCA2 PE , anti-BDCA4 APC , anti-CD80 PE , anti-CD83 FITC , and anti-HLA-DR . Assessors were blinded for all histological quantifications.pDCs were purified from peripheral blood mononuclear cells obtained from blood buffy coats of healthy donors by Ficoll separation followed by enrichment using a CD304 Microbeads kit (Miltenyi Biotech). pDCs were cultured in RPMI 1640 + GlutaMAX (Gibco) supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin and stimulated with 10 µg/ml human DNA (Biochain) complexed with 50 µg/ml LL37 (Proteogenix) with or without 1 µg/ml anti-TNF antibodies , or 100ng/ml recombinant human TNF (RnD). After 48 h of culture, IFN-α was measured in cell-free supernatants by enzyme-linked immunosorbent assay (Mabtech).hBDCA2-DTR (hCLEC4C-DTR) mice were bred at our facility. Skin injury was performed as previously described43. Briefly, mice were anesthetized and their lower backs shaved using clippers, and then depilated using the commercially available Veet cream. After cream removal with a paper tissue, 10 gentle strokes of commercially available tape  were applied to the lower back. Dosage of antibodies applied was deduced from therapeutic use in humans and injected intraperitoneally as follows: 1500 µg of anti-TNF  at days −1 and 0; 200 µg of anti-TCRβ  at days −2, 0, 2, and 4; and 250 µg of anti-IFNAR  at days −1, 0, 1, and 3. We used Remicade because it was previously shown to efficiently block both human and mouse TNF60. However, as a positive control, we used a mouse-specific anti-TNF antibody  and sex-matched mice were used for all experiments. Female wild-type Balb/c mice were purchased from Jackson Laboratory, U-test was used for analysis of human gene expression and histological analysis. To investigate an association between pDCs and type I IFN gene expression, the Spearman’s rank-correlation coefficient was calculated. For preclinical mouse data, Student’s t-test was used to perform statistical analyses. All testing was two-sided, and a p-value of <0.05 was considered to indicate statistical significance. All analyses were performed with GraphPad Prism 6.0.Unpaired non-parametric Mann–Whitney All relevant data are available from the corresponding authors upon reasonable request.Supplementary Information


The innate immune response is a central process that is activated during pathogenic infection in order to maintain physiological homeostasis. It is well known that dexamethasone (Dex), a synthetic glucocorticoid, is a potent immunosuppressant that inhibits the cytokine production induced by bacterial lipopolysaccharides (LPS). Nevertheless, the extent to which the functional groups of Dex control the excessive activation of inflammatory reactions remains unknown. Furthermore, importantly, the role of Dex in the innate immune response remains unclear. Here we explore the mechanism of LPS-induced TNF-α secretion and reveal p38 MAPK signaling as a target of Dex that is involved in control of tumor necrosis factor-α (TNF-α)-converting enzyme (TACE) activity; that later mediates the shedding of TNF-α that allows its secretion. We further demonstrate that the 11-hydroxyl and 21-hydroxyl groups of Dex are the main groups that are involved in reducing LPS-induced TNF-α secretion by activated macrophages. Blockage of the hydroxyl groups of Dex inhibits immunosuppressant effect of Dex during LPS-induced TNF-α secretion and mouse mortality. Our findings demonstrate Dex signaling is involved in the control of innate immunity. Activation of the innate immune system maintains physiological homeostasis when a host has a pathogenic infection, trauma or irradiation –3. The iThe ADAM  family of transmembrane metalloproteinases controls the ectodomain shedding of various substrates –15. The Glucocorticoids have been well-documented as immunosuppressants and have the ability to inhibit cytokine responses in activated macrophages  through Here, we examined whether Dex is able to modulate TACE activity and control LPS-induced innate immunity, and, if it is able to, how it occurs. We used macrophages (RAW264.7 cells) as the model system, since it has been well documented that these cells are able to release pro-inflammatory cytokines such as TNF-α in response to LPS . We founTo test the hypothesis that the innate immune responses of macrophages are able to be limited by glucocorticoids acting through a glucocorticoid receptor , we firsWe next examined the effects of Dex on LPS-induced TNF-α production. Real-time q-PCR revealed that TNF-α mRNA induction occurred as early as 2 hours after LPS stimulation and then subsequently decreased Figure . Later, To examine the effect of Dex on the cellular distribution of TNF-α in activated macrophages, activated RAW cells (LPS stimulation for 2h) were continuously treated with LPS only or LPS accompanied by Dex for 22 hours, and then immunolabelled using antibodies against TNF-α. Confocal images with orthogonal views were reconstituted from the stack of two-dimensional images and these revealed that there was enrichment of cellular TNF-α at the peripheral regions of the cells treated with LPS and Dex, and this contrasted with the diffused pattern of TNF-α present in LPS-stimulated cells Figure . PreviouTACE has been shown to promote TNF-α secretion via the efficient shedding of the preform of TNF-α (pro-TNF-α) into the mature soluble form that is released, which results in increased levels of TNF-α in the culture medium , 38. We in vitro TACE inhibitor assay kit (TACE inhibitor screening assay kit), we showed that Dex did not inhibit the activity of purified TACE proteins  were differentiated from the bone marrow cells collected from 6-8 weeks old mice using L-cell conditional medium [Murine monocytes/macrophages (RAW264.7) were provided by Prof. Ping-Hui Tseng's laboratory and were maintained in DMEM-high glucose (Invitrogen) supplemented with 10% heat inactivated FBS (Invitrogen) and 1% antibiotics solution  under 5% COl medium .Lipopolysaccharide  and dexamethasone (Dex) were purchased from Sigma. Dexamethasone-fluorescein (Dex-FITC) was purchased from Thermo Fisher. RU486 was purchased from Merck. Ac-Dex and DiAc-Dex were produced via a series of chemical reactions by Prof. Chung-Wai Shiau's laboratory.The mouse protocols were approved by the Institutional Animal Care and Use Committee (IACUC) of National Yang-Ming University. The mice were housed on a 12-hour light and 12-hour dark cycle. The mice used for the experiments were 8 to 10 weeks old and were of C57BL/6 male.The sources of the antibodies and their dilutions were as follows: rabbit anti-TNF-α ; mouse anti-Rab11 ; rabbit anti-MAPK (ERK1/2) ; rabbit anti-phospho-p44/42 MAPK (ERK1/2) (Thr202/Tyr204) ; mouse anti-JNK ; rabbit anti-phospho-JNK (Thr183/Tyr185) ; rabbit anti-p38 MAPK ; rabbit anti-phospho-p38 MAPK (Thr180/Tyr182) ; rabbit anti-TAK1 ; rabbit anti-phospho-TAK1 (Thr184/187) ; rabbit anti-IKK-α/β ; rabbit anti-phospho-IKK-α (Ser176)/IKK-β (Ser177) ; mouse anti-β-actin (GeneTex GTX629630); dilution for Western blotting: 1/1000); mouse anti-GAPDH ; mouse anti-TNF receptor I (CD120a) ; goat anti-TACE (ADAM17) ; Alexa Fluor 488-anti-mouse IgG ; Alexa Fluor 488-anti-rabbit IgG ; Alexa Fluor 568-anti-rabbit IgG ; Cy5-anti-goat IgG ; HRP-AffiniPure mouse anti-rabbit IgG (Jackson ImmunoResearch 211-032-171); and HRP-AffiniPure goat anti-mouse IgG (Jackson ImmunoResearch 115-035-174).RNA was extracted from cells using TRIzol reagent (Invitrogen) and total RNA was precipitated according to the manufacturer's instructions. The RNA products were reverse-transcribed using Transcriptor First Strand cDNA Synthesis kits (Roche) and random hexamer primers. The cDNA products were amplified by PCR using a KAPA SYBR® FAST ABI Prism™ One-Step qRT-PCR kits (KAPA BIOSYSTEMS). The CPH gene was used as a reference gene. Quantification of the target mRNA was carried out by the ΔΔCT method.The qPCR primers for TNF-α were 5′-CGTAGGCGATTACAGTCACGG-3′ and 5′-GACCAG GCTGTCGCTACATCA-3′. The qPCR primers for TACE were 5′-TGTGAGCGGTGACCACGAGAAT-3′ and 5′-TTCATCCACCCTGGAGTTGCCA-3′. The qPCR primers for CPH were 5′-ATGGTCAACCCCACCGTGT-3′ and 5′-TTCTTGCTGTCTTTGGAACTTTGTC-3′.To analyze the level of TNF-α secretion, samples of medium and serum were assayed for TNF-α content using Mouse TNF-α DuoSet ELISA kits (R&D systems) according to the manufacturer's instructions. The results are presented graphically using Excel software (Microsoft).iLas multi-modal of TIRF (Roper)/spinning disk confocal  microscope system and a ProEMCCD camera (Princeton). All confocal images were captured and processed using Metamorph software (Molecular Device). To provide orthogonal projections of a given cell, Metamorph image analysis software was used.Cells were fixed and immunostained using a method that has been described previously . BrieflyCells were washed twice with ice-cold PBS and lysed with RIPA solution  supplemented with protease inhibitor cocktails and phosphatase inhibitor cocktails (Roche) at 4°C for 20 min. The cell lysates were then sonicated, which was followed by centrifugation in order to collect the supernatants for protein concentration quantification by Bradford protein assay (Bio-Rad). Equal amounts of total protein per sample was run on SDS-PAGE (SDS-polyacrylamide gel electrophoresis) and then transferred to PVDF membrane (Millipore). The blots were probed using the indicated primary antibodies. Visualization was carried out using mouse anti-rabbit horse radish peroxidase-conjugated secondary antibody (HRP-AffiniPure mouse anti-rabbit IgG) or goat anti-mouse horse radish peroxidase-conjugated secondary antibody (HRP-AffiniPure goat anti-mouse IgG) as appropriate follow by Immobilon Western Chemiluminescent HRP substrate (Millipore). The results were captured using a Luminescence Image System (FUJIFILM).To assess the cell surface expression of TNF receptor I (CD120a) and TACE (ADAM17), cells were immunostained using a method that has been described previously . BrieflyTo analyze TACE activity, cell lysate samples were assayed for TACE activity using SensoLyte® 520 TACE Activity Assay kits (Anaspec) according to the manufacturer's instructions. The results are presented graphically using Excel software (Microsoft).To examine the inhibitory effects of dexamethasone on TACE activity, TACE inhibitor screening assay kits assays (BioVision) were performed according to the manufacturer's instructions. The results are presented graphically using Excel software (Microsoft).Statistical significance was measured by Student's t-test.


Quercus faginea were evaluated in relation to summative chemical composition and non-polar and polar extracts composition, including an assessment of antioxidant properties (DPPH and FRAP). Twenty trees from two sites in Portugal were analysed. Heartwood had approximately two times more solvent extractible compounds than sapwood (on average 19.0% and 9.5%). The lipophilic extractible compounds were below 1%, and most of them were polar e.g. ethanol-soluble compounds corresponded to 65% of total extractives in heartwood and 43% in sapwood. Lignin content was similar in sapwood and heartwood (28.1% and 28.6% of extractive-free wood respectively) as well as the sugar composition. Site did not influence the chemical composition. The lipophilic extractible compounds from both sapwood and heartwood included mainly saturated fatty acids (23.0% and 36.9% respectively) and aromatic compounds were also abundant in sapwood (22.9%). The ethanol-water extractibles had a high content of phenolic substances . The polyphenolic composition was similar in heartwood and sapwood with higher content of ellagitannins (168.9 and 153.5 mg tannic acid/g of extract in sapwood and heartwood respectively) and very low content of condensed tannins. The antioxidant activity was very high with IC50 of 2.6 μg/ml and 3.3 μg/ml for sapwood and heartwood respectively, as compared to standard antioxidants (IC50 of 3.8 μg/ml for Trolox). The ferric reducing ability was 2.8 and 2.0 mMol Trolox equivalents/g extract of heartwood and sapwood respectively. The variability between trees was low and no differences between the two sites were found. Q. faginea showed a very good potential for cooperage and other applications for which a source of compounds with antioxidant properties is desirable.Heartwood and sapwood of  Quercus faginea Lam., identified by A. Fabião) is a species from Fagaceae family, native to the Western Iberian Peninsula and the North African countries of Morocco, Tunisia, and Algeria. It is a medium-sized deciduous or semi-evergreen tree growing to a height of 20 m and a diameter of 80 cm. This species occupied once (XV-XVI centuries) much of the Portuguese territory and the wood was valued and intensively exploited for naval construction ×100, where Abs0 was the absorbance of the blank and Abs1 was the absorbance in the presence of the extract at different concentrations.The radical scavenging activity of each sample was calculated by the DPPH inhibition percentage as follows: I % = [ calculated from the calibration curve with the Trolox solution concentrations and the percentage of scavenging effect on the DPPH radical.The IC3.6H2O solution in a ratio of 10:1:1 in volume. A sample (100 μl) of extract or standard was then added to 3 ml of FRAP reagent and the reaction mixture was incubated at 37°C for 30 min. The absorbance was measured at 593 nm in comparison with a blank. Aqueous solutions of known Trolox concentrations in the range of 0–0.5 Mmol/L were used for the calibration curve, and the results were expressed as Mmol Trolox equivalents/ g dry mass.The ferric reducing antioxidant power (FRAP) assay was followed as described by . The FRAp ≤ 0.05) among the corresponding mean values was determined using one-way analysis of variance (ANOVA) followed by Duncan’s multiple-comparison test, and the Student’s t-test, Shapiro-Wilk normality test was also made using Sigmaplot® statistical software, version 11.0.All results were expressed as mean and standard deviation (SD). The significance of differences  amounts of extractible compounds than sapwood: approximately two times more with on average 19.0% and 9.5% in heartwood and sapwood respectively.There were considerable chemical differences between heartwood and sapwood as regards solvent extractible compounds content . Heartwop = 0.001) . However, there was a difference between the proportion of the ethanol and water soluble compounds in heartwood ethanol-soluble compounds represented the largest proportion of total extractives (65%) while in sapwood it was the water-soluble extractibles that had the highest proportion . The amount of dichloromethane soluble extractibles was very small (below 1%) similar in heartwood and sapwood, but statistically different  = 6.60; = 0.001) .Q. rubra contained 6.2% and 8.2% total extractibles in sapwood and heartwood, respectively, which included sixteen phenolic compounds. Hernández and Salazar [Q. coccolobilofia, 8.3% and 8.9% in Q. durifolia, 9.2% and 11.6% in Q. rugose, and 8.2% and 10.6% in Q. oleoides. Ruiz-Aquino et al. [Q. laurina, and 8.2% and 8.9% in Q. crassifolia.Comparative studies of heartwood—sapwood solvent extractible compounds in other oak species have shown in general a heartwood enrichment in these non-structural compounds, although to a variable extent depending on species. Seikel et al.  reported Salazar  reportedo et al.  found noEucalyptus globulus [Acacia melanoxylon [Tectona grandis [The higher content in solvent extractible compounds of heartwood in relation to sapwood is considered a heartwood specific feature . Howeverglobulus , 30, 4.0anoxylon , 9.2% an grandis .The heartwood contained slightly less lignin than sapwood  and Q. crassifolia (24.9% in sapwood and 25.2% in heartwood), and by Lourenço et al. [Q. suber sapwood (23.8%).The lignin content reported for o et al.  for Q. lo et al.  for Q. sQ. suber sapwood [The carbohydrate composition was similar in heartwood and sapwood . The maj sapwood .Q. faginea trees and the sapwood and heartwood chemical composition was not influenced by the sites (and therefore by the different ages of trees) e.g. p = 0.131 for heartwood solvent extractible compounds, p = 0.153 for extractive-free heartwood lignin content.Overall there was little chemical variation between the Q. faginea sapwood and heartwood are reported in The identified lipophilic extractible compounds in Triterpenes were also identified in both wood tissues, being higher in heartwood; arjunic acid was the major constituent . This can be explained by the tree defence mechanism against external aggressors as fungi, accumulating triterpenes, sterols, flavonoids and other secondary metabolites during heartwood formation .Authors found a significant positive relationship between the compositions of the lipophilic extractibles on both sapwood and heartwood tissues (p<0.05) concerning alkanols, alkanoic acids, sterols and triterpenes, from both sites . The samThe major difference between sapwood and heartwood lipophilic extracts relies on their aromatics content, reaching in average for the two sites 22.8% of all compounds for sapwood, against 5.3% in heartwood.Q. faginea heartwood extraction with 50% methanol-water or the values of 8.3%, 7.8% and 7.9% for Q. pyrenaica, Q. robur and Q.petraea respectively [The results show a clear difference between heartwood and sapwood. The obtained extraction yield was high (on average 14.7% and 6.0% for heartwood and sapwood); these values are under the determined total solvent extractible compounds content  due to tectively .Q. faginea heartwood extracts are similar to those reported in the literature (no reports on sapwood extract composition were found). Cadahía et al. [Q. faginea heartwood methanolic extracts (50% methanol-water) total phenolics of 50.5 and 89.2 mg GAE/g wood, respectively, and 42.6 and 56.6 mg GAE/g of wood for Q robur, 53.7 and 70.3 mg GAE/g of wood for Q. petraea, and 44.8 and 56.6 mg GAE/g of wood for Q. pyrenaica heartwood. Alañón et al. [Q. pyrenaica, 72.6, 48.9 and 33.3 mg GAE/g of wood for Q. robur, Q. petraea and Q. alba respectively.The major part of solvent extractibles is made up of phenolic substances  that repa et al.  and Ferna et al.  reportedn et al.  obtainedQ. faginea heartwood and sapwood showed a similar profile, with a higher content in hydrolysable tannins (ellagitannins) and very small amounts of condensed tannins. This is in accordance with the literature [Q. faginea an ellagitannin content of 4.0 and 8.5 mg ellagic acid/g wood. The values are also higher when compared to the 5.1–5.9 mg ellagic acid/g heartwood of Q. pyrenaica, 8.5–12.1 mg ellagic acid/g heartwood of Q.petraea and 7.6–8.2 mg ellagic acid/g heartwood of Q. robur [Q. pyrenaica with 18.2–23.0 mg ellagic acid/g wood.The polyphenolic composition of the aqueous ethanolic extracts of terature , 15, 17.terature  and Cadaterature  who showQ. robur , 12. HowQ. robur  report hQ. faginea wood  = 3.18; p = 0.080). The same was reported for Q. pyrenaica heartwood [Q. alba and Q. robur, Miller et al. [The site did not influence the content of total phenolics of eartwood , 42, 43.r et al.  referredQ. faginea heartwood and sapwood extracts were evaluated by measurement of the scavenging capacity against a given radical (DPPH assay) and by the ferric reducing antioxidant power (FRAP). The results are summarised in The antioxidant characteristics of 50 of 2.6 μg/ml and 3.3 μg/ml respectively) when compared to Trolox (IC50 3.8 μg/ml) which is used as an antioxidant standard compound. Ellagitannins should be the main compounds responsible for the antioxidant capacity of the extracts since they have a higher ability to donate a hydrogen atom and to support the unpaired electron as compared to low molecular weight phenolic compounds [Both heartwood and sapwood extracts exhibited high antioxidant activities  are similar to those reported for wood extracts of other oaks: 65.3 mg Trolox equivalents /g of wood and 112.9 mg Trolox equivalents /g of wood for American and French oaks respectively .Quercus spp. Alañón et al. [Q. pyrenaica, Q. robur, Q. petraea and Q. alba in mMol of Trolox equivalents/g of wood as 0.54, 0.82, 0.59 and 0.39 respectively. In methanol extracts of American and French oaks, the FRAP values were 0.29 and 0.45 mMol Trolox equivalents/g of wood, respectively [The heartwood and sapwood ethanolic extracts present FRAP activity with a mean value of 2.8 mMol Trolox/g extracts of heartwood and 2.1 mMol Trolox/g extracts of sapwood. The reducing ability of the extracts showed an increasing trend with the polyphenol content, as expected. Phenolic compounds can act as reducing agents, hydrogen donors, singlet oxygen quenchers or metal chelators due to their redox properties and consequently antioxidant capacity. The heartwood extract, with the highest concentration, exhibited the highest reducing capability , while the sapwood extract possessed the lowest ferric reducing abilities . The present results were compared to the values of other extracts from wood of n et al.  evaluateQ. faginea allow considering their potential use for the maturation and aging of alcoholic beverages, as previously suggested [Overall the chemical features of the polar extracts of heartwood and sapwood of uggested –14. AlsoQ. faginea are clearly outsingled with a well-established border. Chemically, heartwood and sapwood differ considerably in their extractives content, namely in polar extractives e.g. compounds soluble in ethanol and water, which are approximately two-fold in heartwood in relation to sapwood. The ethanol-water extracts are rich in phenolics, especially the heartwood extracts. The content of ellagitannins is high and of condensed tannins is low and similar in both heartwood and sapwood extracts. The antioxidant properties of Q. faginea heartwood and sapwood extracts are excellent and above those of standard antioxidant compounds.The heartwood and sapwood of Q. faginea heartwood and sapwood are mainly constituted by saturated alkanoic acids and sterols. Aromatic compounds were also identified in considerable extent only in the sapwood lipophilic extracts.Lipophilic extracts from The variability between trees is low and no differences between trees of different ages from two sites were found.Q. faginea shows a very good potential for cooperage and other applications for which a source of compounds with antioxidant properties is desirable.


Ankylosing spondylitis (AS) is an inflammatory rheumatic disease with impact on axial skeleton, peripheral joints and enthuses, and it may result in severe disabilities of those parts. Tumor necrosis factor-α (TNF-α) inhibitors are considered as an effective treatment for patients with active AS. In this study, we conducted a network meta-analysis to compare the clinical outcomes of active AS patients treated with TNF-α inhibitors. Randomized controlled trials (RCTs) evaluating the efficacy and safety of TNF-α inhibitors were retrieved in literature search and selected for meta-analysis. Changes in ASAS20 response, ASAS40 response and BASDAI 50% response were regarded as efficacy outcomes; serious adverse events (SAE) and all cause withdrawals were regarded as safety outcomes. Both traditional pairwise meta-analysis and network meta-analysis were performed. The results showed that adalimumab and infliximab had better clinical outcomes. Infliximab consistently appeared to be the most effective TNF-α inhibitors with a high risk of adverse events for patients with active AS; meanwhile, adalimumab ranked highest with respect to adverse effects with efficacy secondary to infliximab. As a result, we were unable to conclude the optimal TNF-α inhibitor and this issue should be solved by future researchers. Ankylosing spondylitis (AS) is an inflammatory rheumatic disease which affects the axial skeleton, peripheral joints and enthuses. It is characterized by inflammatory back pain, which not only leads to both structural and functional disability but also affects one’s life qualityAS can be controlled by relieving symptoms , improving physical function, and delaying or avoiding structural damages, which can result in physical impairments and deformitiesCurrently, TNF-α inhibitors in common use include adalimumab, etanercept, golimumab and infliximab. Although the efficacy and safety of each TNF-α inhibitor have been assessed in clinical trials, there appeared to be considerable variation with respect to the efficacy and safety indicators in the literatures. In order to provide concrete evidence for clinical practice, it is of great importance to perform a thorough comparison of all relevant TNF-α inhibitors. This study enabled us to achieve this objective through implementation of a network meta-analysis which takes adalimumab, etanercept, golimumab and infliximab into account.PubMed, EMBASE, the Cochrane and Scopus Database were searched for published RCTs investigating the efficacy of TNF-α inhibitors for AS patients from January 2001 to August 2015. Key terms including “TNF-α inhibitors”, “ankylosing spondylitis” and “randomized controlled trials” were used with no other restrictions. The detailed research strategy of each database was shown in RCTs were eligible for this study if the efficacy or safety is evaluated between a TNF-α inhibitor and another TNF-α inhibitor (or placebo) for active AS patients management. Trials were included into the network meta-analysis if they met the following criteria: (i) trials were randomized-design and patients were over 18 years of age; (ii) AS was diagnosed based on the 1984 modified New York criteriaData was independently extracted by two authors. Study design, characteristics of participants, treatment of pharmaceuticals, follow-up time and relevant outcomes were documented. Any disagreement regarding to data extraction was determined by a third investigator. For cross-over trials, if a washout period was presented before the cross-over time and results of the two phases were reported separately, the results of the second phase were recorded as a separate study. In this study, we analyzed the changes in ASAS20 response, ASAS40 response and BASDAI 50% response as efficacy outcomes; serious adverse events (SAE) and all cause withdrawals were considered as safety outcomes.The Jadad Scale was used for quality assessment of case-control studies in meta-analysis. A paper reporting a clinical trial could therefore receive a Jadad score of between zero and five; scores equal to or above 3 were considered as of high quality.First, traditional meta-analyses were conducted on studies directly comparing one TNF-α inhibitor with a placebo. Then, Bayesian network meta-analyses were performed to compare over different TNF-α inhibitors.Q-statistic and a P-value of less than 0.01 was considered significant. The I2 test was also used to quantify heterogeneity (ranging from 0 to 100%). P < 0.01 for Q-test or I2 > 50% indicated the existence of heterogeneity across the studies. Random-effect model (DerSimonian-Laird method) was used in order to minimize the effect of heterogeneity. All statistical analysis in these meta-analyses was conducted using STATA version 12.0 .In traditional meta-analyses, odd ratios (ORs) with their corresponding 95% CIs were used to assess the efficacy or safety of TNF-α inhibitors. Heterogeneity was examined using the Cochran’s P values for the null hypothesis that there is no significant inconsistency between direct and indirect evidence.In addition to traditional meta-analyses, which directly compared two different treatments, network meta-analyses were also performed to form indirect comparisons. The network meta-analyses were performed with a random-effects model based on a Bayesian framework using Markov Chain Monte Carlo methods in WinBUGS . A probabilistic analysis was performed to estimate rank probabilities based on network meta-analysis. The rank probabilities were summarized for each drug in order to obtain the surface under the cumulative ranking curve (SUCRA) as previously describedA total of 1,855 studies were retrieved from databases and relevant articles. Sixteen studies131415161718192021222324252627P > 0.05)  .P < 0.05) were observed from network meta-analyses. Adalimumab, etanercept, golimumab, infliximab showed significant difference comparing to placebo in both ASAS20 and BASDAI 50%. Etanercept did not prove its significant efficacy in ASAS40. It is estimated that all inhibitors had no significant variation to placebo in safety issues.We carried out comparisons among these four inhibitors through network meta-analysis. ORs and their corresponding 95% credible intervals (CrIs) from network meta-analyses were presented in In secondary analyses, we compared the estimated rank probabilities of different pharmaceutics concerning various outcomes. Adalimumab had the highest efficacy in ASAS20 response, whereas infliximab had higher rank probabilities in both ASAS40 and BASDAI 50% responses . GolimumResults from traditional and network meta-analyses were presented in In this meta-analysis, we reviewed the clinical outcomes of AS patients treated with various TNF-α inhibitors. The corresponding results showed that four TNF-α inhibitors could significantly alleviate clinical symptoms of AS. Although indirect and direct comparisons were conducted, no statistical differences were found among adalimumab, infliximab, etanercept and golimumab. Therefore, we were unable to conclude which TNF-α inhibitor had the best performance in terms of both efficacy and safety.®), etanercept (Enbrel®), golimumab (Simponi®) and infliximab (Remicade®) have been developed to target the function of TNF-α and reduce pain, swelling and inflammation of AS patientsTNF-α is a biological agent which is primarily produced by macrophages and activated monocytes during inflammatory responses. It can also induce the production of other proinflammatory cytokines. Apart from that, TNF-α stimulates endothelial cells in order to express adhesion molecules so that leukocytes can be attracted into inflammatory joints. TNF-α can also promote metallo proteinase synthesis and inhibit the synthesis of proteoglycans in cartilageet al. indicated that adalimumab, etanercept, infliximab and golimumab had better performances than the placebo with respect to ASAS40, while no significant difference was found between these inhibitors and placebo when withdrawals and serious adverse events were taken in to account. Results from our study were consistent with those provided by Maxwell et al.The studied four drugs affect the function of TNF-α via different mechanisms. Adalimumab is a recombinant human IgG1 monoclonal antibody specific to human TNF-αOur study provided exclusive evidence for assessing the current available TNF-α inhibitors that are used for managing AS patients. Our network meta-analysis incorporated all comparisons of available TNF-α inhibitors into a single analysis. In addition, a total of 2,574 AS patients were included in our research in order to provide a ranking of these TNF-α inhibitors when both efficacy and adverse effects were taken into consideration. However, there was no significant evidence to conclude the optimal TNF-α inhibitor for managing AS patients.Nevertheless, there are several perspectives which concern about the limitation of our research due to its nature or design. First of all, variations in treatment duration or dose may have significant impact on the overall outcomes and we are unable to cope with this issue since a large number of randomized clinical trials with different designs were included in our research. Secondly, the number of comparisons or the sample size for each comparison varied significantly and some studies may have an unexpectedly significant impact on the overall effect size while others may not have such an impact. For instance, only two RCTs assessing golimumab were included in our research and this certainly did not provide sufficient evidence. Thirdly, other confounding factors such as treatment delivery methods may have influence on the clinical outcomes of AS patients. For example, different delivery methods of interventions may result in different reasons of withdrawals and thus affect the evaluation of safety outcomes. In this study, we observed that patients treated with intravenous infusion infliximab exhibited the highest rate of withdrawal and we suspected that intervention delivery approaches may be associated with the likelihood of withdrawals.In summary, the four TNF-α inhibitors have superior clinical performance for managing AS patients when compared with placebo. However, we were unable to conclude the optimal TNF-α inhibitor and this issue should be solved by future researchers.How to cite this article: Liu, W. et al. Efficacy and safety of TNF-α inhibitors for active ankylosing spondylitis patients: Multiple treatment comparisons in a network meta-analysis. Sci. Rep.6, 32768; doi: 10.1038/srep32768 (2016).


The Keeling curve has become a chemical landmark, whereas the papers by Charles David Keeling about the underlying carbon dioxide measurements are not cited as often as can be expected against the backdrop of his final approval. In this bibliometric study, we analyze Keeling’s papers as a case study for under-citedness of climate change publications. Three possible reasons for the under-citedness of Keeling’s papers are discussed: (1) The discourse on global cooling at the starting time of Keeling’s measurement program, (2) the underestimation of what is often seen as “routine science”, and (3) the amount of implicit/informal citations at the expense of explicit/formal (reference-based) citations. Those reasons may have contributed more or less to the slow reception and the under-citedness of Keeling’s seminal works. Keeling had proposed that he could deploy a new analytical tool called infrared (IR) gas analyzer to perform continuous measurements of CO2 in air samples. The first analyzer was sent to Antarctica and a second one was prepared for installation aboard a research ship. In March 1958, an analyzer was installed on the top of Mauna Loa (Hawaii) with the intention to measure pristine air over the Pacific Ocean. Keeling soon detected strong seasonal variations in CO2 levels, oscillating from peak levels in the northern hemisphere spring (before the beginning of the growing season) and minimum levels in autumn. Since 1960, Keeling’s data revealed that CO2 levels were rising steadily in what became known as the “Keeling curve”. The data collection of Keeling’s measurement program is the longest continuous record of atmospheric CO2 in the world and shows that its concentration has grown from 315 ppm (parts per million) in 1958 to 408 ppm in May 2015 and is correlated to usage of fossil fuel as energy source.Charles David Keeling (1928–2005) started his scientific career as chemist at the Department of Geochemistry at Caltech, studying groundwater in the pristine Big Sur wilderness. Around 1956, Roger Revelle, director of the Scipps Institution of Oceanography near San Diego , invited Keeling to perform atmospheric carbon dioxide . He was stimulated by Callendar (reading his papers) and by Plass . “He wants to measure COn” Weart , p. 78672 concentration every few years or every decade. (3) He continued to measure, although the up and down of the CO2 concentration measured within the first year seemed to confirm the previously measured inconsistent data—and the general impossibility to measure a baseline CO2 concentration. (4) He refused to replace his gas analyzer by newer instruments to avoid calibration problems and to ensure a highly consistent data row. In 1963, the work almost had to shut down. It was the so-called Sputnik-shock after the launch of the Soviet satellite in 1957 that boosted funding for all areas of science and education and allowed Keeling to continue his measurements at Mauna Loa.One of his most important characteristics was his persistency and subject-specific determination: (1) He refused a job offer (for an indoor job in a dark cellar lab) from the Division of Meteorological Research in the Weather Bureau in Washington, DC. (2) Under Roger Revelle at Scripps Institution of Oceanography he refused to take only snapshots of the COIn this study, we undertake a bibliometric analysis of Keeling’s publications. Bibliometrics is based on the assumption that important contributions to science receive high citation numbers. However, this is not always the case: Keeling’s contributions were definitely important for climate change research. However, we will demonstrate that his most important early publications are lower cited than expected in consideration of their importance for climate change. Their citation profiles are reminiscent of cases of slow reception and weak recognition. We will explore reasons for the under-citedness of Keeling’s papers.In a first step, the profile of his publication history and the impact of his publications have been looked at. These analyses are based on Web of Science  data. We have searched for Keeling’s scientific publications and found 79 papers published between 1953 and 2004 . Is seems that Keeling always used his first and middle name initials—no relevant papers authored by C. Keeling could be found. Based on the publication set of 79 papers, we have established the WoS citation report.In a second step, we broadened the perspective and analyzed whether slow reception  and under-citedness are specific for Keeling’s papers or reflect general patterns of certain climate change papers. As in step 1, we used WoS data for this analysis.2 concentration in the earth’s atmosphere only and investigated Keeling’s citation impact on the field. The analysis is based on the search and retrieval functions of the databases offered by Chemical Abstracts Service (CAS), a division of the American Chemical Society (ACS). The CAS literature database (CAplusSM) covers scientific publications and patents since around the early 1800’s (including the references cited therein since the publication year 1996 of the database records). The CAS chemical substance database (CAS REGISTRYSM) contains all chemical species mentioned within disclosures in chemistry and related fields, identified and registered by the CAS Registry system. All compound records are associated with a unique CAS Registry Number® (CAS RN®). Both databases are connected to each other via the CAS RN. The coverage and indexing of these subject specific databases are optimized in particular for searching literature dealing with chemical processes like the analysis of compounds.In a third step, we focused on publications dealing with the measurement of the COIn our bibliometric analysis, we have investigated the lifework of Keeling from a quantitative (bibliometric) perspective. The bibliometric analysis has been performed in several steps by using different databases: ®) International. The CAplus publication records contain index terms . We searched for either one of the terms “anal?” or “occurr?” in combination  with either of the terms “air” or “atmospher?” (? = truncation symbol to include “atmospheric” etc.) in combination with CO2 in the form of its CAS RN. This query returns all publications, which have words beginning with “anal” or “occurr” in their index terms, if the same index term group also contains “air” or a word starting with “atmospher”, and if the CAS RN of CO2 is listed in that index term group. The cited references within the resulting publication set  have been extracted.In collaboration with CAS, we used both databases via the new platform of the Scientific and Technical Information Network , in particular of Keeling’s papers.We present in Fig. Keeling’s publication output is manageable. According to Fig. Since Fig. 2 concentration in the atmosphere. They discuss the increased productivity of vegetation (based on satellite observations) and thereby the lengthening of the active growing season as a consequence of the recent global warming trend.Although Keeling’s seminal papers from  and 1976The WoS analyze function enables to establish the time evolution of the citations per year (the citation history) of a specific paper. We present in Fig. Note that the 1958 paper deals with measurements in rural areas near the US Pacific coast. The 1958 paper and the In the case of papers that do not attract significant attention until decades after their publication, such as the Keeling  paper, oThe slow reception of Keeling’s papers is no exception in the climate change research literature. An inspection of the citation history of the papers assigned to the peaks in the RPYS spectrogram of the overall climate change research literature  The annual number of climate change papers was determined using the WoS query ‘ti =  and py = 1950–2015’. This query is based on a carefully constructed and tested query  resulted in a quite similar spectrogram.In agreement with the results reported above, the RPYS does not reveal the importance of Keeling’s works in the form of comparatively highly cited references within the subject specific literature: There are no pronounced peaks in the reference publication years 1960 and 1976, respectively, indicating the impact of Keeling’s early works on his Mauna Loa measurements of the atmospheric CO2 in water and sea water, respectively. Studies about the solubility of CO2 in water concern the long discussed and most important question, which amount of the CO2 released by fossil fuel burning will remain in the atmosphere, and which portion will be solved in the oceans. In other words, such studies are connected to the question: Is the increasing CO2 concentration of any relevance with regard to an anthropogenic greenhouse effect—and by this to future climate?The only distinct peak in the spectrogram of Fig. The underestimation of precursor papers/forerunners has also been observed in other fields: Marx and Bornmann , 2013 an2 level in the earth’s atmosphere for many decades? What are possible reasons for comparably low citation counts for Keeling’s landmark papers?In consideration of the importance of Keeling’s and others’ papers for climate change research, we ask for the reasons of the slow reception (in terms of citation impact). Why did the scientific community underestimate the importance of a steadily rising CO2  were primarily interested in the past climate, particularly in the mystery of the ice ages . Concerning the future climate, the dominant sentiment was that “warmer is better”. In the 1950s the popular press began to carry articles about global cooling, which was on the public agenda until the 1970s .Global cooling became an observable trend, stimulating speculations about the coming of a new ice age. Some experts began to ask whether the warming within the decades before 1940 had been an illusion. Later, scientists found out that the decline of the temperature was not seen in the southern hemisphere and concluded that the temperature drop after 1940 was largely due to a rise in industrial pollution (i.e. haze caused by aerosols), augmented by a long-term cycle in the Pacific Ocean. Further information can be found on the website of the science historian Spencer Weart  The discussion on global cooling at the starting time of Keeling’s measurement program, (2) the underestimation of what is often seen as “routine science”, and (3) the amount of implicit/informal citations at the expense of explicit/formal (reference-based) citations.Our case study revealed the discrepancy between the current assessment of Keeling’s lifework and the comparatively low citation impact of his decisive papers on CO2 measurements at Mauna Loa. But “every few years, funding agencies sought to end his support because the work was judged to be ‘routine’ rather than novel research”  a scientific contribution is not always what it looks as seen from a citation perspective, (2) bibliometric data should always be interpreted alongside expert knowledge, and (3) that funding agencies should reconsider their strategy and support long term projects.


Expression of IL-1β, IL-6 and IL-10 was assessed by ELISA. TNF-α induced autophagy as determined by increased LC3-II  and decreased p62  when compared to control. Additionally, TNF-α decreased α-SMA  and SM22  protein levels. Consequently, TNF-α induced migration , proliferation , and the secretion of IL-6 , type I collagen  and osteopontin . Inhibition of autophagy prevented all the TNF-α-induced phenotypic changes. TNF-α induces phenotype switching in A7r5 cell line by a mechanism that required autophagy. Therefore, autophagy may be a potential therapeutic target for the treatment of atherosclerosis.Vascular smooth muscle cells (VSMC) dedifferentiation from a contractile to a synthetic phenotype contributes to atherosclerosis. Atherosclerotic tissue has a chronic inflammatory component with high levels of tumor necrosis factor-α (TNF-α). VSMC of atheromatous plaques have increased autophagy, a mechanism responsible for protein and intracellular organelle degradation. The aim of this study was to evaluate whether TNF-α induces phenotype switching of VSMCs and whether this effect depends on autophagy. Rat aortic Vascular smooth A7r5 cell line was used as a model to examine the phenotype switching and autophagy. These cells were stimulated with TNF-α 100 ng/mL. Autophagy was determined by measuring LC3-II and p62 protein levels. Autophagy was inhibited using chloroquine and siRNA Beclin1. Cell dedifferentiation was evaluated by measuring the expression of contractile proteins α-SMA and SM22, extracellular matrix protein osteopontin and type I collagen levels. Cell proliferation was measured by [ Vascular smooth muscle cells (VSMCs) synthetic phenotype is characterized by an increased migration, proliferation and extra-cellular matrix (ECM) secretion . HoweverVascular remodeling and chronic inflammation characterize Atherosclerosis. Indeed, the inflammatory cytokine TNF-α expression increases in atherosclerotic tissue and contributes to VSMC migration and proliferation  by a mecIn this work, we used A7r5 cell line as a VSMC model to examine the phenotype switching and autophagy. We showed that TNF-α induces autophagy in A7r5 cells by a NF-kB-dependent mechanism. More importantly, TNF-α induces dedifferentiation, along with a migratory, proliferative and inflammatory phenotype in A7r5 cells through an autophagy-dependent mechanism. Altogether, our findings suggest that autophagy is a key mediator on the TNF-α-induced phenotype switching in A7r5 cells.Rattus novergicus) -thymidine incorporation [Proliferation of VSMC was determined using MTT assay  and [3H]poration . A7r5 miporation .2 1.25 mmol/L for 18 h at 37°C and then stained with Coomassie blue (Merck).Electrophoresis was performed with the supernatant of cell cultures using 7.5% polyacrylamide gels with gelatin 8.5 mg/mL. Proteins in the gel were renatured by using 2.5% Triton-X-100 in PBS for 30 min, incubated with Tris-HCl 50 mmol/L pH 8, NaCl 200 mmol/L, CaCl5’-ACT GCC TTC CCT ACT TCA CAA GTC-3’ & reverse 5’-ACT CCA GGT AGA AAC GGA ACT CCA-3’; Pabpn1 rat forward 5’- GTT GGC AAT GTG GAC TAT GG-3’ & reverse 5’-AAC AGG GAC TCA TCT AAG GC-3’.The levels of IL-1β, IL-6 and IL-10 were measured from A7r5 cell supernatants using Quantikine ELISA kits (R&D Systems), according to the manufacturer’s instructions. For quantitative RT-PCR, total RNA was extracted with TRIzol reagent (Invitrogen) and reverse transcribed. Equal amounts of cDNA were subjected to real-time PCR using SYBR green (Applied Biosystems) as described . Data wet test or ANOVA, depending on the conditions. Post-tests are indicated in each figure legend. Differences were considered significant at p<0.05.Results are shown as mean ± SEM of at least three independent experiments. Data was analyzed by p<0.05) and LC3-II increase   (p<0.01), whereas p62 was restored to baseline  . Additioctively) . These rctively) . In ordebaseline , suggestbaseline . These dIt has been previously established that the NF-κB pathway is required for the induction of autophagy by multiple stimulus . Therefop<0.01 and 2.32±0.46, p<0.01, respectively)   and contractile (α-SMA and SM22) protein levels were evaluated. A7r5 cells were stimulated with TNF-α (100 ng/mL) for 48 h and the aforementioned protein levels were determined by western blot. TNF-α induced an increase in type I collagen and osteopontin   and a rectively) . All TNFctively) . Treatmectively) . Moreovectively) . Taken tp<0.01) and transmigration   for 24 h in the presence or absence of CQ. The results showed that TNF-α induces migration measured by wound closure  . The eff p<0.05) . Moreove p<0.05) .In order to confirm these results, autophagy initiation was also blocked by downregulation of Beclin1 using siRNA and TNF-α-induced migration and transmigration were evaluated. As presented in 3H]-thymidine incorporation    for 24 h in the presence or absence of autophagy inhibition. TNF-α induced A7r5 cell proliferation assessed by the MTT assay and [ctively) . Howeverctively) . TherefoCurrently, much remains to be elucidated in regards to cytokine expression and release in VSMC. Therefore, we sought to evaluate if these cells develop an inflammatory phenotype upon TNF-α administration and whether it depends on autophagy. A7r5 cells do not express detectable baseline or TNF-α-induced levels of IL-1β and IL-10 measured by ELISA and RT-qPCR (data not shown).p<0.01 and 258±53, p<0.01, respectively)  . Moreovectively) . Thus, rTNF-α is one of the most relevant cytokines present in the blood and tissues of atherosclerotic patients , 22. Thiet al. that TNF-α induces autophagy in VSMC [et al. demonstrated that classical autophagy inductors, such as starvation and rapamycin, requires IKK complex for optimal induction of autophagy in vivo [It has been previously described by Jia  in VSMC . However in VSMC . Our res in vivo . Moreove in vivo .It has been previously demonstrated that VSMC undergoing vascular remodeling show a higher proliferation and migration rate and TNF-α also promote these features , 25. HowAutophagy was recently reported to be necessary for the motility of metastatic cancer cells . In thesTNF-α is a major pro-inflammatory mediator in several cell types, especially in the immune system . We obseet al. reported that autophagy mediates an unconventional secretory pathway of IL-1β release [Alternatively, Dupont  release . This se release . Further release . Also, t release . Thus, fRegarding autophagy, there are pharmacological compounds that specifically regulate autophagic activity, such as CQ and spautin-1. In this context, it has been described that spautin-1 inhibits autophagy in an Akt-independent pathway inhibiting PDGF-induced hyperproliferation of VSMC . AccordiIn conclusion, our findings suggest that TNF-α elicits a VSMC phenotype switching through an autophagy-dependent mechanism.S1 Fig(A) Western blot analysis of LC3-II and GAPDH in A7r5 cells cultured during 24 h in DMEM + 2 mM pyruvate supplemented with 0, 2, 5 and 10% FBS . (B) Western blot analysis of LC3-II and GAPDH in A7r5 cells incubated with PDGF-BB (10 nM) at 0, 6, 24 and 48 h . Data are expressed as mean ± SEM and analyzed by one-way ANOVA followed Dunnett post-test.(EPS)Click here for additional data file.