Telemedicine draws on information technologies in order to enable the delivery of clinical health care from a distance. Twitter is a social networking platform that has 316 million monthly active users with 500 million tweets per day; its potential for real-time monitoring of public health has been well documented. There is a lack of empirical research that has critically examined the potential of Twitter polls for providing insight into public health. One of the benefits of utilizing Twitter polls is that it is possible to gain access to a large audience that can provide instant and real-time feedback. Moreover, Twitter polls are completely anonymized.The overall aim of this study was to develop and disseminate Twitter polls based on existing surveys to gain real-time feedback on public views and opinions toward telemedicine.Two Twitter polls were developed utilizing questions from previously used questionnaires to explore acceptance of telemedicine among Twitter users. The polls were placed on the Twitter timeline of one of the authors, which had more than 9300 followers, and the account followers were asked to answer the poll and retweet it to reach a larger audience.In a population where telemedicine was expected to enjoy big support, a significant number of Twitter users responding to the poll felt that telemedicine was not as good as traditional care.Our results show the potential of Twitter polls for gaining insight into public health topics on a range of health issues not just limited to telemedicine. Our study also sheds light on how Twitter polls can be used to validate and test survey questions. Telemedicine draws on information technologies in order to enable the delivery of clinical health care from a distance ,2. Telemtweets; 500 million tweets are posted per day [retweet. One of the differences between Twitter and Facebook is that the majority of Twitter accounts are completely public. Twitter also has the concept of hashtags, expressed by placing the “#” symbol at the start of a word and allowing users to categorize a topic and form discussions around it. For instance, it is possible to search Twitter for the hashtag “#telemedicine,” which will display all tweets that have used the hashtag.Twitter is a social networking platform that has 316 million monthly active users and its potential for real-time monitoring of public health has been well documented . Anyone Twitter has been used previously as a platform to disseminate guidelines and perform polls by the European Association of Urology ,6. TwittHowever, there is a lack of empirical research that has critically examined the potential of Twitter polls for providing insight into public health. Twitter is a platform with a demographic that is college educated and where users are likely to be aware of new forms of technology. This makes examining opinions toward telemedicine an interesting case.One of the benefits of utilizing Twitter polls is that it is possible to gain access to a large audience that can provide instant and real-time feedback. Moreover, Twitter polls are completely anonymized and it is not possible to learn the identity of a user completing a poll, nor is it possible for one user to vote on more than one occasion. However, it must be noted that Twitter users could hold several accounts that could allow a single user to vote several times. Our results will be of interest to health authorities, policy makers, and academics interested in telehealth. They are also likely to be of interest to health authorities around the world seeking low-cost, real-time survey methods, as well as researchers interested in a critical examination of Twitter polls.The overall aim of this study was to better understand opinions related to telemedicine on Twitter and to assess the potential of Twitter polls to validate and test survey questions.In this study, we devised two Twitter polls using questions from previous questionnaires to explore acceptance of telemedicine among Twitter users. We distributed the polls on the Twitter timeline belonging to one of the authors and asked the followers of the account to answer the poll and retweet it to reach a larger audience. The Twitter handle that we used for this study, @jvalaball, has more than 9300 followers. The followers of the Twitter account that was utilized had a worldwide audience that mainly derived from Spain (40%), the United States (27%), and the United Kingdom (11%). pinned to the top of the Twitter timeline used in the project for 7 days. By pinning a tweet, it will permanently be placed at the top of a Twitter user’s account, such that any new visitor will see the tweet appear at the top of a user’s timeline.Our first Twitter poll was distributed in May 2016 and was Yes or No. We used a question from the telemedicine satisfaction questionnaire, a validated questionnaire developed by Yip et al in 2002 . The queThe poll was retweeted 51 times and had 6698 impressions . It received a total of 108 votes, 89.8% (97/108) of which were positive and 10.2% (11/108) negative.Better, About the same, Not as good, and Not sure. The poll was retweeted 49 times and had 4364 impressions. The second poll was posted during November 2017, and it was also pinned to the top of the timeline for 7 days. For this Twitter poll, we used a question from the physician questionnaire in the European Union project Health Optimum . The queNot as good as traditional care, 18.6% (21/113) found the quality of care to be About the same, 22.1% (25/113) rated the quality of care as Better, and 20.4% (23/113) responded that they were Not sure about the level of care.Overall, the poll received a total of 113 votes. A total of 38.9% (44/113) of the respondents stated that they rated the quality of care delivered by telemedicine as Our study provided an overview of a novel experiment where targeted Twitter polls were used to assess acceptance of telemedicine among Twitter users. We argue that this tool could be used to quickly perform surveys to assess the opinion of users regarding acceptance of telemedicine in order to obtain rapid feedback of new questionnaires before validating them. An advantage of using Twitter polls is that many can be created and disseminated in very little time as opposed to traditional questionnaires and surveys, which can become resource heavy. One of the key benefits of utilizing social media platforms such as Twitter is the very low cost when compared to traditional survey-based methods. In certain departments with low budgets, Twitter could be used as a tool to gain initial public opinion feedback before a survey could be devised. The first poll showed an overwhelming support toward telemedicine as an acceptable way to receive health care services. In the second poll, which asked Twitter users to rate the quality of care delivered by telemedicine when compared to the quality of traditional care, the majority of users found that telemedicine was not as good as traditional care. This highlights how the design of a question can potentially influence the results of a survey. Our method could be used to conduct testing on survey questions and to compare the answers to ensure they are consistent. Although the two Twitter polls received a very high number of impressions—6698 and 4364, respectively—the response rates were comparatively low. This could be due to the fact that the questions were not attractive enough to the audience for them to feel compelled to reply. It must be noted that one of the limitations of using Twitter for gauging public opinion through the use of Twitter polls is that its users are not representative of the general population in terms of demographics . A furthOur study has demonstrated the potential of Twitter polls for gaining insight into public health topics such as telemedicine and for shedding light on how Twitter polls can be used to validate and test survey questions. Twitter polls could be utilized by health authorities to gain early real-time feedback on public views and opinions on a range of health issues not just limited to telemedicine. Key strengths are the speed at which Twitter polls can be formulated as well as their low cost.


Meconium-stained amniotic fluid is observed in approximately 10–15% of all deliveries; however, only 5% of neonates with meconium-stained amniotic fluid develop meconium aspiration syndrome (MAS). Although foetal distress and subsequent sympathetic stimulation have been considered as the primary upstream events of MAS, this clinical complication sometimes occurs due to other pathologies, such as intraamniotic inflammation. The aim of this study was to investigate whether the incidence of MAS is associated with the presence of funisitis and chorioamnionitis in term neonates with meconium-stained amniotic fluid.Between April 2013 and March 2015, a total of 95 term neonates with meconium-stained amniotic fluid, who were hospitalized at a neonatal intensive care unit, were enrolled in the study. The placenta and umbilical cord were histopathologically examined. Clinical variables and histopathological findings associated with the incidence of MAS were studied.p < 0.05). The multivariate model comprised funisitis , and elevated acute-phase inflammatory reaction score were associated with increased incidence of MAS (all In neonates with meconium-stained amniotic fluid, funisitis, as well as low 1-min Apgar score and male sex, was identified as an independent variable for MAS development. Intraamniotic inflammation might be involved in the pathological mechanisms of MAS. Meconium-stained amniotic fluid is observed in approximately 10–15% of all deliveries , which hTo establish early, reliable biomarkers of MAS, precise understanding of the pathological mechanism is essential. Although asphyxia and subsequent sympathetic stimulation have been considered to trigger foetal passage of meconium into amniotic fluid , MAS somStudies suggested that meconium-stained amniotic fluid is more likely to be contaminated with bacteria, endotoxin, and inflammatory mediators than clear amniotic fluid, and often accompanied by clinical evidence of intraamniotic inflammation . MeconiuWe performed a prospective observational study to investigate whether the incidence of MAS is associated with the presence of funisitis and chorioamnionitis.This study was conducted under the approval of the Ethics Committee of Nagoya West Medical Center . The internal review board advised that there is no statutory requirement for parental consent for the use of clinical and histopathological data, which are collected as a routine clinical care in the unit.Between April 2013 and March 2015, 969 inborn neonates were hospitalized at a level-II neonatal intensive care unit (NICU) of Nagoya West Medical Center , which provides intensive care for virtually all sick neonates, except for those who require advanced life support treatments, such as haemodialysis/filtration, cardiac surgery, and extracorporeal membrane oxygenation. Of these, 239 inborn neonates ≥37 weeks of gestation were admitted within 24 h of birth, whose blood samples on day 0 were available. Subsequently, 99 neonates were enrolled in the study after excluding 10 neonates with major congenital anomalies and 130 neonates without meconium-stained amniotic fluid .Presence of meconium-stained amniotic fluid was evaluated at birth under visual observation as previously described . MAS was1-acid glycoprotein (α1AG), and haptoglobin were measured using turbidimetric immunoassay . CRP > 0.3 mg/dl, α1AG > 20 mg/dl, and haptoglobin > 13 mg/dl were regarded as positive , αpositive ; the acupositive . Venous The clinical variables were compared between neonates with and without MAS. Apgar scores (≤7) and cord blood pH (<7.1) were divided using clinically relevant thresholds. All statistical analyses were performed using R  and its Japanese interface  . Logistip < 0.05; A total of 99 term neonates with meconium-stained amniotic fluid, histopathological examination was not available in four neonates, whose data were not considered further. Of 95 remaining neonates, 36 neonates (37.9%) developed MAS, none of whom showed positive blood culture. Gestational age, caesarean delivery, and incidence of premature rupture of the membranes and cord blood acidosis did not differ between neonates who developed and did not develop MAS . UnivariMeconium aspiration syndrome is one of the most commonly recognized respiratory diseases in neonates, however, the exact pathological mechanism leading to foetal meconium passage into the amniotic fluid and its aspiration is still unknown. Our study suggested that, in neonates with meconium-stained amniotic fluid, funisitis, as well as low 1-min Apgar scores and male sex, was associated with increased incidence of MAS, pointing toward the role of inflammatory factors in the pathophysiology of MAS. Further large-scale studies are required to delineate the role of antenatal inflammation in the development of meconium-stained amniotic fluid and MAS.Previous studies highlighted that MAS development is associated with low cord blood pH and Apgar scores at 5 min , supportIn our study, funisitis was associated with an increased incidence of MAS, supporting a hypothesis that intraamniotic inflammation might be an additional factor involved in the development of MAS. A study that investigated autopsy cases with histopathologic evidence of intrauterine meconium exposure found that pulmonary inflammation was associated with inflammation of the umbilical cord, as well as meconium aspiration . Thureen1AG, and haptoglobin, were employed, which are produced in the liver in response to intrinsic/extrinsic stimuli and subsequent elevation of IL-1ß and IL-6 (In our study, MAS development was associated with funisitis, but not chorioamnionitis. It is possible that chorioamnionitis might represent a maternal inflammatory response to intraamniotic infection, whereas funisitis might be predominantly represent the foetal inflammatory response, which is accompanied by elevation of inflammatory cytokine and procytokine levels. Intraamniotic inflammation occurs more frequently in the presence of meconium-stained amniotic fluid than that in clear amniotic fluid, as shown by elevated amniotic fluid IL-6 levels in neonates with meconium-stained amniotic fluid . In animand IL-6 . AlthougIt is known that inflammatory responses are sex-dependent; for example, studies in vivo demonstrated greater release of proinflammatory cytokines after in vivo lipopolysaccharide stimulation in male foetuses than in female foetuses . LambermSeveral limitations of our study were noted, which need to be considered when interpreting our findings. Although we performed standard histopathological examinations of the placenta and umbilical cord for all participating neonates, the study population is relatively small, which did not allow the assessment of some important clinical cofounders. To minimize the population bias, our study was conducted at a single level-II unit recruiting only inborn neonates. However, we were not able to include neonates, who did not have meconium-stained amniotic fluid, and those, who were not hospitalized at NICU, leaving uncertainly around the role of intraamniotic inflammation in the entire birth cohort. Several important clinical variables associated with antenatal inflammation and hypoxic-ischemic stress, such as maternal fever, infection and finding from cardiotocography, were not incorporated within the analysis, leading to a difficulty in discriminating acute chorioamnionitis and funisitis from chronic ones. A prospective cohort study is currently underway to elucidate the role of intraamniotic inflammation on MAS development in all neonates born at our hospital by involving extensive maternal and neonatal variables.The incidence of MAS was associated with funisitis, as well as low 1-min Apgar scores and male sex, suggesting that intraamniotic inflammation might also be involved in the pathological mechanisms of MAS. Further large-scale prospective studies are needed to delineate the detailed mechanism of MAS and allow its efficient prediction and prevention.10.7717/peerj.7049/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.7049/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.7049/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.7049/supp-4Supplemental Information 4Click here for additional data file.


The K-rich inclusions show significantly different 40Ar/38ArCl values compared to the 38ArCl-rich inclusions, indicating that it is difficult to constrain the quartz formation age using only fluid inclusions containing excess 40Ar. The highest obtained 40Ar/36Ar value from the fluid inclusions is consistent with an expected value of the Ongeluk plume source, suggesting that the quartz precipitation was driven by Ongeluk volcanism. Considering the fluid inclusion generations and their compositions, the hydrothermal system was composed of crustal fluid and magmatic fluid without seawater before the beginning of a small amount of seawater input to the hydrothermal system.Fluid inclusions in hydrothermal quartz in the 2.4 Ga Ongeluk Formation, South Africa, are expected to partially retain a component of the ancient seawater. To constrain the origin of the fluid and the quartz precipitation age, we conducted Ar–Ar dating for the quartz via a stepwise crushing method. The obtained argon isotopes show two or three endmembers with one or two binary mixing lines as the crushing proceeds, suggesting that the isotopic compositions of these endmembers correspond to fluid inclusions of each generation, earlier generated smaller  As r. As r3])One of the best methods to estimate the compositions of ancient seawater is the study of fluid inclusions in hydrothermal quartz precipitated in drainage cavities and interstitial spaces between seafloor pillow lavas because such hydrothermal quartz is presumably formed via mixing between the subseafloor hydrothermal fluid and seawater –17. Howe40Ar has many reservoirs, such as the atmosphere and metamorphic fluid. 40Ar, except atmospheric 40Ar (40ArA) and radiogenic 40Ar (40ArR), is referred as excess 40Ar (40ArE). The ubiquitous 40ArE in natural samples often causes considerable difficulties in Ar–Ar dating. To overcome this problem, several studies have provided useful methods for data analysis, e.g. isochron diagrams for stepwise crushing analyses of fluid inclusions (e.g. )ns , andns e.g. ,20,22). ,22. 40Ar70 000; –26), mid, mid40Ar and oce. Recent methods ,31. ThesIn this study, we focused on the 2.4 Ga Ongeluk Formation, Transvaal Supergroup, South Africa. This formation is composed of submarine lava flows that are thought to have erupted during the Palaeoproterozoic Snowball Earth Event, as suggested by palaeomagnetic studies and geological field observations ,32–34. T2.a). The clastic rocks in the Makganyene Formation do not contain detrital zircons younger than 2.4 Ga )ndmember . TherefoThe results of the isotope ratio show a continuous change in the composition as the crushing proceeds. This change probably resulted from the change in the generation of the crushed fluid inclusions from the later formed larger inclusions to the earlier formed smaller inclusions . In prev5.3.5.3.1.σ) for GU84 and 2701 ± 43 Ma (2σ) for GU103-1. The analysed quartz samples should ideally have similar ages because these samples were collected from outcrops in the same locality even though the obtained ages vary from 2114 ± 312 Ma for GU84 to 2701 ± 43 Ma for GU103-1.The stepwise crushing experiments for the Ar–Ar dating provided ages of 2114 ± 312 Ma , indicating the absence of significant recrystallization and any resulting escape of argon. A second possibility is mixing between the argon from K-rich inclusion and the excess argon without a correlation with chlorine from the (secondary) fluid inclusions. This effect may have influenced all the samples of GU84; especially in the early steps of GU84 (c), it may have disturbed the obtained ages and intercept value of 40ArA+E/36ArA for each sample.There are several effects that can potentially make the Ar–Ar age younger than the true age. One possibility is a break in the closed system for argon, such as the recrystallization of quartz. However, quartz retains its microcrystalline texture c,e, indi of GU84 c, it mayNevertheless, the sample GU103-1 is probably unaffected by this effect because the 3D distribution of argon isotopes is in good agreement with the three-component mixing model. Based on this consistency, 2701 ± 43 Ma for GU103-1 is the best constrained age. However, this age is significantly older than the eruption age of the Ongeluk lavas (2426 ± 3 Ma); even the ages of igneous and sedimentary rocks vary from 2642 ± 3 to 1928 ± 4 Ma in the Griqualand West Basin. Such a discrepancy between the true and estimated ages is possibly explained by four mechanisms described below.40ArR and K in K-rich minerals. Previously, it has been reported that Ar–Ar ages for approximately 120 µm thick muscovite obtained via the crushing method were older than those obtained via the heating and laser ablation methods [40ArR compared to 39ArK during crushing process; 40ArR is loosely retained in minerals compared to 39ArK, which is contained in the crystal lattice of K-bearing minerals. However, Ca/K values in the later crushing steps of GU103-1 and GU103-2 higher than 1 are inconsistent with the significant presence of a K-rich mineral.First, the discrepancy can be caused by a different physical and chemical property between  methods . This ph39ArK in minerals trapped in fluid inclusion via irradiation and release of 40ArR via alteration between fluids and minerals in the fluid inclusion. During neutron irradiation, 39ArK recoils over several hundreds of nanometres. When the K-bearing minerals are very small e.g. less than 1 µm, a significant amount of 39ArK is recoiled into the fluid. Conversely, 40ArR in minerals is slowly released over a few billion years of alteration at temperatures close to room temperature. Younger ages from K-bearing minerals (1027 ± 41 Ma) than expected from the depositional age (approx. 1526 Ma) were reported by a previous study of Ar–Ar dating of middle Proterozoic quartz with K-bearing minerals as the main K reservoir in fluid inclusions using crushing and heating methods . This geriments  that may39ArK recoiling out from the inclusions would possibly make the apparent age older than the depositional age. This K-loss process is insufficient to affect the quartz samples because neutron-irradiated quartz grains have sizes of several hundreds of micrometres.Third, in the case of a K-rich component contributed primarily by K-rich fluid inclusions with minimal contributions from minerals, only the 40ArE/38ArCl values between EM2 and EM3 can explain the discrepancy. The three-component mixing model and the regression plane assume a constant 40ArE/38ArCl ratio in each sample. When this assumption is inappropriate, the obtained age is different from the true formation age. The gap between the apparent age of 2.7 Ga and the expected depositional age of 2.4 Ga implies that the assumption of the three-component mixing model is inappropriate including GU103-1 due to the non-constant 40ArE/38ArCl value between the Cl-rich endmember and the K-rich endmember.Fourth, in the case of EM3 contributed by K-rich fluid inclusions, the different We cannot clearly decide which mechanism is the most suitable to explain the discrepancy of the four described above due to the absence of a heating analysis. However, the Ca/K values and the degassing profiles suggest that the last mechanism is the most likely of the four.5.3.2.40ArA+E/36ArA and 38ArCl/36ArA for GU103-1 show two mixing lines between EM1 mixed with EM2 (EM2′) and EM3 mixed with EM1 (EM3′) and between EM1 and EM2, suggesting that the 40ArE/38ArCl ratio of EM3 is different from that of EM2 in these samples (c). The bulk regression line for GU103-1 shows small uncertainty and a 40ArE/38ArCl value of 340 ± 37 (95% confidence). However, the mixing line between EM2′ and EM3′ of GU103-1 indicates that the 40ArE/38ArCl value of EM3 is higher than approximately 468, approximately 40% higher than the 40ArE/38ArCl value from the total regression line, based on a slope of a line connecting between EM1 and a data point of the last step. The EM3 of GU103-2 also shows a similar minimum 40ArE/38ArCl value of approximately 464. These data clearly show that the fluid inclusions with apparently constant 40ArE/38ArCl value can involve different 40ArE/38ArCl components. These types of fluid inclusions induce misinterpretation when constraining the mineral formation age and the 40ArA/36ArA value of the palaeoatmosphere. However, apparent ages of 2114 ± 312 Ma from GU84 and 2701 ± 43 Ma from GU103-1 obtained by the three-component mixing model varies around the expected depositional age of 2.4 Ga. These variations may imply that all the primary fluid inclusions formed at approximately 2.4 Ga and that the 40ArE/38ArCl value of the mixing line between EM2′ and EM3′ is slightly different from that of EM2.Assuming that the analysed hydrothermal quartz and all the fluid inclusions formed at 2.4 Ga, the diagrams of  samples c. The bu5.4.40ArE/38ArCl values compared with EM2. The 40ArE/36ArA value of EM3 is lower than that of EM2 . EM2, which is trapped in moderate-sized inclusions of moderate generations has a Cl- and 40ArE-rich composition and is poor in K. However, EM2 of all samples except GU84 shows a lower Ca/K value than 1, indicating a higher abundance of K than of Ca. The lower abundances of the two major cations in the fluid imply that EM2 has an Na-rich composition, i.e. EM2 is an Na-rich fluid. The clear presence of 40ArE in EM2 and EM3 indicates a lack of seawater and surface water in the Ongeluk hydrothermal system. A hydrothermal system separated from ambient seawater implies a presence of a cap rock, such as the sulfate minerals in the modern seafloor hydrothermal system  in the Ongeluk interpillow jaspilites, indicative of the decomposition of organic matter in the underlying sedimentary rocks during seafloor hydrothermal circulation [40ArA+E/36ArA values much over 4000 in the samples when the 40ArA+E/36ArA value of the Ongeluk plume source is assumed to have been similar to those of 2.4 Ga oceanic island basalts with values of approximately 4000 [40ArA+E/36ArA value was consistent with the expected 40Ar/36Ar value of MORB source mantle at 3.5 Ga [40ArA+E/36ArA of 4100 similar to the expected value for the Ongeluk plume source supports the idea that the fluid inclusions formed due to hydrothermal circulation driven by the Ongeluk volcanism. EM1 shows the atmospheric composition compared with EM2 and EM3. In particular, EM1 of GU103-1 shows a 40ArA+E/36ArA value similar to the atmospheric value and fluid inclusions trapped in hydrothermal/relict minerals in meta-gabbros of modern oceanic crusts [40ArA+E/36ArA values with a significant difference between that from GU84 and GU103-1 are higher than modern atmospheric value and similar to that of the fluid inclusions in meta-gabbros, suggesting the small amounts of seawater in the Ongeluk hydrothermal system.The continuous change of the extracted argon gas composition from the quartz corresponds to the sizes and generation of fluid inclusions, as discussed in §5.2.5, representing the evolution of Ongeluk hydrothermal fluid. The assumption for the quartz depositional age of 2.4 Ga enables the following interpretation. EM3 which is trapped in smaller inclusions of early generations is enriched in K with various Ca/K ratio among samples and has higher or similar em (e.g. ). The hyely 4000 . In prevt 3.5 Ga ,31. Therc crusts . The atmc crusts ,35 and tc crusts ,13. HowePreviously, the seafloor hydrothermal circulation hosted by the Ongeluk volcanism was primarily explained by interactions between seawater-derived hydrothermal fluids and the volcanic rocks e.g. ,13,33). ,33. 13,36.36Ar-rich larger inclusions, 38ArCl- and 40Ar-rich moderate-sized ones and 39ArK- and 40Ar-rich smaller fluid inclusions.Stepwise crushing extraction of the quartz samples reveals that the fluid inclusions with different size are different argon compositions, The 3D regressions for the obtained argon isotope data clearly indicate that the compositional variations are formed by three major components: K-rich fluid, excess argon-rich fluid and atmospheric argon-rich fluid.40ArE/38ArCl value of K-rich fluid from the Cl-rich fluid.An age of 2732 ± 45 Ma for GU103-1 was obtained from the 3D plot of the three-component mixing model, which is significantly older than reported ages for the Ongeluk volcanism (2425.5 ± 2.6 Ma). The apparent gap of approximately 300 Ma was probably caused by the different 40ArE/38ArCl.It is difficult to constrain a depositional age based on Ar–Ar dating by crushing method using only fluid inclusion containing excess argon due to the large variation of 40ArA+E/36ArA value (4100) of the age-corrected data as 2.4 Ga from the Ongeluk fluid inclusions probably represents the contribution of the deep crustal fluid from older sediments below the Ongeluk Formation and derived from the plume source of the Ongeluk volcanism, suggesting that these fluid inclusions were formed at 2.4 Ga.The highest The presence of excess argon indicates (a) little seawater contribution in the Ongeluk hydrothermal system. The Ongeluk hydrothermal fluid evolves from crustal fluid and magmatic fluid without seawater into a little seawater and these fluids.


We realized that the affiliations of the authors were incorrectly listed in our paper published in Molecules recently . The cor1,2, Lin Zhang 1,2, Tao Yi 1 and Jing-Kui Tian 1,2Hong-Wei Fu 1 zxxtjk@gmail.com (H-W.F.)Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China; E-mail: 2 zhanglin@zju.edu.cn (L.Z.)Zhejiang Provincial Key Laboratory of Chinese Medicine Screening, Exploitation and Medicinal Effectiveness Appraisal for Cardio-Cerebral Vascular and Nervous System, The Key Laboratory of Biomedical Engineering of the Ministry of Education, Hangzhou 310027, China; E-mail:


Uncontrolled proliferation is a hallmark of cancer and can be assessed by labelling breast tissue using immunohistochemistry for Ki67, a protein associated with cell proliferation. Accurate measurement of Ki67-positive tumour nuclei is of critical importance, but requires annotation of the tumour regions by a pathologist. This manual annotation process is highly subjective, time-consuming and subject to inter- and intra-annotator experience. To address this challenge, we have developed Proliferation Tumour Marker Network (PTM-NET), a deep learning model that objectively annotates the tumour regions in Ki67-labelled breast cancer digital pathology images using a convolution neural network. Our custom designed deep learning model was trained on 45 immunohistochemical Ki67-labelled whole slide images to classify tumour and non-tumour regions and was validated on 45 whole slide images from two different sources that were stained using different protocols. Our results show a Dice coefficient of 0.74, positive predictive value of 70% and negative predictive value of 88.3% against the manual ground truth annotation for the combined dataset. There were minimal differences between the images from different sources and the model was further tested in oestrogen receptor and progesterone receptor-labelled images. Finally, using an extension of the model, we could identify possible hotspot regions of high proliferation within the tumour. In the future, this approach could be useful in identifying tumour regions in biopsy samples and tissue microarray images. It ranks as the fifth cause of death from cancer and the most frequent cause of cancer death in women in less developed regions2. According to the World Health Organisation, invasive ductal carcinoma (IDC) is the most common type of breast cancer in both women and men, accounting for about 75% of all breast cancers. IDC is typically characterised by a group of malignant epithelial tumours, with invasion of adjacent tissues that have a tendency to metastasize to distant sites and that do not exhibit sufficient characteristics of a specific histological type, such as lobular or tubular carcinoma.Breast cancer is a heterogeneous disease consisting of several molecular and genetic subtypes, each with characteristic differences in clinical, biological and imaging patterns3 with high tumour spread rates leading to worse patient outcomes. The identification of tumour proliferation rests on the identification and enumeration of mitoses in haematoxylin and eosin (H&E) stained tissues, or the use of immunohistochemistry (IHC) to label a proliferation marker such as Ki67 for proliferating cells. The Ki67 labelling index provides strong prognostic and predictive information on response to chemotherapy4 although it is prone to intra- and inter-observer variation5. To aid in the variation, digital image analysis (DIA) can be used to speed up the process and has been demonstrated to have good correlation with pathology scores6, but is limited by the need to identify the tumour area, which requires detailed pathology annotation. While methods exist to do this without input from a pathologist, including using a pan-tumour marker, the accurate and automated segmentation of breast cancer into tumour and non-tumour regions is challenging. The automated segmentation of tumours using Ki67 labelling is particularly problematic because Ki67 expression is not limited to the tumour and the tumour can contain Ki67-negative as well as Ki67-positive nuclei. Additionally, the segmentation of alternative IHC markers, for example oestrogen receptor (ER) and progesterone receptor (PR), requires re-training of the DIA.Tumour proliferation rate is an important prognostic biomarker7. High resolution images can be prepared for histology slides and computational tools using DIA are provided by several manufacturers, to aid in the reproducible quantification of cells and cellular expression8. Deep convolutional neural networks (DCNNs) have recently achieved state-of-the-art performance in various applications such as image classification9 and object detection10. Unlike the traditional hand-crafted feature approaches, deep learning represents an end-to-end feature learning, using a large amount of training data to learn high-level structural features and thereby discriminate between the classes of interest. Deep learning has been used successfully to automatically segment the epithelial and stromal regions in breast tissue using histological images stained using H&E11. Geert Litjens et al. also demonstrated that deep learning segmentation in prostate cancer gland detection significantly overlapped with pathologist annotation12. Therefore, the deep learning approach can serve as a good feature extractor for better data representation13.Digital pathology (DP) is becoming a significant part of the pipeline in research and clinical laboratoriesIn this paper, we describe a fully automated invasive breast tumour region identification system using a deep learning approach, termed Proliferation Tumour Marker Network (PTM-NET). PTM-NET can accurately detect the tumour area in IHC-stained breast cancer samples without pre-processing procedures, such as image colour unmixing or colour normalisation. In addition, following tumour identification we demonstrate that PTM-NET can identify regions of high proliferation using an activation filter map. Finally, PTM-NET can also be used to identify breast tumour regions from ER- or PR-labelled IHC without any additional training or modifications to the algorithm.From the 102 samples used for analysis, the samples were split in a representative manner between training, testing and validation cohorts  validation cohort and 15 Ki67-labelled images from the Vilnius University (VU) cohort, values for the true-positive rate (TPR), true-negative rate (TNR), false-positive rate (FPR), false-negative rate (FNR), positive predictive value (PPV) and negative predictive value (NPV) were calculated, along with the Dice coefficient (DC) Fig. . SimilarWhile the combined PPV, NPV and Dice coefficients were good, there were significant differences between the AZ and VU cohorts in the validation set, with a significance attached to the TNR, FPR and NPV Table  with theThere was a significant difference in accuracy of PTM-NET when combined dataset were separated by tumour grade, with a PPV of 71.7%, NPV of 86.8% and Dice coefficient of 0.76 for high grade tumours compared to a PPV of 66.4%, NPV of 91.4% and Dice coefficient of 0.7 for low grade (Grade 2 or less) tumours. When separating the combined dataset by the percentage of tumour present in the tissue sample, there was a significant difference only in NPV, with a PPV of 67.6%, NPV of 92.1% and Dice coefficient of 0.72 for tissue samples with less than 50% tumour, compared to a PPV of 71.3%, NPV of 86.3% and Dice coefficient of 0.76 for tissue samples with greater than 50% tumour  is increasing and has been employed in several applications in pathology, including the identification of invasive tumour19. In normal and tumour regions of breast cancer tissue, Ki67 expression is non-uniform, varying across a wide range from zero to high levels of expression. Therefore, it poses a particularly difficult challenge for the training of a tissue segmentation algorithm. To overcome this challenge, we created custom CNN architecture (PTM-NET), to generate a probability map for the tumour and non-tumour regions with the exclusion of infiltrating cells, that demonstrated high segmentation accuracy, with a mean Dice value of 0.74 compared to a set of ground truth images annotated by a pathologist. Where discrepancies occurred, these were isolated small groups of tumour cells (10–30 cells) separated from the larger tumour regions. Interestingly, the NPV for the VU dataset was significantly lower than the AZ dataset, probably reflecting the lower percentage (35%) of images within the training set. These images, while using the same clone of Ki67 antibody, differ in hue and intensity of the diaminobenzidine (DAB) and haematoxylin. Surprisingly, there was a significant difference in the prediction value when separated by tumour grade, with PTM-NET showing a greater predictive value in high grade samples and samples with high tumour content (>50%). As this cannot be due to training, since the training set was intentionally split equally between grades, this must reflect the difference in tissue architecture in high grade samples and reflects the fragmentary nature of the samples with smaller percentage tumour, where the model does not perform well. However, the NPV and PPV are still good in these samples, demonstrating the applicability of the model but where further modification could improve it.Ki67 is an important marker in diagnostic use and has been demonstrated to predict recurrence-free survival after short-term endocrine treatmentet al. who used a three-layer network with one fully connected and an input image size of 101 × 101 × 3, generating a dice value of 0.6714. Whilst the increase in number of layers may be of importance, a direct comparison of the four-layer PTM-NET to the sixteen-layer VGG-NET, trained and tested on the same samples, showed that PTM-NET generated a higher tumour segmentation accuracy and significantly higher PPV, with no significant difference in segmentation of the non-tumour region. This is likely because histopathology tumour images possess colour and textural properties that are captured only using a few convolution filters and the sixteen layers of VGG-NET may add little value and comes at the expense of additional computation and higher GPU requirements.PTM-NET represents a four-layer network with two fully connected networks and input image size of 64 × 64 × 3, in contrast to Cruz-Roa et al.20 achieved an impressive PPV of 98%, distinguishing malignant from benign tumour on H&E stained slides, using the Inception_V3 (INV3) and Inception_resnet_V2 (IRV2) models to perform both binary and multi-class classification of the BreaKHis breast cancer image data. However, since they used transfer learning on a large set of small 700 × 460 RGB micrographs from the database, preselected by a pathologist to have tumour present, this approach does not represent a real-world scenario. Additionally, PTM-NET is designed to use IHC images and is tested on slides prepared at two different sites, with increased complexity due to differences in hue and intensity of staining both the DAB and haematoxylin channels.PTM-NET is also designed for the analysis of whole slide IHC images, which is relevant for the advance of digital pathology in clinical labs worldwide that analyse a wide variety of normal tissue types and lymphocytic infiltrates, and heterogeneity of tumour and their tumour regions. In a recent advance, Xie 21 where the proliferation rate is visually estimated by the pathologist over a hotspot area that could contain between 500 to 2000 cells23. This renders the identification and quantification of hotspot areas as well as the calculation of the proliferation rate complicated and time consuming. The pathologist is required to zoom out to 1X magnification (10 microns/pixel) depending on the size of the screen used and the size of the tissue sample; at this magnification many cell nuclei are smaller and will not be clearly visible. Several methods have been employed previously, including those described in a recent article by Narayanan et al.24 who used a fine-tuned VGG network based on hyper column feature maps (DeepSDCS) at a cellular resolution to detect, simultaneously segment the cells and generate seed labels used in the classification of different cell types such as stromal, lymphocytes, Ki67-positive and negative cells. Such a method relies on the accuracy of cellular segmentation before classifying into Ki67-positive/negative and tumour-positive/negative. In contrast, PTM-NET uses the reverse logic and takes the information generated from the PTM-NET classifier to segment tumour in the first instance and then analyse the Ki67-fraction within the tumour region, both of which are equally valid. Alternatively, Saha et al.17 employed a cut-off value of 15% between regions of low and high proliferation and the analysis used regions of interest on the patch images, in contrast to the PTM-NET methodology with a cut-off value of 20% combined with validation on the whole slide image. Hence this approach can be ultimately employed to minimise multiple rounds of low to high power zoom on a tissue sample to find the best sampling region. When implemented, this should improve pathologist workflow and minimise potential error.The identification of hotspots as well as the quantification of IHC Ki67-positivity are of critical importance for the prognosis and the treatment of breast cancer. Counting Ki67-positive cells in the hot spot, the region of highest concentration of Ki67-positive tumour cells, is a method increasingly used in pathologyFinally, and another demonstration of the applicability of the PTM-NET, a selection of AZ tissue samples from the Ki67 study were stained for ER and PR. While the sample number stained and analysed was small, the segmented tissue samples were visually examined and showed a high level of accuracy for the detection of tumour and non-tumour cells. Under visual examination, the only regions of discrepancy where FP or FN regions could be identified, was due to such areas not being represented in the original Ki67 training set. This discrepancy could be easily rectified and suggests that a single well-trained algorithm could be used for all IHC in breast cancer when utilising a well-designed CNN model, such as PTM-NET.Tissue blocks from 87 patients diagnosed with invasive ductal carcinoma (IDC) were acquired from commercial sources through the AstraZeneca (AZ) Biobank. From these, 45 blocks were used for training PTM-NET, 12 for the testing and subsequent re-training of the initial PTM-NET model and the remaining 30 were used for the validation. Of these 45 blocks used for training, 29 were from AZ and remaining 16 from Vilnius University (VU). Of the 45 blocks used for the validation cohort, 30 were from AZ and 15 from VU.AZ has a governance framework and processes in place to ensure that commercial sources have appropriate patient consent and ethical approval in place for collection of the samples for research purposes including use by for-profit companies. The AZ biobank in the UK is licensed by the human tissue authority (Licence No. 12109) and has national research ethics service committee (NREC) approval as a research tissue bank (RTB) (REC No 17/NW/0207) which covers the use of the samples for this project.2O2 in H2O 20 minutes, washed in TBS-Tween and incubated in Serum Free Protein Block  for 20 minutes. Sections were then treated with anti-Ki67 for 60 minutes and mouse Dako Envision+/HRP solution (Agilent) for 30 minutes before developing with Di-AminoBenzidine (Agilent) for 10 mins and counterstaining with Carazzi’s Haematoxylin. For oestrogen receptor (ER) and progesterone receptor (PR), the DAKO ER/PR Pharm Dx kit (Agilent) was utilised and all reagents, including antibodies were dispensed from the kit. All IHC was performed at room temperature. For Ki67-labelling performed at VU, the same antibody clone was used as for AZ, but stained using a Ventana BenchMark XT autostainers with on-board antigen retrieval and detected using the ultraView Universal DAB kit 25.Four, 4μm consecutive sections were taken from the formalin-fixed paraffin-embedded (FFPE) blocks and stained for H&E, Ki67, ER and PR (Table Slides were digitized using a Leica Aperio AT2 whole slide scanner at 20x objective (Leica), with a scan resolution of 0.5 µm per pixel.Using H&E slides as a reference, invasive tumour regions were annotated on the training and the validation cohort of samples by a pathologist on the Ki67-labelled images. This formed the basis of the “ground truth” for comparison of the accuracy of the algorithm. The annotations were generated using Aperio ImageScope v11.1.2 on the down sampled image with the apparent magnification of x10 which contained sufficient contextual information to train the PTM-NET model. All selected training samples contained distinct examples of tumour, and non-tumour, and captured different expression of Ki67 within IDC  of four layers as shown in Fig. The mean normalised input RGB  image patches of size W × H  was convolved to convolution layers with N  filter banks with kernel size of M × M  to generate the network activation map. For each input image patch, after the convolution process, the feature map will be equal to the number of filter banks. Dependant on the size of the input image, kernel size and the stride, the size of the output feature map can be estimated using:f (x) = max 26.To train the CNN model, image patches of size 64 × 64 pixel (W × H) were generated using a sliding window technique within the annotated region. The patches were extracted with a pixel resolution of approximately 0.5 µm per pixel at 20x objective. The training patches were categorised as tumour (positive examples) and non-tumour (negative examples) and labelled according to the supervised training. The convolution process was followed by a max pooling operation which reduces the feature map size by half as the kernel size was 2 × 2. In addition to performing down sampling of the feature map, max pooling provides the feature map resistance to the translation-invariant. The ReLU activation used in the study is of the form 27. Using this procedure significantly improved the training time and reduced the computational time required when training a model for classification purposes. A stochastic gradient descent algorithm was used to update the network weights to minimise the loss function. The softmax has the property of a smooth gradient so that the back-propagated error is not subject to discontinuities, allowing for easier training. The trained SMC classifier yielded an output based on equation  of tumour were marked in red and low probability in orange (0.65 < p < 0.75) or yellow (0.55 < p < 0.65). The non-tumour regions were marked in blue as shown in Fig. As part of the PTM-NET model, inflammatory infiltrate was specifically excluded. For this, an infiltrate model was trained with annotated down-sampled images from three representative breast cancer samples, classifying into positive probability of lymphocytic infiltrate and zero probability. The output from this model identifying the infiltrate region is shown in the Supplementary Fig. We validated the accuracy of the PTM-NET classifier in whole slide images by comparing the predictions of tumour regions made by the PTM-NET in the validation data set against the corresponding ground-truth regions annotated by a pathologist on the same 45 images, included slides from both the AZ and VU cohorts. A quantitative evaluation was performed by measuring the Dice coefficient, positive predictive value (PPV), negative predictive value (PPV), true positive rate (TPR), true negative rate (TNR), false positive rate (FPR) and false negative rate for (FNR) across all 45 validation slides. In addition, the mean and standard deviation performance measures were calculated for each validation data cohort.28 (VGG16) used in this study. Top layer weights were initialized from the ImageNet9. Two fully connected layers followed by a softmax layer were added to the top layer with random weights initialization to achieve binary classification. To train the VGG-NET to perform tumour classification, the total samples were divided into seventy percent training and thirty percent testing data. Dice, PPV and NPV were estimated to determine the performance of the trained model versus the ground truth manual annotation.A transfer learning approach was adopted to train the VGG-NETThe overall workflow of the breast tumour detection and high proliferation identification system is shown in Fig. From these restricted tumour regions, DAB-stained cells were extracted using threshold and morphological operations from the activation map as a binary image and overlaid on the original image for visual control. The subsequent binary mask of the DAB cells was used to generate the centroid (blue) for every identified cell and cluster centres (red) were identified using a subtractive clustering method. A circle was drawn with a diameter of 100 pixels for every cluster centre and used to estimate the Ki67 ratio  within the circle to estimate if there was a region of high proliferation. Finally, on the original image, the regions where the Ki67 ratio was greater than 20 percent were annotated in red circle.We validated the accuracy of the PTM-NET classifier in both ER and PR-labelled breast cancer samples, choosing three ER or PR-labelled whole slide images from the validation cohort and comparing the ground-truth annotation of the Ki-67 regions to the PTM-NET evaluation of the ER or PR-labelled images.Supplementary Figures and Tables


A combination of laboratory, histopathological and microbiological tests for diagnosis of prosthetic joint infection (PJI) have been strongly recommended. This study aims to characterize the accuracy of individual or group tests, such as culture of sonicate fluid, synovial fluid and peri-implant tissue, C-reactive protein (CRP) and histopathology for detection of early, delayed and late PJI.A prospective study of patients undergoing hip or knee arthroplasty from February 2009 to February 2014 was performed in a Spanish tertiary health care hospital. The diagnostic accuracy of the different methods was evaluated constructing receiver-operating-characteristic (ROC) curve areas.p = 0.033. There was a significantly larger difference for ROC area in the synovial fluid culture for delayed (0.803) than for early (0.781) and late infections (0.679), p = 0.039. The comparison of the areas under the ROC curves for the two microbiological tests showed that sonicate fluid was significantly different from peri-implant tissue in delayed  and late PJI . The conjunction of preoperative parameters, synovial fluid culture and CRP, improved the accuracy for late PJI (p = 0.01). The conjunction of histopathology and sonicate fluid culture increased the area under ROC curve of sonication in early (0.917 vs 1.000); p = 0.06 and late cases (0.901 vs 0.999); p < 0.001.One hundred thirty consecutive patients were included: 18 (13.8%) early PJI, 35 (27%) delayed PJI and 77 (59.2%) late PJI. For individual parameters, the area under the ROC curve for peri-implant tissue culture was larger for early (0.917) than for delayed (0.829) and late PJI (0.778), For early PJI, sonicate fluid and peri-implant tissue cultures achieve the same best sensitivity. For delayed and late PJI, sonicate fluid culture is the most sensitive individual diagnostic method. By combining histopathology and peri-implant tissue, all early, 97% of delayed and 94.8% of late cases are diagnosed. The conjunction of histopathology and sonicate fluid culture yields a sensitivity of 100% for all types of infection. The successful management of patients with prosthetic joint infection (PJI) depends on an early and accurate diagnosis. Despite significant progress in the diagnosis of PJI, there is currently no single routinely used clinical or laboratory test that achieves optimal diagnostic accuracy . Over thThe aim of this study was to compare the diagnostic accuracy of the individual or grouped tests such as sonicate fluid, synovial fluid and peri-implant tissue culture, C reactive protein (CRP) and histopathology for the detection of early, delayed and late PJI, as this would allow us to enhance the diagnosis of each type of orthopedic implant infection.The study was conducted in a tertiary health care hospital, which performs approximately 500 hip and 300 knee arthroplasties per year. From February 2009 to February 2014, all consecutive patients aged 18 years or older who underwent a total or partial hip or knee arthroplasty revision at our institution were prospectively logged and followed-up in a database.Patients were excluded from the study if fewer than two peri-implant tissue samples were submitted for culture, if the implant was not obtained or if an evident contamination of the implant was identified in the operating room. Patients who underwent chemotherapy in the 60 days prior to index surgery and with recent periprosthetic fractures were also excluded, due to the acute stress of the treatment that might elevate the neutrophil count number in the histopathology .S. aureus or S. lugdunensis were the microorganisms isolated, only a single positive tissue specimen was required.PJI was diagnosed according to the criteria proposed by Berbari et al. by the presence of at least one of the following : (i) visOur study did not included sonicate fluid culture (SFC) as diagnosis criteria for infection, however, we evaluate the detection validity of SFC for PJI to provide further evidence for its clinical use. The histopathological criteria established by Morawietz and Krem was used to define a standardized evaluation of the periprosthetic membrane . Since eDemographics, co-morbid conditions, type of implant, surgical procedure, antimicrobial treatment and outcome were prospectively gathered using a standardized data collection form. Previous antimicrobial therapy was defined as the administration of antibiotic treatment within the 14-day period before the removal of the orthopaedic implant.Three to 6 deep samples of periprosthetic tissue with the most evident inflammatory changes were collected for histopathology and conventional microbiologic culture. Removed orthopedic implants were sonicated as previously described, and a cutoff of ≥20 colony-forming unit (CFU) per plate was applied to determine a clinically significant result as described previously –15. In a2 test and a Fisher’s exact test, or ANOVA test, as appropriate in each case. For m × n tables, a Fisher’s exact test was estimated using the Monte Carlo method. Sensitivity, specificity and positive and negative predictive values were calculated. Ninety-five percent confidence intervals (95% CI) were calculated as exact binomial confidence intervals. Based on receiver-operating-characteristic (ROC) analysis of the data, the cut-off level for CRP was >1.Statistical analysis was performed using a two-tailed χThe combination of different diagnostic tests was performed using an “or” combination. The combined test acted as a new test: in scheme “or”, two of two negative tests were necessary to consider the composite diagnostic as negative. To outline the composite diagnostic the combination schemes for the diagnostic tests were as follows: i) test 1 positive and test 2 positive, composite diagnostic positive; ii) test 1 positive and test 2 negative, composite diagnostic positive; iii) test 1 negative and test 2 positive, composite diagnostic positive; iv) test 1 negative and test negative, composite diagnostic negative . Under tThe diagnostic accuracy of the different methods was evaluated by constructing ROC curve areas computed using the trapezoidal rule. De Long et al. , method p-value of less than 0.05 (two-sided) was considered statistically significant. Calculations were performed with the SPSS package v19.0 , and the Stata statistical software .A A total of 504 patients were prospectively included during the study-period. Six patients 1.2%) were excluded: 3 because no implant was submitted to microbiology laboratory, 2 because of a recent periprosthetic fracture, and 1 who underwent chemotherapy treatment for breast cancer. Of the remaining 498 patients, 130 (26.10%) were finally diagnosed with infection and, among these, 18 (13.8%) were classified as early, 35 (27%) as delayed and 77 (59.2%) as late PJI. The demographic characteristics of the patients are shown in Table .2% were Enterococcus faecalis isolation and the patient underwent repeat revision arthroplasty due to suspected implant-related infection 16 months later. The results of microbiological and histopathological examinations were negative. At the present time, she needs to walk on two crutches due to aseptic loosening in the opposite symptomatic knee arthroplasty but she does not wish to undergo surgery. According to the definition of PJI, these two cases were classified as false positives and were not included in the 130 PJI defined cases.There were two aseptic failure cases with positive sonicate fluid cultures. None of them did receive antibiotics beyond standard peri-operative prophylaxis. One case, with prosthesis failure between 3 and 12 months after the last implant placement, had isolation of >100 CFU of coagulase-negative staphylococci (CNS) and, after 6 years of follow up, he has a functional recovery and independent walking. The other case, which failed over 12 months after the last implantation, had Table According to individual parameters, in early PJI, both peri-implant tissue and sonicate fluid culture reached the same best sensitivity of 83.3% followed by CRP and the histology with 81.3% and 77.8%, respectively. For delayed PJI, the highest sensitivity was obtained with sonicate fluid culture with 94.3% followed by histology, CRP and peri-implant tissue culture with 87.9%, 82.9% and, 65.7%, respectively. In the case of late PJI, the highest sensitivity was found for the sonicate fluid culture with 80.5% followed by histology, CRP, and peri-implant tissue culture with 74%, 62.3% and 55.8%, respectively.p = 0.033. There was also a significant difference for ROC area in the synovial fluid culture for delayed PJI compare to early and late infections: 0.803 vs. 0.781 vs. 0.679 respectively, p = 0.039.The area under the ROC curve for peri-implant tissue culture was larger for early infections than for delayed and late PJI: 0.917 vs. 0.829 vs. 0.778 respectively, p = 0.005) and late PJI .The comparison of the areas under the ROC curves for the two microbiological tests showed that sonicate fluid culture was significantly different from peri-implant tissue culture in delayed  but not that of early PJI (p = 0.64), however, delayed PJI had a near-significant trend, 0.849 vs. 0.866, (p = 0.07).The conjunction of preoperative parameters such as synovial fluid culture and CRP significantly improved the accuracy of individual diagnosis of CRP in detection of late PJI 0.735 vs. 0.790, (p = 0.06); delayed 0.849 to 0.892, (p = 0.07); and late 0.735 to 0.826, (p < 0.0001)]. On the other hand, the conjunction of CRP and sonicate fluid culture did not improve the individual diagnosis of sonicate fluid for any type of PJI.The addition of CRP to peri-implant tissue culture improved accuracy of individual diagnosis of CRP for all the types of PJI this being especially significant in late PJI [early 0.791 to 0.852, (p = 0.06), in delayed cases from 0.951 to 0.978 (p = 0.15) and in late cases from 0.901 to 0.999 (p < 0.001).The conjunction of histopathology and sonicate fluid culture yielded a sensitivity of 100% for early, delayed and late cases. This combination allowed the area under ROC curve of sonicate fluid culture to increase in early cases from 0.917 to 1.000 (p = 0.06), in delayed from 0.829 to 0.985 (p < 0.001) and in late cases from 0.778 to 0.973 (p < 0.001).By combining histopathology and peri-implant tissue culture, 100% of early, 97% of delayed and 94.8% of late cases were diagnosed. This combination allowed the area under ROC curve of peri-implant tissue culture achieved in early cases to increase from 0.917 to 1.000  was the least prevalent bacteria. In the case of delayed infections, CNS was the most frequently found microorganism (52%) followed by GNB (20%) and S. aureus and Streptococcus spp. (12% each of them). In late infections, CNS was cultured in 70.5% of cases followed by S. aureus (6.6%) and GNB (4.4%).For early infections, CNS and gram negative bacilli (GNB) were the main isolated microorganisms , and S. aureus in the delayed group and 1 with E. coli producing an extended-spectrum beta-lactamase (ESBL) in early PJI.Regarding infections due to multi-resistant bacteria, there were only 2 isolations with methicillin resistant For delayed PJI, in the sonicate fluid culture SCN was isolated in 19 patients and Streptococci in 5 patients, whereas in the peri-implant tissue culture SCN was isolated in only 13 patients and Streptococci in 3.For late PJI, in the sonicate fluid culture SCN was isolated en 43 patients and Streptococci in 3 patients, whereas in the peri-implant tissue culture SCN was isolated in only 35 patients and there was no isolation of Streptococci.The present study assesses the accuracy of the individual or grouped diagnostic tests for PJI, including CRP, histopathology and microbiology tests, based on the time of infection after prosthesis implantation. So far, this is the first study, which compares diagnosis tests according to onset of PJI taking into account Osmon criteria .Regarding individual methods, our study found that both peri-implant tissue and sonicate fluid cultures are highly sensitive and reach the same accuracy for diagnosis of early PJI. This finding might be related to the microorganisms isolated, as CNS, a slow growing and biofilm-producing microorganism caused nearly half of the early cases (47.6%), which may explain the high sensitivity of implant sonication. For delayed and late infections, sonicate fluid culture was the most sensitive diagnostic method studied. Consistent with our findings, a study comparing peri-implant tissue and sonicate fluid cultures found significantly higher sensitivity of implant sonication in cases of chronic infections but not in acute infections. Notwithstanding, in that study delayed and late infections were grouped together since treatment and prognosis are similar .Actually, with the introduction of new diagnostic methods, there have been many cases and much uncertainty about the interpretation of positive cultures with these new techniques. Some authors reported a higher sensitivity from standard cultures than from sonicate fluid cultures  while otIn addition, the clinical implications, together with the different results between studies, show that cultures performed in the clinical microbiology laboratory vary between centers depending in a variety of factors including specimen processing period, prosthetic solution, geographical location, anaerobic culture period and the use of centrifugation and vortexing. A recent meta-analysis of sonication fluid samples showed that 14-day anaerobic culture may improve sensitivity, the use of centrifugation or vortexing may improve specificity and the use of 400 to 500 ml of Ringer’s solution for containers may improve sensitivity and specificity . TherefoOur results identified histopathology as showing the second highest sensitivity out of all the individual parameters in delayed and late infections. Some studies have been able to demonstrate the superiority of the histological diagnosis of PJI over microbiological methods, however, ultrasonication was not used as an add-on test for routine microbiological diagnostic –28. In cAccording to the preoperative parameters, an interesting finding was that the predictive power of individual tests, such as the CRP and synovial fluid culture, which was higher for delayed infections than for other types of PJI. Although our study was related to different types of onset of PJI, the predictive power of CRP was not as good as reported by others . Fink etRegarding the combination of techniques, our main finding was that the conjunction of histopathology and sonicate fluid culture yielded a sensitivity of 100% for all types of infection with specificities ranging from 95.8% to 100%. By combining histopathology and peri-implant tissue, all early, 97% of delayed and 94.8% of late cases were diagnosed with almost 100% specificity.The combined measurement of preoperative parameters such as CRP and synovial fluid produced the highest preoperative predictive power for diagnosing late cases with a marked trend for delayed PJI. Recent studies have evaluated the measurement of synovial CRP in synovial fluid; however, the use of this measurement seems limited and offers no additional diagnostic information beyond serum CRP, which is already widely clinically available .Results of the present study emphasize the idea that PJI is typically a biofilm-associated infection. In this regard, the Infectious Diseases Society of America (IDSA) suggests a gap in the validation of the diagnostic value of sonicate fluid culture and requires a higher level of evidence . In the . However, multiplex PCR of sonication fluid, despite the promising results in PJI, is an expensive test and unfortunately is not commonly available in most clinical laboratories.The limitations of our study are first, it is a single-center study, with potential for uncontrolled selection biases, however, there is no variation in the methodologies, something that does not occur when drawing patients from multiple centers. Second, due to the small sample size for early and delayed PJI, the study may have lacked the ability to detect slight differences among these two types of infection and the diagnostic techniques compared and, some results approached the borderline of significance; also the small sample size may be a limitation towards conducting further analysis by the operative site (hip vs knee), with a larger sample size we might have been able to draw stronger conclusions. Third, the synovial fluid (SF) white cell count (WCC) and polymorphonuclear cell counts were not performed. Several studies have incorporated the SF analysis into a set of diagnostic criteria for PJI. However, it was found that SF-WCC had high statistical heterogeneity depending on the time at which the sample was obtained (preoperative vs intraoperative) and the operative site (hip vs knee). On the other hand, it may not be reliable for a patient with inflammatory arthropathy, the true diagnostic ability of this test depends on whether the synovial fluid aspiration is successful and the optimal cut-off values of these tests may need further large-scale validation. Fourth, a contributing problem is that CRP was acquired in 70% of overall PJI cases and preoperative aspiration of synovial fluid was performed in only 45% of overall PJI patients. Fifth, molecular microbiological methods were not performed, while the value of Polymerase Chain Reaction (PCR) in most studies is based on locally-developed PCR methods that can present unsatisfactory standardization which is why the development of a commercial system that features higher reproducibility would improve PJI diagnosis in most laboratoriesThis study demonstrates that for early PJI, sonication fluid and peri-implant tissue cultures are highly sensitive and reach the same accuracy. For delayed and late cases, sonication fluid is the most sensitive individual diagnostic method. The CRP, as a preoperative parameter, provided more accuracy in diagnosing delayed infections. The combination of a positive histology and sonicate fluid culture allows the diagnosis of all types of infection, and the combination of preoperative parameters, such as CRP and synovial fluid culture, significantly improved the diagnosis of late PJI and approached the borderline of significance for delayed PJI. Therefore, the observed results could have important implications for the accuracy in diagnosis of each type of PJI based on the onset of infection.


Sci., 2015, ; DOI: 10.1039/c5sc02161h.Correction for ‘Ligand survey results in identification of PNP pincer complexes of iridium as long-lived and chemoselective catalysts for dehydrogenative borylation of terminal alkynes’ by Chun-I Lee  The number of the US National Science Foundation grant listed in the manuscript was incorrect. The correct grant number should be CHE-1300299.The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.


In this field, Cherenkov luminescence imaging (CLI) seems to be very attractive by allowing imaging with clinical PET radiotracers with high-throughput capabilities. In this context, we developed a fast CLI method to detect tumour hypoxia with 6 CT-26 cells. FMISO was injected, and simultaneous PET-blood oxygen level dependent (BOLD)-MRI followed by CLI were performed along with immunohistochemistry staining with pimonidazole.Colon cancer model was induced in mice by subcutaneous injection of 1 × 10There was a significant correlation between FMISO PET and CLI tumour uptakes, consistent with the BOLD-MRI mapping. Tumour-to-background ratio was significantly higher for CLI compared with PET and MRI. Immunohistochemistry confirmed tumour hypoxia. The imaging workflow with CLI was about eight times faster than the PET-MRI procedure.CLI is a fast and relevant tool to assess tumour hypoxia. This approach could be particularly interesting for hypoxia-targeting drug development.The online version of this article (10.1186/s13550-018-0464-7) contains supplementary material, which is available to authorized users. Cherenkng (MRI) . Albeit ng (MRI) . Indeed,ng (MRI) . Thus, Cng (MRI)  and clinng (MRI) . In adding (MRI) . Herein,n = 13) were obtained from the animal husbandry of the University of Burgundy . A total of 1 × 106 murine CT26  colon cancer cells were implanted subcutaneously at the right flank of depilated mice. The tumour grew over 18 days until it reached a mean volume of 316 ± 81 mm3. Then, mice were injected intravenously under anaesthesia (2% isoflurane in air) with 10 MBq of FMISO and pimonidazole (60 mg/kg). PET-MRI imaging was performed 120 min post-injection (p.i.), and CLI imaging was performed on the same animal right after the PET-MRI exam (160 min p.i.). The tumour area was depilated 24 h before CLI imaging. All images were decay corrected for quantification. After the imaging sessions, mice were sacrificed and tumours were formalin-fixed (48 h) for pimonidazole immunohistochemistry. 18F-Fluoromisonidazole (FMISO) was synthesised by Pharmimage® within its cyclotron and radiopharmacy platform following an in-house synthetic scheme  and complied with the Ethical Committee and the French Ministry of Higher Education and Research. Six- to 7-week-old-BALB/c female mice n = 3 were ob3 (%IA/mm3).PET and MRI images were simultaneously acquired in a dual-ring SiPM microPET fully integrated in a 7 T preclinical MRI . During imaging, mice were anaesthetised with 2% isoflurane in air. In addition to T1 anatomical images, BOLD-MRI sequences have been performed using multi-gradient echo (MGE) sequence (T2*-weighted acquisition) with the following parameters: TR = 500 ms; TE = 3, 6, 9, 12, 15 and 18 ms; flip angle = 40°; and image matrix = 128 × 128. MRI voxel values have been interpolated using a Python 3 homemade script, to obtain a T2* map (BOLD data). PET images have been reconstructed using a 3D OSEM algorithm . PET quantifications were performed using Vivoquant™ software , and results have been expressed in % injected activity of FMISO per mm2/sr). Signal-to-noise ratio for PET, MRI and CLI following the injection of FMISO was determined by the ratio of the signal from the tumour and a contralateral irrelevant region of interest (muscle).Following PET-MRI imaging, mice underwent CLI imaging performed with an optical imager  equipped with a CCD camera with the following parameters: exposure time 300 s, binning factor 16, field of view 7.5 cm, in open filter mode. Mice were maintained under anaesthesia throughout the procedure (2% isoflurane in air). Results are expressed in radiance  were deparaffinised and immunostained with a commercial kit  to specifically stain pimonidazole and determine the level of hypoxia. Stained sections were analysed using Axioscope A1® coupled to an Axiocam 503® colour camera and ZEN® Software .p value of less than 0.05 was considered significant. Comparisons between more than two groups have been performed using one-way ANOVA, and correlations were determined by the Pearson’s coefficient using GraphPad Prism® 7.0.For all the experiments performed within this study, statistical analyses were achieved using GraphPad Prism® 7.0  and a n = 13; Pearson’s coefficient = 0.8533, p < 0.0001) between FMISO uptake obtained with PET and CLI  between pimonidazole fluorescence microscopy and FMISO-CLI . Therefore, rapid and non-invasive evaluation of tumour hypoxia is crucial to obtain the most accurate signal. Histological measurement of hypoxia requires to euthanize animals, resect the tumour and then fix the tissue in formalin-based solution. Even though these procedures may only require a short period of time, the hypoxic status of the tumour may suffer from some changes . Therefore, we believe that CLI imaging of FMISO is a more representative measure of in vivo tumour hypoxia compared with histology.Important questions can be raised about the clinical applicability of CLI in humans given the differences between preclinical and clinical studies regarding, for instance, injected doses and depth of the sites of interest. In our preclinical study, CLI imaging was performed on a subcutaneous tumour after injection of 10 MBq of FMISO (400 MBq/kg in a mouse of 25 g). As a comparison, current guidelines prescribe a dose of 3.7 MBq/kg in patients for standard PET exams. With a 100-fold lower injected activity and deeper tumour site, CLI imaging in humans remains a challenge and often requires the use of ultrasensitive photon-detecting devices and strict restriction of ambient light  where tuTumour hypoxia can be assessed with CLI induced by FMISO since it is cross-validated by PET-MRI and histology. Interestingly, the workflow for CLI imaging is much more efficient than PET-MRI paving the way for this method to speed-up and strengthen the preclinical development of hypoxia-targeting drugs.Additional file 1:18F-Fluoromisonidazole (FMISO). (DOCX 344 kb)Synthesis and purification of


In patients who require a massive intraoperative transfusion, cold fluid or blood transfusion can cause hypothermia and potential adverse effects. One method by which to prevent hypothermia in these patients is to warm the intravenous fluid before infusion. The aim of this study was to determine the effect of the fluid flow rate on the efficacy of a fluid warmer.  The room air temperature was controlled at 24°C. Normal saline at room temperature was used for the experiment. The fluid was connected to an infusion pump and covered with a heater line, which constantly maintained the temperature at 42°C. The fluid temperature after warming was measured by an insulated thermistor at different fluid flow rates  and compared with the fluid temperature before warming. Effective warming was defined as an outlet fluid temperature of >32°C. p < 0.001). The increases in temperature were 10.9°C ± 0.1°C, 11.5°C ± 0.1°C, 10.2°C ± 0.1°C, 10.1°C ± 0.7°C, and 8.4°C ± 0.2°C at flow rates of 100, 300, 600, 900, and 1200 mL/h, respectively. The changes in temperature among all different flow rates were statistically significant (p < 0.001). The outlet temperature was >32°C at all flow rates.  The room temperature was 23.6°C ± 0.9°C. The fluid temperature before warming was 24.95°C ± 0.5°C. The outlet temperature was significantly higher after warming at all flow rates ( The efficacy of fluid warming was inversely associated with the increase in flow rate. The outlet temperature was <42°C at fluid flow rates of 100 to 1200 mL/h. However, all outlet temperatures reached >32°C, indicating effective maintenance of the core body temperature by infusion of warm fluid. The normal core body temperature in humans is maintained by the hypothalamus and typically ranges from 36.5°C to 37.5°C. The interthreshold range is usually only 0.2°C to 0.4°C. Both general and regional anesthesia inhibit central thermoregulation and increase the interthreshold range to 2°C to 4°C, leading to hypothermia , 2. HypoAccording to this equation, the temperature of the infused fluid should not be <36°C in a normothermic patient to prevent a decrease in the mean body temperature during anesthesia. Jung et al.  developeWarm intravenous infusion is a direct, active method of warming the core body temperature and is suitable for patients who require massive transfusion . The temThis experimental study was performed in the general operating room at Ramathibodi Hospital. The ambient temperature was controlled at 24°C via a room air conditioning system which was close to the operating condition in our institution. Normal saline was used in this study because it is commonly used as an isotonic solution for fluid replacement in operating rooms. The fluid was maintained at the temperature of the operating room, then connected to the infusion pump , which was adjusted to flow rates of 100, 300, 600, 900, and 1200 mL/h, and these ranges of fluid flow rate were commonly infused in major abdominal operation in local setting. A 1.5 m heater line  covered the infusion line. The heater line was set at 42°C. After rinsing the warmed fluid for 5 min at each flow rate to maintain a constant temperature after warming, we recorded the room temperature by the electronic thermometer and the temperature of the fluid before and after passing through the heater line. Each flow rate was studied five times. The outlet temperature was measured at the immediate end of the heater line with an insulated digital thermistor  sealed in a 4 mL test tube. Before the study, the reliability and accuracy of thermistor were checked by repeatedly measuring the neonatal incubator temperature. Effective warming was defined as an outlet temperature of >32°C [t-test or Wilcoxon's test was used to assess the statistical significance of the changes in temperature before and after warming, and an independent t-test or the Mann–Whitney U test was used to assess the changes in temperature between each flow rate and the next higher one. All statistical analyses were two-sided with a significance level at p value < 0.05.Statistical analyses were performed using SPSS v.20.0 for Windows , and a data chart was produced using Microsoft Excel 2007. All fluid temperatures are reported as mean ± standard deviation. The normality assumption for continuous variables was assessed by the Shapiro–Wilk test. A paired p < 0.001). The maximum outlet temperature was 36.9°C at a flow rate of 300 mL/h. The fluid temperature increased by 10.9°C ± 0.1°C, 11.5°C ± 0.1°C, 10.2°C ± 0.1°C, 10.1°C ± 0.7°C, and 8.4°C ± 0.2°C at flow rates of 100, 300, 600, 900, and 1200 mL/h, respectively (p < 0.001) except the change between 600 and 900 mL/h. A linear trend was found for the entire set of flow rates  can effectively increase infused fluid temperature for replacement therapy in uneventful abdominal operation. However, further research is required to see if more rapid flow rate can still generate optimal temperature or not. It is also worthwhile to compare the performance of some traditional fluid warming devices because some devices can effectively warm the fluid at higher flow rate than in our study.Intravenous fluid warming is an alternative warming method by which to prevent hypothermia in patients who required a massive fluid transfusion. In the present study, the effectiveness of warming was inversely associated with the increase in the flow rate. Although the outlet temperature was <42°C as the set point of the heater line, all tested flow rates  were able to increase the outlet temperature to >32°C, which was considered to be effective fluid warming fluid.


While VZV DNA and antigen have been detected in acute and chronic VZV keratitis, it is unclear whether productive infection of corneal cells is ongoing or whether residual, noninfectious VZV antigens elicit inflammation. Herein, we examined VZV-infected primary human corneal epithelial cells (HCECs) and keratocytes (HKs) to elucidate the pathogenesis of VZV keratitis.HCECs and HKs were mock- or VZV infected. Seven days later, cells were examined for morphology, proinflammatory cytokine and matrix metalloproteinase (MMP) release, ability to recruit peripheral blood mononuclear cells (PBMCs) and neutrophils, and MMP substrate cleavage.Both cell types synthesized infectious virus. VZV-infected HCECs proliferated, whereas VZV-infected HKs died. Compared to mock-infected cells, VZV-infected HCECs secreted significantly more IL-6, IL-8, IL-10, and IL-12p70 that were confirmed at the transcript level, and MMP-1 and MMP-9; conditioned supernatant attracted PBMCs and neutrophils and cleaved MMP substrates. In contrast, VZV-infected HKs suppressed cytokine secretion except for IL-8, which attracted neutrophils, and suppressed MMP release and substrate cleavage.in vitro model for VZV infection of corneal epithelial cells. Furthermore, the proliferation and persistence of VZV-infected HCECs suggest that these cells may serve as viral reservoirs if immune clearance is incomplete. Finally, the finding that VZV-infected HKs die and suppress most proinflammatory cytokines and MMPs may explain the widespread death of these cells with unchecked viral spread due to ineffective recruitment of PBMCs.Overall, VZV-infected HCECs recapitulate findings of VZV keratitis with respect to epithelial cell proliferation, pseudodendrite formation and creation of a proinflammatory environment, providing an  Varicella zoster virus (VZV) produces varicella then establishes latency in ganglionic neurons, including cranial nerve ganglia.4Epithelial and stromal keratitis are corneal complications of VZV and can lead to vision loss after acute disease and during recurrence due to long-term corneal scarring and haze.614Based on its rapid resolution upon corticosteroid treatment, stromal keratitis was thought to be an immune response to viral antigens within the corneal stroma, comprised of stromal fibroblasts underlying the epithelial cell layer.Since there is no animal model for VZV keratitis, we infected primary human corneal epithelial cells (HCECs) and stromal fibroblasts  with VZV and analyzed the cytopathology and inflammatory responses. Understanding the role of ongoing virus replication and inflammation in VZV keratitis can guide antiviral and corticosteroid therapy, mitigating ocular damage and vision loss produced by VZV.https://fiji.sc/).The VZV Gilden strain (GenBank #MH379685) and primary HCECs and HKs from adult human cornea  were used. Cells were confirmed by immunofluorescence antibody assay (IFA) and quantified by Fiji image processing software . Cells were co-cultured with lysates from uninfected or VZV-infected human fetal lung fibroblast  for 24 hours (200 plaque-forming units [PFU]/cm2),HCECs were seeded in corneal epithelial cell medium containing HCEC growth supplement/1% penicillin-streptomycin  and neutrophils purified from whole blood  using the direct human neutrophil isolation kit  were used.At 7DPI, HCECs and HKs were analyzed by IFA using primary mouse anti-cytokeratin 18 , rabbit anti-fibronectin , mouse anti-VZV glycoprotein B , and rabbit anti-GAPdH  antibodies then secondary donkey anti-mouse AlexaFluor 594 IgG and donkey and anti-rabbit AlexaFluor 488 antibodies  as described.VZV-infected HCECs were harvested at 3, 5, and 7 DPI; each sample was serially diluted onto 70% confluent monolayers of uninfected HFLs in DMEM F12 medium with 10% FBS (ATCC) to demonstrate virus transmission from HCECs to HFLs. Similarly, VZV-infected HKs at 3, 5, and 7 DPI were diluted onto HFLs. Three days later, HFLs were stained with 0.1% crystal violet and PFUs/mL were calculated.Proinflammatory cytokines  and MMPs in supernatants (MMP-1/2/3/9/10) were measured using commercial assays  as described.21RNA was extracted, cDNA transcribed then analyzed by RT-qPCR using primers to cytokines above and glyceraldehyde-3-phosphate-dehydrogenase , as described.http://imagej.nih.gov/ij/; provided in the public domain by the National Institutes of Health, Bethesda, MD, USA). Positive control for PBMC and neutrophil migration was chemokine (C-C motif) ligand 2  diluted in either HCEC or HK medium (20 pg/mL) and 50 ng/mL IL-8 (Biolegend), respectively. Negative controls for PBMC and migration assays were HCEC and HKs medium only. To determine specificity for IL8-induced neutrophil migration in infected supernatant, mouse monoclonal IL-8 blocking antibody was added at 1 μg/mL . Fold differences of VZV-infected cells and positive and negative controls were relative to mock-infected cells.At 7 DPI, mock- and VZV-infected supernatants were assayed for their ability to attract PBMCs and neutrophils. Supernatants were loaded in a 96-well chemotaxis plate , 5-μm pore size filters placed on top, immune cells pipetted onto filters , and plate incubated at 37°C for 4 hours. Filters were removed; migrating immune cells were visualized by microscopy and quantified using ImageJ 3D Objects Counter . MMP-1 enzyme (500 pg/mL) served as positive control and MMP-1 enzyme plus MMP inhibitor  served as negative control . Relative fluorescence units were measured using a fluorescent plate reader (excitation/emission: 485 nm/520 nm).q-value ≤ 0.05) using the two-stage step-up method of Benjamini et al.P < 0.05, **P < 0.01, ***P < 0.001).Statistical analysis was performed using graphing software . Cytokine and MMP differences among mock- and VZV-infected cells were determined by multiple unpaired t-tests with a False Discovery Rate , consistent with proliferating VZV-infected HCECs over time. In contrast, input VZV-infected HKs at 3, 5, and 7 DPI significantly decreased PFU/mL in HFLs at 14,666 ± 1171, 8333 ± 1353 and 5400 ± 493 (n = 3), consistent with dying VZV-infected HKs over time. Supernatant from infected cells were unable to transmit virus to HFLs demonstrating the absence of cell-free virus (data not shown).Aside from VZV-infected corneal cells having the capacity to spread VZV to adjacent cells of the same type, VZV-infected HCECs and HKs were tested for their ability to transmit VZV to another cell type by cell-to-cell spread. VZV-infected HCECs and HKs at 3, 5, and 7 DPI were cocultured with uninfected HFLs; PFUs were counted at 3 DPI. The PFUs observed were due to VZV-infected HFLs since input VZV-infected HCECs perish in DMEM F12 medium, and input infected HKs were present in low amounts and are morphologically distinguishable. VZV-infected HKs die and form cell clearings, whereas VZV-infected HFLs swell and form syncytia. Input VZV-infected HCECs at 3, 5, and 7 DPI significantly increased the PFU/mL in HFLs at 367 ± 219, 2300 ± 82, and 5250 ± 204, respectively . VZV-infected HKs significantly decreased cell counts at 5 and 7 DPI relative to 1 DPI: 3 DPI (0.99 ± 0.07), 5 DPI (0.64 ± 0.07), 7 DPI .Phase-contrast imaging indicated that VZV-infected HCECs proliferate; VZV-infected HKs sloughed off. Compared to mock- (n = 5). To verify cytokine expression, transcripts were analyzed by RT-qPCR and fold differences between VZV- to mock-infected HCECs determined . To verify cytokine expression, transcripts from mock- and VZV-infected HKs were analyzed by RT-qPCR and fold differences between VZV- to mock-infected HKs determined . In contrast, VZV-infected HK supernatant did not increase PBMC migration (0.99 ± 0.16), consistent with the suppression of proinflammatory cytokines. Media only control had less migrating PBMCs than CCL2 diluted in HK medium .Relative to mock-, VZV-infected HCEC supernatant significantly increased PBMC infiltration (1.73 ± 0.15), consistent with the elevated proinflammatory cytokine levels detected. Media only control had less migrating PBMCs than CCL2 diluted in HCEC medium .Relative to mock-, VZV-infected HCEC supernatant significantly increased neutrophil migration (1.29 ± 0.04). Anti-IL-8 in VZV-infected supernatant, αIL-8 with IL-8 cytokine diluted in media and media only did not significantly increase migration . IL-8 cytokine diluted in HCEC medium significantly increased neutrophil infiltration . The ability of αIL-8 to block migration of neutrophils indicates that IL-8 in the infected conditioned media was responsible for neutrophil chemotaxis.Compared to mock-, VZV-infected HK supernatant significantly increased neutrophil migration (3.01 ± 0.68), consistent with the elevated levels of IL-8 (a neutrophil chemoattractant). Anti-IL-8 in VZV-infected HK supernatant, αIL-8 with IL-8 cytokine in media and media only did not significantly increase migration . IL-8 diluted in HK medium significantly increased PBMC infiltration . In contrast, VZV-infected HK supernatant significantly decreased MMP-1 (0.28 ± 0.01), MMP-2 , MMP-3 (0.66 ± 0.06) and MMP-9 (0.56 ± 0.04); MMP-10 was not detected (n = 3).Compared to mock-, VZV-infected HCEC supernatant significantly increased MMP-1 (1.70 ± 0.29) and MMP-9 (1.61 ± 0.07), but not MMP-3 or MMP-10; MMP-2 was not detected (n = 3). Positive control MMP-1 enzyme had increased activity (1.78 ± 0.03) compared to the negative control that was not detected.Compared to mock-, VZV-infected HCECs significantly increased MMP activity (1.44 ± 0.06), whereas VZV-infected HKs significantly decreased MMP activity  for the disparities in transcript and secreted cytokine levels remain to be determined. Consistent with the increased proinflammatory cytokine levels, VZV-infected HCEC supernatant recruited PBMCs and neutrophils in a migration assay, consistent with inflammatory infiltrates observed in VZV-infected corneal epithelium of patients. The number of proinflammatory cytokines secreted by VZV-infected HCECs is greater than that previously reported in VZV-infected human brain vascular adventitial fibroblasts (HBVAFs), vascular smooth muscle cells (HBVSMCs), perineurial cells (HPNCs) and HFLs.In VZV-infected HCECs, levels of secreted MMP-1 and MMP-9 were significantly increased, unlike the HSV-1-induced increases in MMP-2 and MMP-9 in HCECs.Compared to mock-infected HKs, VZV-infected HKs only secreted significantly higher levels of IL-8 that was confirmed at the transcript level, similar to HSV-1-induced IL-8 secretion in keratocytes;33Overall, our data shows that VZV-infected HCECs recapitulate the clinical findings seen in patients with VZV keratitis, including epithelial cell proliferation, pseudodendrite formation and increased proinflammatory cytokines and MMPs, providing an in vitro model for VZV infection of HCECs. The proliferation and persistence of VZV-infected HCECs suggest that these cells may serve as viral reservoirs if immune clearance is incomplete. Finally, the VZV-induced death of HKs and suppression of most proinflammatory cytokines and MMPs may explain the widespread death of these cells with unchecked viral spread due to ineffective recruitment of PBMCs.