Do spikes in Twitter chatter about a firm precede unusual stock market trading activity for that firm? If so, Twitter activity may provide useful information about impending financial market activity in real-time. We study the real-time relationship between chatter on Twitter and the stock trading volume of 96 firms listed on the Nasdaq 100, during 193 days of trading in the period from May 21, 2012 to September 18, 2013. We identify observations featuring firm-specific spikes in Twitter activity, and randomly assign each observation to a ten-minute increment matching on the firm and a number of repeating time indicators. We examine the extent that unusual levels of chatter on Twitter about a firm portend an oncoming surge of trading of its stock within the hour, over and above what would normally be expected for the stock for that time of day and day of week. We also compare the findings from our explanatory model to the predictive power of Tweets. Although we find a compelling and potentially informative real-time relationship between Twitter activity and trading volume, our forecasting exercise highlights how difficult it can be to make use of this information for monetary gain. Financial firms and academic researchers have recently begun to study the predictive value of information gathered from social media –3. A recintra-day responses of the stock market in relation to the spread of news on Twitter. Such effects may be transitory and dissipate within hours or minutes, and it is precisely these effects that many algorithmic traders would like to exploit in real time. Moreover, interesting details about the rates at which signals propagate and dissipate on the Twitter social network and in the financial markets can be observed at time-resolutions of minutes; these may be missed entirely when the data is aggregated at the daily level.Although researchers have begun to study the relationship between patterns observed on Twitter and stock market activity at daily levels of aggregation, we do not yet have much understanding of amount of social media chatter about a firm even if we are agnostic about the qualitative features of the sentiment expressed in the chatter. Our analysis differs from common panel data analyses in that we are interested in spikes representing unusual Twitter activity. In fact, while most analyses in prior information systems (IS) literature focus on the average behavior (“regression to the mean”), we are particularly interested in events that are unusual and that would be considered a statistical outlier by most standards. That is, in this study we are interested in events that lead to unusually high levels of Twitter activity. We investigate whether such Twitter spike events lead to sudden spikes in trading volumes, a common measure of market activity levels, in the stock for each firm.In this study, we consider the real-time relationship between chatter on Twitter and the trading volume of 96 firms listed in the Nasdaq 100, during 193 days of trading in the period from May 21, 2012 to September 18, 2013. In contrast to research in [Introductory textbooks of statistics often conclude that outliers might be discarded from analysis or investigated separately from the remainder of the data . The reaThe detection and analysis of unusual events is not quite as common as the investigation of the mean behavior, and its discussion has gained more attention outside the IS literature, e.g. in biosurveillance . In factUnusual Twitter events  are rather rare; most of the time, Tweets mentioning specific firms arrive at a moderate rate. We illustrate this with two mini-case examples below; but the general pattern of low Twitter mentions of a firm punctuated by well-defined and clearly visible spikes is one we see in virtually every full time-series of Twitter activity for firms in our sample. In order to properly compare the effect of unusual Twitter events with that of the “typical” rate of Tweets, it is necessary to create suitable counterfactuals. In this work, we adopt a quasi-experimental design, in which we identify observations featuring firm-specific spikes in Twitter activity, and randomly assign each observation to a ten-minute increment matching on the firm symbol and a number of repeating time indicators that match spikes to non-spikes to within the same day and time of week at the half hour level. We examine the extent that unusual levels of chatter on Twitter about a firm portend an oncoming surge of trading of its stock within the hour, over and above what would normally be expected for the stock in a given day of week or time of day.Our results suggest that, through monitoring of chatter on Twitter about firms listed on the Nasdaq 100, observing spikes of chatter affords a reliable and non-trivial amount of foresight into oncoming surges in trading volume. Sometimes Twitter messages have causal impacts. A recent hoax that spread on Twitter claimed that President Obama was injured by an explosion at the White House; this caused a temporary drop of 150 points in the Dow Jones industrial average . GeneralThis study has two primary objectives as a research contribution. First, the study represents a microscope upon the diffusion of information in social networks that becomes observable at a resolution of minutes. Second, our approach allows a better understanding of how social networks and financial markets simultaneously respond in real-time to external events, drawing contrasts in the speed in which information is propagated in both types of spaces. Unlike traditional time-series approaches that consider spikes as anomalies in the data that need to be removed, we treat spikes as central to the analysis because they represent real reactions to news and have tangible impacts that should not be ignored. We ask the following research questions: 1) To what extent is there a predictive relationship between the spread of new information on Twitter about a firm, and the reaction in the financial markets? 2) Is there a measurable and predictable difference in the time it takes for new information to spread in Twitter and the time it takes for that information to be absorbed in the financial markets? We also compare the findings from our explanatory model to the predictive power of Tweets in attempt to address some of the ongoing conundrums in the IS literature .We study the real-time relationship between chatter on Twitter and the trading volume of Nasdaq 100 firms during 193 days of trading in the period from May 21, 2012 to September 18, 2013. Data was not collected during weekends and holidays, when financial markets were closed. One author operated the data-collection program on a daily basis; but data collection was sometimes disrupted during extended travel or disruptions in internet connectivity. Since our emphasis is on investor rather than consumer sentiment, we excluded the most common household names listed in the Nasdaq 100 from this study: Facebook, Microsoft, Intel, Google and Apple. We used the Nasdaq 100 list as of May 1, 2012 (Facebook had not yet been listed). Among Nasdaq 100 firms, these particular firms have a dominant presence on Twitter in terms of their frequency of being mentioned. The comments we observed on Twitter mentioning these household names are seldom directly related to their financial performance and more often represent consumer sentiment.During the 193 trading days, we collected a stream of continuous Twitter feeds of messages that mention the common names of 96 firms listed in the Nasdaq 100 index . We counted the number of Twitter messages in which each firm is mentioned in each ten-minute period. We use the open third-party application-programming interface Twitter4J to connect to Twitter’s public “garden hose”, a random 1% stream. In total, we counted 35 million messages mentioning the 96 firms during the study period. Using an automated screen scraper, we also gathered Yahoo! Finance data at the beginning and end of each ten-minute period, in particular for the price and trading volume of each stock. Our data collection methods comply with the terms of service of both Twitter and Yahoo.We restricted the sample to the hours of trading from 10 am to 4 pm eastern standard time (EST), eliminating observations during the first half hour of trading when daily trading volumes are highly irregular. Each unit of observation represents a ten-minute period during all but the first half-hour in each day of trading, for each of the 96 firms over the 193 trading days. This results in a total of 618,261 observations that we draw from for this study.th, 90th, and 99th percentile levels for the firm Adobe to be 0.185, 0.248 and and 0.802 Twitter mentions per second (TPS), respectively. As we elaborate below, percentile calculations are calculated with reference to an expanding training-period window that ends prior to the week of measurement. Between 10:59 AM and 11:10 AM of that day, we observe 1.028 Twitter messages per second (TPS) mentioning Adobe, a level that exceeds the 99th percentile threshold for that firm. Thus, this ten-minute period represents a Twitter spike event that we define as an instance of the treatment. As this time period falls on a Thursday morning, our procedure selects randomly among all other observations for Adobe that occur on some other Thursday morning between 11 and 11:30 am. In this case, the selected control instance was January 17, 2013 beginning at 11:02 am; where the observed Twitter mentions per second (TPS) is below Adobe’s 99th percentile TPS threshold, for the training-window period that ended in the week prior Adopting a quasi-experimental design, we identified observations featuring firm-specific spikes in Twitter activity , and assigned each Twitter spike to a randomly selected ten-minute increment matching on the firm and a number of repeating time indicators. This resulted in a final data sample of 11,241 observations in 5,480 treatment-control group pairings for 94 firms (two out of 96 firms did not have sufficient Twitter activity to form such pairings). To provide insight into the measurable effects contained in the data, we consider some case studies that we extracted from the data early in the study period in June 2012. These examples, among many others, provide some guidance in operationalizing constructs related to Twitter activity and financial markets reactions at the appropriate levels of granularity in time.On June 4, 2012, NetFlix announced that it would develop its own content delivery network (CDN) that it called “Open Connect” (openconnect.netflix.com). The news had a sudden impact upon the price and trading volume of stock for Akamai Technologies (AKAM). Akamai is a leading provider of content delivery network services to NetFlix . The proOn June 11, 2012, at approximately 2:40 pm Eastern time, Apple announced the launch of new mapping software for its iO6 devices, leading to speculation that its next versions of the iPhone would be equipped with its own voice-enabled GPS service with turn-by-turn navigation [The two case examples highlighted above are among many in the dataset that we studied to gain a better understanding of the effects that are present in the data. Exploration of the data suggests that spikes of Twitter activity are much more pronounced over smaller durations of time, whereas the financial market reactions take longer to register the effects and result in more gradual slopes.th percentile in the mentions of the common name of the firm per minute, which is calculated with reference to an expanding training-period window that ends prior to the week of measurement. While events exceeded the 99th percentile threshold are probably most salient for identifying spikes, our analysis is broadly consistent when applied at 80th and 90th percentile thresholds. We consider stock market reactions in the forty minutes immediately after a spike in Twitter activity. To capture trading volume spikes, we identify instances in which trading volume reaches the 99th percentile level, as defined based on prior activity for each firm. We illustrate these definitions in Guided by visual exploration of the data, we define a spike in Twitter activity for a firm as the 99th percentile thresholds changed each week from June 21, 2012 to September 17, 2013. As shown, the threshold definitions for Adobe are sensitive to two particularly large spikes that occurred in the middle of the study period.To identify spikes in Twitter activity or changes in trading volume, we used a procedure to determine the percentile thresholds beginning with an initial training period comprised of the first month of data. These thresholds are firm-specific; that is, our procedure determines a unique threshold level for each firm that changes each week of the study period based on the trading history of data for that firm ending in the prior week. The training window then expands one week at a time, to determine the percentile threshold levels for each subsequent week. This method ensures that we use only past data to determine the threshold values for spikes in Twitter as well as future financial trading activity. In We define changes in trading volume over the subsequent forty-minutes, which we define in four distinct units of ten-minute increments as in ΔTradingVolume30mini,t, ΔTradingVolume20mini,t, and ΔTradingVolume10mini,t as changes in trading volume over the subsequent three, two, and single 10-minute increments respectively.Similarly, we defined the quantities th percentile for the firm; consider, for example, how this pairing was done for the Twitter spike listed in Our general estimation model is a within-estimator (i.e. fixed-effects) to implement a differences-in-differences analysis, in which groups are defined through random treatment-control assignment. To create treatment-control groups, we identified Twitter spike events as treatments and randomly assigned each such observation to a control observation in which Twitter activity was observed to be under the 99th percentile thresholds for eligible controls is based on the expanding training-period window that ends in the week prior to each observation. Random control selections were done conditional on matching of the firm symbol, as well as multiple repeating time indicators such as day of week, hour of day, and half hour. Our four-step procedure for identification of treatment-control groups is outlined in Control observations were drawn from past data, and the method for determining the 99We used a lagged-model framework, such that the levels of Twitter activity are measured for the ten-minute increments immediately preceding the periods in which the dependent variable measures begin:i represents a treatment-control group, and t represents a ten-minute interval. The model in 1 as the effect of a Twitter spike event in any ten-minute time increment on trading volume in the periods of 40, 30, 20, and 10 minutes beginning in the subsequent ten-minute time increment. The effect is stated as a difference over what may otherwise be expected in the matched control time periods. In other words, the model measures the extent that a Twitter spike for a firm signals an oncoming surge in trading volume, over and above the level that is normally expected for the firm in the given day of week and time of day. We also control for movements in the stock price and trading volume in the prior ten-minute period, averaged over all Nasdaq 100 firms included in the sample.We begin with this specific model because overall changes in stock market activity are considered to be strongly associated with the activity for each specific firm. The finance literature has explored time-series patterns in stock market volumes and trading activity in depth; and thus, we do not attempt to replicate that line of scholarship here. We are motivated primarily by how Twitter activity may portend oncoming changes in trading volume. Note that in While we do not posit any kind of causal relationship between chatter on Twitter and movements in the stock market, our model allows us to examine whether chatter on Twitter pertaining to a firm can serve as a signal to predict unusually high levels of trading volume in subsequent minutes within the next hour. Because trading activity for a given firm can be unusually high in regularly occurring intervals, for example in a time of the day for a particular day of the week , our model accounts for this through the treatment-control group matching.th percentile based on an expanding training-period window. To identify trading volume events, we used the same expanding training-period window that was used to identify spikes in Twitter activity, again to ensure that only past data was used to define the thresholds that mark these events. We used firm-specific thresholds at the 99th percentile to define unusual upward surges in trading volume. Here again we use a within-estimation empirical framework, this time implementing a differences-in-differences model through a logistic fixed-effects panel estimator appropriate for binary dependent variables. We also code the dependent variable as a binary indicator representing unusually high increases in trading volume or changes in stock market price, at the 99Dependent variable definitions are presented in We next consider the results of the fixed-effects panel implementations of the difference-in-differences, which we present in Tables In order to compare the explanatory analyses from the previous section to the predictive power of Tweets, we conducted forecasting exercises to predict trading volume changes in the subsequent forty minutes for each time period during the trading day. For time period t representing a ten-minute interval, the models predict the change in trading volume from periods t + 1 to t + 4. Forecasting was done with OLS regression models that were trained upon expanding one-week windows. We built an initial model on a training period that began on July 1, 2012 and ended on March 31, 2013, and that expanded one-week at a time to form predictions in each subsequent week. This model is specified below in th, 90th, and 99th percentiles. These Twitter spikes are identified using the methods detailed in DayOfWeek), hour of day (HourOfDay), and half hour .The forecasting model includes an indicator variable for each firm to account for firm fixed-effects; and separate binary indicators representing Twitter spike events at the 80i. We also run forecasting models that omit Twitter-related predictors represented by coefficients β1 through β7, but include all other predictors listed in 2 in regression estimates of 2 and adjusted R2 are the same, both at approximately 0.71, whether or not the seven terms are included in the model. This is probably because of the large number of regressors already in the model: Note that the fixed effect ui is short-hand notation referring to 95 additional coefficient terms in the regression model, one for each firm in the sample except a reference unit, in addition to indicator variables for day of week and hour/half-hour of day. With one observation for each ten-minute period for each firm, the sample size of the first training window period ending on March 31, 2013 is 321,796 data points, and subsequently grows each week as the training window expands. We use the resulting training model coefficient estimates to produce running forecasts in the subsequent week.In the above, the individual firm fixed effect is modeled as ui,(t+1 to t + 5) show a rather well-defined scatter around the diagonal line. This suggests that there is no systematic bias in our predictions. The values of RMSE and MAPE suggest that inclusion of the Twitter-related predictors into our forecasting model results in slightly improved predictions. The level of improvement, in the order of magnitude of 0.2%, does not appear very large. However, day traders and high frequency traders might find even a 0.2% advantage per trade quite lucrative. Yet, our results may also suggest that in order to forecast more accurately, one may have to steer away from interpretable linear models to more black-box algorithmic models; see also [The results are shown in see also . We did In practice, it is extremely difficult for individual investors to capitalize on newly released public information by trading in the stock markets. The speed of information exchange, ever-faster financial trading networks, and liquidity of financial markets, all ensure that market prices almost instantly absorb news as it is released to the public, denying arbitrage opportunities to all but exclusive groups of institutional traders . For allNevertheless, news can spread on Twitter much more quickly than it can be absorbed in the financial markets. For an individual person, the act of sending or relaying a message on Twitter is often fast and effortless, without immediate financial consequences. Executing a trade on the stock market based on the same information can be a cognitively and emotionally taxing process that requires more time. Thus, information may spread on large social networks such as Twitter before the financial markets can process it.Our results suggest that a spike in chatter on large-scale social networks such as Twitter about any firm may signal an impending surge in trading activity of the firm’s stock, whether or not the Twitter spike is causal. This not only presents an opportunity to harness valuable information for participants in financial markets; but also provides greater insight into the types of information that spread on large-scale social networks such as Twitter. We observe the potential for distinct speeds at which information diffuses in Twitter in comparison to the time it takes for financial traders to process and act upon that information. We observe effects that are statistically significant, transitory, and require finite amounts of time. As such, signals propagating in Twitter may be useful to traders seeking to exploit small delays in the diffusion of news and the relatively slow responsiveness of the markets. We believe that statistical models employing real-time data from large social networks can apply not just to the financial markets, but also to other areas of electronic commerce in which consumer sentiment can have measurable effects in real-time, such as in online auctions or online merchandising. Moreover, from the perspective of industry research, it is possible to reveal and quantify more clearly the dependencies between firms in an industry ecosystem.S1 DatasetA compressed.zip file containing two comma delimited files. The file named TwitterYahooFinalSample_PLoSOne.csv contains data used for Tables (ZIP)Click here for additional data file.S1 List of Firms(XLSX)Click here for additional data file.S1 Mapping of Variable Names to Data Fields(DOCX)Click here for additional data file.


Peer review is widely used to assess grant applications so that the highest ranked applications can be funded. A number of studies have questioned the ability of peer review panels to predict the productivity of applications, but a recent analysis of grants funded by the National Institutes of Health (NIH) in the US found that the percentile scores awarded by peer review panels correlated with productivity as measured by citations of grant-supported publications. Here, based on a re-analysis of these data for the 102,740 funded grants with percentile scores of 20 or better, we report that these percentile scores are a poor discriminator of productivity. This underscores the limitations of peer review as a means of assessing grant applications in an era when typical success rates are often as low as about 10%.DOI:http://dx.doi.org/10.7554/eLife.13323.001 Most funding agencies employ panels in which experts review proposals and assign scores to them based on a number of factors . However, several studies have suggested significant problems with the current system of grant peer review. One problem is that the number of reviewers is typically inadequate to provide statistical precision . ResearcAlthough there is general agreement that peer review can discriminate sound grant applications from those containing serious flaws, it is uncertain whether peer review can accurately predict those meritorious applications that are most likely to be productive. An analysis of over 400 competing renewal grant applications at one NIH institute  found no correlation between percentile score and publication productivity of funded grants . A subseIn contrast, a recent analysis of over 130,000 grant applications funded by the NIH between 1980 and 2008 concluded that better percentile scores consistently correlate with greater productivity . AlthougWe therefore performed a re-analysis of the same dataset to specifically address this question. Our analysis focused on subset of grants in the earlier study  that wer2 = 0.0078), suggesting that the overall ability of review groups to predict application success is weak at best. A random forest model indicated that only ~1% of the variance in productivity could be accounted for by percentile ranking  and citation productivity (the top 49.99% had ≥128 citations). Substantial overlap is evident between percentile scores for grants in the upper and lower halves based upon both publication productivity  and citaAn ROC curve  confirmsThese observations suggest that despite the overall ability of reviewers to discriminate between extremely strong grant applications and the remainder, they have limited ability to accurately predict future productivity of meritorious applications in the range relevant to current paylines. This may contribute to a pervasive sense of arbitrariness with regard to funding decisions and dissatisfaction with the peer review system . PerhapsIt should be noted that percentile scores and publication/citation productivity are not necessarily independent. Citation practices have evolved over time, and citations per paper approximately doubled between 1980 and 2004 . This maThese observations have important implications for the grant peer review system. If reviewers are unable to reliably predict which meritorious applications are most likely to be productive, then reviewers might save time and resources by simply identifying the top 20% and awarding funding within this group on a random basis or according to programmatic priorities. In this regard, we refer to our recent suggestion that the NIH consider a modified lottery system  and noteIn summary, while our analysis confirms that peer review has some ability to discriminate between the quality of proposals based on citation productivity over the entire range of percentile scores , this doA database derived from 102,740 research project (R01) grants funded by the NIH from 1980 through 2008 was obtained from Danielle Li (Harvard University) and Richard Nakamura (NIH Center for Scientific Review). The data included the number of publications acknowledging grant support within 5 years of grant approval (from PubMed), the number of citations of those publications through 2013 (Web of Science) and the percentile score of the application (rounded to the nearest whole number). Parametric statistical analyses were performed in Prism . The receiver operating characteristic (ROC) and random forest analyses were performed using R  and the pROC  and rand


The aim of this study was to assess how mean systemic pressure (Psm) and resistance to venous return (Rvr) behave during passive leg raising (PLR) in cases of fluid responsiveness and fluid unresponsiveness.In 30 patients with an acute circulatory failure, in order to estimate the venous return curve, we constructed the regression line between pairs of cardiac index (CI) and central venous pressure (CVP). Values were measured during end-inspiratory and end-expiratory ventilatory occlusions performed at two levels of positive end-expiratory pressure. The x-axis intercept was used to estimate Psm and the inverse of the slope to quantify Rvr. These measurements were obtained at baseline, during PLR and after fluid infusion. Patients in whom fluid infusion increased CI by more than 15 % were defined as “fluid-responders”.2/mL, p = 0.71). In fluid-responders, Psm, CVP and the Psm-CVP gradient significantly increased during fluid infusion while the Rvr did not change. In fluid-nonresponders, CVP and Psm increased significantly during fluid infusion while the Psm-CVP gradient and Rvr did not change.In fluid-responders (n = 15), CVP and Psm significantly increased  during PLR. The Psm-CVP gradient significantly increased by 20 ± 30 % while Rvr did not change significantly during PLR. In fluid-nonresponders, CVP and Psm increased significantly but the Psm-CVP gradient did not change significantly during PLR. PLR did not change the intra-abdominal pressure in the whole population  and in patients with intra-abdominal hypertension at baseline . In the latter group, PLR increased Psm from 22 ± 11 to 27 ± 10 mmHg (p <0.01) and did not change Rvr (5.1 ± 2.6 to 5.2 ± 3 mmHg/min/mPLR significantly increased Psm without modifying Rvr. This was also the case in patients with intra-abdominal hypertension. In case of fluid responsiveness, PLR increased venous return by increasing Psm to a larger extent than CVP. In patients with fluid unresponsiveness, PLR increased Psm but did not change the Psm–CVP gradient. Fluid infusion induced similar effects on Psm and Rvr. According to the Guyton model of circulation, systemic venous return is determined by two components . The firper se, that is to say, to an absence of increase in cardiac output to a significant increase in cardiac preload.Passive leg raising (PLR) has been developed as a test to predict fluid responsiveness. This postural manoeuvre is supposed to transfer a significant volume of venous blood toward the intrathoracic compartment . HoweverEstimating Psm and Rvr at the bedside has been difficult for many years, as it requires measurement of intravascular systemic pressure during cardiac arrest , 6. ReceDecision of the attending physician to perform a PLR test and fluid infusionAge ≥18 yearsMechanical ventilation in the volume assist control mode State of consciousness allowing 15-sec expiratory and inspiratory occlusions to be performed, as assessed by visual observation of the airway pressure curve displayed by the ventilatorThe study was performed in the medical intensive care unit of a University Hospital. It was approved by the Institutional Review Board of our institution (Comité pour la protection des personnes Ile-de-France VII). Deferred informed consent was requested from the patient’s surrogate as soon as possible. As the patient recovered consciousness, deferred informed consent was requested from the patient. If the patient or his/her next of kin refused to consent, the patient’s data were not entered into the analysis. Patients were included in the study if they met all the following criteria:Patients were excluded if PLR was contraindicated .All patients had an internal jugular vein catheter and a thermistor-tipped arterial catheter  in the femoral artery. The pressure sensors connected to the arterial and venous lines were referenced to the right atrium, corresponding to the axillary line, 5 cm below the sternal angle and zeroing was performed against atmospheric pressure. Arterial pressure, CVP and intra-abdominal pressure were continuously recorded by using data acquisition software . The beat-to-beat values of stroke volume derived from pulse contour analysis performed by the PiCCO2 device were computerised by using the PiCCOWin 4.0 software (Pulsion Maquet). These beat-to-beat values of stroke volume were then averaged over a 2-sec period and cardiac index (CI) was calculated over this period. Calibration of pulse contour analysis-derived estimation of stroke volume was performed by transpulmonary thermodilution with injection of three cold saline boluses (15 mL each) at baseline and after fluid infusion.2O, then at PEEP set for reaching a plateau pressure of 28–30 cmH2O [The method used to estimate Psm and Rvr has been previously described . It is bDuring each respiratory hold, we recorded the extreme values of CI (averaged over 2 sec) and the value of CVP at the same time. A regression line was computed  between the four pairs of measurements obtained at baseline, during PLR and after volume expansion. This regression line is assumed to equate the venous return line. The Psm was estimated as the pressure corresponding to the x-intercept of the regression line, as described in the model proposed by Guyton et al. . Rvr wasIntra-abdominal pressure was estimated by the bladder pressure after the injection of 25 mL of saline solution in the Foley catheter with the patient in the semi-recumbent position . The abdAt baseline, a first set of measurements was performed, including arterial pressure, CVP, intra-abdominal pressure and CI measured by transpulmonary thermodilution. Four respiratory holds, two at end-expiration and two at end-inspiration, were randomly performed as described above in order to estimate Psm and Rvr at baseline.After performing these respiratory occlusions, when CI had returned to its baseline value, a PLR test was performed. For this purpose and as previously described , 11, theAfter these recordings, the patient was moved back to the semi-recumbent position and CI was allowed to stabilise. After stabilisation, fluid infusion was performed by infusing 500 mL saline over 10 minutes. After fluid infusion, a third series of respiratory holds was performed in order to estimate Psm and Rvr. All other treatments were kept unchanged during the study period.t test with Bonferroni correction for repeated measurements. Patients in whom fluid infusion increased CI by more than 15 % were defined as fluid-responders. Comparison between fluid-responders and fluid-nonresponders was performed using the two-tailed Student’s t test. Comparison of proportions was performed with the chi-square test. A receiving operating characteristics (ROC) curve was constructed to assess the ability of the PLR-induced changes in CI to detect fluid responsiveness. The best cutoff value of PLR-induced changes in CI was defined as the one providing the best Youden index. Statistical significance was defined by a p value <0.05. The statistical analysis was performed using MedCalc 11.6.0 software .Date are expressed as mean ± SD or frequency . All quantitative data were normally distributed (Kolmogorov test). Comparison between the three time points of the study was performed using the paired Student’s Thirty patients were included in the study. Their characteristics are summarised in Table Considering the whole population at baseline, increased intra-abdominal pressure was present  in 16 patients. It was of grade I (between 12 and 15 mmHg ) in seveIn the 15 fluid-responders, PLR increased CI by 17 ± 20 %  or in patients with intra-abdominal hypertension at baseline . In the latter group, PLR increased Psm from 22 ± 11 to 27 ± 10 mmHg (p <0.01) and did not change Rvr .PLR did not change the intra-abdominal pressure in the whole population  curve of 0.98 ± 0.03.This study showed that PLR significantly increased Psm without modifying Rvr. This was also the case in patients with intra-abdominal hypertension. In the case of fluid responsiveness, PLR increased venous return by increasing Psm to a larger extent than CVP. In patients with fluid unresponsiveness, PLR increased Psm but did not change the Psm–CVP gradient and the cardiac output. Fluid infusion induced similar effects on Psm and Rvr.We estimated the two main determinants of systemic venous return by using heart–lung interactions. By varying the intrathoracic pressure during inspiratory and expiratory holds at two different levels of PEEP, we obtained a series of pairs of measurements of CVP and CI, which were assimilated to right atrial pressure and venous return, respectively. Assimilation of right atrial pressure by CVP is universally accepted. Assimilation of venous return by cardiac output is also accepted in apneic steady-state conditions such as end-expiratory and end-inspiratory occlusion. Initially developed by Maas and co-workers , this mePLR is used as a test for predicting fluid responsiveness. The test is based on the assumption that it increases the stressed blood volume by inducing the gravitational transfer of venous blood from the inferior limbs and the splanchnic compartment toward the cardiac cavities . NeverthFirst, PLR induced significant increases in Psm and CVP in the whole population of patients, confirming that it actually represents a powerful preload challenge . These rOne of the major interests of the study was to analyse the effects of PLR depending on the fluid responsiveness status. In patients with fluid responsiveness, both PLR and fluid administration increased the Psm-CVP gradient, which resulted from a larger increase in Psm than in CVP. This increase in the pressure gradient for venous return was associated with an increase in cardiac output and no change in Rvr. Physiologically, Psm depends on vascular compliance and on the volume of venous blood that is submitted to the strain of the venous reservoir walls, i.e., stressed blood volume . As fluiIn patients with fluid unresponsiveness, neither PLR nor fluid infusion increased the Psm-CVP gradient Fig. . Indeed,A first potential limitation of this study is that we could not compare the estimation of Psm and Rvr obtained by using heart-lung interactions and the theoretical reference that consists in measuring vascular pressures during cardiac arrest. In our study, Psm was estimated from the extrapolation of the regression lines between CVP and CI pairs of measurement. However, we cannot exclude that this part of the relationship between CVP and CI is non-linear, contrary to what was described by Guyton in animals . ValiditIn conclusion, in preload-dependent patients, PLR and fluid infusion increased Psm and increased CVP to a lesser extent while Rvr remained unchanged. This resulted in an increase in venous return. In preload-independent patients, venous return was unchanged by both PLR and fluid infusion, as CVP and Psm increased to a similar extent.PLR increases Psm and CVP, confirming that PLR represents a powerful preload challengeEffects of PLR depend on the fluid responsiveness statusIn patients with fluid responsiveness, both PLR and fluid administration increased the Psm-CVP gradient, which resulted from a larger increase in Psm than in CVP. This increase in the pressure gradient for venous return was associated with an increase in cardiac output and no change in RvrPLR and volume expansion have the same effects on venous return in the case of fluid responsiveness or fluid unresponsiveness.


However, little is known about micro regional distribution of hypoxia, glycolysis and proliferation in spontaneous solid tumours. The overall aim was simultaneous investigation of micro regional heterogeneity of 64Cu-ATSM (13h) and 18F-FDG (2h) enabled simultaneous investigation of micro regional distribution of hypoxia and glycolysis in 145 tumour pieces from four spontaneous canine soft tissue sarcomas. Pairwise measurements of radioactivity and gene expression of endogenous markers of hypoxia , glycolysis , proliferation (Ki-67) and angiogenesis (VEGFA and TF) were performed. Dual tracer autoradiography was compared with Ki-67 immunohistochemistry.Exploiting the different half-lives of 64Cu-ATSM and 18F-FDG was rather similar within each tumour section as reflected in moderate positive significant correlations between the two tracers  based on pixel-to-pixel comparisons of autoradiographies and gamma counting of tumour pieces. 64Cu-ATSM and 18F-FDG correlated positively with gene expression of GLUT1 and GLUT3, but negatively with HIF-1α and CAIX. Significant positive correlations were seen between Ki-67 gene expression and 64Cu-ATSM  and 18F-FDG . Ki-67 gene expression more consistently correlated with 18F-FDG than with 64Cu-ATSM.Micro regional heterogeneity in hypoxia and glycolysis within and between tumour sections of each tumour piece was observed. The spatial distribution of 64Cu-ATSM and 18F-FDG uptakes and distributions showed significant moderate correlations at the micro regional level indicating overlapping, yet different information from the tracers.18F-FDG better reflected cell proliferation as measured by Ki-67 gene expression than 64Cu-ATSM.Micro regional heterogeneity of hypoxia and glycolysis was documented in spontaneous canine soft tissue sarcomas.  Phenotypic and cellular heterogeneity within malignant tumours has been recognized since the early days of cancer biology –3. All cKey factors in the microenvironment of malignant tumours include hypoxia, glycolysis and proliferation. Cancer cells cover their energy demands through aerobic glycolysis (Warburg effect), which also supplies the necessary building blocks for the biomass formed during continuous cancer cell proliferation –14. Rapi18F]fluoro-D-glucose (18F-FDG) is routinely used to detect malignant tumours due to their high glycolytic activity. Furthermore it has been proposed as a surrogate marker of tumour hypoxia [18F-FDG uptake with different hypoxia PET tracers have been inconsistent and have shown regional and tumour type differences in distribution of hypoxia and glycolysis [Insights into the molecular aspects of the tumour microenvironment can be accomplished non-invasively via positron emission tomography (PET). 2-deoxy-2-[ hypoxia  due to tycolysis –36.4-methylthiosemicarbazone) (Cu-ATSM) [64Cu-ATSM and 18F-FDG have been performed in canine and human cancer patients showing from moderate or strong positive significant correlations [64Cu-ATSM and 18F-FDG at the micro regional level using autoradiography have only been performed in preclinical rodent tumour studies showing poor correlations [18F-fluoroazomycin arabinoside (FAZA) with gene expression of endogenous markers of hypoxia in small tumour fragments [One promising hypoxia selective PET tracer is Cu-diacetyl-bis(NCu-ATSM) , which lCu-ATSM) , 38. Onlelations  to no sielations . Howeverelations –45. Receragments . A technragments .64Cu-ATSM and 18F-FDG, their potential spatial overlap as well as their correlation to endogenous markers of hypoxia ), glycolysis (hexokinase 2 (HK2) and glucose transporters GLUT1 and GLUT3), proliferation (Ki-67) and angiogenesis  and tissue factor (TF)). The canine cancer model allowed for these studies to be performed in “full-size” compared to human patients. It was hypothesized that micro regional heterogeneity would be observed; that 64Cu-ATSM and 18F-FDG would show varying degrees of overlap and correlation; that 64Cu-ATSM would correlate positively with gene expression of endogenous markers of hypoxia, glycolysis and angiogenesis due to the important function of HIF-1α in their regulation; that 18F-FDG would correlate with gene expression of genes involved in glycolysis; and lastly that 64Cu-ATSM and 18F-FDG also to some extent would reflect tumour proliferation.However, little is still known about the in vivo micro regional heterogeneity of the tumour microenvironment regarding hypoxia, glycolysis and proliferation in spontaneous solid tumours, as it is difficult to obtain adequate and representative tumour samples from human cancer patients. As stated earlier, knowledge about the micro regional heterogeneity of the tumour microenvironment may be of importance for the prognosis of a cancer patient and furthermore could pave the way for more targeted and individualized treatment approaches. Therefore the aim was to simultaneously investigate the micro regional heterogeneity of hypoxia and glycolysis in canine cancer patients with spontaneous soft tissue sarcomas, examining the uptake and distribution of The study protocol was approved by the Animal Ethics and Administrative Committee at the Department of Veterinary Clinical and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark. Owners provided written informed consent prior to inclusion of their dog.Canine cancer patients with histologically confirmed soft tissue sarcoma admitted to the University Hospital for Companion Animals at the Department of Veterinary Clinical and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen Denmark from 2011 to 2013 were eligible for inclusion, if curable intent surgery was not possible or the owner declined advanced surgery. Treatment consisting of tumour debulking followed by radiotherapy was offered through participation in the study. Four canine cancer patients were prospectively enrolled in the study . As the 64Cu-ATSM and 18F-FDG uptake based on gamma counting.Due to missing data and too high reference gene instabilities (see below) patient 1 was included only in the final analysis of the correlation between 64Cu-ATSM  was injected intravenously as a bolus. 64Cu-ATSM activity in the syringe was recorded and the decay corrected injected activity calculated. The mean received dose of 64Cu-ATSM activity was 8.2 MBq/kg (range 7.2–9.5 MBq/kg). Approximately 1 hour prior to surgery the patients received an intravenous bolus of 18F-FDG  with a mean activity of 7.8 MBq/kg (range 7.3–8.0 MBq/kg). All patients were monitored after tracer administration. By utilizing the different half-lives of these two tracers it was possible to investigate the two molecular targets (hypoxia and glycolysis) simultaneously. Patients were pre-medicated, anesthetized and monitored during surgery according to the standards of the University Hospital for Companion Animals . All patients received post-operative analgesia, and three patients  received post-operative radiotherapy of 6.5–7 Gy once a week for 4 weeks.The canine cancer patients had a full oncological workup including staging. Prior to planned surgery, the patients were fasted for 12 hours and blood samples confirmed normal serum glucose concentrations and readiness for anaesthesia. Approximately 3 hours prior to surgery As soon as a tumour was excised, it was cut into variable sized pieces for either a) autoradiography (AR) and immunohistochemistry (IHC) (2–4 pieces per tumour) or b) gamma counting and quantitative real-time polymerase chain reaction (qPCR) (30–45 pieces per tumour). A total of 145 tumour pieces from the four soft tissue sarcomas were examined.18F-FDG + 64Cu-ATSM). After approximately 12 hours for residual 18F-FDG to decay, slides were re-exposed to the phosphor imaging screen for approximately 11 hours before the second reading. This was to ensure that the second scan strictly expressed 64Cu-ATSM. All exposures were conducted at 4°C and slides subsequently frozen and stored at -80°C until IHC analysis.Tumour pieces for AR and IHC (approximately 10x10x10 mm) were snap-frozen in liquid nitrogen and transferred to dry ice. Tumour pieces were embedded with Jung Tissue freezing Medium® , cut into 8μm tissue sections on a cryostat  and thaw mounted on microscope slides . Ten to twenty cryostat sections 215 μm apart were made for each tumour. Slides with tissue sections were exposed to a phosphor imaging screen  for approximately 2 hours. The exposed imaging screens were scanned at 600 dpi (pixel size 42 μm) using a phosphor imaging system  to obtain the first image (18F-FDG + 64Cu-ATSM and pure 64Cu-ATSM exposure) were co-registered using a rigid transformation and built-in functions, and their intensity corrected linearly for exposure time and scan time difference. By subtracting the pure 64Cu-ATSM from the combined image, an estimated pure 18F-FDG exposure was obtained. After quadruple down sampling to reduce registration artefacts, direct pixel-to-pixel correlation of the two radioisotopes was made and plotted in a scatter plot. Two Gaussian distributions were fitted to the data using hierarchical cluster analysis, one for image background and one for the AR slide itself, and linear fits and coefficients of determination (R2) were calculated for each distribution.All AR images (pixel size 42 μm) were exported from OptiQuant™ Image Analysis Software  in TIFF format and transferred for further analysis using in-house developed software in MATLAB . The AR images of individual tumour tissue sections were cropped from a large image of the whole phosphor imaging screen containing multiple sections. The two individual tumour images  and 10 min soak with 2% bovine serum albumin (BSA). Incubation with monoclonal mouse anti-human antibody for Ki-67 antigen clone MIB-1  lasted 1 hour at room temperature at a concentration of 1:200. This antibody also cross-reacts with canine Ki-67. The secondary antibody, an anti-mouse peroxidase labelled polymer  was linked to the primary antibody during 40 min of incubation and peroxidase activity was visualized through addition of DAB+ Subtrate Chromogen  as detection system. Counterstaining was performed with Mayer’s haematoxylin  before dehydration and mounting of cover glass. Each of the described steps until and including DAB+ was followed by rinses with PBS, except between the BSA and primary antibody. An 8 μm cryostat section of canine epidermis was included in the run as a positive control of staining only in the basal layer. The rest of the epidermis simultaneously served as negative control tissue. Furthermore a negative control, where the primary antibody was replaced with nonsense anti-body  was also included.Four to eight cryostat sections (most often 430 μm apart) from each canine cancer patient previously used for AR were thawed, followed by fixation in acetone at 4◦C for 10 min. Manual staining procedures were performed as follows: After air-drying, antigen retrieval with microwave heating in citrate buffer, followed by a rinse in phosphate buffered saline (PBS) with 0.1% Tween, was performed. Endogenous enzyme activity was blocked through 8 min soak with Dako REAL64Cu-ATSM and 18F-FDG AR images. IHC images (one per tumour) were processed and manually co-registered with AR images of 64Cu-ATSM and 18F-FDG using ImageJ  and MATLAB . Colour de-convolution of the IHC images focussing on the brown colour indicating Ki-67 staining and the blue colour indicating nuclear staining was performed. The original IHC image rescaled to a pixel size of 42 μm (size of AR) using pixel averaging with bicubic interprolation was used for manual co-registration with AR images. A mask drawn on the original IHC image to exclude areas of tissue folding or defects was after rescaling overlaid on the AR images, rescaled brown IHC image and rescaled blue IHC image before data extraction. Furthermore the rescaled brown IHC and blue IHC images were dichotomized to be either positive (1) or negative (0) for the respective stains before data extraction. The corresponding values of Ki-67 immunostaining relative to blue nuclear staining (number of positive brown pixels/number of positive blue pixels) and of tracer uptake (counts) were evaluated for areas of 10 x 10 pixels (420 μm x 420 μm) at a time by taking the average. Pixels lining the edges of the mask delineating the tissue of interest were excluded during this procedure and an equal cell distribution within the examined tissue of interest was assumed. Hereby correlations between Ki-67 IHC as a marker of proliferation and tracer uptake of 64Cu-ATSM (hypoxia) or 18F-FDG (glycolysis) could be established.IHC slides were scanned with Axio Scan.Z1slide scanner  and images (pixel size 0.22 μm) processed in ZEN 2012 software . Images resized to 25% (pixel size 0.88 μm) of the original were exported from ZEN 2012 as PNG files for subsequent analysis of correlation between the Ki-67 IHC and later . Tubes were weighed and using a WIZARD2® Automatic gamma counter  gamma counting of each tumour piece was performed twice approximately 19 hours apart. Thereby the first gamma count would be a mixed count of 64Cu-ATSM and 18F-FDG, while the second count represented solely 64Cu-ATSM. Empty Eppendorf tubes were counted to correct for the background count rate. The counting efficiency of the equipment was 9.43 x 10−2 for 64Cu-ATSM and 0.54 for 18F-FDG.The 30–45 tumour pieces (approximately 5 x 5 x 5 mm) from each tumour, to be evaluated by gamma counting and qPCR, were individually placed in pre-weighed Eppendorf tubes containing RNA® Excel® for mac 2011, version 14.3.8, Microsoft Corporation, Remond, WA, USA) were applied to solve the following two equations with two unknowns with regard to 64Cu-ATSM and 18F-FDG.Based on the gamma counting data, the matrix functions MMINVERSE and MMULT  by taking the weight of the tumour piece, the canine patient’s body weight (BW) and the injected dose into consideration [deration , 48:SUVlater for approximately 24 hours at 4°C before removing the supernatant and storing the tissue at -80°C until use.The gamma counted tumour pieces were stored in RNA®RT , including the 4-bromoanisole (BAN) step to further eliminate genomic DNA contamination, was used according to the manufacturer’s protocol to extract total RNA from each tumour piece (13.4–510.9 mg). The tissue was lysed and homogenized in Precellys®-24 .RNAzolThe total RNA concentration was determined by NanoDrop 1000  to be between 262.4 and 4368.3 ng RNA/μL. To further minimize genomic DNA contamination, all RNA samples were treated with rDNAse  according to the manufactures’ protocol, except for the incubations which were performed at 75°C for 15 minutes and at 4°C for 15 minutes. The quality of the isolated RNA was measured as the RNA Integrity Number (RIN) on a 2100 Bioanalyzer . RIN values were between 5.4 and 9, verifying good RNA quality .TM QPCR cDNA synthesis kit : Seven μL RNA (0.3 μg) + 2.45 μL oligo (dT) primer (0.1 μg/μL) + 0.55 μL random primer (0.1 μg/μL) + 10 μL First Strand Master Mix (2x) + 1 μL StrataScript RT-Rnaseblock; ending up with a final volume of 21 μL cDNA. RT was performed on a MasterCycler Gradient : incubation at 25°C for 5 minutes , 42°C for 15 minutes (cDNA synthesis) and 95°C for 5 minutes (termination of cDNA synthesis). Immediately after the cDNA was cooled down, frozen and stored at -20°C.Total RNA (0.3 μg) was reverse transcribed (RT) using AffinityScriptβ-Glucuronidase  and proteasome subunit, beta type, 6  were selected as reference genes, based on our previous study of suitable reference genes in similar tissues [PLUS software [Canine  tissues . In brie tissues . Primer  tissues  implemensoftware  (BiogazeIn the present study GUSB and PSMB6 were designed in a TaqMan duplex assay while GOIs were designed as multiplex (duplex or triplex) or simplex TaqMan assays. The following GOIs were investigated: c_Ki-67 (XM_005637893) indicating proliferation; c_CAIX (NM_001145174) and c_HIF-1α (XM_003639201) as measures of hypoxia; c_TF (NM_001024640) and c_VEGFA (NM_0010003175) representing angiogenesis and c_HK2 (XM_003639587), c_GLUT1 (NM_001159326) and c_GLUT3 (NM_0010003308) as indicators of glycolysis.Beacon Designer  was used to design primers and probes. Before designing, all genes were tested for cross homology against the canine genome and checked for secondary structures. Since HIF-1α exists in 2 transcript variants and VEGFA in 3, the amplicons for these two targets were placed in a way including all transcript variants. The optimized conditions for the primers and probes, after testing and securing one melting product and an efficiency of 100 ± 10%, are listed in Gene expression was quantified on the Mx3005P real-time PCR systems from Stratagene  using Brilliant III Ultra-Fast QPCR Master Mix  for the simplex assays and Brilliant Multiplex QPCR Master Mix  for the multiplex assays. The thermal profile for Brilliant III Ultra-Fast reagent was: 3 minutes of denaturation at 95°C, followed by 40 cycles with denaturation at 95°C for 20 seconds and annealing/elongation at 60°C for 20 seconds. For the Brilliant Multiplex reagent, the thermal profile was: denaturation at 95°C for 10 minutes, followed by 40 cycles with denaturation at 95°C for 15 seconds and annealing/elongation at 60°C for 1 minute.Samples were run in duplicates using 1 μL cDNA in a total volume of 20 and 25 μL, respectively. A duplicate NO RT (no reverse transcription), indicating contamination with genomic DNA in the sample, was included for each sample using a dilution of the respective sample mRNA. Furthermore a NTC (no template control) and a dilution curve, used for efficiency calculations, were included alongside three inter-run calibration samples in each run.PLUS software based on the comparative 2-ΔΔCq method [PLUS software accounted for the three inter-run calibrators for correction between PCR runs within the same gene. The data were reported as calibrated normalized relative quantities (CNRQs).qBaseq method  for relaq method . The sof64Cu-ATSM and 18F-FDG calculated as SUV from gamma counts were correlated to each other and the gene expression of GOIs using Spearmann’s rank correlation in Graph Pad Prism . MATLAB  was used for correlating 64Cu-ATSM and 18F-FDG AR images to each other. Processing of data from IHC images and AR images was done using Excel®  and Graph Pad Prism  to calculate Spermann’s rank correlations between Ki-67 immunostaining and 64Cu-ATSM and 18F-FDG in tumour tissue sections. For all statistical analyses a p-value < 0.05 was considered significant.Tumour piece uptake of 64Cu-ATSM uptake, and glycolysis, as per 18F-FDG uptake, within tumour tissue sections from each tumour was observed. Visual examination of AR images of 64Cu-ATSM and 18F-FDG likewise revealed different degrees of micro regional heterogeneity in the uptake of the two tracers between distinct tumour tissue sections from each individual tumour as well as between the four examined sarcomas. However the spatial distribution of 64Cu-ATSM and 18F-FDG was rather similar within each tumour tissue section, which was also reflected in the positive correlations found in all AR images between the spatial distribution of 64Cu-ATSM and 18F-FDG based on pixel-to-pixel analysis  between the two tracers was seen in all four sarcomas . The remainder of the correlations were non-significant (p between 0.2123 and 0.3663) or irrelevant (ρ between -0.1433 and -0.1949). A visual example of the comparison between a Ki-67 IHC image and 18F-FDG and 64Cu-ATSM AR images is given in The proliferative state of the tumours, investigated through Ki-67 IHC, revealed an overall scarce Ki-67 staining with a rather homogenous distribution without any obvious hotspots within the tumour sections. When compared with the uptake of 18F-FDG in all three tumours  and 64Cu-ATSM , while the correlations between the tracers and CAIX gene expression were non-significant. With regard to the expression of GLUT1 and GLUT3 both 64Cu-ATSM and 18F-FDG showed positive correlations, though not significant for GLUT1 versus 18F-FDG  . Similarly, non-significant correlations were seen in the two other tumours (no. 2 and 4) for some of the investigated GOIs and no general trend was found within the significant correlations  . For theelations .To further elucidate the significant correlations between the tracers and gene expressions found in tumour 3, we correlated the gene expression of different GOIs to each other .64Cu and 18F to simultaneously investigate the micro regional heterogeneity of tumour microenvironment hypoxia and glycolysis in canine soft tissue sarcomas using the PET tracers 64Cu-ATSM and 18F-FDG and their relation to endogenous markers of hypoxia, glycolysis, proliferation and angiogenesis. Importantly, it was for the first time in spontaneous canine soft tissue sarcomas simultaneously demonstrated, that the uptake and spatial distribution of each of the tracers 64Cu-ATSM and 18F-FDG showed micro regional distribution differences, while the two tracers showed varying degrees of overlapping micro regional distributions of hypoxia and glycolysis manifested in significant moderate correlations between 64Cu-ATSM and 18F-FDG examined both through gamma counting and autoradiographies. This demonstrates both some overlap but also uniqueness of each of the PET tracers. The expression of the endogenous markers of hypoxia and glycolysis showed similar significant correlations to both 64Cu-ATSM and 18F-FDG in one tumour, while no particular trend was observed in the other tumours or for the endogenous markers of angiogenesis potentially indicating differing molecular expression patterns in the examined soft tissue sarcomas. With regard to tumour cell proliferation, gene expression of Ki-67 and tracer uptake for 18F-FDG correlated closer than Ki-67 and 64Cu-ATSM, indicating that 18F-FDG may be a better marker of proliferation.This unique dual tracer study exploited the different half-lives of 64Cu-ATSM uptake, and glycolysis, as per 18F-FDG uptake, at the micro regional level. It is innovative in simultaneously investigating two PET tracers in a spontaneous syngeneic translational animal cancer model and performing pairwise comparisons of tracer uptake with endogenous markers in small tumour pieces. As hypothesized a moderate significant correlation was found between the uptake and spatial distribution of 64Cu-ATSM and 18F-FDG.As stated above the present study contributes with novel information about tumour heterogeneity of hypoxia, as per 64Cu-ATSM and 18F-FDG using either AR or PET image comparisons. Comparable to the results of the present study, a previous canine study including both sarcomas and carcinomas reported moderate to strong spatial correlations between 64Cu-ATSM (3 and 24 hours post injection) and 18F-FDG based on voxel comparisons of PET images, though without specifying the correlations according to histopathology or uptake in multiple tumour pieces as in the present study [64Cu-ATSM and 18F-FDG PET scan uptakes with immunohistochemical staining for the exogenous hypoxia marker pimonidazole, it was found that tumours with high levels of pimonidazole staining showed high uptake of 64Cu-ATSM (3 hours post injection) and 18F-FDG [64Cu-ATSM AR (25 hours post injection) and pimonidazole IHC revealed similar regional distributions in the most heterogeneous tumour regions. 18F-FDG AR however was not available for comparison with pimonidazole and the study concluded that 18F-FDG might not be a surrogate marker of hypoxia [64Cu-ATSM and 18F-FDG in the present study, a recent study in human patients with head and neck cancer showed no significant difference in the biological tumour volume delineated for radiotherapy planning based on 64Cu-ATSM PET/CT or 18F-FDG PET. However the correlation analysis was not described in detail [61Cu-ATSM and 18F-FDG for sarcomas as it did for carcinomas [64Cu-ATSM uptake between various cancers [Other studies have also investigated the spatial overlap of nt study . In a si 18F-FDG . Further hypoxia . In partn detail . A newerrcinomas , suggest cancers .64Cu-ATSM and 18F-FDG evaluated by AR have been seen in preclinical studies in both mouse [64Cu-ATSM and 18F-FDG PET/CT scans in canine spontaneous carcinomas and sarcomas for dose painting also revealed varying degrees of correlation between dose plans based on the two tracers [18F-FDG based dose painting plans only covered approximately 50% of the hypoxic regions adequately, leading to the conclusion that 64Cu-ATSM and 18F-FDG provide different biological information [64Cu-ATSM and 18F-FDG in canine soft tissue sarcomas, but with the novelty of being evaluated simultaneously and at the micro regional level. In addition, the present study revealed varying degrees of heterogeneous distribution of each individual tracer within and between tumour tissue sections from each tumour, indicating the existence of tumour heterogeneity in hypoxia and glycolysis at the micro regional level. In sum, in some circumstances 64Cu-ATSM and 18F-FDG may be closely related but this is not always the case. Therefore, both tracers have their value when phenotyping tumours and 64Cu-ATSM is justified as an adjunct to 18F-FDG imaging.In contrast to the results of the present study, poor correlations between the micro regional distribution of th mouse , 42, ratth mouse , 44 and th mouse . A very  tracers . Furtherormation . In agre64Cu-ATSM and 18F-FDG respectively and the gene expressions of endogenous markers in small tumour pieces helped elucidate the true connections between tracer uptake and molecular events on a micro regional level and gave insights into micro regional heterogeneity. Significant correlations between the gene expression of endogenous markers of hypoxia and glycolysis and the PET tracer uptakes were seen with similar correlations for both 64Cu-ATSM and 18F-FDG in one tumour (no. 3), while no particular trend was seen for the endogenous markers of angiogenesis or in the other tumours, indicating potential differences in molecular expressions in the investigated tumours.The multiple pairwise measurements of radioactivity attributable to 18F-FDG and the mRNA encoding the glucose transporters GLUT1 and GLUT3 as expected, though only significant for GLUT3. GLUT1 and GLUT3 are the primary transporters facilitating uptake of glucose and thus also the glucose analogue 18F-FDG into the tumour cells [18F-FDG uptake only correlated significantly with GLUT3 may be explained by the higher affinity for glucose and the greater transport capacity of this glucose transporter compared to GLUT1 [64Cu-ATSM and GLUT1 and especially GLUT3 reflects the close link between hypoxia and increased glycolysis in tumours. Both GLUT1 and GLUT3 are known to be up-regulated by HIF-1α during hypoxia [The correlations between tracer uptake and gene expression in tumour 3 revealed a positive correlation between ur cells –61. Thatto GLUT1 . However hypoxia , 64. Int hypoxia , 66. Ano64Cu-ATSM and 18F-FDG with regard to the gene expression of CAIX (though non-significant) and HIF-1α in tumour 3. For 18F-FDG this seems reasonable, since this tracer is primarily a marker of tumour glycolysis. However since the correlations were equal for 64Cu-ATSM and 18F-FDG a similar explanation for the negative correlation for the two PET tracers may also be possible. One reason could be the above-described delay in transcription of hypoxia responsive genes in areas of acute hypoxia. Another explanation could be that HIF-1α is regulated by other factors than tumour hypoxia, such as oncogenic signalling or growth factors [We also found negative correlations between both  factors –69. Prob factors , 71. Sim factors .64Cu-ATSM and 18F-FDG in the tumour pieces from tumour 3  that tumour pieces used for gene expression analysis and IHC originated from different areas of the tumour; 2) that post transcriptional or translational changes happen between the mRNA measured during qPCR and the protein measured during IHC; 3) that Ki-67 IHC staining was too scarce and homogenous; 4) the limitations associated with comparing Ki-67 IHC and AR images having very different image resolutions. However we tried to avoid small regional discrepancies in uptake and possible manual co-registration inaccuracies by investigating areas of 420 x 420 μm without compromising the interest of getting micro regional information.With regard to proliferation as evidenced by Ki-67 protein and gene expression, a discrepancy was seen between the correlations to the two tracers 4Cu-ATSM , indicat18F-FDG e.g. in lymphomas [18F-FDG uptake and Ki-67 grades [18F-FDG uptake from PET scans with Ki-67 immunostaining in contrast to the present study where the micro regional tracer uptake is correlated to gene expression of Ki-67. However a very recent study in human colon cancer showed a positive correlation between maximum 18F-FDG uptake from PET scans and the gene-expression of Ki-67 [64Cu-ATSM and Ki-67 are scarcer and only preclinical comparing immunohistochemistry and autoradiographies [18F-FDG and Ki-67 gene expression in the present study represent special information about the micro regional relationship between glycolysis and proliferation in solid tumours, which is in agreement with our hypothesis.Studies in different human cancers have also revealed significant correlations between Ki-67 and ymphomas , non-smaymphomas , 76 and ymphomas . However7 grades . All theof Ki-67 . Studiesgraphies , 42. The64Cu-ATSM and 18F-FDG uptake is heterogenous across multiple micro regions within and between tumours of the same histopathologic class. At the micro regional level 64Cu-ATSM and 18F-FDG uptakes and distributions only correlated moderately indicating different biological aspects visualized. 18F-FDG better reflected cell proliferation as measured by Ki-67 gene expression than did 64Cu-ATSM. Thus each PET tracer contributes with distinct information about the micro regional environment within the cancer. Using endogenous molecular markers also adds relevant information about the microenvironment and heterogeneity and perhaps a molecular taxonomy of cancers may in the future become as important as that of traditional histopathologic classification of cancers. Since the number and types of tumours in the present study were limited, further studies are necessary to investigate whether the observations in the present study are generalizable.In conclusion this study of canine soft tissue sarcomas revealed that both S1 Fig18 F-FDG and 64Cu-ATSM calculated as standardized uptake value (SUV) from gamma counts. Rows 1 and 2 (A) show data for tumour pieces from canine cancer patient 2, while rows 3 and 4 (B) show results for patient 4. n is the number of tumour pieces included in the final analysis. n.s. not significant. Significant correlations are written in bold.Spearman’s Rank correlations (ρ) and p-values for comparison of gene expressions for GLUT1, GLUT3, HIF-1α, CAIX, VEGFA and TF respectively and tumour uptake of (TIFF)Click here for additional data file.


In contrast, TMPyP2 (P2) produces 1O2 but it is not a G-quadruplex binder. As their photosensitizing activity is currently undefined, we report in this study their efficacy against a melanoma skin tumour and describe an in vitro mechanistic study which gives insights into their anticancer activity.Porphyrin TMPyP4 (P4) and its Cin vivo anticancer activity were tested in melanoma tumour-bearing mice. Porphyrin binding and photocleavage of G-rich mRNA regions were investigated by electrophoresis and RT-PCR. Porphyrin effect on ERK pathway was explored by Western blots.Uptake and antiproliferative activity of photoactivated P2, P4 and C14 have been investigated in murine melanoma B78-H1 cells by FACS, clonogenic and migration assays. Apoptosis was investigated by PARP-1 cleavage and annexin-propidium iodide assays. Biodistribution and 2) in B78-H1 melanoma cells, P4 and C14, but not control P2, caused a strong inhibition of metabolic activity, clonogenic growth and cell migration. Biodistribution studies on melanoma tumour-bearing mice showed that P4 and C14 localize in the tumour. Upon irradiation , P4 and C14 retarded tumour growth and increased the median survival time of the treated mice by ~50% (P <0.01 by ANOVA), whereas porphyrin P2 did not. The light-dependent mechanism mediated by P4 and C14 is likely due to the binding to and photocleavage of G-rich quadruplex-forming sequences within the 5′-untranslated regions of the mitogenic ras genes. This causes a decrease of RAS protein and inhibition of downstream ERK pathway, which stimulates proliferation. Annexin V/propidium iodide and PARP-1 cleavage assays showed that the porphyrins arrested tumour growth by apoptosis and necrosis. C14 also showed an intrinsic light-independent anticancer activity, as recently reported for G4-RNA binders.Thanks to its higher lipophylicity C14 was taken up by murine melanoma B78-H1 cells up to 30-fold more efficiently than P4. When photoactivated (7.2 J/cmin vivo. Evidence is provided that C14 acts through light-dependent (mRNA photocleavage) and light-independent (translation inhibition) mechanisms.Porphyrins P4 and C14 impair the clonogenic growth and migration of B78-H1 melanoma cells and inhibit melanoma tumour growth  An equimolar solution of DMA and porphyrin (10 μM each) was irradiated with a lamp (60 mW) for different periods of time up to 1000 s and the residual fluorescence was recorded between 380 and 550 nm. The residual fluorescence was plotted as a function of time and the experimental curve was best-fitted to a first order decay equation y = exp (-k·t) where k (s-1) is the rate constant and t the time(s)  of P4 was reported to be 0.51  at 660 ± 5 nm, fluence of 193 J/cm2. Three porphyrin/light treatments have been carried out at days 1, 7 and 14. The mice were examined every 2 days for changes in weight, appearance of side effects or signs of sickness. The size of the tumour was measured every 2–3 days by a caliper. The mass (mg) of the tumour was calculated assuming a tumour density of 1 and a tumour volume given by π/6 · a2 · b where a and b are the shorter and larger axes (cm), respectively. The median survival time is defined as the animal’s life spanning from the inoculation of tumour cells till death.Female 6-week old C57/BL6 mice were obtained from Harlan-Nossan  and were maintained in a conventional animal house for 2 weeks. All procedures with animals were carried out in accordance with the National Health Institute Guide for the Care and Use of Laboratory Animals and approved by the Institutional Animal Care Committee at the University of Trieste . In particular every effort was made to avoid unnecessary pain to the animals. The mice, weighing 20 g, were implanted into the upper flank by subcutaneously injection with 2 × 101O2 generation by the photoexcited triplet state of the porphyrins was measured by 9,10-dimethyl-anthracene (DMA). Upon reacting with 1O2, DMA is transformed in the non-fluorescent 9,10-endoperoxide. As this reaction occurs with a high degree of specificity and with 100% chemical quenching of the fluorescence [t) where k (s-1) is the rate constant and t the time [s]. We obtained k for for P4, C14 and P2. Considering that the quantum yield of singlet oxygen generation (ϕΔ) of P4 was reported to be 0.51 : Quantum yield; PDT: Photodynamic therapy; C14: Tri-meso(N-methyl-4-pyridyl)- meso(N-tetradecyl-4-pyridyl) porphine; TMPyP4 (or P4): Meso-tetrakis (N-methyl-4-pyridyl) porphine; TMPyP2 (or P2): Meso-tetrakis (N-methyl-2-pyridyl) porphine; PI: Propidium iodide; PARP-1: Poly-[ADP ribose]-polymerase; CD: Circular dichroism.The authors declare that they have no competing interests.VR, SZ, MZ, SC participated in the design of the experiments; VR performed the cell cultured experiments and partecipated to the in vivo experiments; SZ and MZ performed the in vivo experiments; ED carried out western blots, SC performed RT-PCR, photocleavage and CD experiments; LX conceived the study and wrote the manuscript; VR, MZ and SC contributed to the final drafting of the manuscript. All authors read and approved the final manuscript.Dimethylanthracene assay shows singlet oxygen production by the cationic porphyrins irradiated with a laser at 660 nm.Click here for fileG4-RNA formation in the 5’-UTR of KRAS and NRAS mRNAs.Click here for fileMelanoma B78-H1 cells are characterized by a hyperactive RAS/MEK/ERK pathway.Click here for file


Tumour-to-tumour metastasis is an infrequent pathological phenomenon. Meningioma is the most common intracranial tumour where metastatic deposits may be found, the majority of which arise from breast and lung cancers. We describe an unusual case of occult pulmonary carcinoma metastasis into the intracranial meningioma. A 77-year old lady presented with acutely deteriorating hemiparesis. Her previous medical history was unremarkable. Radiological imaging revealed an expansive lesion, classified as meningioma, which was located parasagittally in the right premotor area. A well-capsulated tumour attached to the dura was removed surgically. The pathological examination demonstrated a mixture of angiomatous meningioma and pulmonary adenocarcinoma. Possible explanations for the development of a composite tumour and pathophysiology are described. Tumours of non-neural origin, besides being aggressive locally, metastasize frequently to the central nervous system, and this represents a relatively common complication of most cancers from elsewhere in the body, including carcinoma of the lung and breast and malignant melanoma –4. On thIn addition to the rising incidence of primary and secondary brain tumours, tumour-to-tumour metastasis may also be encountered in clinical practice , 3. AlthClinically, a variety of signs and symptoms may occur . EpileptSurgical excision is the principal form of treatment of patients with a history of extracranial cancer although stereotactic radiosurgery is becoming a desirable alternative therapeutic option, especially in eloquent areas of the brain –15. A coAn unusual case of adenocarcinoma of the lung metastasis into the intracranial meningioma in a 77-year old lady is presented.A 77-year old lady with a known expansive lesion of the right frontal lobe was admitted to the department due to a rapid deterioration of left-sided hemiparesis. She has been followed up constantly at the neurosurgical outpatient clinic as a result of an expansive lesion, radiologically characterised as meningioma. The lesion has been known for many years, and according to radiological imaging, it had been persistingly unchanged. Until now, the woman was in a good medical condition and did not report of any difficulties in relation to this lesion. However, her neurological condition deteriorated suddenly with weakness of the left limbs. At home, she was walking with progressive difficulty, the gait was unstable and in a few hours, she was not able to stand and walk any more. Additionally, she complained of frontal headache and nausea. Neither loss of consciousness nor seizures were reported by the relatives.During a neurological examination, no consciousness or cognitive deficits were found. Testing of cranial nerves was normal. There was marked muscle weakness of the left side, equally pronounced in the upper and lower extremity. On the left, the muscle tone was slightly lowered. In comparison with the right side, the reflexes in the left upper and lower extremities were weaker. Walking was not possible.MRI of the head revealed progression of the tumour, measuring 3 cm in diameter. It was located parasagittally in the right premotor area, surrounded with an extensive oedema, compressing the cortex. After contrast application, it was enhanced homogenously Fig. . FollowiHistologically, the tumour was an angiomatous meningioma. In the tumour mass, however, malignant tissue arranged in cords and sheets and glandular formations of atypical epithelial cells were present Fig. . MalignaTumour-to-tumour metastasis is a rare entity, first described in 1930 by Fried . It denoCampbell proposed strict criteria for this diagnosis in 1968, which are as follows: (I) existing more than one primary tumour, (II) the recipient is a true neoplasm, (III) the metastatic neoplasm is true metastasis with evident growth in recipient tumour and (IV) exclusion of the tumours that have metastasized to the lymphatic system, where lymphoreticular malignant tumours already exist . AccordiTumour-to-tumour metastasis in the central nervous system is even rarer . Since tMeningiomas have been the most common intracranial neoplasms to harbour metastasis, and the reason for a cancer seeding into meningioma is not precisely known. It is suggested that meningiomas, as generally slow growing and indolent tumours, make a suitable destination for cancer metastasis over a period of time due to the clinical and biological characteristics, such as higher incidence among intracranial neoplasms, hypervascularity, slow growth, low metabolic activity and high collagen and lipid content , 19, 21.Frequently, tumour-to-tumour metastasis is seen in the course of cancer disease and its treatment. It is very unusual that the complications of the tumour-to-tumour metastasis are the first sign of the cancer disease. Clinically, a variety of signs and symptoms may occur . EpileptSurgical excision is the principal form of treatment of patients with a history of extracranial cancer and concomitant solitary intracranial mass and must be preceded by radiographical determination of the extent of the primary lesion and possible metastatic deposits . In ordeAlongside micro-neurosurgery, stereotactic radiosurgery is a desirable treatment modality , 27, 28.Tumour-to-tumour metastasis is a rare but well-known event, especially in case of malignant tumours. Due to high blood flow in the meningioma vessels, invasive pulmonary carcinoma cells may easily be transported and may survive in the meningioma tissue, with the tumour microenvironment positively influencing the growth of the implanted tumour cells.Written informed consent was obtained from the patient for publication of this case report and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.


The “median” method for the reduction of the effect of noise and trigger jitter on waveform data is described. The effectiveness of this method was examined using simulations and experiments and, for typical jitter and noise observed in electrical pulse metrology, is shown to provide reconstructed waveforms with transition durations that accurately match those of the input signal. Also, for aberrations, an upper bound on the error in the amplitude of the aberration is provided. The relationship between tjit and the measured value of the rms jitter, σjit, can be found either by calculation and tables   is described to first order by:f0 is the jitter-free, noise-free waveform, mj is the trigger jitter corresponding to the mth waveform, ms is the noise corresponding to the mth waveform, nt is discrete time with index n, and nt. The average of f [nt]M〉, is the value typically provided by oscilloscopes or other waveform recorders:s[nt]M〉 is the mean value of the noise at nt and Mg[nt] is the amplitude contribution from jitter at nt. Based on our observations and the physical cause of the distributions of s and j, both distributions are normal with zero mean and are independent and identically distributed for each nt and each waveform. The two distributions, one caused by jitter and the other by noise, can be dealt with independently. For noise, therefore, we can write:M, the median, mode, and mean of 〈s[nt]M〉 will yield the same value for a symmetric unimodal distribution, which is 〈s〉, where 〈s〉 = 0 for the situation described herein. This is not the case for the effect of jitter because the coefficient of j is signal (data) dependent [ waveform, just as signal averaging is presently implemented.The method we have examined to reduce the effects of noise and trigger jitter on the reconstructed waveform is based on taking the median value for each instant, σjit (this is discussed in Sec. 5). The median method causes the magnitudes of the local extrema to be underestimated. In Sec. 5, we provide an upper bound for this error and show that it is small compared to the amplitude of the aberration when the duration of the aberration is small compared to σjit. In [The median method has also been examined by other researchers ,8. As thσjit. In . No pulsσjit. In , consequpA, of the step was 1 amplitude unit (au); the rms additive noise, nσ, as a percentage of pA, ranged between 0 % and 50 % ; and the rms jitter, in units of a sampling interval (si), ranged from 0 si to 50 si. σjit = 50 si, nσ = 0.01 pA). σjit. K is the number of iterations (100 in this case), ns is the value of the step at index n, ns,ideal is the value of ideal step at index n, and N is the number of samples (1000 in this case). Since the ideal step had a 50 % duty factor and a low and high state value of 0 au and 1 au, its rms value is 0.707 au. Comparing this value to the rms error shown in We examined the median method for jitter and noise reduction by using simulations and experiments. In the first set of simulations, we used a jittered ideal step with additive noise and examined the error in the reconstructed waveforms as a function of noise and jitter. The additive noise and trigger jitter were both modeled as having a zero-mean normal distribution. The amplitude, t10–90, and overshoot, OS, were compared. The process of determining the pulse parameters starts with obtaining the bimodal histogram of the waveform amplitude values, per methods described in [In the second set of simulations, we used the same ideal step but filtered it with a Butterworth filter  to give the signal features (such as aberrations) typically observed in pulse waveforms. The pulse parameters of transition duration (between the instants corresponding to 10 % and 90 % of pulse amplitude), ribed in . The metribed in .t10–90 = 5.86 si and an OS of 0.08 au. Overshoot was examined because it is the aberration with the largest amplitude and would be the most obviously sensitive to jitter. The duration of the overshoot  is caused by the fact that both the positive and negative transitions of the overshoot contribute to the set of data values in the vicinity of the overshoot. This effect is more clearly shown in OST, (between 50 % reference levels) of the overshoot is approximately 5 si and the transition duration of the cumulative jitter distribution is 2.56 σjit. The results of σjit < OST. OS in the median reconstructed waveform is not sensitive to noise for nσ ≤ 0.1 au. The primary purpose for showing The noise-free waveform had a M, used in the simulation to compute the median for σjit = 2 si and 4 si. The generated waveforms were noise free. Each datum in M waveforms. The transition duration values show a monotonic decrease in value, for both levels of jitter, to the target value of 5.86 si. The overshoot values, on the other hand, do not exhibit a common monotonic change approaching the target value of 0.08 au. For σjit ≥ 4 si, increasing M does not result in the correct value of 0.08 au for overshoot. As discussed earlier, this is because the duration of the overshoot is less than the transition duration of the cumulative jitter distribution. For σjit = 2 si, on the other hand, the target value of 0.08 au for overshoot is obtained. M ≥ 512 is required to achieve convergence to the correct value. Furthermore, the variation in the values of overshoot and transition duration are significant for M < 128.M = 128 waveforms in each set of waveforms.Figs. 11tsys is negligible and, given the same pulse source, the difference in transition duration is caused by the difference in tinstr of the samplers.M, of waveforms used to compute the median on the value of transition duration. This is analogous to the simulation results shown in M waveforms is used in M waveforms were used to produce the results shown in M, namely one that is adequate to obtain reproducible results.OS) and undershoot (US) , fabb = 0.5/Tabb, and Tabb is the duration of the aberration. As a check for TPD = 0 (no jitter), then dmax = 0 (no uncertainty contribution from the jitter), as would be expected. On the other hand, if TPD = N/Tabb, where N is an integer, then dmax would approach Vabb as N increased. This also makes sense because as N increases, jitter would tend to cause the aberration to disappear in the observed waveform. The expanded uncertainties for OS and US should include dmax as well as contributions from other sources [Tabb. Each set of data was the result of the average of 256 waveforms. As shown in OS values. With respect to fidelity of reconstruction of the aberrations in the waveform, “monotonicity” is satisfied if σjit ≤ 0.2 Tabb. The value of k = 4 was used in OS for the noisier signals would be less than dmax. This method for finding a limit in the OS or US uncertainty when σjit/Tabb = 2 did not work because this amount of jitter effectively removed the aberration from the observed waveform.As mentioned earlier, a waveform reconstructed using the median method will exhibit an underestimate of the magnitude of aberrations. Typically, however, it is difficult to estimate the uncertainty in overshoot ( in Ref. ) of the eristics . Consequ sources . Simulattjit ≤ tinstr, the jitter deconvolution and median methods appear to work equally well for removal of jitter from the waveforms.A comparison of waveforms reconstructed using the median method and jitter deconvolution are shown in Figs. 19The simulation results of Secs. 2 and 3 and experimental results of Sec. 4 can be compared by multiplying the simulation horizontal axis values and corresponding waveform parameters by two and then changing the simulation horizontal axis units from si to ps. Accordingly, the jitter of 2 si becomes 4 ps and the simulated transition duration of 5.86 si becomes 11.72 ps, which corresponds to the transition duration of typical high-speed pulse generators. The simulated trigger jitter of 4 ps is at least three times larger than what is typically observed. Consequently, the difficulty in reconstructing the aberrations of a waveform that was experienced in the simulation would not be experienced in reconstructing actual signals used in waveform metrology. In general, the simulation and measurement results show that the median method for reducing the effects of trigger jitter and noise on the acquired waveform is effective. However, to accurately reproduce waveform aberrations, the duration of the aberrations should not be greater than the transition duration of the cumulative jitter distribution.


A high concentration of hyaluronic acid in pleural fluid is suggestive of malignant mesothelioma. However, a relatively high concentration of hyaluronic acid was also seen in the pleural fluid of patients with benign inflammatory diseases. To show the utility of measuring hyaluronic acid levels in pleural fluid to diagnose tuberculous pleurisy, we compared the clinical features and levels of hyaluronic acid in the pleural fluid of patients with and without tuberculous pleurisy.We enrolled 27 patients with infective pleurisy admitted at Teikyo University Hospital from January 2010 to December 2013. Ten patients were diagnosed with tuberculous pleurisy, and 17 with non-tuberculous pleurisy. We reviewed the clinical features and data of all 27 patients and compared the two groups. We analyzed and compared the concentration of hyaluronic acid and adenosine deaminase in their pleural fluid.3 ng/mL); 42.9 ± 23.3 vs. 16.8 ± 17.9, P = 0.003, adenosine deaminase (IU/L); 89.7 ± 33.3 vs. 74.0 ± 90.9, P = 0.032). Receiver operating characteristic analysis revealed no significant difference in the area under the curve of hyaluronic acid and adenosine deaminase volumes in pleural fluid, suggesting their equivalent value as major diagnostic tools for tuberculosis pleurisy.Patients with tuberculous pleurisy tended to have significantly higher concentrations of hyaluronic acid and adenosine deaminase in their pleural fluid (tuberculous pleurisy patients vs. other infective pleurisy patients: hyaluronic acid (× 10Hyaluronic acid concentration in pleural fluid can be a valuable tool for the diagnosis of tuberculous pleurisy. Hyaluronic acid is an anionic, non-sulfated glycosaminoglycan distributed widely throughout connective, epithelial, and neural tissues . In pleuA high concentration of hyaluronic acid (> 100 µg/mL) in pleural fluid is suggestive of malignant mesothelioma . In addiTuberculous pleurisy is one of the major manifestations of a tuberculosis infection. It is difficult to diagnose because the positive culture rate for tuberculosis in pleural fluid is usually low . TherefoIn this study, we analyzed the volume of hyaluronic acid in pleural fluid in patients of tuberculous pleurisy, to determine whether hyaluronic acid can aid in the diagnosis of tuberculous pleurisy.In all, 133 patients (> 18 years) with infective pleurisy that were enrolled for the study were admitted to the Teikyo University Hospital, a 1,200-bed teaching hospital in Tokyo, Japan, from January 2010 to December 2013.Infective pleurisy was diagnosed on the basis of a positive result on culture of pleural fluid and signs and symptoms of pleurisy, such as pleural fluid, fever, chest pain, cough, fatigue, disorientation, hypotension, and respiratory failure.Patients whose pleural fluid was collected for the analysis of adenosine deaminase, lactate dehydrogenase, and hyaluronic acid were included. In contrast, patients who had a history of respiratory malignancy and rheumatoid arthritis were excluded because previous reports have shown that respiratory malignancy including malignant mesothelioma could increase hyaluronic acid in pleural fluid .The endpoint of this study was to show whether hyaluronic acid in pleural fluid was significantly increased in patients with tuberculous pleurisy. In order to assess the diagnostic value of hyaluronic acid concentration in pleural fluid, it was compared with the level of adenosine deaminase.t-test and Fisher’s exact test were used to analyze continuous and categorical data where appropriate.The results are expressed as mean with standard deviation unless otherwise indicated. For univariate analysis, Student’s To assess and compare the diagnostic accuracy of hyaluronic acid and adenosine deaminase for discriminating patients with pleurisy from those with tuberculous pleurisy, we plotted receiver operating characteristic (ROC) curves and calculated the areas under the curves (AUC) for comparison. ROC curves were generated by plotting the relationship of the true positivity (sensitivity) and the false positivity (1 - specificity) at various cut-off points in the tests.All P-values were two-sided, and were considered statistically significant at a value of < 0.05.The Ethics Committee of the University of Teikyo approved this project.Only 33 of 133 met the inclusion criteria. Of them, six patients had histories of respiratory malignant diseases and were excluded. Twenty-seven patients were eventually analyzed: 10 had tuberculous pleurisy and 17 had non-tuberculous infective pleurisy.We summarized demographic data, clinical features, and pleural fluid laboratory data of all 27 patients in There were no significant differences in AUC between the two groups . The optOur study results showed that hyaluronic acid concentration in pleural fluid was significantly increased in patients with tuberculous pleurisy, and that the use of this measurement was non-inferior to that of adenosine deaminase concentration in pleural fluid for the diagnosis of tuberculous pleurisy.Hyaluronic acid can be produced from alveolar epithelial cells or mesothelial cells following local inflammation . Indeed,Some reports have indicated that the production of hyaluronic acid might be promoted by proinflammatory cytokines such as tumor necrosis factor alpha and interleukin 1 beta , 9. ThusMycobacterium tuberculosis in pleural fluid is relatively low [M. tuberculosis infection is often found in the elderly, in whom performing invasive procedures such as pleural biopsy is difficult; consequently, the measurement of adenosine deaminase concentration in pleural fluid has become a valuable diagnostic tool. Furthermore, some reports have shown that infective pleurisy not associated with tuberculosis, e.g., cryptococcosis and brucellosis pleurisy, could also result in a high concentration of adenosine deaminase in pleural fluid [Diagnosis of tuberculous pleurisy using pleural fluid is difficult because the positive culture rate of vely low , often nal fluid , 12. TheThe present study has several limitations. First, it was retrospective, single-center study. Second, the sample size was small, because only hospitalized adult patients with infective pleurisy who met the criteria were enrolled. However, the study results demonstrate for the first time that the measurement of hyaluronic acid concentration in pleural fluid might be a diagnostic aid for tuberculous pleurisy. Nevertheless, a larger-scale prospective study is required to confirm our findings.