We analyze the online response to the preprint publication of a cohort of 4,606 scientific articles submitted to the preprint database arXiv.org between October 2010 and May 2011. We study three forms of responses to these preprints: downloads on the arXiv.org site, mentions on the social media site Twitter, and early citations in the scholarly record. We perform two analyses. First, we analyze the delay and time span of article downloads and Twitter mentions following submission, to understand the temporal configuration of these reactions and whether one precedes or follows the other. Second, we run regression and correlation tests to investigate the relationship between Twitter mentions, arXiv downloads, and article citations. We find that Twitter mentions and arXiv downloads of scholarly articles follow two distinct temporal patterns of activity, with Twitter mentions having shorter delays and narrower time spans than arXiv downloads. We also find that the volume of Twitter mentions is statistically correlated with arXiv downloads and early citations just months after the publication of a preprint, with a possible bias that favors highly mentioned articles. The view from the “ivory tower” is that scholars make rational, expert decisions on what to publish, what to read and what to cite. In fact, the use of citation statistics to assess scholarly impact is to a large degree premised on the very notion that citation data represent an explicit, objective expression of impact by expert authors Recent efforts have investigated the effect of the use of social media environments on scholarly practice. For example, some research has looked at how scientists use the microblogging platform Twitter during conferences by analyzing tweets containing conference hashtags http://arxiv.org), a service managed by Cornell University Library, which has become the premier pre-print publishing platform in physics, computer science, astronomy, and related domains. We examine the relations between three types of responses to the submissions of this cohort of pre-prints, namely the number of Twitter posts (tweets) that specifically mention these pre-prints, downloads of these pre-prints from the arXiv.org web site, and the number of early citations that the 70 most Twitter-mentioned preprints in our cohort received after their submission. In each case, we measure total volume of responses, as well as the delay and span of their temporal distribution. We perform a comparative analysis of how these indicators are related to each other, both in magnitude and time.We study how the scientific community and the public at large respond to a cohort of preprints that were submitted to the arXiv database . As for citation data, we are aware that citations take years to accrue. We do not explore here long-term citation effects, but only the early, immediate response to pre-print submission in the form of citations in the scholarly record. Our citation data pertains to a time period that spans from 5 months to 1 year: it is a fraction of the expected amount of “maturation time” for citation analysis. Citation data must therefore be considered to reflect “early citations” only, not total potential citations.By the nature of our research topic, we are particularly focused on Twitter mentions and arXiv downloads may follow particular temporal patterns. For example, for some articles downloads and mentions may take weeks to slowly increase after submission, whereas for other articles downloads may increase very swiftly after submission to wane very shortly thereafter. The total number of downloads and mentions is orthogonal to these temporal effects, and could be different in either case.delay and the time span, which we define as follows. Let The two parameters that we use to describe the temporal distributions of arXiv downloads and Twitter mentions are The delay, To distinguish between the delay and span of arXiv downloads and twitter mentions, we simply denote As shown in post hoc, ergo propter hoc point of view, in this case the Twitter response occurs immediately and nearly exactly before the peak in arXiv reads, suggesting that social media attention may have led to subsequently higher levels of arXiv downloads.To illustrate delay and span, we examine in detail the response dynamics for an article in the corpus, in In this section, we present three results: descriptive statistics of arXiv downloads and Twitter mentions, a temporal analysis of time span and delay in arXiv downloads and Twitter mention, and a regression analysis between arXiv downloads, Twitter mentions, and early citations. For the descriptive statistics, we keep all 5,752 tweets and 4,415 articles mentioned on Twitter, since we want to show a full picture of our data. For the subsequent temporal and regression analysis we only focus on the 2,800 tweets and 1,710 arXiv articles mentioned by non-bot accounts to avoid spurious effects introduced by automated bot accounts.Some descriptive statistics about the datasets analyzed in this article are presented in In order to better understand how Twitter mentions vary across domain, we show the Complementary Cumulative Distribution Functions (CCDF) of Twitter mentions for all articles in the five most frequently observed subjects domains of In From We investigate the degree by which article citations, denoted Given that each article could have been submitted at any time in a 5 month period, i.e. October 4, 2010 to March 1, 2011, on September 30, 2011 some articles could have had 5 more months than others to accumulate early citations. Therefore the citation counts observed on September 30, 2011 may be biased by the submission date of the article in question. We must therefore include the amount of time that an article has had to accumulate citations since their submission date as an independent variable in our regression models.Let From In The ongoing move to online scholarly communication has introduced new possibilities for measuring scholarly impact. At the same time, it has become more difficult to determine which communities drive a particular form of online impact. For example, usage data, measured as volume of downloads, is generally assumed to reflect the interests and preferences of the general public, but what if the particular online service for which usage data was recorded is dedicated to serving scientists only? What if an online service for scientists increasingly becomes a tool for the general public to learn about scientific findings? The online user communities associated with particular services may in fact overlap to various degrees as the scholarly community progressively moves online and the online public moves toward scholarly information services. Naturally, scholarly impact metrics should acknowledge this new reality.The research presented in this paper is based on data from two services which are arguably associated with and intended for two different audiences. ArXiv.org is focused on offering scientists an online platform to publish pre-prints. Twitter is designed to serve as a micro-blogging services for the public. In this study we did, however, not attempt to conceptualize arXiv downloads solely as scientific impact, and Twitter mentions solely as public chatter. Rather, we measured the correlation and temporal differences between these forms of responses, working under the assumption that these services naturally have overlapping and interacting user communities.Our results, though preliminary, are highly suggestive of a strong tie between social media interest, article downloads, and even early citations. We find that Twitter mentions and arXiv downloads of scholarly articles follow two distinct temporal patterns of activity, with Twitter mentions having shorter delays and narrower time spans than arXiv downloads. We also find that volume of Twitter mention is statistically correlated with that of both downloads and “early” citations, i.e., citations in the scholarly record occurring less than 7 months after the publication of a preprint.We can think of two possible explanations for these results. First, the manner in which Twitter mentions, arXiv downloads and article citations are correlated could indicate a causal relation. Scholars are increasingly exposed to social media such as Twitter, and therefore their scholarly download and citation behavior is unavoidably affected. A paper submitted to arXiv that happens to receive high levels of mentions in social media will, as a result, receive greater exposure among both the general public and scholars. As a consequence, it will receive greater levels of scholarly interest, and higher volumes of downloads and subsequent citations. Our results indeed indicate that early Twitter mentions of a paper seem to lead to more rapid and more intense download levels and subsequently higher citation levels. Second, an equally plausible, alternative explanation for our results lies in the intrinsic quality or popular appeal of different manuscripts. A manuscript of greater quality or appeal, either among the public or the scholarly community, will by virtue of this characteristic enjoy higher levels of mentions on Twitter, higher levels of downloads on arXiv, and higher levels of later citations. As a result these indicators will seem to be correlated, and even causative of each other.We therefore acknowledge that these observations can be the result of a number of distinct or overlapping factors which our methodology confounds and fails to distinguish. Consequently, we caution against drawing the unwarranted conclusion that these results indicate that the scholarly impact of an article can be fully determined by its social media coverage, nor that one could increase the citation rate of an article by merely tweeting about it. The fact that some correlation – no matter how small – was observed between social media coverage, usage, and early citations may nevertheless indicate that the scientific communication process is increasingly affected by the growing societal importance of social media. In future research we will therefore continue to focus on unraveling the potential mechanisms that tie these various factors together. These efforts might shed light on whether and how social media is becoming a component of academic and scholarly life.Our process of determining whether a particular arXiv article was mentioned on Twitter consists of three phases: crawling, filtering, and organization. Tweets are acquired via the Streaming API from Twitter Gardenhose, which represents roughly 10% of the total tweets from public time line through random sampling. We collected tweets whose date and time stamp ranges from 2010-10-01 to 2011-04-30 which results in a sample of 1,959,654,862 tweets.The goal of the data filtering process is to find all tweets that contain a URL that directly or indirectly links to any arXiv.org paper. However, determining whether a paper has or has not been mentioned on Twitter is fraught with a variety of issues, the most important of which is the prevalence of partial or shortened URLs. Twitter imposes a 140 character limit on the length of Tweets, and users therefore employ a variety of methods to replace the original article URLs with alternative or shortened ones. Since many different shortened URLs can point to the same original URLs, we resolve all shortened URLs in our Twitter data set to determine whether any of them point to the articles in our arXiv cohort.We distinguish between four general types of scholarly mentions in Twitter, based on whether they contain:a URL that directly refers to a paper published in arXiv.org.a shortened URL that upon expansion refers to an arXiv.org paper.a URL that links to a web page, e.g. a blog posting, which itself contains a URL that points to an arXiv.org paper.a shortened URL that links to a type (3) mention after expansion.In order to detect these four types of Twitter mentions, we first expand all shortened URLs in our crawled public tweets. We select the top 16 popular URL shortening services, including bit.ly, tinyurl.com, and ow.ly, and expand the shortened URLs in our collection of tweets using their respective APIs. As such, we resolved 98,377,880 short URLs, which were mostly generated by the following URL shorteners: bit.ly (61.3%), t.co (15.2%), fb.me (6.5%), tinyurl.com (6.1%) and ow.ly (4.4%).  From the resulting set, we retain all tweets that contain the term ‘arXiv’ and at least one URL. Next, we associate tweets to arXiv papers by extracting the arXiv ID (substrings matching ‘dddd.dddd’) from any papers mentioned in those tweets. .


In this study, quantitative PCT rCBF measurements in patients with brain tumours were compared to the gold standard PET rCBF with 15O-H2O PET and once with PCT performed over the central part of the tumour. Matching rCBF values in tumour and contralateral healthy regions of interest were compared.On the same day within a few hours, rCBF was measured in ten adult patients with treatment-naïve primary brain tumours, twice using −1 100 ml−1 for PET and 78.9 ± 41.8 ml min−1 100 ml−1 for PCT. There was a significant method by tumour grade interaction with a significant tumour grade rCBF difference for PCT of 32.9 ± 15.8 ml min−1 100 ml−1 for low-grade (WHO I + II) and 81.5 ± 15.4 ml min−1 100 ml−1 for high-grade (WHO III + IV) tumours, but not for PET. The rCBF PCT and PET correlation was only significant within tumours in two patients.PCT overestimated intratumoural blood flow in all patients with volume-weighted mean rCBF values of 28.2 ± 18.8 ml min15O-H2O PET in brain tumours.Although intratumoural blood flow measured by PCT may add valuable information on tumour grade, the method cannot substitute quantitative measurements of blood flow by PET and  The recent technical developments in fast multidetector CT and commercial software solutions have enabled a fast, relatively simple, practical and available approach to assess essential parameters of vascular physiology, namely regional cerebral blood flow (rCBF), regional cerebral blood volume (rCBV) and the permeability-surface area product (PS)—a measure of blood-brain barrier permeability . This te15O-labelled water . Also, we performed unpaired ma Table  and unus2 did not change significantly between the PET and PCT studies. The PaCO2, however, dropped significantly by 0.5 kPa, or 4 mmHg, and the PaO2 increased by 1.9 kPa or 14 mmHg from PET scan to PCT scan. This indicates hyperventilation during performance of PCT.Eight patients were included in the blood gas analysis Table . For the−1 100 ml−1 for PET and 78.9 ± 41.8 (range 14.7–142.2) ml min−1 100 ml−1 for PCT  or high (III–IV) WHO graded gliomas measured at 18.4 ± 8.3 and 21.5 ± 7.0 ml min−1 100 ml−1, respectively (Table p <0.01) in perfusion with values of 32.9 ± 15.8 and 81.5 ± 15.4 ml min−1 100 ml−1 for low and high grade, respectively. Both grey and white matter values were extracted from the patients’ scans . In tumours, volume-weighted mean rCBF values were 28.2 ±18.8 (range 12.1–73.2) ml minp < 0.005; low grade 1.1 ± 0.4; high grade 2.7 ± 0.5), but not for PET , a significant main effect of grade (high grade > low grade) and a significant interaction effect (technique × grade) Fig. . The grar2 = 0.5, slope 1.6; intercept 32.6 ml min−1 100 ml−1; Fig. r2 = 0.7; slope 1.6; intercept 1.0). A Bland-Altman plot over each individual ROI shows bias for PCT rCBF values, which increases with increasing tumour rCBF values , who had a tumour CBF of 45.9 ml min−1 100 ml−1. For the neuroepithelial tumours, results are reported with more variation [−1 100 ml−1, respectively [Other research groups have quantified blood flow of different tumour types of the brain with -H2O PET . Meningiariation –26, and ariation . This isectively .In a study of the clinical applicability of CBF estimated with PCT in patients with gliomas, the normalized values of CBV correlated with tumour grade . Their n18F-fluorodeoxyglucose (FDG) PET scans of brain tumours, and it can be expected to pose a similar problem when identifying angiogenically active tumour tissue with PCT or MRI.Evaluating the relative distribution between rCBF values, there was generally speaking a good match between both PET and PCT images in patients with low-grade glioma Fig.  as well There is evidence to suggest that high angiogenic activity in itself, measured by rCBF with MRI, has predictive information of tumour progression or death independent from tumour grade . The patOn the other hand, PCT and GE-EPI that embrace all, even nonfunctional, vascular segments might add value in tumour grading –31 and oPaCO2 of 4 mmHg would lower the rCBF in grey matter in the order of 8 % [PaCO2 level [2, however, with an augmented response to hypocapnia compared to normal grey matter [As an integrated part of the validation between PCT and PET, various error sources need to be considered. PET and PCT were performed within 1–4 h on the same day and always with PET performed before PCT for practical reasons. We do not have to consider a day-to-day measurement variance pertaining to variations in e.g. antiepileptic or prednisone drug concentration. However, an order effect cannot be excluded. The arterial blood gas analysis Table  showed ar of 8 %  and an er of 8 % . In animO2 level , albeit O2 level . Furthery matter . Whether15O-H2O PET adheres to the fundamental differences between a CT non-diffusible intravascular iodinated medium and a PET freely diffusible tissue tracer. “Perfusion” refers to blood flow per unit functional tissue mass. This would correspond to the attenuation signal that in PCT derives from capillaries. However, because of the inherent limitations of resolution the capillary signal cannot be discriminated from flow in larger vascular structures up- or downstream from the capillary level. These structures are not functional to the tumour tissue, in the sense that there is no exchange of nutrients, medication or tumour-derived humoral factors etc. across the vessel walls, and their presence in the imaging field will increase perfusion measurements erroneously. Adding to the difficulties is the known chaotic and bizarre architecture of blood vessels produced within tumours by chronically activated angiogenesis and an unbalanced mix of proangiogenic signals. Tumour neovasculature is characterized by precocious capillary sprouting, convoluted and excessive vessel branching, distorted and enlarged vessels, erratic blood flow, micro-haemorrhaging, leakiness and arteriovenous shunts [15O-labelled water. So although the rCBF PCT and the PET ROIs are identical and thus influenced equally by the tumour tissue heterogeneity, a purely intravascular tracer will overestimate perfusion because of nonfunctional vascular structures, while a freely diffusible tissue tracer will underestimate perfusion because nonfunctioning tissue such as cysts are interpreted as tumour tissue.An important factor to explain the quantitative and qualitative difference between PCT and s shunts . Thus, es shunts . In fact15O-H2O PET showed that PCT could not be used for absolute measurements of tumour blood flow.In conclusion, our study on gliomatous tumour rCBF determined by PCT compared to the gold standard


The quantification of social media impacts on societal and political events is a difficult undertaking. The Japanese Society of Oriental Medicine started a signature-collecting campaign to oppose a medical policy of the Government Revitalization Unit to exclude a traditional Japanese medicine, “Kampo,” from the public insurance system. The signature count showed a series of aberrant bursts from November 26 to 29, 2009. In the same interval, the number of messages on Twitter including the keywords “Signature” and “Kampo,” increased abruptly. Moreover, the number of messages on an Internet forum that discussed the policy and called for signatures showed a train of spikes.In order to estimate the contributions of social media, we developed a statistical model with state-space modeling framework that distinguishes the contributions of multiple social media in time-series of collected public opinions. We applied the model to the time-series of signature counts of the campaign and quantified contributions of two social media, i.e., Twitter and an Internet forum, by the estimation. We found that a considerable portion (78%) of the signatures was affected from either of the social media throughout the campaign and the Twitter effect (26%) was smaller than the Forum effect (52%) in total, although Twitter probably triggered the initial two bursts of signatures. Comparisons of the estimated profiles of the both effects suggested distinctions between the social media in terms of sustainable impact of messages or tweets. Twitter shows messages on various topics on a time-line; newer messages push out older ones. Twitter may diminish the impact of messages that are tweeted intermittently.The quantification of social media impacts is beneficial to better understand people’s tendency and may promote developing strategies to engage public opinions effectively. Our proposed method is a promising tool to explore information hidden in social phenomena. Much commentary on the impact of social media, such as Twitter, on societal, political, and medical events exists To measure such effects on a time-series of collected public opinions, we developed a statistical model that estimates the contributions of multiple social media. We applied it to the data of a recent signature-collecting campaign to oppose a medical policy in Japan and succeeded in detecting the impacts of Twitter and an Internet forum.On November 20, 2009, the Japanese Society of Oriental Medicine and some patients started a website ny  indicates the nth hour, starting from 19∶00 on November 16, 2009 (n = 1) and ending at 23∶00 on November 30, 2009 (n = N = 341). The original messages on Twitter were obtained from the web site 263y and 264y, which correspond to the two hours from 17∶00 to 19∶00 on November 27, are set as missing observations for the analysis in order to avoid a harmful influence for the estimation because a malfunction of the web server for the signature collection campaign that was probably induced by a surge of accesses to the site largely impeded to collect the signatures; the actual numbers of signatures were only 32 and 588 for the two time points, which were much smaller than those of just before and after the period. Our analysis method can deal with missing observations properly by a Bayesian estimation with Kalman filter algorithm.Three observed time-series data are used in this analysis: hourly counts of signatures, yn ; Twitteryn , respectively. The observation noise component is modeled by a normal distribution: To estimate the impact of the Twitter and Internet forum messages on the number of signatures, we assume that the time-series can be modeled by.nb is a component representing a smooth variation in the number of signatures collected from people influenced by effects other than Twitter and the Internet forum. The component is modeled by the second-order stochastic difference equation nb is modeled by the similarity of slopes in a sequence of time-points, i.e., The baseline effect nt is a component of the contributions of people affected by the Twitter signature collection campaign. It is assumed to be proportional to the number of messages nϕ as follows:The Twitter effect n.nu is a component of the contributions of people affected by the Internet forum in signature collection campaign; it is modeled in the same manner as the Twitter effect, with the number of the messages The forum effect n.n, tn, un,b and nw in the decomposition model of the observed time-series of signatures (ny) (To estimate each of the components res (yn) , we convThe signature count exhibited small variations until November 25; however, from November 26 to 29, it showed a series of aberrant bursts . In the We quantified the impacts of social media on the campaign using the statistical model. A total of 95,362 signatures were gathered on the web. 43,190 were obtained in only four days–from Nov. 27 to Nov. 30, 2009. We decomposed the time-series of signatures into a Twitter effect, Forum effect, and baseline effect ; the latThe cumulative profiles of the observed number of signatures and estimated contributions of the Twitter and Forum effects suggest that the latter could explain a large portion of the observed signatures (78%) during the period . The totThe first surge in signature numbers occurred on Nov. 27 between 1 and 3 AM, off-peak internet hours . At the Although the numbers of messages on both Twitter and the Internet forum were comparably small during the last burst of November 28, the estimated Forum effect was larger . TwitterThere are several limitations to our study. First, we analysed data tweeted in Japanese and limted to Japan only. The performance of our model may be biased and suffer. Second, the demographic of Twitter population that would tweet about “Kampo” may not represent the general population, especially the population that would provide their names and addresses for the petition. Third, we did not analyse tweets and signatures across geography. Creating a “mashup” In conclusion, quantification of impacts of social media on a medical campaign is beneficial to better understand people’s tendency and may promote developing strategies to engage public opinions effectively. Our proposed method is a promising tool to explore big-data information


Follicular fluid was aspirated during oocyte retrieval from 20 infertile patients who developed moderate-to-severe OHSS and 20 age- and body mass index-matched normoresponders. BCAA and tyrosine concentration were measured using enzymatic methods. The follicular fluid BCAA concentration was similar between the two groups (P=0.55), whereas tyrosine concentration was significantly lower in the OHSS compared to that in the normoresponder group (P=0.027) and the BCAA/tyrosine ratio was significantly higher in the OHSS compared to that in the normoresponder group (P=0.034). These results suggest an association between low follicular fluid tyrosine concentration and OHSS. Dopamine receptor agonists may be used as potential anti-OHSS medicines and tyrosine, as a dopamine precursor, may play a role against the development of OHSS.The aim of this study was to compare the branched-chain amino acid (BCAA) and tyrosine concentration in the follicular fluid of infertile women with and without ovarian hyperstimulation syndrome (OHSS) in an  Ovarian hyperstimulation syndrome (OHSS) is one of the serious complications that occur in ovulation induction cycles during infertility treatment or, occasionally, following conception. OHSS is characterized by cystic ovarian swelling and shifting of fluid from the circulation to the third space. Human chorionic gonadotropin plays a central role in the onset of OHSS. Human chorionic gonadotropin increases chemokine production in the granulosa and theca cells surrounding the ovulating oocytes. These chemokines bind to their specific receptors, expressed on local and systemic endothelial cells, which, in turn, transactivate vascular endothelial growth factor receptor-2 and its signaling pathway. This signaling stimulates polymerization of cytoskeletal actin and phosphorylation of adherens junction and tight junction molecules in endothelial cells. These molecular events induce conformational changes in the endothelial cells and vascular hyperpermeability. In its severe form, OHSS potentially leads to life-threatening conditions, such as massive ascites, pleural effusion, oliguria and thrombosis/embolism following hemoconcentration .in vitro fertilization program.Infertile women with polycystic ovarian syndrome (PCOS) are at a high risk for developing OHSS in ovulation induction protocols . PCOS isin vitro fertilization/intracytoplasmic sperm injection cycles  and all the subjects provided informed consent.2 test.The values were compared between the OHSS and the normoresponder group using the two-tailed Student’s t-test, non-parametric Mann-Whitney U test, or Pearson’s χThere were no significant differences in demographics between the patients and the normoresponders, including infertility period, infertility category (85% primary infertility rate in the OHSS and 80% primary infertility rate in the normoresponder group) and the values of basal pituitary hormones and estradiol . HoweverThe incidence of PCOS was significantly higher in the OHSS (40%) compared to that in the normoresponder group . The number of oocytes retrieved, fertilization, blastocyst achievement and blastocyst freezing rates were also significantly higher in the OHSS compared to those in the normoresponder group (P<0.011), as were the cumulative clinical pregnancy and ongoing pregnancy rates within three transfer cycles (P<0.0004). There were no fatal complications in neither of the two groups.The follicular fluid BCAA concentration was similar between the two groups (P=0.55), whereas the tyrosine concentration was significantly lower in the OHSS compared to that in the normoresponder group P=0.027, . CorrespIn this study, the follicular fluid BCAA concentration was found to be similar between infertile patients with and those without OHSS. In the Japanese population, the incidence of obesity in infertile women with PCOS is significantly lower compared to that reported in Caucasian populations . The incThe follicular fluid tyrosine concentration, which was measured as a standard control protein, was found to be significantly lower in OHSS patients compared to that in normoresponders. In the human body, tyrosine is an amino acid which is synthesized from phenylalanine. The ovary is considered to be one of the active sites of tyrosine synthesis . TyrosinDopamine receptor agonists, such as docarpamine and cabergoline, are attracting increasing attention as promising OHSS-preventive agents . The binIn conclusion, our results suggest an association between low follicular fluid tyrosine level and the onset of OHSS. As in the serum, tyrosine concentration in the follicular fluid is easily measurable in the clinical practice and may be a potential predictive marker of OHSS.


In this paper, a novel integrated structure is proposed in order to reduce the axial length of the high speed of a magnetically suspended motor (HSMSM) to ensure the maximum speed, which combines radial displacement sensor probes and the permanent magnet biased radial magnetic bearing in HSMSM. The sensor probes are integrated in the magnetic bearing, and the sensor preamplifiers are placed in the control system of the HSMSM, separate from the sensor probes. The proposed integrated structure can save space in HSMSMs, improve the working frequency, reduce the influence of temperature on the sensor circuit, and improve the stability of HSMSMs. As a result, the performance of electronic components such as amplifiers, resistors, capacitors and diodes of the preamplifier circuit are easily affected by temperature, and they easily burn in the usual environment of vibrations and moisture. Therefore, existing eddy current sensors are not good with respect to compactness, environmental adaptability and so on.In this paper, a novel integrated structure is proposed, which combines the radial displacement sensor with eddy current and the radial magnetic bearing with permanent magnet bias. It takes advantage of the space in the radial magnetic bearing, and the sensor probes are placed on the adjacent stator poles in the axial direction. The preamplifier circuit is placed outside the HSMSM and uses shielded cables to connect the sensor probes. Therefore, the axial size of the HSMSM can be effectively reduced and the modal shape of the rotor can be increased. More importantly, the preamplifier circuit is not directly affected by the temperature. The prototype integrated structure was manufactured and verified by experimental tests, and the performance of the sensor is improved in aspects such as the accuracy of detecting displacements, linearity and temperature drift.2.The proposed novel structure is composed of displacement sensor probes, a permanent magnet biased radial magnetic bearing and an external signal processing circuit. A three-dimensional diagram and exploded view of the proposed novel structure is shown in 0 of sensor probe T1 and the central position A10 of the magnetic pole is equal to the distance between T10 and A40, as well as the distance between T10 and A50 and the distance between T1o and A8o, as shown in T2o, A1o = LT2o, A2o = LT2o, A5o = LT2o, A6o, LT3o, A2o = LT3o, A3o = LT3o, A6o = LT3o, A7o, and LT4o, A3o = LT4o, A4o = LT4o, A7o = LT4o, A8o, as shown in In In practical fact, a pair of differential output probes is composed by sensor probes  as well as the other sensor probes . As shown in As shown in As shown in As we know, the principle of the eddy current displacement sensor is the mutual inductance effect between a high frequency current in coils and detector. Therefore, the detector material has an important influence on the sensitivity and precision of the displacement sensor. Steels such as 45# or 40Cr can often be used considering their stability.3.The 3D FEM analyses are shown in 4.The photograph of the proposed novel structure is shown in The relation between displacement and output voltage is shown in The relationship between the temperature of the sensor probe and the coil current of a channel is shown in the online experiment in 5.A novel integrated structure to reduce the axial length and save space in a HSMSM is proposed, which combines a radial displacement sensor and a radial magnetic bearing. The whole axial length can be reduced 9% in a 4 kW HSMSM system, and the first bending mode will increase about 16%, as well as the maximum critical speed. The presented integrated structure separates the sensor probes from their circuit. Through an analysis of the magnetic field, it is determined that it is weak at the sensor probes, so they are not be influenced by the magnetic field produced by the radial magnetic bearing. A prototype of the proposed integrated structure is manufactured and it has good linear displacement characteristics as well as temperature characteristics according to experimental tests.


The organization listed as providing the grant indicated in the Funding section is incorrect. The correct organization is: FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo).


Alpha-smooth muscle actin+ (α-SMA+) pericytes and/or fibroblasts surrounded these well-vascularized tumour deposits. Using the murine bowel mesentery as an accessible mouse peritoneal tissue that could be easily imaged, and two different transplantable models, we found multiple microscopic tumour deposits after i.p. injection of malignant cells. Attachment to the peritoneal surface was rapid (6–48 h) with an extensive CD45+ leukocyte infiltrate visible by 48 h. This infiltrate persisted until end point and in the syngeneic murine ID8 model, it primarily consisted of CD3+ T lymphocytes and CD68+ macrophages with α-SMA+ cells also involved from the earliest stages. A majority of tumour deposits developed above existing mesenteric blood vessels, but in avascular spaces new blood vessels tracked towards the tumour deposits by 2–3 weeks in the IGROV-1 xenografts and 6 weeks in the ID8 syngeneic model; a vigorous convoluted blood supply was established by end point. Inhibition of tumour cell cytokine production by stable expression of shRNA to CXCR4 in IGROV-1 cells did not influence the attachment of cells to the mesentery but delayed neovascularization and reduced tumour deposit size. We conclude that the multiple peritoneal tumour deposits found in HGSC patients can be modelled in the mouse. The techniques described here may be useful for assessing treatments that target the disseminated stage of this disease. Copyright © 2012 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.High-grade serous ovarian cancer (HGSC) disseminates early and extensively throughout the peritoneal space, causing multiple lesions that are a major clinical problem. The aim of this study was to investigate the cellular composition of peritoneal tumour deposits in patient biopsies and their evolution in mouse models using immunohistochemistry, intravital microscopy, confocal microscopy, and 3D modelling. Tumour deposits from the omentum of HGSC patients contained a prominent leukocyte infiltrate of CD3 There may be controversy about the cell and tissue origin of high-grade serous ovarian cancer (HGSC) There is as yet no appropriate genetic model of HGSC that accurately recapitulates the p53 mutation and general genomic instability characteristic of this malignancy et alIGROV-1 (NCI) and the ID8 (MOSEC) malignant cells  were used for this study. IGROV-1 cells were cultured in RPMI with 10% FCS and ID8 cells were cultured in DMEM with 4% FCS and insulin transferrin sodium selenite supplement . The human cell line most recently underwent 16 loci STR authentication  in September 2011. Both cell lines were infected with a lentiviral eGFP construct (pHRSIN-CSGW-dNotI) to express an eGFP construct , as described previously by Kulbe 6 cells) and ID8 (1 × 107 cells) were injected in 0.3 ml of sterile, endotoxin-free PBS. The animals would reach UKCCCR guidelines for end-point tumour burden ICRF nude mice (Cancer Research UK) were used for the IGROV-1 experiments. C57 Black6 mice  were used for all ID8 experiments. Briefly, IGROV-1  or streptavidin Alexa 633 conjugated to biotin-tagged Lycopersicon esculentum lectin  under terminal anaesthesia. The animals were then killed via cardiac perfusion of a 4% paraformaldehyde (PFA) solution. The mesenteric tissue was removed, washed, and fixed in 4% PFA for 1 hour. The tissue was then blocked and permeabilized in a blocking solution of 3% goat serum, 1% BSA, and 1% Triton-X100. Staining for a variety of cell markers was achieved using both fluorophore-conjugated primary antibodies  and primary antibodies with fluorescently labelled secondary antibodies . MRP14 was used as a tissue neutrophil marker as it labels calcium-binding proteins highly expressed on neutrophils and monocytes. Tissues were washed in PBS and arranged on a slide. The intestine was then removed before embedding the tissue onto the slide in Prolong Gold (Invitrogen). The mesenteric tissue was imaged on both an Ariol computer-controlled microscope  and image analysis system and a Zeiss LSM 510 confocal microscope.Functional blood vessel labelling was achieved by injecting (i.v.) 100 µl of 2 mg/ml lectin solution   and CD3   using a Dako Autostainer Plus with a Super Sensitive Polymer—HRP DAB . Neutrophil elastase (a marker for human neutrophils) (Dako) and α-SMA (a marker for fibroblasts and pericytes) (Sigma) staining was visualized with a biotinylated HRP secondary antibody and DAB staining (Vector Labs). Quantification of the tumour-infiltrating inflammatory cells was determined using the Ariol computer-controlled microscope (Leica Microsystems) and image analysis system. Six of the samples were obtained from patients who had interval debulking surgery after three cycles of carboplatin and paclitaxel chemotherapy; two were obtained from patients undergoing primary debulking surgery.+ lymphocytes and CD68+ macrophages, but there were few neutrophil elastase-positive cells. The deposits were well-vascularized and had many α-SMA-positive pericytes and fibroblasts , fibroblasts, and pericytes (α-SMA—red) with eGFP-positive malignant cells. We next used confocal microscopy to further study the development of blood vessels in the peritoneal deposits, staining the mesothelium for functional blood vessels  .The results described above show that we can model the development of peritoneal deposits of ovarian cancer in mice and that the leukocyte populations resemble those seen in human HGSC. We therefore asked if we could use our methods to measure changes in tumour development. We chose to assess the effects of manipulating expression of the chemokine receptor CXCR4 on peritoneal tumour deposits, as we, and others, have found this receptor to be important in ovarian cancer growth and spread < 0.001) A and 6B.< 0.001) C. We alsr volume D and 6E et alTraditional concepts of metastasis may not apply to some types of ovarian cancer. In HGSC, there is no anatomical barrier to seeding throughout the peritoneal cavity. At the earliest detectable stage, the disease may have already formed multiple small peritoneal deposits and these can probably be generated from a primary cancer no larger than a few centimetres + lymphocytes in solid tumour islets predicts longer survival + T-regulatory cells, which can mediate immune suppression, predict poor patient survival The infiltrating leukocytes found in primary human HGSC tumours are related to both good and bad prognosis, depending on subtype. Increased infiltration of CD8+ and NE+ infiltrate but there did seem to be fewer CD68+ cells in the treated patients.Six of the eight patients studied had already received three cycles of chemotherapy. As a majority of HGSC patients in the UK are now pretreated before surgery, we could only obtain two chemo-naïve samples. We could see no difference between untreated and treated in terms of deposit size, location or the CD3in vivoWe believe that any pre-clinical studies of HGSC in mouse models should study peritoneal spread, as this is the major clinical challenge. There is evidence for a cancer stem cell (CSC) population in HGSC with the capacity for both self-renewal and generating heterogeneous lineages


In our Trust, in line with NICE guidance, all breast cancers undergo preoperative axillary ultrasound and, where indicated, needle testing. The current overall sensitivity of this process in our Trust is 57%. This study investigates tumour size, type and location as possible factors for patient triage to repeat preoperative axillary staging, aiming to increase our preoperative axillary staging sensitivity.This prospective study included all patients diagnosed and operated on for unifocal breast cancer in our Trust from September to December 2010. Descriptive statistics, chi-squared and logistic regression were performed upon data collated at MDT meetings.P < 0.0005). No significant difference was found in nodal disease risk between the histological tumour types, although numbers of lobular and tubular carcinomas were small (P = 0.633). Data suggested a difference  in probability of axillary node metastasis with tumour location with 95% confidence limits.Logistic regression of 101 females implies that as tumour size increases by 1 cm, nodal disease risk is 1.75 times larger, with 95% confidence limits. Comparing tumours <20 mm with those >20 mm, nodal disease risk is 5.818 times larger in the >20 mm group (This study demonstrates a clear and statistically significant association between tumour size and nodal disease. The data also suggest a difference in probability of nodal disease with different tumour location, although increased patient numbers are needed to confirm this. A larger trial for stratifying patients for single or double preoperative staging of the axilla is recommended.


There was an error in a grant number in the funding statement. R01NS073670-01 should be R01 NS073671-01.


Profound hemodynamic changes seen in acute liver failure (ALF) resemble those found in later stages of septic shock. Vasopressor support is frequently required and indiscriminate fluid resuscitation can worsen intracranial hypertension (ICH) and lung injury. Markers of preload dependency have thus far not been studied in this patient group and response to dynamic manoeuvres such as passive leg raising or end expiratory hold cannot be considered safe due to the high incidence of ICH.ALF patients admitted to a tertiary specialist ICU in vasoplegic shock, requiring multiorgan support including controlled mechanical ventilation, had their cardiac output monitored via transpulmonary thermodilution and pulse contour analysis (PiCCO). Markers of fluid responsiveness were compared between responders (CI ≥15%) and nonresponders to a colloid fluid challenge (5 ml/kg IBW). All patients had a transthoracic echocardiogram performed before and after fluid administration. The predictive capacity of stroke volume, pulse pressure variation  and respiratory change in peak aortic velocity ΔV peak for preload dependency was analyzed.R = 0.726, P < 0.001). There was no difference between those defined as responders using a cut-off value of CI or SVI of 10%. When using 15%, seven patients would have been classified differently. The intraclass correlation coefficient for CI and SVI change was 0.83 (0.62 to 0.92), confirmed using Pasing and Blakock regression , suggesting hemodynamic changes in both measures are interchangeable. Using a cut-off value of a change in CI of 15%, only PPV predicted fluid responsiveness . SVV weakly predicted fluid responsiveness in this cohort . While there was a trend toward reduction in ΔV peak  this was not different between those defined as fluid responders by CI (repeated-measures ANOVA P = 0.124) and ΔV peak prior to fluid bolus did not predict a CI response .Twenty-six patients (mean age 40 (13), 15 male:11 female) were assessed, mean APACHE II 23 (4) and SOFA 15 (2). Changes in CI and SVI were closely correlated (Baseline PiCCO parameters predict fluid responsiveness but the respiratory variability in ΔV peak did not predict a CI response to fluid bolus in this cohort. PPV may be a more suitable PiCCO index for assessing fluid requirements in patients with ALF than SVV.


Thermosensitive liposome-mediated drug delivery has been developed as part of research efforts aimed at improving therapeutic efficacy while reducing the associated side effect. Since multiple steps are involved in the transport of drug-loaded liposomes, drug release, and its uptake, mathematical models become an indispensible tool to analyse the transport processes and predict the outcome of anticancer treatment. In this study, a computational model is developed which incorporates the key physical and biochemical processes involved in drug delivery and cellular uptake. The model has been applied to idealized tumour geometry, and comparisons are made between continuous infusion of doxorubicin and thermosensitive liposome-mediated delivery. Results show that thermosensitive liposome-mediated delivery performs better in reducing drug concentration in normal tissues, which may help lower the risk of associated side effects. Compared with direct infusion over a 2-hour period, thermosensitive liposome delivery leads to a much higher peak intracellular concentration of doxorubicin, which may increase cell killing in tumour thereby enhancing the therapeutic effect of the drug.The effectiveness of anticancer treatments is often hampered by the serious side effects owing to toxicity of anticancer drugs and their undesirable uptake by healthy cells  As a common anticancer drug, doxorubicin is widely used in chemotherapy to treat various types of cancer, such as lymphoma, genitourinary, thyroid, and stomach cancer . By inteIn order to improve the therapeutic benefit while reducing toxicity of doxorubicin in normal tissues, various treatment modalities have been developed. Recently, liposome-mediated doxorubicin delivery has been proposed as an alternative to direct intravenous administration. Some animal experiments have shown that liposomal doxorubicin delivery offers better effectiveness of anticancer treatments than bolus injection, but no obvious advantage over continuous infusion was reported . The devFollowing administration, the drug-loaded thermosensitive liposome-based nanoparticles are usually small enough to pass through the vasculature wall and then accumulate in the extracellular space in tumour. Localised heating can be performed several hours after drug administration. Upon heating to the phase transition temperature of the thermosensitive liposome, the encapsulated drug can be released from liposomes at a high rate. Some of the released drug may bind with proteins in blood and be cleared up by blood flow, whereas the rest will permeate through the vasculature wall entering the interstitial space. Drug in the interstitium may also bind with proteins present in the interstitial fluid, and be cleared up by the lymphatic system. Because of the concentration gradient at the interface between tumour and normal tissues, drug exchange takes place between these tissues. The extracellular drug may pass through the cell membrane and be taken up by cells. Drug in tumour cells can also be transported back to the extracellular space. Given the many variables related to the properties of tumour, normal tissues, and anticancer drugs, mathematical models are needed to analyse the drug transport processes described above.Previous numerical studies of liposome-mediated drug delivery have mainly focused on drug uptake by tumour cells with a simplified description of the transport processes involved. Harashima et al. , 10 and In the present study, an improved mathematical model is developed and applied to an idealized geometry consisting of tumour and normal tissues. The model incorporates the key physical and biochemical processes involved, including time-dependent plasma clearance, liposome, and drug transport through the blood and lymphatic vessels, extracellular liposome, and drug transport (convection and diffusion), drug binding with proteins, lymphatic drainage, interactions with the surrounding normal tissues, and drug uptake by tumour cells. Therapeutic effect is evaluated based on the fraction of survival tumour cells by directly solving the pharmacodynamics equation using the predicted intracellular drug concentration. Comparisons are made of the predicted efficacies of direct intravenous administration and thermosensitive liposome-mediated delivery. in vivo data on the heterogeneity of tumour vasculature, solid tumours are usually treated as a spatially homogeneous domain [In solid tumours, the size and branching patterns of microvessels could vary considerably depending on the specific tumour type and its growth stage . For a ss domain –18. If tThis is described byρ and v are the density and velocity of the interstitial fluid, respectively. Fv is the interstitial fluid loss from the blood vessels per unit volume of tumour tissue, and Fly is the fluid absorption rate by the lymphatics per unit volume of tumour tissue. Fv and Fly are given by Starling's lawKv is the hydraulic conductivity of the microvascular wall, S/V is the surface area of blood vessels per unit volume of tumour tissue, pv and pi are the vascular and interstitial fluid pressures, respectively, σT represents the average osmotic reflection coefficient for plasma protein, πv is the osmotic pressure of the plasma, and πi is that of interstitial fluid.where Fly, is related to the pressure difference between the interstitial fluid and lymphatics:Kly is the hydraulic conductivity of the lymphatic wall, Sly/V is the surface area of lymphatic vessels per unit volume of tumour tissue, and ply is the intralymphatic pressure. The lymphatic drainage, μm [τ is the stress tensor which is given byI is the unit tensor. The last term in  [Since the intercapillary distance  is μm [∂(ρv)∂t+∇|v | ≪1) , the inegiven byF=Wμv+12CDrug transport is described by equations for the free and bound drug concentrations in the interstitial fluid and the intracellular concentration.This is described byDfe is the diffusion coefficient of free doxorubicin. The source term, Si, is the net rate of doxorubicin gained from the surrounding environment, which is given bySv, Sb, and Su represent the net doxorubicin gained from the blood/lymphatic vessels, association/dissociation with bound doxorubicin-protein, and influx/efflux from tumour cells, respectively,Ffp is the doxorubicin gained from the blood capillaries in tumour and normal tissues, and Ffl is the doxorubicin loss to the lymphatic vessels per unit volume of tissue. Using the pore model [Ffp and Ffl can be expressed asCfp is the concentration of doxorubicin in blood plasma, σd is the osmotic reflection coefficient for the drug molecules, and Pfe is the permeability of vasculature wall to free doxorubicin. Pef is the transcapillary Peclet number defined asDc is the tumour cell density; ka and kd are the doxorubicin-protein binding and dissociation rates, respectively:where re model –17, 21 ferned by , where DThis is described byDbe is the diffusion coefficient of the bound doxorubicin-protein. Fbe represents the bound doxorubicin crossing the capillary wall into the interstitial fluid, which is given byPbe is the permeability of vasculature wall to bound doxorubicin, and Cbp is the bound doxorubicin concentration in plasma. The transcapillary Peclet number iswhere Vmax is the rate of transmembrane transport, ζ and ɛ are cellular uptake and efflux functions, ke and ki are constants obtained from experimental data fitting, and φ is the volume fraction of extracellular space.Because mainly free doxorubicin can pass through the cell membrane and enter the intracellular space , the ratEquations describing the transport of liposome-mediated drug include encapsulated drug concentration in the interstitial fluid, and released doxorubicin in plasma and interstitial fluid. Equations for drug transport include those for free drug concentration in plasma and interstitial fluid. Bound drug concentration in plasma and interstitial fluid as well as intracellular concentration are described using the same equations given in the preceding section.This is described byDl is the diffusion coefficient of liposome encapsulated drug. The source term Sl is the net rate of liposome encapsulated drug gained from the surrounding environment, which is given bySlp is the amount of liposome encapsulated drug from plasma. Sr represents released drug in the interstitial fluid:Flp is the liposome encapsulated doxorubicin gained from the capillaries in tumour and normal tissues, and Fll is the loss of liposome encapsulated doxorubicin through the lymphatic vessels per unit volume of tissue. Using the pore model for transcapillary exchange, Flp and Fll can be expressed asClp is the concentration of liposome in blood plasma, σl is the osmotic reflection coefficient for the liposome particles, and Pl is the permeability of vasculature wall to liposome. Pel is the transcapillary Peclet number defined asSr, is given bykrel is the release rate of liposome.where This is described byFfp represents the free doxorubicin crossing the capillary wall into the interstitial fluid. VT is tumour volume, VB is plasma volume, and VD is the volume of distribution, which is a pharmacological theoretical volume that a drug would have to occupy to provide the same concentration as it is currently in blood plasma. CLfp is the plasma clearance of drug. ka and kd are the association and disassociation rates with proteins.where This is described byCLbp is the plasma clearance of bound doxorubicin. where This is described bySf is the net rate of doxorubicin gained from the surrounding environment, which is given byThe source term sly (see –14) and andSf isly (see ).fmax is the cell-kill rate constant and EC50 is the drug concentration producing 50% of fmax. kp and kg are cell proliferation rate constant and physiologic degradation rate, respectively. In this study, cell proliferation and physiologic degradation are assumed to reach equilibrium at the beginning of each treatment.During anticancer treatment, tumour cell density may change due to cell killing as a result of drug effect, tumour cell proliferation, and physiological degradation. This can be described by a pharmacodynamics model as given below:A 2D idealized model with a realistic tumour size  is used Since the growth of tumour and normal tissues is ignored, all the geometric and transport parameters used in this study are assumed to be constant. These are summarized in Tables −10 and 3.42 ± 0.78 × 10−9 m/s, respectively. In normal granulation tissues permeability of the same liposomes was 0.8 − 0.9 × 10−9 m/s at the same temperature. Wu et al. [−9 m/s and 2.5 ± 0.8 × 10−9 m/s, respectively. The mean values of the above measurements are adopted in this study. Vascular permeability coefficient measures the capacity of a blood vessel (often capillary in tumour) wall to allow for the flow of substances, typically nutrients or pharmaceutical agents in and out of the vasculature. The permeability of polyethylene glycol coated liposomes of 100 nm through tumour capillaries was measured at 37°C by Yuan et al.  and Wu eu et al.  also meaGaber et al.  noticed The reflection coefficient determines the efficiency of the oncotic pressure gradient in driving transport across the vascular wall. It is related to the sizes of drug and pores on the vasculature wall . For theBecause the size of liposome is much larger than the pore size on the vasculature wall in normal tissues (24–60 nm in diameter ), the reR(t) is the percentage of drug released at exposure time t; Rc is the total percentage of drug released at a given heating temperature. This equation is used to fit the experimental data obtained at 42°C [Thermosensitive liposome is designed to release its contents rapidly on heating . The rel at 42°C . From th Direct Continuous Infusion. The doxorubicin concentration in blood plasma is modelled as an exponential decay function of time. The form of equation depends on the infusion mode. For continuous infusion, a triexponential decay function is assumed based on the plasma pharmacokinetics of doxorubicin:D is the dose of doxorubicin and T is the infusion duration. A, B, and C are compartment parameters and α, β, γ are compartment clearance rates.(1)Cfp) and bound (Cbp) doxorubicin in plasma are given bys is the percentage of bound doxorubicin, which is 0.75 in this study.Free doxorubicin in plasma can easily bind with proteins, such as albumin. Greene et al.  found th Thermosensitive Liposome-Mediated Drug Release. The liposome encapsulated doxorubicin concentration in blood plasma is found to follow a 2-exponential decaying function of time [A1 and A2 are compartment parameters, and k1 and k2 are compartment clearance rates.(2) of time , writtenBecause the time scale for the simulation is assumed to be short enough to ignore the growth of tumour and normal tissues, the interface between the tumour and normal tissue as well as the outer surface of normal tissue are fixed. The interface between the tumour and normal tissues is treated as an internal boundary where all variables are continuous. The relative pressure at the outer surface of normal tissues is assumed to be constant at 0 Pa, where zero flux of drug is also specified.−5 and 1 × 10−8, respectively. The equations for the interstitial fluid flow are solved first to obtain a steady-state solution in the entire tumour and its surrounding normal tissues. The obtained pressure and velocity fields are then applied to the equations for drug transport. The second-order implicit backward Euler scheme is used for temporal discretisation, and a fixed time step size of 10 seconds is chosen, which is obtained after time-step sensitivity tests.The mathematical models described above are implemented into ANSYS FLUENT, which is a finite volume based computational fluid dynamics (CFD) code. Mass transfer equations describing the transport of drugs are coded by using the User Defined Scalar (known as UDS). These equations are solved in conjunction with the continuity and momentum equations using numerical algorithms available in FLUENT. Spatial discretisation is performed by employing the second order UPWIND scheme, while pressure-velocity coupling is achieved by the SIMPLEC algorithm. The absolute criteria for residual tolerances for solutions of the Navier-Stokes equations and the drug transport equations are 1 × 10The microenvironment in tumour and normal tissues plays an important role in determining the efficiency of liposome and drug transport. The interstitial fluid pressure (IFP) determines the drug exchange between interstitial fluid and blood plasma, as well as tumour and normal tissues. The mean IFP predicted in the tumour region is 1533 Pa, which is almost identical to the value reported by Baxter and Jain [The spatial distribution of IFP in tumour and normal tissues is shown in 2 encapsulated in thermosensitive liposomes. Liposome encapsulated doxorubicin concentration is a key parameter that determines the doxorubicin concentration in tumour cells. Shown in Liposome encapsulated doxorubicin is administrated into blood in a very short duration, and its concentration in plasma decreases following an exponential decay function of time during the entire treatment period . Its conSince the size of liposome is too large to pass through the vasculature wall in normal tissues , 45, lipThere is evidence for rapid and significant binding between free doxorubicin and proteins in plasma , 22. Pre2. The free and bound doxorubicin concentrations increase rapidly during the initial period following drug administration. For thermosensitive liposome delivery, the doxorubicin concentration remains at zero in the first 24 hours, since no doxorubicin is released from liposome before heating is applied. Upon heating to mild hyperthermia at 24 hours which lasts for one hour, doxorubicin is rapidly released from liposome causing much higher concentration in plasma. Because the temperature of tumour falls back to 37°C immediately after heating is stopped and assuming that encapsulated doxorubicin remains trapped within the core of liposome, the concentration declines rapidly to a low level. Although the concentration with both modes of administration drops to a low level after the infusions ends, 2-hour continuous infusion of nonencapsulated doxorubicin gives a slightly higher concentration over time.For direct infusion of nonencapsulated doxorubicin, the infusion duration is 2 hours as recommended in the literature , and theFree and bound extracellular concentrations of doxorubicin in tumour and normal tissues are shown in Figures −5 kg/m3 [e) [e. Values for AUCe under 2-hour infusion and thermosensitive liposome delivery are compared in e in the first 48 hours of the treatment, suggesting that 2-hour direct infusion of doxorubicin is likely to cause more cell death in normal tissues than thermosensitive liposome delivery. Despite thermosensitive liposome delivery gives higher peak values for both free and bound extracellular concentrations of doxorubicin in normal tissues, the concentration level is still lower than the half maximal (50%) inhibitory concentration (IC) of doxorubicin in normal tissue, which is 4.13 × 10−5 kg/m3 . Howeverg/m3 [e) , 49. A sBecause heating can be controlled and localised in tumour, the temperature in normal tissues would be lower than the hyperthermia temperature required for the release of doxorubicin from liposomes. During the heating period, doxorubicin enters normal tissue only by diffusion and convection from tumour. This leads to doxorubicin being mainly concentrated in the region surrounding the tumour, as shown in Compared with the study reported by El-Kareh and Secomb , lower fAlthough the present numerical study offers some new insight into how anticancer treatment efficacy could be affected by different drug delivery modes, the mathematical models involve a number of assumptions. For example, realistic changes in tumour temperature during heating and after heating are ignored, and step changes are specified instead. In clinical practice, tumour temperature profiles (temperature versus time curve) may vary depending on the heating method applied. Moreover, temperature distribution in tumour tissue is likely to be nonuniform. These factors can influence the outcome of anticancer treatments. Other assumptions include an idealised geometry for the tumour and normal tissues, uniform transport properties, and a uniform distribution of microvasculature for administration of anticancer drug.Doxorubicin delivery into solid tumour by direct continuous infusion and thermosensitive liposome are studied by mathematical modelling, and the anticancer effectiveness is evaluated in terms of the survival fraction of tumour cells. Our computational results show that thermosensitive liposome-mediated delivery offers a lower drug concentration in normal tissues than direct infusion of nonencapsulated doxorubicin, which may help reduce the risk of associated side effects. In addition, thermosensitive liposome delivery achieves a significantly higher peak intracellular concentration, and hence more rapid and effective tumour cell killing in a short time period of treatment.


Multiple myeloma is a malignant proliferation of plasma cells that mainly affects bone marrow. Pleural effusions secondary to pleural myelomatous involvement have rarely been reported in the literature. As it is rarely detected, we aimed to report a case in which pleural effusion of a multiple myeloma was confirmed as true myelomatous involvement by flow cytometry method. A 52-years old man presented to our clinic with chest and back pain lasting for 3 months. On the chest radiography, pleural fluid was detected in left hemithorax. Pleural fluid flow cytometry was performed. In the flow cytometry, CD56, CD38 and CD138 found to be positive, while CD19 was negative. True myelomatous pleural effusions are very uncommon, with fewer than 100 cases reported worldwide. Flow cytometry is a potentially useful and simple method for detection of pleural fluid involvement in multiple myeloma. Multiple myeloma is malign proliferation of plasma cells and it mainly affects bone marrow; however, it may affect thorax as skeletal abnormalities, plasmocytomas, pulmonary infiltrates and pleural effusionAs it is rarely detected, we aimed to report a case in which pleural effusion of a multiple myeloma was confirmed as true myelomatous involvement by flow cytometry method.th and 12th ribs and pleural thickening at paravertebral site on the left. In laboratory evaluations, following findings were observed: hemoglobin: 7.1g/dl.(14–18), white blood cells:11.26 × 103/μL(4.8–10.8), platelets: 787 × 103/μL., creatinine: 3.1mg/dl.(0.6–1.1).Uric acid: 6.1 mg/dl (2.6–6), calcium: 14.7mg/dL (8.8–10.6), total protein:10.7g/dL (6.4–8.3), albumin: 1.8g/dL (3.5–5.2). Serum protein electrophoresis revealed a hypoalbuminemia  associated with an increase in γ globulins . IgG-Kappa monoclonal paraproteinemia was detected in immunofixation tests . ß-2 microglobulin was found as 2.71 mg/dlL . Bence-Jones protein was found to be negative in 24-hours urine collection. A thoracentesis was performed, which revealed serofibrinous fluid with a protein level of 6.3gr/dl and white blood cell count of 6.65 × 103μL consisting of lymphocytes (80%) and neutrophils (20%).A 52-years old man presented to our clinic with chest and back pain lasting for 3 months. On the chest radiography, pleural fluid was detected in left hemithorax . On thorBacterial and micobacterial culture tests of pleural fluid were reported as negative. Biopsy was performed on the mass at rib, which was reported as CD138(+), CD20(−) plasmocytoma. Pleural fluid flow cytometry was performed by using FACSCalibur flow cytometer . In the flow cytometry, CD56, CD38 and CD138 found to be positive, while CD19 was negative. Bone marrow biopsy was reported as CD38 (+) and CD20(−) atypical plasma cell infiltration. T, 17p13,1 (p53 gene) and 13q14,3(Rb gene) was found as negative by FISH method.The patient was scheduled for VAD  chemotherapy, biphosphonate therapy and involved-field radiotherapy.Multiple Myeloma is a clonal late B-cell disorder in which malignant plasma cells expand and accumulate in the bone marrow, leading to cytopenias, bone resorption and the production (in most cases) of the characteristic monoclonal protein.Myelomatous effusions are rarely seen, in which demonstration of monoclonal protein and atypical plasma cells on pleural fluid electrophoresis and histological diagnosis by pleural biopsy is used as diagnostic procedures. Pleural involvement can be diagnosed by presence of plasma cells in pleural fluid or pleural biopsy. Large persistent pleural effusions refractory to diuretics and thoracentesis are more likely to be due to pleural amyloid infiltration. Agarwall et al. demonstrated that in their case; the pleural fluid cytology did not reveal any myelomatous cell, and the recurrent effusions were secondary to pulmonary amyloidosis.In conclusion, we presented our case; as it has been rarely reported, although flow cytometer is a simple method for detection of pleural fluid involvement in Multiple Myeloma.


When anti-tumour therapy is administered to a tumour-host environment, an asymptotic tapering extremity of the tumour cell distribution is noticed. This extremity harbors a small number of residual tumour cells that later lead to secondary malignances. Thus, a method is needed that would enable the malignant population to be completely eliminated within a desired time-frame, negating the possibility of recurrence and drug-induced toxicity.In this study, we delineate a computational procedure using the inverse input-reconstruction approach to calculate the unknown drug stimulus input, when one desires a known output tissue-response . The asymptotic extremity is taken care of using a bias shift of tumour-cell distribution and guided control of drug administration, with toxicity limits enforced, during mutually-synchronized chemotherapy (as Temozolomide) and immunotherapy (Interleukin-2 and Cytotoxic T-lymphocyte).Quantitative modeling is done using representative characteristics of rapidly and slowly-growing tumours. Both were fully eliminated within 2 months with checks for recurrence and toxicity over a two-year time-line. The dose-time profile of the therapeutic agents has similar features across tumours: biphasic (lymphocytes), monophasic (chemotherapy) and stationary (interleukin), with terminal pulses of the three agents together ensuring elimination of all malignant cells. The model is then justified with clinical case studies and animal models of different neurooncological tumours like glioma, meningioma and glioblastoma.The conflicting oncological objectives of tumour-cell extinction and host protection can be simultaneously accommodated using the techniques of drug input reconstruction by enforcing a bias shift and guided control over the drug dose-time profile. For translational applicability, the procedure can be adapted to accommodate varying patient parameters, and for corrective clinical monitoring, to implement full tumour extinction, while maintaining the health profile of the patient. Using the standard values of α = 0.02 and D = 150 mg for bleomycin (maximum dose tolerated)  has been well used in other fields in the form of stimulus reconstruction approach. This enables one to accurately quantify an antecedent unknown stimulus, using the knowledge of the observed response pattern. In our case, the methodology enables one to reconstruct the input drug stimulus, which will accurately produce complete tumour elimination as the output in finite time.So, in the equation (2), one puts T at time t decreases exponentially in a monotonic manner:The cell-drug interactions are governed by first-order chemical reaction rate kinetics  using whT0 is initial population, T′ is the time derivative of T, and κT is the drug’s cytotoxic rate constant , one may omit the input feed of reactants which causes the reaction to slow, but not become zero even after any length of time leaving a small asymptotic level of reaction rate R persisting at a time t, since first-order kinetics indicates R′ + κ R = 0.As in the case of a biochemical reactor, conditions may arise where the synthesis rate ive time . To exply′ + k y = 0. Here the y-axis denotes the system parameter, as reaction rate or altitude, while the x-axis denotes time. The equation implies that at infinite time, the curve meets the y-axis, if the coordinates at this meeting point is t† and y†, then y† = 0, as t† = ∞. Nevertheless, in the exponential systems above, we elucidate that the systems dynamics can be halted in a finite time , if one implements the concept of an adjustable or tunable bias shift, denoted by y*. This enables the performance curve to approach a value y → y* , as t → ∞ . Putting this expression of T′ = −κT (T – T*) in eq. (7), we obtain the condition that needs to be followed to induce complete tumour regression:Now, for enforcing the tumour elimination using the tracking approach mentioned earlier, we use the previously described tumour growth rate as gT1 and gT2:Rewriting eq. (11), we obtain the terms containing the pharmacological effect functions where,UM and UL), which, if implemented, will ensure that the tumour cells undergo complete extinction.Eq. (12) furnishes the relationship of the blood concentration of the antitumour agents (Transposing the eq. (13),bT , should take care of two aspects: (i) the condition required for enforcing tumour cell population elimination, i.e. the expression -κT (T – T*), and (ii) the tumour cell growth due to biological processes, i.e. fT  indicates that the relaxation decay term bT has negative value if tumour is to regress , thus here bT ≤ 0. From eq. (12):Eq. 13) implies that, for proper therapy, the combined therapy-induced anti-tumour effect term,  implies UM and UL), which, if implemented, will ensure the tumour cells undergo complete extinction.Eq. (12) furnishes the relationship of the blood concentration of the antitumour agents , we have the cost function for the tumour cell compartment as:However the blood concentrations rT1 and rT2 are the weighting factors of the two therapeutic entities, temozolomide and cytotoxic T-cells. The cost function JT needs to be minimized, and at the same time eq. (12) should be obeyed as a constraint. The minimization of the cost function, JT = ½ [rT1 UM 2 + rT2 UL 2], is evidently a constrained optimization problem, and the optimality can be customarily solved by the method of Lagrange multiplier, λ. Thereby the augmented cost function J can be written as:Here J with respect to the two variables UM and UL. Thereby, we find out the minimization conditions, i.e., ∂J/∂UM = 0, and ∂J/∂UL = 0, whence we get the expressions for the two variables:where the right sided expression within the second brackets {….} in eq. (16) incorporates the left side of the constraint equation [eq. 14(A)], as required by the Lagrange’s method. To pursue the minimization of the augmented cost function, we differentiate Temozolomide efficiency term,Cytotoxic T-lymphocyte efficiency term,where,Further, from eq. (10):Solving the two equations of the last line, we obtain the desired values, M* (blood concentration of temozolomide) and L* (cytotoxic T-cell population):M and UL, we substitute their values from eq. (17) and (18) respectively. Thence, we arrive at the desired values of the temozolomide blood level and cytotoxic T-cell population, which, if implemented, will regress the tumour fully:In the last two equations, for the terms UDesired temozolomide blood level:Desired cytotoxic T-cell population:I* and vL*, of this compartment’s input parameters (interleukin-2 blood level and tumour-infiltrating lymphocyte injection dose-rate), which would drive the compartments output, namely the cytotoxic T-cell population, L to its desired value L* mentioned in the last paragraph. This enforced driving needs to be faster than the earlier compartment (tumour cell compartment) and requires to be done till time tr, point P, when all the tumour cells have become eliminated . Using similar logic of the reduction of the deviate or error [eq. (6)], we obtain:We use the methodology of guided tracking principle of control systems analysis, which we have already used for the tumour cell population compartment [eq. (5)]. Likewise, we enforce the situation of the cytotoxic T-cell population fT (Xn) + bT [i.e. eq. (7)]. We now desire to formulate the characteristic rate formulation of the cytotoxic T-cell compartment in terms of L′. Thus, we express the temporal dynamics equation of L′ , now denoted as UI, is the therapeutic efficiency relationship of interleukin-2. Evidently from eq. (26), the term bL signifies the total cytotoxic T-cell activation effect by the two immunotherapeutic inputs: the interleukin efficiency term UI and the tumour-infiltrating lymphocyte administration term vL , i.e. bT ≥ 0. Actually, eq. (29) furnishes the relationship of the characteristics of the antitumour agents, interleukin and tumour infiltrating lymphocytes (UI and vL), which if implemented, will ensure the full elimination of tumour.Note here that vL Table . Indeed,I) and tumour-infiltrating lymphocyte . The cost function for this compartment is:In this compartment, the two antitumour agents are nterleukin-2 and Tumour-infiltrating lymphocytes. As explained for the earlier compartment, we need to minimize the toxicity cost function for the compartment where the two therapeutic input functions arriving at the compartment are Interleukin-2 ] should be obeyed. Solving using the Lagrange multiplier method, we arrive at:.where Interleukin-2 efficiency term,Tumour-infiltrating lymphocyte injection dose-rate,whereUI = {(pILI)/(gI + I)} , we obtain the desired value of I* (blood concentration of Interleukin-2):By transposing UI from eq. (32) to eq. (35), we get the desired value of tumour-infiltrating lymphocyte injection dose-rate which will eliminate the tumour:Substituting Desired interleukin-2 blood concentration,vM*, of this compartment’s input parameter (temozolomide injection dose-rate), which would drive the compartment’s output, namely the patient’s blood level of temozolomide M to its desired value M* as given in eq. (22). This driving needs to be done faster than the preceding compartment (T-cell compartment), and is to be done by time tp, point P . Using similar reasoning as the earlier compartment, we get M′ = −κM (M – M*). Eliminating M′ from this equation and from eq. (S.5), we have:We need to implement the condition that EI′ + κIEI = 0 where the deviate EI = (I – I*) Thus, I′ = −κI(I – I*). By eliminating I′ using eq. (S.6), we note:The condition necessary to be implemented is that For this compartment, we can write from eq. (33) that:ts of the regression process (the time duration in which 90% of tumour has regressed), and is taken to be around 1–2 months. The tumour regression rate constant κT = 4/ts; so if ts is 60 days, κT = 0.067 per day. On the other hand, the dynamics of the successively preceding compartments (e.g. chemo-therapy and cytotoxic T-cell compartments) need to be faster, as they causally influence the tumour cell compartment, and thus need to change more rapidly if they are to have a controlling influence on the tumour cell compartment , is chosen to be higher that κL.These are rate constants of the tumour cell compartment, temozolomide compartment, cytotoxic T-cell compartment and interleukin compartment, respectively, as regression occurs under the action of multi-modal therapy. These are calculated from the desired rate of tumour regression, expressed as settling time Thus,T and then we obtain the value of the other constants in terms of κT :As explained earlier, in control analysis , a thumbκT = 0.067/day, we get κL = 0.201/day, κM = 0.201/day, and κI = 0.603/day. It may be noted that the tumour elimination time tp , provides the value of bias shift T*.In the above example, since, rT1, rT2, rL1, rL2 are needed for minimizing the toxicity cost of the therapy. It is these parameters that give a control to the investigator for maneuvering the tumour elimination process, under adjustable dosing of the drugs. Initially, the values of the tuning parameters rT1, rT2, rL1, rL2 which appear in derivation, are chosen by specific quantitative conditions , thereby the task is to suitably choose or optimize the value of the other tuning parameter rT2. Correspondingly, rL1 and rL2 are respectively the toxicity cost weighting factors of interleukin-2 and tumour-infiltrating lymphocytes, which act on the cytotoxic T-cell compartment and produces the toxicity cost JL. Similarly, this cost can be minimized by normalizing rL1 = 1, and then optimizing rL1. All the tuning parameter values must be greater than or equal to zero. Using the upper and lower bounds that needs to be followed by the cellular and pharmacological variables , becomes imaginary or negative. If this happens, the particular drug injection rate  is stopped and kept suspended (its dosage is henceforth taken to be zero), the simulation is continued with the presence of the other two drugs. As long as the dosage of the drug-A calculated remains negative or imaginary, drug-A is not injected, and the drug A’s value for the subsequent simulation steps becomes zero, while the other two drugs, whose calculated dosages continue to be positive numbers, are continued to be administered. Only when the computed dose of the drug-A turns positive, then the injection of drug-A is resumed according to that dose. To wit, the following alternative situations occur:During the analysis and modeling procedure, the control variables, i.e. parameters of the therapeutic agents, should have positive values and not attain negative nor imaginary values, even though the system dynamics [eq. (S.1-S.7)] has fractional exponent at some places [eq. (S.7)]. The situation is amended in case if any of the computed control variables, or of the three injected drug dosage rates calculated ((i) The dosage parameters of all three drugs remain positive throughout the time duration: If so, all are given.(ii) One drug becomes negative or imaginary for a particular time interval: this drug is stopped during that interval, and the other two drugs are continued as per the modeling procedure with the first omitted(iii) Two drugs become negative or imaginary: here the remaining drug with a positive drug dosage rate is administered.(iv) All the three drugs violate the positivity condition: then no drug is given, until one or more drug rates become positive at a subsequent time, upon which the administration of the drug/s is resumed.If case (ii) is applicable, then there will be three different combinations for two drugs at a time by permutation: (a) temozolomide and interleukin, or (ii) temozolomide and tumour infiltrating lymphocytes, or (iii) interleukin and tumour infiltrating lymphocytes. If case (iii) is relevant, then the three possibilities are: temozolomide or interleukin or tumour infiltrating lymphocytes. The mathematical formulation for cases (ii) and (iii) are derived later -14A)], and thereby obtain the temporal profile of the varying dose-rate of the therapeutic agents, by solving eq. (37)-39) which respectively furnish the temporal profile of temozolomide, interleukin-2 and tumour-infiltrating lymphocytes, which would eliminate the tumour cell population. In Figure 4A], and  which reT0) of 2 × 107, natural killer cell population of 105, cytotoxic T-cell population of 5 × 104 and circulating lymphocytes of 109 cells (see endnote b before the References). From this T0 value, we get a bias shift T* = 1.8986 × 105 cells [right-side of eq. (4)] (see subsection on “Determining tumour regression rate constants” above). All the other constants used in the model are given in Table a = 0.301 per day, and the deceleration rate of logistic tumour growth b = 1.01x10-8. We start the simulation procedure increasing the temporal step by one minute each time  of 2 × 107, natural killer cell population of 105, cytotoxic T-cell population of 5 × 104 and circulating lymphocyte of 109 cells were considered to simulate the system. The same tumour settling time and tumour elimination time-line is given, and a bias of T* = 1.8986 × 105 cells is taken. The other parameters for the tumour system are given in Table a value is higher here than the earlier tumour, the current tumour needs more treatment duration than the former. Likewise, we perform the computation for a time span of 800 days, to ensure the permanency of regression far across time. The values of the biological and pharmacological entities are displayed in Figure For the faster growing tumour such as astrocytoma or glioma grade III, we take tumour cell growth rate, (i) the dosages of the therapeutic agents drops to zero from the tumour extinction time onwards(ii) none of the cell populations or dosing of therapeutic agents crosses the bounds Table , and(iii) the dosages of the three agents are approximately the same fraction of the upper bound .We also discern that the therapy period is considerably longer in high-grade tumour, basically since the tumour growth rate is about 50% more than the slow-growing tumour  and tumour system parameters . We performed 500 random simulations to check for complete tumour elimination, in both tumour grades. We found that tumour extinction for oligodendroglioma occurred in 100% cases if the variation was 0%, and in 98% of the cases as the coefficient of variation was increased to 10%  no injections of any of the three therapeutic agents are required any time after the time point of extinction of tumour, which does not recur later Figure G-8I; , so as to induce a higher level of immune activation that would enable complete elimination of glioma cells Figures D and 9C.This population initially increases and then plateaus in both tumours Figures F and 9D,Similar to the Circulating lymphocytes, the NK cells show saturation behaviour for both high and low grade tumours Figures E and 9D.modus operandi of our approach is based on the following aspects: firstly, the tumour elimination process is a manifestation of exponential diminution with bias shifting; secondly, the procedure works on fundamentally two complimentary modes of tumour cell reduction:To justify, the (a) Reducing the tumour cell proliferation, as by chemotherapy or chemical alkylation (DNA damage);(b) Increasing the tumour cell lysis by anti-tumour lymphocytes, which are activated by cytokine modulation .the five-pattern profile: activation of antitumour lymphocyte , chemomodulative DNA damage , interleukin (stationary), natural killer cell (saturation), and circulating lymphocyte (saturation), (ii) the two kinetic conditions: bias shift and exponential decrementing dynamics [the tumour trajectory formula of eq. (4)].Further, the model developed shows that to induce tumour regression, the three antitumour entities should have three distinct temporal profiles: (1) biphasic intensity for lymphocyte activation, (2) monophasic intensity for activation of chemomodulative DNA damage , (3) stationary intensity of cytokine activation (interleukin-2). The experimental studies in animals -39 and c3, indicating about 250% variation on the baseline level. Secondly, for proper monitoring of a patient, the declining tumour load T can be weekly or semiweekly estimated non-invasively, by MRI amide-proton transfer imaging, which maps the cell proliferation intensity by amide mobility [There are two aspects where the approach could be improved. Firstly, one can increase the system robustness, which diminishes as the individual patient-specific fluctuations increases (see earlier subsection on Robustness). For real-time implementation, we can use the neuro-adaptive controller, which can reliably follow the desired mathematical trajectory of the tumour cell population curve Figure B, and camobility . This emT(t) with the actual real-life tumour cell population T(t) as the treatment process goes over months, the actual population T(t) being estimated radiologically at suitable discrete time intervals of τ , then the value at that moment of the actual number of tumour cells Tk can be used as the input value of tumour cell population at time point tk in the simulation procedure for going to the next time point tk+1 as per the procedure in Figure In Figure τ Figure . The aimFor monitoring while the therapy is in progress, one need to have at suitable time intervals, the overall measurements of the serum temozolomide and interleukin-2 levels, as well as the populations of cytotoxic T-cells, natural killer cells and circulating lymphocyte in the blood Figure . Only apOne may observe that during therapy Figures E-8F, theWe have formulated the distinctive dose-time relationship of chemotherapy and of immunotherapy (interleukin-2 and cytotoxic lymphocyte), such that their orchestrated functioning, that incorporates a biphasic temporal profile for T-cell, ensures that all the tumour cells are eliminated within a desired time. Excess toxicity to the host is avoided, as the circulating lymphocyte and natural killer cells in blood are protected. The approach is patient-specific as the formulation depends on the tumour load, the levels of cytotoxic T lymphocytes, cytokine interleukin, natural killer cells and circulating lymphocytes. All these parameters vary with the individual patients, and hence the different therapeutic dose-time profile obtained for each specific patient will be optimally suited from her. The formulation put forward use of an innovative approach of bias shift, control systems analysis, performance cost minimization and inverse construction of drug input. The limitation of the proposed model is that we have not considered the effect of tumour cells resistant to the chemotherapy drug and also not taken care of angiogenesis process where the drug permeability to the tumour cells will hinder. A broad-based gamut of findings from animal experimentation and clinical investigations are shown to corroborate with the tumour extinction approach developed. An unanticipated but noteworthy finding is the importance of giving a terminal pulse of the therapeutic agents before the end of the therapy, so that all the tumour cells become extinct and there is no extra drug-induced toxicity, the natural killer cells and circulating lymphocytes being within the physiological limits. This is in contrast to the generally prevailing view in clinical medicine, which advocates tapering off of the therapy in the later stages. This tapering may cause tumour recurrence in clinical praxis, as there is no intensive spike of the therapeutic agents to eradicate all the malignant cells.To summarize, information from temporal dynamics of both the endogenous and exogenous tumour regression has been used to explore the mechanism and elucidation of integrative functioning of various therapeutic modalities, whose combined effect eliminates the malignant tumour as corroborated with experimental findings. The method proposed can be of wide-ranging application, and can be adapted for application to conditions where there is involvement of chemotherapy and/or immunotherapy. The procedure delineated is also applicable to other tumour systems, as it offers a principled approach to tumour containment and thus an incisive prospect for probing towards further biological and clinical situations.aClarifications on upper/lower limits of biological and therapeutic parameters (Table).rs Table.Circulating lymphocyte population: The normal blood volume under active circulation is 3.5-4.5 litres. The higher limit of lymphocytes in individuals can be up to 100 × 109 cells/litre [11 cells. In contrast, the lowest lymphocyte count for patients for duration of 6–12 months across therapy can be 663–1160 cells/μl [9 cells, which the patient can tolerate up to 6 months.ls/litre . Taking cells/μl . Taking Natural Killer Cells population: The upper limit of NK cell is 13% of lymphocyte population, with CD56/CD16 surface protein being the marker for these lymphocytes [11 cells, we have the maximum value of NK cell population in the individual to be 5.85 × 1010 cells. On the other hand, the lower limit of NK cells (CD56/CD16 lymphocytes) is 0, occurring in people having natural killer cell deficiency condition [phocytes . As the ondition , and a pondition . Hence wTumour-specific Cytotoxic (CD8+) T-cell population: The upper limit of this cell population for a patient is 20150 cells/mm3[10 cells. Furthermore, tumour-specific cytotoxic T-cells, that are specifically active against a particular malignant lesion, has been known to come into play if the tumour is present, and to decay away if the tumour undergoes elimination [cells/mm3. Using amination . Hence, Temozolomide chemotherapy infusion dosage rate: Maximum daily dosage [2/day, i.e., 4.45 mg/kg body. wt. per day. One may choose not to give it, so the lower limit is 0.y dosage  of temozInterleukin-2 immunotherapy infusion dosage rate: Maximum infusion given is 7.2 × 104 International Units (I.U.)/kg/day [Tumour-infiltrating lymphocytes (TIL) immunotherapy cumulative dosage: The maximum cumulative dose for a patient over the whole duration of therapy is 13.7 × 1010 cells [10 cells . LikewisbDetermination of initial number of the cells for numerical experimentation.Tumour cells: The number of cells in a tumour which becomes clinically detectable is 108 occupying a volume of 1 cm3, out of these 107 cells are malignant, while the rest are stromal cells [8 malignant cells as our initial condition.al cells . For ourCirculating lymphocytes: We also note the range of normal values: leucocyte count = 4000 to 11000/mm3, the fraction of lymphocytes are 15-40%, and actively pulsating blood volume under circulation is 3.5-4.5 litres. To be cautious for ensuring tumour regression, we will consider the lower values in the range. By multiplying the requisite aforesaid values, we have the circulating lymphocyte population in the person as 2.1 × 109 cells. Again to be on safer side, we take half of the population for our study .Natural killer cells: The fraction of NK cells is 1-13% of circulating lymphocytes [9 cells given above. Further, there is available data on the antitumour effector factor of NK cells during study of endogenous tumour regression, namely 1:50 as target tumour cell: effector NK cell ratio, i.e. a value of 2% [5 cells. As earlier, we will consider half this value to be on the safe side, i.e. NK cell population = 105 cells.phocytes , we takeue of 2% . MultiplCytotoxic T-cells: The fraction of cytotoxic CD8+ T-cells is 1-3% of circulating lymphocytes [5 cells, say 105 cells. As per the lower end, we have taken half, i.e. 5 × 104 cells as the CTL population.phocytes . The cytphocytes . One alsphocytes . Using tThe authors declare that they do not have any competing interests.The research was conceived, designed and planned by PR and RP. The modelling and numerical experiments were performed and the biological studies investigated by SK, VPSR, CK, RP and PR. All authors examined and evaluated the data. SK, VPSR and PR wrote the manuscript, with participation from CS and RP. All authors read and approved the final manuscript.Supporting Methods.Click here for fileSupporting Analysis.Click here for file


This correction will replace the previous correction made by PLOS ONE. There is more information that is missing from the Funding Disclosure.The first sentence in the Funding Statement should read:This research was supported in part by a National Geographic Society Discovery grant (awarded to DYY and DL), a SSHRC Partnership grant (awarded to DL), the Tula Foundation and Hakai Network for Coastal Peoples and Ecosystems.


Some information was missing from the funding section. The correct funding statement should read: This study was supported in part by grant AI28900 from the National Institutes of Health, grant 3401-11 from the Leukemia and Lymphoma Society and grant 1024929 from the National Health and Medical Research Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Both Daniel Gough and David Levy are corresponding authors. Dr. Gough can be reached here : daniel.gough@monash.edu


Sonographic B-lines are commonly observed in cases of increasing extravascular lung water (EVLW). These findings became prominent when interstitial and alveolar tissues were filled with fluid ,2. Thus,Transthoracic portable ultrasound before and after fluid resuscitation was performed. Patients with pleural disease were excluded. The B-lines were measured in 23 lung zones. The total numbers of B-lines seen in each patient were counted as the total B-line score (TBS). The primary outcome was to demonstrate the increase of TBS after fluid resuscitation. The secondary outcome was to examine the magnitude of the incremental number of TBS.P < 0.0001, 95% CI 13.47 to 33.67). This increase was found in all areas of measurement. In particular, the number of B-lines measured at the anterior axillary line area very well correlated to the TBS  and its increment had reverse correlation to the PaO2/FiO2 ratio . The volume of fluid per one B-line increasing was 119 ± 134 ml. The interobserver reliability between two ultrasound readers was very high . The changing of TBS did not correlated to that of the chest radiologic score for EVLW assessment . There was no linear correlation observed between net fluid balance and total number of increasing B-lines.Twenty patients were enrolled in this study. All patients had septic shock. Six of all had lung involvement. Twelve patients received mechanical ventilation. The mean of net fluid balance was +2,228 ± 1,982 ml and the mean of duration between two ultrasound measurements was 31 ± 13 hours. The means of TBS at pre and post fluid therapy were 37 ± 26 and 64 ± 29 respectively (The number of B-lines definitely increased after fluid resuscitation in shock and correlated to the deterioration of pulmonary gas exchange. These data support the benefit of transthoracic portable ultrasound for assessment of the increment of EVLW in shock patients receiving fluid resuscitation.