Online social media such as Twitter are widely used for mining public opinions and sentiments on various issues and topics. The sheer volume of the data generated and the eager adoption by the online-savvy public are helping to raise the profile of online media as a convenient source of news and public opinions on social and political issues as well. Due to the uncontrollable biases in the population who heavily use the media, however, it is often difficult to measure how accurately the online sphere reflects the offline world at large, undermining the usefulness of online media. One way of identifying and overcoming the online–offline discrepancies is to apply a common analytical and modeling framework to comparable data sets from online and offline sources and cross-analyzing the patterns found therein. In this paper we study the political spectra constructed from Twitter and from legislators' voting records as an example to demonstrate the potential limits of online media as the source for accurate public opinion mining, and how to overcome the limits by using offline data simultaneously. The proliferation of online social media services such as Twitter is widely recognized as signifying a revolution in how we utilize information and understand the way our society communicates. It also presents an open opportunity for any interested party to utilize the massive data accrued from such services for understanding the world as well as ourselves , 2. AmonTo those looking to mine for information from social media as a proxy for conducting polling, the potential disparity between the online and the offline world due to the unknowable demographics raises serious doubts on the usefulness of online social media, particularly where the offline reality is essential such as politics. In such cases the lack of a systematic method by which to compare the online and the offline would make finding relevant, useful information from online data infeasible. When researchers study such problems, they could circumvent this issue by restricting the scope of their study to the online sphere so that by design the real-world public at large is of little concern. This may be appropriate when the issue being studied is pertinent only to the online world; for instance, when the issue affects the online sphere only, or when one declares that their only interest is in the opinions of the online sphere. While certain scientific discoveries can still be made this way since the data themselves are nonetheless novel and interesting, the inability to draw conclusions regarding many issues pertinent to the offline potentially cripples the potential of online social media.In order to overcome such a problem, it is important to identify the discrepancies between the online and the offline quantitatively. One way of achieving that is to apply a common analytical and modeling framework to comparable data sets representing the online and the offline so that we can perform a cross-analysis of the patterns found therein. In this paper we demonstrate the process using as an example the landscape of political spectra  constructed from distinct online and offline data of the U.S. and South Korea, two nations with modern representative democracies. Specifically we study the political spectra of the legislators in each country, constructed in two ways. First, we construct a spectrum from the Twitter followership network, which we take as representing the online political landscape. There are some direct evidence that connection on social networks are biased towards those with similar political viewpoints. In a well-known study, Adamic and Glance showed a clear partisan divide on the blogosphere . RegardiWe utilized the following four data sets: (i) The Twitter followership network of the members of the 111th U.S. Senate; (ii) the roll call of the Senators on legislative bills; (iii) The Twitter followership network of the members of the 18th Korean National Assembly; and (iv) the roll call of the Assembly members between the years 2008 to 2010 (covering three quarters of the term). The Twitter data are current as of June 2011, and Twitter users who followed two or more legislators were included in the data. The data contain all legislators who owned an official Twitter account at the time of data collection and maintained their seat during the entire term considered. This resulted in 67 senators (out of 100), 698 roll calls, and 139 806 Twitter users for the U.S., and 194 assembly members (our of 299), 1 119 roll calls, and 124 341 Twitter users for Korea. Both countries have two-party systems, the Republican Party (GOP) and the Democratic Party (DEM) that account for 99% of the U.S. Senate, and the Grand National Party (GNP) and the Korean Democratic Party (DP) that account for 86% of the Korean National Assembly. [The data were modeled as bipartite networks. See The data introduced above can be modeled as bipartite networks on which the political spectra are constructed using matrix decomposition methods , 14. In We consider the offline, roll call-based spectra as revealing the true political positions of the legislators as they results from their actual actions of voting. Furthermore, we accept the roll call-based spectra as more accurately reflecting true political positions of the offline public and the society, as the legislators’ actions are likely to be heavily influenced by public interests.The political spectra of the legislators thus constructed are shown in Fig A close examination of these spectra can reveals the disparities between the online and the offline. First, we find a stark indication of the significance of the disparity between Twitter and the offline in the majority-minority reversal in both countries: The majority of Twitter users align themselves with the minority parties. The mean and the median positions, and the overall distribution of the spectra are shown in Second, the comparison between the roll call-based spectra of the legislators as the proxy for the offline public at large and the spectra of the Twitter users also reveals a similar disparity. Although we cannot directly compare the online and offline spectra on the same plot because of the differences in scale, the comparison of relative biases in the distribution is still valid. For the U.S., the spectrum of the Twitter users is heavily skewed towards GOP in sharp contrast to the DEMs who are favored in the spectrum of the offline public Fig  and 2B. τ-coefficient , with 0.68 ± 0.02 for the U.S. and 0.48 ± 0.01 for Korea  is immediately noticeable for both nations, and accordingly the roll call-based spectra and the Twitter-based spectra of the legislators exhibit some degree of overall agreement (similarity) as measured by Kendall’s This indicates that Korean Twitter population is more noticeably impervious to the political positions of the legislators, resulting in the very low level of intra-party correlations between the Twitter based spectrum and the roll call based spectrum of the legislators. In U.S. the correlation is more sizable.These disparities between the Twitter users and the offline public show the limits of Twitter as a source for fair and accurate opinion mining, especially pertaining to political or social issues. In fact, the majority-minority reversal shows us that Twitter may be functioning as a de facto “alternative media.”The spread structures of the Twitter users’ spectra in Fig D. It is non-negative, i.e. D ≥ 0, 0 when two distributions being compared are identical, and positive otherwise. To account for random fluctuations we calculate the Z-score of KL divergences (see ZD as a function of the number of politicians (degree) k that a user follows, shown in ZD ≳ 1 implies partisanship, while ZD ≲ −1 implies the opposite. The radius of the disc around each plot point is proportional to the the log of the number of users with the given degree k. The figure shows the widespread nature of partisanship on Twitter [A widely-used measure for the discrepancy between two probability distributions is the Kullback-Leibler (KL) divergence  Twitter , with thIn this paper we proposed a method to compare the political spectra of the online and offline public. We found significant discrepancies between the online and the offline, demanding caution when one tries to use online social media such as Twitter as source for fair and accurate opinion mining. A quantitative measurement of political biases found widespread partisanship among Twitter users.While our work shows the potential limits of the correctness of online social media-based findings, it is still meaningful that were able to obtain the political spectra of Twitter users based on their behavior observed objectively. This enables us to overcome uncertainty in methods based on self-assessment of political positions –20 causeN-dimensional space such that the distance between objects match given dissimilarities as much as possible. Specifically, CMDS aims to produce the coordinates of the objects that minimize a loss function called STRAIN [N = 1 to produce a linear political spectrum that agrees with the common way of thinking of political polarization (e.g. left wing versus right wing).CMDS is a method to embed a set of objects in a pre-defined d STRAIN . In thisCMDS is performed by the eigendecomposition of the matrix made from the dissimilarities. The quality of the resulting coordinates in reproducing the given dissimilarities is defined as the fraction of the first eigenvalue over the sum of all positive eigenvalues. The values are 0.297 for Korea and 0.872 for the U.S. with the roll call data, and 0.113 for Korea and 0.093 for the U.S. with the Twitter data. The noticeably high value for the U.S. roll call data may be due to the stable two-party system of the U.S.While one could theoretically gain more information from higher-dimension CMDS, we could not observe meaningful patterns allowing straightforward political interpretation beyond the first. Our 1-dimensional solutions show clear separations of the legislators along party lines, thus justifying the political interpretation given.i and j asFx denotes the set of the followers of legislator x. The Kulczynski dissimilarity is recommended as an alternative to the commonly used Jaccard measure when the size of Fx exhibits a wide range as in our Twitter data  votes of legislator x. We ignore the yea votes here. The motivation for this is that most bills that pass are bipartisan, potentially limiting the discriminating power of the yea votes.The dissimilarity between legislators From the legislator-Twitter data we estimated the spectrum of the legislators first on their Twitter followership, and then used it to obtain the spectrum of Twitter users. A dual approach could have been employed where we perform CMDS on the Twitter users by defining the dissimilarities between the users based on the set of legislators that they follow, and determining the positions of the legislators based on the results. While there is no reason to object this approach in principle, its computational cost was prohibitive, requiring several tens of gigabytes of memory to store the dissimilarity matrix alone.τ quantifies the similarity between two orderings of objects. Letting  and  be the orderings we wish to compare, Kendall’s τ rank correlation coefficient is defined asKendall’s rank correlation C is the number of  pairs such that the  satisfies xi > xj and yi > yj or it satisfies xi < xj and yi < yj. D is the number of  pairs such that the  satisfies xi > xj and yi < yj or it satisfies xi < xj and yi > yj.In this definition τ were estimated through the jackknife method [Errors in Kendall’s e method .Kullback-Leibler divergence (KL divergence) measures deviation of a probability distribution from a reference probability distribution. Here the reference probability distribution is the fractions of seats occupied by each party, i.e. the probability that a random user would follow a legislator from a given party.Pi be the fraction of the number of legislators from party i among the number of the legislators, and pij be the fraction of the number of legislators from party i followed by Twitter user j among all legislators followed by j. Then the KL divergence Dj of Twitter user j isN is the number of parties. KL divergence is non-negative. A large Dj means that user j’s followership pattern does not agree with the reference probability, i.e. favors one party disproportionately when in a two-party system.Let D⟩k of Twitter users who follow k legislators can be obtained easily from the empirical data, for statistical significance we use the Z-scoreμk and σk are the expectation and standard deviation of the KL divergence when a user choose to follow a party according to the reference probability Pi. If we assume a Twitter user follows k legislators randomly with uniform distribution, then the probability of n1+⋯+nN = k and While the average KL divergence ⟨ZD(k) ≳ 1 indicates that Twitter users follow a biased set of legislators, and that the bias is larger than the typical random fluctuation. Similarly, ZD(k) ≲ −1 means that Twitter users follow the parties in close agreement with the reference probability Pi. As k approaches the actual number of legislators pi, j converges to Pi, ZD(k) becomes more negative (since D → 0).S1 DatasetEach line is a record for a legislator. The first column is the Twitter ID, and the second column is the name. The last column is the party of the legislator. The file is encoded with UTF-8 for Korean characters.(TXT)Click here for additional data file.S2 DatasetEach line is a followee-follower relation. The first column is the Twitter ID of a legislator, and the second column is the Twitter ID of a follower of the legislator.(ZIP)Click here for additional data file.S3 DatasetEach line is a record of a legislator. The first column is the name of the legislator, and the rest columns are votes. A vote is encoded as 1 for yea, 2 for nay, 3 for abstention, and 4 for absence. The file is encoded with UTF-8 for Korean characters.(TXT)Click here for additional data file.S4 DatasetThe format is the same with (TXT)Click here for additional data file.S5 DatasetThe format is the same with (ZIP)Click here for additional data file.S6 DatasetThe format is the same with (TXT)Click here for additional data file.


The population of cells that make up a cancer are manifestly heterogeneous at the genetic, epigenetic, and phenotypic levels. In this mini-review, we summarise the extent of intra-tumour heterogeneity (ITH) across human malignancies, review the mechanisms that are responsible for generating and maintaining ITH, and discuss the ramifications and opportunities that ITH presents for cancer prognostication and treatment. Cancers of all types are now recognised to consist of highly diverse populations of cells3, where ITH is detectable at the genetic, epigenetic, and phenotypic levels  has been documented for many decades, initially from a morphological perspective5 should come as no surprise6. Carcinogenesis is an evolutionary process whereby somatic cells acquire random (epi)mutations that alter their phenotype, and the fittest new clones clonally expand because of the action of Darwinian natural selection7; repeated rounds of mutation and natural selection can lead to the development of a malignant cancer clone that is capable of migration and growth in remote sites. Diversity in the evolving cancer ecosystem is inevitable because it fuels the evolutionary fire; there can be no “survival of the fittest” if all the cells in the tumour have the same fitness.When viewed through the lens of evolutionary biology, the sometimes extreme levels of diversity present in cancers44. When multiplied by the billions of cell divisions required to produce even the smallest clinically detectable tumour (with a volume of approximately 1 cm3), this low level of mutation can potentially generate tremendous within-tumour genetic diversity. Moreover, the rate at which diversity is generated in a tumour is typically accelerated by genetic instability, likely a consequence of replication stress45, and the “mutator phenotype”46 that is a feature of most solid tumours. Furthermore, there can be rare but catastrophic DNA replication errors occurring during a single mitosis that can lead to the production of daughter cells with grossly altered genomes48.There are many mechanisms that contribute to ITH, and these can be broadly classified as “cell autonomous” or “non-cell autonomous”. An example of a cell-autonomous mechanism is the persistence of small numbers of errors that occur during DNA replication49. Therefore, it is conceivable that we will soon be able to envisage mapping the genetic diversity of an entire tumour at cellular resolution. However, an important question is how much of this information about genetic ITH will prove to be clinically relevant? Intriguingly, in some cancers (lung11 and colon14), the key driver mutations are proven to be clonal , although spatially localised drivers have been found in other cancer types (e.g. kidney9). The obvious question then becomes how many, if indeed any, of the heterogeneous mutations are important for tumour growth? Clearly, there is a need to discriminate between ITH that is attributable to “mutational noise” (e.g. the background mutation rate) and that which is in some sense “functional” for tumour development. Mathematical modelling of the amount of ITH that should be expected in a growing tumour can be helpful here, as an increase or decrease of ITH compared with expectation reveals “important” evolution in the tumour (our effort to implement such a model50 is discussed briefly below). An alternative empirical approach would require the concurrent measurement of genotype and phenotype so that genetic ITH can be related to the presence of (minority) cell populations with biologically distinct function. Interestingly, measurement of the behaviour of different clones within a colon cancer, including their sensitivity to cytotoxic drugs, revealed marked differences in behaviour between tumour regions, without concomitant differences in genotype15. This study in particular highlights how genetic ITH can be a poor proxy for functional heterogeneity, and the latter is clearly of greater clinical relevance, especially ITH of drug response.A result of recent advances in next-generation sequencing is that single-cell whole genome sequencing is now possible (and indeed transcriptome sequencing too)51, a form of CSC architecture may significantly contribute to ITH in solid tumours. There is evidence of a CSC hierarchy in mouse models of brain cancer53, where a small subpopulation of cells is responsible for sustaining tumour growth. The definition of a CSC continues to be debated54, but if CSCs are defined simply as the population of cells with long-term self-renewal capability, then following debulking chemotherapy tumour regrowth is determined by the prevalence of the CSC phenotype amongst the surviving cells18. The question of the plasticity of the stem cell phenotype is an interesting topic of discussion. It is noteworthy that observations of cell populations growingin vitro55 and genetically engineered mouse models of intestinal tumourigenesis56 suggest that bidirectional phenotype switching can occur , questioning whether the “stemness” of a tumour cell is strictly intrinsically defined. Plasticity of a CSC phenotype can be viewed as a form of epigenetic heterogeneity within the tumour.A further source of ITH is the persistence of a cancer stem cell (CSC) hierarchy, a factor that may be described as either cell-autonomous (i.e. “stemness” is governed by a cell’s genetic or epigenetic makeup) and non-cell autonomous . Although a strict hierarchy of differentiation is described as a feature of haematopoietic cancers62; in a highly diverse microenvironment, different cellular phenotypes may be selected for or against in different regions of the tumour. For example, any sizeable tumour will inevitably contain areas of hypoxia and normoxia. In a hypoxic region, an anaerobically metabolising cell is expected to have a fitness advantage over an aerobically metabolising cell and so should repopulate the hypoxic region, but the opposite should be true in a normoxic region63. Such different phenotypes may be genetically determined (such as the case of VHL/HIF1 mutants in kidney cancer)64, or they may be a consequence of plasticity in cellular behaviours15, and furthermore there may be feedback between the tumour cells and their microenvironment that drives specialisation of tumour cells and the concomitant strengthening of microenvironmental gradients.Heterogeneity in the microenvironment of a cancer can be described as a non-cell autonomous driver of cancer cell diversity67. Intermixing of clonally distinct populations is a feature of many tumours and has been particularly well documented in gliomas16. If interactions between cells within a tumour are critical for tumour maintenance, then reducing the tumour population size below some critical threshold—called an Allee threshold—may be an effective cancer treatment61. The logic behind this idea is that small populations are not able to produce sufficient density of cooperative factors that are necessary for tumour maintenance (such as diffusible growth factors) and so small populations are unviable. The interplay between clones that areproducers (e.g. angiogenic cells) versusconsumers  should, theoretically, also influence phenotypic diversity within a cancer. Clearly, tumour cells can also benefit from factors produced by the stromal cells in their microenvironment68.Clonal interference refers to the situation where two or more clones of similar fitness each impede the growth of the other by competing equally well for limited resources, and theoretical models of cancer growth predict that clonal interference should slow cancer evolution and lead to the longer-term maintenance of multiple distinct clonal populations69. Interactions between tumour cells and the immune system also shape ITH: for example, the immune system predates the tumour cells, and tumour cells that have evolved to avoid immune detection will persist and perhaps clonally expand in the tumour70.A further example of a non-cell autonomous driver of ITH is the interactions between subclones of the tumour. In experimental systems, synergistic or predatory interactions between phenotypically distinct clones drive tumour growthecosystems60. In the cancer ecosystem, the relative fitness conferred by a new mutation is defined by microenvironmental context57, where the context refers to both the neighbourhood of tumour and stromal cells and more broadly diffusible factors. Importantly, the ecological viewpoint provides a single framework to understand the seemingly distinct contributions of cell-autonomous and non-cell autonomous factors to ITH. This is because the ecological view forces us to recognise that all the evolution within a tumour is only ever driven by selection in the current microenvironment context: cell-autonomous drivers provide an advantage to the cell in their current context, and non-cell autonomous factors can drive evolution by changing that context.Recognising that there is a dynamic interplay between tumour cells and their microenvironment, and between tumour cells themselves, means that cancers are best viewed as complex evolving50. We note that this kind of approach can help to delineate “important” ITH from the inconsequential “mutational noise” mentioned above. More generally, such models can be used to generate new hypotheses concerning the generation and maintenance of ITH, and the effects of ITH on tumour evolution, and to predict response to therapy and recurrence.Multi-faceted networks are challenging to understand, and consequently computational or mathematical models are increasingly recognised as essential tools to integrate and interpret the complex, multi-scale data derived from the interplay between tumour subclones and their interactions with the microenvironment . Relatedly, genetic analysis is employed to predict the likely efficacy of targeted therapies by testing whether the tumour contains pre-existing resistant clones; for example, colorectal cancers are screened forKRAS mutations as the presence of aKRAS mutant clone means that the anti-epidermal growth factor receptor (anti-EGFR) antibody cetuximab will be an ineffective treatment75.Traditionally, cancer prognosis has been determined by the presence or absence of a particular feature within a tumour. Histopathology remains the mainstay of this approach: tumours are scored for stage (how far the cancer has invaded) and grade . Molecular markers have entered clinical practice too, most prominently assessment of oestrogen receptor (ER), progesterone receptor (PR), and HER2 status in breast cancer8. Epistatic interactions (perhaps driven by unmeasured genes) could also potentially alter the prognostic value of individual molecular feature (http://cancergenome.nih.gov), is that every tumour appears molecularly unique or, in other words, that there appear to be many different ways to produce a particular cancer. This rampantinter-tumour heterogeneity may preclude a “one size fits all” approach to feature-based prognostic markers since a particular prognostic feature is unlikely to work for all cancers of a particular type  by the degree of diversity present in that population  and so naive genetic ITH measures may have limited prognostic value.Empirical studies support the idea of quantification of ITH as a prognostic biomarker. Originally, measurements of intra-lesion genetic diversity within the premalignant condition Barrett’s oesophagus were found to be strong predictors of cancer development risk86 or polymerase-epsilon87 defective tumours of the colon), which are expected to have very high levels of genetic ITH, paradoxically have very good prognoses90. Similarly, with an expression-based signature to quantify the degree of chromosomal instability (CIN) in breast cancers, a measure which likely correlates with ITH, the patients with the worst outcome were those whose tumours had intermediate levels of CIN, whereas patients whose tumours had very high levels of CIN had better outcomes91. One possible explanation for these data is that very high mutation rates, while generating lots of diversity that makes the tumour more evolvable, also generate lots of deleterious variants that impede clone growth. A further contributing factor to this favourable prognosis  is the elevated immunogenicity of tumours with a high mutation rate, a feature that is the result of the generation of many immunogenic neo-antigens, which stimulate the host immune system92. Effective ITH-based prognostic markers will need to address such complexities of tumour evolution to be broadly useful. We note too that existing broad molecular classifications of cancer  can have significant prognostic value themselves, and consequently ITH-based measures may be useful only to stratify within such molecularly defined subgroups.Intriguingly, tumours that have underlying defects in DNA repair (such as mismatch repair97. Pre-existing ITH can be (indirectly) attributed as the underlying cause of these treatment failures. Tumours, at the stage when they are treated, contain many billions of cells and it appears an almost mathematical certainty that at least a few of these cells will have evolved a therapy-resistant phenotype. Indeed, in patients with colorectal cancer treated with the anti-EGFR antibody cetuximab, empirical measurements show that KRAS-mutant clones that likely existed at undetectably low frequency prior to the initiation of therapy expand exponentially at the administration of therapy98 and similar dynamics have been observed in a variety of other malignancies, including lung100, leukaemia101, and melanoma103.Resistance to chemo- and targeted-therapy, and concomitant treatment failure, occurs in the majority of cases104. An exciting but radically different approach is to try to use the presence of ITH itself in order to increase the efficacy of cancer treatment105. The approach, termedadaptive therapy, is based on the maxim that “nothing comes for free”. This is the idea that resistance to a particular therapeutic agent inevitably carries some cost to the cancer cell, so that in theabsence of therapy a resistant clone is at a disadvantage and so will be outcompeted by any remaining sensitive cells. The adaptive therapy approach is therefore to pulse the drug in such a manner that when the drug is present the sensitive cell population is killed off and the resistant cell population prospers, whereas when the drug is absent the resistant population is outcompeted by the sensitive population. In theory, if the sensitive population is able to outcompete the resistant population sufficiently well, this treatment regime will mean that the tumour remains sensitised to the drug over long time frames while the size of tumour population as a whole is effectively constrained107. In a melanoma xenograft model, this idea appears efficacious108. Translation of this idea to the clinic would require the development of appropriate monitoring tools that indicate when to provide/withdraw treatment; this is clearly a major challenge in itself. It is important to note that the idea behind adaptive therapy is not to affect a cancer cure—indeed, the idea is predicated on the assumption that this is impossible with a single agent—but rather to extend the effectiveness of a particular agent indefinitely and in so doing make the cancer a chronic rather than fatal disease.Is ITH an insurmountable barrier to effective cancer treatment? It is clearly a major challenge, and accordingly one approach that has been suggested is to try to suppress the level of ITH within a tumour (by targeting the drivers of genetic instability) in order to provide a more homogenous tumour that may be more pliable with treatmentsteer the evolutionary response of a tumour to treatment110. The general idea of these innovative proposals is that it is possible to predict which phenotypes will emerge from a heterogeneous population under treatment, and so the next therapy can be applied in order to treat the emergent clone. This logic provides the rationale behind attempts to “vertically” combine targeted therapies (targeting multiple members of the same pathway) to prevent resistance to a single agent emerging because of the selective pressure the mono-agent provides for the emergence of a clone with a mutation in a gene downstream of the original drug target112. In general, combination therapies may also help to tackle the issue of pre-existing resistance in heterogeneous tumours, since the chance of a cell being doubly resistant to two different therapies should be proportional to the probability that the cell is resistant to any one therapy individually, and so if mono-therapy resistance is rare in the tumour cell population, then doubly resistant cells should be vanishingly rare indeed113. An emerging theme of combination therapy is the use of both targeted and non-specific therapies, such as immunotherapy. For example, a combination of BRAF and MEK inhibitors with adoptive cell transfer (ACT) immunotherapy in a BRAF-driven mouse model of melanoma has been shown to induce complete tumour regression114. However, it should be noted that combining multiple therapies may carry the cost of increased toxicity to normal tissue. Together, these data and theoretical studies suggest that therapeutic regimes will need to bepersonalised for the evolutionary response of an individual tumour in order to effectively tackle the problem of ITH.Other approaches have been proposed that similarly attempt toITH is an inevitable feature of all cancers and presents challenges to our understanding of tumour biology and our ability to prognose and treat cancer. But it is our opinion that these challenges are not insurmountable, and in fact understanding the processes generating ITH provides us with a window to understand the very drivers of carcinogenesis itself. From a more clinical perspective, quantification of ITH offers an exciting opportunity to improve prognostication, and exploiting rather than ignoring ITH has the potential to improve the efficacy of existing cancer therapies.


The aim of this research was to study the levels of 8-oxo-7,8-dihydro-2′-deoxyguanosine (8-oxodG) in tumour tissue samples of colorectal carcinoma based upon immunohistochemical detection and compare those results with patients’ outcome.Tumour blocks of patients surgically treated for colorectal cancer were evaluated by 8-oxodG immunohistochemical staining. The expression was analysed in 500 tumour cells. The percentage of positive cells, as well as staining intensity, was recorded, and Allred score was calculated. For each patient, data of age, gender, tumour size and location, margin status, histologic grade, tumour stage, lymph node status, vascular invasion, overall survival, and therapy protocols were collected. Tumour grade was divided into two groups as low and high grade.n = 138, Mantel–Cox, chi-square 4.116, degree of freedom (df) = 1, p < 0.05; 10-year survival, n = 134, Mantel–Cox, chi-square 4.374, df = 1, p < 0.05). The results showed a positive correlation between Allred score and high tumour grade , as well as with non-polypoid tumour growth . There was no significant difference of 8-oxodG expression related to age, sex, blood group, size and tumour site, distance from the edge of the resected tumour margin, lymph nodes involvement, and vascular invasion.In this study, 146 consecutive patients with primary colorectal carcinoma were included. All data were available for 138 patients, and they were included in this research. There were 83 male and 55 female patients; the median age was 64 years (range 35–87 years). The results showed shorter 5- and 10-year survival in patients with 8-oxodG positive tumour cells (5-year survival, In this study, the positive correlation between 8-oxodG presence in the tumour cells, worse clinical outcome, higher tumour grade, and flat morphology was found. Colorectal cancer (CRC) originates from the colonic mucosal epithelial cells and represents a genomic disease that is the result of the cumulative effect of external and hereditary factors. Progression of the tumour is due to the clonal cell expansion with a selective growth advantage compared to surrounding epithelial cells. It is well known that cells located at the bottom of the crypt are more susceptible to oxidative damage due to the presence of hydrogen peroxide, in relation to the well-differentiated surface cells . These cThe hypothesis of this study is higher levels of 8-oxo-7,8-dihydro-2′-deoxyguanosine (8-oxodG) in tumour tissue samples of colorectal carcinoma correlate with higher tumour grade and worse survival.The study was a retrospective cohort between 1 January 1999 and 31 December 2014 at the Department of Surgery University Hospital Center Zagreb, Croatia. The study was approved by the Ethics Committee of the University Hospital Center Zagreb and the Ethics Committee of the Medical School Zagreb, Croatia.In this study, 138 tumour paraffin blocks were used from 138 eligible patients with primary colorectal carcinoma, out of 146 consecutive patients surgically treated at our institution between 1 January 1999 and 31 December 2000, excluding hereditary CRCs. During the surgery, presence of any distant metastases was recorded. Data of age, gender, location and size of tumour, tumour stage, histological grade, lymph node status, vascular invasion, distance from the nearest edge of resected margin, overall survival in months, and therapy protocols were collected for 146 patients, resulting in left censoring applied to 8 patients due to missing most of the data. The patients alive after 31 December 2014 were right censored at survival analysis. The survival status of 134 patients was available for 15 years of follow-up. According to histological tumour grade, the patients were divided into two groups: low grade (well- and moderately differentiated tumours) and high grade (poorly differentiated tumours).For diagnostic purposes after fixation, tumours were paraffin embedded, cut, and stained with a standard method (haematoxylin–eosin) for light microscope analysis. For additional immunohistochemical staining, 4-μm cuts were used. Primary antibody anti-8 hydroxyguanosine  was used for specimen staining using immunoperoxidase avidin–biotin method in an automatic stainer . Positive control in immunohistochemical staining was epidermis from hairless mouse stained with this antibody. Negative control was the same tissue with avoiding primary antibody. The antigen–antibody reaction was visualized by 3,3-diaminobenzidine tetrahydrochloride (DAB).The percentage of positive cells and staining intensity in a total of 500 counted tumour cells per patient sample were recorded. The intensity of staining was classified into four grades as negative (no staining), weak, medium, and intensive by observation under different microscopic magnification Fig. . Allred t test procedure and Pearson’s chi-square test where applicable. Survival was analysed using Cox regression models and Kaplan–Meier curves with starting point defined as the end of surgical treatment and end points defined as mortality at 15 years of follow-up. The Spearman’s test examined correlations and log-rank test examined statistical differences. Significance was determined by a probability value <0.05, with all p values two-sided.All analyses were performed using SPSS for Windows version 22 . The descriptive statistic was used. Difference between the groups was assessed using independent N = 138; ρ −0.191, p < 0.05), percentage of positive tumour cells , and Allred score . Shorter 5- and 10-year survival was noticed in the patients with 8-oxodG positive tumour cells  = 1, p < 0.05; 10-year survival, n = 134, Mantel–Cox, chi-square 4.374, df = 1, p < 0.05)    was adjusted for age, gender, and tumour stage as confounders. Those confounders were identified to significantly impact survival estimated by Cox regression models as follows: age  1.028; 95 % confidence interval (CI) range 1.004–1.052), gender , and TNM stage ; 95 % CI range n/a; stage II p < 0.0001; HR 0.210; 95 % CI range 0.100–0.437; stage III p < 0.0001; HR 0.327; 95 % CI range 0.176–0.607; stage IV p = 0.024; HR 0.517; 95 % CI range 0.291–0.917). There was no difference in survival between the two groups of patients based upon Allred score negative versus positive status in tumour tissue and normal mucosis . There was no significant difference of 8-oxodG expression related to age, sex, blood group, size and tumour site, distance from the edge of the resected tumour margin, and lymph nodes involvement. There was no statistically significant correlation between the presence of 8-oxodG within the cytoplasm of tumour cells and the presence of vascular invasion.A positive correlation between the non-polypoid macroscopic appearance of tumours with a higher percentage of 8-oxodG positive tumour cells and higher intensity of reaction as well as higher Allred score was found  are responsible for the formation of considerable number of CRCs. According to the data of Kudo et al.  on 14,01Vascular invasion is related to invasion of peritumoural vascular structures and is associated with poor prognosis of patients with CRC. More often it is the penetration through the wall of the vein responsible for the vascular invasion in colorectal carcinoma . The assAccording to the previous research on the subject of oxidative stress and CRC and the results of this study, it seems that a certain part of the mutations responsible for CRC development may be attributable to the reactive chemical compounds. If the 8-oxodG-influenced CG-TA mutation results in the formation of CRC, these patients would have a worse prognosis. In later follow-up, patients with positive reaction to 8-oxodG are probably influenced more by general health impairment due to oxidative stress rather than the influence of oxidative stress on tumour carcinogenesis. The study results confirmed a positive correlation between level of 8-oxodG in tumour tissue of patients with CRC and higher tumour stage but failed to confirm worse outcome of such patients. The displayed results should be seen as a guideline for the proper formation of future prospective studies with a larger sample size to reach relevant information about oxidative stress influence on survival.


There is a missing grant number in the Financial Disclosure (FD) statement of this article. Please refer to the correct FD statement below:This work was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) 1001 Grant number: 112T272 and Sabanci University. D.G. and A.K are recipients the Turkish Academy of Sciences (TUBA) GEBIP Award. D.G. is a recipient of the EMBO Strategical Development and Installation Grant (EMBO-SDIG) and A.K. is a recipient of the TUBITAK Incentive Award. G.K. and K.A.T. are recipients of Yousef Jameel and TUBITAK-BIDEB PhD Scholarships, respectively. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


Tryptase is a serin protease stored and released from mast cells (MCs) that plays a role in tumour angiogenesis. In this study we aimed to evaluate serum tryptase levels in colo-rectal cancer (CRC) patients before (STLBS) and after (STLAS) radical surgical resection. We also evaluated mast cell density positive to tryptase (MCDPT) and microvascular density (MVD) in primary tumour tissue.A series of 61 patients with stage B and C CRC (according to the Astler and Coller staging system) were selected. Serum blood samples were collected from patients one day before and one day after surgery. Tryptase levels were measured using the UniCAP Tryptase Fluoroenzymeimmunoassay . Tumour sections were immunostained with a primary anti-tryptase antibody  and an anti CD-34 antibody  by means of immunohistochemistry and then evaluated by image analysis methods.The mean ± s.d. STLBS and STLAS was 5.63±2.61 µg/L, and 3.39±1.47 µg/L respectively and a significant difference between mean levels was found: p = 0.000 by t-test. The mean ± s.d. of MCDPT and MVD was 8.13±3.28 and 29.16±7.39 respectively. A strong correlation between STLBS and MVD ; STLBS and MCDPT ; and MCDPT and MVD  was found.Results demonstrated higher STLBS in CRC patients, indicating an involvement of MC tryptase in CRC angiogenesis. Data also indicated lower STLAS, suggesting the release of tryptase from tumour-infiltrating MCs. Serum tryptase levels may therefore play a role as a novel bio-marker predictive of response to radical surgery. In this context tryptase inhibitors such as Gabexate and Nafamostat Mesilate might be evaluated in adjuvant clinical trials as a new anti-angiogenic approach. Angiogenesis is a complex process involved in the growth, invasion, and metastasis of several human and animal tumours vitro and in vivo and is therefore involved in cancer development.Tryptase is a neutral serine protease with a molecular weight of 134 kDa and a tetrameric structure consisting of non-covalently linked subunits. Four different forms of tryptase have been identified in human MCs thus far: α-, β-, γ- and δ-tryptase In this pilot study, we analyzed serum tryptase levels (STLs) in CRC patients before (STLBS) and after (STLAS) radical surgical resection; MC density positive to tryptase (MCDPT); and microvascular density (MVD) in primary tumour tissue in order to correlate each to the others and to evaluate STLs as a possible bio-marker predictive of response to radical surgery. In this context, the inhibition of tryptase by means of gabexate and nafamostat may be evaluated in adjuvant clinical trials as a new anti-cancer therapy th Edition A series of 61 CRC patients observed at the Clinical Surgery Unit of the “Magna Graecia” University of Catanzaro were selected. Helical computed tomography of the thorax, abdomen and pelvis revealed that no patients had distant metastases. Various surgical approaches were used: left and right open hemicolectomy for colon cancer; open anterior resection, total meso-rectal excision and open abdomino-perineal resection for rectal cancer Blood samples were collected between 7:00 and 9:00 a.m. after overnight fasting, one day before and one day after surgical resection of the tumour. The samples were immediately dispensed into test tubes with serum separator tubes without additives  and left for at least 30 min at room temperature to allow for a complete clotting process. The samples were subsequently centrifuged at 1,500×g for 15 min at room temperature and the supernatant was recovered. Patient sera obtained were collected, aliquoted and frozen at −80°C until the analysis phase.Prior to starting the analytical phase, the samples were thawed to room temperature and mixed thoroughly by vortexing at low speed in order to eliminate any residues of fibrin or other particulate matter potentially affecting reproducible results. Lipemic or hemolyzed samples potentially interfering with the assay were excluded from the analytical evaluation.Serum tryptase levels were measured by fluoro-enzyme-immunoassay (FEIA) for each sample, using Uni-CAP100 .For the evaluation of MCPT and MVD a three-layer biotin-avidin-peroxidase system was utilized 2 area). The details were evaluated at x1000 magnification in oil (2 area). In tumour tissue VEGF expression was evaluated  was utilized n in oil  were couvaluated .MCDPT and MVD mean values ±1 standard deviations (SD) were evaluated by two independent observers (G.R. and G.D.) for each tumour sample and in all series of sections. MCDPT mean values ±1 SD was also evaluated for each adjacent normal mucosa. Correlations between STLs, MCDPT, and MVD were calculated using Pearson's (r) analysis. The correlations between the above indexes and the clinico-pathological features listed in The mean ± s.d. STLBS and STLAS were 5.63±2.61 µg/L and 3.39±1.47 µg/L respectively, and a significant difference between mean levels was found: p = 0.000 by t-test , Fig. 4.No other significant correlations between the above indexes and the main clinico-pathological features listed in in vitro and in vivo experimental models; secondly, tryptase is an agonist of the proteinase-activated receptor-2 in vascular endothelial cells that, in turn, induces angiogenesis In this pilot study we have shown for the first time that STLBS strongly correlates with MCDPT and MVD in primary tumour tissue. Our data also demonstrates that STLs decrease following surgical resection in CRC patients with a significantly lower value of STLAS. Due to the release of tryptase from MC activation, we suggest that MCDPT in primary CRC tumour tissue represents the main source of serum tryptase. In our hypothesis, if primary tumour tissue is completely removed STLs should decrease in a day due to their approximately 4-h long life-cycle. For these reasons, we detected STLs 24 h before surgery to evaluate their possible role as a circulating biomarker suggesting the presence of tumour tissue, and again 24 h after treatment to confirm the concentration decrease and, as a consequence, its possible implication of the absence of tumour tissue. We elaborate the background of our hypothesis based on previously published pilot data which suggested an increase of MCDPT in primary tumour tissue, and here our results also confirmed the reported data , 3A. In Therefore, with the primary source of tryptase production no longer existing, after 24 h a significant reduction in STLs should be expected. If elevated STLs persist after surgery, this would suggest that residual tumour tissue remains after surgical resection or, alternatively, that unknown metastases are present. In this context, several tryptase inhibitors such as Gabexate or Nafamostat mesilate may be evaluated in future clinical trials as a new anti-tumour and anti-angiogenic approach.


Fluid overload is associated with adverse outcomes in critical illness; however, better methodology is required for its quantification. Bioelectrical impedance analysis (BIA) represents a noninvasive method for quantification of fluid overload , but hasWe assessed changes in fluid balance and performed daily BIA  over 3 days in consecutive ICU admissions with LOS >72 hours.R2 = 0.48 and 0.37 respectively)  on day 1, equating to excess fluid of 7.2 l (IQR 5 to 13.9). Median right body resistance normalized to height at 50 kHz (R50/h) on day 1 was 214 Ω/m (IQR 187 to 256). Daily change in ECW and R50/h correlated with daily fluid balance between BIA measurements (BIA suggests many patients already have significant fluid overload on the first day of ICU admission. Overall, changes in device-specific algorithms for ECW estimation and measured resistances correlated with recorded fluid balance; however, there were inconsistencies in the number of individual patients. Prospective assessment is required to establish whether BIA measurements can be used to assist fluid management in the ICU.


Medical societies, faculty, and trainees use Twitter to learn from and educate other social media users. These social media communities bring together individuals with various levels of experience. It is not known if experienced individuals are also the most influential members. We hypothesize that participants with the greatest experience would be the most influential members of a Twitter community.We analyzed the 2013 Association of Program Directors in Internal Medicine Twitter community. We measured the number of tweets authored by each participant and the number of amplified tweets (re-tweets). We developed a multivariate linear regression model to identify any relationship to social media influence, measured by the PageRank.Faculty (from academic institutions) comprised 19% of the 132 participants in the learning community (p < 0.0001). Faculty authored 49% of all 867 tweets (p < 0.0001). Their tweets were the most likely to be amplified . Faculty had the greatest influence amongst all participants . Being a faculty member had no predictive effect on influence . The only factors that predicted influence (higher PageRank) were the number of tweets authored (p < 0.0001) and number of tweets amplified (p < 0.0001)The status of “faculty member” did not confer a greater influence. Any participant who was able to author the greatest number of tweets or have more of his/her tweets amplified could wield a greater influence on the participants, regardless of his/her authority. These social media communities offer a new and exciting medium by which knowledge can be shared and transmitted3. These communities bring together individuals/organizations with various levels of experience5. In the traditional learning model, the learners/students are aware of the authority of the teacher. In Twitter learning communities, however, there are many teachers whose levels of experience can vary. As a result, individuals who participate in Twitter learning communities will be learning from multiple teachers of different levels of experience. This variety can pose a problem because inexperienced individuals can exert a great influence over learners. Although experienced teachers are increasingly participating in Twitter learning communities, whether they are also the most influential members within the learning community is unknown6. We hypothesize that participants with the greatest experience would be the most influential members of one such Twitter learning community.A number of medical societies, faculty members, and physician-trainees use social media, specifically Twitter, to learn from and educate other social media usershttp://www.im.org). The AAIM is a consortium of five academically focused organizations that represent Internal Medicine in the United States: 1) the Association of Professors in Medicine, 2) Association of Program Directors in Internal Medicine, 3) Association of Specialty Professors, 4) Clerkship Directors in Internal Medicine, and 5) Administrators in Internal Medicine. We identified the online Twitter community for this conference through the official hashtag designation established by the AAIM: #APDIM13. Unlike the Twitter learning communities from other scientific meetings, the #APDIM13 hashtag was not created ad-hoc by an unofficial group of conference attendees, but was created and endorsed by the conference organizer (AAIM). Only publicly available tweets and their respective metadata (including author usernames) were collected from theHealthcare Hashtag Project from 28 April to 1 May 20137. The Project provides free “firehose” access to researchers who are investigating the use of Twitter at scientific conferences.We analyzed Twitter messages (tweets) from the 2013 Association of Program Directors in Internal Medicine meeting. This meeting was held from 28 April to 1 May 2013 and brought together residents and chief residents in Internal Medicine with faculty members and program directors. The Alliance for Academic Internal Medicine (AAIM) organized the meeting  faculty, 2) trainee or residency program representative, 3) organization, or 4) other or unidentifiable. Using metadata, we examined the Twitter profile page of each participant of the Twitter community. We categorized participants as “faculty” if his/her profile page indicated s/he was a faculty member at an academic institution. We identified trainees or residency program representatives if their profile page indicated they were a 1) resident, 2) chief resident, or if the username/profile stated they were a residency program . We categorized tweets from the AAIM as “organizer”, as they were all participants that represented a third-party organization. We categorized participants as “other” if the profile page was ambiguous or incomplete. We did not perform an internet search of authors whose Twitter profiles were ambiguous because these profiles were deficient in key pieces of information that would have allowed us to identify them correctly . Finally, we calculated the number/proportion of tweets per category. The greater the proportion of tweets authored, the greater the Twitter activity.RT within a tweet. Participants whose tweets were re-tweeted the most exhibited high Twitter activity.In the second analysis, we calculated the number of re-tweets per category. Re-tweets are tweets authored by one participant and re-broadcasted (amplified) to a larger Twitter audience by a second participant. We identified re-tweets by the prefix10. It assigns a unitless decimal value to each participant based on three factors: 1) the number of times the incident participant is mentioned in the online community, 2) the number of different participants who mention the incident participant and 3) the PageRank of each participant that mentions the incident participant13. For example, if a number of participants mentioned participant A many times, participant A would have a high influence and high PageRank. If a smaller number of participants mentioned participant B, his/her influence would be less than that of participant A14. Each tweet contained the necessary data to determine if the author mentioned another participant.We measured Twitter influence using Google’s algorithm. The PageRank algorithm quantifies individual influence within an online communityWe pre-defined “faculty” as individuals from academic institutions who are the most experienced sources of medical information within a Twitter community.NodeXL to calculate PageRank as a continuous variable. We used the Fruchterman-Reingold algorithm to develop a directed network map of influence15. Nominal variables included each of the four categories assigned to a participant . We used the Chi-square test to compare the nominal variables; t-tests and ANOVA for continuous variable comparisons. We developed a multivariate linear regression model, based on standard least squares, to identify the factors that predicted online influence. JMP Pro 10.1 was used to perform all statistical analyses. We performed a word frequency analysis using NVivo 10. This investigation was exempt from review by the Institutional Review Board because the data set is part of the public domain according to Section 102 of the United States Copyright Act16. To the best of our knowledge, this investigation conforms to STROBE guidelines for observational research andSAMPL guidelines for statistical reporting18.The data set was downloaded and analyzed using Microsoft Excel 2013. We considered Twitter activity measured as number of tweets and number of tweets re-tweeted, as a continuous variable. We usedOne hundred thirty two participants authored a total of 867 tweets. Common words used in these tweets included: “great”, “residents”, and “meeting” . We idenThe mean PageRank for all participants was 0.92 (SD 1.31). Faculty members had the greatest mean PageRank of 1.99 (95% CI 1.53–2.46). This PageRank was statistically greater than that for trainees  and those participants who could not be categorized  .Figure We developed the following multivariate linear regression model:PageRank = 0.51 + 0.061*(number of tweets authored) + 0.067*(number of tweets re-tweeted) – 0.25*(1 if category = other) Identifying oneself as a faculty member did not predict the PageRank. The participants that identified themselves as either a trainee or organization did not have higher PageRanks. The model predicted a lower PageRank for those participants who failed to identify themselves or whose identity could not be discerned from their Twitter profile .The two main findings in this investigation are: 1) being an experienced source of medical information has no effect on influence within a Twitter learning community and 2) a large percentage of participants do not provide enough information for one to assess their level of experience.5. These communities bring together participants of various levels and, effectively, allow each participant to assume the role of both learner and teacher. While “learners” are exposed to a number of “teachers” in these communities, not all participants who assume the role of “teacher” are qualified to do so. Teachers are traditionally considered to have experience regarding the subject matter they teach. These features allow teachers to exert influence over the learners. In our investigation, the most experienced sources of medical information (faculty) exerted the greatest influence in the Twitter community. However, they did so because they had the greatest Twitter activity and not because of their status as faculty members.Twitter learning communities are becoming increasingly popular. Both the American Societies of Clinical Oncology and Nephrology  have begun yearly Twitter learning communities to accompany their annual scientific meetingsInfluence that depends only on Twitter activity and not the experience of the composer of a tweet is concerning. Any participant, regardless of his/her experience, could exert a great influence over the community simply by authoring the most tweets. As a result, learners may be receiving medical information from sources of questionable experience. To our knowledge, there has been no literature to support the idea that participants with the greatest Twitter activity are necessarily the most experienced sources of knowledge.1. Even fewer choose to identify their gender/sex (64.2%)1. Ambiguity in one’s professional status poses a unique challenge in the medical community. Currently, physicians who use Twitter face an “identity dilemma”, which results in incomplete, inaccurate, and often ambiguous Twitter profiles19. Such profiles make it hard for the learner to assess the experience of the participant dispensing information. Moreover, ambiguous profiles are antithetical to the American Medical Association’s (AMA) principles of medical ethics20. Both the AMA and Twitter-savvy physicians advocate “ownership of activity” in social media by avoiding anonymity and accurately stating one’s credentials21. Given that we could not identify over half of participants in the #APDIM13 Twitter learning community, it is possible that the current regression model is unable to reveal the predictive power of one’s identity on social media influence.The second and equally concerning finding is the ambiguity in Twitter profiles of a large percentage of participants. Uncategorized participants accounted for 58% of all participants in the #APDIM13 community. While over 91% of Twitter users choose to make their profiles publicly visible, fewer seemed to identify their geographic location (75.3%) or place of origin (71.8%)Two limitations deserve a special mention. First, we were unsuccessful at identifying those individuals whose Twitter profiles were ambiguous. In this investigation, we classified them as “other” because the vague Twitter profiles did not allow us to identify them with reasonable certainty. Second, we could not include additional variables into our prediction model. The ambiguous profiles did not include information about age, gender, and/or location. Had we included these variables into our multivariate linear equation, we would have produced an unreliable prediction model.22. Unlike our method, tie strength changes over time, thereby making it difficult to assess one’s influence within a specific learning community. We also used the PageRank algorithm to quantify social media influence. PageRank is considered to be an accurate measurement of influence within social media networks and offers more insight into a person’s influence than simply counting his/her number of followers23.The greatest strength of this investigation is the method used to measure social media influence. We quantified influence using the number and directionality of mentions within the learning community. Previous studies have used tie strength to measure influenceAs the number of Twitter learning communities grow in number and variety, less emphasis will be placed on using social media to exchange medical knowledge. Rather, a greater focus should and will be made towards how to create communities where experienced teachers can 1) be easily identified and 2) have the greatest influence over learners. We must train students to correctly identify experienced sources of information and train those sources to create clear, unambiguous Twitter profiles to allow for easy identification by students. Until then, individuals who consider themselves as experienced educators must actively use Twitter in order to have the greatest influence on learners .The data can be downloaded from the Healthcare Hashtag Project website using the hashtag #APDIM13. et al. have reported on the factors contributing to social media influence in a medical community, specifically regarding Twitter use related to an Internal Medicine conference. With a growing presence of social media and online tools in medical education, this represents a relevant and timely study. They demonstrated that faculty members were more likely to have tweets amplified (retweeted) but faculty were not necessarily more influential (as measure using PageRank). Factors influencing influence were number of tweets i,e  he/she who shouts the loudest, had the most influence. This has implications for how we get our online material and the quality of the source.DesaiMany users (60%) were uncategorized so this undoubtedly took away from the power of the study/conclusions that can be drawn. This is not the fault of the authors however and is a talking point in its own right.Abstract: This portion is confusing to a casual reader: "Faculty had the greatest influence amongst all participants . Being a faculty member had no predictive effect on influence ."-Regarding PageRank (which many readers will not be familiar with): Does it have a maximum value or is it indefinite? Are RT's included in PageRank?In Results:"We identified less than two of every ten participants as a faculty member based on the information from their Twitter profile ."What does the p value refer to? What is the comparator? This is not immediately obvious to me.Figure 3: Does this add any value?Overall, an interesting & relevant study for medical educators, from authors with a strong track record in this field. It makes us question our sources of online medical material and highlights some issues with twitter use in medical education.Some minor points:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Social media is an increasingly important tool for medical education.  This article adds to the necessary body of literature for a greater understanding of its applications.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


The passive leg raising (PLR) test has been developed for predicting fluid responsiveness. Nevertheless, one could question its ability to induce a significant increase in the stressed blood volume. Also, the fact that some patients do not respond to volume expansion by augmenting cardiac output could theoretically be related to the absence of preload dependence, but also to an insufficient increase in stressed blood volume.To assess the effects of increases in preload induced by passive leg raising (PLR) and fluid infusion on the pressure gradient between mean systemic pressure (Psm) and central venous pressure (CVP) and on the resistance to venous return (Rvr).In 30 patients with an acute circulatory failure, in order to estimate the venous return curve, we constructed the regression line between pairs of cardiac index (CI) and CVP. Values were measured during end-inspiratory and end-expiratory ventilatory occlusions performed at two levels of positive end-expiratory pressure. The x-axis intercept was used to estimate Psm and the inverse of the slope to quantify Rvr. These measurements were obtained at baseline, during PLR and after fluid infusion.In volume-responders (n = 15), Psm increased from 25 ± 13 to 31 ± 13 mmHg during PLR and to 32 ± 17 mmHg after fluid infusion. CVP increased from 7 ± 3 mmHg to 9 ± 4 mmHg during PLR and to 9 ± 4 mmHg after fluid infusion. Thus, in these patients, the Psm-CVP gradient increased significantly during PLR and fluid infusion. By contrast in non-volume responders, PLR and fluid infusion increased Psm and CVP to a similar extent, so that the Psm-CVP gradient was unchanged. Rvr was modified neither by PLR nor by fluid infusion in both groups.In preload-dependent patients, PLR and fluid infusion increased venous return owing to an increase in Psm higher than in CVP, with unchanged Rvr. in preload-independent patients, neither PLR nor fluid infusion changed venous return, Psm-CVP gradient and Rvr.


This paper presents some biomedical applications that involve fluid-structure interactions which are simulated using the Immersed Finite Element Method (IFEM). Here, we first review the original and enhanced IFEM methods that are suitable to model incompressible or compressible fluid that can have densities that are significantly lower than the solid, such as air. Then, three biomedical applications are studied using the IFEM. Each of the applications may require a specific set of IFEM formulation for its respective numerical stability and accuracy due to the disparities between the fluid and the solid. We show that these biomedical applications require a fully-coupled and stable numerical technique in order to produce meaningful results. In the past decade, the interest in developing novel simulation techniques for modeling fluid-structure interactions revived due to the increasing demands in capabilities to accurately and efficiently study biomedical applications. Biomedical applications often involve fluid (blood) interacting with soft tissues. In some cases, the fluid can also be air, which has a disparate density compared to the soft tissues.Since soft tissues come in with all forms, shapes and sizes, it is more convenient to set up a simulation using non-boundary-fitted modeling technique. The non-boundary-fitted approaches avoid the re-meshing process by defining independent meshes for the fluid and solid respectively. The solid can freely move on top of the fluid grid without deforming the surrounding fluid. A widely used numerical approach for bio-interface applications is the immersed boundary (IB) method, which was initially proposed by Peskin to study the blood flow around heart valves –7. The mThe problem existing in the non-boundary-fitted approaches mentioned above is the lack of more realistic representations of the solid, which hinders the accurate assessment of the material behavior and its deformation. Since the solid and the fluid domains are fully-coupled, a slight inaccuracy in estimating the solid solution may even affect the surrounding fluid solutions. This problem may propagate over time and cause instabilities in the solution or convergence issues. The immersed finite element method (IFEM) –22 was di.e. the interpolation and the distribution of the velocity and the forces between the two domains, is quite robust when the solid behaves very much like the fluid. However, if there exists high discontinuity in density as well as other intrinsic parameters in the solid and the fluid, the force and the velocity to be interpolated between the two fields can no longer provide consistent convergence. Therefore, a semi-implicit algorithm for the immersed finite element [The two-way coupled approach,  element  was deveThe semi-implicit IFEM algorithm works well when the fluid dominates the dynamics of the system, in which the solid moves and deforms by following the fluid flow. However, when the solid dynamics or inertia must be taken into account, letting the solid follow the fluid movement may lead to unrealistic solid deformation and sometimes even causes the distortion of the solid mesh, because it is not appropriate to still approximate the solid behaviors using only the fluid velocity. A modified IFEM algorithm (mIFEM)  was thenIn this paper, we first provide a detailed derivation and descriptions of the mathematical formulations for IFEM, the semi-implicit IFEM, and the modified IFEM. Then, we will show several biomedical applications that used IFEM algorithms. The choice of the algorithm used for each application is dependent on the nature of the fluid-structure interactions involved, which comes clear as the readers go through the motivations of each of the algorithm development.s, which is completely immersed in a fluid domain Ωf, as illustrated in FSI, where “FSI” represents a line if Ω is a two-dimensional domain or a surface if Ω is in three-dimensions. The interface ΓFSI coincides with the solid boundary Γs. The nomenclature involved can be partitioned into two categories: one belongs to the solid and the other to the fluid. The notations associated with the solid have superscript s to distinguish them from those of the fluid f.Let us consider a deformable structure that occupies a finite domain, Ωs, is constructed, it overlaps with the entire domain Ω filled with fluid. Since both the solid and the “artificial” fluid co-exist in Ωs, it is also referred to as the “overlapping domain”, Ω̅, i.e., Ωs = Ω̅, in later text. This is illustrated in The fluid exists everywhere in the domain, Ω. This assumption allows us to generate fluid and solid meshes and solve fluid and solid equations independently, thus avoiding frequent mesh updating schemes required to track the fluid-structure interface. In the IFEM, the solid immersed in the fluid domain occupies a physical space or volume in the computational domain. Therefore, when the solid domain, ΩThe interface between the fluid and the solid must abide by the matching velocity (or no-slip) and traction boundary conditions. This assumption states that the sol id boundary moves together with the artificial fluid boundary or vice-versa, and the surface traction on both domains are equal and opposite. This assumption allows appropriate coupling to occur between the two domains.The solid must always remain immersed in the fluid domain to avoid inaccurate interpolations at the fluid-solid interface.Before showing derivations, we first need to state three assumptions:vf  and the solid nodal velocity vs , such that:The solid and fluid meshes are constructed independently, therefore it is impossible to have the moving solid boundary nodes exactly coinciding with the fluid nodes in Ω. An interpolation function, ϕ, must be used to couple the fluid velocity field fFSI,s  is distributed to the fluid domain fFSI,f  as:Similarly, the interaction force calculated in the solid (overlapping) domain This two-way coupling is necessary to ensure the stability and convergence of the algorithm. There are multiple ways of performing the interpolation process. The interpolation function, ϕ, can be acquired through the discretized Dirac delta function , the shaC0 continuity.The derivation of IFEM starts from the principle of virtual work or the weak form, which is used for standard finite element analysis. The weak forms of the derived equations are equivalent to their strong forms if the weak form solution is smooth enough to satisfy at least svδ can be expressed as:The virtue work in the solid domain with a test function, σs is the stress which is directly related to the internal force that is determined by the material types and properties. The term ssü, is the inertial force and ρsig is the body or external force.The terms in the bracket of To include the artificial fluid related terms without contradicting the equilibrium, s and Ω̅ belong to the same physical space, we can rearrange this equation to yield:The added terms are underlined. One can notice that they sum to zero. Since ΩWe define the first term in FSI,sf. It represents the interaction force acting on the solid from the fluid. Once it is evaluated, the nodal forces are then distributed onto the fluid domain as FSI,ff. This force, then, becomes the driving force for the fluid. Since this interaction force is first evaluated in the solid domain on the solid nodal points, it is therefore labeled as FSI,ff. The interaction force only exists in the overlapping region and its immediate surroundings. Its value diminishes to zero at places outside the region.Using the no-slip assumption made in Assumption Now, combining the work done by the real fluid and the artificial fluid described in In the IFEM formulation, the term xs and the fluid velocity vf from the previous time step n − 1,Given the structural configuration fFSI,s on solid material points, using Evaluate the nodal interaction forces fFSI,s to fFSI,f using interpolation function Distribute the material nodal force onto the fluid grid, from vf and pressure fp implicitly using n,Solve for fluid velocities i.e. from vf to vs, as in Interpolate the velocities in the fluid domain to the material points, us = vsΔt and go back to step Update the positions of the structure using An outline of the IFEM algorithm can be illustrated as follows:In the IFEM, small time step has to be used to ensure the stability of the coupling procedure because the solid domain and fluid domain are coupled to each other explicitly at every time step. Since the Navier-Stokes equations are solved implicitly, such small time step requirement due to the coupling stability makes the whole algorithm numerically inefficient especially for the cases when the solid properties are very different from the fluid. Semi-implicit coupling between the fluid and solid domain is then introduced in order to enlarge the stability range.previous time step. Therefore, the coupling between the two domains is considered explicit. In üs) and the solid internal stress term ∇·σs are evaluated based on the solid nodal velocity which is interpolated from the fluid velocity of the previous time step, n − 1:n − 1, the interaction force is effectively n−1. It is then passed onto the Navier-Stokes momentum equation to solve for v and p at the current time step n :Although the force or the work is balanced seamlessly in the strong and weak forms at every time step, the fluid domain is numerically balanced with the fluid-structure interaction force evaluated based on the solid configuration of the n except the last term where the interaction force is evaluated from the previous time step n − 1. The last term can be related to the current interaction force by taking the Taylor's expansion at time step n:Noting that every term in The error due to the explicit coupling can be approximated by substituting s/ρf − 1, the stiffness ratio K/ρf and the gravity ratio (ρs ρf − 1) g, respectively. Here, K is an equivalent Young’s modulus of the solid representing the stiffness of the solid material. If any of these terms are large, the resulting error due to the coupling would be large. These large errors often result in instability or divergence of the solution.Based on the definition of the fluid-structure interaction force in fFSI,s is re-defined in the solid domain, which only includes the internal forces for the fluid and solid from the original definition in To alleviate the numerical issues caused by the restrictions in time step size of explicit coupling and the convergence problem due to highly disparate properties between the fluid and the solid domains, a semi-implicit approach is introduced . In the fFSI,s is distributed to the fluid domain as in the original IFEM. The Navier-Stokes equations now must also be re-defined as follows:The rest of the terms in the original explicit formulation I (x), is to identify the real fluid region Ωf, the artificial fluid region or the solid region Ωs, and the fluid-structure interface ΓFSI, in the computational domain Ω. The value of the indicator function is ranged between 0 and 1 where it is 0 if an entire element belongs to the fluid and 1 if an entire element belongs to the solid. This newly revised fluid’s momentum equation combines the inertial and the gravity terms in the original FSI force equation.Here, the indicator function, Gf is interpolated byTo further improve the algorithm, we enhance the indicator function so that it can accommodate high density ratios between the fluid and the solid. This means that the interfacial elements have the transitional indicator values. To do so, the elements that contain the fluid-solid interface have varying indicator value that transits from 0 to 1 by solving Poisson’s equation :(21)∇2In is the unit outward normal of the solid interface and ϕ (x − xs) is the same interpolation function used by the velocity interpolation function and force distribution. The boundary conditions are given as,Here, Since the fluid-solid interface moves, this indicator function is updated at every time step based on the relative position of the solid domain in the entire computational domain. Comparing to the original IFEM algorithm, the inertial and the external force terms in the original interaction force formulation are now been considered in the governing equation and can be evaluated iteratively with the most updated velocity field. This is, therefore, considered as semi-implicit IFEM algorithm.K/ρ̅. Noting that in the semi-implicit form, ρ̅ is defined in K/ρs. For most of the cases, the solid density will be larger than the fluid density, which reduces the coupling error comparing to K/ρf from the original explicit form. Therefore, although the solid internal force is still computed explicitly, the coupling error for the semi-implicit scheme is smaller than the explicit scheme.In this formulation, the solid internal stress is still dependent on the fluid solutions from the previous time step. Even though this term is evaluated explicitly, the semi-implicit scheme still significantly improves the convergence of the solution when the solid material is very stiff. If we re-visit the coupling error Overall, this semi-implicit scheme relaxes the small time step requirement and ensures the stability of the FSI force estimation. In particular, this algorithm can handle a much larger range of fluid and solid properties without sacrificing the computational time.xs and the fluid velocity vf from time step n − 1,Given the structural configuration fFSI,s on the solid material points, using Evaluate the nodal semi-implicit interaction forces fFSI,s to fFSI,f using delta function Distribute the material nodal force onto the fluid grid, from Obtain the indicator field by solving vf and pressure fp implicitly using Solve for fluid velocities vf to vs, as in Interpolate the velocities in the fluid domain to the material points, i.e. from us = vsΔt and go back to step Update the positions of the solid nodes using An outline of the semi-implicit IFEM algorithm is illustrated as follows:dynamics is still been controlled by the artificial fluid. For cases where the solid behavior dominates the entire system or high Reynolds number flows using the IFEM algorithm may lead to unrealistic solid deformation and may even cause the severe distortion of the solid mesh, because it is not appropriate to approximate the solid behavior based on the fluid velocity by letting vs = vf. The idea of the modified IFEM is to let the artificial fluid to behave more like the solid, or letting vf = vs. Doing so allows the solid governing equation to be solved rather than be evaluated. Since the artificial fluid is not real anyway, its role is to produce the same velocity as the solid so that the real fluid realizes the existence of the solid. This modified IFEM algorithm allows the solid behaviors to be estimated more accurately and have stronger influences in the fluid-structure interactions. The detailed rationale and derivations, as well as validation cases were presented in [Although the semi-implicit coupling scheme alleviates some convergence issues when the fluid and solid have large property differences, one can notice that the solid ented in .us and the velocity field vs, the solid equation is solved,In order to find the solid displacement field σs is evaluated using the solid strain tensor εs,ijklc and ηijkl provide various choices of solid material constitutive laws such as linear elastic, viscolinear elastic, hyper-elastic, etc.The solid stress The boundary condition of the solid domain can be applied using either Dirichlet boundary condition described in n is the outward normal of the fluid-structure interface ΓFSI. These boundary conditions are evaluated based on the fluid velocity (vf) and stress (σf) on the fluid-structure interface solved from the fluid equations at previous time step. Δt is the time step size.Here, i.e. solving the artificial fluid governing equation so that fv = sv in Ω̅. To accomplish this, the artificial fluid property, such as the density, should mimic that of the solid.Once the solid solution is obtained, the next step is to make the artificial fluid to follow the solid, The continuity equation of the artificial fluid (Ω̅) can be written as,s) as the solid. Therefore, the artificial fluid is described as a pseudo-compressible fluid:s is used for the compressibility of the artificial fluid. The continuity equation in the artificial fluid domain (Ω̅) can be eventually written as follows,It is also necessary that the artificial fluid has the same compressibility  flowing in blood vessels, especially when bifurcation happens, is important in estimating the nonuniform hematocrit distribution that would affect the microvasular oxygen distribution, the effective viscosity of blood in microvessels and the distribution of other metabolites. Learning the behaviors of diseased RBCs that have abnormal rigidity, radius and shape, can be helpful in designing medical therapy. Using the established IFEM method, we can simulate the motion and the deformation of the red blood cell within the vessels, and study in detail how the geometry of bifurcation and fluid field affect and direct which daughter branch the RBC flows.w0 is the diameter of the mother vessel; w1 and w2 are the diameters of the daughter vessels on the top and the bottom, respectively; β1 and β2 are the respective branching angles of the daughter vessels; the branching fillet radii r0, r1 and r2 are given as 3µm to make the vessel branching transition smooth; Q0, Q1 and Q2 represent the flow rate of each vessel. A RBC is placed near the inlet of the vessel. The radius of the RBC is also given as 2.66 µm. The incoming velocity of the mother vessel is set to be a constant as 0.1 cm/s. The branching angles β1 and β2 are set to be equal and constant, β1 = β2 = π/4. In this study, we set the diameter of the mother branch to be w0 = 8 µm, and consider two sets of diameter ratios, dr= w1/w2, to be 1 and 1.44. When the diameter ratio is 1, it is considered as symmetric bifurcation; when it is not 1, then it is considered as asymmetric.The geometry of a bifurcated blood vessel is shown in Q1/Q2 = 3, the blood cell tends to move to the daughter branch with a higher mass flow rate. When the daughter branches are asymmetric in geometry but with the same mass flow rate Q1/Q2 = 1, as shown in A multi-blood cell case is also simulated and the result is shown in For individuals with an occlusive vascular disease, blood flowing to an organ or to a distal body part is impaired by narrowed arteries with fatty deposits or calcium accumulations. Angioplasty was introduced by Dr. Andreas Gruentzig in the mid to late 1970’s and is widely used today. The area of arterial blockage is dilated with the help of a catheter that has an inflatable small balloon at its tip. Then, the plaque is squeezed along the artery wall. A decade later, stenting was introduced by Dr. Julio Palmaz in 1988 to improve the angioplasty procedure. Like angioplasty, coronary stents physically open the channel of constricted arterial segments. During stenting, a catheter delivers a balloon and a surrounding stent to the location of the blockage area. The balloon deploys the stent, remains inflated for 30 seconds and then is deflated. At the end of the process, the expanded stent is embedded into the wall of the diseased artery and holds it open.Here, we mainly focus on the deployment process of balloon-expanding stents. Balloon expandable stents are typically made of stainless steel tubing mounted over an angioplasty balloon and then plastically deformed to their final diameter by high pressure balloon inflation.et al. [et al. [et al. [et al. [Colombo et al.  and Gold [et al.  demonstr [et al.  showed e [et al.  suggesteIn our previous work , we builThe balloons used for stenting are made of very stiff polyamide (nylon) material ,45. The A stent is a cylindrical and symmetrical assembly of inter-connected diamond-shaped members. In this particular model, we use the Medtronic AVE Modular stents S7 , Finally, the stent is mounted around the balloon as shown in The fluid domain represents blood in the artery where the stent is deployed. We consider a straight artery segment with a length of 16 mm and a lumen diameter of 4.5 mm. Our computational model assumes that the fluid is everywhere in the domain. Although the balloon is not inflated with the same fluid as the surrounding blood, a liquid (with color) that has similar properties as the blood is normally inserted into the balloon for easier visualization in surgeries. Therefore, it is a reasonable assumption to have homogeneous fluid in the entire computational domain. Blood is an incompressible fluid consisting of a suspension of deformable particles  in a Newtonian liquid (plasma). Except within the microcirculation, blood can be considered homogeneous with constant density and viscosity. The parameters and properties used for the fluid domain to perform the simulation can be found in 2.The crimped stent is deformed by a radial force from the balloon. Experiments performed by Dumoulin and Cochelin , and MooOur primary goal is to study the deformation of the stent during its deployment. During deployment, the diameter of the stent increases from 1.64 mm to 2.82 mm. As expected, the stent deforms uniformly in its radial direction, expanding by 1.7 times its initial diameter for our given material. The strength and the long term in-vivo performance of the stent can be determined from the stress distributions with the goal of minimizing vascular injury. The Von Mises stress distribution along the stent is reported at different time steps in Voice is produced by the vibration of vocal folds. The vocal folds are a pair of pliable structures located within the larynx at the top of the trachea, see An accurate numerical simulation of the vocal folds vibration can help us obtain a better understanding of the dynamics of the voice production in human beings. Due to the complicated nature of this problem, the numerical model has to fulfill the following requirements. First, the numerical model has to represent completely a coupled fluid-structure interaction system. Second, the numerical model should perform well when there exists large density ratio between the fluid and structure because the density of the vocal fold muscle is close to water and the density ratio between the vocal fold muscle and the airflow is about 1000. Third, the motion and deformation of the structure have to be predicted accurately with complicated geometry and material descriptions because the vocal folds have complex shape and layer-structures and are viscoelastic materials. The mIFEM is a perfect numerical method to perform the simulation of this complex problem.f = 1.3×10−3g/cm3 and the viscosity of the fluid isρ−4 g/cm·s.μ = 1.8×10The geometry of the self-oscillated vocal folds model is shown in E = 10 kPa, whereas E = 40 kPa for the body layer. The densities of both cover and body layer are assumed to be the same as ρs = 1.0g/cm3. The Poisson ratio is ν = 0.3. Two vocal folds have the exact same geometry and material description, sit in the fluid channel symmetric about the central line. A constant total pressure boundary condition of inP = 1 kPa is applied at the channel inlet and the outflow boundary is given at the channel exit. No-slip and no-penetration boundary conditions are applied on the channel walls and on the vocal fold surfaces.The vocal fold muscle is considered as isotropic viscoelastic material. The vocal fold is assumed to have layered structure, outside cover layer (red) and inside body layer (green). The cover layer is much softer than the body layer. For the cover layer the Young’s modulus is A snapshot of the fluid velocity field at two typical instances during a steady vibration cycle are shown in Gw), shown in upGw and downGw represent the opening width for the up and down vocal folds, respectively. This figure shows that the simulation captures the vocal folds to have a repeated opening and closing process. When the glottis width is zero or near-zero, then the vocal folds are closed, there is no air flowing through. The pressure starts to build up in the upstream of the vocal channel. As the pressure increases, it starts to push the vocal folds to open and eventually reaches a maximum glottis width, the high pressure is released. The vocal folds then return back to its closed position, and the whole process restarts. The glottis width for the up and down vocal folds do not equal each other over cycles, indicating that the vocal folds motion is asymmetric although the vibrational frequency is found to be the same. The vibrational magnitude is slightly off from each other. To find out the vibration frequency, FFT is performed on the up and down glottis widths and the volume flow rate, Q. The power spectra are plotted in The asymmetrical airflow causes an asymmetrical pressure distribution in regions near the vocal folds and change in the vibration pattern. The minimum distance between the vocal fold surface and the central line is measured to represent the half glottis width (In this paper, we reviewed IFEM algorithms that had been developed over the past decade. The IFEM method is a numerical scheme that adopts the non-boundary-fitted mesh approach and fully couples the fluid-structure interaction by interpolation of the interacting domains. The fluid and solid domains are solved independently using finite element method and coupled with each other within one time step through fluid-structure interaction force. The original IFEM algorithm is considered as explicitly coupling for the fluid and solid, which can lead to instability issue when time step is not sufficiently small. The semi-implicit IFEM algorithm tackles this problem by modifying the FSI force and adopting the concept of the indicator function. It extends the stability range of the numerical scheme and allows us to consider the fluid-structure interaction problems when the fluid and solid properties are very different from each other, for example high density ratio between the solid and fluid, and the solid with relatively large stiffness. For high Reynolds number flows,, the modified IFEM algorithm performs better due to its accurate description of the solid motion and deformation through capturing the dynamics of the solid motion. Three biomedical applications are studied using these IFEM algorithms to simulate soft tissues interacting with fluid where the fluid can be either blood or air.


Individualised Goal Directed Fluid Therapy, monitoring Cardiac Output, or other surrogates of tissue oxygenation, to guide intra-venous (IV) fluid delivery, is a cornerstone of Enhanced Recovery. Debate surrounds the optimal timing, volume and targets for IV fluid, but the physiological consequences of head-down positioning, pneumoperitoneum (laparoscopic) and the implications of hypoperfusion or oedema for anastomotic integrity, demand scrupulous attention to fluid management in colorectal surgical patients. Flow monitoring devices  can reduce complications and current guidance stipulates their use .Our objectives were to review local peri-operative fluid management and assess the extent to which Goal Directed Fluid Therapy impacts on outcome in colorectal surgical patients.- What is compliance with intra-operative Cardiac Output monitoring?- How does Cardiac Output monitoring influence peri-operative fluid therapy and patient outcome?A retrospective notes analysis was performed on patients who underwent elective major colorectal surgery at the Royal Berkshire Hospital between January and May 2012. Demographics, co-morbidities and intra-operative and post-operative data were recorded, including haemodynamic monitoring, fluid management and post-operative destination. Outcomes recorded included post-operative complications, length of stay and 30-day mortality.65 patients were included: mean age 67.6 years; 57% male.5% operations were open, 77% laparascopic and 18% laparoscopic converted to open.- Cardiac Output monitoring is not used in a sufficient number of patients.- Patients with Cardiac Output monitoring received more fluid intra-operatively, had higher complication rates and length of stay. This probably reflects a higher risk cohort, whose complexity is identified early and who are treated aggressively.- Restriction of intra-operative IV fluid to 1 or 2 litres, made little difference to the total peri-operative intake.- Post-operative fluid was better restricted in the High Dependency Unit.To optimise fluid management through the peri-operative pathway, we propose:- Intra-operative Cardiac Output monitoring should be universally implemented in major colorectal surgical patients.- Patients should be nursed post-operatively in high dependency areas, which are equipped and staffed to facilitate tight, focused, individualised fluid management.