It is commonly thought that large asexual populations evolve more rapidly than smaller ones, due to their increased rate of beneficial mutations. Less clear is how population size influences the level of fitness an asexual population can attain. Here, we simulate the evolution of bacteria in repeated serial passage experiments to explore how features such as fitness landscape ruggedness, the size of the mutational target under selection, and the mutation supply rate, interact to affect the evolution of microbial populations of different sizes.We find that if the fitness landscape has many local peaks, there can be a trade-off between the rate of adaptation and the potential to reach high fitness peaks. This result derives from the fact that whereas large populations evolve mostly deterministically and often become trapped on local fitness peaks, smaller populations can follow more stochastic evolutionary paths and thus locate higher fitness peaks. We also find that the target size of adaptation and the mutation rate interact with population size to influence the trade-off between rate of adaptation and final fitness.Our study suggests that the optimal population size for adaptation depends on the details of the environment and on the importance of either the ability to evolve rapidly or to reach high fitness levels. Understanding the factors that influence the evolution of microbial populations not only provides fundamental insights into evolutionary processes -4, but iIt is widely believed that the higher supply of beneficial mutations allows large asexual populations to adapt more rapidly to new environments compared to small populations -13. HoweOn the other hand, a small population will generate only a subset of all possible 1-step beneficial mutations, with few mutations that confer large fitness effects ,15,16. BN0 identical clones. The bacteria go through D rounds of division, and each bacterium produces offspring depending on fitness, f, as 2f. After D divisions, serial transfer, modeled as multinomial sampling, reduces the population size back to N0 which initiates another round of exponential growth. This procedure is iterated until the desired number of generations is reached. Because bacterial death is ignored, the only way a given clone can be eliminated is if it is not sampled during serial transfer [We simulate the evolution of bacteria as they undergo repeated cycles of growth and serial dilution ,14. At ttransfer ,18.L mutants that can be reached by a single mutation. The ancestral clone is assigned a fitness value of 1 and the fitness values for the L mutants are 1 + si, with values for si drawn from an exponential distribution p(s) = αe-αs [L 1-step mutants is generated with a probability μ. Whenever a new mutant is generated, it obtains its own neighborhood of L 1-step mutants with fitness values of 1 + si , the 1-step neighborhood of a newly created clone with fitness 1 + si can generate mutants that have higher or lower fitness, depending on the randomly chosen values of si for these mutants. This generates a potentially rugged fitness landscape with multiple peaks. L can be interpreted as the mutational target size of selection, or more generally the number of possible beneficial mutations from a given starting genotype.Every clone is assigned a 1-step neighborhood of  = αe-αs -23. ThisL-mutant neighborhood is chosen, we can tune the fitness landscape from one that is completely smooth to one that is completely rugged. For the smooth landscape, each newly created mutant is assigned a mutant neighborhood that is identical to that of the ancestral strain. In other words, the mutation does not alter the fitness effects of any subsequent mutations that might be obtained. Under these conditions there is a single fitness peak. At the other extreme, for a completely rugged landscape, every new mutant is assigned an entirely new L-mutant neighborhood with values for the fitness effect of each new mutation re-sampled from p(s). This means that a new mutation changes the fitness effects of all other possible mutations. In this scenario there is no correlation between the fitness effects of the L-mutants from a parent clone and those available to its mutant offspring. By changing the fraction, r, of the L sites that are replaced, we can tune the amount of ruggedness of the landscape from smooth (r = 0) to completely rugged (r = 1). By considering a broad range of values for r and L, we can explore a range of scenarios in order to identify conditions where changes in parameters lead to qualitative changes in adaptation. Figure r = 0.6.By adjusting the rules for how the fixed fitness landscape, rather than to differences arising from the fact that different lineages experience distinct ecological or genetic conditions [While the ability to tune between smooth and rugged fitness landscapes is analogous to that of previously studied NK-models and their variants -31 therenditions ,33. The local fitness peak, where, if asexual, they can become trapped. In contrast, a smaller population size allows for more stochastic trajectories on the fitness landscape, and this can occasionally lead to higher fitness peaks. The transition from more stochastic to more deterministic trajectories occurs as the mutation supply rate, S, becomes so large that a population is able to completely sample all possible 1-step beneficial mutations, i.e. if S ≈ L [S = Neμ. For the three initial population sizes we consider here, N0 = 102, 104 and 106, an effective population size given by Ne ≈ DN0 [μ = 10-6, the mutation supply rates are Ss = 0.001, Sm = 0.1 and Sl = 10 for the small, medium and large populations respectively. We initially choose the size of the 1-step neighborhood to be L = 50, which means Sl ≈ L, Sm <L and Ss « L. Thus we expect the large population to evolve mostly deterministically, while the medium population is expected to evolve somewhat slower, but with the potential of reaching higher fitness peaks. Because the small populations have Ss « 1, they are expected to operate in the strong selection weak mutation limit, where evolution will be slow because it is limited by the infrequent creation of beneficial mutations [On a smooth fitness landscape, all populations will eventually reach the sole fitness peak, with the larger populations doing so more rapidly. However, this can change during adaptation on a rugged landscape, as explained above. Here, large populations are expected to evolve almost deterministically. This allows them to quickly reach the highest if S ≈ L . The mututations ,36.r = 1). An example of the results for a single landscape is shown in Figures For each of the three population sizes, we simulated 100 evolutionary trajectories for 50 different rugged fitness landscapes , populations of intermediate size retain their ability to sample the fitness landscape more broadly and to reach higher fitness peaks than large populations. However, as the landscape becomes smoother (r = 0.1), this advantage disappears. This is expected, since for a completely smooth landscape (r = 0), there is only a single globally optimum peak which would be reached by all populations eventually, simply more rapidly by the larger ones. The small populations are less affected by the change in ruggedness because they are still far away from any peak on which they could become stuck. This is confirmed by the mean final ranks  peak, compared with the L = 50 situation (see Rank and CV in Table L = 5 with L = 50). For small L, the small populations are less disadvantaged in terms of adaptive "speed" and are able to more frequently, although still quite rarely overall, reach higher fitness peaks than larger populations .For instance for L = 500) scenario, evolution for the large population becomes markedly more stochastic (see CV in Table L = 50 with L = 500). This supports the intuitive understanding that if more beneficial mutations are accessible, the population size that optimizes the trade-off between the speed of adaptation and the magnitude of the adaptive response shifts towards larger populations. Indeed, in the limit of L → ∞, every clone has access to all possible other mutants, in essence reducing the system to a smooth landscape on which the large populations are always favored [For the large ( favored ,34.S, and the mutant neighborhood, L, are relevant for determining whether adaptation tends to be dominated by stochastic or deterministic change. The mutation supply rate is the product of population size and mutation rate. It is known that population size and mutation rate can have differential effects on the evolutionary dynamics [Above, we explained how the relation of the mutation supply rate, dynamics ,40,41. FWe therefore examined how changing mutation rate and population size, while keeping mutation supply rate fixed, influenced the results of our simulations. As Figure It is generally accepted that large populations will tend to evolve more rapidly than smaller ones. This is caused by two related factors. First large populations have an increased supply of beneficial mutations each generation, which decreases the waiting time for new advantageous mutations. Second, large populations have increased access to mutations that confer large benefits. These factors imply that larger populations gain an advantage by taking larger adaptive steps during population evolution. However, as we have shown in a previous study , sometimWe further showed that for a rather smooth fitness landscape, there is no advantage in following more stochastic adaptive trajectories; however, even an intermediate amount of ruggedness can be sufficient to occasionally favor more stochastically evolving populations of smaller size. Experimental studies suggest that at least some amount of ruggedness is present in natural situations ,44-47. WLastly, we found some evidence that for a fixed mutation supply rate, small populations evolved more rapidly and more stochastically, which allowed them to reach higher fitness compared to larger populations in a majority of simulations. We suggest that this can be attributed to clonal interference acting in larger populations, which limits the amount of within population variation and can retard the rate of adaptation . To keepAlthough medium and small populations can exceed the fitness of larger populations, we note that this outcome does not occur in all, or even most, simulations. More important, the degree to which this result is realized is highly dependent upon underlying landscape architecture. For example, as is most clearly evident in Figure N0 = 100.As with any model, we have made several simplifications. For instance we excluded death of bacteria and only allowed the loss of novel mutants to occur through stochastic loss during sampling via serial dilution. The inclusion of stochastic drift  would liA second simplification is our exclusive focus on asexual populations. A number of studies have shown that the incorporation of recombination can help to overcome clonal interference or can help populations to more easily escape from local fitness peaks -53, thouWe used our simulation to study populations that ranged in size over 4 orders of magnitude. In this range, we found that our large populations exhibited clonal interference and very rarely escaped from local fitness peaks. However, a number of recent studies suggest that if the population size is large enough, the impact of clonal interference might be reduced ,55-57. AIn summary, we have shown that for asexual populations evolving on rugged fitness landscapes, there can be a trade-off between speed of adaptation and the attainable fitness, which strongly depends on the underlying fitness landscape. This suggests that the optimal population size likely depends on both the details of the fitness landscape and the relative importance of speed versus final fitness.AH and DR conceived of the study. AH wrote and analyzed the simulations. AH and DR wrote the manuscript. Both authors read and approved the final manuscript.


The goal of this paper is to develop a mathematical model that analyzes the selective advantage of the SOS response in unicellular organisms. To this end, this paper develops a quasispecies model that incorporates the SOS response. We consider a unicellular, asexually replicating population of organisms, whose genomes consist of a single, double-stranded DNA molecule, i.e. one chromosome. We assume that repair of post-replication mismatched base-pairs occurs with probability  Genetic repair is an essential component of cellular genomes. Without mechanisms for repairing damaged and mutated DNA, genomes could not achieve sufficient information content to code for the variety and complexity of modern terrestrial life Genetic repair mechanisms fall into two main categories: Those that correct base mis-pairings during the replication cycle of a cell, and those that repair mutated and damaged DNA during the growth (G) phase of the cellular life cycle Two important examples of the first class of repair mechanisms are DNA proofreading and mismatch repair (MMR). DNA proofreading is a repair mechanism that is built into the DNA replicases themselves. During daughter strand synthesis, an erroneously matched base is excised, and a second attempt at a base pairing is made Two important examples of the second class of repair mechanisms are Nucleotide Excision Repair (NER) and the SOS response The SOS response is a genomic repair mechanism that only activates when there is extensive damage to the cellular genome. When DNA damage is sufficiently extensive, the cell stops growing, and the SOS repair pathways attempt to restore complementarity to the genome In recent work with quasispecies models of evolutionary dynamics, quasispecies models This paper continues the theme of incorporating various details characteristic of cellular genomes by developing a quasispecies model that takes into consideration the SOS repair mechanism. The model is highly simplified, and therefore only a first step in developing proper evolutionary dynamics equations with SOS repair. Nevertheless, because our model is analytically tractable, we believe it is a useful and important initial approach to mathematically modeling the evolutionary aspects of the SOS repair pathway. A proper modeling of the SOS response is an essential component of developing a quantitative theory of mutation-propagation in cellular organisms, which is important for understanding phenomena such as the emergence of antibiotic drug resistance in bacteria, and cancer in multicellular organisms We consider a unicellular population of asexually replicating organisms, whose genomes consist of a single DNA molecule, i.e. one chromosome. The genome may then be denoted by fitness landscape. It should be emphasized that fitness in this model only refers to replication rate. In particular, this model does not consider cell death.We let The semiconservative replication of the DNA genomes happens in three stages:Strand separation, whereby each strand of the chromosome separates to act as a template for daughter strand synthesis.Daughter strand synthesis. We assume a genome and base-independent mismatch probability Lesion repair, where any post-replication mismatches are removed. Here, there is no longer the parent-daughter strand discrimination that was available during daughter strand synthesis, so in contrast to DNA proofreading and mismatch repair, lesion repair has a In our simplified model, the SOS response is triggered if a given genome has at least  by  see .In this paper, we will consider the behavior of the model in the limit of infinite sequence length. If The assumption of infinite sequence length is a common assumption in quasispecies theory, because it is the mathematical formalization of the long genome-length regime that makes the neglect of backmutations exact. While finite genome length effects need to be considered in dynamic fitness landscapes, where adaptation to specific genomes is important Finally, we assume that the fitness landscape is defined by a master genome The justification for this choice of fitness landscape is as follows: If a genome has a fixed mutation, then neither DNA strand corresponds to either of the master strands However, in the case of a mismatch where one of the bases is the same as the corresponding base in one of the master strands, the information contained in the master genome is still preserved in one of the strands, so that the organism is assumed to remain viable if the total number of such mismatches does not exceed some cutoff value For convenience, We can develop the infinite sequence length equations for our model, assuming an initially prepared clonal population consisting entirely of the wild-type (mutation-free) genome The Hamming distance between two sequences Now, in the limit of infinite sequence length, it may be shown that, with probability As a result, we can define a strand ordering A given genome Note that the fitness landscape depends only on By the symmetry of the fitness landscape, and by the symmetry of the initial population distribution, we can group all genomes of identical To develop the dynamical equations for both the We first consider the case where this genome is not undergoing the SOS response. Then, due to the semiconservative nature of DNA replication, this genome is being destroyed at a rate given by If the The probability of a given set of mutations corresponding to The degeneracy is given by If We also obtain an overall transition probability of It is important to note from the Furthermore, note that strands Then for the genomes Then for the population number A similar expression is obtained for the population number It should also be noted that, by the symmetry of the fitness landscape, we have that Taking into consideration the contribution to Note that we do not write down the dynamical equations for The factor of It should be noted that this factor is missing in the contribution to The factor of To obtain the steady-state behavior of our model, we begin by introducing some definitions that will allow us to simplify the calculations.where we set We can add these equations to obtain,For the purposes of computing the mean fitness at steady-state, we can simplify the system of equations somewhat by defining To obtain the steady-state behavior of this system of equations, we begin by first solving for the steady-state of the population undergoing SOS repair.For For This expression has the form of the recursion relation, If we define Using a similar argument, we obtain,For the steady-state value of Plugging our expressions for From these equations we may derive the equality,Beyond the error catastrophe, the mutation rate is sufficiently high that the selective advantage for remaining localized about the The error catastrophe occurs at the mutation rate for which the two expressions for the mean equilibrium fitness become equal. As with previous quasispecies models, the error catastrophe here also corresponds to a localization to delocalization transition over sequence space We now proceed to consider the behavior of the steady-state mean fitness for a number of limiting cases, in order to better understand our model. We consider the following cases: (1) Case 1:Case 2:Case 3:Taking If we assume that Therefore, for large Now, because In the regime where Now, because Therefore, in any case, we can maximize the SOS response will confer a maximum selective advantage if it is activated when and only when the genome has sustained sufficient genetic damage so that it will be unviable without SOS repair. However, it should be emphasized that this only holds when We reach the conclusion that, when the fitness penalty for having a non-viable genome is sufficiently great, We developed stochastic simulations of a unicellular population capable of undergoing the SOS response, in order to numerically test the analytical predictions of our model. We consider a constant population of genomes that is cycled over every time step. During each cycle, every genome is allowed to replicate with a probability We assume that the population initially consists of a clonal population of wild-type (mutation-free) genomes. The fitness of a given genome If a genome replicates during a cycle, then it is removed from the population, and the two daughters are added to the population of genomes. To maintain a constant population size, another, randomly chosen genome is removed from the population as well. Because this approach is simply the stochastic implementation of the quasispecies dynamics of the system, it converges to the infinite population, continuous time result as the population size gets larger and the time steps get smaller.If a daughter genome is produced that has at least The stochastic simulation is allowed to run for a sufficient number of time steps so that the mean fitness of the population does not change significantly, at which point the system is assumed to be at steady-state.This paper developed a quasispecies approach for describing the evolutionary dynamics of a unicellular population that incorporated a simplified model of the SOS response. The model was a generalization of the single-fitness-peak landscape that is often used in quasispecies theory to study various problems in evolutionary dynamics. The model was shown to be analytically solvable, and it was found that the solution led to a maximal selective advantage to the SOS response in a manner that is broadly consistent with the behavior of actual organisms. Specifically, we showed that the SOS response should only be activated in a cell with a sufficiently damaged genome that it will be unviable if the SOS response is not activated. In such a situation, the cell has “nothing to lose,” meaning that it is better to attempt to repair the genome and risk introducing deleterious mutations, than it is to leave a highly damaged genome alone.For future research, it will be important to consider more realistic models that will allow for quantitative models that can be used in collaboration with experiment. Because the SOS response is a genetic repair pathway that works in conjuction with other cellular repair pathways, a proper understanding of the SOS response is important for developing a coherent theory of mutation-propagation that will be useful for understanding the emergence of antibiotic drug resistance in bacteria, and cancer in multicellular organisms


An essential, conserved characteristic of proneural genes is their ability to restrict their own activity to single or a small number of progenitor cells from their initially broad domains of expression. This is achieved through a process called lateral inhibition. A regulatory element, the sensory organ precursor enhancer (SOPE), is required for this process. First identified in Drosophila, the SOPE contains discrete binding sites for four regulatory factors. The SOPE of the Drosophila asense gene is situated in the 5' UTR.An increasing number of publications demonstrate conservation of function of cis-regulatory elements without sequence similarity. In invertebrates such functional conservation has only been shown for closely related species. Here we demonstrate the existence of an ancient arthropod regulatory element that functions during the selection of neural precursors. The activity of genes of the asense-like genes in species belonging to all four arthropod groups . The SOPEs of the spider Cupiennius salei and the insect Tribolium castaneum are shown to be functional in transgenic Drosophila. This would place the origin of this regulatory sequence as far back as the last common ancestor of the Arthropoda, that is, in the Cambrian, 550 million years ago.Through a manual comparison of consensus binding site sequences we have been able to identify a SOPE in UTR sequences of The SOPE is not detectable by inter-specific sequence comparison, raising the possibility that other ancient regulatory modules in invertebrates might have escaped detection. They are initially expressed during development in groups/domains of equivalent neuroectodermal cells. An essential, conserved characteristic of proneural genes is their ability to restrict their own activity to single or a small number of progenitor cells within these domains  Gal4 , UAS-sc (FlyBase ). UAS-co1 aseflies were crossed to hsp70Gal4 > UAS Dm-ase, hsp70Gal4 > UAS Tc-ase and hsp70Gal4 > UAS CsASH2 flies, respectively, and allowed to lay eggs in culture bottles for 3 days. Heat shocks were preformed between 16 hours and 8 hours before puparium formation. Heat shock expression was driven by three 1-hour heat shocks at 37°C, separated by 2 hour intervals at 25°C. Wings were mounted in glycerol and analyzed under a compound microscope (Leica).Gal4 lines that activated the constructs in the expression domains of toll-8, patched, scabrous and ac. Flies of the appropriate genotype were selected, mounted and the bristles were counted under the dissecting microscope (Leica). Statistical analysis was performed using Microsoft Excel.Three independent lines were generated for each UAS construct (ORF only and ORF+SOPE) and crossed to four different D. pulex (Daphnia_pulex 2006-09 JGI) and S. maritima  were searched using tblastn with the ASH and Ase proteins of D. melanogaster, T. castaneum and C. salei as queries. Hits with relevant homology to the bHLH domain were further characterized. Three genes were identified in the D. pulex genome: an ASH homologue (JGI_V11_254034), an ase homologue (JGI_V11_254038) and a truncated copy of ase (JGI_V11_232740). In the S. maritima genome only one ASH homologue was identified; a second gene analyzed was too divergent in the basic region of the bHLH domain to be classified as an ASH gene.The sequenced genomes of ASH and ase genes from the insects D. melanogaster and T. castaneum, the crustaceans T. longicaudatus and D. pulex, the chelicerate C. salei and the myriapods G. marginata and S. maritima. Amino acid sequences have been aligned with ClustalW2 [Phylogenetic trees were constructed using all lustalW2 , manualllustalW2 . The reslustalW2 .AC: achaete; AS-C: Achaete-Scute complex; ASE: asense; ASH: achaete-scute homologue; ATO: atonal; BHLH: basic helix-loop-helix; BP: base pair; NF: nuclear factor; SC: scute; ORF: open reading frame; SOP: sensory organ precursor; SOPE: sensory organ precursor enhancer; UTR: untranslated region.The authors declare that they have no competing interests.Conceived and designed the experiments: SA, PS, AS. Performed the experiments and analyzed the data: SA, BN, AS. Wrote the manuscript: AS, PS. Contributed to writing and editing: BN.asense rescue experiment. Comparison of the number of stout bristles exhibiting differentiation defects in 1 aseflies (first column) and 1 aseflies carrying hsp70Gal4 > UAS Dm-sc, hsp70Gal4 > UAS Dm-ase, hsp70Gal4 > UAS Tc-ase and hsp70-Gal > UAS-CsASH2 transgenesClick here for fileAlignment of the SOP enhancer elements. Nucleotide sequence alignments of the individual transcription factor binding sites of Drosophila, Tribolium, Daphnia, Strigamia and Cupiennius.Click here for fileSequence logos of the SOPE boxes. Graphic representation of the aligned sequences of the SOPE binding sites of Drosophila melanogaster, Tribolium castaneum, Daphnia pulex, Strigamia maritima and Cupiennius salei.Click here for fileMisexpression experiment. Comparison of the number of ectopic bristles in flies carrying the UAS-ase/ASH2 ORF only and ORF+SOPE constructs.Click here for filesenseless 5' UTRSOP enhancer in the . Arrangement of the SOPE boxes in the 5' UTR of the senseless transcript.Click here for filease-like UTRsSecondary structure of the . Graphic representation of the secondary structure of the UTRs of the Drosophila melanogaster, Tribolium castaneum and Daphnia pulex asense genes and the UTRs of Strigamia maritima ASH and Cupiennius salei CsASH2.Click here for file


Systemic Epstein-Barr-virus-positive T cell lymphoproliferative disease of childhood is an extremely rare disorder, characterized by clonal proliferation of Epstein-Barr-virus-infected T cells with an activated cytotoxic phenotype. The disease is more frequent in Asia and South America, with only few cases reported in Western countries. A prompt diagnosis, though often difficult, is a necessity due to the very aggressive clinical course of the disease.+, Epstein-Barr-encoded-RNA-positive T cell phenotype with clonal rearrangement of the T cell receptor genes, the final diagnosis being systemic Epstein-Barr-virus-positive T cell lymphoproliferative disease. On reviewing the literature we found only 14 similar cases, all presenting with very aggressive clinical courses and requiring extensive phenotyping and molecular techniques for final diagnosis.We report the clinicopathological features of fulminant T cell lymphoproliferative disease that arose in the setting of acute primary Epstein-Barr virus infection. Our patient, a 23-year-old man, presented to our facility with persisting fever, hepatosplenomegaly and severe pancytopenia. On bone marrow biopsy, an abundant lymphoid infiltrate was observed. Immunophenotypic and molecular studies revealed that the atypical lymphoid cells displayed a CD8Though extremely rare, this disease can occur in Europe, and a comprehensive diagnostic approach is thus recommended in all case of Epstein-Barr-virus-positive lymphoproliferative disorders. Unfortunately, at present no specific treatment is available; however, prompt administration of anti- Epstein-Barr virus treatment and rapid attempts to control the hemophagocytic syndrome are indicated. Howeval blood . UnfortuIn addition, EBV is implicated in the pathogenesis of different types of lymphoproliferative diseases (LPD), which are related to diverse immune alterations or peculiar clinical backgrounds . Typical+ LPD, and is currently classified as systemic EBV+ T cell LPD of childhood in the World Health Organization classification of tumors of hematopoietic and lymphoid tissues [In this regard, fulminant mononucleosis has recently been demonstrated to be a monoclonal CD8 tissues . This en tissues ; the dis tissues .+, CD3+, CD8+, CD56-, and TIA+ [+. Neoplastic cells have monoclonally rearranged T cell receptor (TCR) genes, and consistent Epstein-Barr encoded RNA (EBER) positivity at in situ hybridization (ISH). Differential diagnosis mainly concerns reactive conditions as well as aggressive natural killer (NK) cell leukemia.At morphology, neoplastic T cells are usually small and lack significant cytological atypia . HoweverHere, we report the clinicopathological features of fulminant T-LPD that arose in the setting of acute primary EBV infection in our patient, characterized by a monoclonal proliferation of EBV-infected T cells.9 cells/L, white blood cells 2.2 × 109 cells/L, neutrophils 410 × 109 cells/L, lymphocytes 1.570 × 109 cells/L), increased LDH, signs of disseminated intra-vascular coagulopathy (CID), and anti-EBV IgM positivity, while a chest X-ray showed diffuse pulmonary infiltrates. No prior immunological abnormalities were recorded.A 23-year-old Caucasian man was hospitalized for persisting fever resistant to conventional therapies. On physical examination, our patient presented with marked hepatosplenomegaly and abnormal sounds at thoracic auscultation. Laboratory findings consisted of severe pancytopenia  Figure . Immunohescribed ,10.Based on the above findings, a final diagnosis of systemic EBV+ T cell LPD of childhood was made. Our patient was initially treated with two sequential doses of VP16 with moderate improvement of his clinical and laboratory data. In particular, the fever transiently improved, hepatosplenomegaly was reduced, and coagulation parameters were partially corrected; however, severe peripheral blood cytopenia persisted. Soon after, the patient developed a fever recrudescence in association with pulmonary fungal infection.-, CD3+, CD4-, CD8+, EBER- small lymphocytes and absence of the previously observed CD8+ large cells. VP16 was then replaced with cyclosporine, obtaining a white blood cell count increase and a further decline of splenomegaly, but with no improvement in thrombocytopenia. A third bone marrow biopsy showed an increased cellularity with reappearance of numerous CD8+ lymphocytes and evident hemophagocytosis.A second bone marrow biopsy was performed, revealing (hypo)aplasia with a minimum percentage of CD79aOur patient then developed rectal hemorrhages only treatable with surgery, which turned out to be sustained by microvascular thrombosis on histological examination. Finally, after a short period of relative good health, our patient had a relapse of rectal bleeding and died soon after with cerebral manifestations.Systemic EBV+ T cell LPD of childhood is a rare disorder characterized by an aggressive disease course and dismal prognosis . As deatOn reviewing the literature, we found only 14 cases reported in Western countries ,11-14, s+ phenotype, two a CD4+ phenotype and one showed double positivity for CD8 and CD4; in one case phenotype was not interpretable and in three cases it was not reported. TCRα presented with clonal rearrangements in nine out of 10 patients and EBV genome was clonal in all but one case. In contrast, among patients with T cell LPD after CAEBV infection two were CD4+, one was CD45RO+ and one presented with an admixture of CD8+ and CD4+ lymphocytes; three cases presented with monoclonal patterns with regard to rearrangement of both TCRα genes and EBV genome. In the remaining case, only the EBV positivity was assessed.Our review of Western cases showed that four of those 14 patients developed a T cell LPD after CAEBV infection ,11, and TCRα rearrangement in systemic EBV+ T cell LPDs, and also sCD3/CD8 negativity, CD56 positivity and germline TCRα patterns in aggressive NK cell leukemia cases.In all described cases, an accurate diagnostic investigation including clinical, morphological, immunohistochemical, and molecular analyses was necessary in order to formulate a correct diagnosis. In particular, the differential diagnosis with aggressive NK cell leukemia was based on surface sCD3 and CD8 positivity, CD56 negativity, and evidence of + LPDs. In fact, though, at present, specific therapies are not available, the correct description of rare disorders is essential for improving current knowledge and possibly future therapeutic approaches.In conclusion, our case report underlines the importance of a comprehensive diagnostic approach in the management of atypical EBVWritten informed consent was obtained from the patient for publication of this case report and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.The authors declare that they have no competing interests.VT performed research, analyzed data and wrote the manuscript; CA performed research and analyzed data; ES and FB analyzed data; SC and GM were responsible for patient care and provided clinical information; AG, CM, SR, and MTS analyzed data; SAP and PPP performed research, analyzed data and wrote the manuscript. All authors read and approved the final manuscript. VT and CA contributed equally to this work; SAP and PPP contributed equally to this work.


Meta-analyses play an important role in synthesizing evidence from diverse studies and datasets that address similar questions. A major obstacle for meta-analyses arises from biases in reporting. In particular, it is speculated that findings which do not achieve formal statistical significance are less likely reported than statistically significant findings. Moreover, the patterns of bias can be complex and may also depend on the timing of the research results and their relationship with previously published work. In this paper, we present an approach that is specifically designed to analyze large-scale datasets on published results. Such datasets are currently emerging in diverse research fields, particularly in molecular medicine. We use our approach to investigate a dataset on Alzheimer's disease (AD) that covers 1167 results from case-control studies on 102 genetic markers. We observe that initial studies on a genetic marker tend to be substantially more biased than subsequent replications. The chances for initial, statistically non-significant results to be published are estimated to be about 44%  relative to statistically significant results, while statistically non-significant replications have almost the same chance to be published as statistically significant replications . Early replications tend to be biased against initial findings, an observation previously termed Proteus phenomenon: The chances for non-significant studies going in the same direction as the initial result are estimated to be lower than the chances for non-significant studies opposing the initial result . Such dynamic patters in bias are difficult to capture by conventional methods, where typically simple publication bias is assumed to operate. Our approach captures and corrects for complex dynamic patterns of bias, and thereby helps generating conclusions from published results that are more robust against the presence of different coexisting types of selective reporting. In many research fields, meta-analyses play an increasingly important role for synthesizing evidence from studies that have been published in the past. Since not all studies and analyses that have been undertaken are eventually published in the scientific literature, meta-analyses typically rely on incomplete samples of study outcomes. These samples might be biased, because the result of a study or of a specific analysis might influence its chances to become reported. Studies with results that achieve formal statistical significance might, for example, have increased chances to become published Statistical methods have been developed to detect and correct for selective reporting biases Here, we present an approach to quantify selective reporting from large-scale datasets of published results. Such datasets are currently emerging in a number of research fields, including molecular medicine. Our approach extends previous work by Hedges and others We use our approach to investigate data from the AlzGene database Despite different association strengths, there is little reason to believe that fundamental differences exist between markers in how selective reporting bias is acting. Thus, even though selective reporting bias cannot be studied properly on a single marker, because most markers are only covered by a handful of studies each, the combined data are likely highly informative for quantitative models of selective reporting. Our approach exploits this and allows us to investigate whether initial publications are more biased than subsequent replications, and whether early replications are biased against initial results.1,X …, nX are assumed to come from a normal distribution i ∼ NX with a known within-study variance i2σ, an unknown between-study variance 2σ, and an unknown mean effect size Δ. The relative chance for a result to be published is assumed to be depend on i = Xi/σiZ, because the value of iZ typically determines the p-value associated with a study result. It is described by the weight functionWe build our approach on the selection model proposed by Hedges i, σi)ωw. The function changes when i/σiX crosses one of the interval boundaries 1,z =  z. While the parameters 1,w =  w are estimated from the data, the interval boundaries 1,z =  z take fixed, pre-defined values. The discontinuities in the weight function at these pre-defined boundaries may be justified by the salience of p-values such as 0.05 in the current practice of interpreting study outcomes The weight function Applying the weight function to the probability density for the unbiased study results yields a weighted probability density given byi, σi)ωw denote the parameters of the weight function (eq. 1), and the terms ijB are given bywhere 1,X =  X is given byThe log likelihood for the data Δ and 2σ in parallel with the parameters of the weight function, 1,w =  w.This function and first and second derivatives as given in 1,w =  w describing selective reporting bias. We now assume that there are m datasets (1),X =  )X with (i)  =  (X1(i),X … n(i)(i))X. These datasets may differ in the underlying mean (m)Δ and between-study variance (m)2σ. However, all the datasets are assumed to be subject to the same selective reporting bias, i.e. the parameters 1,w =  w are the same for all datasets. For the analysis of the AlzGene dataset, this means that we assume while the effect sizes and between-study variance might be different for different markers, selective reporting affects all markers in the same way. We therefore can simultaneously estimate  (1),Δ =  )Δ, (1),σ =  )σ, and 1,w =  w by maximizing the log likelihoodThe above approach simultaneously quantifies the parameters underlying the distribution of the unbiased data, and the parameters of the weight function i(k)X is associated with a categorical variable i(k)K, different weight functions can be used for the different categories. Instead of using a single weight function as in eq. 5 and 6, weight function (j)ω is used if i(k)K falls into category j. Using this approach we can, for example, use different categories for the initial studies, immediate follow-ups, and subsequent studies. This allows testing whether initial studies on a marker are more biased towards significant outcomes than late studies, or whether immediate replications tend to be biased against the result of the initial study. The use of multiple weight functions for quantifying the Proteus phenomenon is illustrated in Moreover, if each study result Although estimating the parameters for these extensions requires somewhat more effort in handling the data, the likelihood function and its derivatives remain analogous to the ones given by Hedges The unrestricted maximum likelihood approach used in our algorithm may not always converge. In our analysis, convergence problems arise for 22 of the 124 markers. To improve convergence, the between-study variance could be determined by non-iterative methods iX and the corresponding standard errors iσ are estimated using a logistic regression. It should be noted that the p-values associated with observations in our model via the value of i = Xi/σiZ are not necessarily identical to the ones given in the actual publications, where often additional factors such as age, gender or specific diagnosis (such as early-onset AD) might be included, and different statistical and genetic models might be used.The AlzGene database In the analysis presented here, we included results regardless of whether allele frequencies are in Hardy-Weinberg equilibrium. We included only markers with results from at least 4 independent publications, as judged by PubMed identifiers. In total, 124 markers fulfill this criterion. 22 markers are excluded because of convergence issues described above.Categories. For modeling the Proteus phenomenon, we assigned one of three categories to each case-control study: initial findings, early replications, and late replications. Any studies on a specific marker that were published in the same year as the first case-control study on this marker fall into the first category . Studies published within the next two years fall into the second category (early replications). All subsequently published studies fall into the third category (late replications). If a marker appears, for example, in publications from 2000, 2000, 2002, 2004, and 2005, the findings would fall into categories 1, 1, 2, 3 and 3, respectively. In an additional analysis we use PubMed IDs for an alternative categorization. Compared to using the publication year, PubMed IDs allow sorting papers at a finer temporal resolution. However, because particularly for publications from the 90's there is no strict relation between PubMed ID and date of publication, a PubMed ID based ranking is much less robust that a year-wise ranking. Results from the additional analysis are shown in the To estimate selective reporting bias we use fixed-effect models and random-effects models see . We use i, σi)  = 1ω. The weight function is set to one for the outer intervals, i.e. i, σi)  = 1ω. The weight function for this model, and for all following models, is set to one for the outer intervals, i.e. i, σi)  = 1ωw, describing bias for all studies with Z-values that fall in the mid interval.We use a single weight function to model the data, i.e. do not use different categories for initial studies, early replications and late replications. The boundaries are chosen at (I)w and (S)w, and allows detecting whether initial studies are more biased then subsequent ones.This model is similar to Model 1, but uses two different weight functions. One weight function describes bias for the initial study, while the other weight function describes bias for all subsequent studies . The resz =  . The model has four parameters: (I)w for initial studies that fall into the interval between −1.64 and 1.64, (E)1w for early replication studies with z-values between −1.64 and 0, (E)2w for early replication studies with z-values between 0 and 1.64, and (S)w for all subsequent studies that fall into the interval between −1.64 and 1.64. This model allows investigating whether early replication studies differ from later ones, and is designed to match the model for the Proteus phenomenon described in the next paragraph.In this model, a third category is added to separately model early and late replication studies. The additional weight function is described by two additional parameters and uses boundaries at −1.64 and 1.64 and has the opposite sign of the initial study, the weight function is set to (E)1w. If it falls into the interval between −1.64 and 1.64 and has the same sign, weight (E)2w is used. If several studies on a marker are categorized as initial studies, we use the sign of the most extreme result. The Proteus model allows us to investigate whether subsequent studies that contradict the initial one are more likely to be published than studies that are in agreement with the initial one but do not show a strong effect. It has the same number of parameters as Model 3. A comparison between the two models is used to determine whether explicitly taking into account the sign of the initial study for modeling bias in early replications improves the model.As in Model 3, we use three different weight functions, describing initial studies, early replications and late replications. The weight functions and parameters are analogous to the ones used in Model 3. However, the weight function for early replications is assumed to further depend on the outcome of the first study see . If the −1.64 and 1.64 are relatively constant. The weights start increasing as |z| increases over a value of 1.64. This is what one would qualitatively expect for selective reporting bias. Results that do not reach formal statistical significance are less likely published, irrespective of where exactly the result falls. The estimated weights in the 10 intervals covering the region between −1.64 and 1.64 fall roughly around −0.5 which corresponds to about 60% of non-significant results in any of these intervals being reported. For a fixed effects model, where the between-study variances are assumed to be zero  seem to be underrepresented when compared to results with a two-sided p-value smaller than 0.01 (|z| >2.58), and with two-sided p-values between 0.05 and 0.1 (1.64<|z|<1.96). This observation is somewhat surprising for classical publication bias where the p = 0.05 value is considered to be a salient threshold for publication, but is in line with previous observations The high-resolution model reveals an interesting shape of the weight function for results with z-values outside the interval between z = −1.64 and z = 1.64, we used these boundaries in the subsequent models (Model 1–3 and Proteus model). Estimates for the simple random-effects one-parameter model (Model 1) are shown in −0.33 for the random effects model. This corresponds to a probability of reporting of about 72% (95% CI from 58% to 90%) relative to studies that fall into the outer intervals. This estimate is similar to the corresponding estimates in the high-resolution model, but note that we now use different outer intervals . The error in the estimate of the bias is much lower, because in the one-parameter model each interval contains data from several intervals of the high-resolution model (Because the change of the weight function in the high-resolution model is highest at the boundaries of on model .(I)  = −0.81w, suggesting that initial studies with standardized effects in the 1.64 to −1.64 interval have a chance of only 44% (95% CI from 32% to 63%) of being reported, relative to initial studies with results outside that interval. This bias is considerably stronger than the estimates for subsequent studies ((S)  = −0.17log w, respectively, corresponding to a probability of 84% compared with studies outside the interval; 95% CI from 66% to 107%). Thus, initial studies tend to be much more biased than subsequent ones. The potential origin and consequences of this effect are discussed further below. All estimates and their standard errors are summarized in The results for the models designed to investigate the initial study bias and the Proteus phenomenon are summarized in (E)1w and (E)2w, and yields a smaller change in the log likelihood score than the Proteus model. Based on the Akaike Information Criterion (AIC), the Proteus model would be favored for describing the given AD dataset. The single most important parameter in these models is the one to distinguish bias in initial publications from bias in subsequent ones, which indicates that the initial study bias is a very robust phenomenon.The results for the Proteus model show that studies that confirm the direction of the effect of an initial study but do not achieve formal statistical significance face more bias than studies with non-significant results that oppose the initial result in terms of the direction of the effect Click here for additional data file.Supporting Information S2Uncorrected and corrected estimates for the association strengths and between-study variances for the markers included in our analysis.(CSV)Click here for additional data file.


Arca subcrenata is documented in the literature of marine Traditional Chinese Medicine. Polypeptide fraction from A. subcrenata, coded as P2, was demonstrated to possess significant antitumor activity in our previous study. However, the underlying mechanism remains undefined. The present study was carried out to investigate the underlying antitumor mechanism of P2 in human cervical cancer HeLa cells by MTT, FCM, LSCM, and western blot assays. The results revealed that P2 significantly induced apoptosis of HeLa cells in a concentration- and time-dependent manner. High level of ROS was provoked by P2, which was in turn responsible for induction of apoptosis through activation of intrinsic mitochondrial pathway and JNK1/2, p38 MAPK pathways, as well as inhibition of ERK1/2 pathway, as evidenced by the abrogation of P2's effect on HeLa cells preincubated with the ROS scavenger NAC. P2 also was observed to display significant effect on G2/M phase arrest by downregulating the expression of cyclin B1/cdc2 complex and upregulating the expression of p21. These findings demonstrate that P2 induces apoptosis and G2/M phase arrest in HeLa cells through ROS-mediated MAPKs pathways, suggesting that P2 would be worth investigating as a promising agent within the scope of marine drugs for treatment of cervical cancer. Cervical cancer, the second most common type of tumor in females surpassed only by breast cancer , becomesMarine organisms are known for their capability of producing large amount of bioactive materials, with properties such as antitumor, anti-inflammatory, and antioxidant activities mainly due to the marine environment being characterized with high salinity, high pressure, low temperature, low light intensity, and an oligotrophic condition. Cytarabine, a nucleoside firstly obtained from sponge in the deep sea, was approved by FDA in 1969 and is regarded as one of the main drugs used to treat hematologic malignancies , 5. ReceκB pathway [ROS, a series of natural metabolites of intracellular oxygen, plays a predominant role in cellular process and involves multiple signaling pathways as a second messenger , such as pathway , and PI3 pathway . A moder pathway  by induc pathway . ROS wou pathway . Activat pathway , 19. The pathway . Numbers pathway –22. Evid pathway . Low lev pathway . Numbers pathway , 23, 24.Arca subcrenata Lischke, a member from Arcidae family, is classified as a marine invertebrate species [ A. subcrenata [ A. subcrenata can be adopted in the treatment of a number of diseases such as inflammation, tumor, anemia, and dyspepsia in China [ A. subcrenata is mainly composed of polysaccharide complexes and proteins [ A. subcrenata was reported to possess the bioactivity of improving the immunological function such as promotion of the thymus/spleen index of normal mice as well as immunosuppressed mice in vivo tests [ A. subcrenata could inhibit the proliferation of several tumor cell lines in vitro [ A. subcrenata had antioxidant activity against DPPH radicals and hydrogen peroxide [ species . It is wbcrenata . The bodin China . A. subcproteins , 26. Recvo tests . Our prein vitro  and the peroxide . A. subcrenata, displayed significant antitumor activities in both in vitro and in vivo tests [More recently, we had confirmed that P2, a polypeptide fraction fromvo tests . However3--2,5-diphenyltetrazolium bromide (MTT), N-acetylcysteine (NAC), Z-Val-Ala-Asp(OMe)-fluoromethylketone (Z-VAD-FMK), and 2′,7′-dichlorodihydrofluorescein diacetate (DCFH-DA) were purchased from Sigma . RPMI-1640 medium and fetal bovine serum (FBS) were purchased from Gibco . Primary antibodies against cleaved caspases 3, 7, and 9, PARP, p-cdc2, p21, cyclin B1, ERK1/2, p-ERK1/2, p38, p-p38, JNK1/2, p-JNK1/2, and anti-rabbit or anti-mouse IgG horseradish peroxidase- (HRP-) linked secondary antibodies were purchased from Cell Signaling Technology . Primary antibodies against Bax, Bcl-2, and cytochrome c were purchased from Abcam . Primary antibody against GAPDH was from Hangzhou Xianzhi Biotech. Co., Ltd. . Enhanced chemiluminescence (ECL) detection kit for western blot was obtained from Nanjing KeyGEN Biotech. Co., Ltd. . A. subcrenata was purchased from Huangsha seafood market, Guangzhou, China, and identified by Professor Rongmin Yu . The visceral mass of A. subcrenata was washed with distilled water for 3 times and homogenized with phosphate-buffered saline solution . The supernatant was collected after centrifugation and to which ammonium sulfate was added to precipitate the protein components. The crude polypeptides were further purified by ion-exchange chromatography eluted with different concentrations of sodium chloride solution at 4°C and the effluent fractions were detected using a protein detector at 280 nm. P2 was one of the effluent fractions with the highest content of protein. The collected fraction of P2 was concentrated, dialyzed against distilled water at 4°C, and lyophilized.The material ofμg/mL streptomycin, and 100 U/mL penicillin in a cell incubator with 5% CO2 at 37°C.Human cervical cancer HeLa cells were purchased from the American Type Culture Collection  and cultured in RPMI-1640 medium containing 10% FBS, 100 4 cells/mL and incubated with P2 at various concentrations for setting time. The cells were pretreated by inhibitors for 1 h before treatment. Thereafter, MTT solution (5 mg/mL) was added and incubated with cells for another 4 h. The 96-well plates were added with 200 μL/well of DMSO to dissolve the MTT-formazan product and the data were spectrophotometrically estimated at 570 nm. The cell growth inhibitory rate was determined according to the absorption.MTT assay was used to detect the viability of HeLa cells according to the literature with some necessary variations . The celApoptosis measurement was carried out mainly according to the literature  with som4 cells/mL and incubated with 0, 4, 12, and 36 μg/mL of P2 for 48 h. After the treatment with P2, the attached cells were washed twice with PBS and fixed with 4% formaldehyde for 10 min. The fixing solution was then removed and the cells were washed twice with PBS before staining with DAPI solution . After staining for 15 min in the dark, cells were washed again and observed under LSCM . Ten fields were randomly selected to observe and images were taken [Cells were seeded at a density of 4 × 10re taken .5 cells/mL onto 6-well plates and incubated for 48 h with different concentrations of P2. After being treated with P2, cells were harvested and washed twice with cold PBS. Then cells were fixed in 70% alcohol for 12 h at 4°C. The pellet was washed and resuspended again in PBS containing PI (50 mg/mL), Triton X-100 , and RNase A (100 mg/mL). After incubation for 30 min, the cell cycle distribution in each treatment was detected using a flow cytometer. Data were analyzed by WinMDI 2.9 software.Cell cycle distribution analysis was performed according to the literature  with som5/well) were seeded in a 6-well plate for 24 h. After being treated with different concentrations of P2  for 24 h, cells were harvested and washed twice with PBS and then incubated with JC-1 at 37°C for 30 min in the dark. Loss of MMP in HeLa cells was detected by measuring a change in fluorescence intensity using a flow cytometer, with excitation at 484 nm and emission at 501 nm [MMP of HeLa cells in each treatment was measured by FCM using JC-1 staining. Briefly, cells (4 × 105/well) for 24 h. After incubation, cells were treated with different concentrations of P2  for setting time. Then, the cells in each treatment were detached using trypsin-EDTA and washed twice with PBS. Cells were incubated with 10 μM of DCFH-DA for 30 min in the dark. ROS level in each treatment was measured using a flow cytometer, with excitation at 485 nm and emission at 525 nm [DCFH-DA, a fluorescent probe of ROS, was used to measure the generation of intracellular ROS in P2-treated HeLa cells. Cells were seeded in 6-well plates (4 × 10t 525 nm .μg) of protein of each sample were separated on SDS-PAGE and further transferred to a methanol preactivated-polyvinylidene difluoride (PVDF) membrane . The membranes were blocked with 5% nonfat dry milk in Tris-buffered saline containing 0.1% Tween-20 (TBST) for 90 min at room temperature and incubated with primary antibodies at 1 : 400 to 1000 dilutions with 5% bovine serum albumin (BSA) in TBST overnight at 4°C. The blots were washed 3 times with TBST and incubated with HRP-conjugated secondary antibodies (1 : 2000) for 1 h at room temperature. Antibody binding was detected using ECL detection kit.The procedure of western blot was mainly performed according to the literature . HeLa cet-test and P < 0.05 was considered statistically significant. All results are representation of at least three independent tests and data are expressed as mean ± SD.Data were analyzed with Student's +/PI−) and late apoptosis (Annexin V+/PI+) [μg/mL of P2 for 12, 24, and 48 h, both the percentages of early apoptotic cells and late apoptotic cells increased in a concentration- and time-dependent manner  . After b Figures . These rμg/mL of P2. The result showed that Z-VAD-FMK significantly reduced the inhibitory effect of P2 on HeLa cells, indicating that P2's antiproliferative effect was caspase-dependent  [Cell growth and cell division are known to be controlled by several genetically defined cell-cycle checkpoints to ensure the correct progression of cell cycle through different stages . Cell cyn (PCNA) , 37. In n (PCNA) . ROS wasn (PCNA) , 53 and n (PCNA) , 54. In n (PCNA) . Our stuThe current study, for the first time, demonstrates that P2 exerts antitumor effect via induction of apoptosis and cell cycle arrest at G2/M phase. The underlying mechanism is mainly related to ROS-mediated activation of JNK1/2 and p-38 MAPK pathways, inactivation of ERK1/2 pathway, and subsequent trigger of intrinsic mitochondrial apoptotic cell death in a caspase-dependent manner, including the activation of caspases 7, 9, and 3, and shifting the Bcl-2 family proteins. These results exhibit the exciting potential for development of antitumor agents from marine organisms through apoptosis induction and cell cycle arrest by ROS overproduction and targeting MAPKs pathways.


Streptococcus pneumoniae is the most common cause of pediatric and frequent cause of adult empyema. We investigated whether S. pneumoniae can proliferate within human pleural fluid and if growth is affected by the cellular content of the fluid and/or characteristics of pneumococcal surface proteins. Invasive S. pneumoniae isolates (n = 24) and reference strain recovered from human blood or empyema were inoculated (1.5×106CFU/mL) into sterile human malignant pleural fluid samples (n = 11). All S. pneumoniae (n = 25) strains proliferated rapidly, increasing by a median of 3009 (IQR 1063–9846) from baseline at 24hrs in all pleural effusions tested. Proliferation was greater than in commercial pneumococcal culture media and concentrations were maintained for 48hrs without autolysis. A similar magnitude of proliferation was observed in pleural fluid before and after removal of its cellular content, p = 0.728. S. pneumoniae (D39 strain) wild-type, and derivatives (n = 12), each with mutation(s) in a different gene required for full virulence were inoculated into human pleural fluid (n = 8). S. pneumoniae with pneumococcal surface antigen A (ΔpsaA) mutation failed to grow (2207-fold lower than wild-type), p<0.001, however growth was restored with manganese supplementation. Growth of other common respiratory pathogens (n = 14) across pleural fluid samples (n = 7) was variable and inconsistent, with some strains failing to grow. We establish for the first time that pleural fluid is a potent growth medium for S. pneumoniae and proliferation is dependent on the PsaA surface protein and manganese.Empyema is defined by the presence of bacteria and/or pus in pleural effusions. However, the biology of bacteria within human pleural fluid has not been studied.  Streptococcus pneumoniae the most common bacterial cause. As many as 20–40% of patients with pneumonia develop a simple parapneumonic pleural effusion [Pneumonia affects 450 million patients worldwide each year,  with Streffusion .S. pneumoniae accounts for the vast majority of pediatric empyema and is among the most frequent causative organisms of empyema in adults [Empyema represents the most severe end of the pleural infection spectrum. It is defined by the presence of bacteria, or pus, in the pleural fluid. n adults –6. The in adults –9.S. pneumoniae seems to have a particular affinity for causing pleural infection, but the reasons why are not fully understood. During pleural infection, the pleural fluid is exudative and contains a diverse milieu of biologically active molecules. Despite the clinical importance of empyema, no studies have investigated the biological interactions between bacteria and pleural fluids. It is unknown, for example, if bacteria are merely shed from the pleural tissues into the fluid and if the fluid influences bacterial growth. On one hand, exudative fluids are rich in nutrients that can potentially enhance microbial growth; on the other hand, many proteins and enzymes  in the fluid can serve to defend against bacterial invasion. These questions are difficult to study in empyema fluid as the presence of bacteria can profoundly alter the pleural fluid composition. In clinical settings, patients are often given systemic antibiotics before the fluid is sampled, taking away the opportunity to study the effects of bacterial growth.Compared to most other pathogens that cause pneumonia, S. pneumoniae strains. We hypothesized that pleural fluid can serve as a growth medium for common empyema bacteria such as S. pneumoniae. We further investigated the role of common pneumococcal surface proteins, in particular nutrient transporters, on pneumococcal growth and survival within human pleural fluid.Pleural effusion is not unique to pleural infection but is the common end-product of a variety of other non-infective pleural diseases/inflammation that leads to vascular hyper-permeability and plasma leak into the pleural cavity. In this paper, we studied whether the pleural fluid of patients with other non-infective pleural etiologies could support survival and growth of Pleural fluid samples were collected from patients attending Sir Charles Gairdner Hospital (SCGH) that required pleural fluid drainage for clinical indications, with approval (SCGH Human Research Ethics Committee 2012–156).g for 10 minutes.Pleural fluid samples were collected using aseptic techniques from patients with pleural effusions of non-infective etiologies. All samples were subjected to routine bacterial culture for which the fluid was injected into and transported in blood culture bottles and processed in an accredited hospital laboratory . Pleural samples confirmed to be culture-negative were used in experiments. Patients who received antibiotics within 72 hours were excluded. Pleural fluid biochemistry  was measured as previously described . SupernaPleural fluids (n = 53) were collected from 45 patients; effusions obtained from the same individual (n = 8) were collected on separate occasions at least 7 days apart. Most samples were malignant pleural effusions  and 10.6% (n = 5) were transudates; . SamplesS. pneumoniae (n = 24) from patients with invasive pneumococcal infection were obtained from PathWest . Serotypes of the isolates include type 1, 11A and 19F from pleural fluid and 1, 6B, 6C, 8 (n = 3), 10A, 11A, 12F, 19A (n = 8), 21, 22F (n = 2) and 35B from blood cultures. In addition, a S. pneumoniae reference strain of capsular serotype 3  was used , Staphylococcus aureus ATCC®9144, Escherichia coli ATCC®105365 and ATCC®25922, Moraxella catarrhalis NCTC®3622, Pseudomonas aeruginosa ATCC®25668 and clinical isolates (n = 5) capable of causing invasive disease including methicillin-resistant S. aureus, P. aeruginosa (n = 2), Klebsiella pneumoniae and Klebsiella oxytoca (PathWest) were used for comparison growth studies with S. pneumoniae  including eumoniae .S. pneumoniae, a capsule type-2 isolate, and its derivatives (n = 11) with mutations in genes required for full virulence have previously been described [Wild-type D39 ives n =  with mutescribed , and thoS. pneumoniae were prepared as previously described [To standardize counts between experiments, frozen inocula of escribed . Inocula6 CFU/mL of bacteria and incubated for 24hrs at 37°C and 5% CO2 unless specified. Baseline inoculum and final concentrations (CFU/mL) were verified by manual counting log serial dilutions from blood agar.Pleural fluid samples  were warmed to 37°C then inoculated with approximately 1.5×10S. pneumoniae isolates (n = 25). Six pairs were inoculated within 6 hours from collection and all within 24 hours (range 1 to 18 hours). Growth was compared in parallel to Todd-Hewitt Broth  containing 17% FCS , [S. pneumoniae clinical isolate (n = 2) controls, at the same starting inoculum of 1.5×106 CFU/mL, for comparison purposes. The CFU/mL of bacteria failing to grow at 24 hours was subsequently measured at 4 and 8 hours in pleural fluid (n = 3).Pleural fluid characteristics and bacteria used in each experiment are presented in ia, WA), , DulbeccΔpsaA in parallel to wild type in pleural fluids (n = 16) with and without supplementation of 3μM manganese chloride tetrahydrate  were performed. The concentration was determined following concentration dependent supplementation experiments ranging from 0.003 to 30μM of manganese.D39 wild and mutant pneumococci (n = 12) were tested in pleural effusions (n = 8). Further experiments with Statistical analyses were performed using SigmaPlot 12.5 . Results are presented as mean (SD) or median (IQR) based on normality of data. The Wilcoxon Signed-Rank test and paired t-test (for non-parametric and parametric data respectively) were used to compare baseline to 24 hour pneumococcal concentrations and Student’s t-test to compare growth between pleural fluid and other medium at various time-points over 48 hours. The Mann-Whitney Rank Sum test was used to compare differences between samples containing cells and correspondent supernatant and the Pearson Correlation was used to determine any relationship between spun and unspun samples. Significance was defined as p<0.05.6 CFU/mL S. pneumoniae (n = 3), with a median increase of 6875 (IQR 3494–15744) fold at 24 hours compared to baseline. Volumes ranging from 1 to 10mL in both tubes and multi-well plates demonstrated similar growth, however volumes less than 1mL had inconsistent or poor growth (data not shown). Inoculum concentrations in the range of 1.5 x 103 to 1.5x 106 CFU/mL demonstrated similar bacterial growth at 24 hours; an initial concentration of 106 CFU/mL was used across experiments. Samples were incubated for 24 hours following inoculation as both S. pneumoniae isolates and D39 mutant strains were found to plateau at this time point (data not shown). No difference in CFU/mL of pneumococci was observed in fluids stored for up to 96 hours at 2–8°C following collection.Significant growth was observed when 3mL volumes of pleural fluid were inoculated with 1.5 × 10S. pneumoniae proliferated rapidly in all (n = 11) effusion samples tested (5 CFU/mL to 1.3 × 109 CFU/mL), p<0.001 after 24 hours. S. pneumoniae growth was consistent with no difference in CFU/mL when data was analyzed as growth per S. pneumoniae isolate, growth in each pleural fluid sample or growth per serotype (p = 0.776).All 25 strains of s tested  by a medS. pneumoniae growth, with a median fold increase at 24 hours of 2857 . Growth of S. pneumoniae in pleural fluid with cells correlated closely to growth in pleural fluid without cells present  THB with fetal calf serum (17%), a supplemented culture medium commonly used to cultivate pneumococci, 2) DMEM, a glucose rich cell culture medium and 3) 0.85% saline as a control. Rapid proliferation of S. pneumoniae was observed in THB with FCS, DMEM and pleural fluid within 8 hours following inoculation , p<0.001. Furthermore, inconsistent growth between strains of the same bacterial pathogen was evident, including within the viridans streptococci group (n = 3) which are a frequent cause of adult empyema. The bacterial pathogens which failed to grow at 24 hours in pleural fluid did not demonstrate increased growth at earlier time points of 4 or 8 hours (data not shown).We investigated the growth of other bacterial pathogens (n = 14) across pleural fluid samples (n = 7). Growth measured at 24 hours was variable across other bacterial pathogens  with a mS. pneumoniae growth in pleural fluid, growth experiments were repeated using the D39 wild-type strain and eleven mutant strains  in eight pleural fluid samples  whereas the D39 wild strain had increased 1214-fold . A decrease in ΔpsaA concentrations from baseline was apparent at 4 hours and further decreased at 8 and 24 hours. Importantly, manganese supplementation of pleural fluid by the addition of 3uM of manganese prior to inoculation restored ΔpsaA proliferation in the pleural fluid, increasing the median fold-change in CFU from 0.11 (0.07–501) to 1546 (509–4775) at 24 hours, p <0.001 (Δpit and ΔadcAII/I mutants respectively had no impact on growth compared to the D39 wild type control (data not shown).To identify factors required for  samples . These m in vivo , 20, 22.p <0.001 . 3μM manS. pneumoniae is a common cause of both pediatric and adult empyema, yet the reasons why this pathogen can cause pleural infections have only been partially investigated. This is the first study to investigate the interactions of bacteria, namely S. pneumoniae, and human pleural fluid ex vivo. We found that pleural fluid provides a rich medium to support the growth of all relevant S. pneumoniae strains tested and that pneumococci appear to be much better adapted to grow in pleural fluid than most other common respiratory pathogens. More importantly, growth of S. pneumoniae in pleural fluid was dependent on PsaA, a manganese transporter making this a potential future therapeutic target. Mutations of other key virulence factors of S. pneumoniae did not affect the growth of pneumococci in human pleural fluid ex vivo.S. pneumoniae from infected pneumonia tissues invades the pleural cavity within 4 hours of infection via transcytosis across the mesothelial cells [We have previously shown in our mouse model that al cells . Little S. pneumoniae consistently proliferated across eleven human exudative pleural fluid samples tested with a median increase of approximately 3000-fold from baseline at 24 hours. In contrast, the median growth of other bacterial pathogens in pleural fluid was approximately 10-fold, and interestingly the growth was variable with some bacterial isolates demonstrating similar levels of growth to S. pneumoniae, whilst other isolates consistently failed to grow.To investigate the capacity of pleural fluid to support bacterial growth we used human pleural effusions, most with malignant etiology. Parapneumonic effusions and malignant pleural effusions are both exudative in nature and have indistinguishable biochemical compositions with elevated protein and lactate dehydrogenase and reduced glucose and pH. The effusions used for this study display the same biochemical features of non-infective simple parapneumonic effusions. Our results show that all 25 isolates of S. pneumoniae was similar to optimal laboratory media at 8 hours demonstrating that pleural fluid represents an excellent growth medium for S. pneumoniae. Furthermore, pneumococci naturally undergo autolysis after reaching stationary phase during growth which was observed in broth at 18 hours, yet bacterial CFU/mL density was maintained for 48 hours after growth in human pleural fluid. This persistent growth is likely to promote significant neutrophil influx and may be a reason why pneumococci are a frequent cause of empyema. This failure of autolysis would ensure S. pneumoniae strains are capable of maintaining high level infection even in a closed environment such as a pleural effusion. The reasons why autolysis did not occur after growth in pleural fluid are not known. The cellular content of the pleural fluid did not influence S. pneumoniae proliferation, indicating it is the pleural fluid itself that provides the medium for S. pneumoniae growth. The ability to grow in pleural fluid did not vary between individual S. pneumoniae isolates or serotypes investigated.When tested in parallel, the growth of S. pneumoniae have been identified as essential for virulence and growth of the bacterium. We investigated eleven of these virulence factors for their role during growth in pleural fluid, using mutants that are known to have reduced growth in blood  [ [psaA mutant. PsaA is a lipoprotein required for manganese transport and resistance to oxidative stress [S. pneumoniae and growth in laboratory medium that has been depleted of manganese. Providing biochemical complementation of the ΔpsaA mutation by supplementing pleural fluid with 3uM manganese was sufficient to restore growth in pleural fluid, confirming that it is the low concentration of manganese that causes failure of growth of the ΔpsaA strain [S. pneumoniae in pleural fluid yet pneumococci are capable of overcoming this using the PsaA transporter. Potential PsaA inhibitors that have recently been identified open opportunities to control S. pneumoniae proliferation in the pleural space [S. pneumoniae with mutations affecting transporters required for iron or zinc uptake was not affected, suggesting that manganese results are specific for growth within pleural fluid.Multiple cell surface proteins that function as adhesins, in complement resistance, autolytic enzymes and nutrient transporters in d ΔlivH) , 24–27 oΔadcAII) , 28, 29.e stress , 30, 31.al space . InteresS. pneumoniae in vitro. Next, due to the nature of the study, experiments were performed in ex vivo pleural fluid and therefore host defenses, including neutrophils that may modify the bacterial proliferation were not accounted for. However, as a proof of principle study our aim was to investigate the direct effect of pleural fluid on S. pneumoniae proliferation. Furthermore, our data showed no difference in proliferation with or without cellular content of the pleural fluid. Finally, we studied 25 invasive strains of S. pneumoniae that had caused empyema or septicemia. Future studies that include other serotypes may be needed, though the very consistent nature of the results indicates that the majority of S. pneumoniae isolates are likely to grow in pleural fluid.The study has a number of potential limitations. We were unable to use pleural fluid of infected etiology in the present study for reasons stated in the introduction. However, malignant pleural effusions have a close resemblance in biochemistry to parapneumonic effusions and were therefore used for the majority of our experiments. Although the cellular content of these effusions may differ, we found that the cellular content of the fluid did not influence the growth/proliferation of S. pneumoniae and may be one reason why pneumococci are a common bacterial cause of empyema. The high density and persistent growth of S. pneumoniae in pleural fluid highlights the importance of draining infected effusions. Pneumococcal growth was independent of the cellular content of pleural fluid but dependent on the PsaA surface protein, making PsaA a potential future therapeutic target. The variable growth of other bacterial pathogens in pleural fluid may account for the lower incidence of otherwise common respiratory pathogens in empyema and warrants further exploration in order to understand pathogenesis of pleural infection.Pleural fluid is a potent growth medium for S1 Table(DOC)Click here for additional data file.


The burden of pleural diseases has substantially increased in the past decade because of a rise in the incidence of pleural space infections and pleural malignancies in a patient population that is older and more immunocompromised and has more comorbidities. This complexity increasingly requires minimally invasive diagnostic options and tailored management. Implications for patients are such that the limitations of current diagnostic methods need to be addressed by multidisciplinary teams of investigators from the fields of imaging, biology, and engineering. Ignored for a long time as an epiphenomenon at the crossroad of many unrelated medical problems, pleural diseases are finally getting the attention they deserve and have spurred a vibrant and exciting field of research. Interestingly, surgical removal of the pleura, or chemical fusion of the visceral and parietal membranes for therapeutic purposes (pleurodesis), seems to have little physiologic effect on lung function or work of breathing in long-term human studies and this raises the question of why such a complex structure was selected for in the first place5.The term pleura refers to the membrane separating the lungs from the chest wall, which is constituted by two leaflets attached to the outer surface of the lung  and the inside of the chest wall , joining at the hilum of the lungs, hence forming a genuine sac surrounding both lungs. The small amount of pleural fluid separating visceral from parietal pleural membranes serves as a lubricant, optimizing respiratory mechanics by facilitating synchronous chest wall and lung movements during respiratory efforts6. Approximately 150,000 of these effusions are caused by malignancy—lung and breast cancer are by far the most common offenders—generally due to parietal pleural involvement in advanced disease7. Other common causes of pleural effusions include congestive heart failure, pleural space infections, pulmonary embolism, and manifestations of connective tissue diseases such as lupus or rheumatoid arthritis. It should be intuitively obvious, given the profound implications of such diagnoses for patients, that timely and accurate diagnosis of unexplained pleural effusions is of considerable importance. Pleural fluid aspiration followed by application of the time-honored Light’s criteria remains a remarkably useful step in the diagnostic algorithm of pleural effusions, although an expanding panel of diagnostic tools in the past decade has slowly transformed our approach to pleural diseases, the increasing complexity of which has motivated the development of multidisciplinary pleural subspecialty programs at many institutions in recent years12. In this brief report, we will attempt to summarize what we believe are the most significant and promising recent advances in the diagnosis of pleural diseases and highlight current research efforts in the field.Notwithstanding this lack of clear physiologic function, pleural disorders represent one of the most common reasons for consultation of lung specialists, and an estimated 1.5 million new pleural effusions identified every year in the US alone are caused by more than 60 distinct disease processes16. In fact, the evidence for improved outcomes and reduced complication rates with ultrasound for thoracic procedures is so overwhelming that its use is now universally considered the standard of care18. Bedside ultrasound examination of the pleura can easily confirm the presence of pleural fluid when conventional chest x-ray findings are equivocal. In addition, it allows an estimation of its volume and complexity and occasionally identifies pleural nodules or masses suggestive of pleural malignancy19. While most physicians use ultrasound merely to mark the site for needle insertion before setting up for the procedure, real-time ultrasound using a sterile sleeve allows sampling of previously unreachable small loculated effusions and occasionally allows parietal pleural biopsies. Furthermore, recent evidence suggests that pre-procedural identification of the intercostal artery, the course of which can be unpredictable, is easily achieved with a high-frequency probe and could help avoid rare but potentially fatal bleeding complications22. Ultrasound examination immediately after the procedure can indirectly suggest the occurrence of pneumothorax by the disappearance of the typical “sliding” sign, representing the sliding of visceral and parietal pleura over each other. Rare but potentially serious bleeding complications from injury of the intercostal artery or one of its collateral vessels may also be identified in the form of rapid re-accumulation of echogenic pleural fluid24.Thoracic ultrasound, widely available in the form of increasingly sophisticated and affordable portable and handheld units, has unquestionably changed the paradigm in bedside medical evaluation. Basic ultrasound education now complements traditional physical examination training at many medical schools, residency, and fellowship programs. Point-of-care ultrasound for diagnosis and management of thoracic diseases in particular is recommended and endorsed by most relevant scientific societies, and there are established training and certification standards26. An intriguing recent report suggests that ultrasound may allow the identification of unexpandable lung before thoracentesis. In some cases, such as when chronic inflammatory effusions have resulted in the creation of a thick peel around the lung, fluid drainage is not accompanied by lung re-expansion. The vacuum generated can expose patients to complications including pain, pneumothorax, and re-expansion pulmonary edema. Transmission of the heartbeat results in different lung movement and deformation changes that can be identified by using specific ultrasound modes such as the M-mode and speckle tracking27. In this small study, ultrasound in fact performed better than pleural manometry, long regarded as the gold standard for the diagnosis of unexpandable lung and the utility of which is being assessed in an ongoing randomized clinical trial .Interestingly, there has been relatively little research on pleural ultrasound as a predictive tool. Two small studies suggest that a highly organized pleural space with loculations and septations during pleural space infection predicts poorer outcomes, but these studies are small and unadjusted for possible confounders28. However, in the absence of such specific findings, computed tomography appears neither sensitive nor specific enough to obviate the need for invasive diagnostic interventions30. Positron emission tomography is often used to assess the probability of malignant pleural effusion but with modest sensitivity and specificity, which in a recent meta-analysis were estimated to be 81% and 74%, respectively31. Recently, magnetic resonance imaging has been proposed as a potentially superior imaging modality for pleural effusions in general and malignant pleural mesothelioma in particular but remains largely underutilized compared with computed tomography34. Radiomic approaches to thoracic diseases represent an early and exciting field in imaging science, and very preliminary work in quantitative imaging for the diagnosis of malignant pleural effusion—and staging and diagnosis of mesothelioma in particular—is generating considerable interest36.There have been comparatively less recent data on other imaging modalities. Whereas loculations (sequestrations of fluid in non-dependent areas) are well identified on computed tomography, the degree of organization within the effusion is better appreciated on ultrasound. In addition, the presence of any of the four following radiologic signs is highly predictive of malignancy: parietal pleural thickening of more than 1 cm, thickened mediastinal pleura, circumferential thickening, and the presence of pleural nodules11. Briefly, at the risk of oversimplifying complex processes, transudative effusions typically result from local alterations of the Starling rules that govern capillary microcirculation in the parietal pleura with increased hydrostatic  or decreased oncotic  pressures, whereas exudative effusions suggest a disease process involving the parietal pleura itself, whether from malignancy, infection, or inflammatory diseases. However, these processes are complex and overlaps are frequent in terms of both pathophysiology and pleural fluid analysis. For example, an estimated 25% of effusions due to congestive heart failure may classify as exudates (“pseudo-exudates”), particularly if the patient is on diuretics37, and conversely a small percentage of documented malignant pleural effusions may classify as transudative effusions. While a high index of suspicion should lead to pleural biopsies in the latter scenario, recent data suggest that the pleural fluid–to–serum albumin gradient could help clarify the etiology of heart failure–related pseudo-exudative pleural effusions38. Pleural and serum brain natriuretic peptide and amino-terminal pro-brain natriuretic peptide levels are also useful to establish heart failure as the cause of the effusion39. Pleural space infections, potentially life-threatening conditions, are usually characterized by typical pleural fluid biochemistry and hence are more straightforward from a diagnostic standpoint, and an interesting recent observation is that the yield of pleural fluid cultures can be increased by 20% by simply inoculating blood culture bottles with pleural fluid at the bedside rather than sending the fluid directly to the laboratory40.The “diagnostic separation of transudates and exudates” using a combination of pleural fluid protein and lactate dehydrogenase levels was published in 1972  and to this day remains the single most important step in determining the etiology of a pleural effusion41, although some reports suggest much lower estimates42. Others focusing on modern immunohistochemistry techniques seem much more optimistic, particularly for mesothelioma, a primary pleural malignancy which traditionally requires pleural biopsies for definitive diagnosis44. In addition, pleural fluid has been shown to be a biospecimen suitable for the majority of molecular analyses required for targeted therapy45. High-throughput techniques and modern molecular techniques promise to identify biomarkers expressed by cancer cells or their environment that could ultimately transform our diagnostic approach. Novel proposed techniques include single-cell mechanophenotyping which evaluates the deformability of pleural cells46, circulating tumor cells and cell-free tumor DNA48, and metabolic-based assays to identify non-leukocyte metabolically active tumor cells49. These recent developments, though exciting, remain preliminary and are still closer to the bench than they are to the clinic. The search for optimal biomarkers has been hampered by a lack of standardized methodology and failure to externally validate promising results, as in the case of fibulin-3, a biomarker once anticipated to transform our approach to malignant mesothelioma53. Large ongoing research projects are attempting to identify reliable alternative candidates.The diagnostic utility of pleural fluid analysis for malignant pleural effusions has been the object of several recent studies. The sensitivity of cytology for malignancy is estimated around 60%, and there is a 15% increase with a second procedure54. A large multicenter observational prospective study to validate this score is ongoing. Similarly, the LENT  score is a clinical prediction model that provides estimates of survival for patients with malignant pleural effusions, which could prove very useful in subject selection in clinical trials and ultimately individualized medical or surgical management of pleural effusions55.An unprecedented number of well-designed randomized controlled studies published in the last decade by a growing international pleural research network have clarified and sometimes transformed patient management for malignant pleural effusions and pleural space infections in particular. However, one major obstacle faced by researchers and clinicians has been the lack of clinical prediction models allowing appropriate patient selection and stratification. The RAPID  score was derived and validated from two large datasets and proposes to risk-stratify patients with pleural space infections, and ultimately its purpose is to individualize management, which currently is subject to local preferences and expertise rather than patient characteristics56. An example from a Bayesian approach to diagnosis might be helpful to illustrate the limitations of this approach. Given a sensitivity for cytology of 60% and a specificity of 100%, a patient with a 50% pre-test probability of pleural malignancy would have, after a negative cytology, a remaining 27% post-test probability of disease. After a second thoracentesis, this number decreases to only approximately 18% (a generous estimate as the same test is used twice). Given the profound implications of a diagnosis of malignant pleural effusion, it should be intuitively obvious that moving forward with additional, ideally minimally invasive, diagnostic tests would be desirable.Approximately 25% of exudative pleural effusions remain without identifiable causes after pleural fluid analysis and cytology. Parietal pleural biopsies are the recommended next step in the diagnostic assessment but often are not pursued as they sometimes require thoracic surgery which, though less invasive since the advent of video-assisted thoracoscopic surgery, remains a major operation requiring general anesthesia, double-lumen endotracheal intubation, large-bore chest tube placement, and a hospital stay. Hence, clinicians often default to observation after an unfruitful pleural fluid analysis61. While pleuroscopy is slowly gaining traction in the US, centers offering the procedure have exponentially increased over the past decade in Europe and other parts of the world and presumably this is due to more favorable regulatory environments and sometimes more selective access to thoracic surgery. The development of dedicated interventional pulmonology training programs in the US, with accreditation standards that include pleuroscopy training endorsed by all relevant medical societies, may facilitate a more widespread adoption of this safe and effective diagnostic modality15. Even less invasive interventions, such as “mini-thoracoscopy”, using increasingly smaller instruments, have been proposed62.Reclaiming a procedure initially introduced by Hans Christian Jacobeus, a Swedish Professor of Internal Medicine at the Karolinska Institute in Stockholm from 1916 to 1937, interventional pulmonologists and pleural specialists have popularized minimally invasive, so-called “local anesthetic” pleuroscopy, which allows pleural exploration and biopsies in awake patients and often is performed on an outpatient basis63.When focal pleural lesions can be identified by computed tomography or ultrasound, percutaneous image-guided biopsies performed by interventional radiologists or pulmonary specialists are a useful and minimally invasive approach to diagnosis which in recent studies has had a yield similar to that of thoracoscopy but without the option to offer definitive treatment in the same setting The rising burden of pleural disease in an increasingly complex patient population demands a more tailored approach to diagnosis and management than ever before. Thoracic ultrasound, the application of new bioassays in addition to foundational biochemical analysis of pleural fluid, the development of models for prognosis and prediction of treatment response, and the resurgence of medical thoracoscopy-pleuroscopy comprise recent advances in pleural disease, and there is a great need for further basic, translational, and clinical research in this field.


The consultation is open until 28 November 2016.On 17 October, the European Centre for Disease Prevention and Control (ECDC) launched a public consultation on the The aim of the consultation is to harvest input for the ECDC expert opinion which should provide European Union/European Economic Area Member States with scientific opinion and expert opinion to support the decision-making process on the possible introduction and monitoring of routine vaccination against rotavirus-induced gastroenteritis in infants.here on how to submit contributions.Please read more