Although altruism is a key principle in our current organ donation and transplantation system, the meanings and implications of the term have been widely debated. Recently, a new type of living organ donation--anonymous and non-directed, also called living altruistic donation (LAD)--has brought the issue into sharper focus. Transplant physicians' views on altruism might influence their attitudes and actions toward living altruistic donors. This study aimed to explore such views among transplant physicians in France and Quebec.A total of 27 French and 19 Quebec transplant physicians participated in individual, semi-structured interviews between October 2004 and December 2005. The majority of these participants associated altruism with gratuitousness and saw altruistic acts as multiple and varied, ranging from showing consideration to saving a person's life.The transplant physicians' discourses on altruism were quite diverse, leading us to question the relevance of the concept in organ transplantation and the appropriateness of the term "living altruistic donation." Altruism and gift-giving have been an integral part of organ transplantation from the outset: the gift of science to humanity; grieving family members who offer to donate the organ of a deceased loved one; recipients who agree to participate in research. Although organ transplantation has become a routine procedure in the past decade, altruism remains a key principle. Indeed, many organ procurement organizations (OPOs) and medical associations such as United Network for Organ Sharing (UNOS) and the American Society of Transplantation (AST) explicitly state that organ donation and transplantation should be based on altruism -4. The WAs this citation suggests, altruism, in the context of organ donation, is often narrowly defined as an absence of monetary exchange and commercialization. The concept of altruism has been studied and debated since the very beginnings of organ transplantation. In the case of living organ donation, many of these studies have examined living organ donors' motivations and psychosocial profiles ,7. Over Healy has shown that altruistic practices are embedded in socio-organizational structures ,13. TranIn this study, we adopted an empirical ethics approach, allowing the facts gathered to inform normative considerations (the "is" contributing to the "ought") -19. ThisThe study was conducted between October 2004 and December 2005. Respondents in both settings were nephrologists or surgeons involved in the field of renal transplantation. In Quebec, the participants were sampled purposively. A total of 22 physicians (nephrologists and transplant surgeons) from all seven hospitals (adult and pediatric) conducting renal transplantation were contacted; 19 agreed to participate. In France, the transplant directors of seven hospitals located in five cities were contacted  . The resInterview transcripts were analyzed using the content and thematic analysis method described by Miles and Huberman . The comA fundamental characteristic of altruism noted by most of the respondents was gratuitousness . The imperative of gratuitousness also emerged in the transplant physicians' opposition to any form of payment for organs. Donors should not intend to benefit from the donation and should not expect any kind of reward. However, four of the physicians in France and one in Quebec felt that gratuitousness--and, by extension, altruism--were impossible. Rather, these physicians believed in psychological egoism  . In otheRelated to gratuitousness is the pleasure associated with giving or with an altruistic act. Opinions were also divided as to whether this pleasure may be reconciled with altruism or whether it constitutes a reward or an incentive. For some respondents, pleasure makes the act of giving egoistic:"I would say that an altruistic person is not altruistic, but egoistic .... This person derives pleasure from donating; therefore he is not so generous, because he benefits from donating." (Quebec physician)Other respondents acknowledged that the pleasure associated with giving might be an acceptable reward for the donor  if it was not expected and was not the key motivating factor.A second feature of altruism described by many of the physicians was its outward or other-focused nature. By definition, an altruistic gesture is directed toward, and involves helping, others. One respondent remarked on the lack of altruism in our society, referring to the many seniors who died during the 2003 heat wave in France, partly as a result of family members' and neighbours' indifference or neglect.Thirdly, for many respondents, altruism involved gift-giving or charity. In the words of one French physician, "[Altruism] is, above all, a gift--an impressive show of generosity."A few respondents said that to be considered altruistic, an act should involve some risk or sacrifice on the part of the giver. Altruistic acts should not be easy and are therefore not common:"Essentially, altruism means giving a part of ourselves, not giving $50 to an organization. Anyone can do that." (Quebec physician)" ... [to be altruistic] is to be prepared to take risks for others." (Quebec physician)Taking risks does not mean going so far as to risk one's life. Many physicians found the idea of dying to save someone else--for example, a person who is unable to swim trying to stop someone from drowning--to be unacceptable."There is always a cost associated with altruism, but this cost shouldn't be too great. It shouldn't involve sacrificing your life." (French physician)Also mentioned with regard to altruistic acts was their voluntary and beneficial nature. Finally, one French transplant physician associated altruism with love. For him, the degree of war and conflict in the world proves that altruism does not exist.During the interviews, respondents were asked to list altruistic actions. At one end of the spectrum were acts of common courtesy such as giving up one's seat on the bus; at the other were heroic acts like rescuing someone. In between were actions such as donating money or goods to a charity; volunteer work; deceased organ donation; the donation of blood, sperm, eggs and tissue; humanitarian aid ; and living organ donation. However, not all respondents considered living organ donation to be altruistic under all circumstances. Wanting to donate one's heart while still alive, or donating a portion of one's lung or liver would not qualify as altruistic intentions or acts because they would involve sacrificing or risking one's life in order to help another person. Respondents mentioned three other situations they did not consider altruistic: helping someone while harming another person, dying for a cause, and neglecting loved ones in order to save strangers. There was a strong sense that charity begins at home.In his seminal essay on gift exchange in traditional societies, Marcel Mauss argued that gift-giving is governed by three obligations: to give, to receive and to repay. The paradox of a gift exchange, according to Mauss, is that it is free but obligatory. To refuse to participate in a gift exchange is to refuse the other and create a rupture that could lead to war. Gift-giving serves to forge ties and alliances among groups and therefore has a strong moral component . Renée CThese scholarly works are focused on gift exchange rather than altruism. However, for many, the two are closely linked. This was certainly the case in this study, where many respondents viewed altruism as equivalent to gift-giving. In a recent article, Shaw has shown that for New Zealand intensivists and transplant coordinators, gift-giving was also linked with altruism .A common denominator in the definitions of altruism provided by the transplant physicians in our study was gratuitousness. Some respondents (mainly the French transplant physicians) felt that the pleasure experienced by donors ruled out the possibility of altruism, since the latter implies an absence of reward or reciprocity. This view coincides with Derrida's view on gift-giving. For this French philosopher, to give means to expect absolutely nothing in return from the recipient. The latter should not acknowledge receiving anything and the donor should not recognize his or her gesture of generosity, as this will lead to feelings of gratification . Is thisIt is interesting to consider the relationship between altruism, gratuitousness and gift-giving in light of the cited works by Mauss, Godbout, and Fox and Swazey. A purist stance implies that altruism involves both intent and consequences. How can a gratuitous view of altruism, which excludes any form of return, be reconciled with gift-giving? As seen previously, reciprocity is an integral part of gift-giving. The unclear relationship between gratuitousness, altruism and gift-giving could translate into misunderstandings or uneasiness on the part of transplant physicians. What is the cause of this discomfort? Here, we can only speculate. Are transplant physicians uncomfortable about the thing given, namely an organ? Is it the fact that transplant physicians and institutions are the initial recipients of the gift of an organ? Is it because we use an ethics of gift exchange, generosity and altruism to justify organ procurement and transplantation, and to downplay the associated elements of sacrifice, mutilation and risk of death? Does Western capitalism make us forget that altruism is possible? Further studies are needed to explore these issues.This exploratory study describes views on altruism offered by a sample of French and Quebec transplant physicians. These views are embedded in social, cultural, legal and political contexts, and cannot be extrapolated beyond these settings. Our qualitative methodology allowed us to offer some explanations as to why LAD is not widely performed in France and Quebec. The interview data also shed light on how transplant physicians, in their close and daily involvement with organ donation, see altruism. Their perspectives and discourses on the subject help us better understand their assessments, judgments and expectations regarding living organ donors, which subsequently inform the ethical guidelines adopted by medical or transplant communities. Of course, it is important to bear in mind that these views might differ from those of other transplant stakeholders, such as patients on the waiting list, potential donors and the general public. Further qualitative and quantitative studies are needed to explore these views and their bearing on policies and practices.Altruism plays a central role in transplantation and the promotion of organ donation. However, the concept has multiple meanings for transplant physicians. Our data lead us to question whether altruism is overly simplistic, confused or even useful in the field of organ transplantation. Our study has shown how transplant physicians are uneasy with notions of altruism and gift-giving. Is LAD a misnomer? Shaw has shown that the gift metaphor is useful for the public promotion of organ donation but ambiguous for professionals . Some miIn 2006, Marie-Chantal Fortin received an educational grant from Roche Canada to lead a workshop titled "Ethical and Juridical Challenges in the Face of the Organ Shortage" at the World Medical Congress held in Toulouse, France, in August 2006.MCF designed the study, conducted the interviews, analyzed the data and drafted the manuscript. MDL participated in the data analysis and independently coded 10% of the raw data. She also provided input on the draft manuscript. MJH participated in the review of the manuscript. HD supervised the design and the completion of the study. He helped review the draft manuscript. All authors read and approved the final manuscript.


Transparent exopolymer particles (TEP) and the presence of compounds sharing molecular formulae with saturated fatty acids and sugars, as well as dissolved organic matter (DOM) compounds containing nitrogen (N) and phosphorus (P) were higher on Transect 1 than on Transect 2, while oxygen concentrations showed an opposite pattern. N2 fixation rates (up to ~1 nmol N L-1 d-1) were higher in Transect 1 than in Transect 2, and correlated positively with TEP, suggesting a dependence of diazotroph activity on organic matter. The scores of the multivariate ordination of DOM molecular formulae and their relative abundance correlated negatively with bacterial abundances and positively with N2 fixation rates, suggesting an active bacterial exploitation of DOM and its use to sustain diazotrophic activity. Sequences of the nifH gene clustered with Alpha-, Beta-, Gamma- and Deltaproteobacteria, and included representatives from Clusters I, III and IV. A third of the clone library included sequences close to the potentially anaerobic Cluster III, suggesting that N2 fixation was partially supported by presumably particle-attached diazotrophs. Quantitative polymerase chain reaction (qPCR) primer-probe sets were designed for three phylotypes and showed low abundances, with a phylotype within Cluster III at up to 103nifH gene copies L-1. These results provide new insights into the ecology of non-cyanobacterial diazotrophs and suggest that organic matter sustains their activity in the mesopelagic ocean.Dinitrogen (N Although our data cannot confirm that the heterotrophic diazotrophs detected actively take up the DOM compounds observed, the differential distribution of organic matter observed between the two transects surveyed, in parallel with distinct patterns of N2 fixation, suggests that these microorganisms benefit from in situ DOM pools, which provide them with energy to support their N2 fixation activity. These results are a step towards a better understanding of mesopelagic diazotrophy, a process which could potentially increase global N inputs to the ocean.The interactions between organic matter and microbes can only be revealed by integrative approaches where equal efforts are made on chemical compound characterization and functional and molecular microbial diversity . To the S1 Fig(A) Transect 1, and (B) Transect 2, and the same order for each transect with variables (C-D) salinity,  NOx , and (G-H) PO43- (μM). Measurement points are shown with dots.(a) temperature (°C) for (TIFF)Click here for additional data file.S2 Fig(A) Total bacterial abundance (cells mL-1) in Transect 1, and (B) Transect 2.(TIFF)Click here for additional data file.S3 Fig(A) the first and (B) the second coordinate PCoA scores with the intensity of all molecular formulae.The color code is the correlation coefficient of (TIFF)Click here for additional data file.S1 Methods(DOCX)Click here for additional data file.S1 Results(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


The effect of bisacodyl on the treatment of rats with slow transit constipation(STC) was studied. Forty-five female Wister rats were divided into controlgroup, STC group, and STC bisacodyl group. The immunohistochemical method wasused to determine interstitial cells of Cajal (ICC) and the expression of c-Kitprotein. Body mass and the number of defecations were significantly decreased inthe STC group compared with the control group on the 100th day afterdiphenoxylate administration, while dry weight of feces was significantlyincreased and the intestinal transit time was prolonged. There were significantdifferences in the number of defecations, dry weight of feces, and intestinaltransit time among the three groups. The number of defecations was higher, dryweight of feces was lower, and intestinal transit time was shorter in the STCbisacodyl group compared to the STC group. In addition, ICC basement membranedissolution occurred in the colon wall of the STC group. The connection betweenICC and surrounding cells was destroyed, and the nucleus shrunken to differentdegrees. Moreover, c-Kit expression in the STC group was significantly lowerthan the control group. The connection between ICC and surrounding cells in theSTC bisacodyl group was significantly stronger than the STC group, and thenumber of ICC and the expression of c-Kit were increased. Bisacodyl could reducethe severity of STC in rats by increasing the number of ICC and the expressionof c-Kit. Slow transit constipation (STC) is an intractable condition characterized byprolonged transit time of colonic contents that is caused by the decrease of colonicmotility. STC can induce abdominal distention, stomachache, defecation difficulty,perianal diseases, and is related with cardiovascular and cerebrovascular diseasesand colon cancer . The etiBisacodyl is a common drug for the treatment of STC, which was first used in 1985.Later, researchers found that bisacodyl promoted and propagated motor activity inpatients with severe STC . HoweverRats have a mild disposition, good tolerance, and high success rate in experimentalresearch, therefore they are the first choice for the study of STC . The STCIn this study, we established an STC rat model and treated STC rats with bisacodyl.Our aim was to study the relationship between bisacodyl and ICC, and explore thepotential underlying mechanism of STC.Female Wister rats, weighing 120–140 g, were purchased from Shanghai LaboratoryAnimal Center of Chinese Academy of Science (China), and kept in 50–70% humidityat room temperature. All procedures were approved by the Ethics Committee of thePudong New Area People's Hospital.Forty-five female Wister rats were kept in separate cages for three days. Thirtyrats were divided into two groups: STC group and STC bisacodyl group. The other15 rats were used as the control group.™1·d™1), while rats in the controlgroup were treated with the same amount of physiological saline orally. After100 days of diphenoxylate treatment, rats in the STC bisacodyl group weretreated with bisacodyl (20 mg·kg™1·d™1) for 30 days. Thenumber of defecations, feces dry weight, and body weight of rats were recordedevery day. One week after drug withdrawal, the rats were fasted for 24 h. Therats received 100 g/L activated carbon (2 mL) and then the time from theintragastric administration to the first black stool was recorded. The rats werecaged individually and the number of defecations was measured by the metaboliccage (pellet/day). Feces were dried by natural air at room temperature and thenweighed (mg/pellet).The rats in the STC groups were given diphenoxylate by intragastricadministration  wascut from the colon 5 cm away from ileocecal valve. Then, the intestinal tissuewas washed with PBS and fixed with 4% paraformaldehyde for paraffinembedding.The sections (about 5 μm) were mounted on slides and baked on a 75°C bakinginstrument  for 10 min. Xylene solution was usedfor dewaxing. The procedures for hematoxylin-eosin staining were as follows:hydration for 10 s with anhydrous ethanol, 95% ethanol, and 80% ethanol; washwith water for 2 min; hematoxylin staining for 8 min; wash with flowing waterfor 1 min; 1% hydrochloric acid ethanol for 5 s; wash with flowing water for 20s; 1% eosin staining for 2 min; wash with water for 2 min; dehydration ofgradient ethanol for 3 min; dehydration of xylene for 5 min; and seal withneutral gum.The procedures of c-Kit protein immunohistochemical staining were as follows:dewax and dewater the sections; soak with PBS for 5 min; sodium citrate forrestoration; natural cooling for 5 min; wash with cold water for 10 min; soakwith PBS for 5 min; incubate with 3% hydrogen peroxide solution for 15 min; washwith PBS for 2 min; add rabbit anti-mouse c-Kit polyclonal antibody  and incubate for 1.5 h; wash with PBS for 2 min; add enzymelabeled second antibody for 30 min; wash with PBS for 2 min; adddiaminobenzidine (DAB) colored solution for 10 min; wash with distilled water;hematoxylin for counterstaining for 5 min; dehydration of gradient ethanol for 5min; dehydration of xylene for 5 min; wash with PBS for 3 times; and seal withneutral gum.Specimens were observed under a light microscope (×100). Blue nucleus and browncytoplasm were determined as c-Kit-positive cells (ICC cells). Five highmagnification views (×100) were randomly selected in each slice, and the numberof ICC cells was calculated in each field. c-Kit expression was proportional tothe area or concentration of the brown cytoplasm. The areas of five highmagnification fields (×100) were measured by Image-Pro Plus 6.0, and the changeof c-Kit expression is reported as the percentage of area size.t-test, and differencesamong three groups were tested by one-way ANOVA. Non-normally distributed datawere analyzed by non-parametric analyses, with P<0.05 consideredstatistically significant.SPSS, version 20.0, was used for data analysis (USA). Measurement data arereported as means±SD. For normally distributed data, differences between twogroups were tested using Student's th and 100th day after feeding diphenoxylatesuspension, body mass in STC rats was significantly lower than control rats. These findingsindicated that the STC model was successfully established.As shown in th day after feeding diphenoxylate suspension, the number ofdefecations per day in the STC group was less than that of the control group(P<0.05), while dry weight of feces and intestinal transit time were greaterthan that of the control group (P<0.05) (At the 100(P<0.05) .After one month of bisacodyl treatment, there were significant differences in thenumber of defecations, dry weight of feces, and intestinal transit time amongthe control group, STC group, and STC bisacodyl group (P=0.000). The number ofdefecations in the STC bisacodyl group was greater than that of the STC group(P<0.05), while dry weight of feces and intestinal transit time in the STCbisacodyl group were less than in the STC group (P<0.05) .In the STC group, the ICC basement membrane was dissolved in the colon wall. Theconnection between ICC and surrounding cells was destroyed, and the nucleus wasshrunken to different degrees. In the STC group, the number of ICC in a singlefield of vision was 9.51±2.08, and c-Kit expression was (13.05±3.12%), whichwere significantly lower than the control group (P=0.000).Compared with the STC group, the connection between ICC and surrounding cells inthe STC bisacodyl group was significantly strengthened, and the number of ICCs(19.04±3.51) and the expression of c-Kit (46.28±7.09%) were also increased(P=0.000) .Constipation can severely reduce life quality of the patients and bring financialburden to the patients and their families ,9. RecenBisacodyl is a new drug that increases intestinal motility by the following possiblemechanisms : increasIn the current study, the 100th day, when STC rats had the most pronounced differencein body mass, was selected as the experimental time point to observe the effect ofbisacodyl. The symptoms of STC rats were significantly relieved at the 30th dayafter feeding bisacodyl, because the number of defecations increased, dry weight offeces decreased and the intestinal transit time shortened. These findings indicatedthat bisacodyl improved the constipation of rats and had a good therapeuticeffect.invitro and in vivo (The digestive tract, like the heart, has natural pacemakers . The con in vivo . ReductiThe c-Kit receptor is a transmembrane protein encoded by proto-oncogene c-kit thathas tyrosine kinase activity . It is aAs a limitation, we did not observe the time for reversal of the STC model afterstopping diphenoxylate treatment and there is a possibility that part of theresponse of bisacodyl is due to this time.


Matryoshka-like mazes with nested shells, we show that a circular maze has the best filtration efficiency. Results on the mean first-passage time reveal that ABPs escape faster from the center of the maze, while RTPs reach the center from the rim more easily. According to our simulations and a rate theory, which we developed, ABPs in steady state accumulate in the outermost region of the Matryoshka-like mazes, while RTPs occupy all locations within the maze with nearly equal probability. These results suggest a novel technique for separating different types of self-propelled particles by designing appropriate confining geometries without using chemical or biological agents.A diverse range of natural and artificial self-propelled particles are known and are used nowadays. Among them, active Brownian particles (ABPs) and run-and-tumble particles (RTPs) are two important classes. We numerically study non-interacting ABPs and RTPs strongly confined to different maze geometries in two dimensions. We demonstrate that by means of geometrical confinement alone, ABPs are separable from RTPs. By investigating  Active particle systems have attracted a lot of attention during the last decades124567891011121314161718E. Coli bacteria and Chamydomonas algaeruns, with constant speed v0 and reorient randomly, tumble, with rate α. On the other hand, active Brownian particles (ABPs) refer to the second class of self-propelled particles with spherical Janus particles and non-tumbling mutant strains of E. Coliv0 but their direction changes gradually due to their rotational diffusivity Dr. Despite their different moving strategies, RTPs and ABPs behave similarly on large length and time scales; like passive Brownian particles (PBP) they exhibit translational diffusion. More precisely, a pure ABP and a pure RTP diffuse with the same translational diffusion constant, if (d−1)Dr = α, where d is spatial dimension222324252627With respect to their swimming strategies, self-propelled particles are categorized into two main classes. On the one hand, one considers run-and-tumble particles (RTPs) such as motile i.e., when leaving this regimeSeveral experimental and theoretical studies have demonstrated that after an encounter with a wall microswimmers slide along the wall for some time, before they leave it182930312930313334363738394042434445184247484950Salmonella or other microbes infect host cells with different efficiencies1954Despite of their similar properties in bulk and confinement on large scales, ABPs and RTPs interact differently with obstacles on small scalesi.e., proceeding from the entrance to the exit, has been a challenging problem for many centuries. For example, using chemotaxis, active particles are able to solve mazes and find the shortest path towards the exit5758In this article, we present such a strategy by investigating active particles in suitable geometries analogous to mazes, which are illustrated in We consider a single self-propelled particle that is either an ABP or RTP. We place it at a specific starting point inside the maze and monitor its dynamics until it reaches the target point using two-dimensional Brownian dynamics simulations. To obtain the full statistics for an ensemble of non-interacting active particles, we perform many simulation runs to generate different realizations for the same initial condition.The position vectors of both model particles evolve according to the same over-damped dynamicsv0 is the particle’s self-propulsion speed, which is kept constant during the simulations, and its intrinsic direction of motion is given by the unit vector θ is the orientation angle with respect to an arbitrarily chosen x axis. The particle interacts with walls by a simple repulsive force . More details about the numerical method are provided in the The unit vector Most importantly, ABP and RTP differ in the dynamics governing their orientation angle. An ABP changes its self-propulsion direction continuously due to rotational diffusion. Thus,Dr is the particle’s rotational diffusion constant and η is unbiased (<η(t)> = 0) and uncorrelated (<η(t)η(t′)> = δ(t−t′)) white noise. In contrast, an RTP changes its direction abruptly with rate α at each time step,where Ti. The tumble angle Δθi is selected with equal probability from the range between 0 and 2π.The sum runs over all tumble events that stochastically happen at times d−1)Dr = α  is fulfilledτr = 1/Dr is the persistence time of an ABP, on which the particle orientation decorrelates. For an RTP the equivalent time τr = 1/α is the mean run time between two tumble events. To characterize the motion of both particle types, we will use the persistence numberAt long times the motion of ABPs and RTPs becomes diffusive in unconfined space. The respective diffusion constants agree if the equivalence condition  maze. Each maze is characterized as follows.Matryoshka doll. The inner-shell radius is r1 = 1.8R and the other ones obey ri−ri−1 = 2.05R . So the width of each circular annulus (zone) is a little larger than the diameter of the particle. Using these values, the particle effectively moves on one-dimensional circular paths with varying speed depending on particle orientation R and are opposite to each other for neighboring shells.outwards). In the second case, we initially place the particle in the outermost annulus and wait until it reaches the central zone of the maze (inwards). In both cases, the initial particle orientation is along We study the ability of both active-particle types to solve the maze in two cases. In the first case, we initially place the particle in the central zone of the maze and wait until it reaches the outer zones  maze to find out how the absence of regularity of the previous geometries affects the particle behavior. The non-regular maze studied here  is a braPr but it also depends on the type of the self-propelled particle. Pr. However, the ABP (solid symbols) always reaches the openings in the confining walls faster than the RTP (open symbols). Both observations can be understood by carefully analyzing how the self-propelled particles move along the walls. At large persistence number Pr (strong confinement regime), the ABP performs a random walk along convex boundaries. While its mean orientation is aligned with the local boundary normal, the momentary orientation fluctuates about the normal and thereby leads to short drift motionsPr, both particles more often detach from the walls and the exit times increase accordingly.We find that the MFPT not only changes systematically with persistence number Pr. In this case the particle orientation is mostly aligned with the local boundary normal and the orientation angle θ also describes the location of the ABP in the annulus [see θ0 (the position of the previous opening) either to θ1 or θ2 [see θ1 and θ2. Using the standard formalism61θ0 for reaching one of the absorbing boundaries can be calculated:In case of an ABP, a simple random-walk model describes the asymptotic behavior of the MFPT in the limit of large persistence number ulus see . We can r θ2 see . This isi, we choose θ0 = π. Furthermore, the angles θ1 and θ2 depend on the shell index, which we summarize in the Pr  needed to reach opening Pr. This is because at large Pr, the velocity vector of the ABP always points radially outward and rotational diffusion typically only allows for small changes in its direction. Thus, when arriving at an opening, it is very unlikely that the ABP fully reverses its orientation Pr since the ABP is faster and therefore spends less time close to the opening. Thus, the MFPT ultimately diverges. In contrast, an RTP always has a finite probability to reverse its direction and thereby to enter the openings towards the inner parts of the maze. We also find a non-monotonic behavior in the MFPT for an ABP entering the 4th opening. At small Pn or small v0 the ABP hardly moves towards the opening. Thus, the time to reach it and thereby the MFPT increases again with decreasing Pn ∝ v0. A similar non-monotonic behavior is also reported in recent results on optimal search strategies for an ABP looking for a target at the center of a confining circular domainThe most interesting result arises by initializing the self-propelled particles from the outermost zone of the maze [inwards case in n in the circular maze with probability pn. In pn/n for different Pr, where the zone index n is proportional to the zone radius. Clearly, this probability is constant for PBPs that do not have any internal driving. They spread over the whole maze with uniform density just by thermal diffusion. However, at large Pr, ABPs only accumulate in the outermost zone (index 5), while the inner zones remain unoccupied. This is in agreement with the diverging MFPTs plotted in Pr ≈ 1 do ABPs also reach the center of the maze. In contrast, RTPs with their ability to perform large reorientations also settle in the inner zones even at large Pr. All these findings are nicely illustrated in When we let our simulations run for a sufficiently long time, both in the outwards and the inwards case, the particle distribution reaches the same steady state and the particles occupy zone pn(t) for occupying zone n are elements of the state vector |ψ(t)> = |p1, p2, p3, p4, p5>, which evolves in time according toWhen generating the steady state distributions, one has to be sure that the system has relaxed to the steady state starting from an initial condition. To estimate the relevant relaxation times, we developed a linear rate theory. The probabilities Q contains the transition or hoping rates kmn between neighboring zones as introduced in Q is given in the Pr, we determined the rates kmn from simulations of the outwards case, which corresponds to the initial condition |ψ(t)> = |>. The eigenvalues of Q are the relaxation rates of characteristic relaxation modes. One zero eigenvalue belongs to the steady state, which we discussed in τrelax, one generally has to wait before steady state is reached. We plot τrelax versus Pr in The transition matrix Pr for an ABP is more pronounced in the circular maze [see Pr. In addition, while in the circular maze the MFPTs of ABP and RTP at small Pr differ from each other, especially for the innermost opening [see Pr, while they easily leave walls with negative curvature49To find out how wall curvature affects particle separation, we examine the square maze of maze see . Thus, tning see , they arPr = 1.5 all particle types now show a uniform distribution throughout the square maze. In addition, the active particles strongly accumulate in the corners of the square maze, especially at large Pr, which is in line with previous experimental observations on both ABPs19495442433940The steady-state distributions of the square maze plotted in th opening at large Pr, it is more probable for it to return to the outermost zone rather than to enter the 3rd opening, since for most of the time its orientation points radially outwards. Therefore, the nested structure of Matryoshka-like mazes drastically decreases the probability for the ABP to enter the inner regions of the maze and amplifies the difference between the two active-particle types. This feature is missing in the non-regular maze.Finally, we investigated the non-regular maze of Pr. However, corner accumulation is still observable in this maze and it is more pronounced at large Pr. Interestingly, at Pr = 1.5 the particle density increases from the top (starting point) to the bottom (end point), while for large Pr the distribution is more uniform throughout the maze. Finally, we tested whether the size of the maze affected these results by performing simulations with a maze twice as large but with the same structure. Again, no difference was observed between both active-particle types (results not shown).We also determined the spatial probability distribution of both active-particle types in steady state see . It doesIn this article we have explored the behavior of two important and widespread types of active particles inside different mazes. Namely, non-interacting active Brownian particles and run-and-tumble particles strongly confined in Matryoshka-like mazes show large differences both in their mean first-passage times and their stationary probability distributions. In particular, run-and-tumble particles enter the maze and easily move towards the center, while active Brownian particles escape faster towards the rim. In steady state, persistent ABPs accumulate in the outermost region of the maze, while RTPs spread more uniformly in all regions. This peculiar phenomenon is more pronounced in mazes with curved boundaries. Our work suggests Matryoshka-like mazes as excellent devices to separate different types of active particles from each other with potential technological and biomedical applications. In contrast, a random maze is not able to distinguish between both active-particle types.In experiments, regular mazes can be fabricated using microfabrication techniques1929653863How to cite this article: Khatami, M. et al. Active Brownian particles and run-and-tumble particles separate inside a maze. Sci. Rep.6, 37670; doi: 10.1038/srep37670 (2016).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


MLH1, revealed an extremely high rate (77%) of mutations that lead to defective splicing. This finding is confirmed by extending the sampling to five other exons in the MLH1 gene. Further analysis suggests a more general phenomenon of defective splicing driving Lynch Syndrome. Of the 36 mutations tested, 11 disrupted splicing. Furthermore, analyzing past reports suggest that MLH1 mutations in canonical splice sites also occupy a much higher fraction (36%) of total mutations than expected. When performing a comprehensive analysis of splicing mutations in human disease genes, we found that three main causal genes of Lynch Syndrome, MLH1, MSH2, and PMS2, belonged to a class of 86 disease genes which are enriched for splicing mutations. Other cancer genes were also enriched in the 86 susceptible genes. The enrichment of splicing mutations in hereditary cancers strongly argues for additional priority in interpreting clinical sequencing data in relation to cancer and splicing.Substitutions that disrupt pre-mRNA splicing are a common cause of genetic disease. On average, 13.4% of all hereditary disease alleles are classified as splicing mutations mapping to the canonical 5′ and 3′ splice sites. However, splicing mutations present in exons and deeper intronic positions are vastly underreported. A recent re-analysis of coding mutations in exon 10 of the Lynch Syndrome gene,  MLH1, one of the causal genes of Lynch Syndrome. We found that a high fraction of the MLH1 coding mutations resulted in disrupted splicing. To further investigate a more general role of defective splicing across human disease genes, simulation strategies were used to identify 86 disease genes prone to splice site mutations. In these 86 genes, there was an enrichment of cancer genes including the three main casual genes of Lynch Syndrome . Thus, it appears defective splicing may be the main driver of Lynch Syndrome and other cancers. Genes prone to splicing mutations have certain features that allow for the comprehensive prediction of splicing-prone diseases genes in the human genome. Our findings strongly argue for additional clinical sequencing prioritization in both cancer genes and genes prone to splice site mutations.To understand the extent to which disrupted pre-mRNA splicing causes human disease, we re-analyzed coding mutations in  To kscovered –6. Howevpression .MLH1, reveal a high proportion of previously uncharacterized ESM (17 of the 20 or 77%) [The sequences that encode for proteins (exons) and the intervening, noncoding sequences (introns) are known to have an important regulatory role in an RNA processing mechanism known as precursor messenger RNA (pre-mRNA) splicing. Variants that alter the regulatory regions necessary for splicing typically result in the deletion of large portions of the coding sequence and generally result in a non-functional protein . Among t or 77%) . In fact or 77%) .Materials and Methods). Of these 86 SSM-prone genes, three were the main causal genes of Lynch Syndrome , which account for 32%, 39%, and 14% of Lynch Syndrome cases, respectively [Here, we present a comprehensive analysis of splicing mutations in human disease. We report 86 genes enriched for SSM, in patients that present with hereditary disease  altered the splicing of exon 10 [MLH1 exon 10 was indicative of high levels of splicing phenotypes in exonic mutations across all genes, a larger pool of exonic variants  was analyzed using a high-throughput reporter assay, MaPSy [MLH1 exons. Of the 36 pathogenic MLH1 exonic mutations surveyed with MaPSy, 11 (30.5%) affected splicing (S1 Table) in an in vivo minigene assay and in an in vitro splicing assay. On average, disease causing point mutations disrupt splicing 10% of the time  [MLH1 disease alleles was almost three times higher than the background rate of splicing disruption in disease alleles. Mapping potential exonic splicing regulatory sequences (ESRs) [MLH1 exons analyzed in MaPSy revealed exon mutations that altered splicing resulted in a greater difference in wild type (wt)–mutant (mt) ESR scores than mutations not resulting in a splicing defects . MLH1 missense and nonsense mutations were found to frequently disrupt splicing in vitro and in vivo: 6/22 (27%) missense mutations and 5/14 (36%) nonsense mutations. Taken together, this data a) confirms the previous report that exonic mutations in MLH1 frequently disrupt splicing b) exonic mutations that alter ESR signals are more likely to result in a splicing defect, and c) suggests that the rate of splicing disruption is not homogenous across genes (i.e. MLH1 is an outlier).A recent analysis of coding mutations located in exon 10 of  exon 10 . To see MLH1 gene. Of the five exons that were included in this study, three had no ESMs. However, all the exonic mutations in exon 8 (6/6) and 71% (5/7) of the mutations in exon 15 significantly altered splicing . Thus, it appears that certain exons in MLH1 are more prone to splicing disruption. To investigate the possibility that certain exons may be more prone to ESMs, a permutation approach was used to identify exons that exceeded the expected number of ESMs discovered . 11 of the 2,061 exons analyzed using MaPSy were predicted with a P < 0.01 to have more ESM than expected . Remarkably, two of these 11 exons identified in the simulation as being enriched for ESMs were MLH1 exon 8 and exon 15, further confirming the previous finding.Interestingly, ESMs were also disproportionately distributed among the exons within the MLH1 mutations, the representation of MLH1 alleles in the fractions of the in vitro spliceosomal assembly assay was examined . Here, the accumulation of an allele in intermediate complexes was interpreted as an indication that the allele blocked the next stage of spliceosome assembly [MLH1, the disruption occurred later. 63% of exonic splicing mutations were primarily blocked at the A complex in transition to the B complex and 37% were blocked at the B complex . Several mutants reduce more than one step in the assembly . As expected, adjacent mutations that were close enough to fall within the same cis-element shared a similar pattern of disruption. In effect, these clusters of variants mutationally defined a particular cis-elements required for particular spliceosomal transitions .To mechanistically investigate the defective splicing of assembly . In geneassembly , 18, howMLH1 (as shown by the MaPSy 5K panel) may be due to chance or the enrichment for splicing mutations in the gene/disease. To eliminate the null hypothesis, Monte Carlo (MC) simulations were used to generate a distribution of SSM frequencies for each gene given the total number of mutations reported in that gene . Of the ~3,600 disease genes reported in the HGMD, 86 genes, including the three main casual Lynch Syndrome genes , had more SSM than expected based on the distribution of SSM in the HGMD dataset (S2 Table).The surprisingly high fraction of disease-causing splicing mutations both reported in the splice-sites and unreported in exonic positions of P = 1.48 x 10−9, Kruskal-Wallis, Fig 3B). These results suggest that the 86 SSM-prone genes are not only prone to SSMs but also to ESMs, with three ESMs in the 86 SSM-prone genes being validated in individual minigene constructs .Although SSM generally have a severe impact on splicing outcome by disrupting the essential interactions with the core spliceosome components, variants located within the exonic sequence can also alter splicing by disrupting the myriad of exonic splicing regulatory (ESR) elements . Using tS2 Table). The initial report of an association between MLH1 and splicing mutations also associated other cancer related genes such as BRCA1, BRCA2, and NF1 with disrupted splicing. Furthermore, Gene Ontology (GO) enrichment analysis [P = 2.53x10-2, S3 Table), a pathway commonly associated with cancer phenotypes [P < 0.01, permutation test, Fig 4A). Not only were cancer genes overrepresented in the SSM-prone genes, but they also contained 1.5-fold more SSM and 1.4-fold more ESM than the rest of the genes in the HGMD . When further dividing the cancer genes into oncogenes and tumor suppressor genes (TSG), it became apparent that TSG have more SSM and ESM than the rest of the genes in the HGMD . However, this enrichment for SSM and ESM was not apparent when comparing oncogenes to the rest of the genes in the HGMD . Thus, it appears that TSG are more prone to splicing dysfunction most likely due to their loss-of-function disease mutational mechanism.We next sought to determine if a certain class of disease genes were overrepresented in the 86 SSM-prone genes  [P = 2.54 x 10−14, Kruskal-Wallis, S5 Fig). Thus a trivial explanation for predisposition of the 86 SSM-prone genes is the larger mutational target presented by their higher number of splice sites. To determine if the SSM-prone genes were predisposed due to the number of introns, we repeated the MC simulation normalizing for the number of introns . Surprisingly, this correction did not dramatically alter the result. After normalization, about 74.4% (64/86) of the genes that were significantly enriched for splice site mutations, were present in the recalculated SSM-prone gene list (S5 Table).A number of genomic and sequence features have been implicated in the context of splicing , 22–25. 4 Table) , 26–28, S5 Fig). To determine the relative contribution of each feature to the classification, several machine learning approaches were trained on the HGMD mutation dataset. Briefly, the Random Forest (RF) [Fig 3A; for feature ranking please see Materials and Methods). The model indicates that HI genes and genes with less structured exons have a higher risk of being frequently affected by SSM . In addition to feature prioritization, the classifier was also used to predict additional genes that may be prone to SSM but had not yet been identified as human disease genes. To test the performance of both classifiers, ROC curve analysis was performed. The mean area under the curve was measured for both machine learning models. The RF model was the most predictive . A control classifier trained to predict genes that were not prone to SSM  was considerably less accurate, presumably because this category is lower confidence with fewer associated mutations overall.In addition to having more introns, the 86 SSM-prone genes are generally more haploinsufficient (HI), have shorter and more structured exons (predicted to have more base-pairing interactions), and less conserved variants found in the exomes of ~60,000 healthy individuals  est (RF)  and a LoAs haploinsufficiency was an important feature in the prediction of SSM predisposition (upper category) and splicing defects generally result in a severe loss of gene function, it is possible that the degree of haploinsufficiency largely determines a genes predisposition to SSM. However, the RF model still performed well with HI removed (AUC = 0.805). Therefore, it does not appear that there is a single dominant feature such as HI or the number of introns that drives the accuracy of the predictor. Instead it is most likely a combination of features that determine a genes predisposition to SSM. This analysis suggests that the prediction of genes predisposed to SSM using a broad spectrum of features is feasible and could potentially be used to identify new disease genes that are prone to splicing mutations.Fig 5C). While the classifier was run at a range of stringencies. Using a probability cutoff of 0.6–0.86 returned by the classifier, 499 genes were predicted to be SSM-prone (see S7 Table). It is possible that these 499 genes were not previously identified as disease genes because their function was required for organismal viability. To explore the degree to which variation can be tolerated in these 499 genes, the aggregated exome sequencing data from 60,706 presumably healthy individuals provided by Exome Aggregation Consortium (ExAC) [P = 6.1043e-18, Mann-Whitney). This analysis suggests that the splicing elements in the predicted SSM-prone genes are evolving under a higher level of selective pressure. However, this analysis considers all variations equivalently making no distinction between neutral variants and clear loss of function variants. For the variants that fall within the splice sites, position weight matrix (PWM) models can be used to evaluate whether a variant represents a stronger or weaker match to the splice site consensus. In other words, PWM can potentially distinguish loss of function splicing mutants from neutral variation. In this analysis, variants that greatly weaken the match to a splice site model  and would be expected to result in a loss of function are four fold underrepresented in common single nucleotide polymorphisms (SNPs). This suggests a scenario where loss of function variants are eliminated from the variant pool before the SNP can reach a reasonable frequency in the population. Conversely, variants that fall within the 5′ ss but strengthen the agreement of the site to the consensus tend to accumulate in the high frequency set . The same trend is observed in variants that localize to the 3′ ss . An independent measure of selection can be found in analysis that maps obvious loss-of function variants to the predicted SSM-prone genes. For example, 3,230 genes that were depleted of predicted protein-truncating variants (PTV’s) in the exomes of 60,706 individuals are a gold standard for genes in which loss of function variants are poorly tolerated [S7 Fig, P = 7.53e-98 Fisher’s Exact, S7 Table).In order to identify new disease genes that are prone to splicing mutations, the predictive model was applied to ~13,000 non-disease associated genes (m (ExAC)  was crosolerated . While PP = 2.20e-2) and mitosis (P = 5.08e-5) were the two functions enriched in predicted SSM-prone genes . Since the hallmark of cancer is generally the abnormal growth and division of cells, it is possible that mutations within this set may play some yet undiscovered role in cancer. While a more complete characterization of these genes awaits future study, an online browser has been developed to visualize the splicing results of the exonic mutations assessed in the SSM-prone cancer genes studied using MaPSy (S9 Table).The lower proportion of ExAC variants located in the genomic genes predicted to be SSM-prone and the enrichment of PTV-intolerant genes in the SSM-prone genes suggests that they are intolerant to variation and appear to be functionally important genes. It is therefore more likely that splice disrupting variants that map to these genes will be deleterious. To gain more insight into the uncharacterized set of predicted SSM-prone genes, GO Enrichment analysis was performed. Regulation of cell cycle . The degree to which exonic mutations affect splicing also vary across exons. For example, in MLH1, all of the ESM occurred in two of the five exons tested . Earlier work on spliceosome assembly suggested a mechanism where the spliceosome ‘commit’ to splice sites early in the process [Fig 2). Overall, the MaPSy assay demonstrated a three-fold increase in likelihood that a missense mutation in MLH1 would result in a splicing defect. This study confirms earlier findings of high frequency of splicing defects in MLH1 mutants, but also suggests that the Lynch Syndrome genes, MLH1, MSH2 and PMS2, and the other tested genes are outliers and are prone to splicing disruption.Recent analysis of mutations in  process . In contFig 3). The discovery of a class of genes prone to splicing mutations, led to an exploration of what features and cellular functions that predisposed splicing genes encode. GO term analysis indicated that many of these genes were involved in cancer initiation and progression. Defining a set of ‘cancer’ genes at the intersection of the COSMIC and HGMD dataset revealed a significant elevation of SSM and ESM in cancer genes, including genes involved in Lynch Syndrome . Cancer genes are enriched in the SSM-prone genes . Cancer genes in this category have higher predicted haploinsufficiency than cancer genes associated with lower levels of SSMs . Machine learning was used to determine other features associated with the SSM-prone genes . In general, no single feature dominated, rather a combination of features determined whether a disease gene was prone to splicing mutations. However, there are certain properties of splicing mutation that warrant further consideration. Splicing disruptions are potent loss of function mutations. This property probably explains the evidence of haploinsufficiency in the SSM-prone genes. Finally, unlike protein coding variants, splicing variants could have tissue specific affects. Consistent with a model of tissue specific affects, Lynch syndrome causes a wide variety of cancer types. While beyond the scope of this work, further studies will be needed to explore tissue specific differences in splicing for Lynch syndrome mutations.A major conclusion drawn from this study is the existence of a class of diseases that are often caused by splicing mutations (i.e. SSM and ESM). The role that splicing defects plays in genetic disease varies across disease genes but genes with elevated SSM also have elevated ESMs . The finding that more than half of the 499 predicted SSM-prone genes also do not tolerate premature stop codons is further indication of strong selection . While it is beyond the scope of this work to define the role and function of each of these genes, there is an indication that many relate to cancer. Of the 12 GO terms enriched in this set, 4 categories were also associated with the original set of cancer genes suggesting the existence of novel cancer genes (comparison of COSMIC cancer gene GO terms and 499 predicted SSM-prone gene GO terms). Taken together these findings suggest a set of genes that should be prioritized in the analysis of clinical sequencing data with a particular emphasis on cancer.As there is a high medical importance in discovering new cancer genes, the random forest classifier that was trained on the set of 86 SSM-prone genes was applied across the entire genome to reveal a set of 499 predicted SSM-prone genes. One possibility is these 499 SSM-prone genes could be targets of splicing factors that contain dominant oncogenic mutations  –35. HighMLH1 mutations assessed for splicing defects mapped to internal exons and were selected based on their classification of being disease causing (DM) with a previously undocumented role in splicing. The splicing efficiency of wildtype and mutant exons was calculated as below:ispl is the count for spliced output ii,inp is the count for input i, and n is the number of species that were analyzed in the library pool. MaPSy experiments in vivo and in vitro were performed as previously described [The 36 exonic escribed . Brieflyin vivo splicing reporters were generated using overlapping PCR and consists of the Cytomegalovirus (CMV) promotor, Adenovirus (pHMS81) exon with part of its downstream intron at the 5′ end, followed by the 200-mer library, and exon 16 of ACTN1 with part of intron 15 and the bGH polyA signal sequence at the 3′ end. The resulting in vivo reporters were transfected into human embryonic kidney hek293T cells. After 24 hours of transfection, RNA was extracted and both the input reporters and spliced species were sequenced.The in vitro splicing reporters have a similar design to the in vivo reporters, but exclude the ACTN1 exon, and the CMV promoter was replaced with the T7 promoter. The in vitro splicing reporters were obtained through in vitro transcription using T7 RNA Polymerase. The resulting RNA was then used for splicing reactions in 40% HeLa-S3 nuclear extract. Pools of the input and spliced RNAs were converted to cDNA and prepped for deep sequencing.The emi and imi is the counts for the minor allele in the selected pool and input, respectively, emj and imj is the counts for the major allele in the selected pool and input, respectively. For each wildtype-mutant pair, the allele that splices more efficiently is assigned as the major allele.The allele ratios between wildtype and mutant exons in the different spliceosomal fractions were obtained as follows:Wildtype and mutant sequences of exon 15 of MLH1 (NM_000249.3:c.1684C-T), exon 2 of BRCA1 (NM_007294.3:c.5425G-T) and exon 12 of OPA1 (NM_015560.2:c.1199C-T) were synthesized by Synbio Tech  and incorporated into MaPSy in vivo backbone (Adenovirus (HMS81) and ACTN1 exon 15 by overlapping PCR . MaPSy cMLH1 exons assayed with MaPSy . The ‘ESR wt/mt difference’ in S1 Table was computed as the wild type-mutant difference in hexamer scores (17).Hexamer ESEs and ESSs were downloaded from published data (17). A sliding window of 1 nucleotide was used plot the predicted ESEs and ESSs in the Disease causing splicing and coding sequence mutations (DM–disease mutations) were selected from the Human Genome Mutation Database (HGMD). The mutations were then classified as SSM, missense, or nonsense mutations. To be considered an SSM, the variant was required to be within the canonical splice-sites (-3 to +6 positions at the 5′ ss and -20 to +3 at the 3′ ss) and labeled as a splicing mutation by HGMD. The number of missense, nonsense, and SSM mutations were determined for each intron-containing gene.Eq 1), the proportion of SSM given the total mutations in each gene was simulated 1,000 times. Genes falling outside the simulated values represent genes that have more  or less  SSM than expected  based on the distribution of mutations types within the dataset.The total number of mutations were plotted against the total number of SSM in a gene. Weighted random sampling was then used to construct a 99.9% confidence interval that capitulates the expected number of SSM given the total number of mutations within a gene. Using the proportion of total SSM to total mutations in the HGMD as a weight for random sampling (Eq 2). To obtain a weight for random sampling using this ratio, the number of SS and the length of the CDS for each gene was used to obtain a unique SSM to total mutations weight for each gene (Eq 3). The corresponding weights were then used as before to simulate 1,000 times the expected number of SSM for each gene. Genes that have more or less SSM than the simulated values have more or less SSM than expected, respectively.The ratio in the previous simulation was normalized to the total number of splice sites (ss) and the total length of the coding sequence (CDS) in the HGMD dataset (Eq 4), the proportion of ESM given the total mutations in each exon was simulated 1,000 times. Exons falling outside the simulated values represent genes that have more  ESM than expected  based on the total number of mutations tested using MaPSy and the total number of ESMs identified using MaPSy.The total number of mutations tested using MaPSy per exon were plotted against the total number of mutations that altered splicing (ESM). Weighted random sampling was then used to construct a 99.9% confidence interval that capitulates the expected number of ESM given the total number of mutations tested in an exon. Using the proportion of total ESM to total mutations in the an exon as a weight for random sampling  was downloaded and intersected with the list of HGMD genes. A permutation test was then performed to determine if cancer genes were overrepresented in the SSM-prone genes.ESS, ESE, and ESR’s were downloaded from published data  and the kth trees) in a forest are constructed based off a different sub-sample (bootstrap sample) from the original training set and then averaged to provide unbiased estimates of predicted values. Two-thirds of the training set was used for the construction of the kth trees with the remaining one-third (out-of-bag data) used for cross-validation and estimates of variable importance. Default parameters were used to construct the random forest model, with the exception that ‘strata’ was used to sample the majority class (genes with the expected number of SSM) to make the frequency of the expected class closer to the frequency of the rarest class (genes with more SSM than expected). Variable importance was measured by the degree of model accuracy decrease with the permutation of a single predictor variable. The larger the mean decease in accuracy, the more important the variable is deemed in the classification of the data.R implementation of random forest, package ‘randomForest’ , was useR implementation of logistic regression, ‘glm’ function, was used to generate a predictive model for distinguishing SSM-prone genes from non-SSM-prone genes. Logistic regression is a classification method that relies on fitting a regression curve given a set of predictor variables and categorical response variables. Again, two-thirds of the data was used to construct the model with the remaining one-third of the data used for cross-validation. Default parameters were used to construct the logistic regression model, with the exception that ‘family = ‘ was set to binomial.The random forest model generated from the HGMD dataset was then applied to the rest of the testable genes in genome. Testable genes were required to be void of a previously described disease phenotype by HGMD, contain introns, and have sufficient genomic feature data. This resulted in ~13,000 genes that could be tested using the random forest predictive model. The ‘predict’ function with ‘type = ‘ set to ‘prob’ was used to predict SSM-prone genes based on a probability estimate. A probability threshold of > 0.6 was set to select SSM-prone genes, which resulted in 499 predicted SSM-prone gene.n = 497, after intersection) and genes predicted with a high probability (prob > 0.6) to have the expected number of SSM (n = 5995). The average ExAC SS region variants per splice site were plotted for genes predicted to be prone SSM and genes with the expected number of SSM.All low frequency (MAF < 0.01%) single nucleotide ExAC variants reported in the splice site regions of genes (-3 to +6 position at the 5′ ss and -20 to +3 position at the 3′ ss) were counted for each gene and divided by the number of SS’s. The list of ExAC SS region variants per SS was then intersected with the genomic genes tested using the random forest model. The genes were then divided into genes predicted to be SSM-prone (n = 486). All ExAC variants that fall within the splice sites (both 3′ and 5′) of the 486 genes were scored using the Maximum entropy model for splice sites (PMID: 15285897). The ExAC variants were separated based on their minor allele frequency into rare (MAF < 0.01%) and common (MAF > 1%). The entire distribution of scores and the two classes of alleles were plotted. The collapsed plots based on splice site score threshold were also generated.The 499 predicted SSM-prone genes were intersected with RefSeq database and only the ones having RefSeq transcript id were retained for the downstream analysis  were intersected with the list of genomic genes analyzed with the random forest model. 1,746 PTV-intolerant genes were analyzed using the random forest model. 281 of 1,746 were predicted to be prone to SSM. The intersection of the two datasets was plotted as a Venn diagram and significance was determined using the Fischer’s exact test.S1 FigMLH1 exons analyzed with MaPSy. Positions of exonic mutations assayed are highlighted in blue (no effect on splicing) and red (resulting in defective splicing). Positions that had more than one mapped mutation are bold. The sequences for both the branch point sequence (BP Seq) and polypyrimidine tract (PY Tract) are also noted.Predicted ESE’s (bottom brackets) and ESS’s (upper brackets)  were map(TIF)Click here for additional data file.S2 FigB. List of exons enriched for ESMs (P < 0.01).ESM versus all HGMD exonic mutations tested in MaPSy exons with regions of 99.9% confidence interval shown in gray. (TIF)Click here for additional data file.S3 FigA. The results from RT-PCR of the output RNA (spliced species) from MaPSy for three replicates is shown. B. Spliceosomal complexes  visualized in native gels for the MaPSy heterogeneous library substrates. C. Migration of RNA splicing intermediates from MaPSy heterogeneous library substrates.(TIF)Click here for additional data file.S4 FigP < 0.01, Mann-Whitney U test).Average percent SSM and ESM in COSMIC identified oncogenes vs non-oncogenes and TSG vs non-TSG listed in HGMD. Star indicates a significant difference between gene groups ((TIF)Click here for additional data file.S5 FigP-values calculated using Kruskall-Wallis test.Average number of introns, exon length, SS ∆G, HI score, and ExAC variant conservation score in genes with more SSM than expected , expected SSM , and less SSM than expected . (TIF)Click here for additional data file.S6 FigA. Common variants are depleted from the category of variants that cause loss of splice site signal at the 3′ splice site. B. Rare variants are enriched in the range of the splice site signal scores that abolish 3′ splice site recognition.(TIF)Click here for additional data file.S7 FigP = 7.53e-98, Fisher Exact).Enrichment of ExAC’s PTV-intolerant genes in the 499 genomic genes predicted to be susceptible to SSM ((TIF)Click here for additional data file.S1 Table(XLS)Click here for additional data file.S2 Table(XLS)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(XLS)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLS)Click here for additional data file.S8 Table(PDF)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.


Rivaroxaban is a direct oral anticoagulant (DOAC) approved as an important alternative to warfarin in patients with nonvalvular atrial fibrillation. We report the case of an 87-year-old man with past medical history of nonvalvular atrial fibrillation on rivaroxaban and recently started amiodarone for pulseless ventricular tachycardia who presented to our hospital with intermittent chest pain and was diagnosed with spontaneous hemopericardium causing cardiac tamponade. The culprit drugs were discontinued, and the patient was treated with emergent pericardiocentesis. Both rivaroxaban and amiodarone are substrates for the CYP3A4 hepatic pathway, and concomitant use can result in increased plasma rivaroxaban levels causing an increased propensity to bleeding. While most physicians are cognizant of the need for renal dosing of rivaroxaban, this article aims to increase awareness of its interactions with drugs that are also metabolized through the same hepatic CYP450 pathway. Rivaroxaban is a direct oral anticoagulant approved by the United States Food and Drug Administration (FDA) for stroke prevention in nonvalvular atrial fibrillation . AmiodarAn 87-year-old male with medical history of paroxysmal atrial fibrillation on 20 mg of rivaroxaban daily, recent pulseless ventricular tachycardia with implantable cardiac defibrillator in situ, and nonischemic cardiomyopathy (ejection fraction 35%) presented to the emergency room (ER) with intermittent chest pain and light headedness of 2 days duration. Chest pain was exertional, left sided, and pleuritic. He also reported shortness of breath but denied cough, fever, or any other infectious. Of note, he was commenced on 200 mg amiodarone daily four months prior to presentation following an episode of syncope due to pulseless ventricular tachycardia.Initial vital signs showed blood pressure of 89/60 mmHg with pulse rate of 59/min, temperature of 98.2°F, and respiratory rate of 14 breaths/min with normal oxygen saturation of 100% on ambient air. On physical examination, he was in no acute distress and was alert and oriented to time, place, and person. Jugular venous distention was noted on neck examination. Heart sounds were muffled, but lung fields were clear to auscultation. Peripheral pulses were also weak but palpable.His EKG on arrival to the emergency room showed new widespread ST segment elevation . His troChest X-ray revealed enlargement of the cardiomedistinal silhouette. CT chest revealed new moderate-sized pericardial effusion .Transthoracic echocardiogram (TTE) revealed large circumferential pericardial effusion and evidence for tamponade physiology with multichamber collapse and significant respirophasic variation to the mitral inflow pattern . Please Given the patient's history of intermittent chest pain and presence of ST segment elevation on EKG, our initial suspicion was an ST segment elevation myocardial infarction. However, at the time of evaluation by the cardiologist, the patient had no chest pain and serial troponin levels remained normal. Notably, patient had coronary angiography done 9 months prior to index presentation revealed minimal plaquing of the coronary arteries. We now know that the hypotension, muffled heart sounds, and jugular venous distention were part of the Beck's triad of cardiac tamponade. The lactic acidosis, acute kidney injury, and elevated liver enzymes were likely the result of tissue hypoperfusion from hypotension while the leucocytosis was likely reactive.Based on the initial finding of ST segment elevation on EKG, he received a loading dose of 324 mg of ASA by EMS. Amiodarone was discontinued due to deranged liver and renal functions. We also held his rivaroxaban due to abnormal clotting profile and finding of hemopericardium on imaging studies. He had urgent pericardiocentesis in the catheterization laboratory with initial drainage of 825 mLs of sanguinous fluid, and postprocedural TTE showed evidence of resolution of tamponade. The pericardiocentesis catheter was left in place to drain any residual collections, and patient was transferred to the intensive care unit. He had daily TTE at the bedside, and his pericardial drainage volume progressively reduced from 160 cc on day 1 to 100 cc on day 2 to 80 cc on day 3 postpericardiocentesis when the drain was removed. Pericardial fluid analysis was negative for organisms or malignancy.Our patient experienced significant improvement of his symptoms following pericardiocentesis. His blood pressure normalized, and IV dopamine was discontinued immediately after pericardiocentesis. His renal function also improved significantly evidenced by a reduction in creatinine to normal at the time of discharge. He went home 5 days after admission and was instructed to stop his amiodarone and rivaroxaban in the mean time until his office appointment with his cardiologist.Based on the Naranjo causality assessment scale , there iSerious bleeding events have been reported with rivaroxaban, mainly intracranial and gastrointestinal , but onlAltered hepatic metabolism resulting from coadministration with amiodarone and transient renal impairment synergistically resulted in supratherapeutic serum levels of rivaroxaban in our patient resulting in nontraumatic hemopericardium and eventual cardiac tamponade.Elimination of rivaroxaban occurs through a dual pathway—hepatic metabolism occurs via the CYP450 pathway (mainly by CYP3A4 and CYP2J2) while renal excretion via P-glycoprotein-mediated secretion [When used for stroke prophylaxis in patients with nonvalvular atrial fibrillation, clinical trials evaluating the safety and efficacy of rivaroxaban using the cockroft-gault formula recommend that for creatinine clearance greater than 50 mL/minute as was the case in our patient prior to this admission, a standard dose of 20 mg once daily is adequate. However, for CrCl between 15 and 50 mL/minute, a dose adjustment to 15 mg once daily may be considered. Most clinicians recommend discontinuing rivaroxaban in acute kidney injury or CrCl less than 15 mL/minute . No guidAndexanet alfa, a recombinant modified human factor Xa protein, is an antidote recently approved by the United States Food and Drug Administration (FDA)  for patiHowever, there are neither routinely available laboratory tests to measure the anticoagulant effect of rivaroxaban nor consensus guidelines on the use of prothrombin complex concentrates, recombinant factor VIIa, and the newly approved reversal agents in patients on direct oral anticoagulants.This article aims to alert clinicians to this rare but increasingly reported side effect of rivaroxaban. A high index of suspicion is needed for recognition and diagnosis of spontaneous hemopericardium. Caution should be observed especially in elderly patients with declining renal function and increased likelihood of polypharmacy. With increasing use of rivaroxaban and other novel anticoagulants and the recent approval of a new reversal agent, more research is needed to develop monitoring laboratory parameters to determine and monitor their therapeutic range.