Survival in Southern European patients waitlisted for kidney transplant after graft failure: A competing risk analysis

Background Whether patients waitlisted for a second transplant after failure of a previous kidney graft have higher mortality than transplant-näive waitlisted patients is uncertain. Methods We assessed the relationship between a failed transplant and mortality in 3851 adult KT candidates, listed between 1984–2012, using a competing risk analysis in the total population and in a propensity score-matched cohort. Mortality was also modeled by inverse probability weighting (IPTW) competing risk regression. Results At waitlist entry 225 (5.8%) patients had experienced transplant failure. All-cause mortality was higher in the post-graft failure group (16% vs. 11%; P = 0.033). Most deaths occurred within three years after listing. Cardiovascular disease was the leading cause of death (25.3%), followed by infections (19.3%). Multivariate competing risk regression showed that prior transplant failure was associated with a 1.5-fold increased risk of mortality (95% confidence interval [CI], 1.01–2.2). After propensity score matching (1:5), the competing risk regression model revealed a subhazard ratio (SHR) of 1.6 (95% CI, 1.01–2.5). A similar mortality risk was observed after the IPTW analysis (SHR, 1.7; 95% CI, 1.1–2.6). Conclusions Previous transplant failure is associated with increased mortality among KT candidates after relisting. This information is important in daily clinical practice when assessing relisted patients for a retransplant.This study was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) (grant ICI14/00016) from the Instituto de Salud Carlos III co-funded by the Fondo Europeo de Desarrollo Regional±FEDER, RETICS (REDINREN RD16/0009/0006, RD16/0009/0031

Effective Rheology of Bubbles Moving in a Capillary Tube

We calculate the average volumetric flux versus pressure drop of bubbles moving in a single capillary tube with varying diameter, finding a square-root relation from mapping the flow equations onto that of a driven overdamped pendulum. The calculation is based on a derivation of the equation of motion of a bubble train from considering the capillary forces and the entropy production associated with the viscous flow. We also calculate the configurational probability of the positions of the bubbles.Comment: 4 pages, 1 figur

The Drosophila junctophilin gene is functionally equivalent to its four mammalian counterparts and is a modifier of a Huntingtin poly-Q expansion and the Notch pathway

[EN] Members of the Junctophilin (JPH) protein family have emerged as key actors in all excitable cells, with crucial implications for human pathophysiology. In mammals, this family consists of four members (JPH1-JPH4) that are differentially expressed throughout excitable cells. The analysis of knockout mice lacking JPH subtypes has demonstrated their essential contribution to physiological functions in skeletal and cardiac muscles and in neurons. Moreover, mutations in the human JPH2 gene are associated with hypertrophic and dilated cardiomyopathies; mutations in JPH3 are responsible for the neurodegenerative Huntington's disease-like-2 (HDL2), whereas JPH1 acts as a genetic modifier in Charcot-Marie-Tooth 2K peripheral neuropathy. Drosophila melanogaster has a single junctophilin (jp) gene, as is the case in all invertebrates, which might retain equivalent functions of the four homologous JPH genes present in mammalian genomes. Therefore, owing to the lack of putatively redundant genes, a jp Drosophila model could provide an excellent platform to model the Junctophilin-related diseases, to discover the ancestral functions of the JPH proteins and to reveal new pathways. By up-and downregulation of Jp in a tissue-specific manner in Drosophila, we show that altering its levels of expression produces a phenotypic spectrum characterized by muscular deficits, dilated cardiomyopathy and neuronal alterations. Importantly, our study has demonstrated that Jp modifies the neuronal degeneration in a Drosophila model of Huntington's disease, and it has allowed us to uncover an unsuspected functional relationship with the Notch pathway. Therefore, this Drosophila model has revealed new aspects of Junctophilin function that can be relevant for the disease mechanisms of their human counterparts.This work was supported by project grants from Association Francaise contre les Myopathies [AFM 18540 to M.I.G.], Instituto de Salud Carlos III (ISCIII) [PI12/000453 and PI15/000187 to C.E.], Generalitat Valenciana [PROMETEOII/2014/067 to R.A. as partner], and a collaborative grant from the International Rare Diseases Research Consortium (IRDiRC) and ISCIII [IR11/TREAT-CMT to M.I.G. (partner 12) and C.E. (partner 6)]. C.E. has a 'Miguel Servet' contract funded by the ISCIII and Centro de Investigacion Principe Felipe [CPII14/00002]; M.C. was the recipient of a Santiago Grisolia award from Generalitat Valenciana [GrisoliaP/2013/A/044].Calpena-Corpas, E.; Lopez Del Amo, V.; Chakraborty M; Llamusi, B.; Artero R; Espinos, C.; Galindo, MI. (2018). The Drosophila junctophilin gene is functionally equivalent to its four mammalian counterparts and is a modifier of a Huntingtin poly-Q expansion and the Notch pathway. Disease Models & Mechanisms. 11(1). https://doi.org/10.1242/dmm.029082S11

Serum osteoprotegerin is associated with pulse pressure in kidney transplant recipients

Pulse pressure (PP) reflects increased large artery stiffness, which is caused, in part, by arterial calcification in patients with chronic kidney disease. PP has been shown to predict both cardiovascular and cerebrovascular events in various patient populations, including kidney transplant (KTX) recipients. Osteoprotegerin (OPG) is a marker and regulator of arterial calcification, and it is related to cardiovascular survival in hemodialysis patients. Here we tested the hypothesis that OPG is associated with increased pulse pressure. We cross-sectionally analyzed the association between serum OPG and PP in a prevalent cohort of 969 KTX patients (mean age: 51 +/- 13 years, 57% male, 21% diabetics, mean eGFR 51 +/- 20 ml/min/1.73 m2). Independent associations were tested in a linear regression model adjusted for multiple covariables. PP was positively correlated with serum OPG (rho = 0.284, p < 0.001). Additionally, a positive correlation was seen between PP versus age (r = 0.358, p < 0.001), the Charlson Comorbidity Index (r = 0.232, p < 0.001), serum glucose (r = 0.172, p < 0.001), BMI (r = 0.133, p = 0.001) and serum cholesterol (r = 0.094, p = 0.003). PP was negatively correlated with serum Ca, albumin and eGFR. The association between PP and OPG remained significant after adjusting for multiple potentially relevant covariables (beta = 0.143, p < 0.001). We conclude that serum OPG is independently associated with pulse pressure in kidney transplant recipients

Primary versus secondary source of data in observational studies and heterogeneity in meta-analyses of drug effects: a survey of major medical journals

The data from individual observational studies included in meta-analyses of drug effects are collected either from ad hoc methods (i.e. "primary data") or databases that were established for non-research purposes (i.e. "secondary data"). The use of secondary sources may be prone to measurement bias and confounding due to over-the-counter and out-of-pocket drug consumption, or non-adherence to treatment. In fact, it has been noted that failing to consider the origin of the data as a potential cause of heterogeneity may change the conclusions of a meta-analysis. We aimed to assess to what extent the origin of data is explored as a source of heterogeneity in meta-analyses of observational studies.publishe

The infrared-radio correlation of star-forming galaxies is strongly M⋆-dependent but nearly redshift-invariant since z ∼ 4

Over the past decade, several works have used the ratio between total (rest 8‒1000 μm) infrared and radio (rest 1.4 GHz) luminosity in star-forming galaxies (qIR), often referred to as the infrared-radio correlation (IRRC), to calibrate the radio emission as a star formation rate (SFR) indicator. Previous studies constrained the evolution of qIR with redshift, finding a mild but significant decline that is yet to be understood. Here, for the first time, we calibrate qIR as a function of both stellar mass (M⋆) and redshift, starting from an M⋆-selected sample of > 400 000 star-forming galaxies in the COSMOS field, identified via (NUV ‒ r)/(r ‒ J) colours, at redshifts of 0.1 < z < 4.5. Within each (M⋆,z) bin, we stacked the deepest available infrared/sub-mm and radio images. We fit the stacked IR spectral energy distributions with typical star-forming galaxy and IR-AGN templates. We then carefully removed the radio AGN candidates via a recursive approach. We find that the IRRC evolves primarily with M⋆, with more massive galaxies displaying a systematically lower qIR. A secondary, weaker dependence on redshift is also observed. The best-fit analytical expression is the following: qIR(M⋆, z) = (2.646 ± 0.024) × (1 + z)( ‒ 0.023 ± 0.008)-(0.148 ± 0.013) × (log M⋆/M⊙ ‒ 10). Adding the UV dust-uncorrected contribution to the IR as a proxy for the total SFR would further steepen the qIR dependence on M⋆. We interpret the apparent redshift decline reported in previous works as due to low-M⋆ galaxies being progressively under-represented at high redshift, as a consequence of binning only in redshift and using either infrared or radio-detected samples. The lower IR/radio ratios seen in more massive galaxies are well described by their higher observed SFR surface densities. Our findings highlight the fact that using radio-synchrotron emission as a proxy for SFR requires novel M⋆-dependent recipes that will enable us to convert detections from future ultra-deep radio surveys into accurate SFR measurements down to low-M⋆ galaxies with low SFR

Search for neutral MSSM Higgs bosons decaying to a pair of tau leptons in pp collisions

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Measurement of top quark–antiquark pair production in association with a W or Z boson in pp collisions at √s=8 TeV

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