607 research outputs found

    Resolving the relationships of Paleocene placental mammals

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    © 2015 Cambridge Philosophical Society. The 'Age of Mammals' began in the Paleocene epoch, the 10million year interval immediately following the Cretaceous-Palaeogene mass extinction. The apparently rapid shift in mammalian ecomorphs from small, largely insectivorous forms to many small-to-large-bodied, diverse taxa has driven a hypothesis that the end-Cretaceous heralded an adaptive radiation in placental mammal evolution. However, the affinities of most Paleocene mammals have remained unresolved, despite significant advances in understanding the relationships of the extant orders, hindering efforts to reconstruct robustly the origin and early evolution of placental mammals. Here we present the largest cladistic analysis of Paleocene placentals to date, from a data matrix including 177 taxa (130 of which are Palaeogene) and 680 morphological characters. We improve the resolution of the relationships of several enigmatic Paleocene clades, including families of 'condylarths'. Protungulatum is resolved as a stem eutherian, meaning that no crown-placental mammal unambiguously pre-dates the Cretaceous-Palaeogene boundary. Our results support an Atlantogenata-Boreoeutheria split at the root of crown Placentalia, the presence of phenacodontids as closest relatives of Perissodactyla, the validity of Euungulata, and the placement of Arctocyonidae close to Carnivora. Periptychidae and Pantodonta are resolved as sister taxa, Leptictida and Cimolestidae are found to be stem eutherians, and Hyopsodontidae is highly polyphyletic. The inclusion of Paleocene taxa in a placental phylogeny alters interpretations of relationships and key events in mammalian evolutionary history. Paleocene mammals are an essential source of data for understanding fully the biotic dynamics associated with the end-Cretaceous mass extinction. The relationships presented here mark a critical first step towards accurate reconstruction of this important interval in the evolution of the modern fauna. Biological Review

    Refining Our Understanding of the Flow Through Coronary Artery Branches; Revisiting Murray's Law in Human Epicardial Coronary Arteries

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    Background: Quantification of coronary blood flow is used to evaluate coronary artery disease, but our understanding of flow through branched systems is poor. Murray’s law defines coronary morphometric scaling, the relationship between flow (Q) and vessel diameter (D) and is the basis for minimum lumen area targets when intervening on bifurcation lesions. Murray’s original law (Q α D(P)) dictates that the exponent (P) is 3.0, whilst constant blood velocity throughout the system would suggest an exponent of 2.0. In human coronary arteries, the value of Murray’s exponent remains unknown. Aim: To establish the exponent in Murray’s power law relationship that best reproduces coronary blood flows (Q) and microvascular resistances (Rmicro) in a bifurcating coronary tree. Methods and Results: We screened 48 cases, and were able to evaluate inlet Q and Rmicro in 27 branched coronary arteries, taken from 20 patients, using a novel computational fluid dynamics (CFD) model which reconstructs 3D coronary anatomy from angiography and uses pressure-wire measurements to compute Q and Rmicro distribution in the main- and side-branches. Outputs were validated against invasive measurements using a Rayflow™ catheter. A Murray’s power law exponent of 2.15 produced the strongest correlation and closest agreement with inlet Q (zero bias, r = 0.47, p = 0.006) and an exponent of 2.38 produced the strongest correlation and closest agreement with Rmicro (zero bias, r = 0.66, p = 0.0001). Conclusions: The optimal power law exponents for Q and Rmicro were not 3.0, as dictated by Murray’s Law, but 2.15 and 2.38 respectively. These data will be useful in assessing patient-specific coronary physiology and tailoring revascularisation decisions

    Validation of a novel numerical model to predict regionalized blood flow in the coronary arteries

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    Aims: Ischaemic heart disease results from insufficient coronary blood flow. Direct measurement of absolute flow (mL/min) is feasible, but has not entered routine clinical practice in most catheterization laboratories. Interventional cardiologists, therefore, rely on surrogate markers of flow. Recently, we described a computational fluid dynamics (CFD) method for predicting flow that differentiates inlet, side branch, and outlet flows during angiography. In the current study, we evaluate a new method that regionalizes flow along the length of the artery. Methods and results: Three-dimensional coronary anatomy was reconstructed from angiograms from 20 patients with chronic coronary syndrome. All flows were computed using CFD by applying the pressure gradient to the reconstructed geometry. Side branch flow was modelled as a porous wall boundary. Side branch flow magnitude was based on morphometric scaling laws with two models: a homogeneous model with flow loss along the entire arterial length; and a regionalized model with flow proportional to local taper. Flow results were validated against invasive measurements of flow by continuous infusion thermodilution (Coroventisâ„¢, Abbott). Both methods quantified flow relative to the invasive measures: homogeneous (r 0.47, P 0.006; zero bias; 95% CI -168 to +168 mL/min); regionalized method (r 0.43, P 0.013; zero bias; 95% CI -175 to +175 mL/min). Conclusion: During angiography and pressure wire assessment, coronary flow can now be regionalized and differentiated at the inlet, outlet, and side branches. The effect of epicardial disease on agreement suggests the model may be best targeted at cases with a stenosis close to side branches.</p

    Sex differences in coronary microvascular resistance measured by a computational fluid dynamics model

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    BackgroundIncreased coronary microvascular resistance (CMVR) is associated with coronary microvascular dysfunction (CMD). Although CMD is more common in women, sex-specific differences in CMVR have not been demonstrated previously.AimTo compare CMVR between men and women being investigated for chest pain.Methods and resultsWe used a computational fluid dynamics (CFD) model of human coronary physiology to calculate absolute CMVR based on invasive coronary angiographic images and pressures in 203 coronary arteries from 144 individual patients. CMVR was significantly higher in women than men (860 [650–1,205] vs. 680 [520–865] WU, Z = −2.24, p = 0.025). None of the other major subgroup comparisons yielded any differences in CMVR.ConclusionCMVR was significantly higher in women compared with men. These sex-specific differences may help to explain the increased prevalence of CMD in women

    Osteoprotegerin and Myocardial Fibrosis in Patients with Aortic Stenosis

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    Left ventricular myocardial fibrosis in patients with aortic stenosis (AS) confers worse prognosis. Plasma osteoprotegerin (OPG), a cytokine from the TNF receptor family, correlates with the degree of valve calcification in AS, reflecting the activity of the tissue RANKL/RANK/OPG (receptor activator of nuclear factor κΒ ligand/RANK/osteoprotegerin) axis, and is associated with poorer outcomes in AS. Its association with myocardial fibrosis is unknown. We hypothesised that OPG levels would reflect the extent of myocardial fibrosis in AS. We included 110 consecutive patients with AS who had undergone late-gadolinium contrast enhanced cardiovascular magnetic resonance (LGE-CMR). Patients were characterised according to pattern of fibrosis (no fibrosis, midwall fibrosis, or chronic myocardial infarction fibrosis). Serum OPG was measured with ELISA and compared between groups defined by valve stenosis severity. Some 36 patients had no fibrosis, 38 had midwall fibrosis, and 36 had chronic infarction. Patients with midwall fibrosis did not have higher levels of OPG compared to those without fibrosis (6.78 vs. 5.25 pmol/L, p = 0.12). There was no difference between those with midwall or chronic myocardial infarction fibrosis (6.78 vs. 6.97 pmol/L, p = 0.27). However, OPG levels in patients with chronic myocardial infarction fibrosis were significantly higher than those without fibrosis (p = 0.005)

    Evaluation of models of sequestration flow in coronary arteries—Physiology versus anatomy?

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    Background: Myocardial ischaemia results from insufficient coronary blood flow. Computed virtual fractional flow reserve (vFFR) allows quantification of proportional flow loss without the need for invasive pressure-wire testing. In the current study, we describe a novel, conductivity model of side branch flow, referred to as ‘leak’. This leak model is a function of taper and local pressure, the latter of which may change radically when focal disease is present. This builds upon previous techniques, which either ignore side branch flow, or rely purely on anatomical factors. This study aimed to describe a new, conductivity model of side branch flow and compare this with established anatomical models. Methods and results: The novel technique was used to quantify vFFR, distal absolute flow (Qd) and microvascular resistance (CMVR) in 325 idealised 1D models of coronary arteries, modelled from invasive clinical data. Outputs were compared to an established anatomical model of flow. The conductivity model correlated and agreed with the reference model for vFFR (r = 0.895, p < 0.0001; +0.02, 95% CI 0.00 to + 0.22), Qd (r = 0.959, p < 0.0001; −5.2 mL/min, 95% CI −52.2 to +13.0) and CMVR (r = 0.624, p < 0.0001; +50 Woods Units, 95% CI −325 to +2549). Conclusion: Agreement between the two techniques was closest for vFFR, with greater proportional differences seen for Qd and CMVR. The conductivity function assumes vessel taper was optimised for the healthy state and that CMVR was not affected by local disease. The latter may be addressed with further refinement of the technique or inferred from complementary image data.The conductivity technique may represent a refinement of current techniques for modelling coronary side-branch flow. Further work is needed to validate the technique against invasive clinical data
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