Analysis of 786 NF1 mutation-positive subjects with clinical diagnosis of neurofibromatosis type 1 (NF1) allowed to identify the heterozygous c.5425C>T missense variant (p.Arg1809Cys) in six (0.7%) unrelated probands (three familial and three sporadic cases), all exhibiting a mild form of disease. Detailed clinical characterization of these subjects and other eight affected relatives showed that all individuals had multiple cafè-au-lait spots, frequently associated with skinfold freckling, but absence of discrete cutaneous or plexiform neurofibromas, Lisch nodules, typical NF1 osseous lesions or symptomatic optic gliomas. Facial features in half of the individuals were suggestive of Noonan syndrome. Our finding and revision of the literature consistently indicate that the c.5425C>T change is associated with a distinctive, mild form of NF1, providing new data with direct impact on genetic counseling and patient management. 相似文献
Isolated GH deficiency (IGHD) is characterized by genetic heterogeneity, both in familial and sporadic cases. To determine if this statement can be applied to the Russian population, we performed screening for mutations in the GH-1 gene in children living in Russia with IGHD. Twenty-eight children from 26 families with total IGHD were studied. DNA fragments, covering each of four (2-5) exons of GH-1 were amplified using PCR. Single-strand conformation polymorphism analysis followed by direct DNA sequencing identified five heterozygous mutations of splicing in intron 2, intron 3, and exon 4 of GH-1; three of them were not previously reported. We concentrated here on dominant-negative mutations causing IGHD type II, which were as follows: 1) A>T transversion of the second base of the 3'-acceptor splice site of intron 2 (IVS2 -2A>T); 2) T>C transition of the second base of the 5'-donor splice site of intron 3 (IVS3 +2T>C); 3) G>A transition of the first base of the 5'-donor splice site of intron 3 (IVS3 +1G>A). Our data indicate allelic heterogeneity of IGHD type II (IGHD II). However, all mutations in Russian IGHD II patients affect splicing, a striking difference from the mutation spectrum of other IGHD forms. The IVS2 -2A>T mutation is the first identified mutation in intron 2 of GH-1. The 5'-donor splice site of intron 3 of GH-1 is a mutational hot spot, and the IVS3 +1G>A mutation can be considered to be a common molecular defect in IGHD II in Russian patients. 相似文献
With laboratory and numerical work, we demonstrate that one of the main diffusion coefficients and the smaller eigenvalue of the Fick diffusion matrix are invariant to the number of methylene groups of the alcohol in ternary mixtures composed of an aromatic (benzene), a ketone (acetone) and one of three different alcohols (methanol, ethanol or 2-propanol). A critical analysis of the relationship between the kinetic and thermodynamic contributions to the diffusion coefficients allows us to explain this intriguing behaviour of this class of mixture. These findings are reflected by the diffusive behaviour of the according binary subsystems. Our approach provides a promising systematic framework for future investigations into the important and challenging problem of transport diffusion in multicomponent liquids.The Fick diffusion coefficient matrix of three ternary mixtures composed of an aromatic (benzene), a ketone (acetone) and one of three different alcohols (methanol, ethanol or 2-propanol) is investigated with laboratory and numerical work.Multicomponent diffusion plays a crucial role in various natural and industrial processes involving mass transfer.1–3 Liquids appearing in nature and technical applications are essentially multicomponent. However, only data on binary diffusion coefficients are relatively abundant because the diffusion behavior of ternary and higher mixtures is much more complex.4,5 Describing the isothermal–isobaric diffusion of a ternary mixture by Fick’s law requires four different diffusion coefficients that are composition dependent. The presence of cross diffusion coefficients aggravates the interpretation and data processing in experimental work, resulting in large uncertainties.6,7 Thus, efforts are being made to develop new methods for analysis of multicomponent diffusion explicitly addressing various degrees of complexity.8–10 Predictive equations for multicomponent diffusion of liquids mostly rely on extensions of the Darken relation,11–13 which is only valid for ideal mixtures.14 The underlying physical phenomena in non-ideal mixtures are not well understood and the lack of experimental data impedes the development and verification of new predictive equations.The objective of this study was not only to measure and predict the Fick diffusion coefficient matrix for a series of ternary liquid mixtures, rather, the emphasis lied on understanding common features and whether they can be related to the behavior of the pure components and binary subsystems. Three ternary mixtures that are composed of organic compounds were selected, i.e. an aromatic, a ketone and an alcohol. Throughout, the first two components were benzene (1) and acetone (2) and the third component was one of the alcohols, methanol, ethanol or 2-propanol. For each mixture, nine state points along a composition path with a constant content of benzene, x1 = 0.33 mol mol−1, were studied under ambient conditions (298.15 K and 0.1 MPa). Seven of the state points were ternary mixtures and two were binary subsystems. To obtain reliable results for the Fick diffusion coefficient matrix, two complementary approaches were used, i.e. experiments and predictive molecular simulations. This combination allows for a critical analysis and leads to a deeper understanding of the underlying phenomena.14,15The Taylor dispersion technique was utilized for the experiments.16,17 In this method, a small quantity of mixture with a slightly different composition is injected into a laminar stream. It disperses due to convection and diffusion while flowing through a capillary tube and the refractive index is measured at its end to sample the concentration distribution. We have used the same apparatus as in previous works.6,7 The Fick diffusion matrix is obtained by fitting working equations to the measured signal, i.e. the Taylor peak. The mathematical model of the Taylor dispersion technique was originally developed on the basis of Fick’s law in the volume reference frame. In a ternary mixture, two molar fluxes Jvi relative to a volume averaged velocity are related to gradients of molar concentration ∇Ci with four diffusion coefficients Dvij. Alternatively, fluxes expressed in the molar reference frame Ji are relative to a molar averaged velocity and the mole fraction gradients ∇xi act as a driving force1with molar density ρ. The fluxes of all three components are constrained by ΣJi = 0. The main diffusion coefficients D11 and D22 relate the flux of one component to its own mole fraction gradient and the cross diffusion coefficients D12 and D21 describe the coupling of the flux of one component with the gradient of the other. The third component does not appear in eqn (1) explicitly, but in general it affects all four diffusion coefficients. The transformation of experimental data from the volume to the molar reference frame (Dvij to Dij) could be done here on the basis of the pure component volumes (see the ESI†).Equilibrium molecular dynamics (MD) simulations were employed in this work, allowing for examination at the microscopic scale. The underlying molecular models were rigid, non-polarizable force fields of united atom type, consisting of a varying number of Lennard–Jones, point charge, dipole and quadrupole sites (see the ESI†). Note that the force field parameters were adjusted to pure fluid properties only so that all simulation results for the mixtures are strictly predictive. Diffusion coefficients were sampled with the Green–Kubo formalism, based on integrated correlation functions of net velocities of the contained species.11,15 Thereby, phenomenological coefficients Δij were obtained, associating the diffusive fluxes with the chemical potential gradients ∇μi2with gas constant R and temperature T. Fluxes Ji correspond to the molar reference frame as in eqn (1).The diffusion coefficients from experiment and simulation are related to different driving forces so that the chemical potential gradients have to be transformed to the mole fraction gradients for their comparison.18 This transformation is contained in the thermodynamic factor matrix Γ3with the activity coefficient of species i being γi, which expresses the non-ideality of a mixture with respect to the composition. This relationship shows that the Fick diffusion coefficients are actually the product of two contributions, a kinetic Δij and a thermodynamic Γij. The separate observation of these two contributions promotes understanding of the underlying physical phenomena. In the present study, the thermodynamic factor was calculated using the Wilson excess Gibbs energy (gE) model, using parameters fitted to experimental vapor–liquid equilibrium data of the binary subsystems (see the ESI†). This combination of MD simulation results with a gE model was successfully used in previous work to predict Fick diffusion coefficients, including several binary subsystems of the ternary mixtures studied here.19The four elements of the Fick diffusion coefficient matrix were determined for the three ternary mixtures, benzene + acetone + methanol/ethanol/2-propanol, for nine different compositions, each at ambient temperature and pressure.Results for the first main element of the diffusion matrix D11, which relates the flux of benzene to its own mole fraction gradient, are shown in Fig. 1(a). The experimental data agree quantitatively with the molecular simulation data. D11 increases with the acetone content in the ternary mixture. Since mixtures with a constant mole fraction of benzene (x1 = 0.33 mol mol−1) were studied throughout, the left edge of Fig. 1(a) corresponds to the binary limit of benzene + alcohol, while the right edge corresponds to that of benzene + acetone. Analysis of the ternary diffusive fluxes implies the following asymptotic behavior of the diffusion coefficients towards the binary limits:7 (i) at the infinite dilution limit, x2 → 0, the ternary coefficient D11 tends to the binary Fick diffusion coefficient of benzene + alcohol; (ii) at the other limit, x3 → 0, D11 − D12 = D22 − D21 → Dbin (benzene + acetone) should hold. The present experimental and simulation results for D11 are consistent with these asymptotic limits.Open in a separate windowFig. 1Top: The main Fick diffusion coefficient (molar reference frame) of benzene D11 in the three ternary mixtures benzene (1) + acetone (2) + alcohol (3) at a constant benzene mole fraction x1 = 0.33 mol mol−1 from experiment (triangles) and MD simulation combined with the Wilson gE model (circles). Both data sets were sampled at the same compositions, but are slightly shifted in the plot for visibility reasons. The symbols at the edges of this plot are the binary diffusion coefficients of benzene + alcohol (x2 → 0) and of benzene + acetone (x3 → 0). Bottom: The binary Fick diffusion coefficient of the subsystems benzene + alcohol and benzene + acetone. Most of the binary experimental data were taken from the literature.20–27An inspection of Fig. 1(a) provides an unexpected finding: the main element D11 is almost identical for all three mixtures along the examined composition path, i.e. it is independent of the contained type of alcohol. To explain this intriguing behavior of D11, the properties of the pure components are considered first (see
Open in a separate windowAll five components are liquid under ambient conditions so that their self-diffusion coefficients are of the same order of magnitude. Molar masses M and molar densities ρ, indicating the differences in mass and size of the molecules, give an introductory idea of their diffusion behavior. Benzene molecules are heavier and larger than acetone molecules, resulting in a self-diffusion coefficient D0 that is only about half that of acetone. The three alcohols are characterized by increasing mass and size in the order: methanol, ethanol, 2-propanol. Although the methanol molecules are the smallest, due to hydrogen bonding interactions, their self-diffusion coefficient is similar to that of benzene. Molecules associated by hydrogen bonds propagate as an assembly, which significantly slows down their mobility. This is not only the case for methanol, but also for ethanol and 2-propanol. Because these molecules are larger, the bonded clusters are also larger and thus even slower. This hydrogen bonding behavior of the alcohol molecules causes micro-heterogeneity and cluster formation in mixtures with other fluids,28,29 which influences their kinetic and thermodynamic behavior.Next, the binary subsystems of the ternary systems with different types of alcohol were examined. The Fick diffusion coefficient of the three binary benzene + alcohol mixtures and that of benzene + acetone is shown in Fig. 1(b). The benzene mole fraction, x1 = 0.33 mol mol−1, which was constant along the ternary composition path, is marked in the plot by a dashed vertical line. The binary Fick diffusion coefficient of all three benzene + alcohol mixtures has almost the same value in the concentration range around equimolar composition. However, at both infinite dilution limits (x1 → 0 and x1 → 1), the benzene + methanol system has a higher Fick diffusion coefficient than the benzene + ethanol or benzene + 2-propanol systems. The open question is why the Fick diffusion coefficients are similar in a wide composition range. Although these binary data are available in the literature, to the best of our knowledge, they have never been discussed from this point of view.In a binary mixture there is only a single Fick diffusion coefficient and eqn (3) reduces toD = ĐΓ,4where Đ is the Maxwell–Stefan (MS) diffusion coefficient. Đ represents the kinetic contribution to the diffusion behavior, which was sampled here using MD simulations from net velocity correlation functions, while Γ corresponds to the thermodynamic non-ideality, which was calculated using the Wilson gE model. Both contributions are separately shown in Fig. 2(a) and (c) for the three binary benzene + alcohol mixtures. The largest kinetic contribution, i.e. the MS diffusion coefficient, appears for benzene + methanol, followed by benzene + ethanol, which is also slightly larger than that of benzene + 2-propanol (see Fig. 2(a)). The same order was observed for the self-diffusion coefficient of the pure alcohols, which also decreases from methanol over ethanol to 2-propanol. The non-ideal composition dependence of the MS diffusion coefficient is a consequence of the hydrogen bonding behavior of the alcohols. The formation of clusters causes a correlated propagation of molecules. This leads to significant contributions of velocity correlations between unlike molecules,12,14,30 which are considered MS diffusion coefficient sampling (see the ESI†). The thermodynamic factor exhibits the converse order: benzene mixed with methanol is the most non-ideal with the smallest thermodynamic factor, followed by ethanol and 2-propanol. Multiplying these two contributions leads to a similar Fick diffusion coefficient over a wide composition range of the three binary mixtures.Open in a separate windowFig. 2(a) The Maxwell–Stefan diffusion coefficient Đ of the three binary mixtures benzene + alcohol, (b) the phenomenological coefficient Δ11 of the ternary mixtures from MD simulation, (c) the thermodynamic factor Γ of the three binary mixtures and (d) the thermodynamic factor Γ11 of the ternary mixtures from the Wilson gE model.Building on this understanding, we further demonstrate that a similar interplay between kinetic and thermodynamic contributions is responsible for the independence of D11 of the alcohol type for ternary mixtures of benzene and acetone with methanol, ethanol or 2-propanol. It follows from eqn (3) that D11 = Δ11Γ11 + Δ12Γ21. Molecular simulation data show that the first term dominates the sum, while the second term is negligibly small. The kinetic Δ11 and thermodynamic Γ11 contributions of the first term are depicted in Fig. 2(b) and (d). Indeed, as in the binary case, methanol exhibits the highest kinetic and the lowest thermodynamic contribution, providing that the product Δ11Γ11 is the same for all considered types of alcohol. It can thus be concluded that the interplay between kinetics and thermodynamics leads to similar binary and ternary diffusion coefficients for mixtures of benzene and acetone with methanol, ethanol or 2-propanol. To examine the clustering behavior of the alcohols in the ternary mixtures, hydrogen bonding statistics were sampled using molecular simulations on the basis of geometric criteria31 (see Fig. 3). Most of the alcohol molecules are bonded to dimers and trimers within the ternary mixtures. The fractions of monomers, dimers, trimers and tetramers are almost identical for all three alcohols.Open in a separate windowFig. 3Hydrogen bonding statistics obtained from MD simulation in the three ternary mixtures benzene + acetone + alcohol, i.e. methanol (red), ethanol (blue) and 2-propanol (green), at a constant benzene mole fraction x1 = 0.33 mol mol−1.An important remaining question is whether the quantitative similarity of the binary and ternary diffusion coefficients can also relate to the second main Fick diffusion coefficient of the studied ternary mixtures. The diffusion coefficient D22, characterizing the diffusive flux of acetone under its own mole fraction gradient, is shown in Fig. 4(a). The presence of benzene affects D22, resulting in a less steep increase of that coefficient with higher acetone content. On average, D22 is 1.5 to 2 times larger than D11, which is in agreement with the twice as large self-diffusion coefficient of acetone compared to that of benzene. D22 is fairly similar for ethanol and 2-propanol and noticeably higher for methanol. The binary diffusion coefficient of acetone + alcohol, shown in Fig. 3(b), resembles the behavior of D22 in the ternary mixtures. As in the preceding discussion of D11 and the corresponding binary subsystems, we decomposed the diffusion coefficient D22 = Δ21Γ12 + Δ22Γ22 into its kinetic and thermodynamic contributions. Molecular simulation data show that the cross term Δ21Γ12 is again negligibly small. The kinetic contributions for the ternary Δ22 as well as for the binary Đ (acetone + alcohol) mixtures are identical in the case of ethanol and 2-propanol, but much larger in the case of methanol. However, here the thermodynamic contributions for mixtures with methanol (Γ22 and Γ) cannot compensate for the large kinetic values. Separate analysis of kinetics and thermodynamics is a novel way for understanding diffusion.Open in a separate windowFig. 4Top: The main Fick diffusion coefficient (molar reference frame) of acetone D22 in the three ternary mixtures benzene (1) + acetone (2) +alcohol (3) at a constant benzene mole fraction x1 = 0.33 mol mol−1 from experiment (triangles) and MD simulation combined with the Wilson gE model (circles). Both data sets were sampled at the same compositions, but are slightly shifted in the plot for visibility reasons. Bottom: The binary Fick diffusion coefficient of the subsystems acetone + alcohol and acetone + benzene. Most of the binary experimental data were taken from the literature.20,24,25We may thus draw the conclusion that for the liquid ternary mixtures benzene + acetone + alcohol, the qualitative behavior of the main coefficients D11 and D22 can directly be related to the binary subsystems, including the influence of contained alcohols on the composition dependent diffusion coefficients.An important feature of ternary diffusion are the cross effects that cannot be related to binary behavior. As is often the case, the two cross coefficients of the studied ternary mixtures are significantly smaller than the main ones. The cross coefficient of benzene D12, relating the flux of benzene to the mole fraction gradient of acetone, has mostly small negative values for all three ternary mixtures, except for small alcohol concentrations in the mixture with methanol, where it is positive. The second cross coefficient of acetone D21 must be zero at the limit x2 → 0, which is confirmed by the trend of the data. At the other limit x3 → 0, the coefficients are positive and increasing towards the limit of diluted alcohol, with the highest values in the mixture with methanol and the lowest for 2-propanol.In contrast to the individual elements of the Fick diffusion coefficient matrix, the eigenvalues of the matrix do not depend on the reference frame or on the order of components. Furthermore, a constraint imposed by the second law of thermodynamics is that the eigenvalues of the Fick diffusion coefficient matrix must be real and positive for a thermodynamically stable mixture. The eigenvalues of the diffusion matrix obtained by experiment and simulation fulfil these specifications. They show the same variation with composition and dependence on the type of alcohol, which was already observed for the main elements of the diffusion matrix (see Fig. 5). The larger eigenvalue D1 increases with acetone content and shows higher values in the ternary mixture with methanol, while it is slightly lower for ethanol and 2-propanol. This correlates with the behavior of D22. The smaller eigenvalue D2, like the main coefficient D11, is independent of the type of alcohol for the three studied ternary mixtures.Open in a separate windowFig. 5Eigenvalues of the Fick diffusion coefficient matrix of the three ternary mixtures benzene (1) + acetone (2) + alcohol (3) at a constant benzene mole fraction x1 = 0.33 mol mol−1 from experiment (triangles) and MD simulation combined with the Wilson gE model (circles).Fick diffusion coefficients of three different ternary mixtures, i.e. benzene + acetone + methanol/ethanol/2-propanol, were analyzed. Two complementary approaches were utilized to obtain reliable data, experiments and molecular simulation. We identified an important feature of this class of mixture (an aromatic, a ketone and an alcohol): namely that one of the main diffusion coefficients D11, where D11 < D22, and the smaller eigenvalue D2 are independent of the alcohol type along the studied composition path. This insight was reflected in another finding that the Fick diffusion coefficient of the binary benzene + alcohol subsystems also does not depend on the alcohol type. The underlying mechanism of this unusual behavior was explained by separately considering the kinetic and thermodynamic contributions to the diffusion coefficients. The results presented here provide a promising framework for future systematic investigations into the important and challenging problem of diffusion in multicomponent liquid mixtures. In order to provide a more substantial understanding of phenomena occurring in multicomponent mixtures, the present study can be continued and extended by replacing one main component of the ternary mixture, e.g. benzene, with another aromatic substance, e.g. toluene. 相似文献
Native T1 may be a sensitive, contrast-free, non-invasive cardiovascular magnetic resonance (CMR) marker of myocardial tissue changes in patients with pulmonary artery hypertension. However, the diagnostic and prognostic value of native T1 mapping in this patient group has not been fully explored. The aim of this work was to determine whether elevation of native T1 in myocardial tissue in pulmonary hypertension: (a) varies according to pulmonary hypertension subtype; (b) has prognostic value and (c) is associated with ventricular function and interaction.
Methods
Data were retrospectively collected from a total of 490 consecutive patients during their clinical 1.5 T CMR assessment at a pulmonary hypertension referral centre in 2015. Three hundred sixty-nine patients had pulmonary hypertension [58?±?15 years; 66% female], an additional 39 had pulmonary hypertension due to left heart disease [68?±?13 years; 60% female], 82 patients did not have pulmonary hypertension [55?±?18; 68% female]. Twenty five healthy subjects were also recruited [58 ±4 years); 51% female]. T1 mapping was performed with a MOdified Look-Locker Inversion Recovery (MOLLI) sequence. T1 prognostic value in patients with pulmonary arterial hypertension was assessed using multivariate Cox proportional hazards regression analysis.
Results
Patients with pulmonary artery hypertension had elevated T1 in the right ventricular (RV) insertion point (pulmonary hypertension patients: T1?=?1060?±?90 ms; No pulmonary hypertension patients: T1?=?1020?±?80 ms p <?0.001; healthy subjects T1?=?940?±?50 ms p <?0.001) with no significant difference between the major pulmonary hypertension subtypes. The RV insertion point was the most successful T1 region for discriminating patients with pulmonary hypertension from healthy subjects (area under the curve?=?0.863) however it could not accurately discriminate between patients with and without pulmonary hypertension (area under the curve?=?0.654). T1 metrics did not contribute to prediction of overall mortality (septal: p =?0.552; RV insertion point: p =?0.688; left ventricular free wall: p =?0.258). Systolic interventricular septal angle was a significant predictor of T1 in patients with pulmonary hypertension (p <?0.001).
Conclusions
Elevated myocardial native T1 was found to a similar extent in pulmonary hypertension patient subgroups and is independently associated with increased interventricular septal angle. Native T1 mapping may not be of additive value in the diagnostic or prognostic evaluation of patients with pulmonary artery hypertension.
Introduction: Myocarditis and its sequelae remain an unconquered clinical problem, disproportionately affecting the young. Several hurdles beset myocarditis, including non-specific symptoms, heterogeneous clinical presentation, dynamic disease stages, underscored by an absence of an easy diagnostic test or a specific treatment.
Areas covered: The current diagnostic means are poorly equipped to counter the challenge; the gold standard by invasive endomyocardial biopsy relies on availability of expert procedural and reading skill. The tissue diagnostic criteria were developed to improve readers agreement with clinical diagnosis, and not based on evidence for differential treatment or improved prognosis. The Lake-Louise Criteria represented a first step towards a non-invasive diagnosis. They require extensive imaging, which is insufficiently robust with poor diagnostic confidence and tissue pathophysiological validation; they similarly lack evidence of improved outcome by guiding clinical management. T1 and T2 mapping are a step-change, providing robust, short and quantifiable imaging application, which can veritably reflect the dynamic and heterogeneous underlying disease.
Expert commentary: T1 and T2 mapping harbours a unique potential for an objective non-invasive disease recognition and treatment discovery in myocarditis. These measures should enter independently into clinical experimentation, with a high priority for outcome and therapeutic studies. 相似文献
UK Biobank, a large cohort study, plans to acquire 100,000 cardiac MRI studies by 2020. Although fully-automated left ventricular (LV) analysis was performed in the original acquisition, this was not designed for unsupervised incorporation into epidemiological studies. We sought to evaluate automated LV mass and volume (Siemens syngo InlineVF versions D13A and E11C), against manual analysis in a substantial sub-cohort of UK Biobank participants. Eight readers from two centers, trained to give consistent results, manually analyzed 4874 UK Biobank cases for LV end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF) and LV mass (LVM). Agreement between manual and InlineVF automated analyses were evaluated using Bland–Altman analysis and the intra-class correlation coefficient (ICC). Tenfold cross-validation was used to establish a linear regression calibration between manual and InlineVF results. InlineVF D13A returned results in 4423 cases, whereas InlineVF E11C returned results in 4775 cases and also reported LVM. Rapid visual assessment of the E11C results found 178 cases (3.7%) with grossly misplaced contours or landmarks. In the remaining 4597 cases, LV function showed good agreement: ESV ?6.4?±?9.0 ml, 0.853 (mean?±?SD of the differences, ICC) EDV ?3.0?±?11.6 ml, 0.937; SV 3.4?±?9.8 ml, 0.855; and EF 3.5?±?5.1%, 0.586. Although LV mass was consistently overestimated (29.9?±?17.0 g, 0.534) due to larger epicardial contours on all slices, linear regression could be used to correct the bias and improve accuracy. Automated InlineVF results can be used for case-control studies in UK Biobank, provided visual quality control and linear bias correction are performed. Improvements between InlineVF D13A and InlineVF E11C show the field is rapidly advancing, with further improvements expected in the near future. 相似文献
Reliable biomarkers for predicting subsequent sepsis among patients with suspected acute infection are lacking. In patients presenting to emergency departments (EDs) with suspected acute infection, we aimed to evaluate the reliability and discriminant ability of 47 leukocyte biomarkers as predictors of sepsis (Sequential Organ Failure Assessment score?≥?2 at 24 h and/or 72 h following ED presentation).
Methods
In a multi-centre cohort study in four EDs and intensive care units (ICUs), we standardised flow-cytometric leukocyte biomarker measurement and compared patients with suspected acute infection (cohort-1) with two comparator cohorts: ICU patients with established sepsis (cohort-2), and ED patients without infection or systemic inflammation but requiring hospitalization (cohort-3).
Results
Between January 2014 and February 2016, we recruited 272, 59 and 75 patients to cohorts 1, 2, and 3, respectively. Of 47 leukocyte biomarkers, 14 were non-reliable, and 17 did not discriminate between the three cohorts. Discriminant analyses for predicting sepsis within cohort-1 were undertaken for eight neutrophil (cluster of differentiation antigens (CD) CD15; CD24; CD35; CD64; CD312; CD11b; CD274; CD279), seven monocyte (CD35; CD64; CD312; CD11b; HLA-DR; CD274; CD279) and a CD8 T-lymphocyte biomarker (CD279). Individually, only higher neutrophil CD279 [OR 1.78 (95% CI 1.23–2.57); P?=?0.002], higher monocyte CD279 [1.32 (1.03–1.70); P?=?0.03], and lower monocyte HLA-DR [0.73 (0.55–0.97); P?=?0.03] expression were associated with subsequent sepsis. With logistic regression the optimum biomarker combination was increased neutrophil CD24 and neutrophil CD279, and reduced monocyte HLA-DR expression, but no combination had clinically relevant predictive validity.
Conclusions
From a large panel of leukocyte biomarkers, immunosuppression biomarkers were associated with subsequent sepsis in ED patients with suspected acute infection.
To investigate the role of psychiatric dimensions, behavioral or substance addictions and demographical variables as determinants of pathological gambling among nursing students.
Design
Multicenter cross-sectional study.
Methods
From June to October 2015 a survey was carried out among Italian Nursing students. Data were collected using a six-section tool.
Findings
Nursing students who completed the survey numbered 1083, 902 (83.3%) had some problems with gambling and 29 (2.7%) showed pathological gambling. Percentage of pathological gambling was significantly associate with illicit drug/alcohol use (65.5%; p = 0.001) and with male gender (58.6%) comparing to student nurse with non-pathological gambling (20%) and those with some problem (24.2%). Significant main effect was observed for IAT score (Beta = 0.119, t = 3.28, p = 0.001): higher IAT scores were associated with higher SOGS scores.
Conclusions
Italian nursing students have some problems with gambling and pathological gambling problem, and males are those who have more problems. Results might be useful for faculties of health professionals to identify students at risk in an early stage, to direct prevention tailored interventions.
Clinical relevance
Nursing faculties should be aware of the prevalence of Gambling among students.Prevention interventions should be planned to minimize the risk of gambling behavior in the future nurses' health care workers. 相似文献