Grading system to assess clinical performance of closed-loop glucose control

Closed-loop control of the glucose concentration in type 1 diabetes has been the subject of extensive research over the last 3 decades. Building on the recent progress in continuous glucose sensing techniques, several prototypes of a closed-loop system have been developed. To complement existing measures of glucose control, we designed a grading system specifically designed to provide clinical assessment of closed-loop systems including that of glucose controllers. The system introduces six grades, A-F, describing the level of control and the therapeutic intervention during outside-meal and postprandial conditions. Grades A and B represent excellent and good glucose control, respectively, without the need for a corrective therapeutic action. Grade C represents suboptimal control with a recommendation for a corrective action. Grade D represents poor control requiring a corrective action. Grades E and F represent very poor and life-threatening control, respectively, with a need for an immediate corrective action or requiring external assistance. The outcome of grading is the quantification of time spent in each grade. The grading system is exemplified using data obtained with a model predictive controller within an in silico simulation environment. We conclude that the grading system provides suitable means to assess efficacy and safety of glucose controllers complementing existing measures of glucose control.     

Differential expression of galectin-3 in medullary thyroid carcinoma and C-cell hyperplasia

OBJECTIVE AND DESIGN: Galectin-3 is a beta-galactoside-binding protein that plays a role in cell adhesion and tumour progression. It was shown recently to diagnose malignant follicular thyroid lesions accurately. The reliability of this marker in the differential diagnosis between medullary thyroid carcinoma and C-cell hyperplasia was studied by immunohistochemistry. PATIENTS: Tissue specimens were obtained from 34 patients belonging to families with medullary thyroid carcinoma who underwent prophylactic thyroidectomy for RET gene mutation and/or abnormally increased plasma calcitonin levels. RESULTS: Galectin-3 was expressed in 23 of 25 cases of medullary thyroid carcinoma and in none of the nine cases of C-cell hyperplasia only, giving a sensitivity of 92% and a specificity of 100% for the diagnosis of carcinoma. A significant association was found between higher galectin-3 expression and occurrence of lymph node metastases (P < 0.05). CONCLUSIONS: Galectin-3 is a reliable diagnostic marker of medullary thyroid carcinoma, and its use may provide relevant information for prognosis and therapy.     

Management of bone marrow biopsy related bleeding risks: a retrospective observational study

Journal of Thrombosis and Thrombolysis - Bone marrow biopsies are largely used for the diagnosis and prognostic of various hematological diseases. Complications are rare but can be as serious as...     

Linked selection and recombination rate variation drive the evolution of the genomic landscape of differentiation across the speciation continuum of Ficedula flycatchers

Speciation is a continuous process during which genetic changes gradually accumulate in the genomes of diverging species. Recent studies have documented highly heterogeneous differentiation landscapes, with distinct regions of elevated differentiation (“differentiation islands”) widespread across genomes. However, it remains unclear which processes drive the evolution of differentiation islands; how the differentiation landscape evolves as speciation advances; and ultimately, how differentiation islands are related to speciation. Here, we addressed these questions based on population genetic analyses of 200 resequenced genomes from 10 populations of four Ficedula flycatcher sister species. We show that a heterogeneous differentiation landscape starts emerging among populations within species, and differentiation islands evolve recurrently in the very same genomic regions among independent lineages. Contrary to expectations from models that interpret differentiation islands as genomic regions involved in reproductive isolation that are shielded from gene flow, patterns of sequence divergence (d_xy and relative node depth) do not support a major role of gene flow in the evolution of the differentiation landscape in these species. Instead, as predicted by models of linked selection, genome-wide variation in diversity and differentiation can be explained by variation in recombination rate and the density of targets for selection. We thus conclude that the heterogeneous landscape of differentiation in Ficedula flycatchers evolves mainly as the result of background selection and selective sweeps in genomic regions of low recombination. Our results emphasize the necessity of incorporating linked selection as a null model to identify genome regions involved in adaptation and speciation.Uncovering the genetic architecture of reproductive isolation and its evolutionary history are central tasks in evolutionary biology. The identification of genome regions that are highly differentiated between closely related species, and thereby constitute candidate regions involved in reproductive isolation, has recently been a major focus of speciation genetic research. Studies from a broad taxonomic range, involving organisms as diverse as plants (Renaut et al. 2013), insects (Turner et al. 2005; Lawniczak et al. 2010; Nadeau et al. 2012; Soria-Carrasco et al. 2014), fishes (Jones et al. 2012), mammals (Harr 2006), and birds (Ellegren et al. 2012) contribute to the emerging picture of a genomic landscape of differentiation that is usually highly heterogeneous, with regions of locally elevated differentiation (“differentiation islands”) widely spread over the genome. However, the evolutionary processes driving the evolution of the differentiation landscape and the role of differentiation islands in speciation are subject to controversy (Turner and Hahn 2010; Cruickshank and Hahn 2014; Pennisi 2014).Differentiation islands were originally interpreted as “speciation islands,” regions that harbor genetic variants involved in reproductive isolation and are shielded from gene flow by selection (Turner et al. 2005; Soria-Carrasco et al. 2014). During speciation-with-gene-flow, speciation islands were suggested to evolve through selective sweeps of locally adapted variants and by hitchhiking of physically linked neutral variation (“divergence hitchhiking”) (Via and West 2008); gene flow would keep differentiation in the remainder of the genome at bay (Nosil 2008; Nosil et al. 2008). In a similar way, speciation islands can arise by allopatric speciation followed by secondary contact. In this case, genome-wide differentiation increases during periods of geographic isolation, but upon secondary contact, it is reduced by gene flow in genome regions not involved in reproductive isolation. In the absence of gene flow in allopatry, speciation islands need not (but can) evolve by local adaptation, but may consist of intrinsic incompatibilities sensu Bateson-Dobzhansky-Muller (Bateson 1909; Dobzhansky 1937; Muller 1940) that accumulated in spatially isolated populations.However, whether differentiation islands represent speciation islands has been questioned. Rather than being a cause of speciation, differentiation islands might evolve only after the onset of reproductive isolation as a consequence of locally accelerated lineage sorting (Noor and Bennett 2009; Turner and Hahn 2010; White et al. 2010; Cruickshank and Hahn 2014; Renaut et al. 2014), such as in regions of low recombination (Nachman 2002; Sella et al. 2009; Cutter and Payseur 2013). In these regions, the diversity-reducing effects of both positive selection and purifying selection (background selection [BGS]) at linked sites (“linked selection”) impact physically larger regions due to the stronger linkage among sites. The thereby locally reduced effective population size (N_e) will enhance genetic drift and hence inevitably lead to increased differentiation among populations and species.These alternative models for the evolution of a heterogeneous genomic landscape of differentiation are not mutually exclusive, and their population genetic footprints can be difficult to discern. In the cases of (primary) speciation-with-gene-flow and gene flow at secondary contact, shared variation outside differentiation islands partly stems from gene flow. In contrast, under linked selection, ancestral variation is reduced and differentiation elevated in regions of low recombination, while the remainder of the genome may still share considerable amounts of ancestral genetic variation and show limited differentiation. Many commonly used population genetic statistics do not capture these different origins of shared genetic variation and have the same qualitative expectations under both models, such as reduced diversity (π) and skews toward an excess of rare variants (e.g., lower Tajima''s D) in differentiation islands relative to the remainder of the genome. However, since speciation islands should evolve by the prevention or breakdown of differentiation by gene flow in regions not involved in reproductive isolation, substantial gene flow should be detectable in these regions (Cruickshank and Hahn 2014) and manifested in the form of reduced sequence divergence (d_xy) or as an excess of shared derived alleles in cases of asymmetrical gene flow (Patterson et al. 2012). Under linked selection, predictions are opposite for d_xy (Cruickshank and Hahn 2014), owing to reduced ancestral diversity in low-recombination regions. Further predictions for linked selection include positive and negative relationships of recombination rate with genetic diversity (π) and differentiation (F_ST), respectively, and inverse correlations of the latter two with the density of targets for selection. Finally, important insights into the nature of differentiation islands may be gained by studying the evolution of differentiation landscapes across the speciation continuum. Theoretical models and simulations of speciation-with-gene-flow predict that after an initial phase during which differentiation establishes in regions involved in adaptation, differentiation should start spreading from these regions across the entire genome (Feder et al. 2012, 2014; Flaxman et al. 2013).Unravelling the processes driving the evolution of the genomic landscape of differentiation, and hence understanding how genome differentiation unfolds as speciation advances, requires genome-wide data at multiple stages of the speciation continuum and in a range of geographical settings from allopatry to sympatry (Seehausen et al. 2014). Although studies of the speciation continuum are emerging (Hendry et al. 2009; Kronforst et al. 2013; Shaw and Mullen 2014, and references therein), empirical examples of genome differentiation at multiple levels of species divergence remain scarce (Andrew and Rieseberg 2013; Kronforst et al. 2013; Martin et al. 2013), and to our knowledge, have so far not jointly addressed the predictions of alternative models for the evolution of the genomic landscape of differentiation. In the present study, we implemented such a study design encompassing multiple populations of four black-and-white flycatcher sister species of the genus Ficedula (Fig. 1A,B; Supplemental Fig. S1; for a comprehensive reconstruction of the species tree, see Nater et al. 2015). Previous analyses in collared flycatcher (F. albicollis) and pied flycatcher (F. hypoleuca) revealed a highly heterogeneous differentiation landscape across the genome (Ellegren et al. 2012). An involvement of gene flow in its evolution would be plausible, as hybrids between these species occur at low frequencies in sympatric populations in eastern Central Europe and on the Baltic Islands of Gotland and Öland (Alatalo et al. 1990; Sætre et al. 1999), although a recent study based on genome-wide markers identified no hybrids beyond the F₁ generation (Kawakami et al. 2014a). Still, gene flow from pied into collared flycatcher appears to have occurred (Borge et al. 2005; Backström et al. 2013; Nadachowska-Brzyska et al. 2013) despite premating isolation (for review, see Sætre and Sæther 2010), hybrid female sterility (Alatalo et al. 1990; Tegelström and Gelter 1990), and strongly reduced long-term fitness of hybrid males (Wiley et al. 2009). Atlas flycatcher (F. speculigera) and semicollared flycatcher (F. semitorquata) are two closely related species, which have been less studied, but may provide interesting insights into how genome differentiation evolves over time. Here, we take advantage of this system to identify the processes underlying the evolution of differentiation islands based on the population genetic analysis of whole-genome resequencing data of 200 flycatchers.Open in a separate windowFigure 1.A recurrently evolving genomic landscape of differentiation across the speciation continuum in Ficedula flycatchers. (A) Species’ neighbor-joining tree based on mean genome-wide net sequence divergence (d_A). The same species tree topology was inferred with 100% bootstrap support from the distribution of gene trees under the multispecies coalescent (Supplemental Fig. S1). (B) Map showing the locations of population sampling and approximate species ranges. (C) Population genomic parameters along an example chromosome (Chromosome 4A) (see Supplemental Figs. S2, S4 for all chromosomes). Color codes for specific–specific parameters: (blue) collared; (green) pied; (orange) Atlas; (red) semicollared. Color codes for d_xy: (green) collared-pied; (light blue) collared-Atlas; (blue) collared-semicollared; (orange) pied-Atlas; (red) pied-semicollared; (black) Atlas-semicollared. For differentiation within species, comparisons with the Italian (collared) and Spanish (pied) populations are shown. Color codes for F_ST within collared flycatchers: (cyan) Italy–Hungary; (light blue) Italy–Czech Republic; (dark blue) Italy–Baltic. Color codes for F_ST within pied flycatchers: (light green) Spain–Sweden; (green) Spain–Czech Republic; (dark green) Spain–Baltic. (D) Distributions of differentiation (F_ST) from collared flycatcher along the speciation continuum. Distributions are given separately for three autosomal recombination percentiles (33%; 33%–66%; 66%–100%) corresponding to high (>3.4 cM/Mb, blue), intermediate (1.3–3.4 cM/Mb, orange), and low recombination rate (0–1.3 cM/Mb, red), and the Z Chromosome (green). Geographically close within-species comparison: Italy–Hungary. Comparisons within species include the geographically close Italian and Hungarian populations (within [close]), and the geographically distant Italian and Baltic populations (within [far]). Geographically far within-species comparison: Italy–Baltic. (E) Differentiation from collared flycatcher along an example chromosome (Chromosome 11) (see Supplemental Fig. S3 for all chromosomes). Color codes for between-species comparisons: (green) pied; (orange) Atlas; (red) semicollared; (dark red) red-breasted; (black) snowy-browed flycatcher. Color codes for within-species comparisons: (cyan) Italy–Hungary; (blue) Italy–Baltic. Flycatcher artwork in panel A courtesy of Dan Zetterström.     

Screening and confirmatory analysis of recombinant human erythropoietin for racing camels' doping control

Recombinant human erythropoietin (rHuEPO) belongs to the therapeutic class of erythropoiesis stimulating agents (ESAs) due to its implication in the creation pathway of red blood cells and thus enhancement of oxygenation. Because of this bioactivity, rHuEPO has been considered as a major doping agent in sports competitions for decades. Over the years, doping control laboratories designed several analytical strategies applied to human and animal samples to highlight any misuse. Even though multiple analytical approaches have been reported, none has yet been dedicated to racing camels. Here, we describe an analytical strategy to test camel plasma samples at screening using an ELISA assay and a targeted nano‐liquid chromatography–high‐resolution tandem mass spectrometry for confirmatory analysis. The method was validated and has been successfully applied to post‐race samples, allowing the detection of a positive case of rHuEPO administration.     

Educational Impact on Apixaban Adherence in Atrial Fibrillation (the AEGEAN STUDY): A Randomized Clinical Trial

Adherence to non-vitamin-K oral anticoagulants (NOACs) may be lower than to vitamin K antagonists because NOACs do not require routine monitoring. We assessed the impact of an educational program on adherence and persistence with apixaban in patients with non-valvular atrial fibrillation (NVAF). Patients with NVAF eligible for NOACs with one or more stroke risk factor (prior stroke/transient ischemic attack, age ≥ 75 years, hypertension, diabetes, or symptomatic heart failure) were randomized (1:1) to standard of care (SOC) or SOC with additional educational (information booklet, reminder tools, virtual clinic access). The primary outcome was adherence to apixaban (2.5 or 5 mg twice daily) at 24 weeks. Patients receiving the educational program were re-randomized (1:1) to continue the program for 24 further weeks or to switch to secondary SOC. Implementation adherence and persistence were reassessed at 48 weeks. In total, 1162 patients were randomized (SOC, 583; educational program, 579). Mean implementation adherence ± standard deviation (SD) at 24 weeks was 91.6% ± 17.1 for SOC and 91.9% ± 16.1 for the educational program arm; results did not differ significantly between groups at any time-point. At 48 weeks, implementation adherence was 90.4% ± 18.0, 90.1% ± 18.6, and 89.3% ± 18.1 for continued educational program, SOC, and secondary SOC, respectively; and corresponding persistence was 86.1% (95% confidence interval [CI] 81.3–89.7), 85.2% (95% CI 81.5–88.2), and 87.8% (95% CI 83.4–91.1). Serious adverse events were similar across groups. High implementation adherence and persistence with apixaban were observed in patients with NVAF receiving apixaban. The educational program did not show additional benefits. This study is registered at ClinicalTrials.gov [NCT01884350].     

Myocardial infarction in patients aged 70 years and over

109 subjects aged 70 years (58 women, 51 men; average age 77 years) were hospitalized in the CICU (Cardiology Intensive Care Unit) over the period stretching from 1984 to 1986. The average length of stay in the CICU was 1 week, completed by an average stay of 5 days in the cardiology department. 100 per cent of the patients were followed up. Of the clinical parameters made evident by this study, the authors note that hypertension was the predominant risk factor (52.2 per cent); a history of coronary disease was noted in 60.5 per cent; 26.6 per cent of the patients were hospitalized before the 6th hour, chest pain being typical in 78 per cent versus painless in 11 per cent of patients; topographically, the infarction was anterior in 55 per cent, posterior in 40.4 per cent, and around the circumference in 4.6 per cent of cases; 80.8 per cent of the infarctions were transmural versus 19.2 per cent of infarctions without the Q wave--the latter accounted for a higher hospital mortality rate (38 per cent versus 27.3 per cent). The main complications were disturbances in rhythm (60.6 per cent) and LVI (56.9 per cent). Complications on the form of infections were noted in 15.6 per cent. Apart from the usual indicators of severity (cardiogenic shock, VF, LVI), infarction of the RV and AF had a serious effect on the prognosis. latrogenic disease accounted for 18.9 per cent. From the point of view of prognosis, hospital mortality was 30 per cent; mortality after one year was 44 per cent and 47.7 per cent after 2 years (in a group of 76 subjects).(ABSTRACT TRUNCATED AT 250 WORDS)