Ultrastructure of head organs (anterior adhesive apparatus) and posterior secretory systems of Caballeria liewi Lim, 1995 (Monogenea, Ancyrocephalidae)

Caballeria liewi Lim, 1995, uses adhesive secretions from the head organs and posterior secretory systems to assist in locomotion and attachment. Ultrastructural investigations show that the head organs of C. liewi consist of three pairs of antero-lateral pit-like openings bearing microvilli and ducts leading from two types of uninucleated gland cells (located lateral to the pharynx), one type producing rod-like (S1) bodies with an electron-dense matrix containing less electron-dense vesicles and the second type producing oval (S2) bodies with a homogeneous electron-dense matrix. Interlinking band-like structures are observed between S1 bodies and between S2 bodies. S1 body is synthesised in the granular endoplasmic reticulum, transported to a Golgi complex to be packaged into vesicles and routed into ducts for exudation. The synthesis of the S2 body is unresolved. Haptoral secretions manifested externally as net-like structures are derived from dual electron-dense (DED) secretory body produced in the peduncular gland cells. The DED body consists of a less electron-dense oval core in a homogeneous electron-dense matrix. On exocytosis into the pyriform haptoral reservoir, DED bodies are transformed into a secretion with two types of inclusions (less electron-dense oval and electron-dense spherical inclusions) in an electron-dense matrix. The secretions are further transformed (as small, oval, electron-dense bodies) when transported to the superficial anchor grooves, and on exudation into the gill tissues, the secretions become an electron-dense matrix. Secretory bodies associated with uniciliated structures, anchor sleeves and marginal hooks are also observed.

Which biomarkers are predictive specifically for cardiovascular or for non-cardiovascular mortality in men? Evidence from the Caerphilly Prospective Study (CaPS)

OBJECTIVE: To examine a panel of 28 biomarkers for prediction of cardiovascular disease (CVD) and non-CVD mortality in a population-based cohort of men.

METHODS: Starting in 1979, middle-aged men in Caerphilly underwent detailed medical examination. Subsequently 2171 men were re-examined during 1989-1993, and fasting blood samples obtained from 1911 men (88%). Fibrinogen, viscosity and white cell count (WCC), routine biochemistry tests and lipids were analysed using fresh samples. Stored aliquots were later analysed for novel biomarkers. Statistical analysis of CVD and non-CVD mortality follow-up used competing risk Cox regression models with biomarkers in thirds tested at the 1% significance level after covariate adjustment.

RESULTS: During an average of 15.4years follow-up, troponin (subhazard ratio per third 1.71, 95% CI 1.46-1.99) and B-natriuretic peptide (BNP) (subhazard ratio per third 1.54, 95% CI 1.34-1.78) showed strong trends with CVD death but not with non-CVD death. WCC and fibrinogen showed similar weaker findings. Plasma viscosity, growth differentiation factor 15 (GDF-15) and interleukin-6 (IL-6) were associated positively with both CVD death and non-CVD death while total cholesterol was associated positively with CVD death but negatively with non-CVD death. C-reactive protein (C-RP), alkaline phosphatase, gamma-glutamyltransferase (GGT), retinol binding protein 4 (RBP-4) and vitamin B6 were significantly associated only with non-CVD death, the last two negatively. Troponin, BNP and IL-6 showed evidence of diminishing associations with CVD mortality through follow-up.

CONCLUSION: Biomarkers for cardiac necrosis were strong, specific predictors of CVD mortality while many inflammatory markers were equally predictive of non-CVD mortality.

Feasibility Assessment of Plug and Play Model Predictive Control for use in DC Grids

Distributed control techniques can allow Transmission System Operators (TSOs) to coordinate their responses via TSO-TSO communication, providing a level of control that lies between that of centralised control and communication free decentralised control of interconnected power systems. Recently the Plug and Play Model Predictive Control (PnPMPC) toolbox has been developed in order to allow practitioners to design distributed controllers based on tube-MPC techniques. In this paper, some initial results using the PnPMPC toolbox for the design of distributed controllers to enhance AGC in AC areas connected to Multi-Terminal HVDC (MTDC) grids, are illustrated, in order to evaluate the feasibility of applying PnPMPC for this purpose.

An Adaptive Predictive Control Strategy for RMPPT under Partially Shaded Conditions

As one of key technologies in photovoltaic converter control, Maximum Power Point Tracking (MPPT) methods can keep the power conversion efficiency as high as nearly 99% under the uniform solar irradiance condition. However, these methods may fail when shading conditions occur and the power loss can over as much as 70% due to the multiple maxima in curve in shading conditions v.s. single maximum point in uniformly solar irradiance. In this paper, a Real Maximum Power Point Tracking (RMPPT) strategy under Partially Shaded Conditions (PSCs) is introduced to deal with this kind of problems. An optimization problem, based on a predictive model which will change adaptively with environment, is developed to tracking the global maxima and corresponding adaptive control strategy is presented. No additional circuits are required to obtain the environment uncertainties. Finally, simulations show the effectiveness of proposed method.

On Infinite Time Performance of Nonlinear Model Predictive Controllers

This paper addresses the problem of infinite time performance of model predictive controllers applied to constrained nonlinear systems. The total performance is compared with a finite horizon optimal cost to reveal performance limits of closed-loop model predictive control systems. Based on the Principle of Optimality, an upper and a lower bound of the ratio between the total performance and the finite horizon optimal cost are obtained explicitly expressed by the optimization horizon. The results also illustrate, from viewpoint of performance, how model predictive controllers approaches to infinite optimal controllers as the optimization horizon increases.

Predictive molecular classifiers in colorectal cancer

The introduction of predictive molecular markers has radically enhanced the identification of which patients may benefit from a given treatment. Despite recent controversies, KRAS mutation is currently the most recognized molecular predictive marker in colorectal cancer (CRC), predicting efficacy of anti-epidermal growth factor receptor (anti-EGFR) antibodies. However, other relevant markers have been reported and claimed to identify patients that will benefit from anti-EGFR therapies. This group of markers includes BRAF mutations, PI3KCA mutations, and loss of PTEN expression. Similarly, molecular markers for cytotoxic agents' efficacy also may predict outcome in patients with CRC. This review aims to summarize the most important predictive molecular classifiers in patients with CRC and further discuss any inconsistent or conflicting findings for these molecular classifiers.

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Prognostic and predictive markers in colorectal cancer - The role of new genomic technologies

Colorectal cancer (CRC) is one of the most frequently occurring malignancies worldwide, and the second leading cause of cancer related death in the Western World. Although early stage disease is curable by surgical resection alone, one half of patients with CRC will present with metastatic disease at some stage in the course of their disease. The most active drug in the treatment of CRC is 5-fluorouracil (5-FU) which is used in both the adjuvant and advanced settings. The use of adjuvant therapy is of proven benefit in Stage III CRC, however, its role in Stage II disease is less clear. There is therefore a need to identify those patients with early stage disease who will develop recurrent disease, and who would therefore benefit most from adjuvant treatment. In the advanced setting, the use of irinotecan and oxaliplatin in combination with 5-FU has proven beneficial, with yet further improvements in survival reported with the addition of new targeted agents such as bevacizamab. Despite this, a significant number of patients with advanced disease do not derive any benefit from the chemotherapy they receive, highlighting a need for the development of molecular or genomic markers predictive of response to these chemotherapeutic agents. This review will evaluate the recent advances in pharmacogenomics in CRC, in particular the development of predictive markers of response to chemotherapy. The successful identification of these markers of response will herald an era of personalised treatment, reducing treatment-related toxicity and improving outcome of patients with CRC. -cr 2007 Bentham Science Publishers Ltd.

Predictive Value of Depressive Symptoms for All-Cause Mortality: Findings From the PRIME Belfast Study Examining the Role of Inflammation and Cardiovascular Risk Markers

OBJECTIVES: To improve understanding about the potential underlying biological mechanisms in the link between depression and all-cause mortality and to investigate the role that inflammatory and other cardiovascular risk factors may play in the relationship between depressive symptoms and mortality.

METHODS: Depression and blood-based biological markers were assessed in the Belfast PRIME prospective cohort study (N = 2389 men, aged 50-59 years) in which participants were followed up for 18 years. Depression was measured using the 10-item Welsh Pure Depression Inventory. Inflammation markers (C-reactive protein [CRP], neopterin, interleukin [IL]-1 receptor antagonist [IL-1Ra], and IL-18) and cardiovascular-specific risk factors (N-terminal pro-b-type natriuretic peptide, midregion pro-atrial natriuretic peptide, midregion pro-adrenomedullin, C-terminal pro-endothelin-1 [CT-proET]) were obtained at baseline. We used Cox proportional hazards modeling to examine the association between depression and biological measures in relation to all-cause mortality and explore the mediating effects.

RESULTS: During follow-up, 418 participants died. Higher levels of depressive symptoms were associated with higher levels of CRP, IL-1Ra, and CT-proET. After adjustment for socioeconomic and life-style risk factors, depressive symptoms were significantly associated with all-cause mortality (hazard ratio = 1.10 per scale unit, 95% confidence interval = 1.04-1.16). This association was partly explained by CRP (7.3%) suggesting a minimal mediation effect. IL-1Ra, N-terminal pro-b-type natriuretic peptide, midregion pro-atrial natriuretic peptide, midregion pro-adrenomedullin, and CT-proET contributed marginally to the association between depression and subsequent mortality.

CONCLUSIONS: Inflammatory and cardiovascular risk markers are associated with depression and with increased mortality. However, depression and biological measures show additive effects rather than a pattern of meditation of biological factors in the association between depression and mortality.

Preoperative radial artery volume flow is predictive of arteriovenous fistula outcomes

Objective: Guidelines recommend the creation of a wrist radiocephalic arteriovenous fistula (RAVF) as initial hemodialysis vascular access. This study explored the potential of preoperative ultrasound vessel measurements to predict AVF failure to mature (FTM) in a cohort of patients with end-stage renal disease in Northern Ireland

.Methods: A retrospective analysis was performed of all patients who had preoperative ultrasound mapping of upper limb blood vessels carried out from August 2011 to December 2014 and whose AVF reached a functional outcome by March 2015.

Results: There were 152 patients (97% white) who had ultrasound mapping andan AVF functional outcome recorded; 80 (54%) had an upper arm AVF created, and 69 (46%) had a RAVF formed. Logistic regression revealed that female gender (odds ratio [OR], 2.5; 95% confidence interval [CI], 1.12-5.55; P = .025), minimum venous diameter (OR, 0.6; 95% CI, 0.39-0.95; P = .029), and RAVF (OR, 0.4; 95% CI, 0.18-0.89; P = .026) were associated with FTM. On subgroup analysis of the RAVF group, RAVFs with an arterial volume flow <50 mL/min were seven times as likely to fail as RAVFs with higher volume flows (OR, 7.0; 95% CI, 2.35-20.87; P < .001).

Conclusions: In this cohort, a radial artery flow rate <50 mL/min was associated with a sevenfold increased risk of FTM in RAVF, which to our knowledge has not been previously reported in the literature. Preoperative ultrasound mapping adds objective assessment in the clinical prediction of AVF FTM.