Malnutrition in children with chronic liver disease accepted for liver transplantation : clinical profile and effect on outcome

The nutritional profiles of 37 children (aged 0.5-14.0 years) with chronic liver disease at the time of acceptance for orthotopic liver transplantation (OLTP) have been evaluated using clinical, biochemical and body composition methods. Nutritional progress while waiting for a donor has been related to outcome, whether transplanted or not. At the time of acceptance, most children were underweight (mean standard deviation (s.d.) weight = -1.4 ± 0.2) and stunted (mean s.d. height = - 2.2 ± 0.4), had low serum albumin (27/35) and had reduced body fat and depleted body cell mass (measured by total body potassium - mean % expected for age = 58 ± 5%, n = 15). Mean ad libitum nutrient intake was 63 ± 5% of recommended daily intake (RDI). Those who died while waiting (n = 8) had significantly lower mean initial s.d. weight compared with those transplanted. The overall actuarial 1 year survival of those who were transplanted (mean waiting time = 75 days) was 81% but those who were initially well nourished (s.d. weight >-1.0) had an actuarial 1 year survival of 100%. There were no significant differences in actuarial survival in relationship to age, type of transplant (whole liver or segmental), liver biochemistry or the presence or absence of ascites. Of the total group accepted for OLTP, whether transplanted or not, the overall 1 year survival for those who were relatively well nourished was 88% and for those undernourished (initial s.d. weight <-1.0) was 38% (P<0.003). Declining nutritional status during the waiting period also adversely affected outcome. We conclude that malnutrition and/or declining nutritional status is a major factor adversely affecting survival in children awaiting OLTP. In transplant units where waiting time is greater than 40 days, earlier referral, prioritization of cases and the use of adult donor livers may reduce this risk and efforts to maintain or improve nutritional status deserve further study.

Cardiovascular diseases and vulnerable plaques: data, modeling, predictions and clinical applications PREFACE

Introduction Two symposia on “cardiovascular diseases and vulnerable plaques” Cardiovascular disease (CVD) is the leading cause of death worldwide. Huge effort has been made in many disciplines including medical imaging, computational modeling, bio- mechanics, bioengineering, medical devices, animal and clinical studies, population studies as well as genomic, molecular, cellular and organ-level studies seeking improved methods for early detection, diagnosis, prevention and treatment of these diseases [1-14]. However, the mechanisms governing the initiation, progression and the occurrence of final acute clinical CVD events are still poorly understood. A large number of victims of these dis- eases who are apparently healthy die suddenly without prior symptoms. Available screening and diagnostic methods are insufficient to identify the victims before the event occurs [8,9]. Most cardiovascular diseases are associated with vulnerable plaques. A grand challenge here is to develop new imaging techniques, predictive methods and patient screening tools to identify vulnerable plaques and patients who are more vulnerable to plaque rupture and associated clinical events such as stroke and heart attack, and recommend proper treatment plans to prevent those clinical events from happening. Articles in this special issue came from two symposia held recently focusing on “Cardio-vascular Diseases and Vulnerable Plaques: Data, Modeling, Predictions and Clinical Applications.” One was held at Worcester Polytechnic Institute (WPI), Worcester, MA, USA, July 13-14, 2014, right after the 7th World Congress of Biomechanics. This symposium was endorsed by the World Council of Biomechanics, and partially supported by a grant from NIH-National Institute of Biomedical Image and Bioengineering. The other was held at Southeast University (SEU), Nanjing, China, April 18-20, 2014.

A new method for the in vivo identification of mechanical properties in arteries from cine MRI images: Theoretical framework and validation

Quantifying the stiffness properties of soft tissues is essential for the diagnosis of many cardiovascular diseases such as atherosclerosis. In these pathologies it is widely agreed that the arterial wall stiffness is an indicator of vulnerability. The present paper focuses on the carotid artery and proposes a new inversion methodology for deriving the stiffness properties of the wall from cine-MRI (magnetic resonance imaging) data. We address this problem by setting-up a cost function defined as the distance between the modeled pixel signals and the measured ones. Minimizing this cost function yields the unknown stiffness properties of both the arterial wall and the surrounding tissues. The sensitivity of the identified properties to various sources of uncertainty is studied. Validation of the method is performed on a rubber phantom. The elastic modulus identified using the developed methodology lies within a mean error of 9.6%. It is then applied to two young healthy subjects as a proof of practical feasibility, with identified values of 625 kPa and 587 kPa for one of the carotid of each subject.

Analysis of word embeddings and sequence features for clinical information extraction

This study investigates the use of unsupervised features derived from word embedding approaches and novel sequence representation approaches for improving clinical information extraction systems. Our results corroborate previous findings that indicate that the use of word embeddings significantly improve the effectiveness of concept extraction models; however, we further determine the influence that the corpora used to generate such features have. We also demonstrate the promise of sequence-based unsupervised features for further improving concept extraction.

Designs for phase I clinical trials with multiple courses of subjects at different doses

The goal of this article is to provide a new design framework and its corresponding estimation for phase I trials. Existing phase I designs assign each subject to one dose level based on responses from previous subjects. Yet it is possible that subjects with neither toxicity nor efficacy responses can be treated at higher dose levels, and their subsequent responses to higher doses will provide more information. In addition, for some trials, it might be possible to obtain multiple responses (repeated measures) from a subject at different dose levels. In this article, a nonparametric estimation method is developed for such studies. We also explore how the designs of multiple doses per subject can be implemented to improve design efficiency. The gain of efficiency from "single dose per subject" to "multiple doses per subject" is evaluated for several scenarios. Our numerical study shows that using "multiple doses per subject" and the proposed estimation method together increases the efficiency substantially.

Decision-theoretic designs for dose-finding clinical trials with multiple outcomes

A decision-theoretic framework is proposed for designing sequential dose-finding trials with multiple outcomes. The optimal strategy is solvable theoretically via backward induction. However, for dose-finding studies involving k doses, the computational complexity is the same as the bandit problem with k-dependent arms, which is computationally prohibitive. We therefore provide two computationally compromised strategies, which is of practical interest as the computational complexity is greatly reduced: one is closely related to the continual reassessment method (CRM), and the other improves CRM and approximates to the optimal strategy better. In particular, we present the framework for phase I/II trials with multiple outcomes. Applications to a pediatric HIV trial and a cancer chemotherapy trial are given to illustrate the proposed approach. Simulation results for the two trials show that the computationally compromised strategy can perform well and appear to be ethical for allocating patients. The proposed framework can provide better approximation to the optimal strategy if more extensive computing is available.

Kallikrein-related peptidases in prostate cancer: From molecular function to clinical application

Prostate cancer is a leading contributor to male cancer-related deaths worldwide. Kallikrein-related peptidases (KLKs) are serine proteases that exhibit deregulated expression in prostate cancer, with KLK3, or prostate specific antigen (PSA), being the widely-employed clinical biomarker for prostate cancer. Other KLKs, such as KLK2, show promise as prostate cancer biomarkers and, additionally, their altered expression has been utilised for the design of KLK-targeted therapies. There is also a large body of in vitro and in vivo evidence supporting their role in cancer-related processes. Here, we review the literature on studies to date investigating the potential of other KLKs, in addition to PSA, as biomarkers and in therapeutic options, as well as their current known functional roles in cancer progression. Increased knowledge of these KLK-mediated functions, including degradation of the extracellular matrix, local invasion, cancer cell proliferation, interactions with fibroblasts, angiogenesis, migration, bone metastasis and tumour growth in vivo, may help define new roles as prognostic biomarkers and novel therapeutic targets for this cancer.

Analysis of human immunodeficiency virus type 1 drug resistance in children receiving nucleoside analogue reverse-transcriptase inhibitors plus nevirapine, nelfinavir, or ritonavir (Pediatric AIDS Clinical Trials Group 377)

In Pediatric AIDS Clinical Trials Group 377, antiretroviral therapy-experienced children were randomized to 4 treatment arms that included different combinations of stavudine, lamivudine (3TC), nevirapine (Nvp), nelfinavir (Nfv), and ritonavir (Rtv). Previous treatment with zidovudine (Zdv), didanosine (ddI), or zalcitabine (ddC) was acceptable. Drug resistance ((R)) mutations were assessed before study treatment (baseline) and at virologic failure. Zdv(R), ddI(R), and ddC(R) mutations were detected frequently at baseline but were not associated with virologic failure. Children with drug resistance mutations at baseline had greater reductions in virus load over time than did children who did not. Nvp(R) and 3TC(R) mutations were detected frequently at virologic failure, and Nvp(R) mutations were more common among children receiving 3-drug versus 4-drug Nvp-containing regimens. Children who were maintained on their study regimen after virologic failure accumulated additional Nvp(R) and 3TC(R) mutations plus Rtv(R) and Nfv(R) mutations. However, Rtv(R) and Nfv(R) mutations were detected at unexpectedly low rates.

Sequential allocation in clinical trials

Suppose two treatments with binary responses are available for patients with some disease and that each patient will receive one of the two treatments. In this paper we consider the interests of patients both within and outside a trial using a Bayesian bandit approach and conclude that equal allocation is not appropriate for either group of patients. It is suggested that Gittins indices should be used (using an approach called dynamic discounting by choosing the discount rate based on the number of future patients in the trial) if the disease is rare, and the least failures rule if the disease is common. Some analytical and simulation results are provided.

Gittins indices and constrained allocation in clinical trials

We explore the use of Gittins indices to search for near optimality in sequential clinical trials. Some adaptive allocation rules are proposed to achieve the following two objectives as far as possible: (i) to reduce the expected successes lost, (ii) to minimize the error probability at the end. Simulation results indicate the merits of the rules based on Gittins indices for small trial sizes. The rules are generalized to the case when neither of the response densities is known. Asymptotic optimality is derived for the constrained rules. A simple allocation rule is recommended for one-stage models. The simulation results indicate that it works better than both equal allocation and Bather's randomized allocation. We conclude with a discussion of possible further developments.

Validation of a 4DOF manoeuvring model of a high-speed vehicle-passenger trimaran

This paper presents the validation of a manoeuvring model for a novel 127m-vehicle-passenger trimaran via full scale trials. The adopted structure of the model is based on a model previously proposed in the literature with some simplifications. The structure of the model is discussed. Then initial parameter estimates are computed, and the final set of parameters are obtained via adjustments based on engineering judgement and application of a genetic algorithm so as to match the data of the trials. The validity of the model is also assessed with data from a trial different from the one use for the parameter adjustment. The model shows good agreement with the trial data.

Genetic association analysis of miRNA SNPs implicates MIR145 in breast cancer susceptibility

Background MicroRNAs (miRNAs) are important small non-coding RNA molecules that regulate gene expression in cellular processes related to the pathogenesis of cancer. Genetic variation in miRNA genes could impact their synthesis and cellular effects and single nucleotide polymorphisms (SNPs) are one example of genetic variants studied in relation to breast cancer. Studies aimed at identifying miRNA SNPs (miR-SNPs) associated with breast malignancies could lead towards further understanding of the disease and to develop clinical applications for early diagnosis and treatment. Methods We genotyped a panel of 24 miR-SNPs using multiplex PCR and chip-based matrix assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) analysis in two Caucasian breast cancer case control populations (Primary population: 173 cases and 187 controls and secondary population: 679 cases and 301 controls). Association to breast cancer susceptibility was determined using chi-square (X 2 ) and odds ratio (OR) analysis. Results Statistical analysis showed six miR-SNPs to be non-polymorphic and twelve of our selected miR-SNPs to have no association with breast cancer risk. However, we were able to show association between rs353291 (located in MIR145) and the risk of developing breast cancer in two independent case control cohorts (p = 0.041 and p = 0.023). Conclusions Our study is the first to report an association between a miR-SNP in MIR145 and breast cancer risk in individuals of Caucasian background. This finding requires further validation through genotyping of larger cohorts or in individuals of different ethnicities to determine the potential significance of this finding as well as studies aimed to determine functional significance. Keywords: Association analysis; Breast cancer; microRNA; miR-SNPs; MIR145

Genetic variability of Tomato spotted wilt virus in Australia and validation of real time RT-PCR for its detection in single and bulked leaf samples

The potential for large-scale use of a sensitive real time reverse transcription polymerase chain reaction (RT-PCR) assay was evaluated for the detection of Tomato spotted wilt virus (TSWV) in single and bulked leaf samples by comparing its sensitivity with that of DAS-ELISA. Using total RNA extracted with RNeasy® or leaf soak methods, real time RT-PCR detected TSWV in all infected samples collected from 16 horticultural crop species (including flowers, herbs and vegetables), two arable crop species, and four weed species by both assays. In samples in which DAS-ELISA had previously detected TSWV, real time RT-PCR was effective at detecting it in leaf tissues of all 22 plant species tested at a wide range of concentrations. Bulk samples required more robust and extensive extraction methods with real time RT-PCR, but it generally detected one infected sample in 1000 uninfected ones. By contrast, ELISA was less sensitive when used to test bulked samples, once detecting up to 1 infected in 800 samples with pepper but never detecting more than 1 infected in 200 samples in tomato and lettuce. It was also less reliable than real time RT-PCR when used to test samples from parts of the leaf where the virus concentration was low. The genetic variability among Australian isolates of TSWV was small. Direct sequencing of a 587 bp region of the nucleoprotein gene (S RNA) of 29 isolates from diverse crops and geographical locations yielded a maximum of only 4.3% nucleotide sequence difference. Phylogenetic analysis revealed no obvious groupings of isolates according to geographic origin or host species. TSWV isolates, that break TSWV resistance genes in tomato or pepper did not differ significantly in the N gene region studied, indicating that a different region of the virus genome is responsible for this trait.

Standardization and validation of an induced ovulation model system in buffalo cows: Characterization of gene expression changes in the periovulatory follicle

In bovines characterization of biochemical and molecular determinants of the dominant follicle before and during different time intervals after gonadotrophin surge requires precise identification of the dominant follicle from a follicular wave. The objectives of the present study were to standardize an experimental model in buffalo cows for accurately identifying the dominant follicle of the first wave of follicular growth and characterize changes in follicular fluid hormone concentrations as well as expression patterns of various genes associated with the process of ovulation. From the day of estrus (day 0), animals were subjected to blood sampling and ultrasonography for monitoring circulating progesterone levels and follicular growth. On day 7 of the cycle, animals were administered a PGF2α analogue (Tiaprost Trometamol, 750 μg i.m.) followed by an injection of hCG (2000 IU i.m.) 36 h later. Circulating progesterone levels progressively increased from day 1 of the cycle to 2.26 ± 0.17 ng/ml on day 7 of the cycle, but declined significantly after PGF2α injection. A progressive increase in the size of the dominant follicle was observed by ultrasonography. The follicular fluid estradiol and progesterone concentrations in the dominant follicle were 600 ± 16.7 and 38 ± 7.6 ng/ml, respectively, before hCG injection and the concentration of estradiol decreased to 125.8 ± 25.26 ng/ml, but concentration of progesterone increased to 195 ± 24.6 ng/ml, 24 h post-hCG injection. Inh-α and Cyp19A1 expressions in granulosa cells were maximal in the dominant follicle and declined in response to hCG treatment. Progesterone receptor, oxytocin and cycloxygenase-2 expressions in granulosa cells, regarded as markers of ovulation, were maximal at 24 h post-hCG. The expressions of genes belonging to the super family of proteases were also examined; Cathepsin L expression decreased, while ADAMTS 3 and 5 expressions increased 24 h post-hCG treatment. The results of the current study indicate that sequential treatments of PGF2α and hCG during early estrous cycle in the buffalo cow leads to follicular growth that culminates in ovulation. The model system reported in the present study would be valuable for examining temporo-spatial changes in the periovulatory follicle immediately before and after the onset of gonadotrophin surge.

Validation of in silico-predicted genic SNPs in white clover (Trifolium repens L.), an outbreeding allopolyploid species

White clover (Trifolium repens L.) is an obligate outbreeding allotetraploid forage legume. Gene-associated SNPs provide the optimum genetic system for improvement of such crop species. An EST resource obtained from multiple cDNA libraries constructed from numerous genotypes of a single cultivar has been used for in silico SNP discovery and validation. A total of 58 from 236 selected sequence clusters (24.5%) were fully validated as containing polymorphic SNPs by genotypic analysis across the parents and progeny of several two-way pseudo-testcross mapping families. The clusters include genes belonging to a broad range of predicted functional categories. Polymorphic SNP-containing ESTs have also been used for comparative genomic analysis by comparison with whole genome data from model legume species, as well as Arabidopsis thaliana. A total of 29 (50%) of the 58 clusters detected putative ortholoci with known chromosomal locations in Medicago truncatula, which is closely related to white clover within the Trifolieae tribe of the Fabaceae. This analysis provides access to translational data from model species. The efficiency of in silico SNP discovery in white clover is limited by paralogous and homoeologous gene duplication effects, which are resolved unambiguously by the transmission test. This approach will also be applicable to other agronomically important cross-pollinating allopolyploid plant species.