Glycobiology of neuromuscular disorders

There has been a recent explosion in the identification of neuromuscular diseases caused by mutations in genes that affect carbohydrate metabolism or protein glycosylation. A number of these findings relate to defects in the glycosylation of alpha dystroglycan. Alpha dystroglycan is an essential component of the dystrophin-glycoprotein complex, and aberrant glycosylation of alpha dystroglycan is associated with multiple forms of muscular dystrophy in mice and humans. We review the evidence that defects in dystroglycan glycosylation cause muscular dystrophy. In addition, we review evidence that glycobiology is important in other disorders that affect muscle, including hereditary inclusion body myopathy type II and congenital disorders of glycosylation. Finally, we discuss the long-term potential of glycotherapies for muscle disorders.     

Added predictive value of high-throughput molecular data to clinical data and its validation

Hundreds of 'molecular signatures' have been proposed in the literature to predict patient outcome in clinical settings from high-dimensional data, many of which eventually failed to get validated. Validation of such molecular research findings is thus becoming an increasingly important branch of clinical bioinformatics. Moreover, in practice well-known clinical predictors are often already available. From a statistical and bioinformatics point of view, poor attention has been given to the evaluation of the added predictive value of a molecular signature given that clinical predictors or an established index are available. This article reviews procedures that assess and validate the added predictive value of high-dimensional molecular data. It critically surveys various approaches for the construction of combined prediction models using both clinical and molecular data, for validating added predictive value based on independent data, and for assessing added predictive value using a single data set.     

A new generation of predictive models: The added value of hybrid models for manufacturing processes of therapeutic proteins

Due to the lack of complete understanding of metabolic networks and reaction pathways, establishing a universal mechanistic model for mammalian cell culture processes remains a challenge. Contrarily, data-driven approaches for modeling these processes lack extrapolation capabilities. Hybrid modeling is a technique that exploits the synergy between the two modeling methods. Although mammalian cell cultures are among the most relevant processes in biotechnology and indeed looks ideal for hybrid modeling, their application has only been proposed but never developed in the literature. This study provides a quantitative assessment of the improvement brought by hybrid models with respect to the state-of-the-art statistical predictive models in the context of therapeutic protein production. This is illustrated using a dataset obtained from a 3.5 L fed-batch experiment. With the goal to robustly define the process design space, hybrid models reveal a superior capability to predict the time evolution of different process variables using only the initial and process conditions in comparison to the statistical models. Hybrid models not only feature more accurate prediction results but also demonstrate better robustness and extrapolation capabilities. For the future application, this study highlights the added value of hybrid modeling for model-based process optimization and design of experiments.     

The clinical value of quantitative dynamic scintigraphy in salivary gland disorders

Salivary gland scintigraphy with [^99mTc]pertechnetate was performed in a group of 11 subjects without any known salivary pathology, and in a group of 12 xerostomic patients. Time-activity curves, normalized to background, were generated for each of the four major salivary glands in both groups, and averaged into single curves, representing the function of each gland. Comparing the two sets of curves (patients vs controls) demonstrates that utilizing our “normalization” approach enables one not only to determine whether one group of dynamic curves is significantly different from another, but to decide which of the glands are normal and which are malfunctioning. The sensitivity, specificity, accuracy and predictive values of this procedure are all above 0.90.     

Potential clinical translation of juvenile rodent inactivity models to study the onset of childhood obesity

According to the latest data from the Center for Disease Control and Prevention 17%, or 12.5 million, of children and adolescents aged 2-19 years in the United States are obese. Physical inactivity is designated as one of the actual causes of US deaths and undoubtedly contributes to the obesity epidemic in children and adults. Examining the effects of inactivity on physiological homeostasis during youth is crucial given that 58% of children between the ages 6-11 yr old fail to obtain the recommended 60 min/day of physical activity and 92% of adolescents fail to achieve this goal [Troiano et al. Med Sci Sports Exerc. 40, 2008]. Nonetheless, invasive mechanistic studies in children linking diminished physical activity with metabolic maladies are lacking for obvious ethical reasons. The rodent wheel lock (WL) model was adopted by our laboratory and others to study how different organ systems of juvenile rats respond to a cessation of daily physical activity. Our WL model houses rats in cages equipped with voluntary running wheels starting at 28 days of age. After a certain period of voluntary running (3 to 6 wk), the wheels are locked, thus preventing the rats' primary source of physical activity. The studies discussed herein suggest that obesity-associated maladies including skeletal muscle insulin resistance, hypothalamic leptin resistance, fatty acid oxidation impairments in skeletal muscle and adipose tissue, nonalcoholic fatty liver disease, and endothelial dysfunction are initiated in juvenile animals that are restrained from voluntary exercise via WL. The use of the juvenile rodent WL or other inactivity models will continue to provide a powerful clinical translational tool that can be used for primordial prevention of human childhood obesity.     

Bringing 3D tumor models to the clinic – predictive value for personalized medicine

Current decision‐guiding algorithms in cancer drug treatment are based on decades of research and numerous clinical trials. For the majority of patients, this data is successfully applied for a systemic disease management. For a number of patients however, treatment stratification according to clinically based risk criteria will not be sufficient. The most effective treatment options are ideally identified prior to the start of clinical drug therapy. This review will discuss the implementation of three‐dimensional (3D) cell culture models as a preclinical testing paradigm for the efficacy of clinical cancer treatment. Patient tumor‐derived cells in 3D cultures duplicate the individual tumor microenvironment with a minimum of confounding factors. Clinical implementation of such personalized tumor models requires a high quality of methodological and clinical validation comparable to other biomarkers. A non‐systematic literature search demonstrated the small number of prospective studies that have been conducted in this area of research. This may explain the current reluctance of many physicians and insurance providers in implementing this type of assay into the clinical diagnostic routine despite potential benefit for patients. Achieving valid and reproducible results with a high level of evidence is central in improving the acceptance of preclinical 3D tumor models.     

Muscle contractility in chronic disorders of neuromuscular transmission

Muscle contractility of the thumb was studied in 24 normal subjects, 84 patients with myasthenia and 4 patients with hypothyrosis in response to supramaximal stimulation, on the basis of the time of contraction and semi-relaxation, staircase phenomena, and posttetanic potentiation. During hormonal therapy, the patients with myasthenia and those with hypothyrosis treated by substitution therapy manifested the normalization of the staircase and posttetanic potentiation, reduction of the contraction force and twitch time during single contraction. The comparison of the changes seen in the contraction force, staircase and posttetanic potentiation during examination of the patients over time suggested that the muscle has a regulatory system that determines the force and twitch time of muscle contraction.     

Regulation of mRNA translation in stem cells; links to brain disorders

Translational control of gene expression is emerging as a cardinal step in the regulation of protein abundance. Especially for embryonic (ESC) and neuronal stem cells (NSC), regulation of mRNA translation is involved in the maintenance of pluripotency but also differentiation. For neuronal stem cells this regulation is linked to the various neuronal subtypes that arise in the developing brain and is linked to numerous brain disorders. Herein, we review translational control mechanisms in ESCs and NSCs during development and differentiation, and briefly discuss their link to brain disorders.     

Sensitivity of predictive species distribution models to change in grain size

Predictive species distribution modelling (SDM) has become an essential tool in biodiversity conservation and management. The choice of grain size (resolution) of environmental layers used in modelling is one important factor that may affect predictions. We applied 10 distinct modelling techniques to presence-only data for 50 species in five different regions, to test whether: (1) a 10-fold coarsening of resolution affects predictive performance of SDMs, and (2) any observed effects are dependent on the type of region, modelling technique, or species considered. Results show that a 10 times change in grain size does not severely affect predictions from species distribution models. The overall trend is towards degradation of model performance, but improvement can also be observed. Changing grain size does not equally affect models across regions, techniques, and species types. The strongest effect is on regions and species types, with tree species in the data sets (regions) with highest locational accuracy being most affected. Changing grain size had little influence on the ranking of techniques: boosted regression trees remain best at both resolutions. The number of occurrences used for model training had an important effect, with larger sample sizes resulting in better models, which tended to be more sensitive to grain. Effect of grain change was only noticeable for models reaching sufficient performance and/or with initial data that have an intrinsic error smaller than the coarser grain size.     

The use of AUSRIVAS predictive models to assess the response of lotic macroinvertebrates to dams in south-east Australia

1. Sites immediately below seven dams in Victoria and 12 in southern New South Wales (NSW) were sampled for macroinvertebrates using rapid bioassessment techniques. Specimens were identified to the lowest taxonomic level. The dams had different operating and discharge regimes and thus potentially different downstream effects on this fauna.
2. The AUStralian RIVer Assessment Scheme (AUSRIVAS) predictive models for macroinvertebrate composition were applied to the families and genera observed at Victorian sites and to the families only at the NSW sites. Lists of predicted taxa, their probabilities of occurrence and ratios of observed to expected taxa (O/E scores) were obtained from these models.
3. The mean O/E score for Victoria was 0.46 at the genus level and 0.63 at the family level; for NSW the mean score at the family level was 0.62. There was no significant difference between family level scores for the two states. Thus about 40% of expected families were missing at all sites. Family level scores were highly correlated ( r =0.84–0.86, P  < 0.001) with numbers of families or species at a site.
4. There was no correlation between family O/E scores (or family and species richness) and the degree to which current discharge levels below a dam differ from the natural patterns. Thus increased hydrological deviation did not result in greater biological degradation. In addition, there was no association between these biological characteristics and whether dams released surface or bottom water.
5. All the dams seemed to cause much the same disruption to the fauna. Of the families predicted to have widespread occurrence, 9–12 were found at most sites (tolerant taxa), while a larger number (14–24) were missing (intolerant taxa). The limited recolonisation below dams may well be because of the fact that dams act as barriers to drift, the most prominent route for invertebrate colonists.     

Control of neuromuscular blockade with Gaussian process models

This paper presents Nonlinear Model Predictive Control (NMPC) of neuromuscular blockade induced by atracurium on patients subject to general anesthesia. In order to tackle the high levels of uncertainty in the process behavior, probabilistic and non-parametric Gaussian process models are used in the NMPC approach. The proposed control structure was tested in a bank of models that represent patients subject to general anesthesia under elective surgery. All patients models were stabilized and yield a satisfactory performance.     

Pneumonia in patients with cirrhosis: risk factors associated with mortality and predictive value of prognostic models

Background

Cirrhosis always goes with profound immunity compromise, and makes those patients easily be the target of pneumonia. Cirrhotic patients with pneumonia have a dramatically increased mortality. To recognize the risk factors of mortality and to optimize stratification are critical for improving survival rate.

Methods

Two hundred and three cirrhotic patients with pneumonia at a tertiary care hospital were included in this retrospective study. Demographical, clinical and laboratory parameters, severity models and prognosis were recorded. Multivariate Cox regression analysis was used to identify independent predictors of 30-day and 90-day mortality. Area under receiver operating characteristics curves (AUROC) was used to compare the predictive value of different prognostic scoring systems.

Results

Patients with nosocomial acquired or community acquired pneumonia indicated similar prognosis after 30- and 90-day follow-up. However, patients triggered acute-on-chronic liver failure (ACLF) highly increased mortality (46.4% vs 4.5% for 30-day, 69.6% vs 11.2% for 90-day). Age, inappropriate empirical antibiotic therapy (HR: 2.326 p?=?0.018 for 30-day and HR: 3.126 p?<?0.001 for 90-day), bacteremia (HR: 3.037 p?=?0.002 for 30-day and HR: 2.651 p?=?0.001 for 90-day), white blood cell count (WBC) (HR: 1.452 p?<?0.001 for 30-day and HR: 1.551 p?<?0.001 for 90-day) and total bilirubin (HR: 1.059 p?=?0.002 for 90-day) were independent factors for mortality in current study. Chronic liver failure–sequential organ failure assessment (CLIF-SOFA) displayed highest AUROC (0.89 and 0.90, 95% CI: 0.83–0.95 and 0.85–0.95 for 30-day and 90-day respectively) in current study.

Conclusions

This study found age, bacteremia, WBC, total bilirubin and inappropriate empirical antibiotic therapy were independently associated with increased mortality. Pneumonia triggered ACLF remarkably increased mortality. CLIF-SOFA was more accurate in predicting mortality than other five prognostic models (model for end-stage liver disease (MELD), MELD-Na, quick sequential organ failure assessment (qSOFA), pneumonia severity index (PSI), Child-Turcotte-Pugh (CTP) score).