Inappropriate prescribing and adverse drug events in older people

Inappropriate prescribing (IP) in older patients is highly prevalent and is associated with an increased risk of adverse drug events (ADEs), morbidity, mortality and healthcare utilisation. Consequently, IP is a major safety concern and with changing population demographics, it is likely to become even more prevalent in the future. IP can be detected using explicit or implicit prescribing indicators. Theoretically, the routine clinical application of these IP criteria could represent an inexpensive and time efficient method to optimise prescribing practice. However, IP criteria must be sensitive, specific, have good inter-rater reliability and incorporate those medications most commonly associated with ADEs in older people. To be clinically relevant, use of prescribing appropriateness tools must translate into positive patient outcomes, such as reduced rates of ADEs. To accurately measure these outcomes, a reliable method of assessing the relationship between the administration of a drug and an adverse clinical event is required. The Naranjo criteria are the most widely used tool for assessing ADE causality, however, they are often difficult to interpret in the context of older patients. ADE causality criteria that allow for the multiple co-morbidities and prescribed medications in older people are required. Ultimately, the current high prevalence of IP and ADEs is unacceptable. IP screening criteria need to be tested as an intervention to assess their impact on the incidence of ADEs in vulnerable older patients. There is a role for IP screening tools in everyday clinical practice. These should enhance, not replace good clinical judgement, which in turn should be based on sound pharmacogeriatric training

Abnormal Changes in NKT Cells, the IGF-1 Axis, and Liver Pathology in an Animal Model of ALS

Amyotrophic lateral sclerosis (ALS) is a rapidly progressing fatal neurodegenerative disorder characterized by the selective death of motor neurons (MN) in the spinal cord, and is associated with local neuroinflammation. Circulating CD4+ T cells are required for controlling the local detrimental inflammation in neurodegenerative diseases, and for supporting neuronal survival, including that of MN. T-cell deficiency increases neuronal loss, while boosting T cell levels reduces it. Here, we show that in the mutant superoxide dismutase 1 G93A (mSOD1) mouse model of ALS, the levels of natural killer T (NKT) cells increased dramatically, and T-cell distribution was altered both in lymphoid organs and in the spinal cord relative to wild-type mice. The most significant elevation of NKT cells was observed in the liver, concomitant with organ atrophy. Hepatic expression levels of insulin-like growth factor (IGF)-1 decreased, while the expression of IGF binding protein (IGFBP)-1 was augmented by more than 20-fold in mSOD1 mice relative to wild-type animals. Moreover, hepatic lymphocytes of pre-symptomatic mSOD1 mice were found to secrete significantly higher levels of cytokines when stimulated with an NKT ligand, ex-vivo. Immunomodulation of NKT cells using an analogue of α-galactosyl ceramide (α-GalCer), in a specific regimen, diminished the number of these cells in the periphery, and induced recruitment of T cells into the affected spinal cord, leading to a modest but significant prolongation of life span of mSOD1 mice. These results identify NKT cells as potential players in ALS, and the liver as an additional site of major pathology in this disease, thereby emphasizing that ALS is not only a non-cell autonomous, but a non-tissue autonomous disease, as well. Moreover, the results suggest potential new therapeutic targets such as the liver for immunomodulatory intervention for modifying the disease, in addition to MN-based neuroprotection and systemic treatments aimed at reducing oxidative stress

An empirical Bayesian approach for model-based inference of cellular signaling networks

Background A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways. Results As an illustrative example, a kinetic model for the early signaling events associated with the epidermal growth factor (EGF) signaling network was calibrated against dynamic measurements observed in primary rat hepatocytes. A convergence criterion, based upon the Gelman-Rubin potential scale reduction factor, was applied to the model predictions. The posterior distributions of the parameters exhibited complicated structure, including significant covariance between specific parameters and a broad range of variance among the parameters. The model predictions, in contrast, were narrowly distributed and were used to identify areas of agreement among a collection of experimental studies. Conclusion In summary, an empirical Bayesian approach was developed for inferring the confidence that one can place in a particular model that describes signal transduction mechanisms and for inferring inconsistencies in experimental measurements

Vacuolar ATPase Regulates Surfactant Secretion in Rat Alveolar Type II Cells by Modulating Lamellar Body Calcium

Lung surfactant reduces surface tension and maintains the stability of alveoli. How surfactant is released from alveolar epithelial type II cells is not fully understood. Vacuolar ATPase (V-ATPase) is the enzyme responsible for pumping H+ into lamellar bodies and is required for the processing of surfactant proteins and the packaging of surfactant lipids. However, its role in lung surfactant secretion is unknown. Proteomic analysis revealed that vacuolar ATPase (V-ATPase) dominated the alveolar type II cell lipid raft proteome. Western blotting confirmed the association of V-ATPase a1 and B1/2 subunits with lipid rafts and their enrichment in lamellar bodies. The dissipation of lamellar body pH gradient by Bafilomycin A1 (Baf A1), an inhibitor of V-ATPase, increased surfactant secretion. Baf A1-stimulated secretion was blocked by the intracellular Ca2+ chelator, BAPTA-AM, the protein kinase C (PKC) inhibitor, staurosporine, and the Ca2+/calmodulin-dependent protein kinase II (CaMKII), KN-62. Baf A1 induced Ca2+ release from isolated lamellar bodies. Thapsigargin reduced the Baf A1-induced secretion, indicating cross-talk between lamellar body and endoplasmic reticulum Ca2+ pools. Stimulation of type II cells with surfactant secretagogues dissipated the pH gradient across lamellar bodies and disassembled the V-ATPase complex, indicating the physiological relevance of the V-ATPase-mediated surfactant secretion. Finally, silencing of V-ATPase a1 and B2 subunits decreased stimulated surfactant secretion, indicating that these subunits were crucial for surfactant secretion. We conclude that V-ATPase regulates surfactant secretion via an increased Ca2+ mobilization from lamellar bodies and endoplasmic reticulum, and the activation of PKC and CaMKII. Our finding revealed a previously unrealized role of V-ATPase in surfactant secretion

Production of dust by massive stars at high redshift

The large amounts of dust detected in sub-millimeter galaxies and quasars at high redshift pose a challenge to galaxy formation models and theories of cosmic dust formation. At z > 6 only stars of relatively high mass (> 3 Msun) are sufficiently short-lived to be potential stellar sources of dust. This review is devoted to identifying and quantifying the most important stellar channels of rapid dust formation. We ascertain the dust production efficiency of stars in the mass range 3-40 Msun using both observed and theoretical dust yields of evolved massive stars and supernovae (SNe) and provide analytical expressions for the dust production efficiencies in various scenarios. We also address the strong sensitivity of the total dust productivity to the initial mass function. From simple considerations, we find that, in the early Universe, high-mass (> 3 Msun) asymptotic giant branch stars can only be dominant dust producers if SNe generate <~ 3 x 10^-3 Msun of dust whereas SNe prevail if they are more efficient. We address the challenges in inferring dust masses and star-formation rates from observations of high-redshift galaxies. We conclude that significant SN dust production at high redshift is likely required to reproduce current dust mass estimates, possibly coupled with rapid dust grain growth in the interstellar medium.Comment: 72 pages, 9 figures, 5 tables; to be published in The Astronomy and Astrophysics Revie

ISSN exercise & sport nutrition review: research & recommendations

Sports nutrition is a constantly evolving field with hundreds of research papers published annually. For this reason, keeping up to date with the literature is often difficult. This paper is a five year update of the sports nutrition review article published as the lead paper to launch the JISSN in 2004 and presents a well-referenced overview of the current state of the science related to how to optimize training and athletic performance through nutrition. More specifically, this paper provides an overview of: 1.) The definitional category of ergogenic aids and dietary supplements; 2.) How dietary supplements are legally regulated; 3.) How to evaluate the scientific merit of nutritional supplements; 4.) General nutritional strategies to optimize performance and enhance recovery; and, 5.) An overview of our current understanding of the ergogenic value of nutrition and dietary supplementation in regards to weight gain, weight loss, and performance enhancement. Our hope is that ISSN members and individuals interested in sports nutrition find this review useful in their daily practice and consultation with their clients

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Observing the Observer (I): Meta-Bayesian Models of Learning and Decision-Making

In this paper, we present a generic approach that can be used to infer how subjects make optimal decisions under uncertainty. This approach induces a distinction between a subject's perceptual model, which underlies the representation of a hidden "state of affairs" and a response model, which predicts the ensuing behavioural (or neurophysiological) responses to those inputs. We start with the premise that subjects continuously update a probabilistic representation of the causes of their sensory inputs to optimise their behaviour. In addition, subjects have preferences or goals that guide decisions about actions given the above uncertain representation of these hidden causes or state of affairs. From a Bayesian decision theoretic perspective, uncertain representations are so-called "posterior" beliefs, which are influenced by subjective "prior" beliefs. Preferences and goals are encoded through a "loss" (or "utility") function, which measures the cost incurred by making any admissible decision for any given (hidden) state of affair. By assuming that subjects make optimal decisions on the basis of updated (posterior) beliefs and utility (loss) functions, one can evaluate the likelihood of observed behaviour. Critically, this enables one to "observe the observer", i.e. identify (context-or subject-dependent) prior beliefs and utility-functions using psychophysical or neurophysiological measures. In this paper, we describe the main theoretical components of this meta-Bayesian approach (i.e. a Bayesian treatment of Bayesian decision theoretic predictions). In a companion paper ('Observing the observer (II): deciding when to decide'), we describe a concrete implementation of it and demonstrate its utility by applying it to simulated and real reaction time data from an associative learning task