An endogenous explanation of growth: direct-to-consumer stem cell therapies in PR China, India and the USA.

The recent expansion of direct-to-consumer stem cell therapies (DSCTs) across nations where medical malpractice laws are the strongest globally challenges the causal assumption that low regulatory standards in developing countries bolster DSCTs. Drawing on firm-level data of existing biopharmaceuticals, approved stem cell therapies (SCTs) and DSCT clinics across the USA, PR China and India, this paper provides an innovation studies perspective of the ways in which the paradigmatic shift in fundamental knowledge production - from in vitro to in vivo stem cells - is transforming SCT discovery and delivery. It argues that the endogenous and inherent disruptive attributes of SCTs, rather than exogenous conditions like regulations, provide a substantive explanation for the recent expansion of DSCTs and urges regulatory adaptation to endogenous imperatives for effective governance of SCTs.

Bioinformatics and the Politics of Innovation in the Life Sciences: Science and the State in the United Kingdom, China, and India.

The governments of China, India, and the United Kingdom are unanimous in their belief that bioinformatics should supply the link between basic life sciences research and its translation into health benefits for the population and the economy. Yet at the same time, as ambitious states vying for position in the future global bioeconomy they differ considerably in the strategies adopted in pursuit of this goal. At the heart of these differences lies the interaction between epistemic change within the scientific community itself and the apparatus of the state. Drawing on desk-based research and thirty-two interviews with scientists and policy makers in the three countries, this article analyzes the politics that shape this interaction. From this analysis emerges an understanding of the variable capacities of different kinds of states and political systems to work with science in harnessing the potential of new epistemic territories in global life sciences innovation.

Maternal HIV-1 envelope-specific antibody responses and reduced risk of perinatal transmission.

Despite the wide availability of antiretroviral drugs, more than 250,000 infants are vertically infected with HIV-1 annually, emphasizing the need for additional interventions to eliminate pediatric HIV-1 infections. Here, we aimed to define humoral immune correlates of risk of mother-to-child transmission (MTCT) of HIV-1, including responses associated with protection in the RV144 vaccine trial. Eighty-three untreated, HIV-1-transmitting mothers and 165 propensity score-matched nontransmitting mothers were selected from the Women and Infants Transmission Study (WITS) of US nonbreastfeeding, HIV-1-infected mothers. In a multivariable logistic regression model, the magnitude of the maternal IgG responses specific for the third variable loop (V3) of the HIV-1 envelope was predictive of a reduced risk of MTCT. Neutralizing Ab responses against easy-to-neutralize (tier 1) HIV-1 strains also predicted a reduced risk of peripartum transmission in secondary analyses. Moreover, recombinant maternal V3-specific IgG mAbs mediated neutralization of autologous HIV-1 isolates. Thus, common V3-specific Ab responses in maternal plasma predicted a reduced risk of MTCT and mediated autologous virus neutralization, suggesting that boosting these maternal Ab responses may further reduce HIV-1 MTCT.

Hegemony in the marketplace of biomedical innovation: consumer demand and stem cell science.

The global political economy of stem cell therapies is characterised by an established biomedical hegemony of expertise, governance and values in collision with an increasingly informed health consumer demand able to define and pursue its own interest. How does the hegemony then deal with the challenge from the consumer market and what does this tell us about its modus operandi? In developing a theoretical framework to answer these questions, the paper begins with an analysis of the nature of the hegemony of biomedical innovation in general, its close relationship with the research funding market, the current political modes of consumer incorporation, and the ideological role performed by bioethics as legitimating agency. Secondly, taking the case of stem cell innovation, it explores the hegemonic challenge posed by consumer demand working through the global practice based market of medical innovation, the response of the national and international institutions of science and their reassertion of the values of the orthodox model, and the supporting contribution of bioethics. Finally, the paper addresses the tensions within the hegemonic model of stem cell innovation between the key roles and values of scientist and clinician, the exacerbation of these tensions by the increasingly visible demands of health consumers, and the emergence of political compromise.

Kernel-based logistic regression model for protein sequence without vectorialization.

Protein sequence data arise more and more often in vaccine and infectious disease research. These types of data are discrete, high-dimensional, and complex. We propose to study the impact of protein sequences on binary outcomes using a kernel-based logistic regression model, which models the effect of protein through a random effect whose variance-covariance matrix is mostly determined by a kernel function. We propose a novel, biologically motivated, profile hidden Markov model (HMM)-based mutual information (MI) kernel. Hypothesis testing can be carried out using the maximum of the score statistics and a parametric bootstrap procedure. To improve the power of testing, we propose intuitive modifications to the test statistic. We show through simulation studies that the profile HMM-based MI kernel can be substantially more powerful than competing kernels, and that the modified test statistics bring incremental gains in power. We use these proposed methods to investigate two problems from HIV-1 vaccine research: (1) identifying segments of HIV-1 envelope (Env) protein that confer resistance to neutralizing antibody and (2) identifying segments of Env that are associated with attenuation of protective vaccine effect by antibodies of isotype A in the RV144 vaccine trial.

Health consumers and stem cell therapy innovation: markets, models and regulation.

Global health consumer demand for stem cell therapies is vibrant, but the supply of treatments from the conventional science-based model of innovation is small and unlikely to increase in the near future. At the same time, several models of medical innovation have emerged that can respond to the demand, often employing a transnational value chain to deliver the product. Much of the commentary has approached the issue from a supply side perspective, demonstrating the extent to which national and transnational regulation fails to impose what are regarded as appropriate standards on the 'illicit' supply of stem cell therapies characterized by little data and poor outcomes. By contrast, this article presents a political economic analysis with a strong demand side perspective, arguing that the problem of what is termed 'stem cell tourism' is embedded in the demand-supply relationship of the health consumer market and its engagement with different types of stem cell therapy innovation. To be meaningful, discussions of regulation must recognize that analysis or risk being sidelined by a market, which ignores their often wishful thinking.

Bayesian factor models in characterizing molecular adaptation.

Assessing the selective influence of amino acid properties is important in understanding evolution at the molecular level. A collection of methods and models has been developed in recent years to determine if amino acid sites in a given DNA sequence alignment display substitutions that are altering or conserving a prespecified set of amino acid properties. Residues showing an elevated number of substitutions that favorably alter a physicochemical property are considered targets of positive natural selection. Such approaches usually perform independent analyses for each amino acid property under consideration, without taking into account the fact that some of the properties may be highly correlated. We propose a Bayesian hierarchical regression model with latent factor structure that allows us to determine which sites display substitutions that conserve or radically change a set of amino acid properties, while accounting for the correlation structure that may be present across such properties. We illustrate our approach by analyzing simulated data sets and an alignment of lysin sperm DNA.

Bayesian semiparametric regression models to characterize molecular evolution.

BACKGROUND: Statistical models and methods that associate changes in the physicochemical properties of amino acids with natural selection at the molecular level typically do not take into account the correlations between such properties. We propose a Bayesian hierarchical regression model with a generalization of the Dirichlet process prior on the distribution of the regression coefficients that describes the relationship between the changes in amino acid distances and natural selection in protein-coding DNA sequence alignments. RESULTS: The Bayesian semiparametric approach is illustrated with simulated data and the abalone lysin sperm data. Our method identifies groups of properties which, for this particular dataset, have a similar effect on evolution. The model also provides nonparametric site-specific estimates for the strength of conservation of these properties. CONCLUSIONS: The model described here is distinguished by its ability to handle a large number of amino acid properties simultaneously, while taking into account that such data can be correlated. The multi-level clustering ability of the model allows for appealing interpretations of the results in terms of properties that are roughly equivalent from the standpoint of molecular evolution.

Characterizing molecular adaptation: a hierarchical approach to assess the selective influence of amino acid properties.

MOTIVATION: A number of methods for detecting positive selection in protein coding DNA sequences are based on whether each site/region has a non-synonymous to synonymous substitution rates ratio ω greater than one. However, a site/region may show a relatively large number of non-synonymous mutations that conserve a particular property. Recent methods have proposed to consider as evidence for molecular adaptations how conserving, or radically different, non-synonymous mutations are with respect to some key amino acid properties. While such methods have been useful in providing a qualitative assessment of molecular adaptation, they rely on independent statistical analyses for each amino acid property and typically do not properly adjust for multiple comparisons when selection needs to be assessed at several sites. RESULTS: We consider a Bayesian hierarchical model that allows us to jointly determine if a set of amino acid properties are being conserved or radically changed while simultaneously adjusting for multiple comparisons at the codon level. We illustrate how this model can be used to characterize molecular adaptation in two datasets: an alignment from six class I alleles of the human major histocompatibility complex and a sperm lysin alignment from 25 abalone species. We compare the results obtained with the proposed hierarchical models to those obtained with alternative methods. Our analyses indicate that a more complete quantitative and qualitative characterization of molecular adaptation is achieved by taking into account changes in amino acid properties. AVAILABILITY: The R code for implementing the hierarchical models is freely available at http://www.ams.ucsc.edu/∼raquel/software/.