Sex, drugs and superbugs: The rise of drug resistant STIs

Antimicrobial resistance (AMR) presents a swiftly advancing challenge to a wide range of healthcare and health promotion practices. While rising rates of AMR share some dimensions across contexts, the specificities of field, practice, place and population shape – and at times hinder attempts to stem – the rising tide of this health threat. Sexually transmitted infections (STIs) are one area of healthcare where the threat of AMR has traditionally been met with lethargy. In this paper, we draw on a range of stakeholder perspectives across practice, innovation and regulatory systems in Australia, the US and the UK to understand and examine the evolving nexus of STIs and AMR, including the roles of cultural reception, professional practice and political traction. We argue for a critical sociology of the nexus of sexual health and evolving resistance, which will be instructive for comprehending inaction and informing future developments. We also note that part of this critical sociology must involve challenging stigma concerning sexual practices and people/groups, and recognising the role of communities in driving positive change

"It's a revolving door": Ego-depletion among prisoners with injecting drug use histories as a barrier to post-release success

Background: People who inject drugs (PWID) are overrepresented among prisoner populations worldwide. This qualitative study used the psychological concept of “ego-depletion” as an exploratory framework to better understand the disproportionate rates of reincarceration among people with injecting drug use histories. The aim was to illuminate mechanisms by which prospects for positive post-release outcomes for PWID are enhanced or constricted. Methods: Participants were recruited from a longitudinal cohort study, SuperMIX, in Victoria, Australia. Eligible participants were invited to participate in an in-depth interview. Inclusion criteria were: aged 18+; lifetime history of injecting drug use; incarcerated for >three months and released from custody <12 months previously. Analysis of 48 interviews examined how concepts relevant to the ego-depletion framework (self-regulation; standards; consequences and mitigators of ego-depletion) manifested in participants’ narratives. Results: Predominantly, participants aimed to avoid a return to problematic drug use and recidivism, and engaged in effortful self-regulation to pursue their post-release goals. Post-release environments were found to diminish self-regulation resources, leading to states of ego-depletion and compromising the capacity to self-regulate according to their ideals. Fatalism, stress, and fatigue associated with the transition period exacerbated ego-depletion. Strategies that mitigated ego-depletion included avoidance of triggering environments; reducing stress through opioid agonist therapy; and fostering positive affect through supportive relationships. Conclusions: Post-release environments are ego-depleting and inconducive to sustaining behavioural changes for PWID leaving prison. Corrections’ behaviourist paradigms take insufficient account of the socio-structural factors impacting on an individual's self-regulation capacities in the context of drug dependence and desistance processes. Breaking the cycles of reincarceration among PWID requires new approaches that moderate ego-depletion and facilitate long-term goal-pursuit

ρ(770)0\rho(770)^0, K∗(892)0^*(892)^0 and f0(980)_{0}(980) Production in Au-Au and pp Collisions at sNN\sqrt{s_{NN}} = 200 GeV

Preliminary results on $\rho(770)^0 \to \pi^{+}\pi^{-}$, K$^{*}(892)^{0} \to \pi$K and $f_{0}(980) \to \pi^{+}\pi^{-}$ production using the mixed-event technique are presented. The measurements are performed at mid-rapidity by the STAR detector in $\sqrt{s_{NN}}$= 200 GeV Au-Au and pp interactions at RHIC. The results are compared to different measurements at various energies.Comment: 4 pages, 6 figures. Talk presented at Quark Matter 2002, Nantes, France, July 18-24, 2002. To appear in the proceedings (Nucl. Phys. A

Lambda Polarization in Polarized Proton-Proton Collisions at RHIC

We discuss Lambda polarization in semi-inclusive proton-proton collisions, with one of the protons longitudinally polarized. The hyperfine interaction responsible for the $\Delta$-$N$ and $\Sigma$-$\Lambda$ mass splittings gives rise to flavor asymmetric fragmentation functions and to sizable polarized non-strange fragmentation functions. We predict large positive Lambda polarization in polarized proton-proton collisions at large rapidities of the produced Lambda, while other models, based on SU(3) flavor symmetric fragmentation functions, predict zero or negative Lambda polarization. The effect of $\Sigma^0$ and $\Sigma^*$ decays is also discussed. Forthcoming experiments at RHIC will be able to differentiate between these predictions.Comment: 18 pages, 5 figure

Resonance Production

Recent results on rho(770)^0, K(892)^*0, f_0(980), phi(1020), Delta(1232)^++, and Lambda(1520) production in A+A and p+p collisions at SPS and RHIC energies are presented. These resonances are measured via their hadronic decay channels and used as a sensitive tool to examine the collision dynamics in the hadronic medium through their decay and regeneration. The modification of resonance mass, width, and shape due to phase space and dynamical effects are discussed.Comment: 8 pages, 10 figures, proceedings of the Quark Matter 2004, in Oakland, California, to be published in Journal of Physics G: Nuclear and Particle Physic

Understanding uncertainty in temperature effects on vector-borne disease: A Bayesian approach

Extrinsic environmental factors influence the distribution and population dynamics of many organisms, including insects that are of concern for human health and agriculture. This is particularly true for vector-borne infectious diseases, like malaria, which is a major source of morbidity and mortality in humans. Understanding the mechanistic links between environment and population processes for these diseases is key to predicting the consequences of climate change on transmission and for developing effective interventions. An important measure of the intensity of disease transmission is the reproductive number $R_0$. However, understanding the mechanisms linking $R_0$ and temperature, an environmental factor driving disease risk, can be challenging because the data available for parameterization are often poor. To address this we show how a Bayesian approach can help identify critical uncertainties in components of $R_0$ and how this uncertainty is propagated into the estimate of $R_0$. Most notably, we find that different parameters dominate the uncertainty at different temperature regimes: bite rate from 15-25$^\circ$ C; fecundity across all temperatures, but especially $\sim$25-32$^\circ$ C; mortality from 20-30$^\circ$ C; parasite development rate at $\sim$15-16$^\circ$C and again at $\sim$33-35$^\circ$C. Focusing empirical studies on these parameters and corresponding temperature ranges would be the most efficient way to improve estimates of $R_0$. While we focus on malaria, our methods apply to improving process-based models more generally, including epidemiological, physiological niche, and species distribution models.Comment: 27 pages, including 1 table and 3 figure

Mathematical model of a telomerase transcriptional regulatory network developed by cell-based screening: analysis of inhibitor effects and telomerase expression mechanisms

Cancer cells depend on transcription of telomerase reverse transcriptase (TERT). Many transcription factors affect TERT, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of TERT transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 TERT regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of TERT transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted TERT repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous TERT expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3′-oxime (BIO) predicted unstable TERT repression dependent on noise and expression of JUN, corresponding with observations from a previous study. MYC expression is critical in TERT activation in the model, consistent with its well known function in endogenous TERT regulation. Loss of MYC caused complete TERT suppression in our model, substantially rescued only by co-suppression of AR. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in TERT promoter mutation. RNAi targeting AR, JUN, MXD1, SP3, or TP53, showed that AR suppression does rescue endogenous TERT expression following MYC knockdown in these cells and SP3 or TP53 siRNA also cause partial recovery. The model therefore successfully predicted several aspects of TERT regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme TERT for transcriptional stability

Replica theory for learning curves for Gaussian processes on random graphs

Statistical physics approaches can be used to derive accurate predictions for the performance of inference methods learning from potentially noisy data, as quantified by the learning curve defined as the average error versus number of training examples. We analyse a challenging problem in the area of non-parametric inference where an effectively infinite number of parameters has to be learned, specifically Gaussian process regression. When the inputs are vertices on a random graph and the outputs noisy function values, we show that replica techniques can be used to obtain exact performance predictions in the limit of large graphs. The covariance of the Gaussian process prior is defined by a random walk kernel, the discrete analogue of squared exponential kernels on continuous spaces. Conventionally this kernel is normalised only globally, so that the prior variance can differ between vertices; as a more principled alternative we consider local normalisation, where the prior variance is uniform