BHIVA position statement on HIV, the law and the work of the clinical team.

The purpose of this statement is to outline issues at the interface between HIV transmission and the law and provide guidance to healthcare professionals (HCPs) working in the field of HIV medicine. The guidance is to support work in the UK, and it is important to note that the law in England and Wales differs from that in Scotland and Northern Ireland. Approaches are suggested to deal with these issues consistently, within legal and General Medical Council (GMC) regulatory frameworks and in the context of the public health agenda. The guidance specifically addresses sexual transmission.

Antenatal atazanavir: a retrospective analysis of pregnancies exposed to atazanavir.

INTRODUCTION: There are few data regarding the tolerability, safety, or efficacy of antenatal atazanavir. We report our clinical experience of atazanavir use in pregnancy. METHODS: A retrospective medical records review of atazanavir-exposed pregnancies in 12 London centres between 2004 and 2010. RESULTS: There were 145 pregnancies in 135 women: 89 conceived whilst taking atazanavir-based combination antiretroviral therapy (cART), "preconception" atazanavir exposure; 27 started atazanavir-based cART as "first-line" during the pregnancy; and 29 "switched" to an atazanavir-based regimen from another cART regimen during pregnancy. Gastrointestinal intolerance requiring atazanavir cessation occurred in five pregnancies. Self-limiting, new-onset transaminitis was most common in first-line use, occurring in 11.0%. Atazanavir was commenced in five switch pregnancies in the presence of transaminitis, two of which discontinued atazanavir with persistent transaminitis. HIV-VL < 50 copies/mL was achieved in 89.3% preconception, 56.5% first-line, and 72.0% switch exposures. Singleton preterm delivery (<37 weeks) occurred in 11.7% preconception, 9.1% first-line, and 7.7% switch exposures. Four infants required phototherapy. There was one mother-to-child transmission in a poorly adherent woman. CONCLUSIONS: These data suggest that atazanavir is well tolerated and can be safely prescribed as a component of combination antiretroviral therapy in pregnancy.

Socially responsible mining: the relationship between mining and poverty, human health and the environment.

Increasing global demand for metals is putting strain on the ability of the mining industry to physically keep up with demand (physical scarcity). Conversely, social issues including the environmental and human health consequences of mining as well as the disparity in income distribution from mining revenues are disproportionately felt at the local community level. This has created social rifts, particularly in the developing world, between affected communities and both industry and governments. Such rifts can result in a disruption of the steady supply of metals (situational scarcity). Here we discuss the importance of mining in relationship to poverty, identify steps that have been taken to create a framework for socially responsible mining, and then discuss the need for academia to work in partnership with communities, government, and industry to develop transdisciplinary research-based step change solutions to the intertwined problems of physical and situational scarcity.

Total protein, albumin and low-molecular-weight protein excretion in HIV-positive patients.

BACKGROUND: Chronic kidney disease is common in HIV positive patients and renal tubular dysfunction has been reported in those receiving combination antiretroviral therapy (cART). Tenofovir (TFV) in particular has been linked to severe renal tubular disease as well as proximal tubular dysfunction. Markedly elevated urinary concentrations of retinal-binding protein (RBP) have been reported in patients with severe renal tubular disease, and low-molecular-weight proteins (LMWP) such as RBP may be useful in clinical practice to assess renal tubular function in patients receiving TFV. We analysed 3 LMWP as well as protein and albumin in the urine of a sample of HIV positive patients. METHODS: In a cross-sectional fashion, total protein, albumin, RBP, cystatin C, and neutrophil gelatinase-associated lipocalin (NGAL) were quantified in random urine samples of 317 HIV positive outpatients and expressed as the ratio-to-creatinine (RBPCR, CCR and NGALCR). Exposure to cART was categorised as none, cART without TFV, and cART containing TFV and a non-nucleoside reverse-transcriptase-inhibitor (TFV/NNRTI) or TFV and a protease-inhibitor (TFV/PI). RESULTS: Proteinuria was present in 10.4 % and microalbuminuria in 16.7 % of patients. Albumin accounted for approximately 10 % of total urinary protein. RBPCR was within the reference range in 95 % of patients while NGALCR was elevated in 67 % of patients. No overall differences in urine protein, albumin, and LMWP levels were observed among patients stratified by cART exposure, although a greater proportion of patients exposed to TFV/PI had RBPCR >38.8 µg/mmol (343 µg/g) (p = 0.003). In multivariate analyses, black ethnicity (OR 0.43, 95 % CI 0.24, 0.77) and eGFR <75 mL/min/1.73 m2 (OR 3.54, 95 % CI 1.61, 7.80) were independently associated with upper quartile (UQ) RBPCR. RBPCR correlated well to CCR (r2 = 0.71), but not to NGALCR, PCR or ACR. CONCLUSIONS: In HIV positive patients, proteinuria was predominantly of tubular origin and microalbuminuria was common. RBPCR in patients without overt renal tubular disease was generally within the reference range, including those receiving TFV. RBP therefore appears a promising biomarker for monitoring renal tubular function in patients receiving TFV and for distinguishing patients with normal tubular function or mild tubular dysfunction from those with severe renal tubular disease or Fanconi syndrome.

Efavirenz is associated with severe vitamin D deficiency and increased alkaline phosphatase.

OBJECTIVE(S): To identify factors (including exposure to specific antiretroviral drugs) associated with severe vitamin D deficiency (VDD) in HIV-infected individuals and to explore the effects of severe VDD and antiretroviral drug exposure on serum alkaline phosphatase (ALP) as surrogate marker of bone turnover. DESIGN: Cross-sectional survey of vitamin D status among HIV-infected patients attending for routine clinical care at a large London HIV clinic. METHODS: Severe VDD was defined as 25(OH)D levels of less than 10 microg/l (<25 nmol/l). Multivariate logistic regression analysis was used to identify factors associated with severe VDD and upper quartile ALP levels. RESULTS: Vitamin D levels were measured in 1077 patients and found to be suboptimal in 91%. One-third of patients had severe VDD. Black ethnicity, sampling in winter, nadir CD4 cell count less than 200 cells/microl, and exposure to combination antiretroviral therapy were associated with severe VDD. In analyses restricted to patients on combination antiretroviral therapy, current efavirenz use was significantly associated with severe VDD [adjusted odds ratio 2.0 (95% confidence interval 1.5-2.7)]. Current tenofovir [adjusted odds ratio 3.5 (95% confidence interval 2.3-5.2)] and efavirenz use [adjusted odds ratio 1.6 (95% confidence interval 1.02-2.4)], but not severe VDD [odds ratio 1.1 (0.8-1.5)], were associated with increased bone turnover (upper quartile ALP). CONCLUSION: Efavirenz was associated with severe VDD, a condition associated with multiple adverse health outcomes, and efavirenz and tenofovir with increased ALP. The clinical significance of these findings requires further investigation, given the widespread use of efavirenz and tenofovir in first-line combination antiretroviral therapy.

A hybrid artificial neural network-numerical model for ground water problems.

Numerical models constitute the most advanced physical-based methods for modeling complex ground water systems. Spatial and/or temporal variability of aquifer parameters, boundary conditions, and initial conditions (for transient simulations) can be assigned across the numerical model domain. While this constitutes a powerful modeling advantage, it also presents the formidable challenge of overcoming parameter uncertainty, which, to date, has not been satisfactorily resolved, inevitably producing model prediction errors. In previous research, artificial neural networks (ANNs), developed with more accessible field data, have achieved excellent predictive accuracy over discrete stress periods at site-specific field locations in complex ground water systems. In an effort to combine the relative advantages of numerical models and ANNs, a new modeling paradigm is presented. The ANN models generate accurate predictions for a limited number of field locations. Appending them to a numerical model produces an overdetermined system of equations, which can be solved using a variety of mathematical techniques, potentially yielding more accurate numerical predictions. Mathematical theory and a simple two-dimensional example are presented to overview relevant mathematical and modeling issues. Two of the three methods for solving the overdetermined system achieved an overall improvement in numerical model accuracy for various levels of synthetic ANN errors using relatively few constrained head values (i.e., cells), which, while demonstrating promise, requires further research. This hybrid approach is not limited to ANN technology; it can be used with other approaches for improving numerical model predictions, such as regression or support vector machines (SVMs).

Multiobjective analysis of a public wellfield using artificial neural networks.

As competition for increasingly scarce ground water resources grows, many decision makers may come to rely upon rigorous multiobjective techniques to help identify appropriate and defensible policies, particularly when disparate stakeholder groups are involved. In this study, decision analysis was conducted on a public water supply wellfield to balance water supply needs with well vulnerability to contamination from a nearby ground water contaminant plume. With few alternative water sources, decision makers must balance the conflicting objectives of maximizing water supply volume from noncontaminated wells while minimizing their vulnerability to contamination from the plume. Artificial neural networks (ANNs) were developed with simulation data from a numerical ground water flow model developed for the study area. The ANN-derived state transition equations were embedded into a multiobjective optimization model, from which the Pareto frontier or trade-off curve between water supply and wellfield vulnerability was identified. Relative preference values and power factors were assigned to the three stakeholders, namely the company whose waste contaminated the aquifer, the community supplied by the wells, and the water utility company that owns and operates the wells. A compromise pumping policy that effectively balances the two conflicting objectives in accordance with the preferences of the three stakeholder groups was then identified using various distance-based methods.

Haemorrhagic acute disseminated encephalomyelitis: an unusual case of blindness in an HIV-infected patient.

Acute disseminated encephalomyelitis (ADEM) is a monophasic, polysymptomatic, immune-mediated demyelinating disorder involving central nervous system (CNS) white matter. It is usually seen in association with exanthematous viral illnesses, systemic infections or vaccinations. The haemorrhagic form of ADEM is rarer and follows a more fulminant course. We describe a case of haemorrhagic ADEM in an HIV-infected patient presenting solely with acute onset bilateral blindness and normal retina. ADEM was diagnosed from clinical, laboratory and radiological findings. Although ADEM has been seen with HIV seroconversion illness and in chronic infection, the haemorrhagic form has never been reported in association with HIV. Acute onset blindness is extremely rare in HIV-positive patients. A Medline search revealed very few reports. The importance of considering haemorrhagic ADEM as aetiology of blindness in such a scenario is discussed.

Predicting conductance due to upconing using neural networks.

Artificial neural networks (ANNs) were developed to accurately predict highly time-variable specific conductance values in an unconfined coastal aquifer. Conductance values in the fresh water lens aquifer change in response to vertical displacements of the brackish zone and fresh water-salt water interface, which are caused by variable pumping and climate conditions. Unlike physical-based models, which require hydrologic parameter inputs, such as horizontal and vertical hydraulic conductivities, porosity, and fluid densities, ANNs can "learn" system behavior from easily measurable variables. In this study, the ANN input predictor variables were initial conductance, total precipitation, mean daily temperature, and total pumping extraction. The ANNs were used to predict salinity (specific conductance) at a single monitoring well located near a high-capacity municipal-supply well over time periods ranging from 30 d to several years. Model accuracy was compared against both measured/interpolated values and predictions were made with linear regression, and in general, excellent prediction accuracy was achieved. For example, although the average percent change of conductance over 90-d periods was 39%, the absolute mean prediction error achieved with the ANN was only 1.1%. The ANNs were also used to conduct a sensitivity analysis that quantified the importance of each of the four predictor variables on final conductance values, providing valuable insights into the dynamics of the system. The results demonstrate that the ANN technology can serve as a powerful and accurate prediction and management tool, minimizing degradation of ground water quality to the extent possible by identifying appropriate pumping policies under variable and/or changing climate conditions.

A neural network model for predicting aquifer water level elevations.

Artificial neural networks (ANNs) were developed for accurately predicting potentiometric surface elevations (monitoring well water level elevations) in a semiconfined glacial sand and gravel aquifer under variable state, pumping extraction, and climate conditions. ANNs "learn" the system behavior of interest by processing representative data patterns through a mathematical structure analogous to the human brain. In this study, the ANNs used the initial water level measurements, production well extractions, and climate conditions to predict the final water level elevations 30 d into the future at two monitoring wells. A sensitivity analysis was conducted with the ANNs that quantified the importance of the various input predictor variables on final water level elevations. Unlike traditional physical-based models, ANNs do not require explicit characterization of the physical system and related physical data. Accordingly, ANN predictions were made on the basis of more easily quantifiable, measured variables, rather than physical model input parameters and conditions. This study demonstrates that ANNs can provide both excellent prediction capability and valuable sensitivity analyses, which can result in more appropriate ground water management strategies.