Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana.

Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address these data gaps, recent studies have utilized low-cost sensors. These sensors have varied performance, and little literature exists about sensor intercomparison in Africa. By colocating 2 QuantAQ Modulair-PM, 2 PurpleAir PA-II SD, and 16 Clarity Node-S Generation II monitors with a reference-grade Teledyne monitor in Accra, Ghana, we present the first intercomparisons of different brands of low-cost sensors in Africa, demonstrating that each type of low-cost sensor PM2.5 is strongly correlated with reference PM2.5, but biased high for ambient mixture of sources found in Accra. When compared to a reference monitor, the QuantAQ Modulair-PM has the lowest mean absolute error at 3.04 µg/m3, followed by PurpleAir PA-II (4.54 µg/m3) and Clarity Node-S (13.68 µg/m3). We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R2: 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 µg/m3 for PurpleAir PA-II, Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. Therefore, we used Gaussian Mixture Regression to correct data from the network of 17 Clarity Node-S monitors deployed around Accra, Ghana, from 2018 to 2021. We find that the network daily average PM2.5 concentration in Accra is 23.4 µg/m3, which is 1.6 times the World Health Organization Daily PM2.5 guideline of 15 µg/m3. While this level is lower than those seen in some larger African cities (such as Kinshasa, Democratic Republic of the Congo), mitigation strategies should be developed soon to prevent further impairment to air quality as Accra, and Ghana as a whole, rapidly grow.

Submicron Aerosol Composition and Source Contribution across the Kathmandu Valley, Nepal, in Winter.

The Kathmandu valley experiences an average wintertime PM1 concentration of ∼100 µg m-3 and daily peaks over 200 µg m-3. We present ambient nonrefractory PM1 chemical composition, and concentration measured by a mini aerosol mass spectrometer (mAMS) sequentially at Dhulikhel (on the valley exterior), then urban Ratnapark, and finally suburban Lalitpur in winter 2018. At all sites, organic aerosol (OA) was the largest contributor to combined PM1 (C-PM1) (49%) and black carbon (BC) was the second largest contributor (21%). The average background C-PM1 at Dhulikhel was 48 µg m-3; the urban enhancement was 120% (58 µg m-3). BC had an average of 6.1 µg m-3 at Dhulikhel, an urban enhancement of 17.4 µg m-3. Sulfate (SO4) was 3.6 µg m-3 at Dhulikhel, then 7.5 µg m-3 at Ratnapark, and 12.0 µg m-3 at Lalitpur in the brick kiln region. Chloride (Chl) increased by 330 and 250% from Dhulikhel to Ratnapark and Lalitpur on average. Positive matrix factorization (PMF) identified seven OA sources, four primary OA sources, hydrocarbon-like (HOA), biomass burning (BBOA), trash burning (TBOA), a sulfate-containing local OA source (sLOA), and three secondary oxygenated organic aerosols (OOA). OOA was the largest fraction of OA, over 50% outside the valley and 36% within. HOA (traffic) was the most prominent primary source, contributing 21% of all OA and 44% of BC. Brick kilns were the second largest contributor to C-PM1, 12% of OA, 33% of BC, and a primary emitter of aerosol sulfate. These results, though successive, indicate the importance of multisite measurements to understand ambient particulate matter concentration heterogeneity across urban regions.

Wintertime Air Quality across the Kathmandu Valley, Nepal: Concentration, Composition, and Sources of Fine and Coarse Particulate Matter.

The Kathmandu Valley in Nepal experiences poor air quality, especially in the dry winter season. In this study, we investigated the concentration, chemical composition, and sources of fine and coarse particulate matter (PM2.5, PM10, and PM10-2.5) at three sites within or near the Kathmandu Valley during the winter of 2018 as part of the second Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE 2). Daily PM2.5 concentrations were very high throughout the study period, ranging 72-149 µg m-3 at the urban Ratnapark site in Kathmandu, 88-161 µg m-3 at the suburban Lalitpur site, and 40-74 µg m-3 at rural Dhulikhel on the eastern rim of the Kathmandu Valley. Meanwhile, PM10 ranged 194-309, 174-377, and 64-131 µg m-3, respectively. At the Ratnapark site, crustal materials from resuspended soil contributed an average of 11% of PM2.5 and 34% of PM10. PM2.5 was largely comprised of organic carbon (OC, 28-30% by mass) and elemental carbon (EC, 10-14% by mass). As determined by chemical mass balance source apportionment modeling, major PM2.5 OC sources were garbage burning (15-21%), biomass burning (10-17%), and fossil fuel (14-26%). Secondary organic aerosol (SOA) contributions from aromatic volatile organic compounds (13-23% OC) were larger than those from isoprene (0.3-0.5%), monoterpenes (0.9-1.4%), and sesquiterpenes (3.6-4.4%). Nitro-monoaromatic compounds-of interest due to their light-absorbing properties and toxicity-indicate the presence of biomass burning-derived SOA. Knowledge of primary and secondary PM sources can facilitate air quality management in this region.

Aerosol Mass Spectral Profiles from NAMaSTE Field-Sampled South Asian Combustion Sources.

Unit mass resolution mass spectral profiles of nonrefractory submicron aerosol were retrieved from undersampled atmospheric emission sources common to South Asia using a "mini" aerosol mass spectrometer. Emission sources including wood- and dung-fueled cookstoves, agricultural residue burning, garbage burning, engine exhaust, and coal-fired brick kilns were sampled during the 2015 Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE) campaign. High-resolution peak fitting estimates of the mass spectra were used to characterize ions found within each source profile and help identify mass spectral signatures unique to aerosol emissions from the investigated source types. The first aerosol mass spectral profiles of dung burning, charcoal burning, garbage burning, and brick kilns are provided in this work. The online aerosol mass spectra show that organics were generally the dominant component of the nonrefractory aerosol. However, inorganic aerosol components including ammonium and chloride were significant in dung- and charcoal-fired cookstove emissions and sulfate compounds were major components of the coal-fired brick kiln emissions. Organic mass spectra from both the charcoal burning and zigzag brick kiln were dominated by nitrogen-containing ions thought to be from the electron ionization of amines and amides contained in the emissions. The mixed garbage burning emissions profiles were dominated by plastic combustion with very low fractions of organic markers associated with biomass burning. The plastic burning emissions were associated with enhanced organic signal at mass-to-charge (m/z) 104 and m/z 166, which could be useful fragment ion indicators for garbage burning in ambient aerosol profiles. Finally, a framework for the identification of emission sources using the unit mass resolution organic mass fractions at m/z 55 (f 55), m/z 57 (f 57), and m/z 60 (f 60) is proposed in this work. Plotting the ratio of f 55 to f 57 versus f 60 is found to be effective for the identification of emissions by the fuel type and even useful in separating emissions of similar source types. Although the sample size was limited, these results give further context to the aerosol and gas-phase emission factors presented in other NAMaSTE works and provide a critical reference for future aerosol composition measurements in South Asia.

Estimating and Testing Random Intercept Multilevel Structural Equation Models with Model Implied Instrumental Variables.

This study develops a new limited information estimator for random intercept Multilevel Structural Equation Models (MSEM). It is based on the Model Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) estimator, which has been shown to be an excellent alternative or supplement to maximum likelihood (ML) in SEMs (Bollen, 1996). We also develop a multilevel overidentification test statistic that applies to equations at the within or between levels. Our Monte Carlo simulation analysis suggests that MIIV-2SLS is more robust than ML to misspecification at within or between levels, performs well given fewer that 100 clusters, and shows that our multilevel overidentification test for equations performs well at both levels of the model.

Recognition of Emotions from Situational Contexts and the Impact of a Mind Reading Intervention in Children with Autism Spectrum Disorder.

The present research examined recognition of basic (happy, fear, sad) and self-conscious (pride, embarrassment, guilt) emotions from situational contexts in children with Autism Spectrum Disorder (ASD) and neurotypical children (Study 1). Results showed that children with ASD were less accurate in recognizing fear, embarrassment, and guilt situations than neurotypical children. Additionally, the research explored whether recognition of these emotions from situational contexts could be improved in children with ASD after a 4-week computerized emotion intervention (Study 2). Following the intervention, children showed better recognition of embarrassment and guilt, but no improvement in recognizing fear. In children with ASD, significant negative relations were found between ASD symptomatology and recognition of guilt (Study 1), although ASD symptomatology did not impact the intervention's efficacy (Study 2). Additional explanations for these findings are provided.

An introduction to model implied instrumental variables using two stage least squares (MIIV-2SLS) in structural equation models (SEMs).

Structural equation models (SEMs) are widely used to handle multiequation systems that involve latent variables, multiple indicators, and measurement error. Maximum likelihood (ML) and diagonally weighted least squares (DWLS) dominate the estimation of SEMs with continuous or categorical endogenous variables, respectively. When a model is correctly specified, ML and DWLS function well. But, in the face of incorrect structures or nonconvergence, their performance can seriously deteriorate. Model implied instrumental variable, two stage least squares (MIIV-2SLS) estimates and tests individual equations, is more robust to misspecifications, and is noniterative, thus avoiding nonconvergence. This article is an overview and tutorial on MIIV-2SLS. It reviews the six major steps in using MIIV-2SLS: (a) model specification; (b) model identification; (c) latent to observed (L2O) variable transformation; (d) finding MIIVs; (e) using 2SLS; and (f) tests of overidentified equations. Each step is illustrated using a running empirical example from Reisenzein's (1986) randomized experiment on helping behavior. We also explain and illustrate the analytic conditions under which an equation estimated with MIIV-2SLS is robust to structural misspecifications. We include additional sections on MIIV approaches using a covariance matrix and mean vector as data input, conducting multilevel SEM, analyzing categorical endogenous variables, causal inference, and extensions and applications. Online supplemental material illustrates input code for all examples and simulations using the R package MIIVsem. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Harmonizing altered measures in integrative data analysis: A methods analogue study.

In the current study, we used an analogue integrative data analysis (IDA) design to test optimal scoring strategies for harmonizing alcohol- and drug-use consequence measures with varying degrees of alteration across four study conditions. We evaluated performance of mean, confirmatory factor analysis (CFA), and moderated nonlinear factor analysis (MNLFA) scores based on traditional indices of reliability (test-retest, internal, and score recovery or parallel forms) and validity. Participants in the analogue study included 854 college students (46% male; 21% African American, 5% Hispanic/Latino, 56% European American) who completed two versions of the altered measures at two sessions, separated by 2 weeks. As expected, mean, CFA, and MNLFA scores all resulted in scales with lower reliability given increasing scale alteration (with less fidelity to formerly developed scales) and shorter scale length. MNLFA and CFA scores, however, showed greater validity than mean scores, demonstrating stronger relationships with external correlates. Implications for measurement harmonization in the context of IDA are discussed.

Simplifying the implementation of modern scale scoring methods with an automated R package: Automated moderated nonlinear factor analysis (aMNLFA).

When generating scores to represent latent constructs, analysts have a choice between applying psychometric approaches that are principled but that can be complicated and time-intensive versus applying simple and fast, but less precise approaches, such as sum or mean scoring. We explain the reasons for preferring modern psychometric approaches: namely, use of unequal item weights and severity parameters, the ability to account for local dependence and differential item functioning, and the use of covariate information to more efficiently estimate factor scores. We describe moderated nonlinear factor analysis (MNLFA), a relatively new, highly flexible approach that allows analysts to develop precise factor score estimates that address limitations of sum score, mean score, and traditional factor analytic approaches to scoring. We then outline the steps involved in using the MNLFA scoring approach and discuss the circumstances in which this approach is preferred. To overcome the difficulty of implementing MNLFA models in practice, we developed an R package, aMNLFA, that automates much of the rule-based scoring process. We illustrate the use of aMNLFA with an empirical example of scoring alcohol involvement in a longitudinal study of 6998 adolescents and compare performance of MNLFA scores with traditional factor analysis and sum scores based on the same set of 12 items. MNLFA scores retain more meaningful variation than other approaches. We conclude with practical guidelines for scoring.

Proneness to guilt, shame, and pride in children with Autism Spectrum Disorders and neurotypical children.

Self-conscious emotions (e.g., guilt, shame, and pride) are complex emotions that require self-reflection and self-evaluation, and are thought to facilitate the maintenance of societal norms and personal standards. Despite the importance of self-conscious emotions, most research has focused on basic emotion processing in children with Autism Spectrum Disorders (ASD). Therefore, in the present study, we used the Test of Self-Conscious Affect for Children (TOSCA-C) to assess proneness to, or propensity to experience, the self-conscious emotions guilt, shame, and pride in children with ASD and neurotypical children. The TOSCA-C is designed to capture a child's natural tendency to experience a given emotion across a range of everyday situations [Tangney, Stuewig, & Mashek, 2007]. We also assessed how individual characteristics contribute to the development of proneness to self-conscious emotions, including theory of mind (ToM) and ASD symptomatology. In comparison to neurotypical children, children with ASD showed less proneness to guilt, although all children showed relatively high levels of proneness to guilt. Greater ToM ability was related to more proneness to guilt and authentic pride in children with ASD. Additionally, we found that children with ASD with more severe symptomatology were more prone to hubristic pride. Our results provide evidence of differences in proneness to self-conscious emotions in children with ASD, as well as highlight important mechanisms contributing to how children with ASD may experience self-conscious emotions. Autism Res 2018,11:883-892. ©2017 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: This research examined proneness to guilt, shame, and pride in children with Autism Spectrum Disorders (ASD) and neurotypical children. We found that children with ASD showed less proneness to guilt than neurotypical children. Better understanding of theory of mind was related to greater proneness to guilt and pride, but only for children with ASD. These findings are important because these complex emotions are linked with both positive and negative social behaviors towards others and oneself.

A quantitative approach to evaluating the GWP timescale through implicit discount rates.

The 100-year global warming potential (GWP) is the primary metric used to compare the climate impacts of emissions of different greenhouse gases (GHGs). The GWP relies on radiative forcing rather than damages, assumes constant future concentrations, and integrates over a timescale of 100 years without discounting; these choices lead to a metric that is transparent and simple to calculate, but have also been criticized. In this paper, we take a quantitative approach to evaluating the choice of time horizon, accounting for many of these complicating factors. By calculating an equivalent GWP timescale based on discounted damages resulting from CH4 and CO2 pulses, we show that a 100-year timescale is consistent with a discount rate of 3.3% (interquartile range of 2.7% to 4.1% in a sensitivity analysis). This range of discount rates is consistent with those often considered for climate impact analyses. With increasing discount rates, equivalent timescales decrease. We recognize the limitations of evaluating metrics by relying only on climate impact equivalencies without consideration of the economic and political implications of metric implementation.

Advancing the Study of Adolescent Substance Use Through the Use of Integrative Data Analysis.

A wealth of information is currently known about the epidemiology, etiology, and evaluation of drug and alcohol use across the life span. Despite this corpus of knowledge, much has yet to be learned. Many factors conspire to slow the pace of future advances in the field of substance use including the need for long-term longitudinal studies of often hard-to-reach subjects who are reporting rare and episodic behaviors. One promising option that might help move the field forward is integrative data analysis (IDA). IDA is a principled set of methodologies and statistical techniques that allow for the fitting of statistical models to data that have been pooled across multiple, independent samples. IDA offers a myriad of potential advantages including increased power, greater coverage of rare behaviors, more rigorous psychometric assessment of theoretical constructs, accelerated developmental time period under study, and enhanced reproducibility. However, IDA is not without limitations and may not be useful in a given application for a variety of reasons. The goal of this article is to describe the advantages and limitations of IDA in the study of individual development over time, particularly as it relates to trajectories of substance use. An empirical example of the measurement of polysubstance use is presented and this article concludes with recommendations for practice.

An Empirical Assessment of the Sensitivity of Mixture Models to Changes in Measurement.

The current study explored the extent to which variations in self-report measures across studies can produce differences in the results obtained from mixture models. Data (N = 854) come from a laboratory analogue study of methods for creating commensurate scores of alcohol- and substance-use-related constructs when items differ systematically across participants for any given measure. Items were manipulated according to four conditions, corresponding to increasing levels of alteration to item stems, response options, or both. In Study 1, results from latent class analyses (LCA) of alcohol consequences were compared across the four conditions, revealing differences in class enumeration and configuration. In Study 2, results from factor mixture models (FMM) of alcohol expectancies were compared across two of the conditions, revealing differences in patterns and magnitude of the factor loadings and thresholds. The results suggest that even subtle differences in measurement can have substantively meaningful effects on mixture model results.

Changes in droplet surface tension affect the observed hygroscopicity of photochemically aged biomass burning aerosol.

This study examines the hygroscopic and surface tension properties as a function of photochemical aging of the aerosol emissions from biomass burning. Experiments were conducted in a chamber setting at the UC-Riverside Center for Environmental Research and Technology (CE-CERT) Atmospheric Processes Lab using two biomass fuel sources, manzanita and chamise. Cloud condensation nuclei (CCN) measurements and off-line filter sample analysis were conducted. The water-soluble organic carbon content and surface tension of the extracted filter samples were measured. Surface tension information was then examined with Köhler theory analysis to calculate the hygroscopicity parameter, κ. Laboratory measurement of biomass burning smoke from two chaparral fuels is shown to depress the surface tension of water by 30% or more at organic matter concentrations relevant at droplet activation. Accounting for surface tension depression can lower the calculated κ by a factor of 2. This work provides evidence for surface tension depression in an important aerosol system and may provide closure for differing sub- and supersaturated κ measurements.

Benefits of two mitigation strategies for container vessels: cleaner engines and cleaner fuels.

Emissions from ocean-going vessels (OGVs) are a significant health concern for people near port communities. This paper reports the emission benefits for two mitigation strategies, cleaner engines and cleaner fuels, for a 2010 container vessel. In-use emissions were measured following International Organization for Standardization (ISO) protocols. The overall in-use nitrogen oxide (NO(x)) emission factor was 16.1 ± 0.1 gkW(-1) h(-1), lower than the Tier 1 certification (17 gkW(-1) h(-1)) and significantly lower than the benchmark value of 18.7 gkW(-1) h(-1) commonly used for estimating emission inventories. The in-use particulate matter (PM(2.5)) emission was 1.42 ± 0.04 gkW(-1) h(-1) for heavy fuel oil (HFO) containing 2.51 wt % sulfur. Unimodal (∼30 nm) and bimodal (∼35 nm; ∼75 nm) particle number size distributions (NSDs) were observed when the vessel operated on marine gas oil (MGO) and HFO, respectively. First-time emission measurements during fuel switching (required 24 nautical miles from coastline) showed that concentrations of sulfur dioxide (SO(2)) and particle NSD took ∼55 min to reach steady-state when switching from MGO to HFO and ∼84 min in the opposite direction. Therefore, if OGVs commence fuel change at the regulated boundary, then vessels can travel up to 90% of the distance to the port before steady-state values are re-established. The transient behavior follows a classic, nonlinear mixing function driven by the amount of fuel in day tank and the fuel consumption rate. Hence, to achieve the maximum benefits from a fuel change regulation, fuel switch boundary should be further increased to provide the intended benefits for the people living near the ports.

Emergency medicine resident performed bedside ultrasonography of the gallbladder in non-fasted healthy volunteers.

BACKGROUND: Gallbladder ultrasonography is a commonly performed test in the emergency department. It is unknown whether a non-fasting state alters the visualization of the gallbladder by emergency medicine (EM) residents. OBJECTIVES: We conducted this study to determine whether EM residents are able to visualize the gallbladder in volunteers who have recently consumed a fatty meal. METHODS: This study used a prospective, single-blinded, randomized controlled design. Initial scans were performed on fasting volunteers. A fatty meal was then consumed. Thirty minutes after eating, a different resident, who was unaware of whether the volunteer had eaten or fasted, performed a second scan. To control for operator bias, 10% of subjects remained fasting between scans. Student's paired-samples t-test, Pearson's chi-squared, and McNemar test were determined as appropriate. RESULTS: A total of 92 scans from 46 volunteers were analyzed. EM residents were able to visualize the gallbladder in all 40 pre-prandial scans (100%) and all 40 post-prandial scans (100%). Gallbladder area as measured in the longitudinal axis decreased 20% from a mean baseline of 11.58 +/- 4.86 cm(2) (95% confidence interval [CI] 11.17-12.98) to 9.2 +/- 5.04 cm(2) (95% CI 7.74-10.66, p = 0.0009) after food intake. Total time to scan for the fasting volunteers (110.2 s, 95% CI 84.34-136) did not change significantly from non-fasting volunteers (129.7 s, 95% CI 110.29-149.01, p = 0.153). CONCLUSIONS: EM residents are able to visualize the gallbladder in non-fasted healthy volunteers.

Effect of a four-week course of interleukin-10 on cytokine production in a placebo-controlled study of HIV-1-infected subjects.

Interleukin (IL)-10 suppresses synthesis of the pro-inflammatory cytokines tumor necrosis factor (TNF)alpha, IL-1beta, and interferon (IFN)gamma. Since pro-inflammatory cytokines have been implicated in the production of human immunodeficiency virus type 1 (HIV-1), cytokine synthesis in whole blood cultures were determined during a 4-week course of subcutaneous IL-10 injections in 33 HIV-1-infected patients. Patients were randomized into four groups: placebo (nine), IL-10 at 1 microg/kg/day (nine), IL-10 at 4 microg/kg/day (six) and IL-10 at 8 microg/kg three times per week (nine). Whole blood was obtained at the beginning and conclusion of the study and was stimulated for 24 hours with the combination of IL-18 plus lipopolysaccharide. TNFalpha production in stimulated whole blood was reduced three and six hours after the first injection of IL-10 compared to subjects injected with the placebo. After four weeks of treatment, production of IFNgamma was suppressed in a greater number of patients in the IL-10 treatment groups compared to subjects in the placebo group. Similarly, IL-1beta production was lower in the IL-10 treatment groups compared to subjects receiving placebo. In contrast, after four weeks of IL-10, circulating levels of the anti-inflammatory TNF soluble receptor p55 increased dose-dependently compared to placebo subjects. Patient heterogeneity and small sample size presented difficulties in establishing statistical significance. Although the cytokine changes in our study did not demonstrate statistically significant changes, the data nevertheless reveal that four weeks of IL-10 therapy in HIV-1 infected subjects produced the anticipated suppression of pro-inflammatory cytokines.

The effects of the Roche AMPLICOR HIV-1 MONITOR UltraSensitive Test versions 1.0 and 1.5 viral load assays and plasma collection tube type on determination of response to antiretroviral therapy and the inappropriateness of cross-study comparisons.

BACKGROUND: Because there are limited head-to-head data comparing antiretroviral combinations, physicians are tempted to rely on cross-trial comparisons to evaluate the relative efficacy of HIV drugs. However, a variety of factors can confound these comparisons, resulting in misleading or invalid conclusions. OBJECTIVES: To compare and evaluate the use of: (i) versions 1.0 and 1.5 of the Roche AMPLICOR HIV-1 MONITOR UltraSensitive assay, and (ii) ethylenediaminetetraacetic acid (EDTA) and plasma preparation (PPT) tubes on the proportion of HIV-infected patients who would be classified as virological responders in a multinational clinical trial. STUDY DESIGN: The study utilized was a randomized, double-blind trial comparing the efficacy and safety of atazanavir with efavirenz, each in combination with fixed-dose zidovudine/lamivudine, in antiretroviral-naïve patients. To evaluate the effect of monitor kit version, paired plasma samples from 634 patients at week 48 were analyzed using both versions 1.0 and 1.5 of the monitor kit. To evaluate the effect of collection tube type, paired plasma samples collected from 584 patients at week 52 using both EDTA and PPT tubes were assayed. Patients were classified as responders if HIV-1 RNA levels were below a pre-determined level of quantification (LOQ), both 400 and 50 copies/ml. RESULTS AND CONCLUSIONS: Substantially higher HIV-1 RNA levels were observed with monitor kit version 1.5, resulting in lower response rates. The version 1.0 monitor kit resulted in a 7% increase in patients classified as responders at the LOQ of 400 copies/ml and a 13% increase at the LOQ of 50 copies/ml. Consistently higher response rates (11% higher at the LOQ of 400 copies/ml and 34% higher at the LOQ of 50 copies/ml) were also observed when samples were collected in EDTA tubes compared with PPT tubes. Differences in monitor kit sensitivity and plasma collection procedures are key factors in study results and suggest caution when performing cross-study comparisons.

Body fat and other metabolic effects of atazanavir and efavirenz, each administered in combination with zidovudine plus lamivudine, in antiretroviral-naive HIV-infected patients.

BACKGROUND: Protease inhibitor treatment of human immunodeficiency virus (HIV)-infected individuals has been linked to the development of lipodystrophy. The effects of atazanavir on body fat distribution and related metabolic parameters were examined in antiretroviral-naive patients. METHODS: HIV-positive patients with CD4 cell counts > or = 100 cells/mm3 were randomized to 1 of 2 treatment arms: (1) atazanavir, 400 mg given once daily, plus efavirenz placebo; or (2) efavirenz, 600 mg given once daily, plus atazanavir placebo; each drug was administered with fixed-dose zidovudine (300 mg) and lamivudine (150 mg) given twice daily, and patients were treated for at least 48 weeks. Fat distribution measurements (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT], and total adipose tissue [TAT], as measured by computed tomography; and appendicular fat, truncal fat, and total fat levels, as measured by dual-energy x-ray absorptiometry), metabolic measurements (cholesterol and fasting triglyceride levels), and measurements of insulin resistance (fasting glucose and fasting insulin levels) were made at baseline and at week 48 of treatment for a subgroup of 111 atazanavir recipients and 100 efavirenz recipients. RESULTS: Atazanavir and efavirenz treatments resulted in minimal to modest increases in fat accumulation, as measured by VAT, SAT, TAT, appendicular fat, truncal fat, and total fat levels; results were comparable in both arms. In addition, atazanavir was associated with none of the metabolic abnormalities seen with many other protease inhibitors. CONCLUSIONS: Use of atazanavir for 48 weeks neither resulted in abnormal fat redistribution in antiretroviral-naive patients nor induced other metabolic disturbances commonly associated with HIV-related lipodystrophy. Longer-term assessments (e.g., at 96 weeks) will be important to confirm these findings.