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Frascati international research criteria for HIV-associated neurocognitive disorders (HAND) are controversial; some investigators have argued that Frascati criteria are too liberal, resulting in a high false positive rate. Meyer et al. recommended more conservative revisions to HAND criteria, including exploring other commonly used methodologies for neurocognitive impairment (NCI) in HIV including the global deficit score (GDS). This study compares NCI classifications by Frascati, Meyer, and GDS methods, in relation to neuroimaging markers of brain integrity in HIV.
Two hundred forty-one people living with HIV (PLWH) without current substance use disorder or severe (confounding) comorbid conditions underwent comprehensive neurocognitive testing and brain structural magnetic resonance imaging and magnetic resonance spectroscopy. Participants were classified using Frascati criteria versus Meyer criteria: concordant unimpaired [Frascati(Un)/Meyer(Un)], concordant impaired [Frascati(Imp)/Meyer(Imp)], or discordant [Frascati(Imp)/Meyer(Un)] which were impaired via Frascati criteria but unimpaired via Meyer criteria. To investigate the GDS versus Meyer criteria, the same groupings were utilized using GDS criteria instead of Frascati criteria.
When examining Frascati versus Meyer criteria, discordant Frascati(Imp)/Meyer(Un) individuals had less cortical gray matter, greater sulcal cerebrospinal fluid volume, and greater evidence of neuroinflammation (i.e., choline) than concordant Frascati(Un)/Meyer(Un) individuals. GDS versus Meyer comparisons indicated that discordant GDS(Imp)/Meyer(Un) individuals had less cortical gray matter and lower levels of energy metabolism (i.e., creatine) than concordant GDS(Un)/Meyer(Un) individuals. In both sets of analyses, the discordant group did not differ from the concordant impaired group on any neuroimaging measure.
The Meyer criteria failed to capture a substantial portion of PLWH with brain abnormalities. These findings support continued use of Frascati or GDS criteria to detect HIV-associated CNS dysfunction.
Objectives: Studies of neurocognitively elite older adults, termed SuperAgers, have identified clinical predictors and neurobiological indicators of resilience against age-related neurocognitive decline. Despite rising rates of older persons living with HIV (PLWH), SuperAging (SA) in PLWH remains undefined. We aimed to establish neuropsychological criteria for SA in PLWH and examined clinically relevant correlates of SA. Methods: 734 PLWH and 123 HIV-uninfected participants between 50 and 64 years of age underwent neuropsychological and neuromedical evaluations. SA was defined as demographically corrected (i.e., sex, race/ethnicity, education) global neurocognitive performance within normal range for 25-year-olds. Remaining participants were labeled cognitively normal (CN) or impaired (CI) based on actual age. Chi-square and analysis of variance tests examined HIV group differences on neurocognitive status and demographics. Within PLWH, neurocognitive status differences were tested on HIV disease characteristics, medical comorbidities, and everyday functioning. Multinomial logistic regression explored independent predictors of neurocognitive status. Results: Neurocognitive status rates and demographic characteristics differed between PLWH (SA=17%; CN=38%; CI=45%) and HIV-uninfected participants (SA=35%; CN=55%; CI=11%). In PLWH, neurocognitive groups were comparable on demographic and HIV disease characteristics. Younger age, higher verbal IQ, absence of diabetes, fewer depressive symptoms, and lifetime cannabis use disorder increased likelihood of SA. SA reported increased independence in everyday functioning, employment, and health-related quality of life than non-SA. Conclusions: Despite combined neurological risk of aging and HIV, youthful neurocognitive performance is possible for older PLWH. SA relates to improved real-world functioning and may be better explained by cognitive reserve and maintenance of cardiometabolic and mental health than HIV disease severity. Future research investigating biomarker and lifestyle (e.g., physical activity) correlates of SA may help identify modifiable neuroprotective factors against HIV-related neurobiological aging. (JINS, 2019, 25, 507–519)
Childhood maltreatment is one of the strongest predictors of adulthood depression and alterations to circulating levels of inflammatory markers is one putative mechanism mediating risk or resilience.
To determine the effects of childhood maltreatment on circulating levels of 41 inflammatory markers in healthy individuals and those with a major depressive disorder (MDD) diagnosis.
We investigated the association of childhood maltreatment with levels of 41 inflammatory markers in two groups, 164 patients with MDD and 301 controls, using multiplex electrochemiluminescence methods applied to blood serum.
Childhood maltreatment was not associated with altered inflammatory markers in either group after multiple testing correction. Body mass index (BMI) exerted strong effects on interleukin-6 and C-reactive protein levels in those with MDD.
Childhood maltreatment did not exert effects on inflammatory marker levels in either the participants with MDD or the control group in our study. Our results instead highlight the more pertinent influence of BMI.
Declaration of interest
D.A.C. and H.W. work for Eli Lilly Inc. R.N. has received speaker fees from Sunovion, Jansen and Lundbeck. G.B. has received consultancy fees and funding from Eli Lilly. R.H.M.-W. has received consultancy fees or has a financial relationship with AstraZeneca, Bristol-Myers Squibb, Cyberonics, Eli Lilly, Ferrer, Janssen-Cilag, Lundbeck, MyTomorrows, Otsuka, Pfizer, Pulse, Roche, Servier, SPIMACO and Sunovian. I.M.A. has received consultancy fees or has a financial relationship with Alkermes, Lundbeck, Lundbeck/Otsuka, and Servier. S.W. has sat on an advisory board for Sunovion, Allergan and has received speaker fees from Astra Zeneca. A.H.Y. has received honoraria for speaking from Astra Zeneca, Lundbeck, Eli Lilly, Sunovion; honoraria for consulting from Allergan, Livanova and Lundbeck, Sunovion, Janssen; and research grant support from Janssen. A.J.C. has received honoraria for speaking from Astra Zeneca, honoraria for consulting with Allergan, Livanova and Lundbeck and research grant support from Lundbeck.
Objectives: Human immunodeficiency virus (HIV) disproportionately affects Hispanics/Latinos in the United States, yet little is known about neurocognitive impairment (NCI) in this group. We compared the rates of NCI in large well-characterized samples of HIV-infected (HIV+) Latinos and (non-Latino) Whites, and examined HIV-associated NCI among subgroups of Latinos. Methods: Participants included English-speaking HIV+ adults assessed at six U.S. medical centers (194 Latinos, 600 Whites). For overall group, age: M=42.65 years, SD=8.93; 86% male; education: M=13.17, SD=2.73; 54% had acquired immunodeficiency syndrome. NCI was assessed with a comprehensive test battery with normative corrections for age, education and gender. Covariates examined included HIV-disease characteristics, comorbidities, and genetic ancestry. Results: Compared with Whites, Latinos had higher rates of global NCI (42% vs. 54%), and domain NCI in executive function, learning, recall, working memory, and processing speed. Latinos also fared worse than Whites on current and historical HIV-disease characteristics, and nadir CD4 partially mediated ethnic differences in NCI. Yet, Latinos continued to have more global NCI [odds ratio (OR)=1.59; 95% confidence interval (CI)=1.13–2.23; p<.01] after adjusting for significant covariates. Higher rates of global NCI were observed with Puerto Rican (n=60; 71%) versus Mexican (n=79, 44%) origin/descent; this disparity persisted in models adjusting for significant covariates (OR=2.40; CI=1.11–5.29; p=.03). Conclusions: HIV+ Latinos, especially of Puerto Rican (vs. Mexican) origin/descent had increased rates of NCI compared with Whites. Differences in rates of NCI were not completely explained by worse HIV-disease characteristics, neurocognitive comorbidities, or genetic ancestry. Future studies should explore culturally relevant psychosocial, biomedical, and genetic factors that might explain these disparities and inform the development of targeted interventions. (JINS, 2018, 24, 163–175)
Both qualitative and quantitative research routinely fall short, producing misleading causal inferences. Because these weaknesses are in part different, we are convinced that multimethod strategies are productive. Each approach can provide additional leverage that helps address shortcomings of the other. This position is quite distinct from that of Beck, who believes that the two types of analysis cannot be adjoined. We review examples of adjoining that Beck dismisses, based on what we see as his outdated view of qualitative methods. By contrast, we show that these examples demonstrate how qualitative and quantitative analysis can work together.
Concerns about nutrient loads into our waters have focused attention on poultry litter applications. Like many states with a large poultry industry, Georgia recently designed a subsidy program to facilitate the transportation of poultry litter out of vulnerable watersheds. This paper uses a transportation model to examine the necessity of a poultry litter subsidy to achieve water protection goals in Georgia. We also demonstrate the relationship between diesel and synthetic fertilizer prices and the value of poultry litter. Results suggest that a well-functioning market would be able to remove excess litter from vulnerable watersheds in the absence of a subsidy.
Process tracing is a fundamental tool of qualitative analysis. This method is often invoked by scholars who carry out within-case analysis based on qualitative data, yet frequently it is neither adequately understood nor rigorously applied. This deficit motivates this article, which offers a new framework for carrying out process tracing. The reformulation integrates discussions of process tracing and causal-process observations, gives greater attention to description as a key contribution, and emphasizes the causal sequence in which process-tracing observations can be situated. In the current period of major innovation in quantitative tools for causal inference, this reformulation is part of a wider, parallel effort to achieve greater systematization of qualitative methods. A key point here is that these methods can add inferential leverage that is often lacking in quantitative analysis. This article is accompanied by online teaching exercises, focused on four examples from American politics, two from comparative politics, three from international relations, and one from public health/epidemiology.
Self-assembly is a promising technique to overcome fundamental limitations with integrating, packaging, and generally handling individual electronic-related components with characteristic lengths significantly smaller than 1 mm. Here we briefly summarize the use of capillary and magnetic forces to realize two example microscale systems. In the first example, we use capillary forces from a low melting point solder alloy to integrate 500 μm square, 100 μm thick silicon chips with thermally and chemically sensitive metal-polymer hinge actuators, for potential medical applications. The second example demonstrates a path towards self-assembling 3-D silicon circuits formed out of 280 μm sized building blocks, utilizing both capillary forces from a low melting point solder alloy and magnetic forces from integrated, permanent magnets. In the latter example, the utilization of magnetic forces combined with capillary forces improved the assembly yield to 7.8% over 0.1% achieved previously with capillary forces alone.
Herein we present methods for synthesizing monodisperse mesoporous silica particles and silica particles with bimodal porosity by templating with surfactant micelle and microemulsion phases. The fabrication of monodisperse mesoporous silica particles is based on the formation of well-defined equally sized emulsion droplets using a microfluidic approach. The droplets contain the silica precursor/surfactant solution and are suspended in hexadecane as the continuous oil phase. The solvent is then expelled from the droplets, leading to concentration and micellization of the surfactant. At the same time, the silica solidifies around the surfactant structures, forming equally sized mesoporous particles. We show that hierarchically bimodal porous structures can be obtained by templating silica microparticles with a specially designed surfactant micelle/microemulsion mixture. Oil, water, and surfactant liquid mixtures exhibit very complex phase behavior. Depending on the conditions, such mixtures give rise to highly organized structures. A proper selection of the type and concentration of surfactants determines the structuring at the nanoscale level. Tuning the phase state by adjusting the surfactant composition and concentration allows for the controlled design of a system where microemulsion droplets coexist with smaller surfactant micellar structures. The microemulsion droplet and micellar dimensions determine the two types of pore sizes.
In many biological applications, such as cell therapy and drug delivery, there is a need to enhance diffusion by enabling chemical transport in all three dimensions. We highlight this need by comparing diffusion in a conventional two-dimensional (2D) microwell with diffusion in a three-dimensional (3D) cubic microwell using numerical simulations. We also describe the fabrication of hollow polymeric (and biocompatible) cubic microwells and microwell arrays. We emphasize that since the assembly process is compatible with 2D lithographic patterning, porosity can be precisely patterned in all three dimensions. Hence, this platform provides considerable versatility for a variety of applications.
Abstract. Proportional-hazards models are frequently used to analyze data from randomized controlled trials. This is a mistake. Randomization does not justify the models, which are rarely informative. Simpler methods work better. This discussion is salient because the misuse of survival analysis has introduced a new hazard in epidemiology: It can lead to serious mistakes in medical treatment. Life tables, Kaplan-Meier curves, and proportional-hazards models, aka “Cox models,” all require strong assumptions, such as stationarity of mortality and independence of competing risks. Where the assumptions fail, the methods also tend to fail. Justifying those assumptions is fraught with difficulty. This is illustrated with examples: the impact of religious feelings on survival and the efficacy of hormone replacement therapy. What are the implications for statistical practice? With observational studies, the models could help disentangle causal relations if the assumptions behind the models can be justified.
In this chapter, I will discuss life tables and Kaplan-Meier estimators, which are similar to life tables. Then I turn to proportional-hazards models, aka “Cox models.” Along the way, I will look at the efficacy of screening for lung cancer, the impact of negative religious feelings on survival, and the efficacy of hormone replacement therapy.
What are the conclusions about statistical practice? Proportional-hazards models are frequently used to analyze data from randomized controlled trials.
Abstract. Regression models have been used in the social sciences at least since 1899, when Yule published a paper on the causes of pauperism. Regression models are now used to make causal arguments in a wide variety of applications, and it is perhaps time to evaluate the results. No definitive answers can be given, but this chapter takes a rather negative view. Snow's work on cholera is presented as a success story for scientific reasoning based on nonexperimental data. Failure stories are also discussed, and comparisons may provide some insight. In particular, this chapter suggests that statistical technique can seldom be an adequate substitute for good design, relevant data, and testing predictions against reality in a variety of settings.
Regression models have been used in social sciences at least since 1899, when Yule published his paper on changes in “out-relief” as a cause of pauperism: He argued that providing income support outside the poorhouse increased the number of people on relief. At present, regression models are used to make causal arguments in a wide variety of social science applications, and it is perhaps time to evaluate the results.
A crude four-point scale may be useful:
Regression usually works, although it is (like anything else) imperfect and may sometimes go wrong.
Regression sometimes works in the hands of skillful practitioners, but it isn't suitable for routine use.