Mild Cognitive Impairment, But Not HIV Status, is Related to Reduced Awareness of Level of Cognitive Performance Among Older Adults.

OBJECTIVE: Self-assessment of cognitive abilities can be an important predictor of clinical outcomes. This study examined impairments in self-assessments of cognitive performance, assessed with traditional neuropsychological assessments and novel virtual reality tests among older persons with and without human immunodeficiency virus (HIV) and mild cognitive impairment (MCI). METHODS: One hundred twenty-two participants (82 persons with HIV; 79 MCI+) completed a traditional neuropsychological battery, DETECT virtual reality cognitive battery, and self-reported their general cognitive complaints, depressive symptoms, and perceptions of DETECT performance. Relationships between DETECT performance and self-assessments of performance were examined as were the correlations between general cognitive complaints and performance. These relations were evaluated across HIV and MCI status, considering the associations of depressive symptoms, performance, and self-assessment. RESULTS: We found no effect of HIV status on objective performance or self-assessment of DETECT performance. However, MCI+ participants performed worse on DETECT and traditional cognitive tests, while also showing a directional bias towards overestimation of their performance. MCI- participants showed a bias toward underestimation. Cognitive complaints were reduced compared to objective performance in MCI+ participants. Correlations between self-reported depressive symptoms and cognitive performance or self-assessment of performance were nonsignificant. CONCLUSIONS: MCI+ participants underperformed on neuropsychological testing, while overestimating performance. Interestingly, MCI- participants underestimated performance to approximately the same extent as MCI+ participants overestimated. Practical implications include providing support for persons with MCI regarding awareness of limitations and consideration that self-assessments of cognitive performance may be overestimated. Similarly, supporting older persons without MCI to realistically appraise their abilities may have clinical importance.

Multiresidue extraction of current-use pesticides from complex solid matrices using energized dispersive guided extraction with analysis by gas and liquid chromatography tandem mass spectroscopy.

The development of sample processing techniques that recover a broad suite of pesticides from solid matrices, while mitigating coextracted matrix interferences, and reducing processing time is beneficial for high throughput analyses. The objective of this study was to evaluate the effectiveness of an automated extraction system for pesticide analyses in solid environmental samples. An Energized Dispersive Guided Extraction (EDGE) system was used to evaluate two different extraction solvents in optimizing the extraction of 210 pesticides and pesticide transformation products. A graphitized carbon cleanup step was implemented, and three elution solvents were evaluated separately for analyte recoveries. Recoveries between 70 and 130% were achieved for 167 compounds in a test soil using acetonitrile as an extraction solvent and carbon cleanup with acetonitrile and dichloromethane elutions. Nine field samples (soil, sediment, and biosolids) were extracted using the newly developed method and were compared with a previously validated pressurized liquid extraction (PLE) method using an Accelerated Solvent Extraction (ASE) system. Concentrations obtained from the two methods were comparable (linear R2 > 0.999), suggesting similar performance between the EDGE and PLE extractions in complex matrices. The new method provided slightly better sensitivities in comparison to the PLE method, ranging from 0.09 to 2.56 ng g-1. The method presented here significantly reduces extraction setup and runtimes while also minimizing the volume of carcinogenic solvents (e.g., dichloromethane) used in the laboratory and presents a sensitive multiresidue method for a wide range of pesticides in solid matrices.

Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system.

Nontarget chemical analysis using high-resolution mass spectrometry has increasingly been used to discern spatial patterns and temporal trends in anthropogenic chemical abundance in natural and engineered systems. A critical experimental design consideration in such applications, especially those monitoring complex matrices over long time periods, is a choice between analyzing samples in multiple batches as they are collected, or in one batch after all samples have been processed. While datasets acquired in multiple analytical batches can include the effects of instrumental variability over time, datasets acquired in a single batch risk compound degradation during sample storage. To assess the influence of batch effects on the analysis and interpretation of nontarget data, this study examined a set of 56 samples collected from a municipal wastewater system over 7 months. Each month's samples included 6 from sites within the collection system, one combined influent, and one treated effluent sample. Samples were analyzed using liquid chromatography high-resolution mass spectrometry in positive electrospray ionization mode in multiple batches as the samples were collected and in a single batch at the conclusion of the study. Data were aligned and normalized using internal standard scaling and ComBat, an empirical Bayes method developed for estimating and removing batch effects in microarrays. As judged by multiple lines of evidence, including comparing principal variance component analysis between single and multi-batch datasets and through patterns in principal components and hierarchical clustering analyses, ComBat appeared to significantly reduce the influence of batch effects. For this reason, we recommend the use of more, small batches with an appropriate batch correction step rather than acquisition in one large batch.

Exploring chemical space in non-targeted analysis: a proposed ChemSpace tool.

Non-targeted analysis (NTA) using high-resolution mass spectrometry allows scientists to detect and identify a broad range of compounds in diverse matrices for monitoring exposure and toxicological evaluation without a priori chemical knowledge. NTA methods present an opportunity to describe the constituents of a sample across a multidimensional swath of chemical properties, referred to as "chemical space." Understanding and communicating which region of chemical space is extractable and detectable by an NTA workflow, however, remains challenging and non-standardized. For example, many sample processing and data analysis steps influence the types of chemicals that can be detected and identified. Accordingly, it is challenging to assess whether analyte non-detection in an NTA study indicates true absence in a sample (above a detection limit) or is a false negative driven by workflow limitations. Here, we describe the need for accessible approaches that enable chemical space mapping in NTA studies, propose a tool to address this need, and highlight the different ways in which it could be implemented in NTA workflows. We identify a suite of existing predictive and analytical tools that can be used in combination to generate scores that describe the likelihood a compound will be detected and identified by a given NTA workflow based on the predicted chemical space of that workflow. Higher scores correspond to a higher likelihood of compound detection and identification in a given workflow (based on sample extraction, data acquisition, and data analysis parameters). Lower scores indicate a lower probability of detection, even if the compound is truly present in the samples of interest. Understanding the constraints of NTA workflows can be useful for stakeholders when results from NTA studies are used in real-world applications and for NTA researchers working to improve their workflow performance. The hypothetical ChemSpaceTool suggested herein could be used in both a prospective and retrospective sense. Prospectively, the tool can be used to further curate screening libraries and set identification thresholds. Retrospectively, false detections can be filtered by the plausibility of the compound identification by the selected NTA method, increasing the confidence of unknown identifications. Lastly, this work highlights the chemometric needs to make such a tool robust and usable across a wide range of NTA disciplines and invites others who are working on various models to participate in the development of the ChemSpaceTool. Ultimately, the development of a chemical space mapping tool strives to enable further standardization of NTA by improving method transparency and communication around false detection rates, thus allowing for more direct method comparisons between studies and improved reproducibility. This, in turn, is expected to promote further widespread applications of NTA beyond research-oriented settings.

Using Estrogenic Activity and Nontargeted Chemical Analysis to Identify Contaminants in Sewage Sludge.

Diverse organic compounds, many derived from consumer products, are found in sewage sludge worldwide. Understanding which of these poses the most significant environmental threat following land application can be investigated through a variety of predictive and cell-based toxicological techniques. Nontargeted analysis using high-resolution mass spectrometry with predictive estrogenic activity modeling was performed on sewage sludge samples from 12 wastewater treatment plants in California. Diisobutyl phthalate and dextrorphan were predicted to exhibit estrogenic activity and identified in >75% of sludge samples, signifying their universal presence and persistence. Additionally, the application of an estrogen-responsive cell bioassay revealed reductions in agonistic activity during mesophilic and thermophilic treatment but significant increases in antagonism during thermophilic treatment, which warrants further research. Ten nontarget features were identified (metoprolol, fenofibric acid, erythrohydrobupropion, oleic acid, mestranol, 4'-chlorobiphenyl-2,3-diol, medrysone, scillarenin, sudan I, and N,O-didesmethyltramadol) in treatment set samples and are considered to have influenced the in vitro estrogenic activity observed. The combination of predictive and in vitro estrogenicity with nontargeted analysis has led to confirmation of 12 estrogen-active contaminants in California sewage sludge and has highlighted the importance of evaluating both agonistic and antagonistic responses when evaluating the bioactivity of complex samples.

Identifying Toxicologically Significant Compounds in Urban Wildfire Ash Using In Vitro Bioassays and High-Resolution Mass Spectrometry.

Urban wildfires may generate numerous unidentified chemicals of toxicity concern. Ash samples were collected from burned residences and from an undeveloped upwind reference site, following the Tubbs fire in Sonoma County, California. The solvent extracts of ash samples were analyzed using GC- and LC-high-resolution mass spectrometry (HRMS) and using a suite of in vitro bioassays for their bioactivity toward nuclear receptors [aryl hydrocarbon receptor (AhR), estrogen receptor (ER), and androgen receptor (AR)], their influence on the expression of genetic markers of stress and inflammation [interleukin-8 (IL-8) and cyclooxygenase-2 (COX-2)], and xenobiotic metabolism [cytochrome P4501A1 (CYP1A1)]. Genetic markers (CYP1A1, IL-8, and COX-2) and AhR activity were significantly higher with wildfire samples than in solvent controls, whereas AR and ER activities generally were unaffected or reduced. The bioassay responses of samples from residential areas were not significantly different from the samples from the reference site despite differing chemical compositions. Suspect and nontarget screening was conducted to identify the chemicals responsible for elevated bioactivity using the multiple streams of HRMS data and open-source data analysis workflows. For the bioassay endpoint with the largest available database of pure compound results (AhR), nontarget features statistically related to whole sample bioassay response using Spearman's rank-order correlation coefficients or elastic net regression were significantly more likely (by 10 and 15 times, respectively) to be known AhR agonists than the overall population of compounds tentatively identified by nontarget analysis. The findings suggest that a combination of nontarget analysis, in vitro bioassays, and statistical analysis can identify bioactive compounds in complex mixtures.

Analyzing a broader spectrum of endocrine active organic contaminants in sewage sludge with high resolution LC-QTOF-MS suspect screening and QSAR toxicity prediction.

Endocrine active contaminants (EACs) in environmental samples can pose a range of toxicological threats to ecosystems, especially through their impacts on reproductive pathways mediated by the estrogen receptor. The physicochemical properties of known organic EACs vary greatly and typically require different sample preparation techniques to identify different classes of compounds. EAC sources are similarly diverse, including both endogenous compounds and anthropogenic chemicals found in personal care products, pharmaceuticals, and their transformation products, which are often disposed of to sewers at their end of use. Looking for EACs in sewage sludge proposes a bottom-up, or end-of-use and treatment approach to discover environmentally relevant EACs, since many EACs accumulate in sludges even after application of robust wastewater treatment processes. This study demonstrates an extraction and analytical method capable of detecting a broad spectrum of known and suspected EACs via High Resolution Liquid Chromatography Quadropole Time-of-Flight Mass Spectrometry (LC-QTOF-MS) suspect screening of fourteen California sewage sludge samples. Spike-recovery experiments were performed using twelve carefully selected surrogates to assess different extraction solvents, sample weights, extraction pH values, procedures for combining extracts with different extraction pH's, and solid phase extraction cartridges. Using LC-QTOF-MS, identifications of several other organic compounds in the samples were made, a goal unachievable with unit resolution mass spectrometry. Suspect screening of California sludge samples discovered 118 compounds including hormones, pharmaceuticals, phosphate flame retardants, recreational drugs, antimicrobials, and pesticides. Additionally, 22 of these identified compounds are predicted to interfere with estrogen receptors or other reproductive/developmental pathways based on the VEGA QSAR toxicity prediction model.

Bacterial community structure transformed after thermophilically composting human waste in Haiti.

Recycling human waste for beneficial use has been practiced for millennia. Aerobic (thermophilic) composting of sewage sludge has been shown to reduce populations of opportunistically pathogenic bacteria and to inactivate both Ascaris eggs and culturable Escherichia coli in raw waste, but there is still a question about the fate of most fecal bacteria when raw material is composted directly. This study undertook a comprehensive microbial community analysis of composting material at various stages collected over 6 months at two composting facilities in Haiti. The fecal microbiota signal was monitored using a high-density DNA microarray (PhyloChip). Thermophilic composting altered the bacterial community structure of the starting material. Typical fecal bacteria classified in the following groups were present in at least half the starting material samples, yet were reduced below detection in finished compost: Prevotella and Erysipelotrichaceae (100% reduction of initial presence), Ruminococcaceae (98-99%), Lachnospiraceae (83-94%, primarily unclassified taxa remained), Escherichia and Shigella (100%). Opportunistic pathogens were reduced below the level of detection in the final product with the exception of Clostridium tetani, which could have survived in a spore state or been reintroduced late in the outdoor maturation process. Conversely, thermotolerant or thermophilic Actinomycetes and Firmicutes (e.g., Thermobifida, Bacillus, Geobacillus) typically found in compost increased substantially during the thermophilic stage. This community DNA-based assessment of the fate of human fecal microbiota during thermophilic composting will help optimize this process as a sanitation solution in areas where infrastructure and resources are limited.