Toxic metal(loid) speciation during weathering of iron sulfide mine tailings under semi-arid climate

Toxic metalliferous mine-tailings pose a significant health risk to ecosystems and neighboring communities from wind and water dispersion of particulates containing high concentrations of toxic metal(loid)s (e.g., Pb, As, Zn). Tailings are particularly vulnerable to erosion before vegetative cover can be reestablished, i.e., decades or longer in semi-arid environments without intervention. Metal(loid) speciation, linked directly to bioaccessibility and lability, is controlled by mineral weathering and is a key consideration when assessing human and environmental health risks associated with mine sites. At the semi-arid Iron King Mine and Humboldt Smelter Superfund site in central Arizona, the mineral assemblage of the top 2 m of tailings has been previously characterized. A distinct redox gradient was observed in the top 0.5 m of the tailings and the mineral assemblage indicates progressive transformation of ferrous iron sulfides to ferrihydrite and gypsum, which, in turn weather to form schwertmannite and then jarosite accompanied by a progressive decrease in pH (7.3 to 2.3). Within the geochemical context of this reaction front, we examined enriched toxic metal(loid)s As, Pb, and Zn with surficial concentrations 41.1, 10.7, 39.3 mM kg-1 (3080, 2200, and 2570 mg kg-1), respectively. The highest bulk concentrations of As and Zn occur at the redox boundary representing a 1.7 and 4.2 fold enrichment relative to surficial concentrations, respectively, indicating the translocation of toxic elements from the gossan zone to either the underlying redox boundary or the surface crust. Metal speciation was also examined as a function of depth using X-ray absorption spectroscopy (XAS). The deepest sample (180 cm) contains sulfides (e.g., pyrite, arsenopyrite, galena, and sphalerite). Samples from the redox transition zone (25-54 cm) contain a mixture of sulfides, carbonates (siderite, ankerite, cerrusite, and smithsonite) and metal(loid)s sorbed to neoformed secondary Fe phases, principally ferrihydrite. In surface samples (0-35 cm), metal(loid)s are found as sorbed species or incorporated into secondary Fe hydroxysulfate phases, such as schwertmannite and jarosites. Metal-bearing efflorescent salts (e.g., ZnSO4·nH2O) were detected in the surficial sample. Taken together, these data suggest the bioaccessibility and lability of metal(loid)s are altered by mineral weathering, which results in both the downward migration of metal(loid)s to the redox boundary, as well as the precipitation of metal salts at the surface.24 month embargo; published online: 7 February 2015This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Geochemical Weathering Increases Lead Bioaccessibility in Semi-Arid Mine Tailings

Mine tailings can host elevated concentrations of toxic metal­(loid)­s that represent a significant hazard to surrounding communities and ecosystems. Eolian transport, capable of translocating small (micrometer-sized) particles, can be the dominant mechanism of toxic metal dispersion in arid or semiarid landscapes. Human exposure to metals can then occur via direct inhalation or ingestion of particulates. The fact that measured doses of total lead (Pb) in geomedia correlate poorly with blood Pb levels highlights a need to better resolve the precise distribution of molecularly speciated metal-bearing phases in the complex particle mixtures. Species distribution controls bioaccessibility, thereby directly impacting health risk. This study seeks to correlate Pb-containing particle size and mineral composition with lability and bioaccessibility in mine tailings subjected to weathering in a semiarid environment. We employed X-ray absorption spectroscopy (XAS) and X-ray fluorescence (XRF), coupled with sequential chemical extractions, to study Pb speciation in tailings from the semiarid Arizona Klondyke State Superfund Site. Representative samples ranging in pH from 2.6 to 5.4 were selected for in-depth study of Pb solid-phase speciation. The principle lead-bearing phase was plumbojarosite (PbFe₆(SO₄)₄(OH)₁₂), but anglesite (PbSO₄) and iron oxide-sorbed Pb were also observed. Anglesite, the most bioavailable mineral species of lead identified in this study, was enriched in surficial tailings samples, where Pb concentrations in the clay size fraction were 2–3 times higher by mass relative to bulk. A mobile and bioaccessible Pb phase accumulates in surficial tailings, with a corresponding increase in risk of human exposure to atmospheric particles

<i>Trypanosoma Cruzi</i> (Chagas Disease) Positive Samples By Species And Geographic Origin.

<p><i>Trypanosoma Cruzi</i> (Chagas Disease) Positive Samples By Species And Geographic Origin.</p

<i>Trypanosoma Cruzi</i> (Chagas Disease) Prevalence In Coyotes, Shelter Dogs, Human Residents, And Vectors Of South Texas.

<p><i>Trypanosoma Cruzi</i> (Chagas Disease) Prevalence In Coyotes, Shelter Dogs, Human Residents, And Vectors Of South Texas.</p

Genomic Heterogeneity as a Barrier to Precision Medicine in Gastroesophageal Adenocarcinoma

Gastroesophageal adenocarcinoma (GEA) is a lethal disease where targeted therapies, even when guided by genomic biomarkers, have had limited efficacy. A potential rea-son for the failure of such therapies is that genomic profiling results could commonly differ between the primary and metastatic tumors. To evaluate genomic heterogeneity, we sequenced paired primary GEA and synchronous metastatic lesions across multiple cohorts, finding extensive differences in genomic alterations, including discrepancies in potentially clinically relevant alterations. Multiregion sequencing showed significant discrepancy within the primary tumor (PT) and between the PT and disseminated disease, with oncogene amplification profiles commonly discordant. In addition, a pilot analysis of cell-free DNA (cfDNA) sequencing demonstrated the feasibility of detecting genomic amplifications not detected in PT sampling. Lastly, we profiled paired primary tumors, metastatic tumors, and cfDNA from patients enrolled in the personalized antibodies for GEA (PANGEA) trial of targeted therapies in GEA and found that genomic biomarkers were recurrently discrepant between the PT and untreated metastases. Divergent primary and metastatic tissue profiling led to treatment reassignment in 32% (9/28) of patients. In discordant primary and metastatic lesions, we found 87.5% concordance for targetable alterations in metastatic tissue and cfDNA, suggesting the potential for cfDNA profiling to enhance selection of therapy. SIGNIFICANCE: We demonstrate frequent baseline heterogeneity in targetable genomic alterations in GEA, indicating that current tissue sampling practices for biomarker testing do not effectively guide precision medicine in this disease and that routine profiling of metastatic lesions and/or cfDNA should be systematically evaluated

Genomic heterogeneity as a barrier to precision medicine in gastroesophageal adenocarcinoma

Gastroesophageal adenocarcinoma (GEA) is a lethal disease where targeted therapies, even when guided by genomic biomarkers, have had limited efficacy. A potential rea-son for the failure of such therapies is that genomic profiling results could commonly differ between the primary and metastatic tumors. To evaluate genomic heterogeneity, we sequenced paired primary GEA and synchronous metastatic lesions across multiple cohorts, finding extensive differences in genomic alterations, including discrepancies in potentially clinically relevant alterations. Multiregion sequencing showed significant discrepancy within the primary tumor (PT) and between the PT and disseminated disease, with oncogene amplification profiles commonly discordant. In addition, a pilot analysis of cell-free DNA (cfDNA) sequencing demonstrated the feasibility of detecting genomic amplifications not detected in PT sampling. Lastly, we profiled paired primary tumors, metastatic tumors, and cfDNA from patients enrolled in the personalized antibodies for GEA (PANGEA) trial of targeted therapies in GEA and found that genomic biomarkers were recurrently discrepant between the PT and untreated metastases. Divergent primary and metastatic tissue profiling led to treatment reassignment in 32% (9/28) of patients. In discordant primary and metastatic lesions, we found 87.5% concordance for targetable alterations in metastatic tissue and cfDNA, suggesting the potential for cfDNA profiling to enhance selection of therapy. SIGNIFICANCE: We demonstrate frequent baseline heterogeneity in targetable genomic alterations in GEA, indicating that current tissue sampling practices for biomarker testing do not effectively guide precision medicine in this disease and that routine profiling of metastatic lesions and/or cfDNA should be systematically evaluated

Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies

ABSTRACTBiologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration–time curve (AUC0–672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL