Polychlorinated biphenyls (PCBs) are legacy pollutants that exert toxicities through various mechanisms. In the recent years exposure to PCBs via inhalation has been recognized as a hazard. Those PCBs with lower numbers of chlorine atoms (LC-PCBs) are semi-volatile, and have been reported in the urban air, as well as in the indoor air of older buildings. LC-PCBs are bioactivated to phenols and further to quinone electrophiles with genotoxic/carcinogenic potential. We hypothesized that phenolic LC-PCBs are subject to conjugation and excretion in the urine. PCB3, often present in high concentrations in air, is a prototypical congener for the study of the metabolism and toxicity of LC-PCBs. Our objective was to identify metabolites of PCB3 in urine that could be potentially employed in the estimation of exposure to LC-PCBs. Male Sprague Dawley rats (150–175 g) were housed in metabolism cages and received a single intraperitoneal injection of 600 µmol/kg body weight of PCB3. Urine was collected every four hours; rats were euthanized at 36 h and serum was collected. LC-MS analysis of urine before and after incubation with β-glucuronidase and sulfatase showed that sulfate conjugates were in higher concentrations than glucuronide conjugates and free phenolic forms. At least two major metabolites, and two minor metabolites were identified in urine that could be attributed to mercapturic acid metabolites of PCB3. Quantitation by authentic standards confirmed that approximately 3% of the dose was excreted in the urine as sulfates over 36 hours; with peak excretion occurring at 10–20 h after exposure. The major metabolites were 4’PCB3 sulfate, 3’PCB3 sulfate, 2’PCB3 sulfate, and presumably a catechol sulfate. The serum concentration of 4’PCB3 sulfate was 6.18±2.16 µg/mL. This is the first report that sulfated metabolites of PCBs are formed in vivo. These findings suggest a prospective approach for exposure assessment of LC- PCBs by analysis of phase II metabolites in urine.

Nanoscale metal–organic frameworks (nMOF) materials represent an attractive tool for various biomedical applications. Due to the chemical versatility, enormous porosity, and tunable degradability of nMOFs, they have been adopted as carriers for delivery of imaging and/or therapeutic cargos. However, the relatively low stability of most nMOFs has limited practical in vivo applications. Here we report the production and characterization of an intrinsically radioactive UiO-66 nMOF (89Zr-UiO-66) with incorporation of positron-emitting isotope zirconium-89 (89Zr). 89Zr-UiO-66 was further functionalized with pyrene-derived polyethylene glycol (Py–PGA-PEG) and conjugated with a peptide ligand (F3) to nucleolin for targeting of triple-negative breast tumors. Doxorubicin (DOX) was loaded onto UiO-66 with a relatively high loading capacity (1 mg DOX/mg UiO-66) and served as both a therapeutic cargo and a fluorescence visualizer in this study. Functionalized 89Zr-UiO-66 demonstrated strong radiochemical and material stability in different biological media. Based on the findings from cellular targeting and in vivo positron emission tomography (PET) imaging, we can conclude that 89Zr-UiO-66/Py–PGA-PEG-F3 can serve as an image-guidable, tumor-selective cargo delivery nanoplatform. In addition, toxicity evaluation confirmed that properly PEGylated UiO-66 did not impose acute or chronic toxicity to the test subjects. With selective targeting of nucleolin on both tumor vasculature and tumor cells, this intrinsically radioactive nMOF can find broad application in cancer theranostics.

Background: The displacement of l-thyroxine (T4) from binding sites on transthyretin (TTR) is considered a significant contributing mechanism in polychlorinated biphenyl (PCB)-induced thyroid disruption. Previous research has discovered hydroxylated PCB metabolites (OH-PCBs) as high-affinity ligands for TTR, but the binding potential of conjugated PCB metabolites such as PCB sulfates has not been explored.Objectives: We evaluated the binding of five lower-chlorinated PCB sulfates to human TTR and compared their binding characteristics to those determined for their OH-PCB precursors and for T4.Methods: We used fluorescence probe displacement studies and molecular docking simulations to characterize the binding of PCB sulfates to TTR. The stability of PCB sulfates and the reversibility of these interactions were characterized by HPLC analysis of PCB sulfates after their binding to TTR. The ability of OH-PCBs to serve as substrates for human cytosolic sulfotransferase 1A1 (hSULT1A1) was assessed by OH-PCB–dependent formation of adenosine-3´,5´-diphosphate, an end product of the sulfation reaction.Results: All five PCB sulfates were able to bind to the high-affinity binding site of TTR with equilibrium dissociation constants (Kd values) in the low nanomolar range (4.8–16.8 nM), similar to that observed for T4 (4.7 nM). Docking simulations provided corroborating evidence for these binding interactions and indicated multiple high-affinity modes of binding. All OH-PCB precursors for these sulfates were found to be substrates for hSULT1A1.Conclusions: Our findings show that PCB sulfates are high-affinity ligands for human TTR and therefore indicate, for the first time, a potential relevance for these metabolites in PCB-induced thyroid disruption.

Aromatic organosulfates are identified and quantified in fine particulate matter (PM2.5) from Lahore, Pakistan, Godavari, Nepal, and Pasadena, California. To support detection and quantification, authentic standards of phenyl sulfate, benzyl sulfate, 3-and 4-methylphenyl sulfate and 2-, 3-, and 4-methylbenzyl sulfate were synthesized. Authentic standards and aerosol samples were analyzed by ultra-performance liquid chromatography (UPLC) coupled to negative electrospray ionization (ESI) quadrupole time-of-flight (ToF) mass spectrometry. Benzyl sulfate was present in all three locations at concentrations ranging from 4 – 90 pg m−3. Phenyl sulfate, methylphenyl sulfates and methylbenzyl sulfates were observed intermittently with abundances of 4 pg m−3, 2-31 pg m−3, 109 pg m−3, respectively. Characteristic fragment ions of aromatic organosulfates include the sulfite radical (•SO3−, m/z 80) and the sulfate radical (•SO4−,m/z 96). Instrumental response factors of phenyl and benzyl sulfates varied by a factor of 4.3, indicating that structurally-similar organosulfates may have significantly different instrumental responses and highlighting the need to develop authentic standards for absolute quantitation organosulfates. In an effort to better understand the sources of aromatic organosulfates to the atmosphere, chamber experiments with the precursor toluene were conducted under conditions that form biogenic organosulfates. Aromatic organosulfates were not detected in the chamber samples, suggesting that they form through different pathways, have different precursors (e.g. naphthalene or methylnaphthalene), or are emitted from primary sources.

This paper introduces Prec‐DWARF (Precipitation Downscaling With Adaptable Random Forests), a novel machine‐learning based method for statistical downscaling of precipitation. Prec‐DWARF sets up a nonlinear relationship between precipitation at fine resolution and covariates at coarse/fine resolution, based on the advanced binary tree method known as Random Forests (RF). In addition to a single RF, we also consider a more advanced implementation based on two independent RFs which yield better results for extreme precipitation. Hourly gauge‐radar precipitation data at 0.125° from NLDAS‐2 are used to conduct synthetic experiments with different spatial resolutions (0.25°, 0.5°, and 1°). Quantitative evaluation of these experiments demonstrates that Prec‐DWARF consistently outperforms the baseline (i.e., bilinear interpolation in this case) and can reasonably reproduce the spatial and temporal patterns, occurrence and distribution of observed precipitation fields. However, Prec‐DWARF with a single RF significantly underestimates precipitation extremes and often cannot correctly recover the fine‐scale spatial structure, especially for the 1° experiments. Prec‐DWARF with a double RF exhibits improvement in the simulation of extreme precipitation as well as its spatial and temporal structures, but variogram analyses show that the spatial and temporal variability of the downscaled fields are still strongly underestimated. Covariate importance analysis shows that the most important predictors for the downscaling are the coarse‐scale precipitation values over adjacent grid cells as well as the distance to the closest dry grid cell (i.e., the dry drift). The encouraging results demonstrate the potential of Prec‐DWARF and machine‐learning based techniques in general for the statistical downscaling of precipitation.