The Implications of Using a Physiologically Based Pharmacokinetic (PBPK) Model for Pesticide Risk Assessment

Background: A physiologically based pharmacokinetic (PBPK) model would make it possible to simulate the dynamics of chemical absorption, distribution, metabolism, and elimination (ADME) from different routes of exposures and, in theory, could be used to evaluate associations between exposures and biomarker measurements in blood or urine. Objective: We used a PBPK model to predict urinary excretion of 3,5,6-trichloro-2-pyridinol (TCPY), the specific metabolite of chlorpyrifos (CPF), in young children.Methods We developed a child-specific PBPK model for CPF using PBPK models previously developed for rats and adult humans. Data used in the model simulation were collected from 13 children 3–6 years of age who participated in a cross-sectional pesticide exposure assessment study with repeated environmental and biological sampling. Results: The model-predicted urinary TCPY excretion estimates were consistent with measured levels for 2 children with two 24-hr duplicate food samples that contained 350 and 12 ng/g of CPF, respectively. However, we found that the majority of model outputs underpredicted the measured urinary TCPY excretion. Conclusions: We concluded that the potential measurement errors associated with the aggregate exposure measurements will probably limit the applicability of PBPK model estimates for interpreting urinary TCPY excretion and absorbed CPF dose from multiple sources of exposure. However, recent changes in organophosphorus (OP) use have shifted exposures from multipathways to dietary ingestion only. Thus, we concluded that the PBPK model is still a valuable tool for converting dietary pesticide exposures to absorbed dose estimates when the model input data are accurate estimates of dietary pesticide exposures

Interband Transitions and Critical Points of Single-Crystal Thoria Compared with Urania

The interband transitions of UO2 are validated independently through cathode luminescence. A picture emerges consistent with density functional theory. While theory is generally consistent with experiment, it is evident from the comparison of UO2 and ThO2 that the choice of functional can significantly alter the bandgap and some details of the band structure, in particular at the conduction band minimum. Strictly ab initio predictions of the optical properties of the actinide compounds, based on density functional theory alone, continue to be somewhat elusive

Clinical value of bioelectrical properties of cancerous tissue in advanced epithelial ovarian cancer patients

Currently, there are no valid pre-operatively established biomarkers or algorithms that can accurately predict surgical and clinical outcome for patients with advanced epithelial ovarian cancer (EOC). In this study, we suggest that profiling of tumour parameters such as bioelectrical-potential and metabolites, detectable by electronic sensors, could facilitate the future development of devices to better monitor disease and predict surgical and treatment outcomes. Biopotential was recorded, using a potentiometric measurement system, in ex vivo paired non-cancerous and cancerous omental tissues from advanced stage EOC (n = 36), and lysates collected for metabolite measurement by microdialysis. Consistently different biopotential values were detected in cancerous tissue versus non-cancerous tissue across all cases (p < 0.001). High tumour biopotential levels correlated with advanced tumour stage (p = 0.048) and tumour load, and negatively correlated with stroma. Within our EOC cohort and specifically the high-grade serous subtype, low biopotential levels associated with poorer progression-free survival (p = 0.0179, p = 0.0143 respectively). Changes in biopotential levels significantly correlated with common apoptosis related pathways. Lactate and glucose levels measured in paired tissues showed significantly higher lactate/glucose ratio in tissues with low biopotential (p < 0.01, n = 12). Our study proposes the feasibility of biopotential and metabolite monitoring as a biomarker modality profiling EOC to predict surgical and clinical outcomes

Computational Optogenetics: Empirically-Derived Voltage- and Light-Sensitive Channelrhodopsin-2 Model

Channelrhodospin-2 (ChR2), a light-sensitive ion channel, and its variants have emerged as new excitatory optogenetic tools not only in neuroscience, but also in other areas, including cardiac electrophysiology. An accurate quantitative model of ChR2 is necessary for in silicoprediction of the response to optical stimulation in realistic tissue/organ settings. Such a model can guide the rational design of new ion channel functionality tailored to different cell types/tissues. Focusing on one of the most widely used ChR2 mutants (H134R) with enhanced current, we collected a comprehensive experimental data set of the response of this ion channel to different irradiances and voltages, and used these data to develop a model of ChR2 with empirically-derived voltage- and irradiance- dependence, where parameters were fine-tuned via simulated annealing optimization. This ChR2 model offers: 1) accurate inward rectification in the current-voltage response across irradiances; 2) empirically-derived voltage- and light-dependent kinetics (activation, deactivation and recovery from inactivation); and 3) accurate amplitude and morphology of the response across voltage and irradiance settings. Temperature-scaling factors (Q10) were derived and model kinetics was adjusted to physiological temperatures. Using optical action potential clamp, we experimentally validated model-predicted ChR2 behavior in guinea pig ventricular myocytes. The model was then incorporated in a variety of cardiac myocytes, including human ventricular, atrial and Purkinje cell models. We demonstrate the ability of ChR2 to trigger action potentials in human cardiomyocytes at relatively low light levels, as well as the differential response of these cells to light, with the Purkinje cells being most easily excitable and ventricular cells requiring the highest irradiance at all pulse durations. This new experimentally-validated ChR2 model will facilitate virtual experimentation in neural and cardiac optogenetics at the cell and organ level and provide guidance for the development of in vivo tools

Doped graphene nanohole arrays for flexible transparent conductors

Graphene nanohole arrays (GNAs) were fabricated using nanoimprint lithography. The improved optical transmittance of GNAs is primarily due to the reduced surface coverage of graphene from the nanohole fabrication. Importantly, the exposed edges of the nanoholes provided effective sites for chemical doping using thionyl chloride was shown to enhance the conductance by a factor of 15–18 in contrast to only 2-4 for unpatterned graphene. GNAs can provide a unique scheme for improving both optical transmittance and electrical conductivity of graphene-based transparent conductors

Waiting time for cancer treatment and mental health among patients with newly diagnosed esophageal or gastric cancer: a nationwide cohort study

Background Except for overall survival, whether or not waiting time for treatment could influences other domains of cancer patients’ overall well-being is to a large extent unknown. Therefore, we performed this study to determine the effect of waiting time for cancer treatment on the mental health of patients with esophageal or gastric cancer. Methods Based on the Swedish National Quality Register for Esophageal and Gastric Cancers (NREV), we followed 7,080 patients diagnosed 2006–2012 from the time of treatment decision. Waiting time for treatment was defined as the interval between diagnosis and treatment decision, and was classified into quartiles. Mental disorders were identified by either clinical diagnosis through hospital visit or prescription of psychiatric medications. For patients without any mental disorder before treatment, the association between waiting time and subsequent onset of mental disorders was assessed by hazard ratios (HRs) with 95% confidence interval (CI), derived from multivariable-adjusted Cox model. For patients with a preexisting mental disorder, we compared the rate of psychiatric care by different waiting times, allowing for repeated events. Results Among 4,120 patients without any preexisting mental disorder, lower risk of new onset mental disorders was noted for patients with longer waiting times, i.e. 18–29 days (HR 0.86; 95% CI 0.74-1.00) and 30–60 days (HR 0.79; 95% CI 0.67-0.93) as compared with 9–17 days. Among 2,312 patients with preexisting mental disorders, longer waiting time was associated with more frequent psychiatric hospital care during the first year after treatment (37.5% higher rate per quartile increase in waiting time; p for trend = 0.0002). However, no such association was observed beyond one year nor for the prescription of psychiatric medications. Conclusions These data suggest that waiting time to treatment for esophageal or gastric cancer may have different mental health consequences for patients depending on their past psychiatric vulnerabilities. Our study sheds further light on the complexity of waiting time management, and calls for a comprehensive strategy that takes into account different domains of patient well-being in addition to the overall survival.This study was partly supported by the Swedish Cancer Society (grant No: CAN 2014/417).Peer Reviewe

A shared genetic contribution to breast cancer and schizophrenia

Publisher's version (útgefin grein)An association between schizophrenia and subsequent breast cancer has been suggested; however the risk of schizophrenia following a breast cancer is unknown. Moreover, the driving forces of the link are largely unclear. Here, we report the phenotypic and genetic positive associations of schizophrenia with breast cancer and vice versa, based on a Swedish population-based cohort and GWAS data from international consortia. We observe a genetic correlation of 0.14 (95% CI 0.09–0.19) and identify a shared locus at 19p13 (GATAD2A) associated with risks of breast cancer and schizophrenia. The epidemiological bidirectional association between breast cancer and schizophrenia may partly be explained by the genetic overlap between the two phenotypes and, hence, shared biological mechanisms.This work is supported by the Swedish Research Council (grant number: 2018-00648, to D.L.), Karolinska Institutet Research Foundation (grant number: 2018-01585, to D.L.), Grant of Excellence, Icelandic Research Fund (grant number 163362-051, to U.A.V.), and ERC Consolidator Grant (StressGene, grant number:726413, to U.A.V.). We thank Dr. Agnar Helgason, Dr. Patrick Sulem, and Dr. Kári Stefánsson from deCODE Genetics, Iceland for helpful discussions on data analysis and interpretations. We also acknowledge the shared GWAS statistical summaries of schizophrenia from the Psychiatric Genetics Consortium and breast cancer from the Breast Cancer Association Consortium.Peer Reviewe