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Preeclampsia (PE) is a multifactorial pregnancy disease, characterized by new-onset gestational hypertension with (or without) proteinuria or end-organ failure, exclusively observed in humans. It is a leading cause of maternal morbidity affecting 3-7% of pregnant women worldwide. PE pathophysiology could result from abnormal placentation due to a defective trophoblastic invasion and an impaired remodeling of uterine spiral arteries, leading to a poor adaptation of utero-placental circulation. This would be associated with hypoxia/reoxygenation phenomena, oxygen gradient fluctuations, altered antioxidant capacity, oxidative stress, and reduced nitric oxide (NO) bioavailability. This results in part from the reaction of NO with the radical anion superoxide (O2•-), which produces peroxynitrite ONOO-, a powerful pro-oxidant and inflammatory agent. Another mechanism is the progressive inhibition of the placental endothelial nitric oxide synthase (eNOS) by oxidative stress, which results in eNOS uncoupling via several events such as a depletion of the eNOS substrate L-arginine due to increased arginase activity, an oxidation of the eNOS cofactor tetrahydrobiopterin (BH4), or eNOS post-translational modifications (for instance by S-glutathionylation). The uncoupling of eNOS triggers a switch of its activity from a NO-producing enzyme to a NADPH oxidase-like system generating O2•-, thereby potentiating ROS production and oxidative stress. Moreover, in PE placentas, eNOS could be post-translationally modified by lipid peroxidation-derived aldehydes such as 4-oxononenal (ONE) a highly bioreactive agent, able to inhibit eNOS activity and NO production. This review summarizes the dysfunction of placental eNOS evoked by oxidative stress and lipid peroxidation products, and the potential consequences on PE pathogenesis.Despite evidence for the association between emotion regulation difficulties and posttraumatic stress disorder (PTSD), less is known about the specific emotion regulation abilities that are most relevant to PTSD severity. This study examined both item-level and subscale-level models of difficulties in emotion regulation in relation to PTSD severity using supervised machine learning in a sample of U.S. adults (N=570). Participants were recruited via Amazon’s Mechanical Turk (MTurk) and completed self-report measures of emotion regulation difficulties and PTSD severity. We used five different machine learning algorithms separately to train each statistical model. Using ridge and elastic net regression results in the testing sample, emotion regulation predictor variables accounted for approximately 28% and 27% of the variance in PTSD severity in the item- and subscale-level models, respectively. In the item-level model, four predictor variables had notable relative importance values for PTSD severity. These items captured secondary emotional responding, experiencing emotions as out-of-control, difficulties modulating emotional arousal, and low emotional granularity. In the subscale-level model, lack of access to effective emotion regulation strategies, lack of emotional clarity, and emotional nonacceptance subscales had the highest relative importance to PTSD severity. Results from analyses modeling a probable diagnosis of PTSD based on DERS items and subscales are presented in supplemental findings. Findings have implications for developing more efficient, targeted emotion regulation interventions for PTSD.High rate algal ponds (HRAP) are an alternative to conventional wastewater treatment with the potential for wastewater and biomass reuse. In this study, we report the development and validation of methods for analysing 18 bisphenols (BPs) in the aqueous and biomass phase of HRAP. For aqueous phase samples, obtained LLOQ ranged from 10 to 30 ng/L, and recoveries from 78% to 106%. The relative expanded uncertainty was highest at the lowest spiking level (100 ng/L) and ranged from 27% to 66% (BPA), while for the biomass, the LLOQ ranged from 25 to 75 ng/g dw, recoveries from 84% to 103%. The uncertainty ranged from 16% to 37% (BPA). On average, the influent contained 329, 144, and 21 ng/L of BPA, BPS and 4,4′-BPF, and the effluent 69 ng/L, 94 ng/L and less then LLOQ, respectively. KRT-232 MDM2 inhibitor Only BPA was quantified in the algal biomass. The average removal of BPA was 80%, whereas the removal efficiency of BPS was 32%. To our knowledge, this is the first study analysing a wide range of BPs in both aqueous and biomass phase of HRAP treating real wastewater.The Greater one-horned (GoH) rhinoceros is one of the most charismatic endemic megaherbivores of the Indian subcontinent. Threatened by poaching, habitat loss and disease, the species is found only in small areas of its historical distribution. Increasing demands for rhino horns in chinese traditional medicine has put the existing population under continuing threat, and large profits and low conviction rates make poaching difficult to contain. DNA forensics such as the RhoDIS-Africa program has helped in combating illegal rhino horn trade, but the approach is yet to be optimised for Indian GoH rhinoceros. Here we followed the International Society for Forensic Genetics (ISFG) guidelines to establish a 14 dinucleotide microsatellite panel for Indian GoH rhinoceros DNA profiling. Selected from a large initial pool (n = 34), the microsatellite markers showed high polymorphism, stable peak characteristics, consistent allele calls and produced precise, reproducible genotypes from different types of rhino samples. The panel also showed low genotyping error and produced high statistical power during individual identification (PIDsibs value of 1.2*10-4). As part of the official RhoDIS-India program, we used this panel to match poached rhino carcass with seized contraband as scientific evidence in court procedure. This program now moves to generate detailed allele-frequency maps of all GoH rhinoceros populations in India and Nepal for development of a genetic database and identification of poaching hotspots and trade routes across the subcontinent and beyond.

To evaluate the diagnostic performances of four SARS-CoV-2 IgG antibody immunoassays.

Following immunoassays were studied Abbott’s SARS-CoV-2 IgG assay, Diasorin’s Liaison SARS-CoV-2 S2/S2 IgG assay, Euroimmun’s Anti-SARS-CoV-2 IgG ELISA, and Roche’s Elecsys Anti-SARS-CoV-2 assay. Specificity was retrospectively evaluated with 38 samples from 2019. Sensitivity samples (n = 147) were taken from SARS-CoV-2 real-time PCR-positive patients who developed COVID-19 symptoms ten days earlier.

Mean specificity was 96.6%. Mean sensitivity was 62.7% from ten days after onset of symptoms, 84.4% from 15 days after onset of symptoms, and 87.5% from 20 days after onset of symptoms.

Specificity was high, while Abbott and Roche were 100% specific. Sensitivity increased over time, with Abbott and Roche having the highest sensitivity at all time points with ≥90% from 20 days after symptoms’ onset. These findings may assist in selecting SARS-CoV-2 IgG antibody immunoassays for additional diagnostics, epidemiological research, and vaccine development.