Plasma Profiles Research Articles

The amino acid metabolism pathway of peripheral T lymphocytes and ketamine-induced schizophrenia-like phenotype.

The intricate interplay between peripheral adaptive immune cells and the central nervous system (CNS) has garnered increasing recognition. Given that alterations in cell quantities often translate into modifications in metabolite profiles and that these metabolic changes can potentially traverse the bloodstream and enter the CNS, thereby modulating the progression of mental illnesses, we sought to explore the metabolic profiles of peripheral immune cells in a ketamine-treated mouse model of schizophrenia. We used flow cytometry to scrutinize the alterations in peripheral adaptive immune cells in a ketamine-induced schizophrenia mouse model. Subsequently, we implemented an untargeted metabolomic approach with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) to detect the metabolite profiles of peripheral abnormal lymphocytes and identify differential metabolites present in plasma. We then employed targeted metabolomics using UPLC-MS/MS to quantify the common differential metabolites detected in mouse plasma. Flow cytometry analysis detected a notable increase in the count of peripheral CD3+ T cells in a ketamine-induced schizophrenia mouse model. Subsequent untargeted metabolomics analysis revealed that the amino acid metabolism pathway underwent substantial alterations. A detailed quantification of 22 amino acid profiles in the peripheral plasma indicated significant elevation in the levels of glycine, alanine, asparagine, and aspartic acid. Our ongoing research has yet to conclusively identify the precise amino acid metabolism pathway that serves as the pivotal factor in the manifestation of the schizophrenia-like phenotype induced by ketamine. The peripheral amino acid metabolism pathway is involved in the ketamine-induced schizophrenia-like phenotype. The metabolic profile of peripheral immune cells could provide accurate biomarkers for the diagnosis and treatment of psychiatric diseases.

2D analysis of tokamak divertor-plasma detachment-bifurcation with operational parameters and geometries

UEDGE simulations with density scans for various input power, transport coefficients and outer poloidal leg length are performed to study the conditions for the existence of a bifurcation-like drop of Te at the outer strike point, commonly referred to as a detachment cliff, when transitioning to a detached plasma from an attached plasma in the outer divertor as the upstream density increases (McLean et al., 2015). The simulation results show that a detachment cliff tends to occur with a higher power input regardless of diffusivities and leg length. Further analysis of change of plasma profiles at a cliff indicate that, in addition to the sharp reduction of the E×B drift fluxes in the outer divertor studied in Jaervinen et al., (2018), the substantial change of the Mach number in the outer divertor and the decrease of the outer mid-plane Te due to the radiation front moving across the separatrix into the confinement region above the X-point consistently occur for all UEDGE density scans that have a detachment cliff. UEDGE time-dependent simulation of the evolution of a detachment cliff shows that the rapid increase of radiation above the X-point occurs in a time scale of ∼0.3−0.5ms, which could possibly be the trigger for the formation of a detachment cliff, quicker than the Mach number change in a time scale of ∼1ms and the drop of Te in a time scale of ∼2−3ms in the outer divertor.

Concept of a Diagnostic System for Measuring the Electron Temperature Profile of Plasma from the Intensity of Electron Cyclotron Emission for the TRT Facility

The paper presents a concept of the ECE diagnostic for the TRT facility and estimates the achievable measurement parameters in the baseline scenario. The target spectral region for the diagnostic corresponds to the first harmonic of the ECR frequency in ordinary polarization (O1) and the second harmonic in extraordinary polarization (X2). It is proposed to carry out measurements from the low-field side along two lines of sight: radial and toroidally oblique. The accessible spectral region in terms of the normalized radial coordinate is approximately estimated as –0.9 to 0.9 and –0.1 to 0.9. It is proposed to shape the input wave beam by means of a quasi-optical focusing system that provides a transverse size of the resolved region of approximately 3—5 cm for O1 and 1.2—3 cm for X2. For measurements, it is proposed to use Fourier transform spectrometers with a time resolution of about 10 ms and multichannel heterodyne receivers with a time resolution of about 1 μs. The minimum radial size of the resolved region is estimated to be 3—5 cm for O1 and 2—4 cm for X2, depending on the coordinate.

Plasma triacylglycerol length and saturation level mark healthy aging groups in humans.

Complex lipids, essential components in biological processes, exhibit conserved age-related changes that alter membrane properties and cellular functions and are implicated as biomarkers and contributors to longevity and age-related diseases. While physical activity alleviates age-related comorbidities and physical impairments, comprehensive exploration of the underlying biological mechanisms, particularly at the level of complex lipids, remains limited. However, clinical studies suggest that physical activity may counteract these age-related lipidomic changes, presenting a promising avenue for intervention. We performed lipidomic profiling of plasma from an extensively characterized cohort of young and aged individuals. Annotating 1446 unique lipid species across 24 lipid classes, we found the most prominent difference in older adults was an accumulation of triacylglycerols (TGs), with lower physical activity levels associated with higher TG levels in plasma and reduced physical functionality. Remarkably, lipid species in the TG class did not accumulate uniformly. Rather, our study unveiled a negative correlation between higher physical activity levels and TGs with shorter chain lengths and more double bonds in this demographic. Overall, our research highlights that plasma TG length and saturation level can help mark healthy aging groups in humans. These findings deepen our understanding of how aging affects complex lipids and the influence of physical activity on this process.

Plasma metabolic profile reveals signatures of maternal health during gestational hypertension and preeclampsia without and with severe features.

Preeclampsia, a pregnancy-specific syndrome, poses substantial risks to maternal and neonatal health, particularly in cases with severe features. Our study focuses on evaluating the impact of low molecular weight metabolites on the intricate mechanisms and pathways involved in the pathophysiology of preeclampsia when severe features are present. We aim to pinpoint the distinct metabolomic profile in maternal plasma during pregnancies affected by hypertensive disorders and to correlate the metabolite levels with the clinical characteristics of the study cohort. A total of 173 plasma samples were collected, comprising 36 healthy pregnant women (HP), 52 patients with gestational hypertension (GH), 43 with preeclampsia without (PE-), and 42 with severe features (PE+). Nuclear magnetic resonance spectroscopy and metabolite identification were conducted to establish the metabolomic profiles. Univariate and chemometric analyses were conducted using MetaboAnalyst, and correlations were performed using GraphPad Prism. Our study unveils distinct metabolomic profiles differentiating HP women, patients featuring GH, and patients with PE-and PE+. Our analysis highlights an increase in acetate, N,N-dimethylglycine, glutamine, alanine, valine, and creatine levels in the PE+ group compared to the HP and GH groups. The PE+ group exhibited higher concentrations of N,N-dimethylglycine, glutamine, alanine, and valine compared to the PE-group. Moreover, elevated levels of specific metabolites, including N,N-dimethylglycine, alanine, and valine, were associated with increased blood pressure, worse obstetric outcomes, and poorer end-organ function, particularly renal and hepatic damage. Metabolomic analysis of PE+ individuals indicates heightened disturbances in nitrogen metabolism, methionine, and urea cycles. Additionally, the exacerbated metabolic disturbance may have disclosed renal impairment and hepatic dysfunction, evidenced by elevated levels of creatine and alanine. These findings not only contribute novel insights but also provide a more comprehensive understanding of the pathophysiological mechanisms at play in cases of PE+.

Endurance Exercise Training Alters Lipidomic Profiles of Plasma and Eight Tissues in Rats: a MoTrPAC study.

Endurance exercise training (ExT) induces metabolic, structural, and functional adaptations via lipidomic modifications, yet the systematic elucidation of lipidome alterations in response to ExT remains incomplete. As a part of the Molecular Transducers of Physical Activity Consortium (MoTrPAC), we leveraged non-targeted and targeted lipidomics for the systematic discovery of lipid alterations in the brown adipose tissue, heart, hippocampus, kidney, liver, lung, skeletal muscle gastrocnemius, subcutaneous white adipose tissue, and plasma in response to 1, 2, 4 or 8 weeks of ExT in 6-month-old male and female Fischer-344 rats. This study demonstrates that these tissues, each with distinct lipidomic features, underwent dynamic, sexually dimorphic lipid remodeling. Exercise trained animals showed reduced whole-body adiposity and improved cardiorespiratory fitness, along with enhanced utilization of lipid stores and dynamic triacylglycerol remodeling compared to sedentary controls in all tissues except hippocampus. They also showed modifications in phospholipids, lysophospholipids, oxylipins, and ceramides in several tissues. Coordinated changes across tissues reflect systemic tissue communication, with liver-plasma-heart connection potentially playing a key role in systemic lipid metabolism during ExT. These data will improve our understanding of lipid-associated biological processes underlying the health-promoting benefits of ExT.

Analysis of shared variants between cancer biospecimens.

Mutational data from multiple solid and liquid biospecimens of a single patient is often integrated to track cancer evolution. However, there is no accepted framework to resolve if individual samples from the same individual share variants due to common identity versus coincidence. Utilizing 8,000 patient tumors from The Cancer Genome Atlas (TCGA) across 33 cancer types, we estimated background rates of co-occurrence rates of mutations between discrete pairs of samples across cancers and by cancer type. We developed a mutational profile similarity score (MPS) that uses a large background database to produce confidence estimates that two tumors share a unique, related molecular profile. The MPS algorithm was applied to randomly paired tumor profiles, including patients who underwent repeat solid tumor biopsies sequenced with MSK-IMPACT (n=53,113). We also evaluated the MPS in sample pairs from single patients with multiple cancers (n=2,012), as well as patients with plasma and solid-tumor variant profiles (n=884 patients). In unrelated tumors, nucleotide-specific variants are shared in 1.3% (cancer-type agnostic) and in 10-13% (cancer-type specific) of cases. The mutational profile similarity (MPS) method contextualized shared variants to specify whether patients had a single cancer versus multiple distinct cancers. When multiple tumors were compared from the same patient, and an initial clinicopathologic diagnosis was discordant with molecular findings, the MPS anticipated future diagnosis changes in 28% of examined cases. Use of a novel shared variant framework can provide information to clarify the molecular relationship between compared biospecimens with minimal required input.

Exercise, nutrition, and medicine timing in metabolic health: implications for management of Type 2 Diabetes

Abstract Chrono-medicine considers circadian biology in disease management, including combined lifestyle and medicine interventions. Exercise and nutritional interventions are well-known for their efficacy in managing Type 2 Diabetes, and metformin remains a widely used pharmacological agent. However, metformin may reduce exercise capacity and interfere with skeletal muscle adaptations, creating barriers to exercise adherence. Research into optimising the timing of exercise has shown promise, particularly for glycaemic management in people with Type 2 Diabetes. Aligning exercise timing with circadian rhythms and nutritional intake may maximise benefits. Nutritional timing also plays a crucial role in glycaemic control. Recent research suggests that not only what we eat but when we eat significantly impacts glycaemic control, with strategies like time-restricted feeding (TRF) showing promise in reducing caloric intake, improving glycaemic regulation, and enhancing overall metabolic health. These findings suggest that meal timing could be an important adjunct to traditional dietary and exercise approaches in managing diabetes and related metabolic disorders. When taking a holistic view of Diabetes management and the diurnal environment, one must also consider the circadian biology of medicines. Metformin has a circadian profile in plasma, and our recent study suggests that morning exercise combined with pre-breakfast metformin intake reduces glycaemia more effectively than post-breakfast intake. In this review, we aim to explore the integration of circadian biology into Type 2 Diabetes management by examining the timing of exercise, nutrition, and medication. In conclusion, chrono-medicine offers a promising, cost-effective strategy for managing Type 2 Diabetes. Integrating precision timing of exercise, nutrition, and medication into treatment plans requires considering the entire diurnal environment, including lifestyle and occupational factors, to develop comprehensive, evidence-based healthcare strategies.

Targeted Multiplex Proteomics for the Development and Validation of Biomarkers in Primary Aldosteronism Subtyping.

Primary aldosteronism (PA), a significant cause of secondary hypertension affecting approximately 10% of patients with severe hypertension, exacerbates cardiovascular and cerebrovascular complications even after blood pressure control. PA is categorized into two main subtypes: unilateral aldosterone-producing adenomas (APA) and bilateral hyperaldosteronism (BHA), each requiring distinct treatment approaches. Accurate subtype classification is crucial for selecting the most effective treatment. The goal of this study was to develop novel blood-based proteomic biomarkers to differentiate between APA and BHA subtypes in patients with PA. Five subtyping differential protein biomarker candidates (APOC3, CD56, CHGA, KRT5, and AZGP1) were identified through targeted proteomic profiling of plasma. The subtyping efficiency of these biomarkers was assessed at both the tissue gene expression and blood protein expression levels. To explore the underlying biology of APA and BHA, significant differential pathways were investigated. The five-protein panel proved highly effective in distinguishing APA from BHA in both tissue and blood samples. By integrating these five protein biomarkers with aldosterone and renin, our blood-based predictive methods achieved remarkable ROC AUCs of 0.986 (95% CI: 0.963-1.000) for differentiating essential hypertension (EH) from PA, and 0.922 (95% CI: 0.846-0.998) for subtyping APA versus BHA. These outcomes surpass the performance of the existing Kobayashi score subtyping system. Furthermore, the study validated differential pathways associated with the pathophysiology of primary aldosteronism, aligning with current scientific knowledge and opening new avenues for advancing PA care. The new blood-based biomarkers for PA subtyping hold the potential to significantly enhance clinical utility and advance the practice of PA care.

Individuals with SATB2-associated syndrome have impaired vitamin and energy metabolism pathways.

Special AT-rich sequence-binding protein 2 (SATB2) is a master regulator of gene expression. Mutations of the SATB2 gene results in the SATB2-associated syndrome (SAS), a genetic disorder characterized by neurodevelopmental disabilities and autism-related phenotype. The importance of plasma as an indicator of SAS phenotypes is unknown. We aim to investigate if pathogenic variants in SATB2 are associated with alteration to relevant pathways in the plasma of SAS patients and identify key differentially regulated proteins which may serve as biomarkers to improve diagnostic and future pharmacological approaches. We used well-validated proteomic technologies to determine the proteomic profile of plasma from SAS patients compared to healthy control subjects. Bioinformatical analysis was performed to identify significant proteins and functionally enriched pathways. We identified differentially expressed proteins in the plasma of SAS patients that are significantly involved in metabolism-related pathways. Energy metabolism, glucose metabolism and vitamin metabolism pathways are significantly enriched in SAS patients as compared to healthy controls. Our study linked SATB2 mutations to the impairment of plasma proteins involved in different metabolic pathways in SAS patients.

Time-dependent full-radius integrated modeling of the DTT tokamak main plasma scenarios

Abstract We use an integrated modeling workflow with the transport code ASTRA coupled with the quasi-linear transport model TGLF-SAT2, the neoclassical model NCLASS, the FACIT model for the neoclassical impurity transport and the IMEP routines for the pedestal calculations, in order to predict the evolution of the plasma profiles for the Divertor Tokamak Test facility (DTT) tokamak main scenarios. The simulations cover the whole confined plasma radius, up to the separatrix, and the time evolution of the plasma including the early phase in limiter configuration, the whole current ramp-up phase in L-mode divertor configuration, the L–H transition and part of the stationary H-mode phase. Six fields are predicted, i.e. the ion and electron temperatures, the electron density, two impurity densities and the plasma current. The simulations indicate that the main DTT scenarios are within the technical capabilities of the machine. They also indicate that the DTT full power, full current, full field scenario will be able to operate in H-mode with a duration of the flat-top phase of the order of ∼ 30 s, and plasma parameters allowing a core-edge integrated study of the power exhaust, which is the main mission of the device. The simulations show also a strong flexibility of the DTT plasmas, that allows DTT to study reactor-relevant conditions unexplored by existing tokamaks.

Identification of Biochemical Determinants for Diagnosis and Prediction of Severity in 5q Spinal Muscular Atrophy Using 1H-Nuclear Magnetic Resonance Metabolic Profiling in Patient-Derived Biofluids.

This study explores the potential of 1H-NMR spectroscopy-based metabolic profiling in various biofluids as a diagnostic and predictive modality to assess disease severity in individuals with 5q spinal muscular atrophy. A total of 213 biosamples (urine, plasma, and CSF) from 153 treatment-naïve patients with SMA across five German centers were analyzed using 1H-NMR spectroscopy. Prediction models were developed using machine learning algorithms which enabled the patients with SMA to be grouped according to disease severity. A quantitative enrichment analysis was employed to identify metabolic pathways associated with disease progression. The results demonstrate high sensitivity (84-91%) and specificity (91-94%) in distinguishing treatment-naïve patients with SMA from controls across all biofluids. The urinary and plasma profiles differentiated between early-onset (type I) and later-onset (type II/III) SMA with over 80% accuracy. Key metabolic differences involved alterations in energy and amino acid metabolism. This study suggests that 1H-NMR spectroscopy based metabolic profiling may be a promising, non-invasive tool to identify patients with SMA and for severity stratification, potentially complementing current diagnostic and prognostic strategies in SMA management.

Abnormal proteolytic activity profile in plasma of blood donors according to anti-SARS-CoV-2 IgG titer

This work aims to study whether there is a relationship between titer values of anti-SARS-CoV-2 IgG and changes in proteolytic processes. To confirm this hypothesis, we analyzed the content and activity of Matrix Metalloproteinases (MMPs) as well as the concentration and composition of circulating peptide pools in the plasma of blood donors divided into groups on the basis of anti-SARS-CoV-2 IgG titers. The results of gelatin zymography showed the presence of active MMP-2 in donors’ plasma who recovered from COVID-19. In contrast, collagenases and their complexes were detected in the plasma of donors with no anti-SARS-CoV-2 IgG, while their activity was undetectable in some groups of COVID-19 convalescent individuals. The content of MMPs also differed among the donors with different titers of anti-SARS-CoV-2 IgG. Plasma peptide content was identical among the donors' groups, but there were more peptide fractions in plasma peptide pools of COVID-19 convalescent individuals; furthermore, they all were characterized by the presence of peptides with molecular weights less than 920 Da and greater than 1530 Da. We hypothesized a link between proteolytic alterations and peptide fraction composition. Our data need further validation to confirm the relationship between the titer values of anti-SARS-CoV-2 IgG and the severity of the proteolytic imbalance.

Protein Biomarkers of Adverse Clinical Features and Events in Sarcomeric Hypertrophic Cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is a heterogeneous condition that can lead to atrial fibrillation, heart failure, and sudden cardiac death in many individuals but mild clinical impact in others. The mechanisms underlying this phenotypic heterogeneity are not well defined. The aim of this study was to use plasma proteomic profiling to help illuminate biomarkers that reflect or inform the heterogeneity observed in HCM. The Olink antibody-based proteomic platform was used to measure plasma proteins in patients with genotype positive (sarcomeric) HCM participating in the HCM Registry. We assessed associations between plasma protein levels with clinical features, cardiac magnetic resonance imaging metrics, and the development of atrial fibrillation. We measured 275 proteins in 701 patients with sarcomeric HCM. There were associations between late gadolinium enhancement with proteins reflecting neurohormonal activation (NT-proBNP [N-terminal pro-B-type natriuretic peptide] and ACE2 [angiotensin-converting enzyme 2]). Metrics of left ventricular remodeling had novel associations with proteins involved in vascular development and homeostasis (vascular endothelial growth factor-D and TM [thrombomodulin]). Assessing clinical features, the European Society of Cardiology sudden cardiac death risk score was inversely associated with SCF (stem cell factor). Incident atrial fibrillation was associated with mediators of inflammation and fibrosis (MMP2 [matrix metalloproteinase 2] and SPON1 [spondin 1]). Proteomic profiling of sarcomeric HCM identified biomarkers associated with adverse imaging and clinical phenotypes. These circulating proteins are part of both established pathways, including neurohormonal activation and fibrosis, and less familiar pathways, including endothelial function and inflammatory proteins less well characterized in HCM. These findings highlight the value of plasma profiling to identify biomarkers of risk and to gain further insights into the pathophysiology of HCM.

Metabolomic profiling of human feces and plasma from extrauterine growth restriction infants.

Extrauterine growth restriction (EUGR) affects a substantial proportion of preterm infants and may influence both short-term complications and long-term sequelae. While many preterm infants with EUGR are secondary to small for gestational age (SGA) or very low birth weight (VLBW), a subset of EUGR infants do not exhibit these conditions. The purpose of this study is to investigate the metabolic profiles and biomarkers of EUGR infants in the absence of SGA and VLBW. A total of 100 feces (n = 50) and plasma samples (n = 50) were collected from participants categorized as either EUGR (EUGR group) or non-EUGR (NonEUGR group) in the absence of SGA and VLBW. Metabolites were characterized via UPLC-MS/MS using the Discovery HD4® platform. Data normalization, partial least squares discriminant analysis (PLSDA), and KEGG enrichment analysis of metabolite profiles were performed using the MetaboAnalyst 6.0. The clinical characteristics of preterm infants differed significantly between the EUGR and NonEUGR groups at discharge, including length of stay, weight Z-score, weight, height Z-score, height, head circumference, and fat-free mass. The PLSDA model exhibited clustering within groups and separation between groups. A total of 58 and 71 differential metabolites were identified in feces and plasma samples, respectively. They were involved in pathways such as caffeine, galactose, glutathione, cysteine, and methionine metabolisms. In the feces sample, 1-palmitoyl-galactosylglycerol exhibited a significant negative correlation with the growth characteristics of preterm infants, while 1-palmitoyl-2-palmitoleoyl-GPC displayed the opposite pattern. In plasma samples, androsterone glucuronide displayed a significant positive correlation with the growth characteristics of preterm infants, whereas 2-methoxyhydroquinone sulfate generated an opposite pattern. Moreover, 2-oleoylglycerol and sphinganine-1-phosphate exhibited the highest area under the curve in feces and plasma samples, respectively, according to diagnostic ROC curves. Preterm infants with EUGR, in the absence of SGA and VLBW, exhibit specific clinical characteristics and metabolomic profiles. Sphinganine-1-phosphate and 2-oleoylglycerol may hold promise as diagnostic markers for EUGR in the absence of SGA and VLBW. The objective of this study is to identify the differential metabolites in preterm infants with extrauterine growth restriction (EUGR) in the absence of small for gestational age (SGA) or very low birth weight (VLBW). Preterm infants with EUGR without SGA and VLBW exhibit specific clinical characteristics and metabolomic profiles. Sphinganine-1-phosphate and 2-oleoylglycerol emerged as potential diagnostic biomarkers for EUGR. This study enhances our understanding of the metabolomic profile in preterm infants with EUGR without SGA or VLBW. Our findings will offer valuable evidence for improving nutritional management and shedding light on the associated pathophysiological mechanisms of EUGR.

Gyrokinetic flux-driven simulations in mixed TEM/ITG regime using a delta-f PIC scheme with evolving background

In the context of global gyrokinetic simulations of turbulence using a particle-in-cell framework, verifying the delta-f assumption with a fixed background distribution becomes challenging when determining quasi-steady state profiles corresponding to given sources over long time scales, where plasma profiles can evolve significantly. The advantage of low relative sampling noise afforded by the delta-f scheme is shown to be retained by considering the background as a time-evolving Maxwellian with time-dependent density and temperature profiles. Implementation of this adaptive scheme to simulate electrostatic collisionless flux-driven turbulence in tokamak plasmas show small and nonincreasing sampling noise levels, which would otherwise increase indefinitely with a stationary background scheme. The adaptive scheme furthermore allows one to reach numerically converged results of quasi-steady state with much lower marker numbers.

Development of a sensitive disease-screening model using comprehensive circulating microRNA profiles in dogs: A pilot study

In the veterinary field, the utility of disease-identification models that use comprehensive circulating microRNA (miRNA) profiles produced through measurements based on next-generation sequencing (NGS) remains unproven. To integrate NGS technology with automated machine learning (autoML) to create a comprehensive circulating miRNA profile and to assess the clinical utility of a disease-screening model derived from this profile. The study involved dogs diagnosed with or being treated for various diseases, including tumors, across multiple veterinary clinics (n = 254), and healthy dogs without apparent diseases (n = 91). miRNA was extracted from EDTA-treated plasma, and a comprehensive analysis was conducted of one million reads per sample using NGS. Then autoML technology was applied to develop a diagnostic model based on miRNA. Among these models, the one with the highest performance was chosen for evaluation. The diagnostic model, based on the comprehensive circulating miRNA profile developed in this study, achieved an AUC score of 0.89, with a sensitivity of 85 % and a specificity of 88 % for the disease samples. The miRNA-based diagnostic model demonstrated high sensitivity for disease groups and has the potential to be an effective screening test. This study indicates that a comprehensive miRNA profile in dog plasma could serve as a highly sensitive blood biomarker.

Impacts of an organophosphate pesticide and water restriction on physiology and immunity in the passerine Zonotrichia capensis.

Climate change, specifically rising temperatures and increased frequency of droughts will increase the level of exposure of organisms to chemical pollution. Notably, the impact of increased frequency and duration of drought events and subsequent dehydration on pesticide toxicity remains largely unknown. We evaluated the combined effects of exposure to the pesticide Chlorpyrifos (CPF) and water restriction (WR) on the enzymatic activities of three plasma esterases (acetylcholinesterase, butyrylcholinesterase and carboxylesterase), basal metabolic rate (BMR), leukocyte profile (proportion of heterophils to lymphocytes; H:L ratio), and bactericidal capacity of plasma in the rufous-collared sparrow (Zonotrichia capensis), a common passerine bird in Chile. The activity of the enzyme carboxylesterase decreased in birds exposed only to CPF and birds exposed to both the CPF and WR. In the group exposed to both stressors, the butyrylcholinesterase activity was also reduced. The BMR exhibited an increase in both groups exposed to CPF, with a higher rise observed in the group exposed to both CPF and WR, while the group subjected to WR showed no change. The bactericidal capacity of the plasma decreased significantly in birds exposed to CPF and in those exposed to both the pesticide and WR, while a marginal effect was observed in the group only subjected to WR. The H:L ratio increased in all groups, with the most pronounced effect in birds exposed to both stressors. These results suggest a shift in the energy budget of Z. capensis, favoring the detoxification of the pesticide through esterase activities, at the expense of reduced immunocompetence.

Toward electron temperature profiles in hot-dense plasmas from x-ray spectral ensembles

High repetition rate laser systems enable new strategies for diagnosing plasma behavior with large datasets. Here, we define an ensemble technique that relies on randomized targeting of x-ray tracer micro-stripes. On each shot, a high-intensity laser pulse is focused on a solid target with Ti tracer stripes embedded in an Al foil, randomly targeting a micro-stripe, a portion of a stripe, or a gap between stripes. High-resolution, time-integrated x-ray spectrometers capture line emission from the portion of the micro-stripe that is heated to sufficiently high electron temperatures. Accumulation of many such cases is used to construct ensemble distributions of x-ray line intensities that encompass all relative offsets of the laser focus to the micro-stripe centers. Synthetic intensity distributions are likewise generated using collisional-radiative modeling. Bayesian fitting of modeled to measured intensity distributions establishes the most likely radial temperature profiles, enabling comparison to hydrodynamic models and calling into question the cylindrical symmetry of these micro-stripe-embedded systems. Ensemble techniques have significant potential for high-energy-density plasma diagnostics, especially with the advent of high repetition rate experiments.

Surface cleaning with atmospheric pressure plasma jet investigated by in-situ optical emission spectroscopy and laser-induced breakdown spectroscopy

Cleaning of surfaces with atmospheric pressure plasma jet (APPJ) is investigated by in-situ optical emission spectroscopy (OES) and laser-induced breakdown spectroscopy (LIBS). The APPJ device operates a spark discharge of kW power in Argon gas flow resulting in a powerful plasma jet expanding into air. For cleaning experiments samples are coated with lubricant layers of 1.1 to 7.1 µm thickness. Light collected at the sample surface during APPJ treatment is analyzed spectroscopically and used to monitor the cleaning process. LIBS chemical imaging delivers spatial cleaning profiles of plasma treated surfaces. From measured emission intensities of CN molecular band and Na atomic line the cleaning efficiencies for carbon and Na containing contaminants are determined. The power of APPJ plasma generator, distance between sample and jet nozzle, scan speed, and Argon gas flow are varied to optimize the surface treatment. Thermal and non-thermal processes contribute to cleaning. For the carbon containing contaminant thermal processes are dominating (∼90 %) while also non-thermal processes (∼30–45 %) are relevant for the Na containing component. A cleaned surface area per time from 0.3 to 10 cm2/s is investigated. Cleaning efficiency up to 95 % is obtained highlighting the potential of APPJ surface cleaning under ambient conditions.