Large-scale Plasma Research Articles

Risk of Alzheimer’s disease and genetically predicted levels of 1400 plasma metabolites: a Mendelian randomization study

Alzheimer’s disease (AD) is a metabolic disorder. Discovering the metabolic products involved in the development of AD may help not only in the early detection and prevention of AD but also in understanding its pathogenesis and treatment. This study investigated the causal association between the latest large-scale plasma metabolites (1091 metabolites and 309 metabolite ratios) and AD. Through the application of Mendelian randomization analysis methods such as inverse-variance weighted (IVW), MR-Egger, and weighted median models, 66 metabolites and metabolite ratios were identified as potentially having a causal association with AD, with 13 showing significant causal associations. During the replication validation phase, six metabolites and metabolite ratios were confirmed for their roles in AD: N-lactoyl tyrosine, argininate, and the adenosine 5’-monophosphate to flavin adenine dinucleotide ratio were found to exhibit protective effects against AD. In contrast, ergothioneine, piperine, and 1,7-dimethyluric acid were identified as contributing to an increased risk of AD. Among them, argininate showed a significant effect against AD. Replication and sensitivity analyses confirmed the robustness of these findings. Metabolic pathway analysis linked “Vitamin B6 metabolism” to AD risk. No genetic correlations were found, but colocalization analysis indicated potential AD risk elevation through top SNPs in APOE and PSEN2 genes. This provides novel insights into AD’s etiology from a metabolomic viewpoint, suggesting both protective and risk metabolites.

Application of large-scale and multicohort plasma proteomics data to discover novel causal proteins in gastric cancer

PurposesGastric cancer (GC) is one of the most common malignant tumors threatening human beings and has a poor prognosis. Therefore, exploring unveiled biomarkers or therapeutic targets for the diagnosis and treatment of GC is crucial.MethodsA total of 5772 protein quantitative trait loci (pQTL) were aggregated from four latest large-scale proteomics cohorts. Two-sample Mendelian randomization (two-sample MR) was utilized to identify the causal effect of blood plasma proteins on GC. Heterogeneity, pleiotropy, and directionality analyses were employed to evaluate proteins identified via two-sample MR. The robustness of results was further validated via colocalization. The drug targets of proteins were evaluated to reveal the compounds that can interfere with these proteins.ResultsTen proteins with potential causations in relation to GC were identified: LY6D, SLURP1, MLN, PSCA, THSD1, CFTR, PPM1B, KDM3A, TSC1, and HCG22. Among these proteins, LY6D, SLURP1, and THSD1 were considered as the most reliable biomarkers of GC due to their significant H4 posterior probabilities in colocalization analysis and absence of pleiotropy. Compound 35, nitrosamide, and 0175029-0000 were potential drugs or small molecules targeting LY6D, SLURP1, and THSD1, respectively.ConclusionThis study identified several plasma proteins as potential biomarkers of GC and provided data support and new insights into the early diagnosis, intervention, and therapeutic targets of GC.

Generation of Large-Scale Plasma Jet with Excitation of Bipolar Nanosecond Pulse Voltage in Single-Spiral Electrode Configuration

In this study, a single-outer-spiral electrode with inductance of 20 μH is employed to couple the energy input of a bipolar nanosecond pulse for the purpose of generating a large-scale atmospheric pressure plasma jet. When the spiral electrode is wrapped around a plasma jet tube with a length of 35 cm, the electrical field can be optimized, resulting in a stable laminar flow field, and a plasma jet with a length and diameter larger than 14 cm and 1.2 cm can be generated. A comparative study of the bipolar and unipolar pulse excitation voltages is also conducted, showing that the maximum lengths of the plasma jet excited by a bipolar pulse voltage, positive pulse voltage, and negative are 14 cm, 10 cm, and 7 cm, respectively. The temporal and spatially resolved spectra of the plasma jets excited by both bipolar and unipolar pulses are investigated, respectively, and the main physiochemical processes of the active species and the plasma dynamics’ evolution are discussed.

Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression.

Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression n = 4,479, healthy control n = 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation.

A planar plume array emanating from an atmospheric pressure argon plasma jet employing floating electrodes

Large-scale plasma plumes downstream of plasma jets are in urgent need from a practical viewpoint. In this Letter, an argon plasma jet with floating electrodes is proposed to produce a large-scale planar plume array. Results indicate that with increasing peak voltage (Vp), the planar plume array elongates gradually and scales up in the lateral direction to an optimal value of 90.0 mm. There is only one discharge pulse per voltage half cycle, whose intensity and duration increase with increasing Vp. Moreover, there is a time lag between the initiations of individual plumes. Fast photography reveals that the planar plume array originates from the repeated process of some micro-discharge filaments stretching along the argon stream. By optical emission spectroscopy, the spatial distribution of plasma parameters is obtained, such as electron density, electron temperature, and gas temperature. At last, the planar plume array is employed to test the surface modification of polyethylene terephthalate, for which a uniform modification has been realized with a scan velocity of 1.0 cm/min. These results are of great significance for the development of large-scale atmospheric pressure plasma sources.

Plasma-enabled growth of vertically oriented carbon nanostructures for AC line filtering capacitors

Self-standing vertically oriented carbon nanostructures (VCNs) were synthesized using a large-scale microwave plasma under low-pressure conditions, employing methane as a carbon precursor. The influence of plasma operational and substrate conditions on nanostructure growth and morphology were systematically studied. Furthermore, post-synthesis N-doping of VCNs with nitrogen content of 2.4 at% N was achieved using an Ar-N2 microwave plasma. Plasma-enabled direct deposition of VCNs, both doped and un-doped, onto nickel foils has been accomplished. The assessment of the developed nanostructures as electrodes in high-frequency AC filtering capacitors, has demonstrated an overall capacitance of approximately 480 µF at 100 Hz, with a cut-off frequency of 4 kHz for a phase angle of −45°. The excellent electrochemical performance can be attributed to the appropriate structural and morphological properties peculiar for the directly deposited on nickel foil VCNs providing binder-free electrode fabrication, thus enhancing the electrode’s conductivity and charge transfer kinetics. This plasma-enabled approach for electrode design on a large scale, coupled with excellent filtering performance, paves the way for many applications in high-frequency scenarios, offering an environmentally friendly alternative to conventional electrolytic capacitors.

Polar cap region and plasma drift in pulsars

ABSTRACT Pulsars often display systematic variations in the position and/or intensity of the subpulses, the components that comprise each single pulse. Although the drift of these subpulses was observed in the early years of pulsar research, and their potential for understanding the elusive emission mechanism was quickly recognized, there is still no consensus on the cause of the drift. We explore the electrodynamics of two recently proposed or refined drift models: one where plasma lags behind corotation, connecting the drift with the rotational pole; and another where plasma drifts around the electric potential extremum of the polar cap. Generally, these are different locations, resulting in different drift behaviours, that can be tested with observations. In this study, however, we specifically examine these models in the axisymmetric case, where the physics is well understood. This approach seems counter-intuitive as both models then predict similar large-scale plasma drift. However, it allows us to show, by studying conditions within the sparks for both models, that the lagging behind corotation model is inconsistent with Faraday’s law. The modified carousel (MC) model, where plasma drifts around the electric potential extremum, not only aligns with Faraday’s law, but also provides a future direction for developing a comprehensive model of plasma generation in the polar cap region. Unlike previous models, which considered the drift only inside the discharging regions, the MC model reveals that the electric field between the discharges is not completely screened, and plasma drifts there – a paradigm shift for the drifting subpulse phenomenon.

Metabolic gene function discovery platform GeneMAP identifies SLC25A48 as necessary for mitochondrial choline import.

Organisms maintain metabolic homeostasis through the combined functions of small-molecule transporters and enzymes. While many metabolic components have been well established, a substantial number remains without identified physiological substrates. To bridge this gap, we have leveraged large-scale plasma metabolome genome-wide association studies (GWAS) to develop a multiomic Gene-Metabolite Association Prediction (GeneMAP) discovery platform. GeneMAP can generate accurate predictions and even pinpoint genes that are distant from the variants implicated by GWAS. In particular, our analysis identified solute carrier family 25 member 48 (SLC25A48) as a genetic determinant of plasma choline levels. Mechanistically, SLC25A48 loss strongly impairs mitochondrial choline import and synthesis of its downstream metabolite betaine. Integrative rare variant and polygenic score analyses in UK Biobank provide strong evidence that the SLC25A48 causal effects on human disease may in part be mediated by the effects of choline. Altogether, our study provides a discovery platform for metabolic gene function and proposes SLC25A48 as a mitochondrial choline transporter.

Frozen-field Modeling of Coronal Condensations with MPI-AMRVAC. I. Demonstration in Two-dimensional Models

Large-scale coronal plasma evolutions can be adequately described by magnetohydrodynamics (MHD) equations. However, full multidimensional MHD simulations require substantial computational resources. Given the low plasma β in the solar corona, in many coronal studies, it suffices to approximate the magnetic field to remain topologically fixed and effectively conduct one-dimensional (1D) hydrodynamic (HD) simulations instead. This approach is often employed in studies of coronal loops and their liability to form condensations related to thermal instability. While 1D HD simulations along given and fixed field line shapes are convenient and fast, they are difficult to directly compare with multidimensional phenomena. Therefore, it is more convenient to solve volume-filling, multidimensional versions of the MHD equations where we freeze the magnetic field, transforming it into frozen-field HD (ffHD) equations for simulation. We have incorporated this ffHD module into our open-source MPI-AMRVAC code and tested it using a two-dimensional (2D) evaporation–condensation model to study prominence formation due to radiative losses. The 2D ffHD results are compared with those from actual 2D MHD and pseudo-2D HD simulations, analyzing the differences and their causes. Pseudo-2D studies account for the known flux tube expansion effects. Overall, the performance of 2D ffHD is close to that of 2D MHD and pseudo-2D HD. The 2D tests conducted in this paper will be extended in follow-up studies to 3D simulations based on analytical or observational approaches.

Genetic assessment of efficacy and safety profiles of coagulation cascade proteins identifies Factors II and XI as actionable anticoagulant targets.

Anticoagulants are routinely used by millions of patients worldwide to prevent blood clots. Yet, problems with anticoagulant therapy remain, including a persistent and cumulative bleeding risk in patients undergoing prolonged anticoagulation. New safer anticoagulant targets are needed. To prioritize anticoagulant targets with the strongest efficacy [venous thromboembolism (VTE) prevention] and safety (low bleeding risk) profiles, we performed two-sample Mendelian randomization and genetic colocalization. We leveraged three large-scale plasma protein data sets (deCODE as discovery data set and Fenland and Atherosclerosis Risk in Communities as replication data sets] and one liver gene expression data set (Institut Universitaire de Cardiologie et de Pneumologie de Québec bariatric biobank) to evaluate evidence for a causal effect of 26 coagulation cascade proteins on VTE from a new genome-wide association meta-analysis of 44 232 VTE cases and 847 152 controls, stroke subtypes, bleeding outcomes, and parental lifespan as an overall measure of efficacy/safety ratio. A 1 SD genetically predicted reduction in F2 blood levels was associated with lower risk of VTE [odds ratio (OR) = 0.44, 95% confidence interval (CI) = 0.38-0.51, P = 2.6e-28] and cardioembolic stroke risk (OR = 0.55, 95% CI = 0.39-0.76, P = 4.2e-04) but not with bleeding (OR = 1.13, 95% CI = 0.93-1.36, P = 2.2e-01). Genetically predicted F11 reduction was associated with lower risk of VTE (OR = 0.61, 95% CI = 0.58-0.64, P = 4.1e-85) and cardioembolic stroke (OR = 0.77, 95% CI = 0.69-0.86, P = 4.1e-06) but not with bleeding (OR = 1.01, 95% CI = 0.95-1.08, P = 7.5e-01). These Mendelian randomization associations were concordant across the three blood protein data sets and the hepatic gene expression data set as well as colocalization analyses. These results provide strong genetic evidence that F2 and F11 may represent safe and efficacious therapeutic targets to prevent VTE and cardioembolic strokes without substantially increasing bleeding risk.

Visualization of electrical interaction among close-packed atmospheric pressure plasma jets

Plasma jet arrays can flexibly generate large-scale plasma in ambient air for surface treatment. Close arrangement of plasma jets inevitably induces a complex electrical interaction, which reshapes plasma plumes and destabilizes the treatment process. Understanding electrical interaction is a prerequisite for optimizing parameters in surface treatment. In this study, we employ a linear electro-optic technique to visualize the electrical interaction on the cross section perpendicular to plume trajectories. The strength of the interaction is featured by the density of the deposited surface charge. Results show that plasma jet arrays can simultaneously deposit like-charged groups onto the substrate. However, the electrical interaction is proven to behave with a tendency toward convergence rather than the repulsion caused by electrostatic interaction. It is inferred that electrons at the head of ionization waves (IWs) shield electrostatic repellence between positive charge groups. The collision ionization by electrons in the interval guides the coalescence of adjacent IWs. This study clarifies the electrical interaction between multiple jets, which is instructive for stable control of large-scale non-thermal plasmas for surface treatment.

Establishing Quality Control Metrics for Large-Scale Plasma Proteomic Sample Preparation.

Large-scale plasma proteomics studies have been transformed due to the multiplexing and automation of sample preparation workflows. However, these workflows can suffer from reproducibility issues, a lack of standardized quality control (QC) metrics, and the assessment of variation before liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. The incorporation of robust QC metrics in sample preparation workflows ensures better reproducibility, lower assay variation, and better-informed decisions for troubleshooting. Our laboratory conducted a plasma proteomics study of a cohort of patient samples (N = 808) using tandem mass tag (TMT) 16-plex batches (N = 58). The proteomic workflow consisted of protein depletion, protein digestion, TMT labeling, and fractionation. Five QC sample types (QCstd, QCdig, QCpool, QCTMT, and QCBSA) were created to measure the performance of sample preparation prior to the final LC-MS/MS analysis. We measured <10% CV for individual sample preparation steps in the proteomic workflow based on data from various QC sample steps. The establishment of robust measures for QC of sample preparation steps allowed for greater confidence in prepared samples for subsequent LC-MS/MS analysis. This study also provides recommendations for standardized QC metrics that can assist with future large-scale cohort sample preparation workflows.

Generation of meter-scale nanosecond pulsed DBD and the intelligent evaluation based on multi-dimensional feature parameter extraction

This study introduces a novel meter-scale dielectric barrier discharge (m-DBD) reactor designed to generate large-scale, low-temperature nanosecond pulsed discharge plasma. By employing a modularized gas path, this reactor enables a comprehensive analysis of discharge patterns and uniformity using multi-dimensional discharge parameters. Simulation results reveal optimal gas distribution with ten gas holes in the variable plate and a 40 mm slit depth in the main reactor. Besides, a diagnosis method based on electro-acoustic-spectrum-image (E-A-S-I) parameters is developed to evaluate nanosecond pulsed m-DBD discharge states. It is found that the discharge states are closely related to the consistency of segmental discharge currents, the fluctuation of acoustic signals and the distribution of active particles. Machine learning methods are established to realize the diagnosis of m-DBD discharge pattern and uniformity by E-A-S-I parameters, where the optimized BPNN has a best recognition accuracy of 97.5%. Furthermore, leveraging nanosecond pulse power in Ar/m-DBD enables stable 1120 × 70 mm2 discharge, uniformly enhancing hydrophobicity of large-scale materials from a 67° to 122° water contact angle with maximal fluctuations below 7%. The modularized m-DBD reactor and its intelligent analysis based on multi-dimensional parameter provide a crucial foundation for advancing large-scale nanosecond pulsed plasma and their industrial applications.

A large-scale cold plasma jet: generation mechanism and application effect

Atmospheric pressure cold plasma jets (APCPJs) typically exhibit a slender, conical structure, which imposes limitations on their application for surface modification due to the restricted treatment area. In this paper, we introduce a novel plasma jet morphology known as the large-scale cold plasma jet (LSCPJ), characterized by the presence of both a central conical plasma jet and a peripheral trumpet-like diffuse plasma jet. The experimental investigations have identified the factors influencing the conical and the trumpet-like diffuse plasma jet, and theoretical simulations have shed light on the role of the flow field and the electric field in shaping the formation of the LSCPJ. It is proved that, under conditions of elevated helium concentration, the distributions of impurity gas particles and the electric field jointly determine the plasma jet’s morphology. High-speed ICCD camera images confirm the dynamic behavior of plasma bullets in LSCPJ, which is consistent with the theoretical analysis. Finally, it is demonstrated that when applied to the surface treatment of silicone rubber, LSCPJ can achieve a treatment area over 28 times larger than that of APCPJ under equivalent conditions. This paper uncovers the crucial role of impurity gases and electric fields in shaping plasma jet morphology and opens up the possibility of efficiently diversifying plasma jet generation effects through external electromagnetic fields. These insights hold the promise of reducing the generation cost of plasma jets and expanding their applications across various industrial sectors.

Large-scale plasma proteomics in the UK Biobank modestly improves prediction of major cardiovascular events in a population without previous cardiovascular disease.

Improved identification of individuals at high risk of developing cardiovascular disease would enable targeted interventions and potentially lead to reductions in mortality and morbidity. Our aim was to determine whether use of large-scale proteomics improves prediction of cardiovascular events beyond traditional risk factors (TRFs). Using proximity extension assays, 2919 plasma proteins were measured in 38 380 participants of the UK Biobank. Both data- and literature-based feature selection and trained models using extreme gradient boosting machine learning were used to predict risk of major cardiovascular events (MACEs: fatal and non-fatal myocardial infarction, stroke, and coronary artery revascularization) during a 10-year follow-up. Area under the curve (AUC) and net reclassification index (NRI) were used to evaluate the additive value of selected protein panels to MACE prediction by Systematic COronary Risk Evaluation 2 (SCORE2) or the 10 TRFs used in SCORE2. SCORE2 and SCORE2 refitted to UK Biobank data predicted MACE with AUCs of 0.740 and 0.749, respectively. Data-driven selection identified 114 proteins of greatest relevance for prediction. Prediction of MACE was not improved by using these proteins alone (AUC of 0.758) but was significantly improved by combining these proteins with SCORE2 or the 10 TRFs (AUC = 0.771, P < 001, NRI = 0.140, and AUC = 0.767, P = 0.03, NRI 0.053, respectively). Literature-based protein selection (113 proteins from five previous studies) also improved risk prediction beyond TRFs while a random selection of 114 proteins did not. Large-scale plasma proteomics with data-driven and literature-based protein selection modestly improves prediction of future MACE beyond TRFs.

Constraining ion transport in the diamagnetic cavity of comet 67P

ABSTRACT The European Space Agency Rosetta mission escorted comet 67P for a 2-yr section of its six and a half-year orbit around the Sun. By perihelion in 2015 August, the neutral and plasma data obtained by the spacecraft instruments showed the comet had transitioned to a dynamic object with large-scale plasma structures and a rich ion environment. One such plasma structure is the diamagnetic cavity: a magnetic field-free region formed by interaction between the unmagnetized cometary plasma and the impinging solar wind. Within this region, unexpectedly high ion bulk velocities have been observed, thought to have been accelerated by an ambipolar electric field. We have developed a 1D numerical model of the cometary ionosphere to constrain the impact of various electric field profiles on the ionospheric density profile and ion composition. In the model, we include three ion species: H2O+, H3O+, and $\mathrm{NH_4^+}$. The latter, not previously considered in ionospheric models including acceleration, is produced through the protonation of NH3 and only lost through ion–electron dissociative recombination, and thus particularly sensitive to the time-scale of plasma loss through transport. We also assess the importance of including momentum transfer when assessing ion composition and densities in the presence of an electric field. By comparing simulated electron densities to Rosetta Plasma Consortium data sets, we find that to recreate the plasma densities measured inside the diamagnetic cavity near perihelion, the model requires an electric field proportional to r−1 of around 0.5–2 mV m−1 surface strength, leading to bulk ion speeds at Rosetta of 1.2–3.0 km s−1.

Insight into telomere regulation: road to discovery and intervention in plasma drug-protein targets

BackgroundTelomere length is a critical metric linked to aging, health, and disease. Currently, the exploration of target proteins related to telomere length is usually limited to the context of aging and specific diseases, which limits the discovery of more relevant drug targets. This study integrated large-scale plasma cis-pQTLs data and telomere length GWAS datasets. We used Mendelian randomization(MR) to identify drug target proteins for telomere length, providing essential clues for future precision therapy and targeted drug development.MethodsUsing plasma cis-pQTLs data from a previous GWAS study (3,606 Pqtls associated with 2,656 proteins) and a GWAS dataset of telomere length (sample size: 472,174; GWAS ID: ieu-b-4879) from UK Biobank, using MR, external validation, and reverse causality testing, we identified essential drug target proteins for telomere length. We also performed co-localization, Phenome-wide association studies and enrichment analysis, protein-protein interaction network construction, search for existing intervening drugs, and potential drug/compound prediction for these critical targets to strengthen and expand our findings.ResultsAfter Bonferron correction (p < 0.05/734), RPN1 (OR: 0.96; 95%CI: (0.95, 0.97)), GDI2 (OR: 0.94; 95%CI: (0.92, 0.96)), NT5C (OR: 0.97; 95%CI: (0.95, 0.98)) had a significant negative causal association with telomere length; TYRO3 (OR: 1.11; 95%CI: (1.09, 1.15)) had a significant positive causal association with telomere length. GDI2 shared the same genetic variants with telomere length (coloc.abf-PPH 4 > 0.8).ConclusionGenetically determined plasma RPN1, GDI2, NT5C, and TYRO3 have significant causal effects on telomere length and can potentially be drug targets. Further exploration of the role and mechanism of these proteins/genes in regulating telomere length is needed.

Importance of the dusk-dawn interplanetary magnetic field component (IMF By) to magnetospheric convection in Earth’s magnetotail plasma sheet

The solar wind and its embedded magnetic field, the interplanetary magnetic field (IMF) together with magnetic reconnection power the large-scale plasma and magnetic flux circulation in the Earth’s magnetosphere-ionosphere system. This circulation is termed as convection and its strength is controlled by the north-south IMF component (IMF Bz). In recent years, an interest has arisen to investigate the lesser-known role of the dusk-dawn component (IMF By) in convection. It has been previously known though that prevailing nonzero IMF By can cause plasma flow asymmetries in the high-latitude ionosphere, but how the magnetospheric flows, for instance, in the magnetotail plasma sheet are affected, remains to be investigated. In this article, we introduce the recent progress and the latest achievements in the research of the influence of IMF By on tail plasma sheet convection. The research progress has been rapid and it has revealed that both fast and slow convection are affected in a manner that is in accordance with the asymmetries observed in the ionospheric convection. The results indicate the significance of the IMF By component on magnetospheric convection and they represent a major advance in the field of solar wind-magnetosphere coupling.

Metabolome profiling by widely-targeted metabolomics and biomarker panel selection using machine-learning for patients in different stages of chronic kidney disease

Chronic kidney disease (CKD) is an increasingly prevalent medical condition associated with high mortality and cardiovascular complications. The intricate interplay between kidney dysfunction and subsequent metabolic disturbances may provide insights into the underlying mechanisms driving CKD onset and progression. Herein, we proposed a large-scale plasma metabolite identification and quantification system that combines the strengths of targeted and untargeted metabolomics technologies, i.e., widely-targeted metabolomics (WT-Met) approach. WT-Met method enables large-scale identification and accurate quantification of thousands of metabolites. We collected plasma samples from 21 healthy controls and 62 CKD patients, categorized into different stages (22 in stages 1–3, 20 in stage 4, and 20 in stage 5). Using LC-MS-based WT-Met approach, we were able to effectively annotate and quantify a total of 1431 metabolites from the plasma samples. Focusing on the 539 endogenous metabolites, we identified 399 significantly altered metabolites and depicted their changing patterns from healthy controls to end-stage CKD. Furthermore, we employed machine-learning to identify the optimal combination of metabolites for predicting different stages of CKD. We generated a multiclass classifier consisting of 7 metabolites by machine-learning, which exhibited an average AUC of 0.99 for the test set. In general, amino acids, nucleotides, organic acids, and their metabolites emerged as the most significantly altered metabolites. However, their patterns of change varied across different stages of CKD. The 7-metabolite panel demonstrates promising potential as biomarker candidates for CKD. Further exploration of these metabolites can provide valuable insights into their roles in the etiology and progression of CKD.

Radiation-driven diffusive transport of fast electrons in solar flares

Fast electron scattering on plasma ions due to stimulated Bremsstrahlung is investigated and modeled. Comparison with Coulomb scattering suggests that stimulated Bremsstrahlung scattering can be dominant in low-density, radiation-driven plasmas, provided that the radiation spectrum has a sufficiently high brightness temperature in the neighborhood of the plasma frequency. While stimulated Bremsstrahlung scattering cannot be easily observed in laboratory plasmas due to their small size, it should operate in large-scale astrophysical plasmas, such as those met in the flaring solar corona. The effect of the solar microwave radiation on fast-electron scattering is evaluated through a parameterized flaring corona model. We find that stimulated Bremsstrahlung greatly enhances the fast-electron scattering frequency in the flare magnetic loop, leading the transport of deka-keV electrons to occur in the diffusion regime, characterized by significant precipitation rates. This prediction is consistent with the interpretation of the above-loop-top hard x-ray and microwave emissions from the X3.1 flare of August 24, 2002. Our analysis indicates that stimulated Bremsstrahlung may play an essential role in the dynamics of fast electrons trapped in solar flare loops.