Asymptomatic infection and disappearance of clinical symptoms of COVID-19 infectors in China 2022-2023: a cross-sectional study.

To explore the clinical characteristics of patients infected with SARS-CoV-2 nationwide, especially the effect factors of asymptomatic infection and disappearance of clinical symptoms. A total of 66,448 COVID-19 patients in China who have been diagnosed by nucleic acid test or rapid antigen test were surveyed online (December 24, 2022 to January 16, 2023). Our cross-sectional study used descriptive analyses and binary Logistics regression model to assess the correlation between the clinical characteristics and relative factors, including age, gender, pre-existing conditions, reinfection, vaccination and treatment. A total of 64,515 valid questionnaires were collected. Among included participants, 5969 of which were asymptomatic. The symptoms were mainly upper respiratory symptoms, including dry and itchy throat (64.16%), sore throat (59.95%), hoarseness (57.90%), nasal congestion (53.39%). In binary Logistics regression model, we found that male, no pre-existing conditions, reinfection and vaccination have positive correlations with the appearance of asymptomatic COVID-19 patients. In Cox proportional-hazards regression model, considering all clinical symptoms disappeared in 14 days as outcome, we found that ≤ 60 years old, male, no pre-existing conditions, vaccination and adopted treatment have positive correlations with rapid amelioration of clinical symptoms in COVID-19 patients. The clinical symptoms of the participants were mainly upper respiratory symptoms which were according with the infection of Omicron variant. Factors including age, gender, pre-existing conditions and reinfection could influence the clinical characteristics and prognosis of COVID-19 patients. Importantly, vaccination has positive significance for the prevention and treatment of COVID-19. Lastly, the use of Chinese medicine maybe beneficial to COVID-19 patients, however, reasonable guidance is necessary.

Risk factors for long COVID syndrome in postmenopausal women with previously reported diagnosis of COVID-19.

PURPOSE: Long COVID-19 syndrome occurs in 10-20 % of people after a confirmed/probable SARS-COV-2 infection; new symptoms begin within three months of COVID-19 diagnosis and last > 8 weeks. Little is known about risk factors for long COVID, particularly in older people who are at greater risk of COVID complications. METHODS: Data are from Women's Health Initiative (WHI) postmenopausal women who completed COVID surveys that included questions on whether they had ever been diagnosed with COVID and length and nature of symptoms. Long COVID was classified using standard consensus criteria. Using WHI demographic and health data collected at study enrollment (1993-98) through the present day, machine learning identified the top 20 risk factors for long COVID. These variables were tested in logistic regression models. RESULTS: Of n = 37,280 survey respondents, 1237 (mean age = 83 years) reported a positive COVID-19 test and 425 (30 %) reported long COVID. Symptoms included an array of neurological, cardio-pulmonary, musculoskeletal, and general fatigue, and malaise symptoms. Long COVID risk factors included weight loss, physical and mobility limitations, and specific heath conditions (e.g., history of heart valve procedure, rheumatoid arthritis). CONCLUSIONS: Knowledge of risk factors for long COVID may be the first step in understanding the etiology of this complex disease.

Nano-Fe3O4: Enhancing the tolerance of Elymus nutans to Cd stress through regulating programmed cell death.

Cadmium (Cd) poses a significant threat to plant growth and the environment. Nano-Fe3O4 is effective in alleviating Cd stress in plants. Elymus nutans Griseb. is an important fodder crop on the Qinghai-Tibetan Plateau (QTP). However, the potential mechanism by which nano-Fe3O4 alleviates Cd stress in E. nutans is not well understood. E. nutans were subjected to single Cd, single nano-Fe3O4, and co-treatment with nano-Fe3O4 and Cd, and the effects on morphology, Cd uptake, antioxidant enzyme activity, reactive oxygen species (ROS) levels and programmed cell death (PCD) were studied to clarify the regulatory mechanism of nano-Fe3O4. The results showed that Cd stress significantly decreased the germination percentage and biomass of E. nutans. The photosynthetic pigment content decreased significantly under Cd stress. Cd stress also caused oxidative stress and lipid peroxidation, accumulation of excessive ROS, resulting in PCD, but the effect of nano-Fe3O4 was different. Seed germination, seedling growth, and physiological processes were analyzed to elucidate the regulatory role of nano-Fe3O4 nanoparticles in promoting photosynthesis, reducing Cd accumulation, scavenging ROS, and regulating PCD, to promote seed germination and seedling growth in E. nutans. This report provides a scientific basis for improving the tolerance of Elymus to Cd stress by using nano-Fe3O4.

Fallopian tube wrapping as a cause of catheter malfunction in peritoneal dialysis: a report of two cases and management strategies.

Background: Catheter malfunction is a common problem following the placement of a peritoneal dialysis (PD) catheter, and it is characterized by inadequate dialysate drainage, which can also limit infusion. Common causes include constipation, catheter migration, catheter kinking, omental wrapping, and fibrin obstruction. However, catheter obstruction by other intra-abdominal organs has been observed infrequently. Case Description: We present two cases of female PD patients experiencing catheter dysfunction after catheter implantation. The first case involves a 28-year-old female who suffered from problematic drainage and infusion of dialysate 1 month after catheter insertion, evidenced by catheter displacement from the pelvis on abdominal X-ray. The second case concerns a 49-year-old female PD patient who also encountered a bidirectional catheter malfunction 40 days post-implantation. Conservative methods failed to restore the catheter function in both patients. Laparoscopic examination revealed fallopian tube, not the omentum, was tightly wrapped around the PD catheter in both cases. Finally, laparoscopic surgery with catheter fixation restored the catheter function, enabling continued continuous ambulatory peritoneal dialysis (CAPD) with favorable outcomes. Conclusions: Our findings indicate that healthcare providers should consider fallopian tube wrapping as a potential cause of catheter dysfunction. Prompt consideration and utilization of laparoscopy with catheter fixation can play an important role in restoring catheter function and improving patient outcomes.

Impact on Image Quality and Diagnostic Performance of Dual-Layer Detector Spectral CT for Pulmonary Subsolid Nodules: Comparison With Hybrid and Model-Based Iterative Reconstruction.

OBJECTIVE: To evaluate the image quality and diagnostic performance of pulmonary subsolid nodules on conventional iterative algorithms, virtual monoenergetic images (VMIs), and electron density mapping (EDM) using a dual-layer detector spectral CT (DLSCT). METHODS: This retrospective study recruited 270 patients who underwent DLSCT scan for lung nodule screening or follow-up. All CT examinations with subsolid nodules (pure ground-glass nodules [GGNs] or part-solid nodules) were reconstructed with hybrid and model-based iterative reconstruction, VMI at 40, 70, 100, and 130 keV levels, and EDM. The CT number, objective image noise, signal-to-noise ratio, contrast-to-noise ratio, diameter, and volume of subsolid nodules were measured for quantitative analysis. The overall image quality, image noise, visualization of nodules, artifact, and sharpness were subjectively rated by 2 thoracic radiologists on a 5-point scale (1 = unacceptable, 5 = excellent) in consensus. The objective image quality measurements, diameter, and volume were compared among the 7 groups with a repeated 1-way analysis of variance. The subjective scores were compared with Kruskal-Wallis test. RESULTS: A total of 198 subsolid nodules, including 179 pure GGNs, and 19 part-solid nodules were identified. Based on the objective analysis, EDM had the highest signal-to-noise ratio (164.71 ± 133.60; P < 0.001) and contrast-to-noise ratio (227.97 ± 161.96; P < 0.001) among all image sets. Furthermore, EDM had a superior mean subjective rating score (4.80 ± 0.42) for visualization of GGNs compared to other reconstructed images (all P < 0.001), although the model-based iterative reconstruction had superior subjective scores of overall image quality. For pure GGNs, the measured diameter and volume did not significantly differ among different reconstructions (both P > 0.05). CONCLUSIONS: EDM derived from DLSCT enabled improved image quality and lesion conspicuity for the evaluation of lung subsolid nodules compared to conventional iterative reconstruction algorithms and VMIs.

Integrative genome-centric metagenomics for surface water surveillance: Elucidating microbiomes, antimicrobial resistance, and their associations.

Surface water ecosystems are intimately intertwined with anthropogenic activities and have significant public health implications as primary sources of irrigation water in agricultural production. Our extensive metagenomic analysis examined 404 surface water samples from four different geological regions in Chile and Brazil, spanning irrigation canals (n = 135), rivers (n = 121), creeks (n = 74), reservoirs (n = 66), and ponds (n = 8). Overall, 50.25 % of the surface water samples contained at least one of the pathogenic or contaminant bacterial genera (Salmonella: 29.21 %; Listeria: 6.19 %; Escherichia: 35.64 %). Furthermore, a total of 1,582 antimicrobial resistance (AMR) gene clusters encoding resistance to 25 antimicrobial classes were identified, with samples from Brazil exhibiting an elevated AMR burden. Samples from stagnant water sources were characterized by dominant Cyanobacteriota populations, resulting in significantly reduced biodiversity and more uniform community compositions. A significant association between taxonomic composition and the resistome was supported by a Procrustes analysis (p < 0.001). Notably, regional signatures were observed regarding the taxonomic and resistome profiles, as samples from the same region clustered together on both ordinates. Additionally, network analysis illuminated the intricate links between taxonomy and AMR at the contig level. Our deep sequencing efforts not only mapped the microbial landscape but also expanded the genomic catalog with newly characterized metagenome-assembled genomes (MAGs), boosting the classification of reads by 12.85 %. In conclusion, this study underscores the value of metagenomic approaches in surveillance of surface waters, enhancing our understanding of microbial and AMR dynamics with far-reaching public health and ecological ramifications.

A phylogeny-informed characterisation of global tetrapod traits addresses data gaps and biases.

Tetrapods (amphibians, reptiles, birds, and mammals) are model systems for global biodiversity science, but continuing data gaps, limited data standardisation, and ongoing flux in taxonomic nomenclature constrain integrative research on this group and potentially cause biased inference. We combined and harmonised taxonomic, spatial, phylogenetic, and attribute data with phylogeny-based multiple imputation to provide a comprehensive data resource (TetrapodTraits 1.0.0) that includes values, predictions, and sources for body size, activity time, micro- and macrohabitat, ecosystem, threat status, biogeography, insularity, environmental preferences, and human influence, for all 33,281 tetrapod species covered in recent fully sampled phylogenies. We assess gaps and biases across taxa and space, finding that shared data missing in attribute values increased with taxon-level completeness and richness across clades. Prediction of missing attribute values using multiple imputation revealed substantial changes in estimated macroecological patterns. These results highlight biases incurred by nonrandom missingness and strategies to best address them. While there is an obvious need for further data collection and updates, our phylogeny-informed database of tetrapod traits can support a more comprehensive representation of tetrapod species and their attributes in ecology, evolution, and conservation research.

Overcoming Kinetic Limitations of Polyanionic Cathode toward High-Performance Na-Ion Batteries.

Polyanionic cathodes have attracted extensive research interest for Na-ion batteries (NIBs) due to their moderate energy density and desirable cycling stability. However, these compounds suffer from visible capacity fading and significant voltage decay upon the rapid sodium storage process, even if modified through nanoengineering or carbon-coating routes, leading to limited applications in NIBs. Herein, the Na3(VOPO4)2F cathode material with dominantly exposed {001} active facets is demonstrated by a topochemical synthesis route. Owing to the rational geometrical structure design and thereby directly shortening Na diffusion distance, the electrode delivers a reversible capacity of ∼129 mA h g-1 even at a high rate of 10 C, which is very close to the theoretical capacity of 132 mA h g-1, achieving a high energy density of ∼452 W h kg-1 coupled with a high-power density of 4660 W kg-1. When further served as a cathode for nonaqueous, aqueous-based, and solid-state full NIBs, respectively, our designed Na3(VOPO4)2F always enables superior electrochemical performance due to favorable kinetics.

HES6 Mediates Oxidative Phosphorylation Pathway to Promote Immune Infiltration of CD8+ T Cells in Lung Adenocarcinoma.

Tumor immunotherapy has recently gained popularity as a cancer treatment strategy. The molecular mechanism controlling immune infiltration in lung adenocarcinoma (LUAD) cells, however, is not well characterized. Investigating the immune infiltration modulation mechanism in LUAD is crucial. LUAD patient samples were collected, and HES6 expression and immune infiltration level of CD8+ T cells in patient tissues were analyzed. Bioinformatics was utilized to identify binding relationship between E2F1 and HES6, and enrichment pathway of HES6. The binding of E2F1 to HES6 was verified using dual-luciferase and ChIP experiments. HES6 and E2F1 expression in LUAD cells was detected. LUAD cells were co-cultured with CD8+ T cells, and the CD8+ T cell killing level, IFN-γ secretion, and CD8+ T-cell chemotaxis level were measured. Expression of key genes involved in oxidative phosphorylation was detected, and the oxygen consumption rate of LUAD cells was assessed. A mouse model was constructed to assay Ki67 expression and apoptosis in tumor tissue. High expression of HES6 promoted CD8+ T-cell infiltration and enhanced T-cell killing ability through oxidative phosphorylation. Further bioinformatics analysis, molecular experiments, and cell experiments verified that E2F1 negatively regulated HES6 by oxidative phosphorylation, which suppressed CD8+ T-cell immune infiltration. In addition, in vivo assays illustrated that silencing HES6 repressed tumor cell immune evasion. E2F1 inhibited HES6 transcription, thereby mediating oxidative phosphorylation to suppress immune infiltration of CD8+ T cells in LUAD. The biological functions and signaling pathways of these genes were analyzed, which may help to understand the possible mechanisms regulating immune infiltration in LUAD.

(HTBNet)Arbitrary Shape Scene Text Detection with Binarization of Hyperbolic Tangent and Cross-Entropy.

The existing segmentation-based scene text detection methods mostly need complicated post-processing, and the post-processing operation is separated from the training process, which greatly reduces the detection performance. The previous method, DBNet, successfully simplified post-processing and integrated post-processing into a segmentation network. However, the training process of the model took a long time for 1200 epochs and the sensitivity to texts of various scales was lacking, leading to some text instances being missed. Considering the above two problems, we design the text detection Network with Binarization of Hyperbolic Tangent (HTBNet). First of all, we propose the Binarization of Hyperbolic Tangent (HTB), optimized along with which the segmentation network can expedite the initial convergent speed by reducing the number of epochs from 1200 to 600. Because features of different channels in the same scale feature map focus on the information of different regions in the image, to better represent the important features of all objects in the image, we devise the Multi-Scale Channel Attention (MSCA). Meanwhile, considering that multi-scale objects in the image cannot be simultaneously detected, we propose a novel module named Fused Module with Channel and Spatial (FMCS), which can fuse the multi-scale feature maps from channel and spatial dimensions. Finally, we adopt cross-entropy as the loss function, which measures the difference between predicted values and ground truths. The experimental results show that HTBNet, compared with lightweight models, has achieved competitive performance and speed on Total-Text (F-measure:86.0%, FPS:30) and MSRA-TD500 (F-measure:87.5%, FPS:30).

Graph Adaptive Attention Network with Cross-Entropy.

Non-Euclidean data, such as social networks and citation relationships between documents, have node and structural information. The Graph Convolutional Network (GCN) can automatically learn node features and association information between nodes. The core ideology of the Graph Convolutional Network is to aggregate node information by using edge information, thereby generating a new node feature. In updating node features, there are two core influencing factors. One is the number of neighboring nodes of the central node; the other is the contribution of the neighboring nodes to the central node. Due to the previous GCN methods not simultaneously considering the numbers and different contributions of neighboring nodes to the central node, we design the adaptive attention mechanism (AAM). To further enhance the representational capability of the model, we utilize Multi-Head Graph Convolution (MHGC). Finally, we adopt the cross-entropy (CE) loss function to describe the difference between the predicted results of node categories and the ground truth (GT). Combined with backpropagation, this ultimately achieves accurate node classification. Based on the AAM, MHGC, and CE, we contrive the novel Graph Adaptive Attention Network (GAAN). The experiments show that classification accuracy achieves outstanding performances on Cora, Citeseer, and Pubmed datasets.

Comparative simulations of methanol steam reforming on PdZn alloy using kinetic Monte Carlo and mean-field microkinetic model.

Methanol steam reforming (MSR) is an attractive route for producing clean energy hydrogen. PdZn alloys are extensively studied as potential MSR catalysts for their stability and high CO2 selectivity. Here, we investigated the reaction mechanism using density functional calculations, mean-field microkinetic modeling (MF-MKM), and kinetic Monte Carlo (kMC) simulations. To overcome the over-underestimation of CO2 selectivity by log-kMC, an ads-kMC algorithm is proposed in which the adsorption/desorption rate constants were reduced under certain requirements and the diffusion process was treated by redistributing surface species each time an event occured. The simulations show that the dominant pathway to CO2 at low temperatures is CH3OH → CH3O → CH2O → H2COOH → H2COO → HCOO → CO2. The ads-kMC predicted OH coverage is 2-3 times that of MF-MKM, while they produce similar coverage for other species. Analyses indicate that surface OH promotes the dehydrogenation of CH3OH, CH3O, and H2COOH significantly and plays a key role in the MSR process. The dissociation of water/methanol is the most important rate-limiting/rate-inhibiting step. The CO2 selectivity obtained by the two methods is close to each other and consistent with the experimental trend with temperature. Generally, the ads-kMC results agree with the MF-MKM ones, supporting the previous finding that kMC and MF-MKM predict similar results if the diffusion is very fast and adsorbate interactions are neglected. The present study sheds light on the MSR process on PdZn alloys, and the proposed scheme to overcome the stiff problems in kMC simulations is worthy of being extended to other systems.

Comprehensive analyses show the enhancement effect of exogenous melatonin on fluroxypyr-meptyl multiple phase metabolisms in Oryza sativa for reducing environmental risks.

The role of melatonin (MT), an essential phytohormone controlling the physiological and biochemical reactions of plants to biotic and abiotic stress, in alleviating pesticide phytotoxicity remains unclear. This study explores the effects of MT (0 and 200 mg/L) and six doses of fluroxypyr-meptyl (FLUME) (0-0.14 mg/L) on the physiological response of rice (Oryza sativa). FLUME exposure inhibited the growth of rice seedlings, with MT treatment ameliorating this effect. To determine the biochemical processes and catalytic events involved in FLUME breakdown in rice, six rice root and shoot libraries exposed to either FLUME or FLUME-MT were generated and then subjected to RNA-Seq-LC-Q-TOF-HRMS/MS analyses. The results showed that 1510 root genes and 139 shoot genes exhibited higher upregulation in plants treated with an ecologically realistic FLUME concentration and MT than in those treated with FLUME alone. Gene enrichment analysis revealed numerous FLUME-degradative enzymes operating in xenobiotic tolerance to environmental stress and molecular metabolism. Regarding the FLUME degradation process, certain differentially expressed genes were responsible for producing important enzymes, such as cytochrome P450, glycosyltransferases, and acetyltransferases. Four metabolites and ten conjugates in the pathways involving hydrolysis, malonylation, reduction, glycosylation, or acetylation were characterized using LC-Q-TOF-HRMS/MS to support FLUME-degradative metabolism. Overall, external application of MT can increase rice tolerance to FLUME-induced oxidative stress by reducing phytotoxicity and FLUME accumulation. This study provides insights into MT's role in facilitating FLUME degradation, with potential implications for engineering genotypes supporting FLUME degradation in paddy crops.

Tetraphenylethene-based mononuclear aggregation-induced emission (AIE)-active mechanofluorochromism gold(I) complexes with different auxiliary ligands.

In this study, a series of tetraphenylethene-containing gold(I) complexes with different auxiliary ligands have been synthesized. These complexes were characterized using a variety of techniques including nuclear magnetic resonance spectroscopy, mass spectrometry, and single crystal X-ray diffraction. Their aggregation-induced emission (AIE) behaviors were investigated through ultraviolet/visible and photoluminescence spectrum analyses, and dynamic light scattering measurements. Meanwhile, their mechanofluorochromic properties were also studied via solid-state photoluminescence spectroscopy. Intriguingly, all these mononuclear gold(I) molecules functionalized by tetraphenylethene group demonstrated AIE phenomena. Furthermore, five gold(I) complexes possessing diverse auxiliary ligands exhibited distinct fluorescence changes in response to mechanical grinding. For luminogens 2-5, their solids showed reversible mechanofluorochromic behaviors triggered by the mutual transformation of crystalline and amorphous states, while for luminogen 1, blue-green-cyan three-color solid fluorescence conversion was realized by sequential mechanical grinding and solvent fumigation. Based on this stimuli-responsive tricolored fluorescence feature of 1, an information encryption system was successfully constructed.

Degradation of perineuronal nets in the medial prefrontal cortex promotes extinction and reduces reinstatement of methamphetamine-induced conditioned place preference in female mice.

The high rate of relapse to compulsive methamphetamine (MA)-taking and seeking behaviors after abstinence constitutes a major obstacle to the treatment of MA addiction. Perineuronal nets (PNNs), essential components of the extracellular matrix, play a critical role in synaptic function, learning, and memory. Abnormalities in PNNs have been closely linked to a series of neurological diseases, such as addiction. However, the exact role of PNNs in MA-induced related behaviors remains elusive. Here, we established a MA-induced conditioned place preference (CPP) paradigm in female mice and found that the number and average optical density of PNNs increased significantly in the medial prefrontal cortex (mPFC) of mice during the acquisition, extinction, and reinstatement stages of CPP. Notably, the removal of PNNs in the mPFC via chondroitinase ABC (ChABC) before extinction training not only facilitated the extinction of MA-induced CPP and attenuated the relapse of extinguished MA preference but also significantly reduced the activation of c-Fos in the mPFC. Similarly, the ablation of PNNs in the mPFC before reinstatement markedly lessened the reinstatement of MA-induced CPP, which was accompanied by the decreased expression of c-Fos in the mPFC. Collectively, our results provide more evidence for the implication of degradation of PNNs in facilitating extinction and preventing relapse of MA-induced CPP, which indicate that targeting PNNs may be an effective therapeutic option for MA-induced CPP memories.

Exploring explainable AI features in the vocal biomarkers of lung disease.

This review delves into the burgeoning field of explainable artificial intelligence (XAI) in the detection and analysis of lung diseases through vocal biomarkers. Lung diseases, often elusive in their early stages, pose a significant public health challenge. Recent advancements in AI have ushered in innovative methods for early detection, yet the black-box nature of many AI models limits their clinical applicability. XAI emerges as a pivotal tool, enhancing transparency and interpretability in AI-driven diagnostics. This review synthesizes current research on the application of XAI in analyzing vocal biomarkers for lung diseases, highlighting how these techniques elucidate the connections between specific vocal features and lung pathology. We critically examine the methodologies employed, the types of lung diseases studied, and the performance of various XAI models. The potential for XAI to aid in early detection, monitor disease progression, and personalize treatment strategies in pulmonary medicine is emphasized. Furthermore, this review identifies current challenges, including data heterogeneity and model generalizability, and proposes future directions for research. By offering a comprehensive analysis of explainable AI features in the context of lung disease detection, this review aims to bridge the gap between advanced computational approaches and clinical practice, paving the way for more transparent, reliable, and effective diagnostic tools.

Exploring Porous Flow Behavior of the Decomposed Gas from CH4 Hydrate in Clayey Sediments by Molecular Dynamics Simulation.

A fundamental understanding of the fluid flow mechanism during CH4 hydrate dissociation in nanoscale clayey sediments from the molecular perspective can provide invaluable information for macroscale natural gas hydrate (NGH) exploration. In this work, the fluid flow behaviors of the decomposed gas from CH4 hydrate within clayey nanopores under different temperature conditions are revealed by molecular dynamics (MD) simulation. The simulation results indicate that the key influencing factors of gas-water flow in nanoscale clayey sediments include the diffusion and the random migration of gas molecules. The influencing mechanisms of fluid flow in nanopores are closely related with the temperature conditions. Under a low temperature condition, the gas diffusion process is impeded by the secondary hydrate formation, leading to the decline in gas transport velocity within nanopores. However, it is still noteworthy that the gas-water fluid flow channels are not completely blocked by the occurrence of secondary hydrate. Under a high temperature condition, the significant phenomenon of water migration during gas flow is observed, which can be ascribed to the gas-liquid entrainment effect in nanopores of the clayey sediment. These results may provide valuable implications and fundamental evidence for improving gas production efficiency in future field tests of NGH exploitation in marine sediments.

Unraveling the impact of Spartina alterniflora invasion on greenhouse gas production and emissions in coastal saltmarshes: New insights from dissolved organic matter characteristics and surface-porewater interactions.

Saltmarshes along the Chinese coast are threatened by the invasion of Spartina alterniflora (S. alterniflora). This study was carried out in the Andong Shoal, Hangzhou Bay, China, with the aim of comprehending the intricate impacts of S. alterniflora invasion on greenhouse gases (GHG) production and emissions. To address this issue, we thoroughly examined the chemistry of dissolved organic matter (DOM) and the rate of surface water-porewater interaction. Porewater and surface water samples were collected from farm land, S. alterniflora invaded areas, and Scirpus mariqueter (S. mariqueter) dominated areas. The findings indicated that the invasion of S. alterniflora impeded the interaction between surface water and porewater, resulting in reduced porewater exchange rates within its affected region (0.015-0.440 cm d-1), in contrast to areas dominated by S. mariqueter (9.635-18.232 cm d-1). The invasion also increased dissolved organic carbon concentration in porewater and created a stable and closed soil environment that resulted in DOM with smaller molecule sizes and higher humification levels. The presence of high tryptophan-like fluorescent DOM caused an increase in the production of methane and carbon dioxide in S. alterniflora invaded area. However, both limited surface-porewater exchange and significant differences in GHG concentrations between porewater and surface water suggested that the aerenchyma tissues of S. alterniflora may play an important role in transporting GHG from soil to the atmosphere.

Oral edaravone ameliorates behavioral deficits and pathologies in a valproic acid-induced rat model of autism spectrum disorder.

Autism spectrum disorder (ASD) is neurodevelopmental disorder with a high incidence rate, characterized by social deficits and repetitive behaviors. There is currently no effective management available to treat the core symptoms of ASD; however, oxidative stress has been implicated in its pathogenesis. Edaravone (EDA), a free-radical scavenger, is used to treat amyotrophic lateral sclerosis (ALS) and acute ischemic stroke (AIS). Here, we hypothesized that an oral formula of EDA may have therapeutic efficacy in the treatment of core ASD symptoms. A rat model of autism was established by prenatal exposure to valproic acid (VPA), and the offsprings were orally treated with EDA at low (3 mg/kg), medium (10 mg/kg), and high (30 mg/kg) doses once daily for 28 days starting from postnatal day 25 (PND25). Oral EDA administration alleviated the core symptoms in VPA rats in a dose-dependent manner, including repetitive stereotypical behaviors and impaired social interaction. Furthermore, oral administration of EDA significantly reduced oxidative stress in a dose-dependent manner, as evidenced by a reduction in oxidative stress markers and an increase in antioxidants in the blood and brain. In addition, oral EDA significantly attenuated downstream pathologies, including synaptic and mitochondrial damage in the brain. Proteomic analysis further revealed that EDA corrected the imbalance in brain oxidative reduction and mitochondrial proteins induced by prenatal VPA administration. Overall, these findings demonstrate that oral EDA has therapeutic potential for ASD by targeting the oxidative stress pathway of disease pathogenesis and paves the way towards clinical studies.

DXA-derived abdominal fat free mass to predict MRI skeletal muscle mass in postmenopausal women.

Dual-energy X-ray absorptiometry (DXA) is more available than gold-standard magnetic resonance imaging (MRI), but DXA ability to estimate abdominal skeletal muscle mass (SMM) is unknown. DXA-derived abdominal fat-free mass (FFM; Hologic QDR2000 or QDR4500w) was correlated with single-slice MRI SMM at L4 (N = 69; r QDR2000=0.71, QDR4500w=0.69; p<.0001). Linear regression to predict SMM, including DXA FFM, BMI, and age, resulted in an R-squared of 0.72 and 0.65 for QDR2000 and QDR4500. Bland-Altman limits of agreement were ±21g and ±31g for 2-3 standard deviations from the mean difference. DXA predicted abdominal SSM is a moderate proxy for MRI abdominal SMM.