Antennal Transcriptome Evaluation and Analysis for Odorant-Binding Proteins, Chemosensory Proteins, and Suitable Reference Genes in the Leaf Beetle Pest Diorhabda rybakowi Weise (Coleoptera: Chrysomelidae).

Diorhabda rybakowi Weise is one of the dominant pests feeding on Nitraria spp., a pioneer plant used for windbreaking and sand fixation purposes, and poses a threat to local livestock and ecosystems. To clarify the key olfactory genes of D. rybakowi and provide a theoretical basis for attractant and repellent development, the optimal reference genes under two different conditions (tissue and sex) were identified, and the bioinformatics and characterization of the tissue expression profiles of two categories of soluble olfactory proteins (OBPs and CSPs) were investigated. The results showed that the best reference genes were RPL13a and RPS18 for comparison among tissues, and RPL19 and RPS18 for comparison between sexes. Strong expressions of DrybOBP3, DrybOBP6, DrybOBP7, DrybOBP10, DrybOBP11, DrybCSP2, and DrybCSP5 were found in antennae, the most important olfactory organ for D. rybakowi. These findings not only provide a basis for further in-depth research on the olfactory molecular mechanisms of host-specialized pests but also provide a theoretical basis for the future development of new chemical attractants or repellents using volatiles to control D. rybakowi.

m6Aexpress-enet: Predicting the regulatory expression m6A sites by an enet-regularization negative binomial regression model.

As the most abundant mRNA modification, m6A controls and influences many aspects of mRNA metabolism including the mRNA stability and degradation. However, the role of specific m6A sites in regulating gene expression still remains unclear. In additional, the multicollinearity problem caused by the correlation of methylation level of multiple m6A sites in each gene could influence the prediction performance. To address the above challenges, we propose an elastic-net regularized negative binomial regression model (called m6Aexpress-enet) to predict which m6A site could potentially regulate its gene expression. Comprehensive evaluations on simulated datasets demonstrate that m6Aexpress-enet could achieve the top prediction performance. Applying m6Aexpress-enet on real MeRIP-seq data from human lymphoblastoid cell lines, we have uncovered the complex regulatory pattern of predicted m6A sites and their unique enrichment pathway of the constructed co-methylation modules. m6Aexpress-enet proves itself as a powerful tool to enable biologists to discover the mechanism of m6A regulatory gene expression. Furthermore, the source code and the step-by-step implementation of m6Aexpress-enet is freely accessed at https://github.com/tengzhangs/m6Aexpress-enet.

[Analysis of constituents in different parts of Forsythia suspensa by UPLC-Q-TOF-MS and evaluation of their anti-inflammatory activity].

This study aims to characterize and identify the chemical constituents in 11 parts of Forsythia suspensa by using ultra-performance liquid chromatography-quadrupole time of flight-mass spectrometry(UPLC-Q-TOF-MS) combined with a self-established chemical constituent database, including leaves, flowers, fruits, green F. suspensa, old F. suspensa, and seeds. The quality attributes and differences of different parts of F. suspensa were evaluated by principal component analysis, partial least square discriminant analysis, and other stoichiometric methods. A total of 79 compounds were identified, including 13 phenylethanol glycosides, 10 lignans, 12 flavonoids, 10 organic acids, 14 terpenoids, and 20 other types of compounds. Among them, 34 compounds were the main variables of difference between the different parts of F. suspensa, and the content of each component was relatively higher in the leaves and green F. suspensa. The LPS-induced inflammation model of RAW264.7 cells was applied to study the anti-inflammatory activity of the extracts of the different parts of F. suspensa and the main constituents. The results show that the extracts of green F. suspensa, flower, twig, and stem exhibited anti-inflammatory activity, and the constituents such as forsythoside A, phyllyrin, phillygenin, and(+)-pinoresinol-ß-D-glucopyranoside could significantly inhibit anti-inflammatory activity released by NO. The chemical constituent in different parts of F. suspensa is analyzed comprehensively, and the anti-inflammatory activity is evaluated in this study, which provides a reference for the development and comprehensive utilization of F. suspensa resources.

A generalizable and easy-to-use COVID-19 stratification model for the next pandemic via immune-phenotyping and machine learning.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has affected billions of people worldwide, and the lessons learned need to be concluded to get better prepared for the next pandemic. Early identification of high-risk patients is important for appropriate treatment and distribution of medical resources. A generalizable and easy-to-use COVID-19 severity stratification model is vital and may provide references for clinicians. Methods: Three COVID-19 cohorts (one discovery cohort and two validation cohorts) were included. Longitudinal peripheral blood mononuclear cells were collected from the discovery cohort (n = 39, mild = 15, critical = 24). The immune characteristics of COVID-19 and critical COVID-19 were analyzed by comparison with those of healthy volunteers (n = 16) and patients with mild COVID-19 using mass cytometry by time of flight (CyTOF). Subsequently, machine learning models were developed based on immune signatures and the most valuable laboratory parameters that performed well in distinguishing mild from critical cases. Finally, single-cell RNA sequencing data from a published study (n = 43) and electronic health records from a prospective cohort study (n = 840) were used to verify the role of crucial clinical laboratory and immune signature parameters in the stratification of COVID-19 severity. Results: Patients with COVID-19 were determined with disturbed glucose and tryptophan metabolism in two major innate immune clusters. Critical patients were further characterized by significant depletion of classical dendritic cells (cDCs), regulatory T cells (Tregs), and CD4+ central memory T cells (Tcm), along with increased systemic interleukin-6 (IL-6), interleukin-12 (IL-12), and lactate dehydrogenase (LDH). The machine learning models based on the level of cDCs and LDH showed great potential for predicting critical cases. The model performances in severity stratification were validated in two cohorts (AUC = 0.77 and 0.88, respectively) infected with different strains in different periods. The reference limits of cDCs and LDH as biomarkers for predicting critical COVID-19 were 1.2% and 270.5 U/L, respectively. Conclusion: Overall, we developed and validated a generalizable and easy-to-use COVID-19 severity stratification model using machine learning algorithms. The level of cDCs and LDH will assist clinicians in making quick decisions during future pandemics.

Abdominal physical examinations in early stages benefit critically ill patients without primary gastrointestinal diseases: a retrospective cohort study.

Background: Gastrointestinal (GI) function is critical for patients in intensive care units (ICUs). Whether and how much critically ill patients without GI primary diseases benefit from abdominal physical examinations remains unknown. No evidence from big data supports its possible additive value in outcome prediction. Methods: We performed a big data analysis to confirm the value of abdominal physical examinations in ICU patients without GI primary diseases. Patients were selected from the Medical Information Mart for Intensive Care (MIMIC)-IV database and classified into two groups depending on whether they received abdominal palpation and auscultation. The primary outcome was the 28-day mortality. Statistical approaches included Cox regression, propensity score matching, and inverse probability of treatment weighting. Then, the abdominal physical examination group was randomly divided into the training and testing cohorts in an 8:2 ratio. And patients with GI primary diseases were selected as the validation group. Several machine learning algorithms, including Random Forest, Gradient Boosting Decision Tree, Adaboost, Extra Trees, Bagging, and Multi-Layer Perceptron, were used to develop in-hospital mortality predictive models. Results: Abdominal physical examinations were performed in 868 (2.63%) of 33,007 patients without primary GI diseases. A significant benefit in terms of 28-day mortality was observed among the abdominal physical examination group (HR 0.75, 95% CI 0.56-0.99; p = 0.043), and a higher examination frequency was associated with improved outcomes (HR 0.62, 95%CI 0.40-0.98; p = 0.042). Machine learning studies further revealed that abdominal physical examinations were valuable in predicting in-hospital mortality. Considering both model performance and storage space, the Multi-Layer Perceptron model performed the best in predicting mortality (AUC = 0.9548 in the testing set and AUC = 0.9833 in the validation set). Conclusion: Conducting abdominal physical examinations improves outcomes in critically ill patients without GI primary diseases. The results can be used to predict in-hospital mortality using machine learning algorithms.

Dynamic genomic changes in methotrexate-resistant human cancer cell lines beyond DHFR amplification suggest potential new targets for preventing drug resistance.

BACKGROUND: Although DHFR gene amplification has long been known as a major mechanism for methotrexate (MTX) resistance in cancer, the early changes and detailed development of the resistance are not yet fully understood. METHODS: We performed genomic, transcriptional and proteomic analyses of human colon cancer cells with sequentially increasing levels of MTX-resistance. RESULTS: The genomic amplification evolved in three phases (pre-amplification, homogenously staining region (HSR) and extrachromosomal DNA (ecDNA)). We confirm that genomic amplification and increased expression of DHFR, with formation of HSRs and especially ecDNAs, is the major driver of resistance. However, DHFR did not play a detectable role in the early phase. In the late phase (ecDNA), increase in FAM151B protein level may also have an important role by decreasing sensitivity to MTX. In addition, although MSH3 and ZFYVE16 may be subject to different posttranscriptional regulations and therefore protein expressions are decreased in ecDNA stages compared to HSR stages, they still play important roles in MTX resistance. CONCLUSION: The study provides a detailed evolutionary trajectory of MTX-resistance and identifies new targets, especially ecDNAs, which could help to prevent drug resistance. It also presents a proof-of-principal approach which could be applied to other cancer drug resistance studies.

An externally validated clinical-laboratory nomogram for myocardial involvement in adult idiopathic-inflammatory-myopathy patients.

OBJECTIVES: This study aimed at identifying clinical and laboratory risk factors for myocardial involvement (MI) in idiopathic inflammatory myopathies (IIMs) patients as well as constructing a risk-predicted nomogram for prediction and early identification of MI. METHODS: An IIMs cohort in southeastern China was constructed, including 504 adult IIMs patients who met the inclusion and exclusion criteria, and were hospitalized at four divisions of the First Affiliated Hospital, Zhejiang University School of Medicine from January 1st 2018 to April 30st 2022. After dividing patients into the training cohort and the validation cohort, risk factors for MI were identified through least absolute shrinkage and selection operator regression and multivariate logistic regression. A risk-predicted nomogram was established and validated internally and externally for discrimination, calibration and practicability. RESULTS: In this cohort, 17.7% of patients developed MI and the survival was significantly inferior to that of IIMs patients without MI (P < 0.001). In the training cohort, age > 55 years old (P < 0.001), disease activity > 10 points (P < 0.001), interleukin-17A (IL-17A) > 7.5 pg/ml (P < 0.001), lactic dehydrogenase (LDH) > 425 U/L (P < 0.001), anti-mitochondrial antibodies (AMAs, P = 0.017), and anti-MDA5 antibody (P = 0.037) were significantly correlated with development of MI. A nomogram was established by including the above values to predict MI and was found efficient in discrimination, calibration, and practicability through internal and external validation. CONCLUSION: This study developed and validated a nomogram model to predict the risk of MI in adult IIMs patients, which can benefit the prediction and early identification of MI as well as timely intervention in these patients.

Machine learning identifies cell-free DNA 5-hydroxymethylation biomarkers that detect occult colorectal cancer in PLCO Screening Trial subjects.

Background: Colorectal cancer (CRC) is a leading cause of cancer-related mortality, and CRC detection through screening improves survival rates. A promising avenue to improve patient screening compliance is the development of minimally-invasive liquid biopsy assays that target CRC biomarkers on circulating cell-free DNA (cfDNA) in peripheral plasma. In this report, we identify cfDNA biomarker candidate genes bearing the epigenetic mark 5-hydroxymethylcytosine (5hmC) that diagnose occult CRC up to 36 months prior to clinical diagnosis using the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial samples. Methods: Archived PLCO Trial plasma samples containing cfDNA were obtained from the National Cancer Institute (NCI) biorepositories. Study subjects included those who were diagnosed with CRC within 36 months of blood collection (i.e., case, n = 201) and those who were not diagnosed with any cancer during an average of 16.3 years of follow-up (i.e., controls, n = 402). Following the extraction of 3 - 8 ng cfDNA from less than 300 microliters plasma, we employed the sensitive 5hmC-Seal chemical labeling approach, followed by next-generation sequencing (NGS). We then conducted association studies and machine-learning modeling to analyze the genome-wide 5hmC profiles within training and validation groups that were randomly selected at a 2:1 ratio. Results: Despite the technical challenges associated with the PLCO samples (e.g., limited plasma volumes, low cfDNA amounts, and long archival times), robust genome-wide 5hmC profiles were successfully obtained from these samples. Association analyses using the Cox proportional hazards models suggested several epigenetic pathways relevant to CRC development distinguishing cases from controls. A weighted Cox model, comprised of 32-associated gene bodies, showed predictive detection value for CRC as early as 24-36 months prior to overt tumor presentation, and a trend for increased predictive power was observed for blood samples collected closer to CRC diagnosis. Notably, the 5hmC-based predictive model showed comparable performance regardless of sex and self-reported race/ethnicity, and significantly outperformed risk factors such as age and obesity according to BMI (body mass index). Additionally, further improvement of predictive performance was achieved by combining the 5hmC-based model and risk factors for CRC. Conclusions: An assay of 5hmC epigenetic signals on cfDNA revealed candidate biomarkers with the potential to predict CRC occurrence despite the absence of clinical symptoms or the availability of effective predictors. Developing a minimally-invasive clinical assay that detects 5hmC-modified biomarkers holds promise for improving early CRC detection and ultimately patient survival through higher compliance screening and earlier intervention. Future investigation to expand this strategy to prospectively collected samples is warranted.

Functional division of the dorsal striatum based on a graph neural network.

The dorsal striatum, an essential nucleus in subcortical areas, has a crucial role in controlling a variety of complex cognitive behaviors; however, few studies have been conducted in recent years to explore the functional subregions of the dorsal striatum that are significantly activated when performing multiple tasks. To explore the differences and connections between the functional subregions of the dorsal striatum that are significantly activated when performing different tasks, we propose a framework for functional division of the dorsal striatum based on a graph neural network model. First, time series information for each voxel in the dorsal striatum is extracted from acquired functional magnetic resonance imaging data and used to calculate the connection strength between voxels. Then, a graph is constructed using the voxels as nodes and the connection strengths between voxels as edges. Finally, the graph data are analyzed using the graph neural network model to functionally divide the dorsal striatum. The framework was used to divide functional subregions related to the four tasks including olfactory reward, "0-back" working memory, emotional picture stimulation, and capital investment decision-making. The results were further subjected to conjunction analysis to obtain 15 functional subregions in the dorsal striatum. The 15 different functional subregions divided based on the graph neural network model indicate that there is functional differentiation in the dorsal striatum when the brain performs different cognitive tasks. The spatial localization of the functional subregions contributes to a clear understanding of the differences and connections between functional subregions.

Genomic characters of Anaplasma bovis and genetic diversity in China.

The emergence of Anaplasma bovis or A. bovis-like infection in humans from China and the United States of America has raised concern about the public health importance of this pathogen. Although A. bovis has been detected in a wide range of ticks and mammals in the world, no genome of the pathogen is available up to now, which has prohibited us from better understanding the genetic basis for its pathogenicity. Here we describe an A. bovis genome from metagenomic sequencing of an infected goat in China. Anaplasma bovis had the smallest genome of the genus Anaplasma, and relatively lower GC content. Phylogenetic analysis of single-copy orthologue sequence showed that A. bovis was closely related to A. platys and A. phagocytophilum, but relatively far from intraerythrocytic Anaplasma species. Anaplasma bovis had 116 unique orthogroups and lacked 51 orthogroups in comparison to other Anaplasma species. The virulence factors of A. bovis were significantly less than those of A. phagocytophilum, suggesting less pathogenicity of A. bovis. When tested by specific PCR assays, A. bovis was detected in 23 of 29 goats, with an infection rate up to 79.3% (95% CI: 64.6% ∼94.1%). The phylogenetic analyses based on partial 16S rRNA, gltA and groEL genes indicated that A. bovis had high genetic diversity. The findings of this study lay a foundation for further understanding of the biological characteristics and genetic diversity of A. bovis, and will facilitate the formulation of prevention and control strategies.

Characterization of a pangolin SARS-CoV-2-related virus isolate that uses the human ACE2 receptor.

Various SARS-CoV-2-related coronaviruses have been increasingly identified in pangolins, showing a potential threat to humans. Here we report the infectivity and pathogenicity of the SARS-CoV-2-related virus, PCoV-GX/P2V, which was isolated from a Malayan pangolin (Manis javanica). PCoV-GX/P2V could grow in human hepatoma, colorectal adenocarcinoma cells, and human primary nasal epithelial cells. It replicated more efficiently in cells expressing human angiotensin-converting enzyme 2 (hACE2) as SARS-CoV-2 did. After intranasal inoculation to the hACE2-transgenic mice, PCoV-GX/P2V not only replicated in nasal turbinate and lungs, but also caused interstitial pneumonia, characterized by infiltration of mixed inflammatory cells and multifocal alveolar hemorrhage. Existing population immunity established by SARS-CoV-2 infection and vaccination may not protect people from PCoV-GX/P2V infection. These findings further verify the hACE2 utility of PCoV-GX/P2V by in vivo experiments using authentic viruses and highlight the importance for intensive surveillance to prevent possible cross-species transmission.

Immobilisation remediation of arsenic-contaminated soils with promising CaAl-layered double hydroxide and bioavailability, bioaccessibility, and speciation-based health risk assessment.

Arsenic (As)-contaminated soil poses great health risk to human mostly through inadvertent oral exposure. We investigated CaAl-layered double hydroxide (CaAl-LDH), a promising immobilising agent, for the remediation of As-contaminated Chinese soils. The effects on specific soil properties and As fractionation were analyzed, and changes in the health risk of soil As were accurately assessed by means of advanced in vivo mice model and in vitro PBET-SHIME model. Results showed that the application of CaAl-LDH significantly increased soil pH and concentration of Fe and Al oxides, and effectively converted active As fractions into the most stable residual fraction, guaranteeing long-term remediation stability. Based on in vivo test, As relative bioavailability was significantly reduced by 37.75%. Based on in vitro test, As bioaccessibility in small intestinal and colon phases was significantly reduced by 25.65% and 28.57%, respectively. Furthermore, As metabolism (reduction and methylation) by the gut microbiota inhabiting colon was clearly observed. After immobilisation with CaAl-LDH, the concentration of bioaccessible As(Ⅴ) in the colon fluid was significantly reduced by 61.91%, and organic As (least toxic MMA(V) and DMA(V)) became the main species, which further reduced the health risk of soil As. In summary, CaAl-LDH proved to be a feasible option for immobilisation remediation of As-contaminated soils, and considerable progress was made in relevant health risk assessment.

PM2.5 exposure-induced senescence-associated secretory phenotype in airway smooth muscle cells contributes to airway remodeling.

Fine particulate matter (PM2.5) has been linked to increased severity and incidence of airway diseases, especially chronic obstructive pulmonary disease (COPD) and asthma. Airway remodeling is an important event in both COPD and asthma, and airway smooth muscle cells (ASMCs) are key cells which directly involved in airway remodeling. However, it was unclear how PM2.5 affected ASMCs. This study investigates the effects of PM2.5 on airway smooth muscle and its mechanism. We first showed that inhaled particulate matter was distributed in the airway smooth muscle bundle, combined with increased airway smooth muscle bundle and collagen deposition in vivo. Then, we demonstrated that PM2.5 induced up-regulation of collagen-I and alpha-smooth muscle actin (α-SMA) expression in rat and human ASMCs in vitro. Next, we found PM2.5 led to rat and human ASMCs senescence and exhibited senescence-associated secretory phenotype (SASP) by autophagy-induced GATA4/TRAF6/NF-κB signaling, which contributed to collagen-I and α-SMA synthesis as well as airway smooth muscle remodeling. Together, our results provided evidence that SASP induced by PM2.5 in airway smooth muscle cells prompted airway remodeling.

Dual RNA sequencing of Helicobacter pylori and host cell transcriptomes reveals ontologically distinct host-pathogen interaction.

Helicobacter pylori is a highly successful pathogen that poses a substantial threat to human health. However, the dynamic interaction between H. pylori and the human gastric epithelium has not been fully investigated. In this study, using dual RNA sequencing technology, we characterized a cytotoxin-associated gene A (cagA)-modulated bacterial adaption strategy by enhancing the expression of ATP-binding cassette transporter-related genes, metQ and HP_0888, upon coculturing with human gastric epithelial cells. We observed a general repression of electron transport-associated genes by cagA, leading to the activation of oxidative phosphorylation. Temporal profiling of host mRNA signatures revealed the downregulation of multiple splicing regulators due to bacterial infection, resulting in aberrant pre-mRNA splicing of functional genes involved in the cell cycle process in response to H. pylori infection. Moreover, we demonstrated a protective effect of gastric H. pylori colonization against chronic dextran sulfate sodium (DSS)-induced colitis. Mechanistically, we identified a cluster of propionic and butyric acid-producing bacteria, Muribaculaceae, selectively enriched in the colons of H. pylori-pre-colonized mice, which may contribute to the restoration of intestinal barrier function damaged by DSS treatment. Collectively, this study presents the first dual-transcriptome analysis of H. pylori during its dynamic interaction with gastric epithelial cells and provides new insights into strategies through which H. pylori promotes infection and pathogenesis in the human gastric epithelium. IMPORTANCE: Simultaneous profiling of the dynamic interaction between Helicobacter pylori and the human gastric epithelium represents a novel strategy for identifying regulatory responses that drive pathogenesis. This study presents the first dual-transcriptome analysis of H. pylori when cocultured with gastric epithelial cells, revealing a bacterial adaptation strategy and a general repression of electron transportation-associated genes, both of which were modulated by cytotoxin-associated gene A (cagA). Temporal profiling of host mRNA signatures dissected the aberrant pre-mRNA splicing of functional genes involved in the cell cycle process in response to H. pylori infection. We demonstrated a protective effect of gastric H. pylori colonization against chronic DSS-induced colitis through both in vitro and in vivo experiments. These findings significantly enhance our understanding of how H. pylori promotes infection and pathogenesis in the human gastric epithelium and provide evidence to identify targets for antimicrobial therapies.

Performance of the Iterative OSEM and HYPER Algorithm for Total-body PET at SUVmax with a Low 18F-FDG Activity, a Short Acquisition Time and Small Lesions.

OBJECTIVE: The primary objective of this comparative investigation was to examine the qualitative attributes of image reconstructions utilizing two distinct algorithms, namely OSEM and HYPER Iterative, in total-body 18F- FDG PET/CT under various acquisition durations and injection activities. METHODS: An initial assessment was executed using a NEMA phantom to compare image quality engendered by OSEM and HYPER Iterative algorithms. Parameters such as BV, COV, and CRC were meticulously evaluated. Subsequently, a prospective cohort study was conducted on 50 patients, employing both reconstruction algorithms. The study was compartmentalized into distinct acquisition time and dosage groups. Lesions were further categorized into three size-based groups. Quantifiable metrics including SD of noise, SUVmax, SNR, and TBR were computed. Additionally, the differences in values, namely ΔSUVmax, ΔTBR, %ΔSUVmax, %ΔSD, and %ΔSNR, between OSEM and HYPER Iterative algorithms were also calculated. RESULTS: The HYPER Iterative algorithm showed reduced BV and COV compared to OSEM in the phantom study, with constant acquisition time. In the clinical study, lesion SUVmax, TBR, and SNR were significantly elevated in images reconstructed using the HYPER Iterative algorithm in comparison to those generated by OSEM (p < 0.001). Furthermore, an amplified increase in SUVmax was predominantly discernible in lesions with dimensions less than 10 mm. Metrics such as %ΔSNR and %ΔSD in HYPER Iterative exhibited improvements correlating with reduced acquisition times and dosages, wherein a more pronounced degree of enhancement was observable in both ΔSUVmax and ΔTBR. CONCLUSION: The HYPER Iterative algorithm significantly improves SUVmax and reduces noise level, with particular efficacy in lesions measuring ≤ 10 mm and under conditions of abbreviated acquisition times and lower dosages.

Insomnia is related to long-term atrial fibrillation recurrence following radiofrequency ablation.

OBJECTIVE: Atrial fibrillation (AF), the most common cardiac arrhythmia, presents significant health challenges, and the intricate connection between insomnia and AF has garnered substantial attention. This cohort study aims to investigate the relationship between insomnia and AF recurrences following radiofrequency ablation. MATERIALS AND METHODS: Data were retrieved from an electronic database of patients who underwent radiofrequency ablation for AF. The primary endpoint was AF recurrence. We utilized a multivariable Cox model, coupled with three propensity score methods, for analysis. RESULTS: Between January 1, 2017, and June 1, 2022, 541 patients who underwent radiofrequency ablation for AF were recorded in the database. After excluding 185 patients, the final cohort comprised 356 patients. Among them, 68 were afflicted by insomnia, while 288 were not. Over a median follow-up of 755 days, one patient died, and 130 (36.5%) experienced AF recurrence. Multivariate Cox regression analysis revealed that the insomnia group had a higher risk of AF recurrence compared to the non-insomnia group (HR: 1.83, 95% CI: 1.16-2.89). Further landmark analysis showed no significant difference in AF recurrence rates during the initial 1-year follow-up. However, beyond 1 year, the insomnia group demonstrated a significantly higher AF recurrence rate. As the number of insomnia symptoms increased, the risk of AF recurrence also rose significantly, indicating a dose-response relationship. CONCLUSION: This study establishes a significant link between insomnia and long-term AF recurrence following radiofrequency ablation. It underscores the importance of identifying and addressing insomnia in patients with AF undergoing radiofrequency ablation.

Influences of coexisting aged polystyrene microplastics on the ecological and health risks of cadmium in soils: A leachability and oral bioaccessibility based study.

Whether coexisting microplastics (MPs) affect the ecological and health risks of cadmium (Cd) in soils is a cutting-edge scientific issue. In this study, four typical Chinese soils were prepared as artificially Cd-contaminated soils with/without aged polystyrene (PS). TCLP and in vitro PBET model were used to determine the leachability (ecological risk) and oral bioaccessibility (human health risk) of soil Cd. The mechanisms by which MPs influence soil Cd were discussed from direct and indirect perspectives. Results showed that there was no significant difference in the leachability of soil Cd with/without aged PS. Additionally, aged PS led to a significant decrease in the bioaccessibility of soil Cd in gastric phase, but not in small intestinal phase. The increase in surface roughness and the new characteristic peaks (e.g., Si-O-Si) of aged PS directly accounted for the change in Cd bioaccessibility. The change in organic matter content indirectly accounted for the exceptional increase in Cd bioaccessibility of black soil with aged PS in small intestinal phase. Furthermore, the changes in cation exchange capacity and Cd mobility factor caused by aged PS explained the change in Cd leachability. These results contribute to a deeper understanding about environmental and public health in complicated emerging scenarios.

The complete mitogenome of Argas vulgaris (Filippova, 1961) and its phylogenetic status in subgenus Argas (Acari: Argasidae).

Soft ticks (Ixodida: Argasidae) are ectoparasites of terrestrial vertebrates with worldwide distributions. As one representative group of Argasidae, the genus Argas has an important vectorial role in transmitting zoonotic diseases. However, our knowledge of the subgenus Argas in China is still limited, as most literature only lists occurrence records or describes specific case reports without providing detailed morphological characteristics and further molecular data. This study aims to characterize Argas vulgaris through complete mitochondrial sequencing and morphological diagnostic techniques based on a batch of adult specimens collected from Ningxia Hui Autonomous Regions (NXHAR), North China. The morphology and microstructures of Ar. vulgaris and other lectotypes of argasid ticks in the subgenus Argas were also observed using a stereomicroscope. Following DNA extraction and sequencing, a complete mitochondrial sequence of Ar. vulgaris was assembled and analyzed within a phylogenetic context. The 14,479 bp mitogenome of Ar. vulgaris consists of 37 genes, including 13 genes for protein coding, two for ribosomal RNA, 22 for transfer RNA, and one for control region (D-loops). Phylogenetic analysis of Ar. vulgaris showed 98.27%-100% nucleotide identity with Ar. japonicus, indicating a close relationship between the two tick species. The morphological diagnostic features to differentiate Ar. vulgaris from other ticks within the subgenus Argas included the location of the anus and setae on the anterior lip of the female genital aperture. This study provided high-resolution scanning electron microscope images of female Ar. vulgaris and corresponding molecular data, representing valuable resources for future accurate species identification.

Isolation and characterization of a pangolin-borne HKU4-related coronavirus that potentially infects human-DPP4-transgenic mice.

We recently detected a HKU4-related coronavirus in subgenus Merbecovirus (named pangolin-CoV-HKU4-P251T) from a Malayan pangolin1. Here we report isolation and characterization of pangolin-CoV-HKU4-P251T, the genome sequence of which is closest to that of a coronavirus from the greater bamboo bat (Tylonycteris robustula) in Yunnan Province, China, with a 94.3% nucleotide identity. Pangolin-CoV-HKU4-P251T is able to infect human cell lines, and replicates more efficiently in cells that express human-dipeptidyl-peptidase-4 (hDPP4)-expressing and pangolin-DPP4-expressing cells than in bat-DPP4-expressing cells. After intranasal inoculation with pangolin-CoV-HKU4-P251, hDPP4-transgenic female mice are likely infected, showing persistent viral RNA copy numbers in the lungs. Progressive interstitial pneumonia developed in the infected mice, characterized by the accumulation of macrophages, and increase of antiviral cytokines, proinflammatory cytokines, and chemokines in lung tissues. These findings suggest that the pangolin-borne HKU4-related coronavirus has a potential for emerging as a human pathogen by using hDPP4.

Chemical profile and difference analysis of four parts of Strobilanthes sarcorrhiza by ultrafast flow liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry and multivariate statistical analysis.

Strobilanthes sarcorrhiza (CTS) is a medicinal plant with various pharmacological effects such as tonifying kidney and anti-inflammatory. However, the chemical composition and difference of its four parts (leaves, stems, rhizomes, and root tubers) have been rarely reported. In this study, ultrafast flow liquid chromatography coupled with quadrupole-time-of-flight MS was applied to analyze the chemical profile of CTS and identify 55 compounds, including terpenoids, phenylethanol glycosides, fatty acid derivatives, chain glycosides, flavonoid glycosides, and others. Among these compounds, 34 compounds were first identified in CTS. They were mainly terpenoids, phenylethanol glycosides, fatty acid derivatives, and so forth. Multivariate statistical analysis, such as principal component analysis and orthogonal partial least squares-discriminant analysis were also used to evaluate the difference in chemical compounds from the four parts of CTS. The results showed that phenylethanol glycosides were the main compounds of the underground parts, while terpenoids were the main compounds of the aboveground parts. This study revealed the chemical diversity and similarity of CTS and suggested that the rhizomes could be used as an alternative medicinal part to improve the resource utilization of CTS.