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Henan Province is the main producing area of winter wheat in China， and it is also susceptible to dry-hot wind.At present， the research and forecast of dry-hot wind both are on the weather scale， it is of great significance to study whether the dry-hot wind is predictable from the perspective of climate.This study analyzed the relationship between dry-hot wind days in Henan Province and preceding sea surface temperatures （SST） from 1980 to 2022， as well as potential influencing pathways， based on the dry-hot wind data from Henan Province， NCEP/NCAR reanalysis data， and SST data from the Hadley Centre.The results show that： （1） The mean annual dry-hot wind days in northern， northwestern and central Henan Province is more than that in other regions.The probability of dry-hot wind occurrence in the day gradually increases from south to north， and from late May to early June.The average number of dry-hot wind days in the province has a significant increasing trend.（2） There is a close correlation between the number of dry-hot wind days in Henan and the March-April SST in the northeastern Atlantic-western Mediterranean （key region） on both decadal and interannual scales.The correlations between the above two and the 500 hPa geopotential height during dry-hot wind periods， before and after detrending， indicate significant positive correlation areas in mid-latitude East Asia， located from northern Xinjiang to north of the Lake Baikal， and significant negative correlation areas located from northeastern China to the Japanese archipelago， respectively.Correlations with 850 hPa wind fields show anticyclonic and cyclonic distributions in these significant correlation areas， respectively.There is significant correlation of northerly wind in the rear of cyclone， That is， in years with high SST in the key region in March-April， the dry-hot wind weather in Henan Province was formed under the influence of the Mongolian high pressure ridge and the East Asian trough.The correlations between the key region SST index and the circulation fields exhibits a more obvious zonal teleconnection wave-train from the northeastern Atlantic across Eurasia to northeastern Asia in the middle-latitudes.（3） When the western Pacific subtropical high （WPSH） shifts southward （northward）， the dry-hot wind days are more （fewer）， while the high （low） SST in the key region in March-April can lead to a southward （northward） shift of the WPSH.（4） The differences of meridional mean vortex at 30°N-55°N during dry-hot wind period between years with more dry-hot wind days and years with less days， years with key region high SST index in March-April and years with low SST index， composites of 500 hPa quasi-geostrophic stream function anomalies and wave activity flux during dry-hot wind period in years with high and low SST index all show that there is a zonal wave-train from the northeastern Atlantic to the western Pacific.This indicates that March-April SST in the key region can generate a similar wave train during subsequent dry-hot wind periods， propagating across the Eurasian continent to northeastern Asia， with the circulation pattern in high SST years causing dry-hot wind weather in Henan Province.

Abstract Background SARS-CoV-2, a highly contagious coronavirus, is responsible for the global pandemic of COVID-19 in 2019. Currently, it remains uncertain whether SARS-CoV-2 possesses oncogenic or oncolytic potential in influencing tumor progression. Therefore, it is important to evaluate the clinical and functional role of SARS-CoV-2 on tumor progression. Methods Here, we integrated bioinformatic analysis of COVID-19 RNA-seq data from the GEO database and performed functional studies to explore the regulatory role of SARS-CoV-2 in solid tumor progression, including lung, colon, kidney and liver cancer. Results Our results demonstrate that infection with SARS-CoV-2 is associated with a decreased expression of genes associated with cancer proliferation and metastasis in lung tissues from patients diagnosed with COVID-19. Several cancer proliferation or metastasis related genes were frequently downregulated in SARS-CoV-2 infected intestinal organoids and human colon carcinoma cells. In vivo and in vitro studies revealed that SARS-CoV-2 nucleocapsid (N) protein inhibits colon and kidney tumor growth and metastasis through the N-terminal (NTD) and the C-terminal domain (CTD). The molecular mechanism indicates that the N protein of SARS-CoV-2 interacts with YBX1, resulting in the recruitment of PKM mRNA into stress granules mediated by G3BP1. This process ultimately destabilizes PKM expression and suppresses glycolysis. Conclusion Our study reveals a new function of SARS-CoV-2 nucleocapsid protein on tumor progression.

Abstract Fluid management remains a critical challenge in the treatment of septic shock, with individualized approaches lacking. This study aims to develop a statistical model based on transcriptomics to identify subgroups of septic shock patients with varied responses to fluid strategy. The study encompasses 494 septic shock patients. A benefit score is derived from the transcriptome space, with higher values indicating greater benefits from restrictive fluid strategy. Adherence to the recommended strategy is associated with a hazard ratio of 0.82 (95% confidence interval: 0.64–0.92). When applied to the baseline hospital mortality rate of 16%, adherence to the recommended fluid strategy could potentially lower this rate to 13%. A proteomic signature comprising six proteins is developed to predict the benefit score, yielding an area under the curve of 0.802 (95% confidence interval: 0.752–0.846) in classifying patients who may benefit from a restrictive strategy. In this work, we develop a proteomic signature with potential utility in guiding fluid strategy for septic shock patients.

The viral protein mutations can modify virus-host interactions during virus evolution, and thus alter the extent of infection or pathogenicity. Studies indicate that nucleocapsid (N) protein of SARS-CoV-2 participates in viral genome assembly, intracellular signal regulation and immune interference. However, its biological function in viral evolution is not well understood. SARS-CoV-2 N protein mutations were analyzed in Delta, Omicron, and original strains. Two mutations with a methionine (M) residue at site 203 and a tyrosine (Y) residue at site 377 of the N protein were found in Delta strain but not in Omicron and original strains, and promoted SARS-CoV-2 infection therein. Those mutations, R203M and D377Y, enhanced the inhibitory impact of N protein on the impairment of RIG-I-mediated antiviral signaling, such as IRF3 phosphorylation and IFN-β activation. The viral RNA-binding activity of N protein was promoted by these mutations, effectively attenuating the recognition and interaction of RIG-I with viral RNA compared to the original or other variants. The R203M/D377Y mutations thus enhanced the suppressive activity of the N protein on RIG-I-mediated interferon induction both in vitro and in vivo, which in turn promoted viral replication. This study helps to understand the variability of SARS-CoV-2 in regulating host immunity.

An innovative and miniature photoacoustic spectroscopy (PAS) gas sensor based on a multivariate-coupled amplification photoacoustic cell (MVCA-PAC) with a total length of 100 mm was developed to achieve ultra-sensitive trace CH4 detection. Acoustic pressure distribution simulations reveal that at the first-order resonance frequency, the MVCA-PAC achieves a maximum acoustic pressure approximately 3.9 times higher than that of a conventional photoacoustic cell. The absorption optical path of the MVCA-PAC reached 2068 mm through 22 reflections, resulting in a 2-fold increase in the amplitude of photoacoustic signals compared to the traditional photoacoustic cell with an equivalent absorption optical path. Furthermore, compared to a single-pass photoacoustic cell, the 2-f signal intensity of the MVCA-PAC increased by a factor of 4.5. Allan variance analysis indicated a detection limit of 0.572 ppm for CH4 detection with an averaging time of approximately 300 s. To further improve the measurement precision of the designed sensor, the Chebyshev rational fractional-order filtering (CRFOF) algorithm was introduced for PAS signal processing for the first time. Post-processing results demonstrated a 15.4-fold improvement in measurement precision, achieving a precision of 0.578 ppm. Finally, continuous monitoring of atmospheric CH4 over a 48-hour period validated the reliability and feasibility of the sensor.

The increasing prevalence of non-tuberculous mycobacterium (NTM) infections alongside tuberculosis (TB) underscores a pressing public health challenge. Yet, the mechanisms governing their infection within the lung remain poorly understood. Here, we integrate metagenomic sequencing, metabolomic sequencing, machine learning classifiers, SparCC, and MetOrigin methods to profile bronchoalveolar lavage fluid (BALF) samples from NTM/TB patients. Our aim is to unravel the intricate interplay between lung microbial communities and NTM/Mycobacterium tuberculosis infections. Our investigation reveals a discernible reduction in the compositional diversity of the lung microbiota and a diminished degree of mutual interaction concomitant with NTM/TB infections. Notably, NTM patients exhibit a distinct microbial community characterized by marked specialization and notable enrichment of Pseudomonas aeruginosa and Staphylococcus aureus, driving pronounced niche specialization for NTM infection. Simultaneously, these microbial shifts significantly disrupt tryptophan metabolism in NTM infection, leading to an elevation of kynurenine. Mycobacterium intracellulare, Mycobacterium paraintracellulare, Mycobacterium abscessus, and Pseudomonas aeruginosa have been implicated in the metabolic pathways associated with the conversion of indole to tryptophan via tryptophan synthase within NTM patients. Additionally, indoleamine-2,3-dioxygenase converts tryptophan into kynurenine, fostering an immunosuppressive milieu during NTM infection. This strategic modulation supports microbial persistence, enabling evasion from immune surveillance and perpetuating a protracted state of NTM infection. The elucidation of these nuanced microbial and metabolic dynamics provides a profound understanding of the intricate processes underlying NTM and TB infections, offering potential avenues for therapeutic intervention and management.

C26H26N6O5, monoclinic, P21/c (no. 14), a = 10.9393(1) Å, b = 16.8724(2) Å, c = 13.5836(2) Å, β = 102.843(1)°, V = 2444.43(5) Å3, Z = 4, R gt(F) = 0.0318, wRref (F 2) = 0.0827, T = 173 K.

Abstract Background Predicting patient mortality risk facilitates early intervention in intensive care unit (ICU) patients at greater risk of disease progression. This study applies machine learning methods to multidimensional clinical data to dynamically predict mortality risk in ICU patients. Methods A total of 33,798 patients in the MIMIC-III database were collected. An integrated model NIMRF (Network Integrating Memory Module and Random Forest) based on multidimensional variables such as vital sign variables and laboratory variables was developed to predict the risk of death for ICU patients in four non overlapping time windows of 0–1 h, 1–3 h, 3–6 h, and 6–12 h. Mortality risk in four nonoverlapping time windows of 12 h was externally validated on data from 889 patients in the respiratory critical care unit of the Chinese PLA General Hospital and compared with LSTM, random forest and time-dependent cox regression model (survival analysis) methods. We also interpret the developed model to obtain important factors for predicting mortality risk across time windows. The code can be found in https://github.com/wyuexiao/NIMRF . Results The NIMRF model developed in this study could predict the risk of death in four nonoverlapping time windows (0–1 h, 1–3 h, 3–6 h, 6–12 h) after any time point in ICU patients, and in internal data validation, it is suggested that the model is more accurate than LSTM, random forest prediction and time-dependent cox regression model (area under receiver operating characteristic curve, or AUC, 0–1 h: 0.8015 [95% CI 0.7725–0.8304] vs. 0.7144 [95%] CI 0.6824–0.7464] vs. 0.7606 [95% CI 0.7300–0.7913] vs 0.3867 [95% CI 0.3573–0.4161]; 1–3 h: 0.7100 [95% CI 0.6777–0.7423] vs. 0.6389 [95% CI 0.6055–0.6723] vs. 0.6992 [95% CI 0.6667–0.7318] vs 0.3854 [95% CI 0.3559–0.4150]; 3–6 h: 0.6760 [95% CI 0.6425–0.7097] vs. 0.5964 [95% CI 0.5622–0.6306] vs. 0.6760 [95% CI 0.6427–0.7099] vs 0.3967 [95% CI 0.3662–0.4271]; 6–12 h: 0.6380 [0.6031–0.6729] vs. 0.6032 [0.5705–0.6406] vs. 0.6055 [0.5682–0.6383] vs 0.4023 [95% CI 0.3709–0.4337]). External validation was performed on the data of patients in the respiratory critical care unit of the Chinese PLA General Hospital. Compared with LSTM, random forest and time-dependent cox regression model, the NIMRF model was still the best, with an AUC of 0.9366 [95% CI 0.9157–0.9575 for predicting death risk in 0–1 h]. The corresponding AUCs of LSTM, random forest and time-dependent cox regression model were 0.9263 [95% CI 0.9039–0.9486], 0.7437 [95% CI 0.7083–0.7791] and 0.2447 [95% CI 0.2202–0.2692], respectively. Interpretation of the model revealed that vital signs (systolic blood pressure, heart rate, diastolic blood pressure, respiratory rate, and body temperature) were highly correlated with events of death. Conclusion Using the NIMRF model can integrate ICU multidimensional variable data, especially vital sign variable data, to accurately predict the death events of ICU patients. These predictions can assist clinicians in choosing more timely and precise treatment methods and interventions and, more importantly, can reduce invasive procedures and save medical costs.

Background: 8-oxoguanine DNA glycosylase can maintain genomic stability and integrity. However, it can interfere with the regular DNA damage repair process, possibly leading to the development of cancer and various other human diseases when its activity becomes abnormal. Current methods for detecting 8-oxoguanine DNA glycosylase activity often suffer from low sensitivity, time-consuming procedures, labor-intensive requirements, and the need for specialized equipment and trained professionals for execution. Consequently, there is an urgent need for a portable, user-friendly 8-oxoguanine DNA glycosylase assay that offers swift results and supports real-time testing. Results: We've developed a PERFUME method that combines primer exchange reaction and functionalized G-quadruplex/hemin DNAzyme for sensitive detection of Fpg, a typical 8-oxoguanine DNA glycosylase. Utilizing a single probe and T4 Polynucleotide Kinase (PNK) simplifies the experiment to a one-step reaction at 37 °C in 3 h, reducing sample consumption and improving sensitivity. We chose functionalized hemin cofactors, significantly improving catalytic efficiency and enhancing detection capability. This biosensor detects Fpg activity with a sensitivity as low as 0.11 U mL−1, displaying exceptional sensitivity, selectivity, and interference resistance in human serum and bacterial cell extracts under isothermal conditions. The biosensor demonstrates remarkable selectivity and ability for Fpg inhibitors screening. In addition, this biosensor enables reading the sample's RGB values using a smartphone, facilitating accurate quantification of Fpg activity without the necessity for specialized equipment. Significance: PERFUME simplifies Fpg detection by using a single hairpin and PNK in a one-step process. We utilize FUME to enhance catalytic efficiency, it surpassing the performance of traditional G-quadruplex/hemin DNAzyme methods. This approach excels in analyzing Fpg in human serum and bacterial extracts. It allows quantitative Fpg detection using UV–Vis and smartphones under isothermal conditions, making it valuable for clinical diagnosis.

Pigs play vital roles in the food supply, economic development, agricultural recycling, bioenergy, and social culture. Pork serves as a primary meat source and holds extensive applications in various dietary cultures, making pigs indispensable to human dietary structures. Manual pig counting, a crucial aspect of pig farming, suffers from high costs and time-consuming processes. In this paper, we propose the PDC-YOLO network to address these challenges, dedicated to detecting pigs in complex farming environments for counting purposes. Built upon YOLOv7, our model incorporates the SPD-Conv structure into the YOLOv7 backbone to enhance detection under varying lighting conditions and for small-scale pigs. Additionally, we replace the neck of YOLOv7 with AFPN to efficiently fuse features of different scales. Furthermore, the model utilizes rotated bounding boxes for improved accuracy. Achieving a mAP of 91.97%, precision of 95.11%, and recall of 89.94% on our collected pig dataset, our model outperforms others. Regarding technical performance, PDC-YOLO exhibits an error rate of 0.002 and surpasses manual counting significantly in speed.

Cotton verticillium wilt significantly impacts both cotton quality and yield. Selecting disease-resistant varieties and using their resistance genes in breeding is an effective and economical control measure. Accurate severity estimation of this disease is crucial for breeding resistant cotton varieties. However, current methods fall short, slowing the breeding process. To address these challenges, this paper introduces CVW-Etr, a high-precision method for estimating the severity of cotton verticillium wilt. CVW-Etr classifies severity into six levels (L0 to L5) based on the proportion of segmented diseased leaves to lesions. Upon integrating YOLOv8-Seg with MobileSAM, CVW-Etr demonstrates excellent performance and efficiency with limited samples in complex field conditions. It incorporates the RFCBAMConv, C2f-RFCBAMConv, AWDownSample-Lite, and GSegment modules to handle blurry transitions between healthy and diseased regions and variations in angle and distance during image collection, and to optimize the model’s parameter size and computational complexity. Our experimental results show that CVW-Etr effectively segments diseased leaves and lesions, achieving a mean average precision (mAP) of 92.90% and an average severity estimation accuracy of 92.92% with only 2.6M parameters and 10.1G FLOPS. Through experiments, CVW-Etr proves robust in estimating cotton verticillium wilt severity, offering valuable insights for disease-resistant cotton breeding applications.

Oxidative stress is associated with the development of many diseases, such as chronic inflammation, cardiovascular disease and cancer. Antioxidants can scavenge free radicals in the body, reduce the production of reactive oxygen species (ROS) and effectively prevent oxidative stress. Polysaccharide is a antioxidants that widely found in natural resources, which is a kind of high molecular polymer with antioxidant activity. It can prevent oxidative stress and play a role in disease prevention through various mechanisms. In this paper, the different types of polysaccharides were summarized, such as plant polysaccharides, algal polysaccharides, microbial polysaccharides and animal polysaccharides, and their antioxidant mechanism and application in the treatment of disease were also discussed. we hope this review can offer some theoretical basis and inspiration for the antioxidant activity study and application of polysaccharides from natural resources.

Cotton, a vital textile raw material, is intricately linked to people’s livelihoods. Throughout the cotton cultivation process, various diseases threaten cotton crops, significantly impacting both cotton quality and yield. Deep learning has emerged as a crucial tool for detecting these diseases. However, deep learning models with high accuracy often come with redundant parameters, making them challenging to deploy on resource-constrained devices. Existing detection models struggle to strike the right balance between accuracy and speed, limiting their utility in this context. This study introduces the CDDLite-YOLO model, an innovation based on the YOLOv8 model, designed for detecting cotton diseases in natural field conditions. The C2f-Faster module replaces the Bottleneck structure in the C2f module within the backbone network, using partial convolution. The neck network adopts Slim-neck structure by replacing the C2f module with the GSConv and VoVGSCSP modules, based on GSConv. In the head, we introduce the MPDIoU loss function, addressing limitations in existing loss functions. Additionally, we designed the PCDetect detection head, integrating the PCD module and replacing some CBS modules with PCDetect. Our experimental results demonstrate the effectiveness of the CDDLite-YOLO model, achieving a remarkable mean average precision (mAP) of 90.6%. With a mere 1.8M parameters, 3.6G FLOPS, and a rapid detection speed of 222.22 FPS, it outperforms other models, showcasing its superiority. It successfully strikes a harmonious balance between detection speed, accuracy, and model size, positioning it as a promising candidate for deployment on an embedded GPU chip without sacrificing performance. Our model serves as a pivotal technical advancement, facilitating timely cotton disease detection and providing valuable insights for the design of detection models for agricultural inspection robots and other resource-constrained agricultural devices.

Corona Virus Disease 2019 (COVID-19) is a highly prevalent and potent infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Until now, the world is still endeavoring to develop new ways to diagnose and treat COVID-19. At present, the clinical prevention and treatment of COVID-19 mainly targets the spike protein on the surface of SRAS-CoV-2. However, with the continuous emergence of SARS-CoV-2 Variants of concern (VOC), targeting the spike protein therapy shows a high degree of limitation. The Nucleocapsid Protein (N protein) of SARS-CoV-2 is highly conserved in virus evolution and is involved in the key process of viral infection and assembly. It is the most expressed viral structural protein after SARS-CoV-2 infection in humans and has high immunogenicity. Therefore, N protein as the key factor of virus infection and replication in basic research and clinical application has great potential research value. This article reviews the research progress on the structure and biological function of SARS-CoV-2 N protein, the diagnosis and drug research of targeting N protein, in order to promote researchers’ further understanding of SARS-CoV-2 N protein, and lay a theoretical foundation for the possible outbreak of new and sudden coronavirus infectious diseases in the future.

ABSTRACTThe nucleocapsid protein of SARS-CoV-2 plays significant roles in viral assembly, immune evasion, and viral stability. Due to its immunogenicity, high expression levels during COVID-19, and conservation across viral strains, it represents an attractive target for antiviral treatment. In this study, we identified and characterized a single-stranded DNA aptamer, N-Apt17, which effectively disrupts the liquid-liquid phase separation (LLPS) mediated by the N protein. To enhance the aptamer’s stability, a circular bivalent form, cb-N-Apt17, was designed and evaluated. Our findings demonstrated that cb-N-Apt17 exhibited improved stability, enhanced binding affinity, and superior inhibition of N protein LLPS; thus, it has the potential inhibition ability on viral replication. These results provide valuable evidence supporting the potential of cb-N-Apt17 as a promising candidate for the development of antiviral therapies against COVID-19.IMPORTANCEVariants of SARS-CoV-2 pose a significant challenge to currently available COVID-19 vaccines and therapies due to the rapid epitope changes observed in the viral spike protein. However, the nucleocapsid (N) protein of SARS-CoV-2, a highly conserved structural protein, offers promising potential as a target for inhibiting viral replication. The N protein forms complexes with genomic RNA, interacts with other viral structural proteins during virion assembly, and plays a critical role in evading host innate immunity by impairing interferon production during viral infection. In this investigation, we discovered a single-stranded DNA aptamer, designated as N-Apt17, exhibiting remarkable affinity and specificity for the N protein. Notably, N-Apt17 disrupts the liquid-liquid phase separation (LLPS) of the N protein. To enhance the stability and molecular recognition capabilities of N-Apt17, we designed a circular bivalent DNA aptamer termed cb-N-Apt17. In both in vivo and in vitro experiments, cb-N-Apt17 exhibited increased stability, enhanced binding affinity, and superior LLPS disrupting ability. Thus, our study provides essential proof-of-principle evidence supporting the further development of cb-N-Apt17 as a therapeutic candidate for COVID-19.

Urban ventilation corridors are designed to enhance air quality, alleviate urban thermal conditions, reduce pollution and energy consumption, as well as improve human comfort within cities. They play a pivotal role in mitigating environmental impacts, particularly in densely populated urban areas. Based on satellite remote sensing data, meteorological observations, basic geographic information of Zhengzhou City and its surroundings, and urban planning data, we analyzed the urban wind environment, urban heat island, ecological cold sources, and ventilation potential. The findings reveal several key insights: (1) Dominant winds in Zhengzhou City predominantly originate from the northwest, northeast, and south, influenced by topography and the monsoon climate, with seasonal variations. These wind patterns are crucial considerations for designing primary ventilation corridors. (2) The urban heat island exhibits a polycentric spatial distribution, with intensity decreasing from the city center towards the periphery. Ecological cold sources, primarily situated in the city outskirts, act as reservoirs of fresh air that mitigate the urban heat island effect through designated corridors. (3) A preliminary corridor system, termed “eight primary and thirteen secondary corridors”, is proposed for Zhengzhou City based on an integrated assessment of ventilation potential, urban surface roughness, and sky view factor. This research contributes to advancing the understanding of urban ventilation systems and provides practical insights for policymakers, urban planners, and researchers seeking sustainable solutions to mitigate climate impacts in rapidly urbanizing environments in the region.

During the normal laying and operation of a three-core umbilical cable, AC current can easily lead to AC electrochemical corrosion on the outer surface of the steel tube. To explore the electrochemical corrosion mechanism and the factors affecting the three-core umbilical cable, this paper optimizes the internal induced potential calculation method for three-core umbilical cables. It analyzes the changes in the characteristics of the induced potential and explores the variations in the density of induced current under different conditions. The research results show that by optimizing the calculation method for the induction potential of the umbilical cable’s steel pipe, for the electromagnetic significance of the smallest repeating unit, the induction potential on the steel pipe’s surface exhibited a cyclic change. The peak part of the induction potential is most likely to experience electrochemical corrosion. Additionally, reducing the radius of the outer insulation aperture of the steel pipe and improving the conductivity of seawater will increase the density of the induced current in the insulation aperture, thereby increasing the risk of electrochemical corrosion. As the cable pitch and AC frequency increase, the current density in the steel pipe pores will also rise.

The impending challenge posed by escalating heatwave events due to projected global warming scenarios of 1.5 and 2.0 °C underscores the critical need for a comprehensive understanding of their impact on human health and socio-economic realms. This study delves into the anticipated implications of elevated global temperatures, specifically the 1.5 and 2.0 °C warming scenarios under the SSP2-4.5 and SSP5-8.5 pathways, on population and GDP exposure to heatwaves in China. We also evaluated the aggregated impacts of climate, population, and GDP and their interactions on future socio-economic exposure across China. We leveraged data sourced from the climatic output of Coupled Model Intercomparison Project Phase 6 (CMIP6) for heatwave analysis and integrated population and GDP projections under divergent Shared Socio-economic Pathways (SSPs), including SSP2-4.5 (low emission) and SSP5-8.5 (high-emission). Results indicate a drastic surge in the number of heatwave days under both warming scenarios, particularly in regions like Xinjiang (XJ), North China (NC), and South China (SC) subregions, with a notable disparity in the elevation of heatwave days among different levels. There is an alarming surge in population exposure, escalating approximately 7.94–8.70 times under the 1.5 °C warming scenario and markedly increasing by 14.48–14.75 times by the 2100s relative to the baseline (1985–2014) under the more extreme 2.0 °C warming level. Likewise, the study unveils a substantial elevation in GDP exposure, ranging from 40.65 to 47.21 times under the 1.5 °C warming level and surging dramatically by 110.85–113.99 times under the 2.0 °C warming level. Further analyses disclose that the climate effect predominantly influences changes in population exposure, constituting 72.55–79.10% of the total change. Meanwhile, the interaction effect notably shapes GDP exposure alterations, contributing 77.70–85.99% to the total change. The comprehensive investigation into alterations in population and GDP exposure under varying warming scenarios, coupled with the quantification of each contributing factor, holds paramount importance in mitigating the detrimental repercussions of heatwaves on both human life and socio-economic landscapes.

Abstract Background Gastric cardia adenocarcinoma (GCA) is classified as Siewert type II adenocarcinoma at the esophagogastric junction in Western countries. The majority of GCA patients do not exhibit early warning symptoms, leading to over 90% of diagnoses at an advanced stage, resulting in a grim prognosis, with less than a 20% 5‐year survival rate. Method Metabolic features of 276 GCA and 588 healthy controls were characterized through a widely‐targeted metabolomics by UPLC‐MS/MS analysis. This study encompasses a joint pathway analysis utilizing identified metabolites, survival analysis in both early and advanced stages, as well as high and negative and low expression of HER2 immunohistochemistry staining. Machine learning techniques and Cox regression models were employed to construct a diagnostic panel. Results A total of 25 differential metabolites were consistently identified in both discovery and validation sets based on criteria of p

Annually, more than 15 million people worldwide suffer from stroke, a condition linked to high mortality and disability rates. This disease significantly affects daily life, impairing everyday functioning, executive function, and cognition. Moreover, stroke severely restricts patients’ ability to perform daily activities, diminishing their overall quality of life. Recent scientific studies have identified cuproptosis, a newly discovered form of cell death, as a key factor in stroke development. However, the role of cuproptosis in stroke remains unclear to researchers. Therefore, it is crucial to investigate the mechanisms of cuproptosis in stroke’s pathogenesis. This review examines the physiological role of copper, the characteristics and mechanisms of cuproptosis, the differences and similarities between cuproptosis and other cell death types, and the pathophysiology of cuproptosis in stroke, focusing on mitochondrial dysfunction and immune infiltration. Further research is necessary to understand the relationship between previous strokes and cuproptosis and to clarify the mechanisms behind these associations.

The inhalation of zinc chloride (ZnCl2) smoke is one of common resources of lung injury, potentially resulting in severe pulmonary complications and even mortality. The influence of ZnCl2 smoke on lysine succinylation (Ksucc) in the lungs remains uncertain. In this study, we used a ZnCl2 smoke inhalation mouse model to perform global proteomic and lysine succinylome analyses. A total of 6781 Ksucc sites were identified in the lungs, with injured lungs demonstrating a reduction to approximately 2000 Ksucc sites, and 91 proteins exhibiting at least five differences in the number of Ksucc sites. Quantitative analysis revealed variations in expression of 384 proteins and 749 Ksucc sites. The analysis of protein-protein interactions was conducted for proteins displaying differential expression and differentially expressed lysine succinylation. Notably, proteins with altered Ksucc exhibited increased connectivity compared with that in differentially expressed proteins. Beyond metabolic pathways, these highly connected proteins were also involved in lung injury-associated pathological reactions, including processes such as focal adhesion, adherens junction, and complement and coagulation cascades. Collectively, our findings contribute to the understanding of the molecular mechanisms underlaying ZnCl2 smoke-induced lung injury with a specific emphasis on lysine succinylation. These findings could pave the way for targeted interventions and therapeutic strategies to mitigate severe pulmonary complications and mortality associated with such injuries in humans.

Water droplets containing the SARS-CoV-2 virus, responsible for coronavirus 2019 transmission, were introduced into a controlled-temperature and -humidity chamber. The SARS-CoV-2 virus with green fluorescent protein tag in droplets was used to infect Caco-2 cells, with viability assessed through flow cytometry and microscopic counting. Whereas temperature fluctuations within typical indoor ranges (20°C–30°C) had minimal impact, we observed a notable decrease in infection rate as the surrounding air’s relative humidity increased. By investigating humidity levels between 20% and 70%, we identified a threshold of ≥40% relative humidity as most effective in diminishing SARS-CoV-2 infectivity. We also found that damage of the viral proteins under high relative humidity may be responsible for the decrease in their activity. This outcome supports previous research demonstrating a rise in the concentration of reactive oxygen species within water droplets with elevated relative humidity.

Abstract Background The teaching of critical care medicine is a very important task, especially during the COVID-19 pandemic. The understanding of critical care parameters is the foundation and core, which is conducive to the formation of clinical thinking. This study is to evaluate the training effect of teaching of critical care parameters based on an online platform, and explore the teaching methods of critical care medicine that can help to cultivate trainees’ clinical thinking and practical ability. Methods Questionnaires were released before and after the training through the official new media platform “Yisheng” application (APP) of China Medical Tribune involving 1109 participants. The trainees who filled in the questionnaire in APP and received training were randomly selected as the investigated population. Statistical description and analysis were carried out using SPSS 20.0 and Excel 2020. Results The trainees were mainly attending physicians in tertiary hospitals and above. Among all critical care parameters, trainees paid more attention to critical hemodynamics, respiratory mechanics, severity of illness scoring systems, critical ultrasound, and critical hemofiltration. The degree of satisfaction with the courses was high, especially the course of critical hemodynamics was scored the highest. The trainees believed that the course contents were of great help to clinical work. However, no significant difference was found in the trainees’ understanding or cognition of the connotation of the parameters before and after the training. Conclusion Teaching of critical care parameters based on an online platform is conducive to improving and consolidating the clinical care ability of trainees. However, it is still necessary to strengthen the cultivation of clinical thinking in critical care. In the future, the integration of theory with practice must be strengthened in clinical practice, ultimately achieving the homogeneous diagnosis and treatment of patients with critical illness.

Abstract Viral infection in respiratory tract usually leads to cell death, impairing respiratory function to cause severe disease. However, the diversity of clinical manifestations of SARS-CoV-2 infection increases the complexity and difficulty of viral infection prevention, and especially the high-frequency asymptomatic infection increases the risk of virus transmission. Studying how SARS-CoV-2 affects apoptotic pathway may help to understand the pathological process of its infection. Here, we uncovered SARS-CoV-2 imployed a distinct anti-apoptotic mechanism via its N protein. We found SARS-CoV-2 virus-like particles (trVLP) suppressed cell apoptosis, but the trVLP lacking N protein didn’t. Further study verified that N protein repressed cell apoptosis in cultured cells, human lung organoids and mice. Mechanistically, N protein specifically interacted with anti-apoptotic protein MCL-1, and recruited a deubiquitinating enzyme USP15 to remove the K63-linked ubiquitination of MCL-1, which stabilized this protein and promoted it to hijack Bak in mitochondria. Importantly, N protein promoted the replications of IAV, DENV and ZIKV, and exacerbated death of IAV-infected mice, all of which could be blocked by a MCL-1 specific inhibitor, S63845. Altogether, we identifed a distinct anti-apoptotic function of the N protein, through which it promoted viral replication. These may explain how SARS-CoV-2 effectively replicates in asymptomatic individuals without cuasing respiratory dysfunction, and indicate a risk of enhanced coinfection with other viruses. We anticipate that abrogating the N/MCL-1-dominated apoptosis repression is conducive to the treatments of SARS-CoV-2 infection as well as coinfections with other viruses.

Objective To explore a machine learning model for the early prediction of acute kidney injury (AKI) and to screen the related factors affecting new-onset AKI in the ICU.Methods A retrospective analysis was performed used the MIMIC-III data source. New onset of AKI defined based on the serum creatinine changed. We included 19 variables for AKI assessment using four machine learning models: support vector machines, logistic regression, and random forest. and XGBoost, using accuracy, specificity, precision, recall, F1 score, and AUROC (area under the ROC curve) to evaluate model performance. The four models predicted new-onset AKI 3–6–9–12 h ahead. The SHapley Additive exPlanation (SHAP) value is used to evaluate the feature importance of the model.Results We finally respectively extracted 1130 AKI patients and non-AKI patients from the MIMIC-III database. With the extension of the early warning time, the prediction performance of each model showed a downward trend, but the relative performance was consistent. The prediction performance comparison of the four models showed that the XGBoost model performed the best in all evaluation indicators in all the time point at new-onset AKI 3–6–9–12 h ahead (accuracy 0.809 vs 0.78 vs 0.744 vs 0.741, specificity 0.856 vs 0.826 vs 0.797 vs 0.787, precision 0.842 vs 0.81 vs 0.775 vs 0.766, recall 0.759 vs 0.734 vs 0.692 vs 0.694, Fl score 0.799 vs 0.769 vs 0.731 vs 0.729, AUROC 0.892 vs 0.857 vs 0.827 vs 0.818). In the prediction of AKI 6, 9 and 12 h ahead, the importance of creatinine, platelets, and height was the most important based on SHapley.Conclusions The machine learning model described in this study can predict AKI 3–6–9–12 h before the new-onset of AKI in ICU. In particular, platelet plays an important role.Key messageThe new-onset of AKI in ICU is a common and important problem, which early be identified the risk of AKI can improve patients’ outcomes.We explored MIMIC-III and determined the exact time point of occurrence of AKI as the basis for the new-onset of AKI in ICU.XGBoost model performed the best prediction in all the time point at new-onset AKI 3–6–9–12 h ahead.For patients with the new-onset of AKI in ICU, platelets become an important factor associated with AKI.

This study aims to prepare the self-luminous pavement marking (SLPM) to enhance driving safety and reduce the risk of traffic accident. Firstly, the composition of the SLPM was determined by the mechanical properties and luminous performance tests. The optimal mass proportions of epoxy resin, toughening agent, curing agent, curing accelerator, luminous powder, reflective powder, glass bead and glass powder was 1.0:0.1:0.3:0.006:0.4:0.3:0.25:0.55. In addition, test results showed that the SLPM has satisfactory mechanical properties, durability and luminous performance, which met the engineering requirements for practical application. The afterglow time was more than 11 h at night in laboratory test. Moreover, it is feasible to prepare different colors of the SLPM according to the RGP principle. The afterglow color and luminous intensity of SLPM strongly depend on the luminous material and its proportions. Overall, this paper provides a reference for designing and applying SLPM materials in road engineering.

Vermicompost is a promising amendment for immobilization of cadmium (Cd) in soils; however, its effectiveness can be influenced by rhizosphere environment conditions, such as pH and the presence of low-molecular-weight organic acids (LMWOAs). In this study, a batch experiment was conducted to examine the characteristics of Cd adsorption by vermicompost at different pH (pH = 3, 5, and 7) and after the addition of different LMWOAs (oxalic acid; citric acid; malic acid). Furthermore, a series of morphology and structural analyses were conducted to elucidate the mechanisms of observed effects. The results showed that the adsorption capacity of vermicompost for Cd increased as pH increased, and chemisorption dominated the adsorption process. Changes in pH altered adsorption performance by affecting the -OH groups of alcohol/phenol and the -CH2 groups of aliphatics. Further, the addition of oxalic acid promoted Cd adsorption, and the effect was concentration dependent. Modifying the verimicompost surface with more adsorption sites might be the main reason. Conversely, citric acid and malic acid showed the ability to inhibit Cd adsorption by vermicompost. Citric acid caused a blocking effect by covering flocculent substances on the vermicompost surface while reducing surface adsorption sites by dissolving mineral components such as iron oxides. However, the action of malic acid did not appear to be related to changes in morphology or the structure of vermicompost. Overall, the results of this study partially explain the limited effectiveness of Cd immobilization within the rhizosphere by vermicompost, and provide theoretical support for regulating rhizosphere environments to improve the effectiveness of vermicompost immobilization of Cd.

Wild rice, a natural gene pool for rice germplasm innovation and variety improvement, holds immense value in rice breeding due to its disease-resistance genes. Traditional disease resistance identification in wild rice heavily relies on labor-intensive and subjective manual methods, posing significant challenges for large-scale identification. The fusion of unmanned aerial vehicles (UAVs) and deep learning is emerging as a novel trend in intelligent disease resistance identification. Detecting diseases in field conditions is critical in intelligent disease resistance identification. In pursuit of detecting bacterial blight in wild rice within natural field conditions, this study presents the Xoo-YOLO model, a modification of the YOLOv8 model tailored for this purpose. The Xoo-YOLO model incorporates the Large Selective Kernel Network (LSKNet) into its backbone network, allowing for more effective disease detection from the perspective of UAVs. This is achieved by dynamically adjusting its large spatial receptive field. Concurrently, the neck network receives enhancements by integrating the GSConv hybrid convolution module. This addition serves to reduce both the amount of calculation and parameters. To tackle the issue of disease appearing elongated and rotated when viewed from a UAV perspective, we incorporated a rotational angle (theta dimension) into the head layer's output. This enhancement enables precise detection of bacterial blight in any direction in wild rice. The experimental results highlight the effectiveness of our proposed Xoo-YOLO model, boasting a remarkable mean average precision (mAP) of 94.95%. This outperforms other models, underscoring its superiority. Our model strikes a harmonious balance between accuracy and speed in disease detection. It is a technical cornerstone, facilitating the intelligent identification of disease resistance in wild rice on a large scale.

Smoke bombs are often used in military/fire training, which can produce a large amount of zinc chloride (ZnCl2) smoke. Inhalation of ZnCl2 smoke usually causes acute lung injury (ALI) that would likely develop to acute respiratory distress syndrome (ARDS). However, there is no effective prevention or treatment strategy for the smoke-induced ALI. Resveratrol (RES) is a natural polyphenol with good anti-inflammatory and anti-apoptotic activities, but its low solubility, stability, and bioavailability restrict its clinical application. In this study, an inhalable RES formulation composed of RES-β-cyclodextrin inclusion complexes (RES-β-CD) was prepared for the prevention of ZnCl2 smoke-induced ALI. RES-β-CD powders had a small mass median aerodynamic diameter of 3.61 μm and a high fine particle fraction of 38.84%, suitable for pulmonary inhalation. RES-β-CD exhibited low BEAS-2B cytotoxicity. Pulmonary delivery of RES-β-CD to mice remarkably prevented the smoke-induced ALI with downregulation of TNF-α, IL-1β, STAT3, and GATA3, and upregulation of T-bet and Foxp3. RES-β-CD protected the respiratory function, percutaneous oxygen saturation, physical activity, lung capillary integrity, and lung liquid balance, alleviating inflammation and apoptosis. Pulmonary delivery of the positive drug, budesonide (BUD), also alleviated the smoke-induced ALI by reduction of inflammation and cell apoptosis. RES-β-CD exhibited the regulation of the Th1/Th2 and Treg/Th17 balances, while BUD did not show any effect on immune balances. In conclusion, pulmonary delivery of RES-β-CD is a promising anti-inflammatory and anti-apoptosis strategy for the prevention of ZnCl2 smoke-induced ALI by direct lung drug distribution and regulation of immune balance.

NLRP3 inflammasome mainly controls interleukin-1β (IL-1β) secretion, leading to cell death called pyroptosis constituting a major antiviral host defense and inflammatory diseases upon viral infection. The RAF-MEK1/2-ERK1/2 cascade and downstream c-Jun/Fos and Activator protein-1 (AP1) signaling pathway control the degree of inflammatory response. Influenza A virus (IAV) infection is known to stimulate NLRP3 inflammasome activation and inflammatory responses. Nevertheless, the detailed mechanism by which IAV induces NLRP3 inflammasome activation involved in transcription of pro-IL-1β mRNA remains elusive. In our study, we found that IAV infection promotes pro-IL-1β mRNA transcription and activates NLRP3 inflammasome. Detailed studies reveal that type I interferon (IFN-α/IFN-β) as well as U0126 (a selective inhibitor of MEK-1 and MEK-2) typically inhibit IAV-mediated NLRP3 inflammasome activation via downregulating pro-IL-1β mRNA. Moreover, knock-down of c-Jun decreases pro-IL-1β mRNA and inhibits NLRP3 inflammasome activation upon IAV infection. Overall, the findings uncover that AP-1 signaling pathway promotes NLRP3 inflammasome activation upon IAV infection, which provides a new idea for the therapy of NLRP3 inflammasome-associated inflammatory diseases.

Aiming at the problem that the puncture drag of the medical suture needle produced in the suture process will cause damage to the soft tissue,and the greater the puncture drag is,the higher the damage degree is,a biomimetic model of low-drag medical suture needle was proposed.The biomimetic prototype of low-drag medical suture needle was constructed,and its needle tip taper/radius and needle body curvature were designed based on the morphological/structural characteristics of the thorn in Ziziphus jujube,and vertical stripe grooves were set on the needle body,forming the biomimetic model of low-drag medical suture needles.LS-DYNA software and Coulomb friction model were used to simulate the process of biomimetic low-drag medical suture needle penetrating skin soft tissue.The results show that the needle body with vertical stripe grooves can reduce the puncture drag of low-drag medical suture needle with the thorn tip taper/radius and the thorn body curvature by 34.24%~58.48%,and can reduce the puncture drag of low-drag medical suture needle with the thorn tip taper/radius by 30.26%~49.86%.The biomimetic low-drag medical suture needles were fabricated and their puncture drag was experimentally tested.In the penetrating process,the puncture drag of biomimetic low-drag medical suture needle was reduced by 43.02%.The results reveal the influence mechanism of the morphological/structural characteristics of medical suture needle on its puncture drag,and provide a theoretical reference for the biomimetic design of low-drag medical suture needle.

Abstract This study proposes a novel network model for video action tube detection. This model is based on a location-interactive weakly supervised spatial–temporal attention mechanism driven by multiple loss functions. It is especially costly and time consuming to annotate every target location in video frames. Thus, we first propose a cross-domain weakly supervised learning method with a spatial–temporal attention mechanism for action tube detection. In source domain, we trained a newly designed multi-loss spatial–temporal attention–convolution network on the source data set, which has both object location and classification annotations. In target domain, we introduced internal tracking loss and neighbor-consistency loss; we trained the network with the pre-trained model on the target data set, which only has inaccurate action temporal positions. Although this is a location-unsupervised method, its performance outperforms typical weakly supervised methods, and even shows comparable results with some recent fully supervised methods. We also visualize the activation maps, which reveal the intrinsic reason behind the higher performance of the proposed method.

Allogeneic Hematopoietic stem cell transplantation (HSCT) offers a potential cure for patients with hematologic malignancies. Unfortunately, graft-versus-host disease (GVHD) remains a major obstacle to the greater success of this treatment. Despite intensive research efforts over the past several decades, GVHD is still a major cause of morbidity and mortality in patients receiving allogeneic HSCT. The genetic disparity between donor and recipient is the primary factor that dictates the extent of alloimmune response and the severity of acute GVHD (aGVHD). However, some nongenetic factors are also actively involved in GVHD pathogenesis. Thus, identifying host factors that can be readily modified to reduce GVHD risk is of important clinical significance. We are particularly interested in the potential role of nutrition, as a nongenetic factor, in the etiology and management of aGVHD. In this article, we summarize recent findings regarding how different routes of nutritional support and various dietary factors affect aGVHD. Since diet is one of the most important factors that shape gut microbiota, we also provide evidence for a potential link between certain nutrients and gut microbiota in recipients of allogeneic HSCT. We propose a shifting role of nutrition from support to therapy in GVHD by targeting gut microbiota.

Numerous genetic variants associated with Alzheimer’s disease (AD) have been identified through genome-wide association studies (GWAS), but their interpretation is hindered by the strong linkage disequilibrium (LD) among the variants, making it difficult to identify the causal variants directly. To address this issue, the transcriptome-wide association study (TWAS) was employed to infer the association between gene expression and a trait at the genetic level using expression quantitative trait locus (eQTL) cohorts. In this study, we applied the TWAS theory and utilized the improved Joint-Tissue Imputation (JTI) approach and Mendelian Randomization (MR) framework (MR-JTI) to identify potential AD-associated genes. By integrating LD score, GTEx eQTL data, and GWAS summary statistic data from a large cohort using MR-JTI, a total of 415 AD-associated genes were identified. Then, 2873 differentially expressed genes from 11 AD-related datasets were used for the Fisher test of these AD-associated genes. We finally obtained 36 highly reliable AD-associated genes, including APOC1, CR1, ERBB2, and RIN3. Moreover, the GO and KEGG enrichment analysis revealed that these genes are primarily involved in antigen processing and presentation, amyloid-beta formation, tau protein binding, and response to oxidative stress. The identification of these potential AD-associated genes not only provides insights into the pathogenesis of AD but also offers biomarkers for early diagnosis of the disease.

Abstract Background In sepsis, vitamin D binding protein (VDBP) has been shown to be low-expressed. The current study examined the relationship between serum VDBP level and liver injury in sepsis patients, as well as in a mouse model for sepsis and in cultured liver epithelial cell line exposed to lipopolysaccharide (LPS). Methods The human study included 78 sepsis patients and 50 healthy volunteers. Sepsis patients were categorized into sepsis survivor group (n = 43) and sepsis non-survivor group (n = 35) based on 28-day mortality for data analysis. Adult male C57BL/6 mice were subjected to cecal ligation and puncture (CLP). Serum samples were collected on day 1, 3, 5 and 7 to determine the levels of VDBP, 25-hydroxyvitamin D [25(OH)D3], 1,25-dihydroxyvitamin D [1,25(OH)2D3], interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α). Potential protective effects of VDBP overexpression against LPS-induced liver damage were examined in cultured THLE2 cells. Results Serum levels of VDBP, 25(OH)D3, and 1,25(OH)2D3 were significantly lower in sepsis patients vs. the healthy control (P

A compact and reliable thin-core fiber (TCF) based in-line Mach-Zehnder interferometric (MZI) sensor for refractive index (RI) measurement is proposed and experimentally demonstrated. A section of TCF is sandwiched between a multi-mode fiber (MMF) and a single-mode fiber (SMF) to form the sensing head. In addition, the use of the differential intensity demodulation method not only greatly reduces the system cost, but also solves the problem of power fluctuation in the RI interrogation system. Three samples with different sensing lengths have been made, and the maximum sensitivity of 59.9 dB/RIU has been obtained in the range of 1.332–1.411 RIU, which means the measurement resolution is about 1.669 × 10−4 RIU. Besides, the proposed MZI-based RI sensor is less sensitive to temperature, thus the cross-sensitivity effect can be greatly weakened.

BackgroundThe antiviral activity and underlying mechanism of Patchouli alcohol remain unclear.MethodsThis study evaluated the cytotoxicity, optimal methods for drug administration, anti-influenza A activity of Patchouli alcohol. The antiviral mechanism of Patchouli alcohol was also assessed via qRT-PCR, western blot, hemagglutination inhibiting (HAI) assay, and hemolysis inhibiting assay.ResultsPatchouli alcohol was shown to have low cytotoxicity and its strongest antiviral effect was associated with premixed administration. Patchouli alcohol inhibited virus replication during the early lifecycle stages of influenza A virus infection and specifically prevented expression of the viral proteins, HA and NP. In both the HAI and hemolysis inhibiting assays, Patchouli alcohol was able to block HA2-mediated membrane fusion under low pH conditions. Patchouli alcohol had lower binding energy with HA2 than HA1.ConclusionThese findings suggest that Patchouli alcohol could be a promising membrane fusion inhibitor for the treatment of influenza A infection.

BackgroundThis study was intended to construct a brand new prognostic nomogram after combine clinical and pathological characteristics to increases prognostic value in patients with esophageal squamous cell carcinoma.MethodsA total of 1,634 patients were included. Subsequently, the tumor tissues of all patients were prepared into tissue microarrays. AIPATHWELL software was employed to explore tissue microarrays and calculate the tumor-stroma ratio. X-tile was adopted to find the optimal cut-off value. Univariate and multivariate Cox analyses were used to screen out remarkable characteristics for constructing the nomogram in the total populations. A novel prognostic nomogram with clinical and pathological characteristics was constructed on the basis of the training cohort (n=1,144). What’s more performance was validated in the validation cohort (n=490). Clinical-pathological nomogram were assessed by concordance index, time-dependent receiver operating characteristic, calibration curve and decision curve analysis.ResultsThe patients can divide into two groups with cut-off value of 69.78 for the tumor-stroma ratio. It is noteworthy that the survival difference was noticeable (P

Objective: The present study was developed to explore the impact of acupuncture on the Th17/Treg balance in the brain and the periphery and associated changes in cognitive deficits in a rat model of vascular dementia (VD). Methods: Male Wistar rats (8 weeks old) were randomly assigned to sham-operated (Gs, n = 10), and operation (n = 30) groups. A VD model was established for all rats in the operation group via the permanent bilateral occlusion of the common carotid artery. Behavioral screening of these rats was conducted via a hidden platform trial at 2 months post-operation. These operation group rats were then further subdivided into impaired (Gi) and acupuncture (Ga) groups (n = 10/group). Acupuncture was performed over a 21-day period for rats in the Ga group. A Morris water maze (MWM) test was used to assess cognitive function for rats in all groups. Flow cytometry and fluorescent staining were used to detect Th17 and Treg cells in samples from these animals based on IL-17/FoxP3 or CD4+FoxP3+/CD4+RORγt+ staining profiles. Results: Relative to the Gs group, escape latency values for rats in the Gi group were significantly increased. Following treatment, rats in the Ga group exhibited significant reductions in escape latency values as compared to rats in the Gi group (P 0.05), whereas the frequency of Th17 cells in the peripheral blood and spleen of Ga group rats trended downward relative to the Gi group (P > 0.05). Significantly fewer CD4+RORγt+ and RORγt+ cells were detected in the Ga group relative to the Gi group, whereas CD4+FoxP3+ and FoxP3+ cell counts were increased (P

Severe COVID-19 patients exhibit impaired IFN-I response due to decreased IFN-β production, allowing persistent viral load and exacerbated inflammation. While the SARS-CoV-2 nucleocapsid (N) protein has been implicated in inhibiting innate immunity by interfering with IFN-β signaling, the specific underlying mechanism still needs further investigation for a comprehensive understanding. This study reveals that the SARS-CoV-2 N protein enhances interaction between the human SUMO-conjugating enzyme UBC9 and MAVS. Increased MAVS-UBC9 interaction leads to enhanced SUMOylation of MAVS, inhibiting its ubiquitination, resulting in the inhibition of phosphorylation events involving IKKα, TBK1, and IRF3, thus disrupting IFN-β signaling. This study highlights the role of the N protein of SARS-CoV-2 in modulating the innate immune response by affecting the MAVS SUMOylation and ubiquitination processes, leading to inhibition of the IFN-β signaling pathway. These findings shed light on the complex mechanisms utilized by SARS-CoV-2 to manipulate the host’s antiviral defenses and provide potential insights for developing targeted therapeutic strategies against severe COVID-19.