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Abstract Vibratory rollers are generally used in the process of highway subgrade compaction. In the paper, the vibratory roller—subgrade finite element model was established to simulate the field construction by using ABAQUS. We used Hilbert–Huang Transform to analyze the compaction in the field test from the time–frequency domain. By changing the parameters of the vibratory roller and the filler, the comprehensive influence of the parameters such as the elastic modulus of the filler, rolling speed, excitation force, vibration frequency and thickness of the filler on the compaction quality of the subgrade was investigated. We studied the propagation pattern of vibration waves in three-dimensional space. The study shows that the signals of different frequency bands in the Hilbert spectrum represent the compaction degrees of fillers in different zones. The peak acceleration generally decreases with the increase of horizontal and vertical distance, but there is an increase at the boundary of the vibration field. There is an optimal combination of the excitation force, vibration frequency and thickness of the filler. The vibration waves propagate in the form of an ellipsoid in three dimensions, and the amplitude decreases with distance.

Abstract Purpose This study aims to investigate the risk factors for lymph node metastasis (LNM) in synchronous multiple primary lung cancer (sMPLC) using clinical and CT features, and to offer guidance for preoperative LNM prediction and lymph node (LN) resection strategy. Materials and methods A retrospective analysis was conducted on the clinical data and CT features of patients diagnosed with sMPLC at the Third Affiliated Hospital of Kunming Medical University from January 1, 2018 to December 31, 2022. Patients were classified into two groups: the LNM group and the non-LNM (n-LNM) group. The study utilized univariate analysis to examine the disparities in clinical data and CT features between the two groups. Additionally, multivariate analysis was employed to discover the independent risk variables for LNM. The diagnostic efficacy of various parameters was evaluated using the receiver operating characteristic (ROC) curve. Results Among the 688 patients included in this study, 59 exhibited LNM. Univariate analysis revealed significant differences between the LNM and n-LNM groups in terms of gender, smoking history, CYFRA21-1 level, CEA level, NSE level, lesion type, total lesion diameter, main lesion diameter, spiculation sign, lobulation sign, cavity sign, and pleural traction sign. Logistic regression identified CEA level (OR = 1.042, 95%CI: 1.009-1.075), lesion type (OR = 9.683, 95%CI: 3.485-26.902), and main lesion diameter (OR = 1.677, 95%CI: 1.347-2.089) as independent predictors of LNM. The regression equation for the joint prediction was as follows: logit(p)= -7.569+0.041*CEA level +2.270* lesion type +0.517* main lesion diameter.ROC curve analysis showed that the AUC for CEA level was 0.765 (95% CI, 0.694–0.836), for lesion type was 0.794 (95% CI, 0.751–0.838), for main lesion diameter was 0.830 (95% CI, 0.784–0.875), and for the combine predict model was 0.895 (95% CI, 0.863–0.928). Conclusion The combination of clinical and imaging features can better predict the status of LNM of sMPLC, and the prediction efficiency is significantly higher than that of each factor alone, and can provide a basis for lymph node management decision.

Abstract Both the pyrroloindoline core and N–CF3 moiety hold significant importance in medicinal chemistry. However, to date, no instances of constructing N–CF3-containing pyrroloindolines have been reported. Herein, we present a robust and operationally simple approach to assembling such intriguing skeletons from tryptamine-derived isocyanides through a cascade sequence, which includes an oxidative trifluorination and a subsequent halogenative cyclization. Key to the success lies in the development of a facile conversion of isocyanides to N–CF3 moiety with commercially available reagents N-halosuccinimide and Et3N·HF. The protocol features mild reaction conditions, broad functional group tolerance, good to excellent yields, and high diastereoselectivities. In addition, we demonstrate that the halide substituent within the products serves as a versatile functional handle for accessing diverse C3-quaternary-substituted N–CF3-pyrroloindolines.

Nuclear transporter importin-β1 is emerging as an attractive target by virtue of its prevalence in many cancers. However, the lack of druggable inhibitors restricts its therapeutic proof of concept. In the present work, we optimized a natural importin-β1 inhibitor DD1 to afford an improved analog DD1-Br with better tolerability (>25 folds) and oral bioavailability. DD1-Br inhibited the survival of castration-resistant prostate cancer (CRPC) cells with sub-nanomolar potency and completely prevented tumor growth in resistant CRPC models both in monotherapy (0.5 mg/kg) and in enzalutamide-combination therapy. Mechanistic study revealed that by targeting importin-β1, DD1-Br markedly inhibited the nuclear accumulation of multiple CRPC drivers, particularly AR-V7, a main contributor to enzalutamide resistance, leading to the integral suppression of downstream oncogenic signaling. This study provides a promising lead for CRPC and demonstrates the potential of overcoming drug resistance in advanced CRPC via targeting importin-β1.

Abstract An improved method for identifying sporadic E (Es) layer properties from radio occultation (RO) electron density profiles (EDPs) is presented. The data used are sourced from COSMIC-1 RO EDPs collected between 2006 and 2019, which cover altitudes from 75 to 145 km. Initially, we evaluate the reliability of EDPs using the International Reference Ionosphere 2016 (IRI-2016) model and select only those profiles with a reliability score of 0.6 or higher (on a scale up to 1) for further analysis. Preliminary Es layer inversion results are obtained and validated against electron density data derived from ionosonde fbEs measurements, demonstrating a linear correlation with a coefficient of 0.72 and a mean absolute percentage error of 36.08%. Further verification using RO S4max data shows that this research method achieved an accuracy of 85.3% in identifying Es events. We perform a detailed analysis of Es layer relative occurrence rates, intensity, and thickness. The Es intensity is expressed by NmμEs (the layer’s maximum electron density (Nm) corresponding to the metal (μ) ion), which is estimated from the measured layer peak electron density NmEs by subtracting the ambient E region electron density NeE(hEs) at the height of the layer’s peak hEs, computed from the IRI-2016 model. Our findings reveal that Es layers predominantly occur in mid-latitude regions during summer, with average intensities between $$5\times {10}^{4 }\text{ and }8\times {10}^{4 }\text{el}/{\text{cm}}^{3}$$ 5 × 10 4 and 8 × 10 4 el / cm 3 . The most likely thickness of Es layers is approximately 1.4 km. Additionally, the present study shows that because NeE(hEs) increases during daytime, which leads to increases in NmEs, confirming that NmμEs is the proper parameter for assessing the Es layer intensity, in line with what is suggested by Haldoupis et al. (Haldoupis et al., J Atmos Sol Terr Phys 206:105327, 2020). Graphical Abstract

Abstract Purpose To apply an independent GPU-accelerated Monte Carlo (MC) dose verification for CyberKnife M6 with Iris collimator and evaluate the dose calculation accuracy of RayTracing (TPS-RT) algorithm and Monte Carlo (TPS-MC) algorithm in the Precision treatment planning system (TPS). Methods GPU-accelerated MC algorithm (ArcherQA-CK) was integrated into a commercial dose verification system, ArcherQA, to implement the patient-specific quality assurance in the CyberKnife M6 system. 30 clinical cases (10 cases in head, and 10 cases in chest, and 10 cases in abdomen) were collected in this study. For each case, three different dose calculation methods (TPS-MC, TPS-RT and ArcherQA-CK) were implemented based on the same treatment plan and compared with each other. For evaluation, the 3D global gamma analysis and dose parameters of the target volume and organs at risk (OARs) were analyzed comparatively. Results For gamma pass rates at the criterion of 2%/2 mm, the results were over 98.0% for TPS-MC vs.TPS-RT, TPS-MC vs. ArcherQA-CK and TPS-RT vs. ArcherQA-CK in head cases, 84.9% for TPS-MC vs.TPS-RT, 98.0% for TPS-MC vs. ArcherQA-CK and 83.3% for TPS-RT vs. ArcherQA-CK in chest cases, 98.2% for TPS-MC vs.TPS-RT, 99.4% for TPS-MC vs. ArcherQA-CK and 94.5% for TPS-RT vs. ArcherQA-CK in abdomen cases. For dose parameters of planning target volume (PTV) in chest cases, the deviations of TPS-RT vs. TPS-MC and ArcherQA-CK vs. TPS-MC had significant difference (P 0.05). ArcherQA-CK had less calculation time compared with TPS-MC (1.66 min vs. 65.11 min). Conclusions Our proposed MC dose engine (ArcherQA-CK) has a high degree of consistency with the Precision TPS-MC algorithm, which can quickly identify the calculation errors of TPS-RT algorithm for some chest cases. ArcherQA-CK can provide accurate patient-specific quality assurance in clinical practice.

The application of machine learning (ML) techniques to climate science has received significant attention, particularly in the field of climate predictions, ranging from sub-seasonal to decadal time scales. This paper reviews recent progress of ML techniques employed in climate phenomena prediction and the enhancement of dynamic forecast models, which provide valuable insights into the great potentials of ML techniques to improve climate prediction capabilities with reduced computational time and resource consumption. This paper also discusses several major challenges in the application of ML to climate prediction, including the scarcity of datasets, physical inconsistency, and lack of model transparency and interpretability. Additionally, this paper sheds light on how climate change impacts ML model training and prediction, and explores three key areas with potential breakthroughs: large-scale climate models, knowledge discovery driven by ML, and hybrid dynamical-statistical models, underscoring the important role of the integration of “ML and dynamical models” in building a bridge between the artificial intelligence and climate science.

Background Primary hyperoxaluria type 2 (PH2) is a rare disorder caused by GRHPR mutations. Research on the mutation spectrum and pedigree of PH2 helps in comprehending its pathogenesis and clinical outcomes, guiding clinical diagnosis and treatment.Methods We report a case of PH2 with a three-generational pedigree. The GRHPR genotypes of the family members were confirmed by Sanger sequencing. Urine and blood samples were collected for biochemical analysis. Computational analysis was performed to assess the pathogenicity of the mutations. Cellular experiments based on site-directed mutagenesis were conducted to confirm the effect of mutations on GRHPR expression, activity, and subcellular localization.Results The proband underwent her first kidney transplantation in 2015, and experienced recurrent urinary tract infections and urolithiasis postoperatively. Graft failure occurred in 2018. Whole exome sequencing identified compound heterozygous GRHPR mutations p.G160E/p.P203Rfs*7. The patient underwent a second kidney transplantation in 2019 and maintained good graft function with urine dilution measures. Notably, her brother and sister carried the same mutations; however, only the proband progressed to renal failure. Computational analysis suggested that p.G160E reduced the affinity of GRHPR for coenzymes. Cellular experiments indicated that p.G160E reduced GRHPR activity (p

ABSTRACTObjective: An association exists between immune checkpoint inhibitors and hemophagocytic lymphohistiocytosis (HLH). Therefore, the main objective of this study was to collect data on this rare but potentially life-threatening immune-related adverse reaction to identify the medications that cause it, the clinical characteristics, and effective treatments.Methods: Literature in English and Chinese on immune checkpoint inhibitors causing HLH published from August 2014 to March 2024 was analyzed. Immune checkpoint inhibitors, immunotherapy, anti-PD-1, PD-L1 inhibitors, HLH, hemophagocytic lymphohistiocytosis, hemophagocytic syndrome keywords were used to find the literature on China Knowledge Network, Wanfang, PubMed and Emabase Databases.Results and discussion: Twenty-four studies were included, with a total of 27 patients (18 males and 9 females) with a mean age of 58 years (range 26–86). The mean time to the onset of symptoms was 10.3 weeks (7 days–14 months). The main clinical characteristics were fever, cytopenia, splenomegaly, methemoglobinemia, hypofibrinogenemia, and bone marrow biopsy showed phagocytosis. Twenty-two patients improved after the treatment with steroids, cytokine blocking therapy and symptomatic treatment, four patients died, and one patient was not described.Conclusion: HLH should be not underestimated as a potentially serious adverse effect of immune checkpoint inhibitors since appropriate treatments may save the life of patients.

With the advent of online communities, firms have invested heavily in creating their brand communities as a value co-creation platform to engage with their customers. However, customer interactions in online brand communities may not always be beneficial for firms. Drawing on conservation of resources theory, we explore how interaction failure as a manifestation of value co-destruction leads to negative customer outcomes in online brand communities. The results of an offline scenario-based experiment and an online survey suggest that interaction failure causes negative customer behaviors through two resource-based mediators: perceived usefulness and perceived fairness. Furthermore, we find that different types of interaction failures lead to different degrees of value co-destruction. Specifically, firm-oriented interaction failure leads to more negative customer behaviors than customer-oriented and peer-oriented interaction failures. Our study provides both theoretical and practical implications for value co-creation in online brand communities.

Summary: In patients with polycystic ovary syndrome (PCOS), the concentration of homocysteine (Hcy) in follicular fluid is inversely correlated with oocyte and embryo quality. Nevertheless, other metabolic abnormalities associated with PCOS may also impact oocyte and early embryo quality. Therefore, it remains uncertain whether reproductive function is affected in patients without PCOS with hyperhomocysteinemia (HHcy). Here, we observed reduced fertility, increased ovarian atretic follicles, and reduced oocyte maturation rates in HHcy mice. Proteomic analyses revealed that HHcy causes mitochondrial dysfunction and reduced expression of zona pellucida proteins (ZP1, ZP2, and ZP3) in oocytes. Transmission electron microscopy confirmed abnormal formation of the zona pellucida and microvilli in oocytes from HHcy mice. Additionally, in vitro fertilization (IVF) demonstrated a reduction in the rate of 2-cell embryo formation in HHcy mice. These findings reveal that HHcy reduces female reproductive longevity by affecting follicular development and oocyte quality.

The need for privacy, recognized as a fundamental psychological requirement, has garnered increasing attention as researchers explore the restorative effects of privacy driven activities (PDA). This study employs quantitative experiments and analyses to assess the restorative benefits of PDA within three classical Chinese gardens, demonstrating their superiority over conventional leisure activities in promoting emotional recovery, reducing stress, and restoring attention. The experiment quantifies the restorative effects of PDA versus standard leisure activities using a classic restorative scale and physiological indicators reflecting emotional relief. Regression analyses then identify five key factors influencing the occurrence of PDA, derived from preferred locations and behavioral tendencies observed in the three gardens. Further analyses reveal significant differences in the impacts of these five environmental feature dimensions on the evaluation indicators of “preference for privacy-oriented activities” and “restorative effects”. Among these dimensions, “spatial scale and accessibility” has the greatest impact on the “preference for privacy-oriented environments”, while “spatial atmosphere” and “activity facilities” have the most significant impact on “restorative effects”. The findings suggest that behavioral activities mediate the relationship between environmental factors and restorative effects, highlighting the potential of PDA as a mediating variable for a comprehensive investigation into the pathways and mechanisms influencing restorative environments in research and design.

ObjectiveDespite advancements in understanding panic disorder (PD), its etiology and pathogenesis remain elusive, particularly concerning the influence of psychosocial factors. This study aims to explore the mediating roles of neuroticism and anxiety sensitivity (AS) in the relationship between childhood trauma and PD severity.MethodsWe conducted a cross-sectional analysis involving 84 individuals diagnosed with PD and 112 age- and gender-matched healthy controls (HC). Childhood trauma was assessed using the validated Childhood Trauma Questionnaire (CTQ), while anxiety sensitivity levels were measured using the Anxiety Sensitivity Index-3 (ASI-3). Neuroticism was evaluated using the Chinese Big Five Personality Inventory (CBF-PI-B). The severity of PD was determined using the Panic Disorder Severity Scale (PDSS). Statistical analysis encompassed both correlation and mediation techniques to thoroughly examine the relationships among childhood trauma, neuroticism, AS, and PD severity.ResultsIndividuals with PD exhibited significantly higher CTQ, ASI-3, and neuroticism scores compared to HC (all P

IntroductionUrinary incontinence (UI) significantly impairs women’s quality of life. Identifying its risk factors is essential for developing effective interventions. Sarcopenia, characterized by the accelerated loss of muscle mass and function, is an emerging concern often linked to obesity and abnormal metabolic status, exacerbating various adverse health outcomes. This population-based study aimed to explore the independent and joint associations of sarcopenia, obesity, and metabolic health with UI risk, as well as to evaluate the mediating role of metabolic indicators in these associationsMethodsA total of 3,557 women aged ≥20 years from the National Health and Nutrition Examination Survey were included. Sarcopenia was assessed using the appendicular lean mass index (ALMI), and obesity was defined by body mass index and waist circumference. Metabolic health was evaluated using revised criteria from the National Cholesterol Education Program-Adult Treatment Panel III. UI was identified through responses to the “Kidney Conditions-Urology” questionnaire and classified into stress UI (SUI), urgency UI (UUI), and mixed UI (MUI). Multivariable logistic regression and restricted cubic spline models were used to evaluate the associations and visualize the relationship between ALMI and UI. Mediation models were constructed to assess the mediating role of metabolic indicators.ResultsWe found that sarcopenia was significantly associated with an increased risk of MUI in the general population. Age-specific analysis revealed that sarcopenia is an independent risk factor for SUI in women aged ≥60, and for MUI in women aged 40–59 years. Sarcopenic obesity, particularly under central obesity criteria, further elevated the risk of UI. Notably, women with the metabolically unhealthy obese phenotype with sarcopenia were at the highest risk for both SUI and MUI. Metabolically unhealthy status, glycohemoglobin, vitamin D, and serum albumin levels were partial mediators of these associations.ConclusionOur findings elucidated the complex interactions between sarcopenia, obesity, and metabolic health, underscoring the critical need for integrated therapeutic strategies that address both metabolic health and targeted nutritional interventions, aiming to enhance muscular health and effectively manage and prevent UI.

Objective To address the complexities of distinguishing truth from falsehood in the context of the COVID-19 infodemic, this paper focuses on utilizing deep learning models for infodemic ternary classification detection. Methods Eight commonly used deep learning models are employed to categorize collected records as true, false, or uncertain. These models include fastText, three models based on recurrent neural networks, two models based on convolutional neural networks, and two transformer-based models. Results Precision, recall, and F 1-score metrics for each category, along with overall accuracy, are presented to establish benchmark results. Additionally, a comprehensive analysis of the confusion matrix is conducted to provide insights into the models’ performance. Conclusion Given the limited availability of infodemic records and the relatively modest size of the two tested data sets, models with pretrained embeddings or simpler architectures tend to outperform their more complex counterparts. This highlights the potential efficiency of pretrained or simpler models for ternary classification in COVID-19 infodemic detection and underscores the need for further research in this area.

Abstract As our planet is entering into the “global boiling” era, understanding regional climate change becomes imperative. Effective downscaling methods that provide localized insights are crucial for this target. Traditional approaches, including computationally-demanding regional dynamical models or statistical downscaling frameworks, are often susceptible to the influence of downscaling uncertainty. Here, we address these limitations by introducing a diffusion probabilistic downscaling model (DPDM) into the meteorological field. This model can efficiently transform data from 1° to 0.1° resolution. Compared with deterministic downscaling schemes, it not only has more accurate local details, but also can generate a large number of ensemble members based on probability distribution sampling to evaluate the uncertainty of downscaling. Additionally, we apply the model to generate a 180-year dataset of monthly surface variables in East Asia, offering a more detailed perspective for understanding local scale climate change over the past centuries.

Abstract The observed Southern Ocean sea surface temperature (SST) has experienced prominent inter-decadal variability nearly in phase with the Inter-decadal Pacific Oscillation (IPO), but less associated with the Atlantic Multidecadal Variability (AMV), challenging the prevailing view of Pacific-Atlantic synergistic effects. Yet, the mechanisms of distinct trans-hemispheric connections to the Southern Ocean remain indecisive. Here, by individually constraining the observed cold-polarity and warm-polarity IPO and AMV SSTs in a climate model, we show that the IPO is influential in initiating a basin-wide Southern Ocean response, with the AMV secondary. A tropical Pacific-wide cooling triggers a basin-scale Southern Ocean cold episode through a strong Rossby wave response to the north-to-south cross-equatorial weakened Hadley circulation. By contrast, due to the competing role of tropical Pacific cooling, an Atlantic warming partly cools the Southern Ocean via a weak Rossby wave response to the south-to-north cross-equatorial enhanced Hadley circulation. Conversely, tropical Pacific warming leads to a warm Southern Ocean episode. Our findings highlight that properly accounting for the tropical Pacific SST variability may provide a potential for skillful prediction of Southern Ocean climate change and more reliable estimates of climate sensitivity, currently overestimated by the misrepresented Southern Ocean warming.

Abstract Background Metabolic disorders (MetDs) have been demonstrated to be closely linked to numerous diseases. However, the precise association between MetDs and pulmonary tuberculosis (PTB) remains poorly understood. Method Summary statistics for exposure and outcomes from genome-wide association studies (GWASs) for exposures and outcomes were obtained from the BioBank Japan Project (BBJ) Gene–exposure dataset. The 14 clinical factors were categorized into three groups: metabolic laboratory markers, blood pressure, and the MetS diagnostic factors. The causal relationship between metabolic factors and PTB were analyzed using two-sample Mendelian Randomization (MR). Additionally, the direct effects on the risk of PTB were investigated through multivariable MR. The primary method employed was the inverse variance-weighted (IVW) model. The sensitivity of this MR analysis was evaluated using MR-Egger regression and the MR-PRESSO global test. Results According to the two-sample MR, HDL-C, HbA1c, TP, and DM were positively correlated with the incidence of active TB. According to the multivariable MR, HDL-C (IVW: OR 2.798, 95% CI 1.484–5.274, P = 0.001), LDL (IVW: OR 4.027, 95% CI 1.140–14.219, P = 0.03) and TG (IVW: OR 2.548, 95% CI 1.269–5.115, P = 0.009) were positively correlated with the occurrence of PTB. TC (OR 0.131, 95% CI 0.028–0.607, P = 0.009) was negatively correlated with the occurrence of PTB. We selected BMI, DM, HDL-C, SBP, and TG as the diagnostic factors for metabolic syndrome. DM (IVW, OR 1.219, 95% CI 1.040–1.429 P = 0.014) and HDL-C (IVW, OR 1.380, 95% CI 1.035–1.841, P = 0.028) were directly correlated with the occurrence of PTB. Conclusions This MR study demonstrated that metabolic disorders, mainly hyperglycemia, and dyslipidemia, are associated with the incidence of active pulmonary tuberculosis.

Objective To analyze the reentry of single reagent reactive voluntary blood donors in our center, in order to provide data support and reference for the recall of single reagent reactive blood donors. Methods Collect the information of eligible blood donors from the information system of our center from January 2019 to September 2023 were collected, and the reentry were tracked and retrospectively analyzed. The demographic characteristics, active reentry rate, and re-donation rate of eligible blood donors were analyzed, and the differences between qualified reentry rates and factors affecting re-donation were compared using chi-square test. Results A total of 3 361 blood donors met the reentry criteria, with an active reentry rate of 2.7% (91/3 361) and a qualified reentry rate of 80.21% (73/91). The reentry rate of eligible blood donors showed significant differences in terms of different genders(3.3% vs 2.1%), educational background(3.7% vs 2.3%), blood donation frequency(9.3% vs 4.0% vs 1.3%), testing items(7.7% vs 2.7% vs 1.9% vs 1.3%) and blood donation types(18.1% vs 2.0%) (P

Abstract Understanding both positive and negative impacts of climate change is essential for comprehensively assessing and well adapting to the impacts of changing climate. Conventionally, climate warming is revealed to negatively impact human activities. Here, we reveal that human beings’ performance in anaerobic sports may benefit from climate warming. Using global weather observation and athletes’ performance datasets, we show that world-top athletes’ performances in nearly all athletics anaerobic events (i.e., sprints, jumps and throws) substantially improve as ambient temperature rises. For example, 100 m performance monotonically improves by 0.26 s as ambient temperature rises from 11.8° to 36.4 °C. Using Coupled Model Intercomparison Project Phase 6 datasets, we further show that global warming can substantially improve world-top athletes’ performance in eleven of the thirteen Olympics athletics anaerobic events by 0.27%–0.88% and 0.14–0.48% under high-emission and medium-emission scenarios, respectively, during 1979–2100. Among them, the improvements for 100 m are 0.59% (0.063 s) and 0.32% (0.034 s), respectively. Mechanism analysis shows that the warmed ambient atmosphere can improve competitors’ performance through expanding the air and thus reducing the air resistance to the competitors and throwing implements for hummer throw and all the sprints, hurdling and jumps. Quantitative analysis estimates that this thermodynamic process is essential for the impacts of warmed ambient atmosphere on the performances in these events as physiological processes are.

Abstract In recent years, extensive research has focused on encryption algorithms for square images, with relatively little attention given to nonsquare images. This paper introduces a novel encryption algorithm tailored for nonequal length images, integrating bit-plane chaotic mapping and Arnold transformation. To effectively implement the algorithm, the plain image is initially transformed into two equal-sized binary sequences. A new diffusion strategy is then introduced to mutually diffuse these sequences, followed by the use of a chaotic map to control the swapping of binary elements between them, enabling permutation of bits across different bitplanes. Finally, the positional information of the image is scrambled using the Arnold transform, resulting in the generation of the encrypted image. By utilizing nonequal Arnold transformation parameters and the initial value of the Lorenz chaotic map as keys, the transmission of keys is simplified, and the cryptosystem gains infinite key space to resist brute force attacks. Experimental results and security analysis confirm the effectiveness of the proposed quantum image encryption algorithm in encrypting nonsquare images, demonstrating good performance in terms of nonstatistical properties, key sensitivity, and robustness. Furthermore, simulation experiments based on Qiskit successfully validate the correctness and feasibility of the quantum image encryption algorithm.

Abstract Background Premature ovarian insufficiency (POI) is a major cause of infertility. In this study, we aimed to investigate the effects of the combination of bone marrow mesenchymal stem cells (BMSCs) and moxibustion (BMSCs-MOX) on POI and evaluate the underlying mechanisms. Methods A POI rat model was established by injecting different doses of cyclophosphamide (Cy). The modeling of POI and the effects of the treatments were assessed by evaluating estrous cycle, serum hormone levels, ovarian weight, ovarian index, and ovarian histopathological analysis. The effects of moxibustion on BMSCs migration were evaluated by tracking DiR-labeled BMSCs and analyzing the expression of chemokines stromal cell-derived factor 1 (Sdf1) and chemokine receptor type 4 (Cxcr4). Mitochondrial function and mitophagy were assessed by measuring the levels of reactive oxygen species (ROS), mitochondrial membrane potential (MMP), ATP, and the mitophagy markers (Drp1, Pink1, and Parkin). Furthermore, the mitophagy inhibitor Mdivi-1 and the mitophagy activator CCCP were used to confirm the role of mitophagy in Cy-induced ovarian injury and the underlying mechanism of combination therapy. Results A suitable rat model of POI was established using Cy injection. Compared to moxibustion or BMSCs transplantation alone, BMSCs-MOX showed improved outcomes, such as reduced estrous cycle disorders, improved ovarian weight and index, normalized serum hormone levels, increased ovarian reserve, and reduced follicle atresia. Moxibustion enhanced Sdf1 and Cxcr4 expression, promoting BMSCs migration. BMSCs-MOX reduced ROS levels; upregulated MMP and ATP levels in ovarian granulosa cells (GCs); and downregulated Drp1, Pink1, and Parkin expression in ovarian tissues. Mdivi-1 significantly mitigated mitochondrial dysfunction in ovarian GCs and improved ovarian function. CCCP inhibited the ability of BMSCs-MOX treatment to regulate mitophagy and ameliorate Cy-induced ovarian injury. Conclusions Moxibustion enhanced the migration and homing of BMSCs following transplantation and improves their ability to repair ovarian damage. The combination of BMSCs and moxibustion effectively reduced the excessive activation of mitophagy, which helped prevent mitochondrial damage, ultimately improving ovarian function. These findings provide a novel approach for the treatment of pathological ovarian aging and offer new insights into enhancing the efficacy of stem cell therapy for POI patients. Graphical abstract

Abstract Purpose Based on the quantitative and qualitative features of CT imaging, a model for predicting the invasiveness of ground-glass nodules (GGNs) was constructed, which could provide a reference value for preoperative planning of GGN patients. Materials and methods Altogether, 702 patients with GGNs (including 748 GGNs) were included in this study. The GGNs operated between September 2020 and July 2022 were classified into the training group (n = 555), and those operated between August 2022 and November 2022 were classified into the validation group (n = 193). Clinical data and the quantitative and qualitative features of CT imaging were harvested from these patients. In the training group, the quantitative and qualitative characteristics in CT imaging of GGNs were analyzed by using performing univariate and multivariate logistic regression analyses, followed by constructing a nomogram prediction model. The differentiation, calibration, and clinical practicability in both the training and validation groups were assessed by the nomogram models. Results In the training group, multivariate logistic regression analysis disclosed that the maximum diameter (OR = 4.707, 95%CI: 2.06–10.758), consolidation/tumor ratio (CTR) (OR = 1.027, 95%CI: 1.011–1.043), maximum CT value (OR = 1.025, 95%CI: 1.004–1.047), mean CT value (OR = 1.035, 95%CI: 1.008–1.063; P = 0.012), spiculation sign (OR = 2.055, 95%CI: 1.148–3.679), and vascular convergence sign (OR = 2.508, 95%CI: 1.345–4.676) were independent risk parameters for invasive adenocarcinoma. Based on these findings, we established a nomogram model for predicting the invasiveness of GGN, and the AUC was 0.910 (95%CI: 0.885–0.934) and 0.902 (95%CI: 0.859–0.944) in the training group and the validation group, respectively. The internal validation of the Bootstrap method showed an AUC value of 0.905, indicating a good differentiation of the model. Hosmer–Lemeshow goodness of fit test for the training and validation groups indicated that the model had a good fitting effect (P > 0.05). Furthermore, the calibration curve and decision analysis curve of the training and validation groups reflected that the model had a good calibration degree and clinical practicability. Conclusion Combined with the quantitative and qualitative features of CT imaging, a nomogram prediction model can be created to forecast the invasiveness of GGNs. This model has good prediction efficacy for the invasiveness of GGNs and can provide help for the clinical management and decision-making of GGNs.

IntroductionMajor depressive disorder is a condition involving microbiota–gut–brain axis dysfunction. Increasing research aims to improve depression through gut microbiota regulation, including interventions such as probiotics, prebiotics, and fecal microbiota transplants. However, most research focuses on exogenous depression induced by chronic stress or drugs, with less attention given to endogenous depression. Additionally, research on gut mycobiota in depression is significantly less than that on gut bacteria.MethodsIn the present study, Wistar–Kyoto rats were used as an endogenous depression and treatment-resistant depression model, while Wistar rats served as controls. Differences between the two rat strains in behavior, gut bacteria, gut mycobiota, nervous system, endocrine system, immune system, and gut barrier were evaluated. Additionally, the effects of Lactobacillus helveticus NS8 supplementation were investigated.ResultsWistar–Kyoto rats demonstrated increased depressive-like behaviors in the forced swimming test, reduced sucrose preference in the sucrose preference test, and decreased locomotor activity in the open field test. They also exhibited abnormal gut bacteria and mycobiota, characterized by higher bacterial α-diversity but lower fungal α-diversity, along with increased butyrate, L-tyrosine, and L-phenylalanine biosynthesis from bacteria. Furthermore, these rats showed dysfunction in the microbiota–gut–brain axis, evidenced by a hypo-serotonergic system, hyper-noradrenergic system, defective hypothalamic–pituitary–adrenal axis, compromised gut barrier integrity, heightened serum inflammation, and diminished gut immunity. A 1-month L. helveticus NS8 intervention increased the fecal abundance of L. helveticus; reduced the abundance of Bilophila and Debaryomycetaceae; decreased immobility time but increased climbing time in the forced swimming test; reduced hippocampal corticotropin-releasing hormone levels; decreased hypothalamic norepinephrine levels; increased hippocampal glucocorticoid receptor, brain-derived neurotrophic factor dopamine, and 5-hydroxyindoleacetic acid content; and improved the gut microbiota, serotonergic, and noradrenergic system.ConclusionThe depressive phenotype of Wistar–Kyoto rats is not only attributed to their genetic context but also closely related to their gut microbiota. Abnormal gut microbiota and a dysfunctional microbiota–gut–brain axis play important roles in endogenous depression, just as they do in exogenous depression. Supplementing with probiotics such as L. helveticus NS8 is likely a promising approach to improve endogenous depression and treatment-resistant depression.

Non-invasive neuroimaging has revealed specific network-based resting-state dynamics in the human brain, yet the underlying neurophysiological mechanism remains unclear. We employed intracranial electroencephalography to characterize local field potentials within the default mode network (DMN), frontoparietal network (FPN), and salience network (SN) in 42 participants. We identified stronger within-network phase coherence at low frequencies (θ and α band) within the DMN, and at high frequencies (γ band) within the FPN. Hidden Markov modeling indicated that the DMN exhibited preferential low frequency phase coupling. Phase-amplitude coupling (PAC) analysis revealed that the low-frequency phase in the DMN modulated the high-frequency amplitude envelopes of the FPN, suggesting frequency-dependent characterizations of intrinsic brain networks at rest. These findings provide intracranial electrophysiological evidence in support of the network model for intrinsic organization of human brain and shed light on the way brain networks communicate at rest.

Drought is projected to intensify under warming climate and will continuously threaten global food security. Assessing the risk of yield loss due to drought is key to developing effective agronomic options for farmers and policymakers. However, little has been known about determining the likelihood of reduced crop yield under different drought conditions and defining thresholds that trigger yield loss at the regional scale in Australia. Here, we estimated the dependence of yield variation on drought conditions and identified drought thresholds for 12 Australia's key wheat producing regions with historical yield data by developing bivariate models based on copula functions. These identified drought thresholds were used to investigate drought statistics under climate change with an ensemble of 36 climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that drought-induced yield loss was region-specific. The drought thresholds leading to the same magnitude of wheat yield reduction were smaller in regions of southern Queensland and larger in Western Australia mainly due to different climate and soil conditions. Drought will be more frequent and affect larger areas under future warming climates. Based on our results, we advocate for more effective crop management options, particularly in regions where wheat yield is vulnerable to drought in Australia. This will mitigate potential drought impacts on crop production and safeguard global food security.

Abstract In July and August 2022, the Yangtze River basin (YRB) experienced its hottest summer since 1961. The SINTEX‐F2 seasonal prediction system initialized in early May predicted the hotter‐than‐normal summer due to its successful prediction of central Pacific La Niña, negative Indian Ocean Dipole and the resultant warming in the tropical West Pacific‐East Indian Ocean (TWP_EIO). The common SST forcing explains only about 26% to the heatwave strength, while the internal variations in the anomalous warming in the TWP_EIO and Europe, surplus precipitation in Pakistan, and local land‐air interaction account for approximately 65%, based on the analysis of 108 ensemble members. These factors have collectively increased the maximum temperature over the YRB through the enhancement and westward expansion of western North Pacific subtropical high. Our findings quantify the relative contributions of external forcing and internal variations to the unprecedented hot event, offering insights into its forming mechanism and potential predictability.

Excessive alcohol exposure can cause neurobehavioral deficits and structural alterations in the brain. Emerging research evidence suggests that endoplasmic reticulum (ER) stress plays an important role in alcohol-induced neurotoxicity. Mesencephalic astrocyte-derived neurotrophic factor (MANF) is an ER stress inducible protein and is responsible to maintain ER homeostasis. MANF is highly expressed in both the developing and mature brain. We have previously shown that MANF deficiency exacerbated alcohol induced neurodegeneration and ER stress in the developing brain. However, little is known regarding the role of MANF in alcohol induced neuronal damage in the adult brain. In this study, we used a neuron-specific MANF knockout (KO) mouse model to investigate the effect of MANF deficiency on acute binge alcohol exposure-induced neurobehavioral deficits and ER stress. Adult male and female MANF KO mice and littermate controls received daily alcohol gavage (5 g/kg) for 10 days and then subjected to a battery of neurobehavioral tests including rotarods, balance beam, DigiGait, open field, elevated plus maze, Barnes maze, and three-chamber sociability task. Female MANF KO animals were more susceptible to alcohol-induced body weight loss. Alcohol exposure did not affect motor function, however female but not male MANF KO mice exhibited an increased locomotor activity in open field test. Learning and memory was not significantly impaired, but it was altered by MANF deficiency in females while it was affected by alcohol treatment in males. Both alcohol-exposed male and female MANF KO mice displayed increased sociability. Alcohol induced the expression of ER chaperones GRP78 and GRP94 and altered the levels of several unfolded protein response (UPR) and neuroinflammation markers in MANF KO mice in a sex-specific manner. The expression of MANF interacting proteins neuroplastin, PDIA1, and PDIA6 was increased in MANF KO mice, and was further induced by alcohol. In conclusion, alcohol exposure and neuronal MANF deficiency interacted to alter neurobehavioral outcomes, ER homeostasis and neuroinflammation in a sex-specific manner.

ObjectivesOsteosarcoma (OS) is the primary bone tumor originating from transformed mesenchymal cells. It is unclear whether associations between specific circulating immune cells and OS are causal or due to bias. To clarify whether predicted genetically altered circulating immune cells are associated with OS development, we performed a two-sample Mendelian randomization (MR) analysis.MethodsThe genetic variants strongly associated with immune cell traits as instrumental variables (IVs) were used to perform MR analyses. The effect of specific immune cells on OS risk was measured using the summary statistics from the genome-wide association studies (GWAS).ResultsOur findings indicate that CD80 on CD62L+ myeloid dendritic cell and CD28−CD4−CD8− T-cell absolute count are positively associated with OS (CD80 on CD62L+ myeloid dendritic cell, OR: 3.41 [95% CI: 1.40 to 8.31], p = 0.007; CD28−CD4−CD8− T-cell absolute count, OR: 4.49 [95% CI: 1.29 to 15.62], p = 0.018). It is also found that CD20 has a negative effect on CD24+CD27+ B cell on OS (OR: 0.32 [95% CI: 0.14 to 0.72], p = 0.006) and a similar impact on IgD+ CD38− B cell on OS (OR: 0.19 [95% CI: 0.05 to 0.68], p = 0.011).ConclusionsThese findings illustrate that the genetic predisposition to specific immune cells can exert a causal effect on OS risk, which confirms the crucial role played by immunity in OS development. Particularly, the causal association between immune cells and OS underscores the evidence for exploring the new treatment strategy for OS in the future.

Abstract Accurate detection and attribution of past climate change are crucial for projecting future climate change and formulating proper policies. In this study, we show that the warming of the tropical Indian Ocean contributes to the observed wetting trend in the Tibetan plateau. The warming tropical Indian Ocean can lead to more precipitation around the Arabian Sea. The associated diabatic heating triggers the cyclonic atmospheric response in the lower troposphere over the Arabian Sea and eastern Africa. It also causes the enhancement and westward extension of the western North Pacific subtropical high. The in‐between airflow transports more moisture northward to the plateau, leading to the increased precipitation over the plateau. These large‐scale circulation patterns can be detected from the long‐term trends based on the observations and the large‐ensemble historical simulations. They can also be simulated by an atmospheric general circulation model forced by the observed warming merely in the tropical Indian Ocean.

Summary: The KRAS mutation is the most common oncogenic driver in patients with non-small cell lung cancer (NSCLC). However, a detailed understanding of how self-reported race and/or ethnicity (SIRE), genetically inferred ancestry (GIA), and their interaction affect KRAS mutation is largely unknown. Here, we investigated the associations between SIRE, quantitative GIA, and KRAS mutation and its allele-specific subtypes in a multi-ethnic cohort of 3,918 patients from the Boston Lung Cancer Survival cohort and the Chinese OrigiMed cohort with an independent validation cohort of 1,450 patients with NSCLC. This comprehensive analysis included detailed covariates such as age at diagnosis, sex, clinical stage, cancer histology, and smoking status. We report that SIRE is significantly associated with KRAS mutations, modified by sex, with SIRE-Asian patients showing lower rates of KRAS mutation, transversion substitution, and the allele-specific subtype KRASG12C compared to SIRE-White patients after adjusting for potential confounders. Moreover, GIA was found to correlate with KRAS mutations, where patients with a higher proportion of European ancestry had an increased risk of KRAS mutations, especially more transition substitutions and KRASG12D. Notably, among SIRE-White patients, an increase in European ancestry was linked to a higher likelihood of KRAS mutations, whereas an increase in admixed American ancestry was associated with a reduced likelihood, suggesting that quantitative GIA offers additional information beyond SIRE. The association of SIRE, GIA, and their interplay with KRAS driver mutations in NSCLC highlights the importance of incorporating both into population-based cancer research, aiming to refine clinical decision-making processes and mitigate health disparities.

BackgroundEarlier observational studies have demonstrated a correlation between glioma and the risk of neurodegenerative diseases (NDs), but the causality and direction of their associations remain unclear. The objective of this study was to ascertain the causal link between glioma and NDs using Mendelian randomization (MR) methodology.MethodsGenome-wide association study (GWAS) data were used in a two-sample bi-directional MR analysis. From the largest meta-analysis GWAS, encompassing 18,169 controls and 12,488 cases, summary statistics data on gliomas was extracted. Summarized statistics for NDs, including Alzheimer’s disease (AD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS) and Parkinson’s disease (PD) were obtained from the GWAS of European ancestry. Inverse variance weighted (IVW) method was elected as the core MR approach with weighted median (WM) method and MR-Egger method as complementary methods. In addition, sensitivity analyses were performed. A Bonferroni correction was used to correct the results.ResultsGenetically predicted glioma had been related to decreased risk of AD. Specifically, for all glioma (IVW: OR = 0.93, 95% CI = 0.90–0.96, p = 4.88 × 10−6) and glioblastoma (GBM) (IVW: OR = 0.93, 95% CI = 0.91–0.95, p = 5.11 × 10−9). We also found that genetically predicted all glioma has a suggestive causative association with MS (IVW: OR = 0.90, 95% CI = 0.81–1.00, p = 0.045). There was no evidence of causal association between glioma and ALS or PD. According to the results of reverse MR analysis, no discernible causal connection of NDs was found on glioma. Sensitivity analyses validated the robustness of the above associations.ConclusionWe report evidence in support of potential causal associations of different glioma subtypes with AD and MS. More studies are required to uncover the underlying mechanisms of these findings.

Abstract Background To establish a nomogram to predict the probability of survival of patients with stage II/III gastric cancer (GC) who received incomplete peri-operative adjuvant chemotherapy (PAC). Methods The medical records of stage II/III GC patients who received curative resection and 1 to 5 cycles of PAC from two tertiary hospitals were retrospectively reviewed. Patients were randomly classified into either a training group or validation group at a ratio of 7:3. The nomogram was constructed based on various prognostic factors using Cox regression analysis in the training cohort, and was validated by the validation group. Concordance index and calibration curves were used to evaluate the discrimination and calibration of the nomogram. Additionally, decision curve analysis (DCA) was used to compare the net clinical benefits of the nomogram and eighth version of TNM staging system. Results A total of 1,070 consecutive patients were included and 749 patients were enrolled into the training group. Lower body mass index (

Abstract Background Surgery combined with radiotherapy substantially escalates the likelihood of encountering complications in early-stage cervical squamous cell carcinoma(ESCSCC). We aimed to investigate the feasibility of Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in ESCSCC and minimize the occurrence of adverse events associated with the treatment. Methods A dataset comprising MR images was obtained from 289 patients who underwent radical hysterectomy and pelvic lymph node dissection between January 2019 and April 2022. The dataset was randomly divided into two cohorts in a 4:1 ratio.The postoperative radiotherapy options were evaluated according to the Peter/Sedlis standard. We extracted clinical features, as well as intratumoral and peritumoral radiomic features, using the least absolute shrinkage and selection operator (LASSO) regression. We constructed the Clinical Signature (Clinic_Sig), Radiomics Signature (Rad_Sig) and the Deep Transformer Learning Signature (DTL_Sig). Additionally, we fused the Rad_Sig with the DTL_Sig to create the Deep Learning Radiomic Signature (DLR_Sig). We evaluated the prediction performance of the models using the Area Under the Curve (AUC), calibration curve, and Decision Curve Analysis (DCA). Results The DLR_Sig showed a high level of accuracy and predictive capability, as demonstrated by the area under the curve (AUC) of 0.98(95% CI: 0.97–0.99) for the training cohort and 0.79(95% CI: 0.67–0.90) for the test cohort. In addition, the Hosmer-Lemeshow test, which provided p-values of 0.87 for the training cohort and 0.15 for the test cohort, respectively, indicated a good fit. DeLong test showed that the predictive effectiveness of DLR_Sig was significantly better than that of the Clinic_Sig(P

The construction of dams impedes energy exchange and material circulation in rivers, and the operation of hydropower stations negatively impacts the function of river ecosystems. Countries across the globe have implemented various fish protection countermeasures and conducted many associated hydraulic and ecological studies to mitigate the impact of hydropower development on fish survival, maintain the abundance and diversity of fish populations, and restore riverine and lacustrine fish migration routes and habitats. Remarkably, wild fish generally migrate in groups, whereas most contemporary studies on fish passage facilities focus on individual fish. Hence, the behavioral characteristics of fish schools are worth investigating. Given that schooling behavior is a pervasive feature of fish communities and plays an essential role in dealing with potential risks, improving self-adaptation, expanding resilience, and enhancing population sustainability, this study concentrated on investigating its effect on upstream fish migration and decoding the internal mechanism. We conducted an experiment targeting grass carp (Ctenopharyngodon idella) and silver carp (Hypophthalmichthys molitrix), two economically important freshwater fish species in China, on a non-uniform flow ground with low turbulence. The experiment quantified the impact of fish schooling on their ability to overcome flow barriers by examining the ascending sustainability and swimming performance of five-fish groups and one-fish groups at three flow velocity levels (0.25–0.50 m/s, 0.30–0.60 m/s, and 0.35–0.70 m/s) utilizing a novel index system. The new index system employed nondimensionalized ascending sustainability, first-attempt endurance, and first-attempt ascending energy consumption to indicate the fish's persistent ascending ability, ascending efficiency, and ascending energy cost, respectively. Finally, the ascending trajectory of fish was investigated simultaneously to determine the distribution of trajectory and the distribution of the hydrodynamic force factor at the trajectory points. It could be concluded that (1) the influence of schooling behavior on ascending fish behavior was related to the ascending sustainability of individual fish. Schooling behavior significantly increased the ascending sustainability while producing no specific impact on the ascending efficiency at low-level individual ascending sustainability. Inversely, schooling behavior significantly decreased the ascending efficiency while having no particular effect on high-level individual ascending sustainability. Moreover, the contribution of schooling behavior to the ascending sustainability of grass carp varied with the flow velocity, as the ascending sustainability of the five-fish groups was significantly higher (P = 0.030) and lower (P = 0.048) than the one-fish group at the velocity levels of 0.30–0.60 m/s and 0.35–0.70 m/s, respectively. In contrast, schooling behavior holistically improved the upward swimming of silver carp, significantly increasing the ascending sustainability of the five-fish groups at velocity levels of 0.30–0.60 m/s (P = 0.004) and 0.35–0.70 m/s (P < 0.001). (2) The endurance of the first attempt in the juvenile grass carp group was significantly higher than that of the single fish at the velocity levels of 0.25–0.50 m/s (P < 0.001) and 0.35–0.70 m/s (P = 0.005), while the endurance of the first attempt in the juvenile silver carp group was significantly higher than that of the single fish at the velocity levels of 0.25–0.50 m/s (P < 0.001) and 0.30–0.60 m/s (P = 0.005). The endurance of the first attempt in juvenile silver carp decreased significantly in schools (P < 0.001) solely at the velocity level of 0.35–0.70 m/s. In addition, the flow velocity generally increased the first-attempt endurance and cumulative energy consumption of individual and grouped fish. However, since the burst-coast swimming mode forced on juvenile silver carp in high-velocity conditions significantly improved ascending efficiency, the endurance of the first attempt initially increased and then decreased with the flow velocity. (3) Schooling behavior enabled grass carp to swim with less energy and significantly lowered their energy cost at the velocity level of 0.25–0.50 m/s (P < 0.001), whereas it augmented the accumulated energy consumption of five-fish groups in silver carp and significantly increased their energy cost at the velocity level of 0.25–0.50 m/s (P = 0.050). (4) Collectively, juvenile silver carp could find an ideal ascending trajectory more rapidly than juvenile grass carp. The ascending trajectory of grass carp tended to concentrate at first and then disperse with increasing velocity, whereas the trajectory of silver carp tended to concentrate with increasing velocity. In brief, schooling is an unsubstituted behavior in the upstream migration of fish migrants, motivating and inhibiting the fish's upstream movement performance with its primary effect on locomotion in terms of energy consumption, visual response, and the ability to overcome flow barriers. The findings could improve the design of fish protection measures and provide specific recommendations for the operation of fish passage facilities. For example, when silver carp migrate through a fishway, the additional light source can adequately promote schooling behavior and improve their capacity to overcome flow barriers. When grass carp migrate through a fish passage, the additional light source in the rest pond of the fishway can effectively promote schooling behavior, thereby preventing the return of fish owing to a lack of ascending sustainability. Considering that the hydrodynamic environment of natural waters and the size of fish groups are highly complex and variable, the schooling behavior characteristics of fish groups in different water flow environments should be investigated in future research to enrich the database for the planning and implementation of fish protection engineering measures.

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection has many neurological manifestations, and its effects on the nervous system are increasingly recognized. There has been no systematic analysis of electroencephalography (EEG) characteristics in children exhibiting neurological symptoms of Coronavirus disease 2019 (COVID-19). The primary aim of this study was to describe the EEG characteristics caused by COVID-19 infection in children who were showing neurological symptoms and to assess the relationship between COVID-19-related EEG changes and clinical features in these children. Method: This study included 125 pediatric patients infected with SARS-CoV2 and showing neurological symptoms, and their continuous EEG was recorded. In addition, the demographic and clinical characteristics of these patients were analyzed and the correlation between the two was investigated. Results: Abnormal EEG findings were detected in 31.20% (N = 39) of the patients. Abnormal discharges (43.59%) were the most common EEG abnormalities, followed by background abnormalities (41.03%). The proportion of patients diagnosed with febrile seizure was higher in the normal EEG group than in the abnormal EEG group (P = 0.002), while the opposite was true for epilepsy and encephalitis/encephalopathy (P = 0.016 and P = 0.003, respectively). The independent associated factors of abnormal EEG were age and total length of stay (P