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Acute ischemic stroke represents a critical, life-threatening condition affecting the central nervous system. Intravenous thrombolysis with tissue plasminogen activator (tPA) remains a cornerstone for achieving vascular recanalization in such patients; however, its therapeutic utility is limited, with only approximately 10% of patients benefiting due to the narrow therapeutic window and significant risk of hemorrhagic transformation. Enhancing the efficacy of tPA thrombolysis is therefore imperative. Neutrophils have been identified as key modulators of thrombolytic outcomes, interacting with tPA post-stroke to influence treatment effectiveness. The binding of tPA to low-density lipoprotein receptor-related protein 1 (LRP-1) on neutrophil surfaces induces degranulation and formation of neutrophil extracellular traps (NETs). Conversely, neutrophils impede the thrombolytic action of tPA by obstructing its interaction with fibrin and activating platelets. These findings suggest that targeting neutrophils may hold promise for improving thrombolysis outcomes. This review explores the role of neutrophils in tPA-mediated thrombolysis following acute ischemic stroke, examines neutrophil-associated biomarkers, and outlines potential strategies for enhancing tPA efficacy.

ObjectivesThe emergency response to the COVID-19 pandemic may disrupt hospital management activities of antimicrobial resistance (AMR). This study aimed to determine the changing AMR trend over the period in China when stringent COVID-19 response measures were implemented.MethodsThis retrospective study was conducted in a designated hospital for COVID-19 patients in Guangzhou, China from April 2018 to September 2021. The prevalence of 13 antimicrobial-resistant bacteria was compared before and after the COVID-19 responses through Chi-square tests. Interrupted time series (ITS) models on the weekly prevalence of AMR were established to determine the changing trend. Controlled ITS models were performed to compare the differences between subgroups.ResultsA total of 10,134 isolates over 1,265 days were collected. And antimicrobial-resistant strains presented in 38.6% of the testing isolates. The weekly AMR prevalence decreased by 0.29 percentage point (95% CI [0.05–0.80]) after antimicrobial stewardship (AMS) policy, despite an increase in the prevalence of penicillin-resistant Streptococcus pneumoniae (from 0/43 to 15/43, p

Periodontitis, delineated by the destruction of structures that support teeth, is predominantly propelled by intricate immune responses. Immunomodulatory treatments offer considerable promise for the management of this ailment; however, the modulation of the periodontal immune microenvironment to facilitate tissue regeneration presents a substantial biomedical challenge. Herein, our study investigates the role of Wilms’ tumor 1-associating protein (WTAP), a critical m6A methyltransferase, in the immunomodulation of periodontitis and assesses its viability as a therapeutic target. We observed heightened expression of WTAP in macrophages extracted from gingival tissues impacted by periodontitis, with a strong association with M1 polarization. Via loss-of-function experiments, we demonstrated that diminishing WTAP expression precipitates a transition from M1 to M2 macrophage phenotypes amidst inflammatory conditions, thus improving the periodontal immune landscape. Further, RNA sequencing and indirect co-culture assays indicated that suppressing of WTAP expression modulates osteoimmune responses and enhances the osteogenic differentiation of bone marrow stromal cells. The local deployment of adeno-associated virus-shWTAP in murine models of periodontitis robustly validated the therapeutic promise of targeting WTAP in this disease. Collectively, our findings highlight the crucial role of WTAP in orchestrating macrophage-mediated osteoimmune responses and tissue regeneration in periodontitis, proposing novel avenues for immunotherapeutic interventions in its treatment.

BackgroundAnxiety, depression, and sleep problems are prevalent comorbid mental disorders among university students. The World Health Organization (WHO) emphasized a mental health promotion objective, recommending the consideration of protective health-promoting factors in strategies aimed at preventing mental disorders. Integrating theoretically significant constructs (such as protective factors) enhances our comprehension of the intricate mechanisms that underpin mental disorders. This study employed network analysis to first identify core and bridge symptoms within comorbid mental disorders and then explore how health-promoting lifestyles (HPLs) were associated with these disorders. The ultimate goal is to offer health promotion recommendations to enhance students’ quality of life.MethodsA total of 3,896 qualified university students participated in this study. Anxiety, depression, sleep problems, and HPLs were assessed using the GAD-7, PHQ-9, PSQI, and HPLP-II scales. A Gaussian Graphical Model was used to construct the networks. The Network Comparison Test was applied to determine whether the associations between HPLs and comorbid symptoms vary by gender, educational level, family sibling, and mental health status.ResultsLow energy (PHQ4) had the highest strength centrality, followed by Daytime dysfunction (PSQI7) and Trouble relaxing (GAD4). Five bridge symptoms were identified: Daytime dysfunction (PSQI7), Self-harm even suicide (PHQ9), Sad mood (PHQ2), Low energy (PHQ4), and Feeling afraid (GAD7). Regarding protective HPLs, Physical activity, Spiritual growth, and Stress management generally emerged as the top three central mental health-promoting behaviors.ConclusionTargeting core and bridge symptoms with timely and appropriate interventions can alleviate anxiety, depression, and sleep problems in this population. Moreover, promoting physical activity, fostering spiritual growth, and managing stress are likely to significantly enhance the overall mental health of university students.

Manhole cover damage poses significant threats to road safety and infrastructure integrity, necessitating timely detection and repair. To address this, we introduce an enhanced YOLOX model integrated with ECA (High Efficiency Channel Attention) modules for real-time monitoring using car recorder footage. Our method categorizes manhole cover conditions into three distinct states: normal, broken, and down. By in-corporating ECA-Net before the decoupling head of the YOLOX model, we significantly boost its channel feature extraction abilities, critical for distinguishing subtle changes in cover conditions. Experimental results reveal a substantial increase in mean Average Precision (mAP) to 93.91%, with a notable AP of 92.2% achieved in the detection of the ‘down’ state, historically the most challenging category. Despite the en-hancements, our model maintains a high detection speed, processing at an average rate only five images per second slower than the original YOLOX model. Comparative analyses against leading detection models, in-cluding Faster R-CNN, SSD, and CenterNet, underscore the superiority of our approach in terms of both accuracy and speed, particularly in accurately recognizing the ‘down’ condition of manhole covers. This in-novative model provides a reliable tool for swiftly identifying damaged manhole covers and their precise lo-cations, enabling prompt maintenance actions. By improving the monitoring efficiency of urban infrastruc-ture, our solution contributes to enhanced road safety and the advancement of smart city technologies.

IntroductionThe development of advanced sewage technologies empowers the industry to produce high-quality recycled water, which greatly influences human’s life and health. Thus, this study investigates the mechanism of individuals’ adoption of recycled water from the technology adoption perspective.MethodsEmploying the mixed method of structural equation modeling and artificial neural network analysis, we examined a research model developed from the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) framework. To examine the research model, this study employs a leading web-survey company (Sojump) to collect 308 valid samples from the residents in mainland China.ResultsThe structural equation modeling results verified the associations between the six predictors (performance expectancy, effort expectancy, social influence, facilitating conditions, environmental motivation, and price value), individuals’ cognitive and emotional attitudes, and acceptance intention. The artificial neural network analysis validates and complements the structural equation modeling results by unveiling the importance rank of the significant determinants of the acceptance decisions.DiscussionThe study provides theoretical implications for recycled water research and useful insights for practitioners and policymakers to reduce the environmental hazards of water scarcity.

BackgroundSerum 25-hydroxyvitamin D level is associated with erectile dysfunction (ED) in observational studies. However, whether there is a causal association between them remains uncertain.ObjectiveConduct a two-sample Mendelian randomization (MR) analysis to investigate the causal effect between serum 25-hydroxyvitamin D level and ED risk.MethodGenome-wide association study (GWAS) data of serum 25-hydroxyvitamin D levels comprising 6,896,093 single nucleotide polymorphisms (SNP) from 496,949 people of European ancestry were regarded as exposure for the MR analysis. Additional GWAS data involving 9,310,196 SNPs of 6,175 European ED cases and 217,630 controls were used as outcome data. The MR-Egger, inverse variance weighted (IVW) method, weighted median, simple mode, and weighted mode were employed to evaluate causal effects, among which IVW was the primary MR analysis method. The stability of the MR analysis results was confirmed by a heterogeneity test, a horizontal pleiotropy test, and the leave-one-out method.ResultThere were 103 SNPs utilized as instrumental variables (p < 5 × 10−8). The results of MR analysis showed no causal effects of serum 25(OH) D concentration on ED risks (IVW; OR = 0.9516, 95% CI = 0.7994 to 1.1328, p = 0.5772). There was no heterogeneity and pleiotropy in the statistical models.ConclusionThe present MR study did not support a causal association for genetically predicted serum 25-hydroxyvitamin D concentration in the risk of ED in individuals of European descent.

In serving college students with mental disorders, on-campus mental health professionals have been lacking integrative theoretical frameworks to guide their missions of prevention, remedy, and development facilitation. In the current paper, we propose the positive clinical psychology as a theoretically and practically valuable framework for these missions by narratively reviewing the preventive, remedial, and developmental mechanisms derived from the theory and summarizing the most recent empirical evidence that supports each mechanism. We further discuss why and how these mechanisms and findings can be applied to on-campus mental health services to facilitate the resilience and optimal development of college students with mental disorders. Particularly, the use of resilience-focused and strength-based intervention strategies are promoted for services.

BackgroundDental implants have become an increasingly popular option for replacing missing teeth, and the prevalence of peri-implantitis has also increased, which is expected to become a public health problem worldwide and cause high economic and health burdens. This scenario highlights the need for new therapeutic options to treat peri-implantitis.MethodsIn this study, we proposed a novel sono-responsive antibacterial nanosystem co-loaded with metformin (Met) and bone morphogenetic protein-2 (BMP-2) to promote efficacy in treating peri-implantitis. We introduced the zeolitic imidazolate framework-8 (ZIF-8) as a carrier for hematoporphyrin monomethyl ether (HMME) to enhance the antibacterial effect of sonodynamic antibacterial therapy and tested its reactive oxygen species (ROS) production efficiency and bactericidal effect in vitro. Afterward, HMME-loaded ZIF-8, BMP-2-loaded polylactic acid-glycolic acid (PLGA), and Met were incorporated into gelatin methacryloyl (GelMA) hydrogels to form HMME@ZIF-8/Met/BMP-2@PLGA/GelMA composite hydrogels, and the biocompatibility of which was determined in vitro and in vivo. A bacterial-induced peri-implantitis model in the maxilla of rats was established to detect the effects of the composite hydrogels with adjunctive use of ultrasound on regulating inflammation and promoting bone tissue repair in vivo.ResultsThe results indicated that HMME@ZIF-8 with ultrasound stimulation demonstrated more better ROS production efficiency and antimicrobial efficacy. The composite hydrogels had good biocompatibility. Ultrasound-assisted application of the composite hydrogels reduced the release of the inflammatory factors IL-6 and TNF-α and reduced bone loss around the implant in rats with bacterial-induced peri-implantitis.ConclusionOur observations suggest that HMME@ZIF-8 may be a new good sonosensitizer material for sonodynamic antibacterial therapy. The use of HMME@ZIF-8/Met/BMP-2@PLGA/GelMA composite hydrogels in combination with ultrasound can provide a novel option for treating peri-implantitis in the future.

BackgroundA patient-centered dialysis treatment option requires an understanding of patient preferences for alternative vascular accesses and nephrologists often face difficulties when recommending vascular access to end-stage kidney disease (ESKD) patients. We aimed to quantify the relative importance of various vascular access characteristics to patients, healthcare providers and general population, and how they affect acceptability for patients and healthcare providers.MethodsIn a discrete choice experiment, patients with maintenance hemodialysis (MHD), healthcare providers, and individuals from the general population were invited to respond to a series of hypothetical vascular access scenarios that differed in five attributes: cumulative patency, infection rate, thrombosis rate, cost, and time to maturation. We estimated the respondents’ preference heterogeneity and relative importance of the attributes with a mixed logit model (MXL) and predicted the willingness to pay (WTP) of respondents via a multinomial logit model (MNL).ResultsHealthcare providers (n = 316) and the general population (n = 268) exhibited a favorable inclination toward longer cumulative patency, lower access infection rate and lower access thrombosis rate. In contrast, the patients (n = 253) showed a preference for a 3-year cumulative patency, 8% access infection rate, 35% access thrombosis rate and 1.5 access maturity time, with only the 3-year cumulative patency reaching statistical significance. Among the three respondent groups, the general population found cumulative patency less important than healthcare providers and patients did. Patients demonstrated the highest WTP for cumulative patency, indicating a willingness to pay an extra RMB$24,720(US$3,708) for each additional year of patency time.ConclusionPatients and healthcare providers had a strong preference for vascular access with superior patency. While the general population preferred vascular access with lower thrombosis rates. These results indicate that most patients prefer autogenous arteriovenous fistula (AVF) as an appropriate choice for vascular access due to its superior patency and lower complications than other vascular access types.

BackgroundIn recent years, immunotherapy has been emerging as a promising alternative therapeutic method for cancer patients, offering potential benefits. The expression of PD-L1 by tumors can inhibit the T-cell response to the tumor and allow the tumor to evade immune surveillance. To address this issue, cancer immunotherapy has shown promise in disrupting the interaction between PD-L1 and its ligand PD-1.MethodsWe used mirror-image phage display technology in our experiment to screen and determine PD-L1 specific affinity peptides (PPL-C). Using CT26 cells, we established a transplanted mouse tumor model to evaluate the inhibitory effects of PPL-C on tumor growth in vivo. We also demonstrated that PPL-C inhibited the differentiation of T regulatory cells (Tregs) and regulated the production of cytokines.ResultsIn vitro, PPL-C has a strong affinity for PD-L1, with a binding rate of 0.75 μM. An activation assay using T cells and mixed lymphocytes demonstrated that PPL-C inhibits the interaction between PD-1 and PD-L1. PPL-C or an anti-PD-L1 antibody significantly reduced the rate of tumor mass development in mice compared to those given a control peptide (78% versus 77%, respectively). The results of this study demonstrate that PPL-C prevents or retards tumor growth. Further, immunotherapy with PPL-C enhances lymphocyte cytotoxicity and promotes proliferation in CT26-bearing mice.ConclusionPPL-C exhibited antitumor and immunoregulatory properties in the colon cancer. Therefore, PPL-C peptides of low molecular weight could serve as effective cancer immunotherapy.

Mycoplasma pneumoniae (M. pneumoniae) is a well-recognized pathogen primarily associated with respiratory tract infections. However, in rare instances, it can lead to extrapulmonary manifestations, including myocarditis. We present a case of a 15-year-old male who developed fulminant myocarditis, cardiogenic shock, and cardiac electrical storm attributed to M. pneumoniae infection. He underwent a combination of intra-aortic balloon pump (IABP) and veno-arterial extracorporeal membrane oxygenation (VA-ECMO) for cardiac support, ultimately surviving despite the intracardiac thrombus formation and embolic stroke. Following comprehensive treatment and rehabilitation, he was discharged in stable condition. This case underscores the importance of considering atypical pathogens as potential etiological factors in patients presenting with cardiac complications, especially in the adolescents. It also emphasizes the need for clinical vigilance and effective support for potential cardiac complications arising from M. pneumoniae infection.

IntroductionTo support rigorous evaluation across a national portfolio of grants, the United States Department of Veterans Affairs (VA) Office of Rural Health (ORH) adopted an analytic framework to guide their grantees' evaluation of initiatives that reach rural veterans and to standardize the reporting of outcomes and impacts. Advance Care Planning via Group Visits (ACP-GV), one of ORH's Enterprise-Wide Initiatives, also followed the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. ACP-GV is a national patient-centered intervention delivered in a large, veterans integrated healthcare system. This manuscript describes how RE-AIM was used to evaluate this national program and lessons learned from ORH's annual reporting feedback to ACP-GV on their use of the framework to describe evaluation impacts.MethodsWe used patient, provider, and site-level administrative health care data from the VA Corporate Data Warehouse and national program management databases for federal fiscal years (FY) spanning October 1, 2018–September 30, 2023. Measures included cumulative and past FY metrics developed to assess program impacts.ResultsRE-AIM constructs included the following cumulative and annual program evaluation results. ACP-GV reached 54,167 unique veterans, including 19,032 unique rural veterans between FY 2018 to FY 2023. During FY 2023, implementation adherence to the ACP-GV model was noted in 91.7% of program completers, with 55% of these completers reporting a knowledge increase and 14% reporting a substantial knowledge increase (effectiveness). As of FY 2023, 66 ACP-GV sites were active, and 1,556 VA staff were trained in the intervention (adoption). Of the 66 active sites in FY 2023, 27 were sites previously funded by ORH and continued to offer ACP-GV after the conclusion of three years of seed funding (maintenance).DiscussionLessons learned developing RE-AIM metrics collaboratively with program developers, implementers, and evaluators allowed for a balance of clinical and scientific input in decision-making, while the ORH annual reporting feedback provided specificity and emphasis for including both cumulative, annual, and rural specific metrics. ACP-GV's use of RE-AIM metrics is a key step towards improving rural veteran health outcomes and describing real world program impacts.

The stable operation of a power supply system is inseparable from the work of detecting defects in transmission lines. However, the insulator defect detection model based on deep learning is widely used in wire inspection work. Therefore, this paper proposes an improved YOLOv5s insulator defect detection model in order to solve the problems of insufficient training data and low recognition accuracy of the target detection model in the real-time detection of small target insulator defects. To expand and enhance the training data, experiments were conducted using the addition of noise and random black blocks. The spatial and channel weight coefficients were obtained by adding an attention mechanism (Convolutional Block Attention Module, CBAM), and the dimensions of the input feature maps were transformed to enhance the model’s ability to extract and fuse small target defect features. Experiments show that with Faster RCNN, YOLOv3, SSD and YOLOv4 comparison experiments verified that the algorithm achieves 97.38% detection accuracy for insulators and 93.32% detection accuracy for small target insulator defects with a fast detection speed, which is a better solution to the problem of detecting insulator defects with too small a proportion in the image.

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts’ Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.

Eight new 12,8-eudesmanolide sesquiterpenes, eutypellaolides A–H (1–8), and two new eudesmane-type sesquiterpenes, eutypellaolides I–J (9–10), along with four known 12,8-eudesmanolide compounds 11–14, were isolated from the culture extract of the polar fungus Eutypella sp. D-1 by one strain many compounds (OSMAC) approach. The structures of these compounds were determined through comprehensive spectroscopic data and experimental and calculated ECD analysis. Antibacterial, immunosuppressive, and PTP1B inhibition activities of these compounds were evaluated. Compounds 1 and 11 exhibited strong inhibitory activities against Bacillus subtilis and Staphylococcus aureus, with each showing an MIC value of 2 μg/mL. Compound 9 displayed weak immunosuppressive activity against ConA-induced T-cell proliferation with an inhibitory rate of 61.7% at a concentration of 19.8 μM. Compounds 5, 11, and 14 exhibited weak PTP1B inhibition activities with IC50 values of 44.8, 43.2, and 49.5 μM, respectively.

Diabetic kidney disease (DKD) is a significant contributor to end-stage renal disease worldwide. Despite extensive research, the exact mechanisms responsible for its development remain incompletely understood. Notably, patients with diabetes and impaired kidney function exhibit a hypercoagulable state characterized by elevated levels of coagulation molecules in their plasma. Recent studies propose that coagulation molecules such as thrombin, fibrinogen, and platelets are interconnected with the complement system, giving rise to an inflammatory response that potentially accelerates the progression of DKD. Remarkably, investigations have shown that inhibiting the coagulation system may protect the kidneys in various animal models and clinical trials, suggesting that these systems could serve as promising therapeutic targets for DKD. This review aims to shed light on the underlying connections between coagulation and complement systems and their involvement in the advancement of DKD.

ImportanceEarly neurological deterioration (END) is a critical complication in acute ischemic stroke (AIS) patients receiving intravenous thrombolysis (IVT), with a need for reliable prediction tools to guide clinical interventions.ObjectiveThis study aimed to develop and validate a rating scale, utilizing clinical variables and multisystem laboratory evaluation, to predict END after IVT.Design, setting, and participantsThe Clinical Trial of Revascularization Treatment for Acute Ischemic Stroke (TRAIS) cohort enrolled consecutive AIS patients from 14 stroke centers in China (Jan 2018 to Jun 2022).OutcomesEND defined as NIHSS score increase >4 points or death within 24 h of stroke onset.Results1,213 patients (751 in the derivation cohort, 462 in the validation cohort) were included. The CNS-LAND score, a 9-point scale comprising seven variables (CK-MB, NIHSS score, systolic blood pressure, LDH, ALT, neutrophil, and D-dimer), demonstrated excellent differentiation of END (derivation cohort C statistic: 0.862; 95% CI: 0.796–0.928) and successful external validation (validation cohort C statistic: 0.851; 95% CI: 0.814–0.882). Risk stratification showed END risks of 2.1% vs. 29.5% (derivation cohort) and 2.6% vs. 31.2% (validation cohort) for scores 0–3 and 4–9, respectively.ConclusionCNS-LAND score is a reliable predictor of END risk in AIS patients receiving IVT.

Plants are widely grown around the world and have high economic benefits. plant leaf diseases not only negatively affect the healthy growth and development of plants, but also have a negative impact on the environment. While traditional manual methods of identifying plant pests and diseases are costly, inefficient and inaccurate, computer vision technologies can avoid these drawbacks and also achieve shorter control times and associated cost reductions. The focusing mechanism of Transformer-based models(such as Visual Transformer) improves image interpretability and enhances the achievements of convolutional neural network (CNN) in image recognition, but Visual Transformer(ViT) performs poorly on small and medium-sized datasets. Therefore, in this paper, we propose a new hybrid architecture named FOTCA, which uses Transformer architecture based on adaptive Fourier Neural Operators(AFNO) to extract the global features in advance, and further down sampling by convolutional kernel to extract local features in a hybrid manner. To avoid the poor performance of Transformer-based architecture on small datasets, we adopt the idea of migration learning to make the model have good scientific generalization on OOD (Out-of-Distribution) samples to improve the model’s overall understanding of images. In further experiments, Focal loss and hybrid architecture can greatly improve the convergence speed and recognition accuracy of the model in ablation experiments compared with traditional models. The model proposed in this paper has the best performance with an average recognition accuracy of 99.8% and an F1-score of 0.9931. It is sufficient for deployment in plant leaf disease image recognition.

Surrounding rock pressure is a crucial parameter in tunnel engineering design, and its calculation is a classic challenge. The surrounding rock pressure is influenced by geostress, but existing calculation methods often do not take into account the effect of geostress. In this paper, finite discrete element method (FDEM) is used to study the design values of tunnel surrounding rock pressure under different geostress fields. Firstly, a set of calibration methods for input parameters of FDEM is summarized based on previous studies. Then, taking a high-speed railway tunnel in IV-level surrounding rock as an example, the excavation-induced failure process of the tunnel under the influence of gravity stress field and geostress field is simulated using the FDEM. By comparing the results with those of the finite element method simulation, the rationality of applying FDEM to the simulation of tunnel excavation is demonstrated. Next, a calculation method of surrounding rock pressure design value based on FDEM is proposed by introducing tunnel displacement criterion, and its validity is verified by comparing with the results of the theoretical formula. Finally, the surrounding rock pressure design values under different geostress are analyzed by using this calculation method. The results show that there are significant differences between the gravity stress field and the geostress field in the maximum principal stress distribution, failure zone form and crack distribution. The geostress directly influences the design value of surrounding rock pressure. As the geostress varies from 4MPa to 12MPa, the corresponding design value increases from 49KPa to 1,288KPa, illustrating a quadratic relationship between them. With the corresponding design support force, the displacement of the surrounding rock is controlled within a reasonable range, ensuring the stability of the tunnel is maintained.

BackgroundPreterm birth (PTB) has been linked with ambient particulate matter (PM) exposure. However, data are limited between physiological development of PTB and PM exposure.MethodsTrimester and season-specific PM exposure including PM2.5 and PM10 was collected from Jiaxing between January 2014 and December 2017. Information about parents and 3,054 PTB (gestational age

BackgroundPrior observational studies have found an association between kidney function and cardiovascular diseases (CVDs). However, these studies did not investigate causality. Therefore, the aim of this study is to examine the causal relationship between kidney function and CVDs.MethodsWe utilized data from the eICU Collaborative Research Database (eICU-CRD) from the years 2014-2015 to evaluate the observational association between renal failure (RF) and CVDs. To investigate the causal effects of kidney function (estimated glomerular filtration rate [eGFR] and chronic kidney disease [CKD]) and CVDs (including atrial fibrillation [AF], coronary artery disease [CAD], heart failure [HF], any stroke [AS], and any ischemic stroke [AIS]), we conducted a two-sample bidirectional Mendelian randomization (MR) analysis.ResultsIn the observational analysis, a total of 157,883 patients were included. After adjusting for potential confounding factors, there was no significant association between baseline RF and an increased risk of developing CVDs during hospitalization [adjusted odds ratio (OR): 1.056, 95% confidence interval (CI): 0.993 to 1.123, P = 0.083]. Conversely, baseline CVDs was significantly associated with an increased risk of developing RF during hospitalization (adjusted OR: 1.189, 95% CI: 1.139 to 1.240, P < 0.001). In the MR analysis, genetically predicted AF was associated with an increased risk of CKD (OR: 1.050, 95% CI: 1.016 to 1.085, P = 0.004). HF was correlated with lower eGFR (β: -0.056, 95% CI: -0.090 to -0.022, P = 0.001). A genetic susceptibility for AS and AIS was linked to lower eGFR (β: -0.057, 95% CI: -0.079 to -0.036, P < 0.001; β: -0.029, 95% CI: -0.050 to -0.009, P = 0.005; respectively) and a higher risk of CKD (OR: 1.332, 95% CI: 1.162 to 1.528, P < 0.001; OR: 1.197, 95% CI: 1.023 to 1.400, P = 0.025; respectively). Regarding the reverse direction analysis, there was insufficient evidence to prove the causal effects of kidney function on CVDs. Outcomes remained consistent in sensitivity analyses.ConclusionOur study provides evidence for causal effects of CVDs on kidney function. However, the evidence to support the causal effects of kidney function on CVDs is currently insufficient. Further mechanistic studies are required to determine the causality.

With the increasing demand for biomarker detection in wearable electronic devices, flexible biosensors have garnered significant attention. Additionally, graphene field-effect transistors (GFETs) have emerged as key components for constructing biosensors, owing to their high sensitivity, multifunctionality, rapid response, and low cost. Leveraging the advantages of flexible substrates, such as biocompatibility, adaptability to complex environments, and fabrication flexibility, flexible GFET sensors exhibit promising prospects in detecting various biomarkers. This review provides a concise summary of design strategies for flexible GFET biosensors, including non-encapsulated gate without dielectric layer coverage and external gate designs. Furthermore, notable advancements in sensing applications of biomolecules, such as proteins, glucose, and ions, are highlighted. Finally, we discuss the future challenges and prospects in this field, aiming to inspire researchers to address these issues in their further investigations.

The accurate prediction of electricity prices has great significance for the power system and the electricity market, regional electricity prices are difficult to predict due to congestion issues in regional transmission lines. A regional electricity price prediction framework is proposed based on an adaptive spatial–temporal convolutional network. The proposed framework is expected to better explore regional electricity prices’ spatial–temporal dynamic characteristics in the electricity spot market and improve the predictive accuracy of regional electricity prices. First, different areas of the electricity market are regarded as nodes. Then, each area’s historical electricity price data are used as the corresponding node’s characteristic information and constructed into a graph. Finally, a graph containing the spatial–temporal information on electricity prices is input to the adaptive spatial–temporal prediction framework to predict the regional electricity price. Operational data from the Australian electricity market are adopted, and the prediction results from the proposed adaptive spatial–temporal prediction framework are compared with those of existing methods. The numerical example results show that the predictive accuracy of the proposed framework is better than the existing baseline and similar methods. In the twelve-step forecast example in this paper, considering the spatial dependence of the spot electricity price can improve the forecast accuracy by at least 10.3% and up to 19.8%.

Cardiovascular disease (CVD) is the leading cause of death worldwide. Pyroptosis is a unique kind of programmed cell death that varies from apoptosis and necrosis morphologically, mechanistically, and pathophysiologically. Long non-coding RNAs (LncRNAs) are thought to be promising biomarkers and therapeutic targets for the diagnosis and treatment of a variety of diseases, including cardiovascular disease. Recent research has demonstrated that lncRNA-mediated pyroptosis has significance in CVD and that pyroptosis-related lncRNAs may be potential targets for the prevention and treatment of specific CVDs such as diabetic cardiomyopathy (DCM), atherosclerosis (AS), and myocardial infarction (MI). In this paper, we collected previous research on lncRNA-mediated pyroptosis and investigated its pathophysiological significance in several cardiovascular illnesses. Interestingly, certain cardiovascular disease models and therapeutic medications are also under the control of lncRNa-mediated pyroptosis regulation, which may aid in the identification of new diagnostic and therapy targets. The discovery of pyroptosis-related lncRNAs is critical for understanding the etiology of CVD and may lead to novel targets and strategies for prevention and therapy.

BackgroundTo explore the safety, efficacy, and survival benefits of laparoscopic digestive tract nutrition reconstruction (LDTNR) combined with conversion therapy in patients with unresectable gastric cancer with obstruction.MethodsThe clinical data of patients with unresectable gastric cancer with obstruction who was treated in Fujian Provincial Hospital from January 2016 to December 2019, were analyzed. LDTNR was performed according to the type and degree of obstruction. All patients received the epirubicin + oxaliplatin + capecitabine regimen as conversion therapy.ResultsThirty-seven patients with unresectable obstructive gastric cancer underwent LDTNR, while thirty-three patients received chemotherapy only. In LDTNR group patients, the proportion of nutritional risks gradually decreased, the rate of severe malnutrition decreased, the proportion of neutrophil-lymphocyte ratio (NLR)

Ischemic stroke is a highly complex systemic disease characterized by intricate interactions between the brain and gastrointestinal tract. While our current understanding of these interactions primarily stems from experimental models, their relevance to human stroke outcomes is of considerable interest. After stroke, bidirectional communication between the brain and gastrointestinal tract initiates changes in the gastrointestinal microenvironment. These changes involve the activation of gastrointestinal immunity, disruption of the gastrointestinal barrier, and alterations in gastrointestinal microbiota. Importantly, experimental evidence suggests that these alterations facilitate the migration of gastrointestinal immune cells and cytokines across the damaged blood-brain barrier, ultimately infiltrating the ischemic brain. Although the characterization of these phenomena in humans is still limited, recognizing the significance of the brain-gastrointestinal crosstalk after stroke offers potential avenues for therapeutic intervention. By targeting the mutually reinforcing processes between the brain and gastrointestinal tract, it may be possible to improve the prognosis of ischemic stroke. Further investigation is warranted to elucidate the clinical relevance and translational potential of these findings.

IntroductionA rare pathogen of Infective Endocarditis (IE), the Abiotrophia defectiva, has been known to trigger life-threatening complications. The case discussed here is of a teenager with brain infarction and subarachnoid hemorrhage caused by IE due to A. defectiva.Case reportA 15-year-old girl with movement disorders involving the left limbs and intermittent fevers was admitted to the hospital. A head CT scan revealed cerebral infarction in the right basal ganglia and subarachnoid hemorrhage. Moreover, vegetation on the mitral valve were confirmed by echocardiography. The blood cultures were found to be positive for Gram-positive streptococcus and identified by Vitek mass spectrometry as A. defectiva. She was prescribed vancomycin antibacterial therapy and underwent a surgical mitral valve replacement.ConclusionThis case is suggestive of the fact that A. defectiva is a rare but crucial pathogen of IE-associated stroke. Obtaining early blood cultures and using microbial mass spectrometry could help achieve an accurate diagnosis. Moreover, reasonable anti-infective medications and surgical interventions need to be combined to avoid and/or manage severe complications.

BackgroundIn ST-segment elevation myocardial infarction (STEMI) with the restoration of TIMI 3 flow by percutaneous coronary intervention (PCI), visually defined microvascular obstruction (MVO) was shown to be the predictor of poor prognosis, but not an ideal risk stratification method. We intend to introduce deep neural network (DNN) assisted myocardial contrast echocardiography (MCE) quantitative analysis and propose a better risk stratification model.Methods194 STEMI patients with successful primary PCI with at least 6 months follow-up were included. MCE was performed within 48 h after PCI. The major adverse cardiovascular events (MACE) were defined as cardiac death, congestive heart failure, reinfarction, stroke, and recurrent angina. The perfusion parameters were derived from a DNN-based myocardial segmentation framework. Three patterns of visual microvascular perfusion (MVP) qualitative analysis: normal, delay, and MVO. Clinical markers and imaging features, including global longitudinal strain (GLS) were analyzed. A calculator for risk was constructed and validated with bootstrap resampling.ResultsThe time-cost for processing 7,403 MCE frames is 773 s. The correlation coefficients of microvascular blood flow (MBF) were 0.99 to 0.97 for intra-observer and inter-observer variability. 38 patients met MACE in 6-month follow-up. We proposed A risk prediction model based on MBF [HR: 0.93 (0.91–0.95)] in culprit lesion areas and GLS [HR: 0.80 (0.73–0.88)]. At the best risk threshold of 40%, the AUC was 0.95 (sensitivity: 0.84, specificity: 0.94), better than visual MVP method (AUC: 0.70, Sensitivity: 0.89, Specificity: 0.40, IDI: −0.49). The Kaplan-Meier curves showed that the proposed risk prediction model allowed for better risk stratification.ConclusionThe MBF + GLS model allowed more accurate risk stratification of STEMI after PCI than visual qualitative analysis. The DNN-assisted MCE quantitative analysis is an objective, efficient and reproducible method to evaluate microvascular perfusion.

Background: The prominent mitochondrial metabolic changes of the atrium reportedly have significant impact on electrical signals and structural remodeling which play important roles in the occurrence and development of atrial fibrillation (AF). However, the mechanism is not completely known.Objective: This study was aimed to explore the mitochondrial metabolism reprogrammed in AF patients by integrating metabolomics as well as proteomics of human atrium tissues.Methods and Results: Left atrial tissue samples were harvested from 10 non-valvular AF patients and 10 matched samples from healthy donors for transplantation. In metabolomics analysis, 113 metabolites were upregulated and 10 metabolites were downregulated in AF, where multiple pathways related to mitochondrial energy metabolism were enriched. Correlation analysis between the differentially expressed proteins and metabolites identified several hub proteins related to mitochondrial function including Glycerol-3-phosphate dehydrogenase 2 (GPD2), Synemin (SYNM), Plectin (PLEC), with MCC score of 27, 17, 16, respectively, which have the most interactions with the dysregulated metabolites and ranked at the top in network string interactions scored by MCC method. All 330 differentially expressed proteins including 225 upregulated and 105 downregulated molecules were revealed and analyzed, which identified the downregulation of GPD2 (p = 0.02 and FC = 0.77), PLEC (p < 0.001 and FC = 0.71) and SYNM (p = 0.04 and FC = 0.76) in AF patients. Gene Set Variation Analysis (GSEA) showed mitochondrial metabolism-associated pathways including oxidative phosphorylation (NES: −1.73) and ATP biosynthetic process (NES: −2.29), were dramatically diversified in human AF. In GSVA, the expression levels of GPD2, PLEC, and SYNM were demonstrated to be associated with multiple metabolic pathways related to mitochondrial function (e.g., lipid metabolism and AMP activated protein kinase signaling) and cardiac structural and electrical remodeling (e.g., contractile fiber, ion homeostasis), which were proven vital in the development and maintenance of AF.Conclusion: In all, this study provides new insights into understanding the mechanisms of AF progression, especially the reprogramming mitochondrial metabolism, and identifies several genes related to mitochondrial function as novel targets for AF, which may be involved in the occurrence and development of AF.

IntroductionCognitive impairment includes the abnormality of learning, memory and judgment, resulting in severe learning and memory impairment and social activity impairment, which greatly affects the life quality of individuals. However, the specific mechanisms underlying cognitive impairment in different behavioral paradigms remain to be elucidated.MethodsThe study utilized two behavioral paradigms, novel location recognition (NLR) and novel object recognition (NOR), to investigate the brain regions involved in cognitive function. These tests comprised two phases: mice were presented with two identical objects for familiarization during the training phase, and a novel (experiment) or familiar (control) object/location was presented during testing. Immunostaining quantification of c-Fos, an immediate early gene used as a neuronal activity marker, was performed in eight different brain regions after the NLR or NOR test.ResultsThe number of c-Fos-positive cells was significantly higher in the dorsal part of the lateral septal nucleus (LSD) in the NLR and dentate gyrus (DG) in the NOR experiment group than in the control group. We further bilaterally lesioned these regions using excitotoxic ibotenic acid and replenished the damaged areas using an antisense oligonucleotide (ASO) strategy.DiscussionThese data reinforced the importance of LSD and DG in regulating spatial and object recognition memory, respectively. Thus, the study provides insight into the roles of these brain regions and suggests potential intervention targets for impaired spatial and object recognition memory.

Intercultural sensitivity has been regarded as a very important ability necessary for living in multicultural countries and regions. In this research, the quantitative method was used to explore the intercultural sensitivity of university students in Macao, a typical multicultural region in China. By adopting the Intercultural Sensitivity Scale developed by Chen and Starosta and a sociodemographic questionnaire designed by the authors, this study investigated the intercultural sensitivity level of university students in Macao, and explored whether there were any significant differences in student groups in terms of gender, grade, program (language of instruction adopted in a major), courses related to intercultural communication, overseas experience, and foreign language proficiency. The findings of the study on a sample of 375 participants showed that university students in Macao had a relatively high intercultural sensitivity level. There were significant differences in different student groups in terms of gender, grade, courses related to intercultural communication, and foreign language proficiency. This study has implications for both educational policymakers and educators and can ultimately help university students in a multicultural environment improve their intercultural sensitivity and sustainable development.

One of the core elements of Machine Learning (ML) is statistics and its embedded foundational rules and without its appropriate integration, ML as we know would not exist. Various aspects of ML platforms are based on statistical rules and most notably the end results of the ML model performance cannot be objectively assessed without appropriate statistical measurements. The scope of statistics within the ML realm is rather broad and cannot be adequately covered in a single review article. Therefore, here we will mainly focus on the common statistical concepts that pertain to supervised ML (i.e. classification and regression) along with their interdependencies and certain limitations.

The interferon (IFN) response is the first line of defense against viral invasion and thus plays a central role in the regulation of the immune response. IFN-epsilon (IFN-ε) is a newly discovered type I IFN that does not require viral induction, unlike other type I IFNs. IFN-ε is constitutively expressed in epithelial cells and plays an important role in mucosal immunity. In this study, we evaluated the biological activity of the mink-IFN (MiIFN)-ε gene in prokaryotic cells. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to evaluate IFN-ε expression in different mink tissues. MiIFN-ε was highly expressed in brain, lung, tracheal, kidney, intestinal, bladder, ovarian, and testis tissues. There was no significant difference in MiIFN-ε expression between female and male minks, except in the reproductive system. Expression of the small ubiquitin-like modifier (SUMO3)-MiIFN-ε fusion gene was induced by isopropylβ-d-thiogalactoside, and MiIFN-ε was collected after SUMO-specific protease digestion. We tested the antiviral activity of MiIFN-ε against vesicular stomatitis virus (VSV) in epithelial cells of feline kidney 81 (F81). We used qRT-PCR to analyze the expression of several IFN-stimulated genes (ISGs), including ISG15, 2′-5′ oligoadenylate synthetase (2′-5′OAS1), and myxovirus resistance protein 1 (Mx1). Recombinant IFN-ε induced high ISG expression in F81 cells. Compared with those in the cell control group, expressions of ISG15, Mx1, and 2′-5′ OAS1 in the VSV-GFP control, IFN-ε, and MiIFN-ε-inhibited VSV-GFP groups were significantly increased. Compared with those in the VSV-GFP control group, expressions of ISG15 and 2′-5′ OAS1 in the IFN-ε and MiIFN-ε-inhibited VSV-GFP groups were significantly increased, and the differences were highly significant (p < 0.0001). IFN-ε played an indirect antiviral role. These findings lay the foundation for detailed investigation of IFN-ε in the future.

Several devastating experiences with extreme natural disasters demonstrate that improving power system resilience is becoming increasingly important. This paper proposes a pre-disaster transmission maintenance scheduling considering network topology optimization to ensure the power system economics before disasters and power system resilience during disasters. The transmission line fragility is distinguished and considered in the proposed optimization model to determine the maintenance scheduling of defective lines that minimizes load shedding during disasters. The proposed model is established as a tri-level optimization problem that is further reformulated to a bi-level problem utilizing duality theory. The column-and-constraint generation (C&CG) algorithm is employed to solve the equivalent robust optimization problem. Finally, the proposed model and its solution algorithm are implemented on the modified IEEE RTS-79 system. The significant cost savings and increased resilience illustrate the effectiveness of the proposed model.

ObjectivesTo quantitatively summarize the specific changes in brain structure and function in migraine patients.MethodsA literature screening of migraine was conducted from inception to Sept 1, 2022, in PubMed, Web of Science, Cochrane Library, and Medline databases using the keyword combination of “migraine and MRI.” Activation likelihood estimation (ALE) was performed to assess the differentiation of functional connectivity (FC), regional homogeneity (ReHo), and gray matter volume (GMV) of migraine patients.ResultsEleven voxel-based morphometry (VBM) studies and 25 resting-state fMRI (rs-fMRI) studies (16 FC and 9 ReHo studies) were included in this study. ALE analysis revealed the ReHo increase in the brainstem and left thalamus, with no decreased area. Neither increased nor decreased regions were detected in FC and GMV of migraine patients.ConclusionsThe left thalamus and brainstem were the significantly activated regions of migraine. It is a meaningful insights into the pathophysiology of migraine. The consistent alterated brain areas of morphometrical and functional in migraine patients were far from reached based on current studies.

Ischemic stroke is the most common type of cerebrovascular disease with high disability rate and mortality. The blood-brain barrier (BBB) protects the homeostasis of the brain’s microenvironment and impedes the penetration of 98% of drugs. Therefore, effective treatment requires the better drug transport across membranes and increased drug distribution. Nanoparticles are a good choice for drugs to cross BBB. The main pathways of nano delivery systems through BBB include passive diffusion, adsorption-mediated endocytosis, receptor-mediated transport, carrier-mediated transport, etc. At present, the materials used in brain-targeted delivery can be divided into natural polymer, synthetic polymers, inorganic materials and phospholipid. In this review, we first introduced several ways of nano delivery systems crossing the BBB, and then summarized their applications in ischemic stroke. Based on their potential and challenges in the treatment of ischemic stroke, new ideas and prospects are proposed for designing feasible and effective nano delivery systems.

The outbreak of the novel coronavirus disease 2019 (COVID-19) has had an unprecedented impact worldwide, and it is of great significance to predict the prognosis of patients for guiding clinical management. This study aimed to construct a nomogram to predict the prognosis of COVID-19 patients. Clinical records and laboratory results were retrospectively reviewed for 331 patients with laboratory-confirmed COVID-19 from Huangshi Hospital of Traditional Chinese Medicine (TCM) (Infectious Disease Hospital) and Third Affiliated Hospital of Sun Yat-sen University. All COVID-19 patients were followed up for 80 days, and the primary outcome was defined as patient death. Cases were randomly divided into training (n=199) and validation (n=132) groups. Based on baseline data, we used statistically significant prognostic factors to construct a nomogram and assessed its performance. The patients were divided into Death (n=23) and Survival (n=308) groups. Analysis of clinical characteristics showed that these patients presented with fever (n=271, 81.9%), diarrhea (n=20, 6.0%) and had comorbidities (n=89, 26.9.0%). Multivariate Cox regression analysis showed that age, UREA and LDH were independent risk factors for predicting 80-day survival of COVID-19 patients. We constructed a qualitative nomogram with high C-indexes (0.933 and 0.894 in the training and validation groups, respectively). The calibration curve for 80-day survival showed optimal agreement between the predicted and actual outcomes. Decision curve analysis revealed the high clinical net benefit of the nomogram. Overall, our nomogram could effectively predict the 80-day survival of COVID-19 patients and hence assist in providing optimal treatment and decreasing mortality rates.

Background and objectiveGastric cancer (GC) represents a major factor inducing global cancer-associated deaths, but specific biomarkers and therapeutic targets for GC are lacking at present. Therefore, the present work focused on developing an immune-related genetic signature at the single-cell level for categorizing GC cases and predicting patient prognostic outcome, immune status as well as treatment response.MethodsSingle-cell RNA-sequencing (scRNA-seq) data were combined with bulk RNA-seq data in GC patients for subsequent analyses. Differences in overall survival (OS), genomic alterations, immune status, together with estimated immunotherapeutic outcomes were measured between different groups.ResultsNine cell types were identified by analyzing scRNA-seq data from GC patients, and marker genes of immune cells were also selected for subsequent analysis. In addition, an immune-related signature was established to predict OS while validating the prediction power for GC patients. Afterwards, a nomogram with high accuracy was constructed for improving our constructed signature’s clinical utility. The low-risk group was featured by high tumor mutation burden (TMB), increased immune activation, and microsatellite instability-high (MSI-H), which were related to the prolonged OS and used in immunotherapy. By contrast, high-risk group was associated with microsatellite stability (MSS), low TMB and immunosuppression, which might be more suitable for targeted therapy. Meanwhile, the risk score generated by our signature was markedly related to the cancer stem cell (CSC) index. In addition, the immunotherapeutic response prediction accuracy of our signature was validated in an external dataset IMvigor210 cohort.ConclusionA signature was constructed according to scRNA-seq data analysis. The signature-screened low- and high-risk patients had different prognoses, immune statuses and enriched functions and pathways. Such results shed more lights on immune status of GC, prognosis assessment, and development of efficient immunotherapeutic treatments.

CTRP6, a member of the C1q/TNF-related protein (CTRP) family, has gained increasing scientific interest because of its regulatory role in tumor progression. Previous studies have shown that CTRP6 is closely involved in regulating various pathophysiological processes, including glucose and lipid metabolism, cell proliferation, apoptosis, and inflammation. To date, CTRP6 has been identified as related to eight different malignancies, including lung cancer, oral cancer, gastric cancer, colon cancer, liver cancer, bladder cancer, renal cancer, and ovarian cancer. CTRP6 is reported to be associated with tumor progression by activating a series of related signal networks. This review article mainly discusses the biochemistry and pleiotropic pathophysiological functions of CTRP6 as a new molecular mediator in carcinogenesis, hoping that the information summarized herein could make a modest contribution to the development of novel cancer treatments in the future.

Background: Hepatocellular carcinoma (HCC) refers to the malignant tumor associated with a high mortality rate. This work focused on identifying a robust tumor glycolysis-immune-related gene signature to facilitate the prognosis prediction of HCC cases.Methods: This work adopted t-SNE algorithms for predicting glycolysis status in accordance with The Cancer Genome Atlas (TCGA)-derived cohort transcriptome profiles. In addition, the Cox regression model was utilized together with LASSO to identify prognosis-related genes (PRGs). In addition, the results were externally validated with the International Cancer Genome Consortium (ICGC) cohort.Results: Accordingly, the glycolysis-immune-related gene signature, which consisted of seven genes, PSRC1, CHORDC1, KPNA2, CDCA8, G6PD, NEIL3, and EZH2, was constructed based on TCGA-HCC patients. Under a range of circumstances, low-risk patients had extended overall survival (OS) compared with high-risk patients. Additionally, the developed gene signature acted as the independent factor, which was significantly associated with clinical stage, grade, portal vein invasion, and intrahepatic vein invasion among HCC cases. In addition, as revealed by the receiver operating characteristic (ROC) curve, the model showed high efficiency. Moreover, the different glycolysis and immune statuses between the two groups were further revealed by functional analysis.Conclusion: Our as-constructed prognosis prediction model contributes to HCC risk stratification.

The relationship between exposure to prosocial media content and prosocial behavior has been extensively explored. However, previous studies mainly explore the effect of prosocial media content exposure by comparing an individual’s exposure to the different types of content (i.e., prosocial content or neutral content), and generally focus on traditional media and video games, with less attention given to the increasingly popular new media platforms. In this study, we explored new dimensions by considering individuals’ exposure to different consequences of the same prosocial behavior (i.e., reward, punishment, or no consequences) in the context of short videos. Drawing upon Social Cognitive Theory and the General Learning Model, this experimental study identified the effect of such exposure on subsequent prosocial behavior among adolescents. We found that compared to the no consequences group, exposure to the reward consequence did not significantly predict moral elevation and subsequent prosocial behavior. Meanwhile, exposure to the punishment consequence had a significantly negative effect on subsequent prosocial behavior via moral elevation. Furthermore, the results revealed that empathy moderated the relationship between moral elevation and prosocial behavior, and moral elevation only positively predicted prosocial behavior among those with low empathy. Theoretically, this study deepens our understanding of the impact of exposure to different consequences of prosocial behavior on adolescents’ subsequent prosocial behavior, and highlights the importance of moral elevation and empathy to understand the underlying mechanism. The study also provides some practical implications for parents and practitioners to nurture prosocial behavior among adolescents.

ObjectiveTumor spread through air spaces (STAS) has recently been reported as a novel invasive pattern in lung adenocarcinoma. Thus, this study aimed to investigate the clinicopathological and radiological features in invasive lung adenocarcinoma with tumor STAS.MethodsData of 503 invasive lung adenocarcinoma patients who underwent surgery between 1 January 2015 and 31 December 2021 were collected. The correlations between STAS presence and clinicopathological and radiological characteristics were analyzed. Statistical analysis was performed using SPSS 22.0.ResultsAmong the 503 patients with invasive adenocarcinoma, 247 (47.9%) and 262 (52.1%) patients were positive and negative for STAS, respectively. Compared to STAS-negative adenocarcinoma, STAS was more common in papillary, micropapillary, and solid tumors (p < 0.01); STAS was associated with advanced pT (p = 0.024), pN (p < 0.001), and pTNM (p < 0.001) stage, more lymph node metastases (p < 0.01), more pleural invasion (p < 0.01), and more neurovascular invasion (p = 0.025). The maximum diameter (p < 0.01), the maximum diameters of the solid component (p < 0.01), and the consolidation/tumor ratio (CTR, p < 0.01) were significantly larger in STAS-positive than in STAS-negative adenocarcinoma. Other common computed tomography (CT) features of adenocarcinomas, i.e., lobulation (p < 0.01), spiculation (p < 0.01), vacuole (p < 0.01), air bronchogram (p = 0.020), vascular convergence (p < 0.01), and pleural indentation (p < 0.01) were significantly associated with STAS. In a multivariable analysis, the maximal diameter of the solid component (odds ratio [OR], 2.505; 95% confidence interval [CI], 1.886–3.329), vacuole (OR, 3.301; 95% CI, 1.822–5.980), and spiculation (OR, 2.162; 95% CI, 1.221–3.829) were independent predictors of STAS. The area under the curve (AUC) of the maximal diameter of the solid component was 0.757 (95% CI, 0.714–0.799; p < 0.001), the sensitivity was 73.9%, and the specificity was 69.1% at a cutoff value of 1.18 cm.ConclusionSTAS was significantly correlated with several invasive clinicopathological and radiological characteristics, and the maximal diameter was an independent predictor of STAS. These results will prove helpful in identifying STAS-positive adenocarcinoma by CT before surgical resection.

PurposeThere is a bidirectional relationship between periodontitis and type 2 diabetes mellitus (T2DM). The aim of this study was to further explore the pathogenesis of this comorbidity, screen out ferroptosis-related genes involved in the pathological process, and predict potential drug targets to develop new therapeutic strategies.MethodsCommon cross-talk genes were identified from periodontitis datasets (GSE16134, GSE10334 and GSE106090) and T2DM databases (DisGeNET and GeneCard). Then, GO and KEGG enrichment analyses, PPI network analysis and hub gene identification were performed. The association between ferroptosis and periodontitis with T2DM was investigated by Pearson correlation analysis. Core ferroptosis-related cross-talk genes were identified and verified by qRT-PCR. Potential drugs targeting these core genes were predicted via DGIDB.ResultsIn total, 67 cross-talk genes and two main signalling pathways (immuno-inflammatory pathway and AGE-RAGE signalling pathway) were identified. Pearson correlation analysis indicated that ferroptosis served as a crucial target in the pathological mechanism and treatment of periodontitis with T2DM. IL-1β, IL-6, NFE2L2 and ALOX5 were identified as core ferroptosis-related genes and the qRT-PCR detection results were statistically different. In total, 13 potential drugs were screened out, among which, Echinacea and Ibudilast should be developed first.ConclusionsThis study contributes to a deeper understanding of the common pathogenesis of periodontitis and T2DM and provides new insights into the role of ferroptosis in this comorbidity. In addition, two drugs with potential clinical application value were identified. The potential utility of these drugs requires further experimental investigation.