Browse Articles

Differential regulation of long noncoding RNAs by endogenous and exogenous reactive oxygen species-generating prooxidants in NIH3T3 cells

Long noncoding RNAs (lncRNAs) play crucial roles in various cellular processes, including the response to oxidative stress. However, the relationship between oxidants and lncRNA expression remains poorly understood. This study investigated the effects of endogenous and exogenous prooxidants with reactive oxygen species (ROS)-generating activity on lncRNA expression in NIH3T3 cells. We treated cells with various compounds, including food-derived polyphenols and environmental pollutants, for 24 hours and analyzed lncRNA expression. Treatment with 1 μM exogenous prooxidants (1,2,4-benzenetriol or 1,4-naphthoquinone) resulted in a significant increase (>50-fold) in the expression of several lncRNAs, including Snhg4, Kcnq1ot1, Rmst, Neat1, Gt(ROSA)26Sor, and Peg13. Conversely, exposure to endogenous prooxidants (dopamine and adrenaline), certain food-derived polyphenols (chrysin and 3-O-methylquercetin), or 1,2-naphthoquinone led to a significant decrease (<0.5-fold) in Rmst expression. Similarly, Neat1 expression was significantly reduced in the presence of food-derived polyphenols (luteolin, quercetin, and piceatannol). These findings suggest that prooxidants with distinct redox properties differentially regulate lncRNAs and potentially influence oxidative stress responses. Our results provide new insights into the complex interplay between oxidants and lncRNA regulation. This may have implications for understanding oxidative stress-related pathologies and developing novel therapeutic strategies.

Image aesthetic quality assessment: A method based on deep convolutional capsule network

Image aesthetics assessment (IAA) has become a hot research area in recent years due to its extensive application potential. However, existing IAA methods often overlook the importance of spatial information in evaluating image aesthetics. To address this limitation, this study proposes a novel method called the Deep Convolutional Capsule Network (DCCN), which integrates an improved Inception module with a capsule routing mechanism to enhance the representation of spatial features—an essential yet frequently underexplored aspect in aesthetic evaluation. This design enables the model to effectively extract both global and local aesthetic features while maintaining spatial relationships. To the best of our knowledge, this is the first attempt to apply capsule networks in the IAA domain. Experiments conducted on two benchmark datasets, CUHK-PQ and AVA, demonstrate the effectiveness of the proposed method. The DCCN achieves a classification accuracy of 94.79% on CUHK-PQ, and on AVA, it obtains a Pearson Linear Correlation Coefficient (PLCC) of 0.8408 and a Spearman Rank-Ordered Correlation Coefficient (SROCC) of 0.7394. While the DCCN shows promising results, it exhibits sensitivity to style variations and resolution changes and has relatively high inference complexity due to dynamic routing, which may affect deployment in real-time applications.

Construction and application of a novel urban knowledge model with extended historical and cultural semantics

Historical urban spaces, imbued with profound historical and cultural significance, have evolved from ‘spaces’ into meaningful ‘places’. But they now face the risk of being eroded by rapid urbanisation and forgotten by today’s society, making it challenging to integrate them into modern life. UNESCO’s Historic Urban Landscapes (HUL) Recommendation highlights the importance of understanding the holistic, layered, and dynamic nature of urban heritage. Therefore, focusing on the ancient city district of Guangzhou as a case study, this research explores innovative approaches to integrating holistic semantics of urban places by merging contemporary semantics from geo-big data with historical and cultural semantics from documents and archives to create an urban knowledge model that bridges the gap between the present and the past. Furthermore, using knowledge graph embedding technology, we develop a model capable of entity prediction, similarity calculation, and query retrieval. We propose four key application scenarios for the implementation of the model. First, our research identifies potential cultural spatial connections that contribute to the joint preservation and promotion of historic urban places. Second, we develop a recommendation system that caters to users’ various requests, increasing the visibility of historical places. Third, we predict optimal locations for Time-Honored Brands. Finally, we identify visitor profiles to assist managers in meeting cultural promotion needs. To summarize, the integrated framework proposed in this study demonstrates both methodological efficacy and reusability. It not only helps to deeply explore the historical and cultural connotations, providing a scientific basis for urban planning and cultural inheritance, but also has the potential for enhancing the public’s awareness and participation in historical culture, promoting the sustainable development and prosperity of urban culture.

Correction: Decisions with Uncertain Consequences—A Total Ordering on Loss-Distributions

Gestational diabetes mellitus and its associated factors among women of advanced maternal age in Malaysia: Findings from a national survey

Gestational diabetes mellitus (GDM) is a growing public health concern, particularly among women with advanced maternal age. Understanding the prevalence and associated sociodemographic factors is crucial for targeted interventions. This study aimed to determine the prevalence of GDM and its association with sociodemographic factors among Malaysian women with advanced maternal age. This study utilized data from the National Health and Morbidity Survey 2022: Maternal and Child Health, a nationwide cross-sectional survey employing a two-stage stratified cluster sampling design. GDM was identified based on the result of a modified oral glucose tolerance test (MOGTT) recorded in the mother’s antenatal book. The 75-g MOGTT was performed according to the Clinical Practice Guidelines for the Management of Diabetes in Pregnancy in Malaysia. Sociodemographic variables, including ethnicity, locality, education, employment, and household income, were analysed. Multiple logistic regression was performed to identify factors associated with GDM. The prevalence of GDM among women with advanced maternal age in Malaysia was 33.7% (95% CI: 30.9%−36.5%). Ethnicity was significantly associated with GDM, with Indian women showing the highest prevalence (48.8%) and odds (AOR: 7.31, 95% CI: 2.58–20.72; P < 0.001). Working status was another significant factor, with non-working women having higher odds of GDM compared to working women (AOR: 1.34, 95% CI: 1.01–1.77; P = 0.003). No significant associations were observed for locality, educational level, or household income. The high prevalence of GDM among women with advanced maternal age in Malaysia underscores the urgent need for targeted interventions, particularly among high-risk ethnic groups. Public health strategies should prioritize early screening, culturally tailored programs, and community-based initiatives to address this growing burden. Future research should explore behavioural and genetic determinants to further inform policy and practice.

Post-marketing safety of solriamfetol: A retrospective pharmacovigilance study based on the us food and drug administration adverse event reporting system

Purpose Excessive daytime sleepiness (EDS) seriously affects quality of life and may increase the risk of life-threatening situations, such as motor vehicle accidents. Solriamfetol is a novel medication approved for the treatment of EDS and serves as an alternative to traditional stimulants. This retrospective pharmacovigilance study aimed to analyze adverse events (AEs) related to solriamfetol based on real-world data. Methods Data regarding solriamfetol-related adverse events were retrieved from the FDA Adverse Event Reporting System (FAERS) from Q3 of 2019 to Q1 of 2024. A total of 1550 reports on solriamfetol-related AEs were analyzed using disproportionality analysis to identify AE signals across various organ systems. Results A large proportion of AEs were reported among female patients (64.06%), primarily including those with narcolepsy (38.13%) and obstructive sleep apnea (3.68%). The most frequently reported AEs included headache, anxiety, and drug ineffectiveness, with 46.8% of AEs occurring within 7 days of treatment initiation. Furthermore, solriamfetol was significantly associated with psychiatric and nervous system disorders as well as cardiac and general disorders. Conclusions Solriamfetol-related adverse events were mainly psychiatric, neurological, cardiac, and general disorders, with headache, anxiety, and drug ineffectiveness being the most common. Nearly half of the events occurred within the first week of treatment. Given the limitations of the FAERS database, further prospective studies are needed to confirm these findings.

Investigating the coupling relationships of railway safety risks using the N-K model and complex network theory

To quantitatively analyze the coupling relationships between railway safety risk factors, identify key factors contributing to railway accidents, and develop scientific strategies for accident prevention, this study introduces a complex network-based N-K model to investigate the coupling relationships of railway safety risk factors. First, we identified 18 railway safety risk factors by analyzing case data from railway accidents. The occurrence probabilities and coupling values of these risk factors were then calculated using the N-K model. Subsequently, based on the constructed railway safety risk complex network, reachability and centrality analyses were performed to determine the key factors of railway safety risk. Results indicate that the occurrence of railway accidents is directly proportional to the risk coupling value; the greater the number of coupling factors, the higher the risk value. The coupling of personnel factors and equipment factors is particularly prone to leading to railway accidents. Conversely, effective management of the coupling between personnel and equipment factors can significantly reduce the likelihood of accidents. Inadequate maintenance and unsafe human behavior were identified as critical factors contributing to railway accidents and should be prioritized in prevention efforts.

The effect of a six-month programme of intradialytic cycling on survival and hospitalisations in people requiring haemodialysis: 5-year follow-up of the CYCLE-HD randomised controlled trial

We have previously shown that a six-month programme of intradialytic cycling (IDC) improved cardiovascular structure and function, it is unclear whether these changes are associated with long-term benefits. The aim of this post-trial analysis was to evaluate a programme of IDC on all-cause mortality, hospitalisations and cardiovascular events at five-years. Mortality and hospitalisation data were collected from Hospital Episode Statistics and death certificates. Models were fitted unadjusted and adjusted for age, sex, diabetes, duration of dialysis, and receiving a kidney transplant. Cox proportional hazard models were used for time-to-event analysis to evaluate all-cause mortality. Hospitalisations were analysed using a negative binomial regression model, and length of stay using a generalised linear model. A composite outcome of time to first cardiovascular event, combining cardiovascular mortality and hospitalisations, was evaluated using a Cox model. There was no evidence of a statistically significant effect of treatment allocation on survival (hazard ratio (HR) 1.09, 95% confidence interval (CI): 0.68–1.76, p = 0.71). After adjustment, results remained non-significant (HR 1.22, 95% CI: 0.74–2.01, p = 0.43). There was no evidence of a significant effect on all-cause hospitalisations for unadjusted (p = 0.20) or adjusted (p = 0.25) models. Similar results are reported for cardiovascular hospitalisations (p = 0.30 and p = 0.17). For time to first cardiovascular event there was no evidence of a statistically significant effect (HR 1.39, 95% CI: 0.79–2.72, p = 0.26). The main findings show no evidence that a six-month programme of IDC affected all-cause mortality, hospitalisations, cardiovascular events, or length of stay in hospital at five-years.

A machine learning framework for estimating the probability of blacklegged tick population establishment in eastern Canada using Earth observation data

Ixodes scapularis ticks are the primary vector of Lyme disease (LD) in North America, and their range has expanded into southeastern and southcentral Canada with climate change. This study presents a comprehensive machine learning (ML) framework to estimate the probability of blacklegged tick population establishment as measured using active tick surveillance data. Environmental predictor variables were derived from Earth observation (EO) data at multiple spatial scales to assess their individual contributions in the prediction models. Among the tested ML algorithms, XGBoost emerged as the top-performing model, achieving high sensitivity (0.83) and specificity (0.71) in predicting population establishment. Performance was optimized when using predictor variables derived from a 1 km radius around surveillance sites. Top predictors included cumulative annual degree-days above 0°C and maximum temperature of warmest month, reflecting the importance of temperature in enabling tick survival and reproduction. Additional predictor variables of high importance included silty soil (lower clay content) with slightly higher than average SOC and pH, and land cover types that contained broadleaf forests (percent mixed forest, percent broadleaf) and less urban areas. By integrating ML with open access EO data, this study demonstrates that accurate, easily updatable risk maps can be produced to support public health management of LD, and more broadly, the growing threat of tick-borne diseases in a changing climate.

Harnessing interpretable novel combination of GloVe embedding with deep CNN-BiLSTM neural network for fake news detection

The important issue of fake news to society is how it affects how society runs in terms of decision-making and public perception. Hence, this study is a comparative analysis of innovative hybrid deep learning models and embedding techniques focusing on interpretability using eXplainable Artificial Intelligence (XAI) for fake news detection. The popular fake news dataset is used to design and test a collection of state-of-the-art models, such as GloVe with CNN-BiLSTM, FastText-Bi-LSTM, and logistic regression with TF-IDF against the CNN and GloVe with BiLSTM and CNN models. In terms of accuracy, LSTM without FastText shows a performance of 98.33%, whereas GloVe with BiLSTM and CNN shows a 99.63% performance. Local Interpretable Model-Agnostic Explanations (LIME) is used to clarify how the input features make decisions on the high precision of the model. The integration of such state-of-the-art models with XAI is one of the major contributions of the study, which brings high accuracy as well as interpretability. Our study’s perspective addresses model performance and user trust in the future, laying the foundation for the practical implementation of reliable fake news detection systems.

Recurrent venous thromboembolism and clot distribution in COVID-19 infection: A review by variant type

Research question Do PE distribution and rates of recurrent VTE differ between COVID-19 (CAPE) and non-COVID-19 (NCAPE) patients and among COVID-19 variants? Study design and methods A single-center retrospective chart review of 547 patients with PE admitted from January 2020-October 2022 was conducted. 470 patients did not have COVID-19 infection on admission. 77 patients had COVID-19 infection with 17, 27, and 33 admissions occurring during Alpha, Delta, and Omicron predominance, respectively. Imaging reports, follow-up, and recurrent VTE incidence were extracted. Central clot was defined as saddle or mainstem PE. Clot distribution was classified as central, peripheral, or both. Recurrent VTE was examined at 3 and 6 months. Results NCAPE and CAPE patients had similar patterns of clot distribution overall (P = 0.34, 0.48, & 0.82 for central, peripheral, and both, respectively). Of CAPE patients with solely peripheral PE distribution (N = 49), Omicron comprised 78.8% (N = 26, P = 0.01). Recurrent VTE occurred in 45 patients. The cumulative proportion of recurrent VTE or death did not differ significantly by COVID-19 status (P = 0.12). Conclusions: COVID-19 infection is associated with VTE events which appear to vary in frequency and location among the different variants (Alpha, Omicron, and Delta). VTE risk is highest in the 3 months following COVID-19 infection. Recurrent rate of VTE is similar between patients with COVID-19-associated and non-COVID-19-associated PE. The VTE risk and clot distribution of COVID-19 is evolving over the years as the virus becomes endemic in the United States and warrants further study.

Differential PARP inhibitor responses in BRCA1-deficient and resistant cells in competitive co-culture

Synthetic lethality describes a genetic relationship where the loss of two genes results in cell death, but the loss of one of those genes does not. Drugs used for precision oncology can exploit synthetic lethal relationships; the best described are PARP inhibitors which preferentially kill BRCA1-deficient tumours preferentially over BRCA1-proficient cells. New synthetic lethal targets are often discovered using genetic screens, such as CRISPR knockout screens. Here, we present a competitive co-culture assay that can be used to analyse drugs or gene knockouts with synthetic lethal effects. We generated new BRCA1 isogenic cell line pairs from both a triple-negative breast cancer cell line (SUM149) and adapted pre-existing non-cancerous BRCA1 isogenic pair (RPE). Each cell line of the isogenic pair was transformed with its own fluorescent reporter. The two-coloured cell lines of the isogenic pair were then grown together in the same vessel to create a more competitive environment compared to when grown separately. We used four PARP inhibitors to validate the ability to detect synthetic lethality in BRCA1-deficient cancer cells. The readout of the assay was performed by counting the fluorescently coloured cells after drug treatment using flow cytometry. We observed preferential targeting of BRCA1-deficient cells, by PARPi, at relative concentrations that broadly reflect clinical dosing. Further we reveal subtle differences between PARPi resistant lines compared to BRCA1-proficient cells. Here, we demonstrate the validation and potential use of the competitive assay, which could be extended to validating novel genetic relationships and adapted for live cell imaging.

Speech-in-noise discriminability after noise exposure: Insights from a gerbil model of acoustic trauma

Speech comprehension, especially in the presence of background sounds, allegedly declines as a consequence of noise-induced hearing loss. However, the connection between noise overexposure and deteriorated speech-in-noise perception despite normal audiometric thresholds (hidden hearing loss) is not yet clear. This study investigates speech-in-noise discrimination in young-adult Mongolian gerbils before and after an acoustic trauma to examine the link between noise exposure and speech-in-noise perception. Nine young-adult gerbils were trained to discriminate a deviant consonant-vowel-consonant combination (CVC) or vowel-consonant-vowel combination (VCV) in a sequence of CVC or VCV standards, respectively. The logatomes were spoken by different speakers and masked by a steady-state speech-shaped noise. After the gerbils obtained the behavioral baseline data, they underwent an acoustic trauma and participated in the behavioral experiments again. Applying multidimensional scaling, response latencies were used to generate perceptual maps reflecting the gerbils’ internal representations of the sounds pre- and post-trauma. To evaluate how the discrimination of vowels and consonants was altered after noise exposure, changes in response latencies between phoneme pairs were investigated in relation to their articulatory features. Numbers of intact inner hair cell synapses were counted, and auditory brainstem responses were measured to assess peripheral auditory function. Perceptual maps of vowels and consonants were very similar before and after noise exposure. Interestingly, the gerbils’ overall vowel discrimination ability was improved after the acoustic trauma, even though the gerbils suffered from noise-induced hearing loss with a temporary threshold shift for frequencies above 4 kHz. In contrast, there were only minor changes in the gerbils’ consonant discrimination ability. Moreover, noise exposure showed a differential influence on response latencies for vowel and consonant discriminations depending on the articulatory features. Altogether, the results show that an acoustic trauma followed by a temporary threshold shift is not necessarily linked to speech-in-noise perception difficulties associated with hidden hearing loss.

Editorial Note: Incidence and predictors of HIV related opportunistic infections after initiation of highly active antiretroviral therapy at Ayder Referral Hospital, Mekelle, Ethiopia: A retrospective single centered cohort study

Comparative analysis of AI and expert evaluations in engineering design pedagogy

Background Integrating engineering design processes into science education has become a significant priority in STEM instruction. However, many science teachers face difficulties incorporating these processes due to limited pedagogical expertise. Generative artificial intelligence (GAI) tools such as ChatGPT offer potential support mechanisms by evaluating lesson plans and providing formative feedback. This study investigates the reliability and validity of GAI evaluations compared to expert assessments. Methods This mixed-methods study involved 43 science teachers who received professional development over four months to integrate engineering design into their lesson plans. A total of 52 lesson plans were evaluated using structured and unstructured prompts via ChatGPT 4.5, alongside evaluations by expert mentors. Quantitative data were analyzed using the Intraclass Correlation Coefficient (ICC) and Bland-Altman methods to assess inter-rater consistency. Qualitative data was analyzed through open and deductive coding to interpret differences in evaluation rationale. Results Findings revealed high consistency between structured prompt AI evaluations and expert assessments (ICC = 0.708), while unstructured prompts showed low and non-significant agreement (ICC = 0.076). Qualitative analysis indicated that AI evaluations, particularly those using structured prompts, tend to be more positive and holistic, whereas experts offered more detailed and critical feedback. Differences were also observed in evaluating dcomponents like problem definition, testability, and interdisciplinary integration. Conclusion Structured AI prompts offer reliable and valid evaluation results comparable to expert assessments and could serve as scalable tools in teacher support systems. However, unstructured prompts produce inconsistent outcomes and require refinement. The study highlights both the potential and limitations of using GAI tools for pedagogical evaluation in STEM education.

Retraction: Eucommia ulmoides Cortex, Geniposide and Aucubin Regulate Lipotoxicity through the Inhibition of Lysosomal BAX

Study on spray combustion characteristics of liquid ammonia/dimethyl ether dual fuel based on different injection strategies

Ammonia is a green zero-carbon fuel, yet its low reactivity poses challenges, including difficult ignition and slow combustion rates. Compared to diesel or biodiesel, dimethyl ether (DME) has no C-C bonds, which means it produces almost no soot and has a high cetane number that helps it ignite easily. So, using the highly reactive DME to help ignite liquid ammonia is a good way to make it burn better. This study uses computer simulations to look at how well liquid ammonia and DME work together as fuel with different injection setups. Results indicate optimal DME ignition enhancement at injector spacing L = 6 cm, injection angle 180°, and ammonia energy share 70%, outperforming cases with spacings of 7–8 cm and angles of 150°, 120°, 90°, and 60°. At the same time, having shorter spacing during DME combustion leads to smaller areas for OH but more NH₂ formation, showing that ammonia is more effective at cooling the flame and that more ammonia is being used as fuel. Additionally, when DME is not burning, both OH and NH₂ areas grow larger at shorter spacings, showing that the fuel mixes sooner, the reaction areas get bigger, and the burning process is more complete. Regarding the bimodal NH₂ peaks, the initial peak reflects partial ammonia oxidation that is flame-entrained during DME combustion, while the secondary peak indicates the onset of autoignition, which is characterized by diminished reaction rates and reduced combustion intensity.

A fusion safety and security analysis framework for intelligent and connected vehicles

Driven by advancements in emerging technologies and data-driven innovations, the global automotive industry is focusing on intelligent and connected vehicles (ICVs), which involve complex electronic systems and vast data interactions. Safety concerns have expanded beyond traditional safety measures to include functional safety, safety of the intended functionality (SOTIF), and cybersecurity. Despite their interconnected nature, current methods often address these domains separately, risking incomplete safety assessments. This paper introduces a fusion safety analysis method that evaluates the three domains collectively. By identifying safety attributes and mapping unsafe behaviors to hazardous scenarios, it quantitatively assesses integrated safety risks. An illustrative case study on adaptive cruise control (ACC) highlights the method’s effectiveness, stressing the importance of addressing multi-dimensional safety issues to enhance ICVs safety.

NR3C1-mediated epigenetic regulation suppresses astrocytic immune responses in mice

Comparative single-cell and spatial profiling of anti-SSA-positive and anti-centromere-positive Sjögren’s disease reveals common and distinct immune activation and fibroblast-mediated inflammation

Abstract Sjögren’s disease (SjD) is an autoimmune disease that causes salivary gland dysfunction due to immune-mediated destruction. While autoantibodies such as anti-SSA and anti-centromere (CENT) are associated with distinct clinical manifestations, the molecular features remain to be elucidated. In this study, we apply multi-modal single-cell technologies: single-cell RNA sequencing, T cell and B cell receptor sequencing and spatial transcriptomics to salivary gland lesions, aiming to elucidate common and unique cellular and transcriptional signatures linked to different autoantibody profiles. Our analysis demonstrates that GZMB + GNLY + CD8+ T cells are the main expanded subset across different autoantibody statuses, highlighting their central role in SjD pathogenesis, while the enrichment of memory B cells is more prominent in anti-CENT-positive patients. Cytokine signaling also differs by autoantibody profile, with an activated interferon signature in anti-SSA-positive patients, whereas TGFβ signaling is enhanced in anti-CENT-positive patients. Furthermore, spatial profiling reveals THY1 + fibroblasts, expressing complement genes and chemokines, as key hubs orchestrating inflammation within the salivary glands. These findings deepen our understanding of the pathogenesis of SjD, and may inform the development of targeted and personalized therapeutic strategies.