Search Results (16,051)

UAV communication, leveraging high mobility and flexible deployment, is gradually becoming an important component of 6G integrated air–ground networks. With the expansion of aerial services, air–ground spectrum resources are increasingly scarce, and spectrum sharing and opportunistic access have become key technologies for improving spectrum utilization. Spectrum sensing is the prerequisite for UAVs to perform dynamic access and avoid causing interference to primary users. However, in air–ground links, the channel time variability caused by Doppler effects, carrier frequency offset, and Rician fading can weaken feature separability, making it difficult for deep learning-based spectrum sensing methods to maintain reliable detection in complex environments. In this paper, a dual-stream hybrid-attention spectrum sensing method (DSHA) is proposed, which represents the received signal simultaneously as a time-domain I/Q sequence and an STFT time-frequency map to extract complementary features and employs a hybrid attention mechanism to model key intra-branch dependencies and achieve inter-branch interaction and fusion. Furthermore, a noise-consistent paired training strategy is introduced to mitigate the bias induced by noise randomness, thereby enhancing weak-signal discrimination capability. Simulation results show that under different false-alarm constraints, the proposed method achieves higher detection probability in low-SNR scenarios as well as under fading and CFO perturbations. In addition, compared with multiple typical baselines, DSHA exhibits better robustness and generalization; under Rician channels, its detection probability is improved by about 28.6% over the best baseline. Full article

Traffic accident prediction is of great importance for intelligent transportation systems and public safety management. Unlike conventional traffic flow forecasting tasks, accident data are characterized by low occurrence frequency and highly imbalanced distributions, with near-zero values during most time periods and occasional concentrated bursts. Accident occurrences are also strongly influenced by daily and weekly periodic patterns, resulting in mixed characteristics of low baseline levels, abrupt peaks, and long-term trends. These properties make traditional time series forecasting methods based on stationarity assumptions or single-period modeling less effective. To address this issue, this study proposes a time series forecasting framework that integrates patch-aware local perception with global basis representation. Specifically, this study aims to improve traffic accident time-series forecasting accuracy under sparse and bursty conditions by integrating patch-aware local perception with global basis representation. The patch-level structure captures fine-grained fluctuations in accident sequences by modeling short-term local variations, while basis decomposition provides robust modeling of overall trends through a set of global latent components, leading to complementary effects at both local and global levels. Experimental results on the I-405 highway accident dataset demonstrate that the proposed model significantly outperforms baseline methods, reducing mean squared error (MSE) and mean absolute error (MAE) by approximately 9.7% and 12.6% compared with PatchTST, and by 22.3% and 28.2% compared with Basisformer. Furthermore, experiments on public benchmark datasets ETTh1 and Electricity show that the proposed method achieves comparable or superior performance to mainstream models, indicating its effectiveness and generalization ability across different types of time series scenarios. Full article

The accurate detection of electricity theft is crucial for reducing non-technical losses in smart grids. However, many existing data-driven methods rely on a single data modality, such as either the raw 1D consumption sequence or its transformed 2D image. This single-modality approach may not fully capture the complex spatio-temporal patterns associated with fraudulent behavior. To address this limitation, this paper proposes a novel detection method that integrates Markov Transition Fields (MTFs) with a hybrid neural network. First, this approach uses MTF to convert 1D time-series consumption data into 2D feature images, which enhances state-transition patterns. A parallel Residual Network and Long Short-Term Memory (ResNet-LSTM) architecture is then designed to simultaneously extract global temporal features from the original 1D data and local spatial features from the MTF images, with their fused representation used for classification. Experimental validation on a real-world dataset from the State Grid Corporation of China (SGCC)—comprising 6000 users over 304 days—demonstrates the effectiveness of our approach. The proposed model achieves a detection accuracy of 94.0% on an independent test set of 1200 users, significantly outperforming several state-of-the-art single-modality benchmarks. This work provides a new technical method for intelligent electricity theft prevention system. Full article

Monitoring urban subsidence over ultra-long periods using time-series Interferometric synthetic aperture radar (InSAR) technology is critically important. Conventional approaches, however, face two main limitations: significant atmospheric phase residuals in complex urban settings, and discontinuous temporal time-series with short temporal coverage due to single-platform data constraints. To address these limitations, this study presents a new method for estimating ultra-long-term subsidence time series in urban areas, which combines Interferometric Subset Stacking (ISS) with multi-platform data fusion (DF). The methodology firstly processes TerraSAR-X and Sentinel-1A datasets through differential interferometry and applies ISS for atmospheric phase suppression. Next, bilinear interpolation unifies the spatial resolution and aligns the spatial reference frames of the two datasets. Subsequently, joint modeling derives subsidence velocities. Finally, temporal integration via linear interpolation and moving averaging produces a unified spatio-temporal deformation sequence. Applied to the Beijing region, China, this approach generated a 12-year ultra-long-term subsidence time series result (2012–2024), revealing maximum cumulative subsidence of 1100 mm spatially correlated with groundwater extraction patterns. Validation against Global Navigation Satellite System (GNSS) data showed strong agreement (correlation coefficient: 0.94, Root Mean Square Error (RMSE): 6.3 mm). The method achieved substantial atmospheric reduction—67.7% for Sentinel-1A and 24.1% for TerraSAR-X—representing approximately 15–20% accuracy improvement over conventional Generic Atmospheric Correction Online Service (GACOS) for InSAR. By effectively utilizing multi-platform data, this approach makes fuller use of the available phase information and compensates for the temporal gaps inherent in single-satellite datasets. It thus offers a valuable framework for long-term urban deformation monitoring. Full article

This research explores the development of a decoder-only speech language model (SLM) for Kazakh, a language currently characterized by limited computational resources. Our approach leverages discrete acoustic units synthesized from self-supervised speech representations. Specifically, we utilize a pretrained Wav2Vec 2.0 model to extract continuous latent features, which are then transformed into discrete semantic tokens via the k-means clustering algorithm. These tokens serve as the foundation for training a generative model designed to predict and maximize the likelihood of speech-unit sequences. To facilitate this study, we curated a specialized Kazakh speech corpus by synthesizing and refining multiple publicly available audio datasets. Given the constrained hardware resources available, we conducted large-scale feature extraction and tokenization to train the unit-based model. We evaluated the system’s efficacy using negative log-likelihood and perplexity metrics on independent test sets. The model captures Kazakh vowel harmony but struggles with long-range agglutinative chains. Key observations include the model’s high sensitivity to data quality, tokenization techniques, and specific training hyperparameters. Although constrained by data volume and training time relative to global benchmarks, the model successfully captures the underlying structural patterns in Kazakh speech. This work establishes a vital empirical baseline and suggests future improvements through refined unit discovery and integrated speech-text modeling. Full article

Congenital heart disease (CHD) is the most common congenital anomaly worldwide and poses significant diagnostic challenges due to its structural complexity and frequent association with extracardiac anomalies and genetic abnormalities. While conventional tests such as karyotyping, quantitative fluorescent polymerase chain reaction (QF-PCR), and chromosomal microarray analysis (CMA) are standard first-tier investigations, many cases remain genetically unexplained. Prenatal whole exome sequencing (WES) has emerged as a valuable tool to detect pathogenic single gene variants underlying CHD. This narrative review synthesizes findings from 28 studies involving over 2000 WES-tested fetuses and more than 10,000 CHD cases. The additional diagnostic yield of WES over CMA ranged from 8.0% to 66.7%, with higher yields in syndromic or non-isolated CHD (10–50%) compared to isolated cases (7.1–27.8%). Trio-based WES outperformed proband-only sequencing by improving accuracy, reducing turnaround time, and lowering the rate of variant of uncertain significance (VUS). Prenatal WES not only clarifies genetic etiology but also reveals syndromic diagnoses, allowing CHD to be interpreted within broader multisystem contexts. Integration of phenotypic and genomic data enhances prenatal counseling, prognostication, delivery planning, and postnatal care—advancing precision medicine in fetal cardiology. Full article

(1) Background: In recent years, new pests have been constantly emerging in tea trees, posing a significant threat to tea production. Therefore, it is necessary to monitor and investigate whether new pests have emerged in tea trees. (2) Methods: A new tea pest discovered in a tea garden was identified through mitochondrial cytochrome-c oxidase subunit I (COI) gene sequence analysis and observation of morphological characteristics. Its occurrence pattern was also analyzed in detail, and preliminary control methods were proposed. (3) Results: During the 2023 tea garden pest investigation, we discovered a new tea pest for the first time in a tea garden in Jiepai Town, Hengyang County, Hengyang City, Hunan Province, and identified it as Nysius ericae (Schilling). The results indicated that N. ericae was mainly fed on the upper leaves of tea trees, and high temperature and drought were suitable for its occurrence. Furthermore, various concentrations (1~16 mg/L) of matrine showed significant toxicity against N. ericae under laboratory conditions. (4) Conclusions: Our research has discovered for the first time a new pest of tea trees, providing an important scientific foundation for the monitoring, early warning, and prevention and control of N. ericae in tea gardens, which is of great significance for ensuring the ecological security and tea quality of tea gardens. Full article

Background: Diagnostic discrimination between ischemic stroke (IS) and hemorrhagic stroke (HS) is required for successful intervention with time-critical acute treatments. The available data on blood-based RNA biomarkers and discrimination between IS and HS are limited. This systematic review aimed to examine and summarize the existing literature on potentially useful blood-based RNA biomarkers that may aid in preclinical acute diagnosis. Methods: We systematically reviewed the literature on the ability of blood-based RNA biomarkers to discriminate between IS and HS according to PRISMA guidelines. We searched PubMed, EMBASE, The Cochrane Library, and The Web of Science for eligible randomized controlled trials, observational studies, and case–control studies published in the English language without time limitation. The risk of bias was evaluated using the Newcastle–Ottawa Scale. Results: We included eight studies with a total of 728 patients (436 with IS and 292 with HS) in our review. The study quality was good in five and fair in three investigations. No meta-analysis was performed due to high heterogeneity in methods and study endpoints. Reported biomarkers include miRNA-124-3p, miRNA-16, miRNA-340-5p, lncRNA XIST (X-inactive specific transcript), PFKFB3 mRNA (6-phosphofructo-2-kinase/fructose-2,6-biphosphatase), tRNA derivatives, tRNA fragments, extracellular miRNAs, transcriptome changes, and MCEMP1 gene expression. Assessment techniques varied widely across studies, ranging from RNA sequencing to qPCR, microarray, human transcriptome array, and ELISA. MicroRNA-124-3p, miRNA-340-5p, lncRNA XIST, PFKFB3 mRNA, and MCEMP1 gene expression differed significantly between IS and HS. In one study, principal component analysis and unsupervised learning demonstrated the utility of hierarchical clustering of differentially expressed exons to discriminate between HS and IS. Conclusions: This review demonstrates the utility of single RNA-based targets and clusters that may have diagnostic value in distinguishing IS from HS. However, the current body of evidence is limited by considerable methodological heterogeneity between studies. Registration: This systematic review was prospectively registered on PROSPERO on 21 April 2023 (CRD42023411203). Full article

Ion channels in biological membranes can form spatially localized clusters that exhibit cooperative gating behavior. In this mode, the activity of one channel modulates the opening probability of its neighbors. Understanding such inter-channel interactions is key to elucidating the molecular mechanisms underlying electrochemical signaling and advancing channel-targeted pharmacology. In this study, we introduce a simplified stochastic model of multi-channel gating that allows for systematic analysis of cooperative behavior under controlled conditions. Two information-theoretic metrics, i.e., Shannon entropy and Sample Entropy, are applied to simulated multi-channel datasets, including idealized total current traces and dwell-time sequences of cluster states, to quantify inter-channel cooperativity. We show that the entropic measures display a strong dependency on the strength and type of cooperation (non-, positive, or negative cooperation). The proposed entropy-based framework offers a generalizable and quantitative approach for biomedical data analysis, demonstrating effectiveness in interpreting multi-channel recordings and uncovering cooperative mechanisms in ion channel behavior. The underlying mechanisms by which entropy reflects cooperativity are expected to appear in real recordings, where deviations can further aid in characterizing individual channel features in future work. Full article

The exponential growth of network traffic and the increasing sophistication of cyberattacks have underscored the need for intelligent and real-time Intrusion Detection Systems (IDS). Traditional flow-based IDS models typically analyze each network flow independently, ignoring the temporal and contextual dependencies among flows, which reduces their ability to recognize coordinated or multi-stage attacks. To address this limitation, this paper proposes a Bernoulli-based probabilistic sequence modeling framework that integrates statistical learning with visual feature representation for efficient intrusion detection. The approach begins with a comprehensive data-preprocessing pipeline that performs feature cleaning, encoding, normalization, and sequence aggregation. Each aggregated feature vector is then transformed into a 6 × 6 grayscale image, allowing the system to capture spatial correlations among network features through convolutional operations. A logistic regression model first estimates per-flow attack probabilities, and these are combined using the Bernoulli probability law to infer the likelihood of malicious activity across flow sequences. The resulting sequence-level representations are evaluated using lightweight classifiers such as TinyNet-6 × 6, MobileNetV2, and ResNet18. Experimental results on the CICIDS2017 dataset demonstrate that the proposed method achieves high detection accuracy with reduced computational cost compared to state-of-the-art deep models, highlighting its suitability for scalable, real-time IDS deployment. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

Background: Liquid biopsy, particularly through the analysis of circulating tumor DNA (ctDNA), represents a significant advancement in oncology. Unlike traditional tissue biopsies, ctDNA offers a minimally invasive, real-time approach to cancer management. It has demonstrated considerable potential in early cancer detection, monitoring of therapeutic responses, and assessing minimal residual disease (MRD) to predict recurrence. By enabling comprehensive molecular profiling through a simple blood test, ctDNA supports the core principles of precision oncology, facilitating more personalized and adaptive treatment strategies. Methods: In the following article we describe the recent developments focused on refining ctDNA detection assays to improve sensitivity and specificity. Advanced technologies, including next-generation sequencing (NGS) and digital PCR, are commonly employed. The integration of artificial intelligence (AI) and multi-omics approaches—such as combining genomic, epigenomic, and transcriptomic data—has further enhanced the analytical power of ctDNA assays. Results: Emerging evidence shows that ctDNA-based liquid biopsy enables dynamic, real-time tracking of tumor evolution and therapeutic resistance. Clinical studies have demonstrated its efficacy in detecting early-stage cancers, guiding treatment selection, and predicting relapse with higher accuracy than some conventional methods. Moreover, AI-enhanced algorithms have improved signal detection, allowing for more precise and earlier identification of actionable mutations and MRD. Conclusions: ctDNA analysis via liquid biopsy is poised to revolutionize cancer care by offering a non-invasive, precise, and adaptive tool for tumor characterization and monitoring. Although obstacles remain—particularly regarding assay sensitivity, standardization, and economic feasibility—ongoing technological innovations and multi-omics integration are rapidly advancing its clinical viability. With continued progress, ctDNA-based liquid biopsy is likely to become a cornerstone of routine oncology practice. Full article

Background: SCLC remains an aggressive malignancy with limited therapeutic progress over the past few decades. It remains unclear how the sequence of immunotherapy initiation influences overall survival (OS) in SCLC; we performed a population-based analysis using NCDB to evaluate its association with survival. Methods: SCLC patients in NCDB from 2016 to 2021 were identified to evaluate the impact of immunotherapy on OS. Among ES-SCLC patients, we conducted subsequent analyses to clarify the relationship between the sequence of immunotherapy initiation and OS in the context of chemotherapy and CRT. Results: Among 69,820 eligible patients, 9242 received CRT plus immunotherapy (CRT + IO), and 11,755 received chemotherapy plus immunotherapy (Chemo + IO). In the overall population, adding immunotherapy to chemotherapy or CRT was associated with modestly improved survival. In ES-SCLC, immunotherapy was associated with longer survival in both the Chemo and CRT cohort, while the addition of immunotherapy did not confer benefits in limited-stage SCLC (LS-SCLC). Within the ES-SCLC Chemo + IO cohort, altering the initiated immunotherapy interval (0–90 days) did not show any meaningful difference in survival. By contrast, in the CRT + IO cohort, survival showed benefit in a time-dependent pattern: patients who initiated immunotherapy within 4–7 days after CRT had a trend of survival, which was consistent with the proposed immune activation window. Conclusions: This real-world analysis suggests that immunotherapy was associated with longer survival in ES-SCLC, CRT + IO is associated with improved OS, with a more obvious survival benefit when immunotherapy is initiated within 4–7 days after CRT. These findings hint at the potential importance of immunotherapy initiation sequence and warrant further prospective validation. Full article

Stock price forecasting remains challenging due to the non-linear, noisy, and non-stationary nature of financial time series. Although LSTMs and Transformer-based models have improved sequential modeling, their ability to scale efficiently to long financial sequences remains limited. Recently, selective state space models such as Mamba have emerged as efficient alternatives to self-attention, offering attention-like performance with linear computational complexity. In this study, we systematically evaluate multiple Mamba-augmented Transformer architectures for stock market price forecasting. We further propose CrossMamba, a novel architecture that models cross-sequence interactions between encoder and decoder representations using a causal Mamba block. Experiments on multiple S&P 500 and Yahoo Finance stocks show that CrossMamba achieves superior short-horizon performance with 5-day input windows ($R^2$ up to 0.963), while Hybrid Bi-Mamba performs best for longer horizons, achieving the lowest MAE of 0.67 for 10-day forecasts. Compared with advanced Mamba-based and Transformer baselines, the proposed models achieve competitive accuracy while maintaining substantially improved computational efficiency. These results highlight the effectiveness of Mamba-augmented Transformers as scalable architectures for financial time series forecasting. Full article

Background/Objectives: To overcome bottlenecks in the application of Macleaya cordata extract (MCE) in veterinary traditional Chinese medicine, such as low bioavailability of its active ingredients, gastrointestinal irritation, and muscular toxicity, this study aimed to develop a liposomal nano-delivery system loaded with MCE (MCE-Lips) to achieve the core objective of “enhancing efficacy and reducing toxicity” and to explore its potential application and mechanism of action in treating bacterial diarrhea. Methods: MCE-Lips were prepared using the thin-film dispersion method, and their physicochemical properties—particle size, encapsulation efficiency, and drug loading capacity—were characterized. In vitro, cytotoxicity against skeletal muscle cells and NCM460 intestinal epithelial cells was evaluated using the CCK-8 assay. The release of lactate dehydrogenase (LDH) from skeletal muscle cells was measured with an LDH assay kit. The expression levels of inflammatory factors (TNF-α, IL-6, and IL-1β) in both cell types were determined through ELISA. A fluorescent probe was employed to assess cell membrane integrity. The effect of MCE-Lips on the expression of tight junction proteins (ZO-1, Occludin, and Claudin-5) was evaluated via immunofluorescence. Acute toxicity was examined through H&E staining. A bacterial diarrhea model was established using Escherichia coli in mice, and comprehensive safety and efficacy were assessed through hematological tests and gastrointestinal motility evaluation. Finally, untargeted metabolomics and 16S rRNA sequencing were utilized to investigate the underlying mechanisms of action. Results: The prepared MCE-Lips had an average particle size of 86.49 nm and a high encapsulation efficiency of 89.07%. In vitro experiments demonstrated that MCE-Lips significantly alleviated skeletal muscle cell damage, reduced LDH release (p < 0.05), and effectively inhibited the expression of inflammatory factors IL-6, TNF-α, and IL-1β (p < 0.05). In NCM460 cells, MCE-Lips exhibited a more pronounced inhibitory effect on LPS-induced release of TNF-α (p < 0.01), IL-6 (p < 0.0001), and IL-1β (p < 0.0001) and enhanced intestinal barrier function by upregulating the expression of tight junction proteins ZO-1 (p < 0.001), Occludin (p < 0.01), and Claudin-5 (p < 0.01). In the bacterial diarrhea model, MCE-Lips showed excellent anti-diarrheal efficacy (p < 0.01). Hematological analysis indicated no systemic toxicity. At the endocrine level, the high-dose group significantly reduced motilin (MTL) levels (p < 0.01), which slowed intestinal motility and prolonged chyme retention, thereby alleviating diarrhea symptoms. Mechanistic studies revealed that it acts by regulating the intestinal metabolic profile and microbial community structure, with Desulfovibrio, Enterococcus, and Streptococcus identified as key characteristic differential genera. Conclusions: For the first time, an MCE liposome nanoparticle system was constructed, and untargeted metabolomics combined with 16S rRNA sequencing were employed to elucidate its anti-diarrheal mechanism. MCE-Lips exerts excellent antibacterial diarrhea effects through multiple mechanisms, including direct cytoprotection and anti-inflammatory action, enhancement of the intestinal barrier, regulation of gut function, and remodeling of the gut microecology. This work provides a novel paradigm for plant-derived nano-anti-diarrheal agents. The systematic evaluation of the pharmacodynamics of MCE-Lips in a piglet bacterial diarrhea model will lay a solid foundation for its eventual market application. Full article