GOI-YOLOv8 Grouping Offset and Isolated GiraffeDet Low-Light Target Detection.

In the realm of computer vision, object detection holds significant importance and has demonstrated commendable performance across various scenarios. However, it typically requires favorable visibility conditions within the scene. Therefore, it is imperative to explore methodologies for conducting object detection under low-visibility circumstances. With its balanced combination of speed and accuracy, the state-of-the-art YOLOv8 framework has been recognized as one of the top algorithms for object detection, demonstrating outstanding performance results across a range of standard datasets. Nonetheless, current YOLO-series detection algorithms still face a significant challenge in detecting objects under low-light conditions. This is primarily due to the significant degradation in performance when detectors trained on illuminated data are applied to low-light datasets with limited visibility. To tackle this problem, we suggest a new model named Grouping Offset and Isolated GiraffeDet Target Detection-YOLO based on the YOLOv8 architecture. The proposed model demonstrates exceptional performance under low-light conditions. We employ the repGFPN feature pyramid network in the design of the feature fusion layer neck to enhance hierarchical fusion and deepen the integration of low-light information. Furthermore, we refine the repGFPN feature fusion layer by introducing a sampling map offset to address its limitations in terms of weight and efficiency, thereby better adapting it to real-time applications in low-light environments and emphasizing the potential features of such scenes. Additionally, we utilize group convolution to isolate interference information from detected object edges, resulting in improved detection performance and model efficiency. Experimental results demonstrate that our GOI-YOLO reduces the parameter count by 11% compared to YOLOv8 while decreasing computational requirements by 28%. This optimization significantly enhances real-time performance while achieving a competitive increase of 2.1% in Map50 and 0.6% in Map95 on the ExDark dataset.

Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks.

Multimodal neuroimaging modeling has become a widely used approach but confronts considerable challenges due to heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitates the deployment of advanced computational methods to integrate and interpret these diverse datasets within a cohesive analytical framework. In our research, we amalgamate functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and structural MRI (sMRI) into a cohesive framework. This integration capitalizes on the unique strengths of each modality and their inherent interconnections, aiming for a comprehensive understanding of the brain's connectivity and anatomical characteristics. Utilizing the Glasser atlas for parcellation, we integrate imaging-derived features from various modalities-functional connectivity from fMRI, structural connectivity from DTI, and anatomical features from sMRI-within consistent regions. Our approach incorporates a masking strategy to differentially weight neural connections, thereby facilitating a holistic amalgamation of multimodal imaging data. This technique enhances interpretability at connectivity level, transcending traditional analyses centered on singular regional attributes. The model is applied to the Human Connectome Project's Development study to elucidate the associations between multimodal imaging and cognitive functions throughout youth. The analysis demonstrates improved predictive accuracy and uncovers crucial anatomical features and essential neural connections, deepening our understanding of brain structure and function. This study not only advances multi-modal neuroimaging analytics by offering a novel method for the integrated analysis of diverse imaging modalities but also improves the understanding of intricate relationship between the brain's structural and functional networks and cognitive development.

Reading the m6A-encoded epitranscriptomic information in development and diseases.

N6-methyladenosine (m6A) represents the most prevalent internal and reversible modification on RNAs. Different cell types display their unique m6A profiles, which are determined by the functions of m6A writers and erasers. M6A modifications lead to different outcomes such as decay, stabilization, or transport of the RNAs. The m6A-encoded epigenetic information is interpreted by m6A readers and their interacting proteins. M6A readers are essential for different biological processes, and the defects in m6A readers have been discovered in diverse diseases. Here, we review the latest advances in the roles of m6A readers in development and diseases. These recent studies not only highlight the importance of m6A readers in regulating cell fate transitions, but also point to the potential application of drugs targeting m6A readers in diseases.

A True Random Number Generator Design Based on the Triboelectric Nanogenerator with Multiple Entropy Sources.

The triboelectric nanogenerator (TENG) has the potential to serve as a high-entropy energy harvester, enabling the self-powered operation of Internet of Things (IoT) devices. True random number generator (TRNG) is a common feature of encryption used in IoT data communication, ensuring the security of transmitted information. The benefits of multiplexing TENG and TRNG in resource-constrained IoT devices are substantial. However, current designs are limited by the usage scenarios and throughput of the TRNG. Specifically, we propose a structurally and environmentally friendly design based on the contact-separation structure, integrating heat fluctuation and charge decay as entropy sources. Furthermore, filtering and differential algorithms are recommended for data processing based on TENG characteristics to enhance randomness. Finally, a TENG-based TRNG is fabricated, and its performance is verified. Test results demonstrate a random number throughput of 25 Mbps with a randomness test pass rate approaching 99%, demonstrating suitability for resource-constrained IoT applications.

Challenges and pitfalls in managing lumbosacral hydatid disease: Lessons learned from clinical practice.

OBJECTIVE: Lumbosacral hydatid disease (LHD), a rare skeletal parasitic disease that involves the lumbosacral region. In this study, we summarized the diagnostic and therapeutic procedures for patients with LHD to provide insights into managing this rare disease. METHODS: Between 2000 and 2023, 16 patients diagnosed with LHD were retrospectively analyzed. Each patient's medical records and follow-up details, were carefully assessed. The average follow-up period was 11.25 ± 6.41 years, providing valuable insights into treatment durability and effectiveness. RESULTS: The diagnosis was confirmed via imaging, serological tests, and pathological examination. The clinical symptoms included lumbago with lower limb numbness (25 %) and urinary and fecal incontinence (25 %). All patients underwent surgery, with an average of 2.6 surgeries per patient. Thirteen (81.25 %) patients experienced recurrence postoperatively. CONCLUSION: LHD is a severe and complex skeletal parasitic disease with significant diagnostic and therapeutic challenges. Effective management requires a comprehensive strategy involving surgery and additional therapies.

An efficient ANN SoC for detecting Alzheimer's disease based on recurrent computing.

Alzheimer's Disease (AD) is an irreversible, degenerative condition that, while incurable, can have its progression slowed or impeded. While there are numerous methods utilizing neural networks for AD detection, there is a scarcity of High-performance AD detection chips. Moreover, excessively complex neural networks are not conducive to on-chip implementation and clinical applications. This study addresses the challenges of high misdiagnosis rates and significant hardware costs inherent in traditional AD detection techniques. A novel and efficient AD detection framework based on a recurrent computational strategy is proposed. The framework harnesses an Artificial Neural Network (ANN) embedded within a System on Chip (SoC) to perform sophisticated Electroencephalogram (EEG) analysis. The approach began by employing a reduced IEEE754 single-precision encoding method to hardware-encode the preprocessed EEG data, thereby minimizing the memory storage area. Next, data remapping techniques were utilized to ensure the continuity of the input data read addresses and reduce the memory access pressure during neural network computations. Subsequently, hierarchical and Processing Element (PE) reuse technologies were leveraged to perform the multiply-accumulate operations of the ANN. Finally, a step function was chosen to establish binary classification circuits dedicated to AD detection. Experimental results indicate that the optimized SoC achieves a 70 % reduction in area and a 50 % reduction in power consumption compared to traditional designs. For various neural network models, the detection model proposed in this paper incurs less overhead, with a training speed 3 to 4 times faster than other traditional models, and a high accuracy rate of 98.53 %.

PlantCircRNA: a comprehensive database for plant circular RNAs.

Circular RNAs (circRNAs) represent recently discovered novel regulatory non-coding RNAs. While they are present in many eukaryotes, there has been limited research on plant circRNAs. We developed PlantCircRNA (https://plant.deepbiology.cn/PlantCircRNA/) to fill this gap. The two most important features of PlantCircRNA are (i) it incorporates circRNAs from 94 plant species based on 39 245 RNA-sequencing samples and (ii) it imports the original AtCircDB and CropCircDB databases. We manually curated all circRNAs from published articles, and imported them into the database. Furthermore, we added detailed information of tissue as well as abiotic stresses to the database. To help users understand these circRNAs, the database includes a detection score to measure their consistency and a naming system following the guidelines recently proposed for eukaryotes. Finally, we developed a comprehensive platform for users to visualize, analyze, and download data regarding specific circRNAs. This resource will serve as a home for plant circRNAs and provide the community with unprecedented insights into these mysterious molecule.

Non-apoptotic cell death in osteoarthritis: Recent advances and future.

Osteoarthritis (OA) is the most common degenerative joint disease. Multiple tissues are altered during the development of OA, resulting in joint pain and permanent damage to the osteoarticular joints. Current research has demonstrated that non-apoptotic cell death plays a crucial role in OA. With the continuous development of targeted therapies, non-apoptotic cell death has shown great potential in the prevention and treatment of OA. We systematically reviewed research progress on the role of non-apoptotic cell death in the pathogenesis, development, and outcome of OA, including autophagy, pyroptosis, ferroptosis, necroptosis, immunogenic cell death, and parthanatos. This article reviews the mechanism of non-apoptotic cell death in OA and provides a theoretical basis for the identification of new targets for OA treatment.

The regulatory role of m6A modification in the maintenance and differentiation of embryonic stem cells.

As the most prevalent and reversible internal epigenetic modification in eukaryotic mRNAs, N 6-methyladenosine (m6A) post-transcriptionally regulates the processing and metabolism of mRNAs involved in diverse biological processes. m6A modification is regulated by m6A writers, erasers, and readers. Emerging evidence suggests that m6A modification plays essential roles in modulating the cell-fate transition of embryonic stem cells. Mechanistic investigation of embryonic stem cell maintenance and differentiation is critical for understanding early embryonic development, which is also the premise for the application of embryonic stem cells in regenerative medicine. This review highlights the current knowledge of m6A modification and its essential regulatory contribution to the cell fate transition of mouse and human embryonic stem cells.

Association between cardiac magnetic resonance ventricular strain and left ventricular thrombus in patients with ST-segment elevation myocardial infarction.

BACKGROUND: Myocardial strain can analyze early myocardial dysfunction after myocardial infarction (MI). However, the correlation between left ventricular (LV) strain (including regional and global strain) obtained by cardiac magnetic resonance (CMR) imaging and left ventricular thrombus (LVT) after ST-segment elevation myocardial infarction (STEMI) is unclear. METHODS: The retrospective clinical observation study included patients with LVT (n = 20) and non-LVT (n = 195) who underwent CMR within two weeks after STEMI. CMR images were analyzed using CVI 42 (Circle Cardiovascular Imaging, Canada) to obtain LV strain values. Logistic regression analysis identified risk factors for LVT among baseline characteristics, CMR ventricular strain, and left ventricular ejection fraction (LVEF). Considering potential correlations between strains, the ability of LV strain to identify LVT was evaluated using 9 distinct models. Receiver operating characteristic curves were generated with GraphPad Prism, and the area under the curve (AUC) of LVEF, apical longitudinal strain (LS), and circumferential strain (CS) was calculated to determine their capacity to distinguish LVT. RESULTS: Among 215 patients, 9.3% developed LVT, with a 14.5% incidence in those with anterior MI. Univariate regression indicated associations of LAD infarct-related artery, lower NT-proBNP, lower LVEF, and reduced global, midventricular, and apical strain with LVT. Further multivariable regression analysis showed that apical LS, LVEF and NT-proBNP were still independently related to LVT (Apical LS: OR = 1.14, 95%CI (1.01, 1.30), P = 0.042; LVEF: OR = 0.91, 95%CI (0.85, 0.97), P = 0.005; NT-proBNP: OR = 2.35, 95%CI (1.04, 5.31) ). CONCLUSION: Reduced apical LS on CMR is independently associated with LVT after STEMI.

TCS-Fall: Cross-individual fall detection system based on channel state information and time-continuous stack method.

Background: Falls pose a serious health risk for the elderly, particular for those who are living alone. The utilization of WiFi-based fall detection, employing Channel State Information (CSI), emerges as a promising solution due to its non-intrusive nature and privacy preservation. Despite these advantages, the challenge lies in optimizing cross-individual performance for CSI-based methods. Objective: This study aimed to develop a resilient real-time fall detection system across individuals utilizing CSI, named TCS-Fall. This method was designed to offer continuous monitoring of activities over an extended timeframe, ensuring accurate and prompt detection of falls. Methods: Extensive CSI data on 1800 falls and 2400 daily activities was collected from 20 volunteers. The grouped coefficient of variation of CSI amplitudes were utilized as input features. These features capture signal fluctuations and are input to a convolutional neural network classifier. Cross-individual performance was extensively evaluated using various train/test participant splits. Additionally, a user-friendly CSI data collection and detection tool was developed using PyQT. To achieve real-time performance, data parsing and pre-processing computations were optimized using Numba's just-in-time compilation. Results: The proposed TCS-Fall method achieved excellent performance in cross-individual fall detection. On the test set, AUC reached 0.999, no error warning ratio score reached 0. 955 and correct warning ratio score reached of 0.975 when trained with data from only two volunteers. Performance can be further improved to 1.00 when 10 volunteers were included in training data. The optimized data parsing/pre-processing achieved over 20× speedup compared to previous method. The PyQT tool parsed and detected the fall within 100 ms. Conclusions: TCS-Fall method enables excellent real-time cross-individual fall detection utilizing WiFi CSI, promising swift alerts and timely assistance to elderly. Additionally, the optimized data processing led to a significant speedup. These results highlight the potential of our approach in enhancing real-time fall detection systems.

Self-Assembled Multilayer-Modified Needles Simulate Acupuncture and Diclofenac Sodium Delivery for Rheumatoid Arthritis.

A novel therapeutic approach combining acupuncture and diclofenac sodium (DS) administration was established for the potential treatment for rheumatoid arthritis (RA). DS is a commonly used anti-inflammatory and analgesic drug but has short duration and adverse effects. Acupoints are critical linkages in the meridian system and are potential candidates for drug delivery. Herein, we fabricated a DS-loaded multilayer-modified acupuncture needle (DS-MMAN) and investigated its capacity for inhibiting RA. This DS-MMAN possesses sustained release properties and in vitro anti-inflammatory effects. Experimental results showed that the DS-MMAN with microdoses can enhance analgesia and efficiently relieve joint swelling compared to the oral or intra-articular administration of DS with gram-level doses. Moreover, the combination of acupoint and DS exerts a synergistic improvement in inflammation and joint damage. Cytokine and T cell analyses in the serum indicated that the application of DS-MMAN suppressed the levels of pro-inflammatory factors and increased the levels of anti-inflammatory factors. Furthermore, the acupoint administration via DS-MMAN could decrease the accumulation of DS in the liver and kidneys, which may express better therapeutic efficiency and low toxicity. The present study demonstrated that the acupuncture needle has the potential to build a bridge between acupuncture and medication, which would be a promising alternative to the combination of traditional and modern medicine.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevel-opmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

Inferior Frontal Sulcal Hyperintensity on Fluid-Attenuated Inversion Recovery Is Related to Cerebrospinal Fluid Clearance via Putative Meningeal Lymphatics.

Inferior frontal sulcal hyperintensity (IFSH) on FLAIR sequence may indicate elevated cerebrospinal fluid (CSF) wastes. The objective of this study was to investigate its association with the clearance function of putative meningeal lymphatic vessels (mLVs). We included patients who underwent FLAIR sequence and dynamic contrast MRI with intrathecal administration of contrast agent. The visibility of IFSH was quantitatively assessed by measuring the mean signal intensity of inferior frontal sulci on 2D FLAIR. The clearance function of putative mLVs was defined as the percentage change of signal unite ratio in the parasagittal dura from baseline to 4.5, 15 and 39 hours after intrathecal injection on dynamic contrast MRI. Additionally, imaging markers of cerebral small vessel disease, including white matter hyperintensities and enlarged perivascular spaces, were measured. Correlation analysis and linear regression were employed to verify the association of IFSH with the clearance function of mLVs. A total of 76 patients were included in the study. The visibility of IFSH was found to be associated with the percentage change of signal unite ratio in parasagittal dura from baseline to 15 and 39 hours in adjusted analyses. Furthermore, the visibility of IFSH was positively related to the age, scores of both periventricular and deep white matter hyperintensities, and the grade of enlarged perivascular spaces in centrum semiovale. These findings suggest that the visibility of IFSH on 2D FLAIR may serve as an indicator of clearance dysfunction of mLVs and may be implicated in the development of cerebral small vessel disease.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

Electrical Stimulations Generated by P(VDF-TrFE) Films Enhance Adhesion Forces and Odontogenic Differentiation of Dental Pulp Stem Cells (DPSCs).

Biophysical and biochemical cues of biomaterials can regulate cell behaviors. Dental pulp stem cells (DPSCs) in pulp tissues can differentiate to odontoblast-like cells and secrete reparative dentin to form a barrier to protect the underlying pulp tissues and enable complete pulp healing. Promotion of the odontogenic differentiation of DPSCs is essential for dentin regeneration. The effects of the surface potentials of biomaterials on the adhesion and odontogenic differentiation of DPSCs remain unclear. Here, poly(vinylidene fluoride-trifluoro ethylene) (P(VDF-TrFE)) films with different surface potentials were prepared by the spin-coating technique and the contact poling method. The cytoskeletal organization of DPSCs grown on P(VDF-TrFE) films was studied by immunofluorescence staining. Using atomic force microscopy (AFM), the lateral detachment forces of DPSCs from P(VDF-TrFE) films were quantified. The effects of electrical stimulation generated from P(VDF-TrFE) films on odontogenic differentiation of DPSCs were evaluated in vitro and in vivo. The unpolarized, positively polarized, and negatively polarized films had surface potentials of -52.9, +902.4, and -502.2 mV, respectively. DPSCs on both negatively and positively polarized P(VDF-TrFE) films had larger cell areas and length-to-width ratios than those on the unpolarized films (P < 0.05). During the detachment of DPSCs from P(VDF-TrFE) films, the average magnitudes of the maximum detachment forces were 29.4, 72.1, and 53.9 nN for unpolarized, positively polarized, and negatively polarized groups, respectively (P < 0.05). The polarized films enhanced the mineralization activities and increased the expression levels of the odontogenic-related proteins of DPSCs compared to the unpolarized films (P < 0.05). The extracellular signal-regulated kinase (ERK) signaling pathway was involved in the odontogenic differentiation of DPSCs as induced by surface charge. In vivo, the polarized P(VDF-TrFE) films enhanced adhesion of DPSCs and promoted the odontogenic differentiation of DPSCs by electrical stimulation, demonstrating a potential application of electroactive biomaterials for reparative dentin formation in direct pulp capping.

Global diversity of airborne pathogenic bacteria and fungi from wastewater treatment plants.

Wastewater treatment plants (WWTPs) have been recognized as one of the major potential sources of the spread of airborne pathogenic microorganisms under the global pandemic of COVID-19. The differences in research regions, wastewater treatment processes, environmental conditions, and other aspects in the existing case studies have caused some confusion in the understanding of bioaerosol pollution characteristics. In this study, we integrated and analyzed data from field sampling and performed a systematic literature search to determine the abundance of airborne microorganisms in 13 countries and 37 cities across four continents (Asia, Europe, North America, and Africa). We analyzed the concentrations of bioaerosols, the core composition, global diversity, determinants, and potential risks of airborne pathogen communities in WWTPs. Our findings showed that the culturable bioaerosol concentrations of global WWTPs are 102-105 CFU/m3. Three core bacterial pathogens, namely Bacillus, Acinetobacter, and Pseudomonas, as well as two core fungal pathogens, Cladosporium and Aspergillus, were identified in the air across global WWTPs. WWTPs have unique core pathogenic communities and distinct continental divergence. The sources of airborne microorganisms (wastewater) and environmental variables (relative humidity and air contaminants) have impacts on the distribution of airborne pathogens. Potential health risks are associated with the core airborne pathogens in WWTPs. Our study showed the specificity, multifactorial influences, and potential pathogenicity of airborne pathogenic communities in WWTPs. Our findings can improve the understanding of the global diversity and biogeography of airborne pathogens in WWTPs, guiding risk assessment and control strategies for such pathogens. Furthermore, they provide a theoretical basis for safeguarding the health of WWTP workers and ensuring regional ecological security.

Bombus terrestris Prefer Mixed-Pollen Diets for a Better Colony Performance: A Laboratory Study.

Pollen is a major source of proteins and lipids for bumblebees. The nutritional content of pollen may differ from source plants, ultimately affecting colony development. This study investigated the foraging preferences of Bombus terrestris in regard to four pollen species, i.e., oilseed rape, wild apricot, sunflower, and buckwheat, under laboratory conditions. The results show that B. terrestris diversified their preference for pollens; the bumblebees mostly preferred wild apricot pollen, whereas sunflower pollen was the least preferred. The colonies fed on a mixed four-pollen diet, with a protein-lipid ratio of 4.55-4.86, exhibited better development in terms of the number of offspring, individual body size and colony weight. The colonies fed with buckwheat and sunflower pollens produced a significantly lower number of workers and failed to produce queen and male offspring. Moreover, wild apricot pollen had the richest protein content (23.9 g/100 g) of the four pollen species, whereas oilseed rape pollen had the highest lipid content (6.7 g/100 g), as revealed by the P:L ratios of wild apricot, sunflower, buckwheat, and oilseed rape, which were 6.76, 5.52, 3.50, and 3.37, respectively. Generally, B. terrestris showed feeding preferences regarding different pollens and a mixture of pollens, which ultimately resulted in differences in colony development. The findings of this study provide important baseline information to researchers and developers of nutritive pollen diets for bumblebees.

Exploration of altered miRNA expression and function in MSC-derived extracellular vesicles in response to hydatid antigen stimulation.

Background: Hydatid disease is caused by Echinococcus parasites and can affect various tissues and organs in the body. The disease is characterized by the presence of hydatid cysts, which contain specific antigens that interact with the host's immune system. Mesenchymal stem cells (MSCs) are pluripotent stem cells that can regulate immunity through the secretion of extracellular vesicles (EVs) containing microRNAs (miRNAs). Methods: In this study, hydatid antigens were isolated from sheep livers and mice peritoneal cavities. MSCs derived from mouse bone marrow were treated with different hydatid antigens, and EVs were isolated and characterized from the conditioned medium of MSCs. Small RNA library construction, miRNA target prediction, and differential expression analysis were conducted to identify differentially expressed miRNAs. Functional enrichment and network construction were performed to explore the biological functions of the target genes. Real-time PCR and Western blotting were used for miRNA and gene expression verification, while ELISA assays quantified TNF, IL-1, IL-6, IL-4, and IL-10 levels in cell supernatants. Results: The study successfully isolated hydatid antigens and characterized MSC-derived EVs, demonstrating the impact of antigen concentration on MSC viability. Key differentially expressed miRNAs, such as miR-146a and miR-9-5p, were identified, with functional analyses revealing significant pathways like Endocytosis and MAPK signaling associated with these miRNAs' target genes. The miRNA-HUB gene regulatory network identified crucial miRNAs and HUB genes, such as Traf1 and Tnf, indicating roles in immune modulation and osteogenic differentiation. Protein-protein interaction (PPI) network analysis highlighted central HUB genes like Akt1 and Bcl2. ALP activity assays confirmed the influence of antigens on osteogenic differentiation, with reduced ALP activity observed. Expression analysis validated altered miRNA and chemokine expression post-antigen stimulation, with ELISA analysis showing a significant reduction in CXCL1 expression in response to antigen exposure. Conclusion: This study provides insights into the role of MSC-derived EVs in regulating parasite immunity. The findings suggest that hydatid antigens can modulate the expression of miRNAs in MSC-derived EVs, leading to changes in chemokine expression and osteogenic capacity. These findings contribute to a better understanding of the immunomodulatory mechanisms involved in hydatid disease and provide potential therapeutic targets for the development of new treatment strategies.