HHOMR: a hybrid high-order moment residual model for miRNA-disease association prediction.

Numerous studies have demonstrated that microRNAs (miRNAs) are critically important for the prediction, diagnosis, and characterization of diseases. However, identifying miRNA-disease associations through traditional biological experiments is both costly and time-consuming. To further explore these associations, we proposed a model based on hybrid high-order moments combined with element-level attention mechanisms (HHOMR). This model innovatively fused hybrid higher-order statistical information along with structural and community information. Specifically, we first constructed a heterogeneous graph based on existing associations between miRNAs and diseases. HHOMR employs a structural fusion layer to capture structure-level embeddings and leverages a hybrid high-order moments encoder layer to enhance features. Element-level attention mechanisms are then used to adaptively integrate the features of these hybrid moments. Finally, a multi-layer perceptron is utilized to calculate the association scores between miRNAs and diseases. Through five-fold cross-validation on HMDD v2.0, we achieved a mean AUC of 93.28%. Compared with four state-of-the-art models, HHOMR exhibited superior performance. Additionally, case studies on three diseases-esophageal neoplasms, lymphoma, and prostate neoplasms-were conducted. Among the top 50 miRNAs with high disease association scores, 46, 47, and 45 associated with these diseases were confirmed by the dbDEMC and miR2Disease databases, respectively. Our results demonstrate that HHOMR not only outperforms existing models but also shows significant potential in predicting miRNA-disease associations.

BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks.

Circular RNA (CircRNA)-microRNA (miRNA) interaction (CMI) is an important model for the regulation of biological processes by non-coding RNA (ncRNA), which provides a new perspective for the study of human complex diseases. However, the existing CMI prediction models mainly rely on the nearest neighbor structure in the biological network, ignoring the molecular network topology, so it is difficult to improve the prediction performance. In this paper, we proposed a new CMI prediction method, BEROLECMI, which uses molecular sequence attributes, molecular self-similarity, and biological network topology to define the specific role feature representation for molecules to infer the new CMI. BEROLECMI effectively makes up for the lack of network topology in the CMI prediction model and achieves the highest prediction performance in three commonly used data sets. In the case study, 14 of the 15 pairs of unknown CMIs were correctly predicted.

Immune patterns of cuproptosis in ischemic heart failure: A transcriptome analysis.

Cuproptosis is a recently discovered programmed cell death pattern that affects the tricarboxylic acid (TCA) cycle by disrupting the lipoylation of pyruvate dehydrogenase (PDH) complex components. However, the role of cuproptosis in the progression of ischemic heart failure (IHF) has not been investigated. In this study, we investigated the expression of 10 cuproptosis-related genes in samples from both healthy individuals and those with IHF. Utilizing these differential gene expressions, we developed a risk prediction model that effectively distinguished healthy and IHF samples. Furthermore, we conducted a comprehensive evaluation of the association between cuproptosis and the immune microenvironment in IHF, encompassing infiltrated immunocytes, immune reaction gene-sets and human leukocyte antigen (HLA) genes. Moreover, we identified two different cuproptosis-mediated expression patterns in IHF and explored the immune characteristics associated with each pattern. In conclusion, this study elucidates the significant influence of cuproptosis on the immune microenvironment in ischemic heart failure (IHF), providing valuable insights for future mechanistic research exploring the association between cuproptosis and IHF.

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution.

Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug-disease association prediction. However, most of the existing models neglect the positive influence of non-Euclidean data and multisource information, and there is still a critical issue for graph neural networks regarding how to set the feature diffuse distance. To solve the problems, we proposed SiSGC, which makes full use of the biological knowledge information as initial features and learns the structure information from the constructed heterogeneous graph with the adaptive selection of the information diffuse distance. Then, the structural features are fused with the denoised similarity information and fed to the advanced classifier of CatBoost to make predictions. Three different data sets are used to confirm the robustness and generalization of SiSGC under two splitting strategies. Experiment results demonstrate that the proposed model achieves superior performance compared with the six leading methods and four variants. Our case study on breast neoplasms further indicates that SiSGC is trustworthy and robust yet simple. We also present four drugs for breast cancer treatment with high confidence and further give an explanation for demonstrating the rationality. There is no doubt that SiSGC can be used as a beneficial supplement for drug repositioning.

Bioaccumulation Characteristics of Typical Perfluoroalkyl Compounds in Alfalfa Under Salt Stress.

Medicago sativa, commonly known as alfalfa, is widely distributed worldwide, known for its strong stress resistance and well-developed root system, making it an important plant in ecological restoration research. To investigate the absorption and transport characteristics of alfalfa for typical perfluoroalkyl substances (PFAS) under salt stress, a 30-day indoor greenhouse experiment was conducted. The results showed that alfalfa exhibited varying degrees of absorption and transport for the selected PFAS. The highest BCF (Bioconcentration Factor) for shoot tissue reached 725.4 (for PFBA), and the highest TF (Translocation Factor) reached 53.8 (for PFPeA). Different PFAS compounds exhibited distinct bioaccumulation behaviors, with short-chain PFAS more readily entering the plant's root system and being transported upwards, while long-chain PFAS tended to adsorb to the surface of the root system. Furthermore, salt stress did not significantly affect the uptake of PFAS by alfalfa. This suggests that alfalfa is salt-tolerant and holds great potential for ecological restoration in short-chain PFAS-contaminated sites.

[Expression and significance of hypoxia-inducible factor 1α and Bcl-2/adenovirus E1B19kDa-interacting protein 3 in children with traumatic brain injury]. / ç¼ºæ°§è¯±å¯¼å› å­1α、Bcl-2/è ºç— æ¯’E1B19kDa相互作用蛋白3在儿童创伤性脑损伤中的表达及意义.

OBJECTIVES: To dynamically observe the changes in hypoxia-inducible factor 1α (HIF-1α) and Bcl-2/adenovirus E1B19kDa-interacting protein 3 (BNIP3) in children with traumatic brain injury (TBI) and evaluate their clinical value in predicting the severity and prognosis of pediatric TBI. METHODS: A prospective study included 47 children with moderate to severe TBI from January 2021 to July 2023, categorized into moderate (scores 9-12) and severe (scores 3-8) subgroups based on the Glasgow Coma Scale. A control group consisted of 30 children diagnosed and treated for inguinal hernia during the same period, with no underlying diseases. The levels of HIF-1α, BNIP3, autophagy-related protein Beclin-1, and S100B were compared among groups. The predictive value of HIF-1α, BNIP3, Beclin-1, and S100B for the severity and prognosis of TBI was assessed using receiver operating characteristic (ROC) curves. RESULTS: Serum levels of HIF-1α, BNIP3, Beclin-1, and S100B in the TBI group were higher than those in the control group (P<0.05). Among the TBI patients, the severe subgroup had higher levels of HIF-1α, BNIP3, Beclin-1, and S100B than the moderate subgroup (P<0.05). Correlation analysis showed that the serum levels of HIF-1α, BNIP3, Beclin-1, and S100B were negatively correlated with the Glasgow Coma Scale scores (P<0.05). After 7 days of treatment, serum levels of HIF-1α, BNIP3, Beclin-1, and S100B in both non-surgical and surgical TBI patients decreased compared to before treatment (P<0.05). ROC curve analysis indicated that the areas under the curve for predicting severe TBI based on serum levels of HIF-1α, BNIP3, Beclin-1, and S100B were 0.782, 0.835, 0.872, and 0.880, respectively (P<0.05), and for predicting poor prognosis of TBI were 0.749, 0.775, 0.814, and 0.751, respectively (P<0.05). CONCLUSIONS: Serum levels of HIF-1α, BNIP3, and Beclin-1 are significantly elevated in children with TBI, and their measurement can aid in the clinical assessment of the severity and prognosis of pediatric TBI.

CircEYA3 aggravates intervertebral disc degeneration through the miR-196a-5p/EBF1 axis and NF-κB signaling.

Intervertebral disc degeneration (IDD) is a well-established cause of disability, and extensive evidence has identified the important role played by regulatory noncoding RNAs, specifically circular RNAs (circRNAs) and microRNAs (miRNAs), in the progression of IDD. To elucidate the molecular mechanism underlying IDD, we established a circRNA/miRNA/mRNA network in IDD through standardized analyses of all expression matrices. Our studies confirmed the differential expression of the transcription factors early B-cell factor 1 (EBF1), circEYA3, and miR-196a-5p in the nucleus pulposus (NP) tissues of controls and IDD patients. Cell proliferation, apoptosis, and extracellular mechanisms of degradation in NP cells (NPC) are mediated by circEYA3. MiR-196a-5p is a direct target of circEYA3 and EBF1. Functional analysis showed that miR-196a-5p reversed the effects of circEYA3 and EBF1 on ECM degradation, apoptosis, and proliferation in NPCs. EBF1 regulates the nuclear factor kappa beta (NF-кB) signalling pathway by activating the IKKß promoter region. This study demonstrates that circEYA3 plays an important role in exacerbating the progression of IDD by modulating the NF-κB signalling pathway through regulation of the miR196a-5p/EBF1 axis. Consequently, a novel molecular mechanism underlying IDD development was elucidated, thereby identifying a potential therapeutic target for future exploration.

Fish IL-26 collaborates with IL-10R2 and IL-20R1 to enhance gut mucosal barrier during the antibacterial innate immunity.

IL-26 is a cytokine that is crucial for the maintenance and function of the gut mucosal barrier. IL-26 signaling pathway relies on a heterodimeric receptor complex, which is composed of two distinct subunits, IL-10R2 and IL-20R1. However, there are no reports on the antibacterial immunity of IL-26 and its receptors in fish. For this purpose, in this study we identified IL-26 and its receptors IL-10R2 and IL-20R1 in Carassius cuvieri × Carassius auratus red var. (named WR-IL-26, WR-IL10R2 and WR-IL20R1, respectively). Phylogenetic analysis confirmed the conservation of these genes, with shared structural motifs similar to those found in higher vertebrates. Upon exposure to Aeromonas hydrophila, a common fish pathogen, there was a significant upregulation of WR-IL-26, WR-IL10R2 and WR-IL20R1 in the gut, indicating a potential role in the immune response to infection. A co-immunoprecipitation assay revealed that WR-IL-26 formed complexes with WR-IL10R2 and WR-IL20R1. In vivo experiments demonstrated that administration of WR-IL-26 activated the JAK1-STAT3 signaling pathway and protected the gut mucosa barrier from A. hydrophila infection. Conversely, silencing WR-IL10R2 and WR-IL20R1 via RNA interference significantly attenuated the activation of WR-IL-26-mediated JAK1-STAT3 pathway. These results provided new insights into the role of IL-26 and its receptors in the gut mucosa barrier and could offer novel therapeutic strategies for managing bacterial infections in aquaculture.

Ginkgolide A attenuated apoptosis via inhibition of oxidative stress in mice with traumatic brain injury.

Traumatic brain injury (TBI) is the main cause of death among young adults and the main cause of mortality and disability for all ages groups worldwide. Ginkgolides terpenoid compounds unique to Ginkgo biloba, which have protective effects on cardiovascular and cerebrovascular diseases. The aim of this study is to investigate whether ginkgolide A (GA) can improve TBI in mice and whether it can alleviate cell apoptosis in the brain of TBI mice by reducing oxidative stress. Mice received TBI and GA administration for 7 days. Neurological deficits were monitored and brain tissues were examined for molecular pathological markers. TBI mice had more severer neurobehavioral deficits compared with sham group, which could be improved by administration of GA. GA administration improveed Modified Neurological Severity Scale (mNSS) scores, Grid-Walking test and Rotarod test of TBI mice. The apoptosis increased in TBI mice, and reduced after GA treatment. The biomarkers of oxidative stress 8-OHdG and malondialdehyde (MDA) in the brain of TBI mice increased, while SOD reduced. These changes were reversed after GA administration. These outcomes showed that GA could raise neurobehavioral deficiency of TBI mice. GA treatment could attenuate apoptosis in TBI mice by reducing oxidative stress.

Predicting miRNA-disease Associations Based on Spectral Graph Transformer with Dynamic Attention and Regularization.

Extensive research indicates that microRNAs (miRNAs) play a crucial role in the analysis of complex human diseases. Recently, numerous methods utilizing graph neural networks have been developed to investigate the complex relationships between miRNAs and diseases. However, these methods often face challenges in terms of overall effectiveness and are sensitive to node positioning. To address these issues, the researchers introduce DARSFormer, an advanced deep learning model that integrates dynamic attention mechanisms with a spectral graph Transformer effectively. In the DARSFormer model, a miRNA-disease heterogeneous network is constructed initially. This network undergoes spectral decomposition into eigenvalues and eigenvectors, with the eigenvalue scalars being mapped into a vector space subsequently. An orthogonal graph neural network is employed to refine the parameter matrix. The enhanced features are then input into a graph Transformer, which utilizes a dynamic attention mechanism to amalgamate features by aggregating the enhanced neighbor features of miRNA and disease nodes. A projection layer is subsequently utilized to derive the association scores between miRNAs and diseases. The performance of DARSFormer in predicting miRNA-disease associations is exemplary. It achieves an AUC of 94.18% in a five-fold cross-validation on the HMDD v2.0 database. Similarly, on HMDD v3.2, it records an AUC of 95.27%. Case studies involving colorectal, esophageal, and prostate tumors confirm 27, 28, and 26 of the top 30 associated miRNAs against the dbDEMC and miR2Disease databases, respectively. The code and data for DARSFormer are accessible at https://github.com/baibaibaialone/DARSFormer.

GSLCDA: An Unsupervised Deep Graph Structure Learning Method for Predicting CircRNA-Disease Association.

Growing studies reveal that Circular RNAs (circRNAs) are broadly engaged in physiological processes of cell proliferation, differentiation, aging, apoptosis, and are closely associated with the pathogenesis of numerous diseases. Clarification of the correlation among diseases and circRNAs is of great clinical importance to provide new therapeutic strategies for complex diseases. However, previous circRNA-disease association prediction methods rely excessively on the graph network, and the model performance is dramatically reduced when noisy connections occur in the graph structure. To address this problem, this paper proposes an unsupervised deep graph structure learning method GSLCDA to predict potential CDAs. Concretely, we first integrate circRNA and disease multi-source data to constitute the CDA heterogeneous network. Then the network topology is learned using the graph structure, and the original graph is enhanced in an unsupervised manner by maximize the inter information of the learned and original graphs to uncover their essential features. Finally, graph space sensitive k-nearest neighbor (KNN) algorithm is employed to search for latent CDAs. In the benchmark dataset, GSLCDA obtained 92.67% accuracy with 0.9279 AUC. GSLCDA also exhibits exceptional performance on independent datasets. Furthermore, 14, 12 and 14 of the top 16 circRNAs with the most points GSLCDA prediction scores were confirmed in the relevant literature in the breast cancer, colorectal cancer and lung cancer case studies, respectively. Such results demonstrated that GSLCDA can validly reveal underlying CDA and offer new perspectives for the diagnosis and therapy of complex human diseases.

MAGCDA: A Multi-Hop Attention Graph Neural Networks Method for CircRNA-Disease Association Prediction.

With a growing body of evidence establishing circular RNAs (circRNAs) are widely exploited in eukaryotic cells and have a significant contribution in the occurrence and development of many complex human diseases. Disease-associated circRNAs can serve as clinical diagnostic biomarkers and therapeutic targets, providing novel ideas for biopharmaceutical research. However, available computation methods for predicting circRNA-disease associations (CDAs) do not sufficiently consider the contextual information of biological network nodes, making their performance limited. In this work, we propose a multi-hop attention graph neural network-based approach MAGCDA to infer potential CDAs. Specifically, we first construct a multi-source attribute heterogeneous network of circRNAs and diseases, then use a multi-hop strategy of graph nodes to deeply aggregate node context information through attention diffusion, thus enhancing topological structure information and mining data hidden features, and finally use random forest to accurately infer potential CDAs. In the four gold standard data sets, MAGCDA achieved prediction accuracy of 92.58%, 91.42%, 83.46% and 91.12%, respectively. MAGCDA has also presented prominent achievements in ablation experiments and in comparisons with other models. Additionally, 18 and 17 potential circRNAs in top 20 predicted scores for MAGCDA prediction scores were confirmed in case studies of the complex diseases breast cancer and Almozheimer's disease, respectively. These results suggest that MAGCDA can be a practical tool to explore potential disease-associated circRNAs and provide a theoretical basis for disease diagnosis and treatment.

LMGATCDA: Graph Neural Network With Labeling Trick for Predicting circRNA-Disease Associations.

Previous studies have proven that circular RNAs (circRNAs) are inextricably connected to the etiology and pathophysiology of complicated diseases. Since conventional biological research are frequently small-scale, expensive, and time-consuming, it is essential to establish an efficient and reasonable computation-based method to identify disease-related circRNAs. In this article, we proposed a novel ensemble model for predicting probable circRNA-disease associations based on multi-source similarity information(LMGATCDA). In particular, LMGATCDA first incorporates information on circRNA functional similarity, disease semantic similarity, and the Gaussian interaction profile (GIP) kernel similarity as explicit features, along with node-labeling of the three-hop subgraphs extracted from each linked target node as graph structural features. After that, the fused features are used as input, and further implied features are extracted by graph sampling aggregation (GraphSAGE) and multi-hop attention graph neural network (MAGNA). Finally, the prediction scores are obtained through a fully connected layer. With five-fold cross-validation, LMGATCDA demonstrated excellent competitiveness against gold standard data, reaching 95.37% accuracy and 91.31% recall with an AUC of 94.25% on the circR2Disease benchmark dataset. Collectively, the noteworthy findings from these case studies support our conclusion that the LMGATCDA model can provide reliable circRNA-disease associations for clinical research while helping to mitigate experimental uncertainties in wet-lab investigations.

Liver transcriptome and physiological analyses preliminarily revealed the adaptation mechanisms of Amur grayling (Thymallus arcticus grubei, Dybowski, 1869) fry for dietary lipid nutrition.

The Amur grayling (Thymallus arcticus grubei Dybowski, 1869), a species of potentially economic and research value, is renowned for its tender meat, exquisite flavor, and high nutritional contents. This study was conducted to investigate the physiological adaptation mechanisms to dietary lipids in Amur grayling fry (with average initial weight 4.64±0.03 g). This study involved a 56-day feeding trial with diets containing varying lipid levels (9.07%, 12.17%, 15.26%, 18.09%, 21.16%, and 24.07%, designated as GL1 through GL6, respectively) to explore the impact of dietary lipids on growth performance, intestinal digestion, liver antioxidative function, and transcriptomic profiles. Results showed that The group receiving 18% dietary lipid exhibited a markedly higher weight gain rate (WGR) and specific growth rate compared to other groups, alongside a reduced feed conversion ratio (FCR), except in comparison to the 15% lipid group. Activities of lipase in pancreatic secretion and amylase in stomach mucosa peaked in the 18% lipid treatment group, indicating enhanced digestive efficiency. The liver of fish in this group also showed increased activities of antioxidative enzymes and higher levels of glutathione and total antioxidative capacity, along with reduced malondialdehyde content compared to the 9% and 24% lipid treatments. Additionally, serum high-density lipoprotein cholesterol levels were highest in the 18% group. Transcriptomic analysis revealed four significant metabolic pathways affected: Cholesterol metabolism, Fat digestion and absorption, PPAR signaling, and Fatty acid degradation, involving key genes such as Lipase, Lipoprotein lipase, Fatty acid-binding protein, and Carnitine palmitoyltransferase I. These findings suggest that the liver of Amur grayling employs adaptive mechanisms to manage excessive dietary lipids. Quadratic regression analysis determined the optimal dietary lipid levels to be 16.62% and 16.52%, based on WGR and FCR, respectively. The optimal dietary lipid level for juvenile Amur grayling appears to be around 18%, as evidenced by improved growth performance, digestive function, balanced serum lipid profile, and enhanced liver antioxidative capacity. Exceeding this lipid threshold triggers both adaptive and potentially detrimental liver responses.

Microenvironment-Responsive Hydrogel Reduces Seizures After Traumatic Brain Injury in Juvenile Rats by Reducing Oxidative Stress and Hippocampal Inflammation.

Traumatic brain injury (TBI) is the primary cause of child mortality and disability worldwide. It can result in severe complications that significantly impact children's quality of life, including post-traumatic epilepsy (PTE). An increasing number of studies suggest that TBI-induced oxidative stress and neuroinflammatory sequelae (especially, inflammation in the hippocampus region) may lead to the development of PTE. Due to the blood-brain barrier (BBB), typical systemic pharmacological therapy for TBI cannot deliver berberine (BBR) to the targeted location in the early stages of the injury, although BBR has strong anti-inflammatory properties. To break through this limitation, a microenvironment-responsive gelatin methacrylate (GM) hydrogel to deliver poly(propylene sulfide)60 (PPS60) and BBR (GM/PB) is developed for regulating neuroinflammatory reactions and removing reactive oxygen species (ROS) in the brain trauma microenvironment through PPS60. In situ injection of the GM/PB hydrogel efficiently bypasses the BBB and is administered directly to the surface of brain tissue. In post-traumatic brain injury models, GM/PB has the potential to mitigate oxidative stress and neuroinflammatory responses, facilitate functional recovery, and lessen seizing. These findings can lead to a new treatment for brain injuries, which minimizes complications and improves the quality of life.

TNC upregulation promotes glioma tumourigenesis through TDG-mediated active DNA demethylation.

Gliomas represent the most predominant primary malignant tumor in central nervous system. Thymine DNA glycosylase (TDG) is a central component in active DNA demethylation. However, the specific mechanisms of TDG-mediated active DNA demethylation in gliomas remain unclear. This research indicates TDG expression is overexpressed in gliomas and correlated with poor prognosis. TDG knockdown suppressed the malignant phenotype of gliomas both in vitro and vivo. Notably, RNA-seq analysis revealed a strong association between TDG and tenascin-C (TNC). ChIP-qPCR and MeDIP-qPCR assays were undertaken to confirm that TDG participates in TNC active DNA demethylation process, revealing decreased DNA methylation levels and elevated TNC expression as a result. Silencing TNC expression also suppressed the tumor malignant phenotype in both in vitro and in vivo experiments. Additionally, simultaneous silencing of TNC reduced or even reversed the glioma promotion caused by TDG overexpression. Based on our findings, we conclude that TDG exerts an indispensable role in TNC active DNA demethylation in gliomas. The DNA demethylation process leads to alternations in TNC methylation levels and promotes its expression, thereby contributing to the development of gliomas. These results suggest a novel epigenetic therapeutic strategy targeting active DNA demethylation in gliomas.

Mechanistic insight into glioma through spatially multidimensional proteomics.

Characterizing the tumor microenvironment at the molecular level is essential for understanding the mechanisms of tumorigenesis and evolution. However, the specificity of the blood proteome in localized region of the tumor and its linkages with other systems is difficult to investigate. Here, we propose a spatially multidimensional comparative proteomics strategy using glioma as an example. The blood proteome signature of tumor microenvironment was specifically identified by in situ collection of arterial and venous blood from the glioma region of the brain for comparison with peripheral blood. Also, by integrating with different dimensions of tissue and peripheral blood proteomics, the information on the genesis, migration, and exchange of glioma-associated proteins was revealed, which provided a powerful method for tumor mechanism research and biomarker discovery. The study recruited multidimensional clinical cohorts, allowing the proteomic results to corroborate each other, reliably revealing biological processes specific to gliomas, and identifying highly accurate biomarkers.

Mechanical properties of the sciatic nerve following combined transplantation of analytically extracted acellular allogeneic nerve and adipose-derived mesenchymal stem cells

Abstract Purpose To investigate the effects of Chemically Extracted Acellular Nerves (CEANs) when combined with Adipose-Derived mesenchymal Stem Cell (ADSC) transplantation on the repair of sciatic nerve defects in rabbits. Methods A total of 71 six-month-old Japanese rabbit were used in this study. Twenty rabbits served as sciatic nerve donors, while the other 51 rabbits were randomly divided into Autologous Nerve Transplantation Group (ANT, n=17), CEAN group (n=17) and CEAN-ADSCs group (n=17). In all these groups, the rabbit's left sciatic nerves were injured before the experiment, and the uninjured sciatic nerves on their right side were used as the control (CON). Electrophysiological tests were carried out and sciatic nerves were prepared for histomorphology and stretch testing at 24 weeks post-transplant. Results There were significant differences between ANT and Con groups in amplitude (AMP): P=0.031; motor nerve conduction velocity (MNCV): P=0.029; Maximum stress: P=0.029; and Maximum strain P=0.027. There were also differences between the CEAN and CEAN+ADSCs groups in AMP: P=0.026, MNCV: P=0.024; Maximum stress: P=0.025 and Maximum strain: P=0.030. No significant differences in these parameters were observed when comparing the ANT and CEAN+SACN groups (MNCV: P=0.071) or the CEAN and ANT groups (Maximum stress: P=0.069; Maximum strain P=0.077). Conclusion Addition of ADSCs has a significant impact on the recovery of nerve function, morphology, and tensile mechanical properties following sciatic nerve injury.

Analysis of BMSCs-intervened viscoelasticity of sciatic nerve in rats with chronic alcoholic intoxication

Abstract Purpose: To investigate the impact of bone mesenchymal stem cells (BMSCs) intervention on the viscoelasticity of sciatic nerve in rats with chronic alcohol intoxication (CAI). Methods: The CAI rat models were prepared, divided into model groups, and treated with either BMSCs or basic fibroblast growth factor (bFGF). Then the rats underwent electrophysiological test and the serum levels of malondialdehyde (MDA), superoxide dismutase (SOD), and metallothionein (MT) were measured. Histological observation, stress relaxation test, and creep test were performed for the sciatic nerve of the CAI model in each group. Results: The MDA level of group BMSC was significantly lower (p<0.05) than that of groups MOD (the CIA model) and bFGF. The SOD and MT levels were higher in group BMSC than in groups MOD and bFGF (p<0.05). The motor nerve conduction velocity and amplitude were higher in group BMSC than in groups MOD and bFGF (p<0.05). The amounts of 7200s stress reduction and 7200 s strain increase of the sciatic nerve in group BMSC were greater than those in groups bFGF and MOD (p<0.05). Conclusion: Bone mesenchymal stem cells can improve the metabolism of free radicals, restore the tissue morphology and viscoelasticity of the chronic alcohol intoxication animal model, and positively affect the repairing of the injured sciatic nerve.