Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction.

Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make patients adopt reasonable preventive measures before seizures occur and thus reduce harm to patients. In recent years, deep learning-based methods have made significant progress in solving the problem of epileptic seizure prediction. However, most current methods mainly focus on modeling short- or long-term dependence in EEG, while neglecting to consider both. In this study, we propose a Parallel Dual-Branch Fusion Network (PDBFusNet) which aims to combine the complementary advantages of Convolutional Neural Network (CNN) and Transformer. Specifically, the features of the EEG signal are first extracted using Mel Frequency Cepstral Coefficients (MFCC). Then, the extracted features are delivered into the parallel dual-branches to simultaneously capture the short- and long-term dependencies of EEG signal. Further, regarding the Transformer branch, a novel feature fusion module is developed to enhance the ability of utilizing time, frequency, and channel information. To evaluate our proposal, we perform sufficient experiments on the public epileptic EEG dataset CHB-MIT, where the accuracy, sensitivity, specificity and precision are 95.76%, 95.81%, 95.71% and 95.71%, respectively. PDBFusNet shows superior performance compared to state-of-the-art competitors, which confirms the effectiveness of our proposal.

Size control of ropivacaine nano/micro-particles by soft-coating with peptide nanosheets for long-acting analgesia.

Rationale: To meet the need of long-acting analgesia in postoperative pain management, slow-releasing formulations of local anesthetics (LAs) have been extensively investigated. However, challenges still remain in obtaining such formulations in a facile and cost-effective way, and a mechanism for controlling the release rate to achieve an optimal duration is still missing. Methods: In this study, nanosheets formed by a self-assembling peptide were used to encapsulate ropivacaine in a soft-coating manner. By adjusting the ratio between the peptide and ropivacaine, ropivacaine particles with different size were prepared. Releasing profile of particles with different size were studied in vitro and in vivo. The influence of particle size and ropivacaine concentration on effective duration and toxicity were evaluated in rat models. Results: Our results showed that drug release rate became slower as the particle size increased, with particles of medium size (2.96 ± 0.04 µm) exhibiting a moderate release rate and generating an optimal anesthetic duration. Based on this size, formulations at different ropivacaine concentrations generated anesthetic effect with different durations in rat sciatic nerve block model, with the 6% formulation generated anesthetic duration of over 35 h. Long-acting analgesia up to 48 h of this formulation was also confirmed in a rat total knee arthroplasty model. Conclusion: This study provided a facile strategy to prepare LA particles of different size and revealed the relationship between particle size, release rate and anesthetic duration, which provided both technical and theoretical supports for developing long-acting LA formulations with promising clinical application.

A novel approach for intelligent diagnosis and grading of diabetic retinopathy.

Diabetic retinopathy (DR) is a severe ocular complication of diabetes that can lead to vision damage and even blindness. Currently, traditional deep convolutional neural networks (CNNs) used for DR grading tasks face two primary challenges: (1) insensitivity to minority classes due to imbalanced data distribution, and (2) neglecting the relationship between the left and right eyes by utilizing the fundus image of only one eye for training without differentiating between them. To tackle these challenges, we proposed the DRGCNN (DR Grading CNN) model. To solve the problem caused by imbalanced data distribution, our model adopts a more balanced strategy by allocating an equal number of channels to feature maps representing various DR categories. Furthermore, we introduce a CAM-EfficientNetV2-M encoder dedicated to encoding input retinal fundus images for feature vector generation. The number of parameters of our encoder is 52.88 M, which is less than RegNet_y_16gf (80.57 M) and EfficientNetB7 (63.79 M), but the corresponding kappa value is higher. Additionally, in order to take advantage of the binocular relationship, we input fundus retinal images from both eyes of the patient into the network for features fusion during the training phase. We achieved a kappa value of 86.62% on the EyePACS dataset and 86.16% on the Messidor-2 dataset. Experimental results on these representative datasets for diabetic retinopathy (DR) demonstrate the exceptional performance of our DRGCNN model, establishing it as a highly competitive intelligent classification model in the field of DR. The code is available for use at https://github.com/Fat-Hai/DRGCNN.

Identification of crosstalk genes relating to ECM-receptor interaction genes in MASH and DN using bioinformatics and machine learning.

This study aimed to identify genes shared by metabolic dysfunction-associated fatty liver disease (MASH) and diabetic nephropathy (DN) and the effect of extracellular matrix (ECM) receptor interaction genes on them. Datasets with MASH and DN were downloaded from the Gene Expression Omnibus (GEO) database. Pearson's coefficients assessed the correlation between ECM-receptor interaction genes and cross talk genes. The coexpression network of co-expression pairs (CP) genes was integrated with its protein-protein interaction (PPI) network, and machine learning was employed to identify essential disease-representing genes. Finally, immuno-penetration analysis was performed on the MASH and DN gene datasets using the CIBERSORT algorithm to evaluate the plausibility of these genes in diseases. We found 19 key CP genes. Fos proto-oncogene (FOS), belonging to the IL-17 signalling pathway, showed greater centrality PPI network; Hyaluronan Mediated Motility Receptor (HMMR), belonging to ECM-receptor interaction genes, showed most critical in the co-expression network map of 19 CP genes; Forkhead Box C1 (FOXC1), like FOS, showed a high ability to predict disease in XGBoost analysis. Further immune infiltration showed a clear positive correlation between FOS/FOXC1 and mast cells that secrete IL-17 during inflammation. Combining the results of previous studies, we suggest a FOS/FOXC1/HMMR regulatory axis in MASH and DN may be associated with mast cells in the acting IL-17 signalling pathway. Extracellular HMMR may regulate the IL-17 pathway represented by FOS through the Mitogen-Activated Protein Kinase 1 (ERK) or PI3K-Akt-mTOR pathway. HMMR may serve as a signalling carrier between MASH and DN and could be targeted for therapeutic development.

Painful diabetic neuropathy: The role of ion channels.

Painful diabetic neuropathy (PDN) is a common chronic complication of diabetes that causes neuropathic pain and negatively affects the quality of life. The management of PDN is far from satisfactory. At present, interventions are primarily focused on symptomatic treatment. Ion channel disorders are a major cause of PDN, and a complete understanding of their roles and mechanisms may provide better options for the clinical treatment of PDN. Therefore, this review summarizes the important role of ion channels in PDN and the current drug development targeting these ion channels.

Conserved mechanisms of self-renewal and pluripotency in mouse and human ESCs regulated by simulated microgravity using a 3D clinostat.

Embryonic stem cells (ESCs) exhibit unique attributes of boundless self-renewal and pluripotency, making them invaluable for fundamental investigations and clinical endeavors. Previous examinations of microgravity effects on ESC self-renewal and differentiation have predominantly maintained a descriptive nature, constrained by limited experimental opportunities and techniques. In this investigation, we present compelling evidence derived from murine and human ESCs, demonstrating that simulated microgravity (SMG)-induced stress significantly impacts self-renewal and pluripotency through a previously unidentified conserved mechanism. Specifically, SMG induces the upregulation of heat shock protein genes, subsequently enhancing the expression of core pluripotency factors and activating the Wnt and/or LIF/STAT3 signaling pathways, thereby fostering ESC self-renewal. Notably, heightened Wnt pathway activity, facilitated by Tbx3 upregulation, prompts mesoendodermal differentiation in both murine and human ESCs under SMG conditions. Recognizing potential disparities between terrestrial SMG simulations and authentic microgravity, forthcoming space flight experiments are imperative to validate the impact of reduced gravity on ESC self-renewal and differentiation mechanisms.

POZ/BTB and AT hook containing zinc finger 1 (PATZ1) suppresses differentiation and regulates metabolism in human embryonic stem cells.

Human embryonic stem cells (hESCs) can proliferate infinitely (self-renewal) and give rise to almost all types of somatic cells (pluripotency). Hence, understanding the molecular mechanism of pluripotency regulation is important for applications of hESCs in regenerative medicine. Here we report that PATZ1 is a key factor that regulates pluripotency and metabolism in hESCs. We found that depletion of PATZ1 is associated with rapid downregulation of master pluripotency genes and prominent deceleration of cell growth. We also revealed that PATZ1 regulates hESC pluripotency though binding the regulatory regions of OCT4 and NANOG. In addition, we demonstrated PATZ1 is a key node in the OCT4/NANOG transcriptional network. We further revealed that PATZ1 is essential for cell growth in hESCs. Importantly, we discovered that depletion of PATZ1 drives hESCs to exploit glycolysis which energetically compensates for the mitochondrial dysfunction. Overall, our study establishes the fundamental role of PATZ1 in regulating pluripotency in hESCs. Moreover, PATZ1 is essential for maintaining a steady metabolic homeostasis to refine the stemness of hESCs.

Disulfideptosis-associated lncRNAs reveal features of prognostic, immune escape, tumor mutation, and tumor malignant progression in renal clear cell carcinoma.

PURPOSE: Investigating the role of lncRNAs associated with the latest cell death mode (Disulfideptosis) in renal clear cell carcinoma, as well as their correlation with tumor prognosis, immune escape, immune checkpoints, tumor mutational burden, and malignant tumor progression. Searching for potential biomarkers and targets for renal clear cell carcinoma. METHODS: Downloaded the expression profile data and clinical data of 533 cases of renal clear cell carcinoma from the TCGA database, and randomly divided them into a test set (267 cases) and a validation set (266 cases). Based on previous research, 13 genes associated with Disulfideptosis were obtained. Using R software, lncRNAs with a differential expression that is related to the prognosis of renal clear cell carcinoma and associated with Disulfideptosis were screened out. After univariate Cox regression analysis, Lasso regression analysis, and multivariate Cox regression analysis, lncRNAs with independent predictive ability were obtained. A predictive risk model was established based on the risk scores. Verification was carried out between the obtained high-risk and low-risk groups and their subgroups (including Age, Gender, tumor mutational burden (TMB), tumor grading, and staging). Subsequently, a nomogram was established, and a calibration curve was generated for verification. Performed GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) functional enrichment analyses. Downloaded the values of Tumor Immune Dysfunction and Exclusion (TIDE) for all samples and calculated the difference between the high and low-risk groups. Selected human renal tumor cell lines (786-O, OS-RC-2, A-498, ACHN) and human renal cortex proximal tubule epithelial cell line (HK-2). The RNA expression levels of the above lncRNAs in each cell line were analyzed using RT-qPCR (Real-time Quantitative PCR Detecting System). Used siRNA (small interfering RNA) to knock down FAM225B in 786-O and OS-RC-2 cell lines, and then performed in vitro cell experiments to validate the functional characteristics of FAM225B. RESULTS: Our constructed predictive model includes 5 lncRNAs with an independent predictive ability (FAM225B, ZNF503-AS1, SPINT1-AS1, WWC2-AS2, LINC01338), which can effectively distinguish between patients in high and low-risk groups and their subgroups. The 1, 3, and 5-year AUC (Area Under the ROC Curve) values of the established nomogram are 0.756, 0.752, and 0.781, respectively. The 5-year AUC value is higher compared to other clinical characteristics (Age: 0.598, Gender: 0.488, Grade: 0.680, Stage: 0.717). After the knockdown of FAM225B, the proliferation, migration, and invasion abilities of renal cancer cell lines OS-RC-2 and 786-O all decreased. CONCLUSION: We have constructed and validated a prognostic model based on Disulfideptosis-associated lncRNAs. This model can effectively predict the high or low risk of patient prognosis and can distinguish the tumor cell mutational burden and immune escape capabilities among high-risk and low-risk patients. This predictive model can serve as an independent prognostic factor for renal clear cell carcinoma, providing a new direction for personalized treatment of patients with renal clear cell carcinoma.

Deep Survival Analysis With Latent Clustering and Contrastive Learning.

Survival analysis is employed to analyze the time before the event of interest occurs, which is broadly applied in many fields. The existence of censored data with incomplete supervision information about survival outcomes is one key challenge in survival analysis tasks. Although some progress has been made on this issue recently, the present methods generally treat the instances as separate ones while ignoring their potential correlations, thus rendering unsatisfactory performance. In this study, we propose a novel Deep Survival Analysis model with latent Clustering and Contrastive learning (DSACC). Specifically, we jointly optimize representation learning, latent clustering and survival prediction in a unified framework. In this way, the clusters distribution structure in latent representation space is revealed, and meanwhile the structure of the clusters is well incorporated to improve the ability of survival prediction. Besides, by virtue of the learned clusters, we further propose a contrastive loss function, where the uncensored data in each cluster are set as anchors, and the censored data are treated as positive/negative sample pairs according to whether they belong to the same cluster or not. This design enables the censored data to make full use of the supervision information of the uncensored samples. Through extensive experiments on four popular clinical datasets, we demonstrate that our proposed DSACC achieves advanced performance in terms of both C-index (0.6722, 0.6793, 0.6350, and 0.7943) and Integrated Brier Score (IBS) (0.1616, 0.1826, 0.2028, and 0.1120).

Diagnostic performance of coronary computed tomography angiography stenosis score for coronary stenosis.

BACKGROUND: Coronary computed tomography angiography stenosis score (CCTA-SS) is a proposed diagnosis score that considers the plaque characteristics, myocardial function, and the diameter reduction rate of the lesions. This study aimed to evaluate the diagnostic performance of the CCTA-SS in seeking coronary artery disease (CAD). METHODS: The 228 patients with suspected CAD who underwent CCTA and invasive coronary angiography (ICA) procedures were under examination. The diagnostic performance was evaluated with the receiver operating curve (ROC) for CCTA-SS in detecting CAD (defined as a diameter reduction of ≥ 50%) and severe CAD (defined as a diameter reduction of ≥ 70%). RESULTS: The area under ROC (AUC) of CCTA-SS was 0.909 (95% CI: 0.864-0.943), which was significantly higher than that of CCTA (AUC: 0.826; 95% CI: 0.771-0.873; P = 0.0352) in diagnosing of CAD with a threshold of 50%. The optimal cutoff point of CCTA-SS was 51% with a sensitivity of 90.66%, specificity of 95.65%, positive predictive value of 98.80%, negative predictive value of 72.13%, and accuracy of 91.67%, whereas the optimal cutoff point of CCTA was 55%, and the corresponding values were 87.36%, 93.48%, 98.15%, 65.15%, and 88.60%, respectively. With a threshold of 70%, the performance of CCTA-SS with an AUC of 0.927 (95% CI: 0.885-0.957) was significantly higher than that of CCTA with an AUC of 0.521 (95% CI: 0.454-0.587) (P < 0.0001). CONCLUSIONS: CCTA-SS significantly improved the diagnostic accuracy of coronary stenosis, including CAD and severe CAD, compared with CCTA.

Topical Tacrolimus and Mycophenolic Acid Therapy Synergizes with Low Dose Systemic Immunosuppression to Sustain Vascularized Composite Allograft Survival.

AIM: The goal of this study was to evaluate whether topical administration of tacrolimus (TAC) and mycophenolic acid (MPA) at the transplant site enables vascularized composite allograft (VCA) survival with significant minimization of the dose and adverse effects of systemic TAC (STAC) immunosuppression. MATERIALS AND METHODS: Lewis (Lew) rats received orthotopic hind limb allotransplants from fully mismatched Brown Norway (BN) donors. Group 1 (Controls) received no treatment. Other groups were treated with STAC at a dose of 1 mg/kg/day for 7 days. On post-operative day (POD) 8, the STAC dose was dropped to 0.1 mg/kg/day for Group 2 and maintained at 1 mg/kg for Group 3. Group 4 received topical application of TAC and MPA on the transplanted (Tx) limb starting POD 8 without STAC. Group 5 received topical TAC and MPA on the contralateral non-Tx limb and Group 6 received topical TAC and MPA on the Tx limb starting POD 8 along with low dose STAC (0.1 mg/kg/day). Treatment was continued until the study end point was reached, defined as either grade 3 rejection or allograft survival exceeding 100 days. .We conducted sequential LC-MS/MS measurements to assess TAC and MPA concentrations in both blood/plasma and allograft tissues. Additionally, we evaluated markers indicative of organ toxicity associated with STAC immunosuppression. RESULTS: Compared to controls, topical therapy with TAC+MPA significantly prolonged allograft survival beyond 100 daysat very low dose STAC (0.1 mg/kg/day) (Group 6). The histopathological assessment of the grafts was consistent with the clinical outcomes. .Drug levels in blood/plasma remained low or undetectable, while allograft tissues showed higher drug concentrations compared to contralateral limb tissues (P<0.05). . Urinary creatinine clearance remained within the normal range at 2.5 mL/min. CONCLUSION: Combination therapy with topical TAC and MPA synergizes with a very low dose, corticosteroid- free-STAC regimen and facilitates rejection-free, prolonged VCA survival without morbidity.

Contrastive and adversarial regularized multi-level representation learning for incomplete multi-view clustering.

Incomplete multi-view clustering is a significant task in machine learning, given that complex systems in nature and society cannot be fully observed; it provides an opportunity to exploit the structure and functions of underlying systems. Current algorithms are criticized for failing either to balance data restoration and clustering or to capture the consistency of the representation of various views. To address these problems, a novel Multi-level Representation Learning Contrastive and Adversarial Learning (aka MRL_CAL) for incomplete multi-view clustering is proposed, in which data restoration, consistent representation, and clustering are jointly learned by exploiting features in various subspaces. Specifically, MRL_CAL employs v auto-encoder to obtain a low-level specific-view representation of instances, which restores data by estimating the distribution of the original incomplete data with adversarial learning. Then, MRL_CAL extracts a high-level representation of instances, in which the consistency of various views and labels of clusters is incorporated with contrastive learning. In this case, MRL_CAL simultaneously learns multi-level features of instances in various subspaces, which not only overcomes the confliction of representations but also improves the quality of features. Finally, MRL_CAL transforms incomplete multi-view clustering into an overall objective, where features are learned under the guidance of clustering. Extensive experimental results indicate that MRL_CAL outperforms state-of-the-art algorithms in terms of various measurements, implying that the proposed method is promising for incomplete multi-view clustering.

Effects and mechanisms of perioperative medications on the hypothalamic pituitary adrenal response to surgical injury: A narrative review.

The adrenal gland is a vital endocrine organ, and adrenal steroid synthesis and secretion are closely regulated by the hypothalamic-pituitary-adrenal (HPA) axis in response to various stimuli. Surgery or trauma can activate the HPA axis and induce the secretion of cortisol. Different cortisol responses vary with the grade of surgery. Perioperative medications have the potential to decrease the cortisol level in the body, and both excessive and insufficient cortisol levels after surgery are disadvantageous. The effect of perioperative medications on the HPA response to surgery can be divided into three levels: "adrenal insufficiency (AI)", "stress response inhibition", and "uncertainty". The clinical presentation of AI includes fatigue, nausea, vomiting, abdominal pain, muscle cramps, hypotension, hypovolemic shock and prerenal failure, which may result in fatal consequences. Stress response inhibition can reduce postoperative complications, such as pain and cognitive dysfunction. This is protective to patients during perioperative and postoperative periods. The aim of the present review is to shed light on current evidence regarding the exact effects and mechanisms of perioperative medications on the HPA response to surgical injury and provide the applicable guidance on clinical anesthesia.

Exhaled breath is found to be better than blood samples for determining propofol concentrations in the brain tissues of rats.

The correlation between propofol concentration in exhaled breath (CE) and plasma (CP) has been well-established, but its applicability for estimating the concentration in brain tissues (CB) remains unknown. Given the impracticality of directly sampling human brain tissues, rats are commonly used as a pharmacokinetic model due to their similar drug-metabolizing processes to humans. In this study, we measuredCE,CP, andCBin mechanically ventilated rats injected with propofol. Exhaled breath samples from the rats were collected every 20 s and analyzed using our team's developed vacuum ultraviolet time-of-flight mass spectrometry. Additionally, femoral artery blood samples and brain tissue samples at different time points were collected and measured using high-performance liquid chromatography mass spectrometry. The results demonstrated that propofol concentration in exhaled breath exhibited stronger correlations with that in brain tissues compared to plasma levels, suggesting its potential suitability for reflecting anesthetic action sites' concentrations and anesthesia titration. Our study provides valuable animal data supporting future clinical applications.

Surface reconstruction in amorphous CoFe-based hydroxides/crystalline phosphide heterostructure for accelerated saline water electrolysis.

Developing electrocatalysts with high activity and robust performance for large-scale seawater electrolysis to produce hydrogen holds immense significance. Herein, a highly active bifunctional electrode composed of amorphous cobalt-iron layered double hydroxides (CoFeLDH) and crystalline nickel phosphide (Ni2P) (denoted as CoFeLDH@Ni2P), is employed to boost hydrogen production through seawater electrolysis. The strong interface coupling effectively modifies the electronic structure at active sites, thereby accelerating the catalytic reaction kinetics. Impressively, in situ Raman and post-stability analyses demonstrate a unique reconstruction behavior on the CoFeLDH@Ni2P electrode. Bimetal co-incorporated NiOOH (CoFe-NiOOH) and Ni(OH)2 species are formed during the oxygen evolution reaction (OER), while CoFeLDH@Ni2P can transform into Ni(OH)2 species during the hydrogen evolution reaction (HER) process. Furthermore, the highly negatively charged surface selectively rejects Cl- ions by formed PO43-, endowing CoFeLDH@Ni2P with excellent tolerance and promising durability in saline electrolytes. Consequently, the CoFeLDH@Ni2P electrode exhibits an overpotential of 106 mV for HER at 10 mA cm-2 and 308 mV for OER to achieve 100 mA cm-2 in 1.0 M KOH solution. Additionally, the CoFeLDH@Ni2P(+,-) electrolyzer requires a low cell voltage of 1.56 V to deliver 10 mA cm-2 in 1.0 M KOH + Seasalt. This work presents an appealing strategy for the rational design of advanced electrocatalysts with amorphous-crystalline interfaces, which reveals the source of the activity of transition-metal phosphating compounds in saline water electrolysis.

Spectrum Situation Awareness for Space-Air-Ground Integrated Networks Based on Tensor Computing.

The spectrum situation awareness problem in space-air-ground integrated networks (SAGINs) is studied from a tensor-computing perspective. Tensor and tensor computing, including tensor decomposition, tensor completion and tensor eigenvalues, can satisfy the application requirements of SAGINs. Tensors can effectively handle multidimensional heterogeneous big data generated by SAGINs. Tensor computing is used to process the big data, with tensor decomposition being used for dimensionality reduction to reduce storage space, and tensor completion utilized for numeric supplementation to overcome the missing data problem. Notably, tensor eigenvalues are used to indicate the intrinsic correlations within the big data. A tensor data model is designed for space-air-ground integrated networks from multiple dimensions. Based on the multidimensional tensor data model, a novel tensor-computing-based spectrum situation awareness scheme is proposed. Two tensor eigenvalue calculation algorithms are studied to generate tensor eigenvalues. The distribution characteristics of tensor eigenvalues are used to design spectrum sensing schemes with hypothesis tests. The main advantage of this algorithm based on tensor eigenvalue distributions is that the statistics of spectrum situation awareness can be completely characterized by tensor eigenvalues. The feasibility of spectrum situation awareness based on tensor eigenvalues is evaluated by simulation results. The new application paradigm of tensor eigenvalue provides a novel direction for practical applications of tensor theory.

Local Delivery of Adipose Stem Cells Promotes Allograft Survival in a Rat Hind-Limb Model of Vascularized Composite Allotransplantation.

BACKGROUND: Adipose stem cells (ASCs) are a promising cell-based immunotherapy because of their minimally invasive harvest, high yield, and immunomodulatory capacity. In this study, the authors investigated the effects of local versus systemic ASC delivery on vascularized composite allotransplant survival and alloimmune regulation. METHODS: Lewis rats received hind-limb transplants from Brown Norway rats and were administered donor-derived ASCs (passage 3 or 4, 1 × 10 6 cells/rat) locally in the allograft, or contralateral limb, or systemically at postoperative day 1. Recipients were treated intraperitoneally with rabbit anti-rat lymphocyte serum on postoperative days 1 and 4 and daily tacrolimus for 21 days. Limb allografts were monitored for clinical signs of rejection. Donor cell chimerism, immune cell differentiation, and cytokine expression in recipient lymphoid organs were measured by flow cytometric analysis. The immunomodulation function of ASCs was tested by mixed lymphocyte reaction assay and ASC stimulation studies. RESULTS: Local-ASC-treated recipients achieved significant prolonged allograft survival (85.7% survived >130 days; n = 6) compared with systemic-ASC and contralateral-ASC groups. Secondary donor skin allografts transplanted to the local-ASC long-term surviving recipients accepted permanently without additional immunosuppression. The increases in donor cell chimerism and regulatory T-cells were evident in blood and draining lymph nodes of the local-ASC group. Moreover, mixed lymphocyte reaction showed that ASCs inhibited donor-specific T-cell proliferation independent of direct ASC-T-cell contact. ASCs up-regulated antiinflammatory molecules in response to cytokine stimulation in vitro. CONCLUSION: Local delivery of ASCs promoted long-term survival and modulated alloimmune responses in a full major histocompatibility complex-mismatched vascularized composite allotransplantation model and was more effective than systemic administration. CLINICAL RELEVANCE STATEMENT: ASCs are a readily available and abundant source of therapeutic cells that could decrease the amount of systemic immunosuppression required to maintain limb and face allografts.