Mechanism of KMT2D-mediated epigenetic modification in IL-1ß-induced nucleus pulposus cell degeneration.

BACKGROUND: Intervertebral disc (IVD) degeneration (IVDD) is characterized by structural destruction accompanied by accelerated signs of aging. This study aimed to investigate the mechanism of lysine methyltransferase 2D (KMT2D) in the proliferation, apoptosis, and inflammation of nucleus pulposus cells (NPCs) in IVDD. METHODS: Mouse-derived NPCs were cultured and induced with interleukin-1 beta (IL-1ß) to establish cell models. KMT2D expression was detected by western blot and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). KMT2D expression was interfered with, and the contents of IL-18, IL-6, and tumor necrosis factor (TNF) were detected by enzyme-linked immunosorbent assay. Cell proliferation, apoptosis, and the expression of miR-133a-5p and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 (PFKFB2) were measured. The enrichment of KMT2D and Histone 3 Lysine 4 monomethylation/dimethylation (H3K4me1/2) on the miR-133a-5p promoter was analyzed by chromatin immunoprecipitation and qPCR. The binding of miR-133a-5p and PFKFB2 was analyzed by a dual-luciferase assay. RESULTS: IL-1ß treatment promoted KMT2D expression in NPCs. KMT2D knockdown reduced inflammation and apoptosis and promoted the proliferation of IL-1ß-induced NPCs. Mechanistically, KMT2D upregulated miR-133a-5p expression by increasing the level of H3K4me2 at the miR-133a-5p promoter, thereby promoting the binding between miR-133a-5p and PFKFB2 and downregulating the transcription of PFKFB2. miR-133a-5p overexpression or PFKFB2 knockdown alleviated the protective effect of KMT2D knockdown on IL-1ß-induced NPCs. CONCLUSION: KMT2D promoted miR-133a-5p expression through H3K4me2 methylation, thereby promoting the binding of miR-133a-5p to PFKFB2, reducing the mRNA level of PFKFB2, promoting inflammation and apoptosis of IL-1ß-induced NPCs, and inhibiting NPC proliferation.

Npac Regulates Pre-mRNA Splicing in Mouse Embryonic Stem Cells.

As a reader of tri-methylated lysine 36 on histone H3 (H3K36me3), Npac has been shown to have a significant role in gene transcription elongation. However, its potential implication in RNA splicing remains unknown. Here, we characterized the phenotypes of Npac knockout in mES cells. We discovered that loss of Npac disrupts pluripotency and identity in mESCs. We also found that Npac is associated with many cellular activities, including cell proliferation, differentiation, and transcription regulation. Notably, we uncovered that Npac is associated with RNA splicing machinery. Furthermore, we found that Npac regulates alternative splicing through its interaction with the splicing factors, including Srsf1. Our research thus highlights the important role of Npac in maintaining ESC identity through the regulation of pre-mRNA splicing.

Multimodal fusion network for ICU patient outcome prediction.

Over the past decades, massive Electronic Health Records (EHRs) have been accumulated in Intensive Care Unit (ICU) and many other healthcare scenarios. The rich and comprehensive information recorded presents an exceptional opportunity for patient outcome predictions. Nevertheless, due to the diversity of data modalities, EHRs exhibit a heterogeneous characteristic, raising a difficulty to organically leverage information from various modalities. It is an urgent need to capture the underlying correlations among different modalities. In this paper, we propose a novel framework named Multimodal Fusion Network (MFNet) for ICU patient outcome prediction. First, we incorporate multiple modality-specific encoders to learn different modality representations. Notably, a graph guided encoder is designed to capture underlying global relationships among medical codes, and a text encoder with pre-fine-tuning strategy is adopted to extract appropriate text representations. Second, we propose to pairwise merge multimodal representations with a tailored hierarchical fusion mechanism. The experiments conducted on the eICU-CRD dataset validate that MFNet achieves superior performance on mortality prediction and Length of Stay (LoS) prediction compared with various representative and state-of-the-art baselines. Moreover, comprehensive ablation study demonstrates the effectiveness of each component of MFNet.

ST-SCSR: identifying spatial domains in spatial transcriptomics data via structure correlation and self-representation.

Recent advances in spatial transcriptomics (ST) enable measurements of transcriptome within intact biological tissues by preserving spatial information, offering biologists unprecedented opportunities to comprehensively understand tissue micro-environment, where spatial domains are basic units of tissues. Although great efforts are devoted to this issue, they still have many shortcomings, such as ignoring local information and relations of spatial domains, requiring alternatives to solve these problems. Here, a novel algorithm for spatial domain identification in Spatial Transcriptomics data with Structure Correlation and Self-Representation (ST-SCSR), which integrates local information, global information, and similarity of spatial domains. Specifically, ST-SCSR utilzes matrix tri-factorization to simultaneously decompose expression profiles and spatial network of spots, where expressional and spatial features of spots are fused via the shared factor matrix that interpreted as similarity of spatial domains. Furthermore, ST-SCSR learns affinity graph of spots by manipulating expressional and spatial features, where local preservation and sparse constraints are employed, thereby enhancing the quality of graph. The experimental results demonstrate that ST-SCSR not only outperforms state-of-the-art algorithms in terms of accuracy, but also identifies many potential interesting patterns.

Comparative Analysis of the Feasibility of Myocardial Blood Flow Index Versus CT-FFR in the Diagnosis of Suspected Coronary Artery Disease.

Background: Using fluid dynamic modeling, noninvasive fractional flow reserve (FFR) derived from coronary computed tomography angiography (CCTA) data provides better anatomic and functional information than CCTA, with a high diagnostic and discriminatory value for diagnosing hemodynamically significant lesions. Myocardial blood flow index (MBFI) based on CCTA is a physiological parameter that reflects myocardial ischemia. Thus, exploring the relationship between computed tomography derived fractional flow reserve (CT-FFR) and MBFI could be clinically significant. This study aimed to investigate the relationship between CT-FFR and MBFI and to analyze the feasibility of MBFI differing from CT-FFR in diagnosing suspected coronary artery disease (CAD). Methods: Data from 61 patients (35 males, mean age: 59.2 ± 10.02 years) with suspected CAD were retrospectively analyzed, including the imaging data of CCTA, CT-FFR, and data of invasive coronary angiography performed within one week after hospitalization. CT-FFR and MBFI were calculated, and the correlation between MBFI or CT-FFR and invasive coronary angiography (ICA) was evaluated. Using ICA (value ≥ 0.70) as the gold standard and determining the optimal cutoff value via a diagnostic test, the diagnostic performance of MBFI or CT-FFR was evaluated. Results: MBFI and CT-FFR were negatively correlated with ICA (r = -0.3670 and -0.4922, p = 0.0036 and 0.0001, respectively). Using ICA (value of ≥ 0.70) the gold standard, the optimal cutoff value was 0.115 for MBFI, and the area under the curve (AUC) was 0.833 (95% confidence interval [CI]: 0.716-0.916, Z = 5.357, p < 0.0001); using ICA (value of ≥ 0.70) the gold standard, the optimal cutoff value was 0.80 for CT-FFR, and the area under the curve (AUC) was 0.759 (95% CI: 0.632-0.859, Z = 3.665, p = 0.0002). No significant difference was observed between the AUCs of CT-FFR and MBFI (Z = 0.786, p = 0.4316). Conclusions: MBFI based on CCTA can be used to evaluate myocardial ischemia similar to CT-FFR in suspected CAD; however, it should be noted that CT-FFR is a functional index based on the anatomical stenosis of the coronary artery, whereas MBFI is a physiological index reflecting myocardial mass remodeling.

Geniposide ameliorates diabetic nephropathy in type 2 diabetic mice by targeting AGEs-RAGE-dependent inflammatory pathway.

BACKGROUND: Diabetic nephropathy (DN) is a prevalent complication of diabetes mellitus and the primary cause of morbidity and mortality in end-stage renal disease. The receptor for advanced glycation end products (RAGE) plays a crucial role in mediating AGE-triggered inflammation, which has been implicated in DN pathogenesis. While geniposide, a natural compound, has demonstrated anti-inflammatory and hypoglycemic properties, its potential to mitigate AGE-induced renal inflammation and consequently impede DN progression remains unexplored. PURPOSE: The objective of this study was to ascertain whether geniposide is a novel natural AGEs-RAGE blocker and to investigate its protective effect on renal DN in type 2 diabetic mice. METHODS: Binding affinity between geniposide and RAGE was assessed using MicroScale Thermophoresis (MST), molecular docking, and co-immunoprecipitation. RAGE was then subjected to knockdown and overexpression in cellular experiments to evaluate geniposide's effects on AGE-induced inflammatory responses and the RAGE pathway. Finally, db/db mice were employed to validate the renoprotective effects of geniposide in DN. RESULTS: Geniposide exhibited higher binding affinity to RAGE's V domain than AGEs, competitively inhibiting AGEs-RAGE interaction through hydrogen bonding. It suppressed RAGE expression and RAGE-dependent inflammatory responses to AGEs, comparable to RAGE siRNA effects. In RAGE-overexpressing cells, geniposide further inhibited AGEs-induced ERK1/2 and NFκB P65 activation, reducing inflammatory marker levels. Long-term oral administration of geniposide to db/db mice improved plasma creatinine, urea, and proteinuria levels, ameliorated pathological changes, and downregulated inflammatory factors such as TNF-α and IL-1ß. Moreover, it dose-dependently attenuated enhanced renal expression of RAGE, phosphorylated ERK1/2, IκB-α, and NF-κB P65. CONCLUSION: Geniposide effectively attenuates AGEs-induced RAGE activation by directly blocking AGEs-RAGE signal transduction, thereby mitigating inflammatory responses. These findings suggest that geniposide has potential as a high-affinity RAGE antagonist, potentially playing a crucial role in the treatment of DN.

ERK5 promotes autocrine expression to sustain mitogenic balance for cell fate specification in human pluripotent stem cells.

The homeostasis of human pluripotent stem cells (hPSCs) requires the signaling balance of extracellular factors. Exogenous regulators from cell culture medium have been widely reported, but little attention has been paid to the autocrine factor from hPSCs themselves. In this report, we demonstrate that extracellular signal-related kinase 5 (ERK5) regulates endogenous autocrine factors essential for pluripotency and differentiation. ERK5 inhibition leads to erroneous cell fate specification in all lineages even under lineage-specific induction. hPSCs can self-renew under ERK5 inhibition in the presence of fibroblast growth factor 2 (FGF2) and transforming growth factor ß (TGF-ß), although NANOG expression is partially suppressed. Further analysis demonstrates that ERK5 promotes the expression of autocrine factors such as NODAL, FGF8, and WNT3. The addition of NODAL protein rescues NANOG expression and differentiation phenotypes under ERK5 inhibition. We demonstrate that constitutively active ERK5 pathway allows self-renewal even without essential growth factors FGF2 and TGF-ß. This study highlights the essential contribution of autocrine pathways to proper maintenance and differentiation.

(+)-Catechins Play a Protective Role in Diabetic Kidney Disease by Alleviating EMT through Multiple Pathways.

SCOPE: Diabetic nephropathy (DN), a complication of diabetes mellitus, is becoming a significant global health concern, with no complete cure currently available. Tea is regarded as an essential component of a balanced diet and contains (+)-Catechin (CE), which exert a range of pharmacological effects. Consequently, CE may be a potential treatment for DN. The objective of this study is to examine the protective effects and underlying mechanisms of CE on DN, with a particular focus on the epithelial-mesenchymal transition (EMT) process, which plays a pivotal role in regulating DN. METHODS AND RESULTS: In this study db/db mice are treated with catechins. The results demonstrate that CE reduces obesity and hyperglycemia, improves renal dysfunction and morphological changes in diabetic mice, and inhibits the development of DN through the RAGE/NF-κB signaling pathway. Among them differentially expressed messenger RNA (mRNA) results, those related to EMT, including Cav1, grem2, macrod2, and kap, are identified. To further validate the results, the same experiments are performed on HK-2 cells. CONCLUSIONS: The research results offer novel perspectives by emphasizing the anti-inflammatory properties of CE and their potential role in mitigating DN through the regulation of EMT-related genes such as RAGE, Cav1, grem2, macrod2, and kap.

Microglia Promote Lymphangiogenesis Around the Spinal Cord Through VEGF-C/VEGFR3-Dependent Autophagy and Polarization After Acute Spinal Cord Injury.

Reducing secondary injury is a key focus in the field of spinal cord injury (SCI). Recent studies have revealed the role of lymphangiogenesis in reducing secondary damage to central nerve. However, the mechanism of lymphangiogenesis is not yet clear. Macrophages have been shown to play an important role in peripheral tissue lymphangiogenesis. Microglia is believed to play a role similar to macrophages in the central nervous system (CNS); we hypothesized that there was a close relationship between microglia and central nerve system lymphangiogenesis. Herein, we used an in vivo model of SCI to explored the relationship between microglia and spinal cord lymphangiogenesis and further investigated the polarization of microglia and its role in promoting spinal cord lymphangiogenesis by a series of in vitro experiments. The current study elucidated for the first time the relationship between microglia and lymphangiogenesis around the spinal cord after SCI. Classical activated (M1) microglia can promote lymphangiogenesis by secreting VEGF-C which further increases polarization and secretion of lymphatic growth factor by activating VEGFR3. The VEGF-C/VEGFR3 pathway activation downregulates microglia autophagy, thereby regulating the microglia phenotype. These results indicate that M1 microglia promote lymphangiogenesis after SCI, and activated VEGF-C/VEGFR3 signaling promotes M1 microglia polarization by inhibiting autophagy, thereby facilitates lymphangiogenesis.

A novel approach for automatic classification of macular degeneration OCT images.

Age-related macular degeneration (AMD) and diabetic macular edema (DME) are significant causes of blindness worldwide. The prevalence of these diseases is steadily increasing due to population aging. Therefore, early diagnosis and prevention are crucial for effective treatment. Classification of Macular Degeneration OCT Images is a widely used method for assessing retinal lesions. However, there are two main challenges in OCT image classification: incomplete image feature extraction and lack of prominence in important positional features. To address these challenges, we proposed a deep learning neural network model called MSA-Net, which incorporates our proposed multi-scale architecture and spatial attention mechanism. Our multi-scale architecture is based on depthwise separable convolution, which ensures comprehensive feature extraction from multiple scales while minimizing the growth of model parameters. The spatial attention mechanism is aim to highlight the important positional features in the images, which emphasizes the representation of macular region features in OCT images. We test MSA-NET on the NEH dataset and the UCSD dataset, performing three-class (CNV, DURSEN, and NORMAL) and four-class (CNV, DURSEN, DME, and NORMAL) classification tasks. On the NEH dataset, the accuracy, sensitivity, and specificity are 98.1%, 97.9%, and 98.0%, respectively. After fine-tuning on the UCSD dataset, the accuracy, sensitivity, and specificity are 96.7%, 96.7%, and 98.9%, respectively. Experimental results demonstrate the excellent classification performance and generalization ability of our model compared to previous models and recent well-known OCT classification models, establishing it as a highly competitive intelligence classification approach in the field of macular degeneration.

Semi-Supervised Graph Structure Learning via Dual Reinforcement of Label and Prior Structure.

Graph neural networks (GNNs) have achieved considerable success in dealing with graph-structured data by the message-passing mechanism. Actually, this mechanism relies on a fundamental assumption that the graph structure along which information propagates is perfect. However, the real-world graphs are inevitably incomplete or noisy, which violates the assumption, thus resulting in limited performance. Therefore, optimizing graph structure for GNNs is indispensable and important. Although current semi-supervised graph structure learning (GSL) methods have achieved a promising performance, the potential of labels and prior graph structure has not been fully exploited yet. Inspired by this, we examine GSL with dual reinforcement of label and prior structure in this article. Specifically, to enhance label utilization, we first propose to construct the prior label-constrained matrices to refine the graph structure by identifying label consistency. Second, to adequately leverage the prior structure to guide GSL, we develop spectral contrastive learning that extracts global properties embedded in the prior graph structure. Moreover, contrastive fusion with prior spatial structure is further adopted, which promotes the learned structure to integrate local spatial information from the prior graph. To extensively evaluate our proposal, we perform sufficient experiments on seven benchmark datasets, where experimental results confirm the effectiveness of our method and the rationality of the learned structure from various aspects.

Intersection-union dual-stream cross-attention Lova-SwinUnet for skin cancer hair segmentation and image repair.

Skin cancer images have hair occlusion problems, which greatly affects the accuracy of diagnosis and classification. Current dermoscopic hair removal methods use segmentation networks to locate hairs, and then uses repair networks to perform image repair. However, it is difficult to segment hair and capture the overall structure between hairs because of the hair being thin, unclear, and similar in color to the entire image. When conducting image restoration tasks, the only available images are those obstructed by hair, and there is no corresponding ground truth (supervised data) of the same scene without hair obstruction. In addition, the texture information and structural information used in existing repair methods are often insufficient, which leads to poor results in skin cancer image repair. To address these challenges, we propose the intersection-union dual-stream cross-attention Lova-SwinUnet (IUDC-LS). Firstly, we propose the Lova-SwinUnet module, which embeds Lovasz loss function into Swin-Unet, enabling the network to better capture features of various scales, thus obtaining better hair mask segmentation results. Secondly, we design the intersection-union (IU) module, which takes the mask results obtained in the previous step for pairwise intersection or union, and then overlays the results on the skin cancer image without hair to generate the labeled training data. This turns the unsupervised image repair task into the supervised one. Finally, we propose the dual-stream cross-attention (DC) module, which makes texture information and structure information interact with each other, and then uses cross-attention to make the network pay attention to the more important texture information and structure information in the fusion process of texture information and structure information, so as to improve the effect of image repair. The experimental results show that the PSNR index and SSIM index of the proposed method are increased by 5.4875 and 0.0401 compared with the other common methods. Experimental results unequivocally demonstrate the effectiveness of our approach, which serves as a potent tool for skin cancer detection, significantly surpassing the performance of comparable methods.

Ku70 Binding to YAP Alters PARP1 Ubiquitination to Regulate Genome Stability and Tumorigenesis.

Yes-associated protein (YAP) is a central player in cancer development, with functions extending beyond its recognized role in cell growth regulation. Recent work has identified a link between YAP/transcriptional coactivator with PDZ-binding motif (TAZ) and the DNA damage response. Here, we investigated the mechanistic underpinnings of the cross-talk between DNA damage repair and YAP activity. Ku70, a key component of the nonhomologous end joining pathway to repair DNA damage, engaged in a dynamic competition with TEAD4 for binding to YAP, limiting the transcriptional activity of YAP. Depletion of Ku70 enhanced interaction between YAP and TEAD4 and boosted YAP transcriptional capacity. Consequently, Ku70 loss enhanced tumorigenesis in colon cancer and hepatocellular carcinoma (HCC) in vivo. YAP impeded DNA damage repair and elevated genome instability by inducing PARP1 degradation through the SMURF2-mediated ubiquitin-proteasome pathway. Analysis of samples from patients with HCC substantiated the link between Ku70 expression, YAP activity, PARP1 levels, and genome instability. In conclusion, this research provides insight into the mechanistic interactions between YAP and key regulators of DNA damage repair, highlighting the role of a Ku70-YAP-PARP1 axis in preserving genome stability. Significance: Increased yes-associated protein transcriptional activity stimulated by loss of Ku70 induces PARP1 degradation by upregulating SMURF2 to inhibit DNA damage, driving genome instability and tumorigenesis.

Leveraging Senescent Cancer Cell Membrane to Potentiate Cancer Immunotherapy Through Biomimetic Nanovaccine.

Senescent cancer cells are endowed with high immunogenic potential that has been leveraged to elicit antitumor immunity and potentially complement anticancer therapies. However, the efficacy of live senescent cancer cell-based vaccination is limited by interference from immunosuppressive senescence-associated secretory phenotype and pro-tumorigenic capacity of senescent cells. Here, a senescent cancer cell-based nanovaccine with strong immunogenicity and favorable potential for immunotherapy is reported. The biomimetic nanovaccine integrating a senescent cancer cell membrane-coated nanoadjuvant outperforms living senescent cancer cells in enhancing dendritic cells (DCs) internalization, improving lymph node targeting, and enhancing immune responses. In contrast to nanovaccines generated from immunogenic cell death-induced tumor cells, senescent nanovaccines facilitate DC maturation, eliciting superior antitumor protection and improving therapeutic outcomes in melanoma-challenged mice with fewer side effects when combined with αPD-1. The study suggests a versatile biomanufacturing approach to maximize immunogenic potential and minimize adverse effects of senescent cancer cell-based vaccination and advances the design of biomimetic nanovaccines for cancer immunotherapy.

Study on the Application of Kramers-Kronig Relation for Polyurethane Mixture.

Polyurethane (PU) mixture, which is a new pavement mixture, exhibits different dynamic properties compared to a hot-mixed asphalt mixture (HMA). This paper analyzed whether the Kramers-Kronig (K-K) relation and thermorheologically simple properties applied to the PU mixture. Based on the results, the PU mixture exhibited thermorheologically simple properties within the test conditions. The time-temperature superposition principle (TTSP) was applicable for the PU mixture to construct a dynamic modulus master curve using the standard logistic sigmoidal (SLS) model, the generalized logistic sigmoidal (GLS) model, and the Havriliak-Negami (HN) model. The Hilbert integral transformed SLS and GLS models for the phase angle can accurately fit the measured phase angle data with newly fitted shift factors and predict the phase angle within the viscoelastic range. The core-core and black space diagrams both displayed single continuous smooth curves, which can be utilized to characterize the viscoelastic property of the PU mixture. The K-K relation is applicable for the PU mixture to obtain the phase angle master curve model, storage modulus, and loss modulus from the complex modulus test results with the test temperatures and loading frequencies. The phase angle of the PU mixture at extremely high or low test temperatures cannot be derived from the dynamic modulus data.

Imaging and blood flow characteristics of cerebrovascular fenestration malformation and its relationship with the occurrence of ischemic cerebrovascular disease.

OBJECTIVE: To explore the imaging and transcranial Doppler cerebral blood flow characteristics of cerebrovascular fenestration malformation and its relationship with the occurrence of ischemic cerebrovascular disease. METHODS: A retrospective analysis was conducted on the imaging data of 194 patients with cerebrovascular fenestration malformation who visited the Heyuan People's Hospital from July 2021 to July 2023. The location and morphology of the fenestration malformation blood vessels as well as the presence of other cerebrovascular diseases were analyzed. Transcranial Doppler cerebral blood flow detection data of patients with cerebral infarction and those with basilar artery fenestration malformation were also analyzed. RESULTS: A total of 194 patients with cerebral vascular fenestration malformation were found. Among the artery fenestration malformation, basilar artery fenestration was the most common, accounting for 46.08% (94/194). 61 patients (31.44%) had other vascular malformations, 97 patients (50%) had cerebral infarction, of which 30 were cerebral infarction in the fenestrated artery supply area. 28 patients with cerebral infarction in the fenestrated artery supply area received standardized antiplatelet, lipid-lowering and plaque-stabilizing medication treatment. During the follow-up period, these patients did not experience any symptoms of cerebral infarction or transient ischemic attack again. There were no differences in peak systolic flow velocity and end diastolic flow velocity, pulsatility index and resistance index between the ischemic stroke group and the no ischemic stroke group in patients with basal artery fenestration malformation (P > 0.05). CONCLUSION: Cerebrovascular fenestration malformation is most common in the basilar artery. Cerebrovascular fenestration malformation may also be associated with other cerebrovascular malformations. Standardized antiplatelet and statin lipid-lowering and plaque-stabilizing drugs are suitable for patients with cerebral infarction complicated with fenestration malformation. The relationship between cerebral blood flow changes in basilar artery fenestration malformation and the occurrence of ischemic stroke may not be significant.

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.

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.