[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.

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.

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.

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.

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.

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.

Identification and validation of chromatin regulator-related signatures as a novel prognostic model for low-grade gliomas using translational bioinformatics.

AIMS: The purpose of this study is to explore the potential biological role and prognostic significance of chromatin regulators (CRs) in low-grade gliomas (LGGs). MAIN METHODS: CRs were obtained from the FACER database. Transcription profiles of LGG patients were collected from the TCGA and CGGA databases. Differentially expressed CRs (DECRs) between LGGs and normal controls were identified using DESeq2. The consensus clustering algorithm was employed to distinguish subtypes of LGGs based on prognosis-related DECRs. The differences in clinical and molecular characteristics between different subtypes were explored. R packages, GSVA, ssGSEA, and ESTIMATE were utilized to elucidate the tumor microenvironment and activated pathways in different subtypes. Subsequently, a CRs-related signature was developed using LASSO Cox regression. Its performance was evaluated by Kaplan-Meier curve and ROC curve analyses. In vitro experiments were performed to explore the function of JADE3 in LGGs, which predominantly expressed in glioma cells. KEY FINDINGS: We identified 43 DECRs and two CRs-related subtypes of LGGs. The subtype characterized by shorter survival displayed significant enrichment for pathways associated with DNA damage response and repair, along with heightened immune cell infiltration. Furthermore, the CRs-based signature exhibited excellent prognostic performance in both the TCGA and CGGA databases. Knockdown of JADE3 significantly increased the invasion, migration, and proliferation abilities of Hs683. SIGNIFICANCE: Our study reveals the aberrant expression and prognostic value of CRs in LGGs. It emphasizes the potential regulatory role of CRs in the microenvironment and DNA damage repair in LGGs. JADE3 could be a possible therapeutic target for LGGs.

Evolutionarily conserved IL-22 participates in gut mucosal barrier through its receptors IL-22BP, IL-10R2 and IL-22RA1 during bacterial infection in teleost.

IL-22 is a critical cytokine of epithelial mucosal barrier. In humans, IL-22 signals through a heteroduplex receptor consisting of IL-22R and IL-10Rß. In fish, IL-22 and its receptors homologues have been cloned in a number of species, however, no studies have been reported how the receptors are involved in IL-22 transduction. For this purpose, in this study we identified IL-22 and its soluble receptor IL-22BP and transmembrane receptors IL-22RA1 and IL-10R2 in Carassius cuvieri × Carassius auratus red var. (named WR-IL-22, WR-IL-22BP, WR-IL10R2 and WR-IL22RA1, respectively). WR-IL-22, WR-IL-22BP, WR-IL10R2 and WR-IL22RA1 were relatively conserved in the evolutionary process, sharing the same conserved domains as their higher vertebrate homologues. When the fish were infected with the Aeromonas hydrophila, the expression of WR-IL-22, WR-IL-22BP, WR-IL10R2 and WR-IL22RA1 were significantly induced in the gut. The co-IP assay showed that WR-IL-22 not only interacted with WR-IL-22BP, but also with WR-IL10R2 and WR-IL22RA1. When introduced in vivo, WR-IL-22 activated the JAK1-STAT3 axis and protected the gut mucosa from A. hydrophila infection. However, overexpression of WR-IL-22BP or knockdown of transmembrane receptors WR-IL10R2 and WR-IL22RA1 significantly inhibited the activation of WR-IL-22-mediated JAK1-STAT3 axis and promoted bacterial colonization in the gut. These results provided new insights into the role of IL-22 and its receptors in the gut mucosa barrier and immune response in teleost.

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.

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.

Clay-based 1D-2D halloysite&g-C3N4 nanostructured meat floss for photocatalytic hydrogen evolution.

Graphitic carbon nitride (g-C3N4) has drawn extensive attention with some features including visible-light response as non-metallic semiconductor, low cost in raw material and green pollution-free for environment, but suffers from some issues such as fast charge carriers' recombination, easy aggregation, etc. In this work, the 1D-2D HNTs&g-C3N4-X binary materials similar to meat floss pattern in a series of halloysite loading amounts are designed via a facile electrostatic self-assembly strategy with debris g-C3N4 after cell pulverizing treatment and HNTs that outwardly modified by cetyltrimethylammonium bromide (CTAB) as the building blocks. The halloysite-mediated satellite-core material displays a photocatalytic of H2 evolution performance with the highest evolution rate of 137.0 µmol g-1 h-1 in visible light condition with no co-catalysts, and is ∼3.4 times that of bulk g-C3N4, mainly benefiting from the reduced nanometer size of debris g-C3N4 and enhanced interface dispersion ability by HNTs, resulting in ameliorative separation efficiency of photogenerated charge carriers. This research conclusively provides the new perspective towards the performance enhancement of water splitting of g-C3N4 in raw clay mineral modification mode and broadens the applications of mineral-based composite in the renewable energy utilization field.

Comparison of reconstruction nails versus dual implants in the treatment of ipsilateral femoral neck and shaft fractures in adults: a meta-analysis and systematic review.

OBJECTIVE: There is no consensus on the optimal treatment for ipsilateral femoral neck and shaft fractures. This meta-analysis aims to assess the effectiveness of reconstruction nails and dual implants in treating ipsilateral femoral neck and shaft fractures to provide a basis for decision-making when selecting the optimal approach. METHODS: Relevant articles were retrieved from Pubmed, Embase, and Cochrane databases using the keywords "neck of femur", "shaft" and "fracture fixation" from inception until November 17, 2022. The screening process of the studies was conducted independently by two assessors, who assessed each study's eligibility and two assessors assessed the quality. Then compared differences in outcome measures using RevMan 5.3 software. RESULTS: A total of ten retrospective cohort studies were included. There were no significant differences in union time, union rate, union-related complications (malunion, nonunion, delayed union) of femoral neck and shaft fractures, osteonecrosis of the femoral head, and functional outcomes (Friedman-Wyman scoring system) (P > 0.05). CONCLUSION: Our pooled estimates indicated that reconstruction nails and dual implants for ipsilateral femoral neck and shaft fractures could yield satisfactory surgical results, and that there is no difference between the two treatment methods. TRIAL REGISTRATION: This meta-analysis was registered on the PROSPERO website (registration number: CRD42022379606).

An efficient circRNA-miRNA interaction prediction model by combining biological text mining and wavelet diffusion-based sparse network structure embedding.

MOTIVATION: Accumulating clinical evidence shows that circular RNA (circRNA) plays an important regulatory role in the occurrence and development of human diseases, which is expected to provide a new perspective for the diagnosis and treatment of related diseases. Using computational methods can provide high probability preselection for wet experiments to save resources. However, due to the lack of neighborhood structure in sparse biological networks, the model based on network embedding and graph embedding is difficult to achieve ideal results. RESULTS: In this paper, we propose BioDGW-CMI, which combines biological text mining and wavelet diffusion-based sparse network structure embedding to predict circRNA-miRNA interaction (CMI). In detail, BioDGW-CMI first uses the Bidirectional Encoder Representations from Transformers (BERT) for biological text mining to mine hidden features in RNA sequences, then constructs a CMI network, obtains the topological structure embedding of nodes in the network through heat wavelet diffusion patterns. Next, the Denoising autoencoder organically combines the structural features and Gaussian kernel similarity, finally, the feature is sent to lightGBM for training and prediction. BioDGW-CMI achieves the highest prediction performance in all three datasets in the field of CMI prediction. In the case study, all the 8 pairs of CMI based on circ-ITCH were successfully predicted. AVAILABILITY: The data and source code can be found at https://github.com/1axin/BioDGW-CMI-model.

ICGA combined with EP monitoring in microclipping of cerebral aneurysms.

Cerebral aneurysm is one of the common cerebrovascular diseases in neurosurgery, and rupture of cerebral aneurysm is the most important cause of spontaneous subarachnoid hemorrhage. How to precisely clip the aneurysm has been a topic worth discussing, so the authors explore the value of ICGA combined with electrophysiological monitoring in the microclipping of cerebral aneurysms. Using the method of retrospective analysis of cases, 661 patients with cerebral aneurysms admitted to the Department of Neurosurgery, Zhongnan Hospital of Wuhan University, from 2021.8 to 2022.10 were studied, 390 patients with aneurysm clipping were included, and patients with Hunt-Hess classification ≥ 4 were excluded, and whether to use ICGA combined with EP in microclipping of the ruptured and unruptured aneurysm in pterional approach was investigated at the time of discharge, respectively. The MRS and total hospital days were compared to investigate the value of ICGA combined with EP in the microclipping of cerebral aneurysms. All 390 patients enrolled in the group had successful aneurysm clipping, 178 patients were screened for ruptured aneurysm pterional approach and 120 patients for unruptured aneurysm pterional approach access; the MRS at discharge was significantly lower in the ICGA combined with EP group than in the no-EP group for ruptured aneurysm pterional approach microclipping (p < 0.001), and the mean number of days in hospital was significantly lower (p < 0.01). Patients in the ICGA combined with EP group in microclipping of unruptured aneurysms with pterional approach also had significantly lower MRS at discharge compared with patients in the ICGA alone group (p < 0.001), with no statistically significant difference in the mean number of days in hospital (p = 0.09). In open cerebral aneurysm microclipping, ICGA combined with EP monitoring for both ruptured and unruptured aneurysms can effectively reduce the false-negative rate of ICGA, significantly reduce the incidence of postoperative neurological deficits, and shorten the total hospital stay to some extent. ICGA combined with EP monitoring may be an effective means to reduce the rate of false clipping of the penetrating vessels and to avoid stenosis or occlusion of the aneurysm-carrying artery and is worth promoting in microclipping of cerebral aneurysms except for Hunt-Hess ≥ 4.

KS-CMI: A circRNA-miRNA interaction prediction method based on the signed graph neural network and denoising autoencoder.

Circular RNA (circRNA) plays an important role in the diagnosis, treatment, and prognosis of human diseases. The discovery of potential circRNA-miRNA interactions (CMI) is of guiding significance for subsequent biological experiments. Limited by the small amount of experimentally supported data and high randomness, existing models are difficult to accomplish the CMI prediction task based on real cases. In this paper, we propose KS-CMI, a novel method for effectively accomplishing CMI prediction in real cases. KS-CMI enriches the 'behavior relationships' of molecules by constructing circRNA-miRNA-cancer (CMCI) networks and extracts the behavior relationship attribute of molecules based on balance theory. Next, the denoising autoencoder (DAE) is used to enhance the feature representation of molecules. Finally, the CatBoost classifier was used for prediction. KS-CMI achieved the most reliable prediction results in real cases and achieved competitive performance in all datasets in the CMI prediction.

Sound non-reciprocity based on synthetic magnetism.

Synthetic magnetism has been recently realized using spatiotemporal modulation patterns, producing non-reciprocal steering of charge-neutral particles such as photons and phonons. Here, we design and experimentally demonstrate a non-reciprocal acoustic system composed of three compact cavities interlinked with both dynamic and static couplings, in which phase-correlated modulations induce a synthetic magnetic flux that breaks time-reversal symmetry. Within the rotating wave approximation, the transport properties of the system are controlled to efficiently realize large non-reciprocal acoustic transport. By optimizing the coupling strengths and modulation phases, we achieve frequency-preserved unidirectional transport with 45-dB isolation ratio and 0.85 forward transmission. Our results open to the realization of acoustic non-reciprocal technologies with high efficiency and large isolation, and offer a route towards Floquet topological insulators for sound.

Ultrasonic-Enabled Nondestructive and Substrate-Independent Liquid Metal Ink Sintering.

Printing or patterning particle-based liquid metal (LM) ink is a good strategy to overcome poor wettability of LM for its circuits' preparation in flexible and printed electronics. Subsequently, a crucial step is to recover conductivity of LM circuits consisting of insulating LM micro/nano-particles. However, most widely used mechanical sintering methods based on hard contact such as pressing, may not be able to contact the LM patterns' whole surface conformally, leading to insufficient sintering in some areas. Hard contact may also break delicate shapes of the printed patterns. Hereby, an ultrasonic-assisted sintering strategy that can not only preserve original morphology of the LM circuits but also sinter circuits on various substrates of complex surface topography is proposed. The influencing factors of the ultrasonic sintering are investigated empirically and interpreted with theoretical understanding by simulation. LM circuits encapsulated inside soft elastomer are successfully sintered, proving feasibility in constructing stretchable or flexible electronics. By using water as energy transmission medium, remote sintering without any direct contact with substrate is achieved, which greatly protect LM circuits from mechanical damage. In virtue of such remote and non-contact manipulation manner, the ultrasonic sintering strategy would greatly advance the fabrication and application scenarios of LM electronics.

The role of the gut microbiome and its metabolites in cerebrovascular diseases.

The gut microbiome is critically involved in maintaining normal physiological function in the host. Recent studies have revealed that alterations in the gut microbiome contribute to the development and progression of cerebrovascular disease via the microbiota-gut-brain axis (MGBA). As a broad communication network in the human body, MGBA has been demonstrated to have significant interactions with various factors, such as brain structure and function, nervous system diseases, etc. It is also believed that the species and composition of gut microbiota and its metabolites are intrinsically linked to vascular inflammation and immune responses. In fact, in fecal microbiota transplantation (FMT) research, specific gut microbiota and downstream-related metabolites have been proven to not only participate in various physiological processes of human body, but also affect the occurrence and development of cerebrovascular diseases directly or indirectly through systemic inflammatory immune response. Due to the high mortality and disability rate of cerebrovascular diseases, new treatments to improve intestinal dysbacteriosis have gradually attracted widespread attention to better ameliorate the poor prognosis of cerebrovascular diseases in a non-invasive way. This review summarizes the latest advances in the gut microbiome and cerebrovascular disease research and reveals the profound impact of gut microbiota dysbiosis and its metabolites on cerebrovascular diseases. At the same time, we elucidated molecular mechanisms whereby gut microbial metabolites regulate the expression of specific interleukins in inflammatory immune responses. Moreover, we further discuss the feasibility of novel therapeutic strategies targeting the gut microbiota to improve the outcome of patients with cerebrovascular diseases. Finally, we provide new insights for standardized diagnosis and treatment of cerebrovascular diseases.