Nano-mechanical Immunoengineering: Nanoparticle Elasticity Reprograms Tumor-Associated Macrophages via Piezo1.

The mechanical properties of nanoparticles play a crucial role in regulating nanobiointeractions, influencing processes such as blood circulation, tumor accumulation/penetration, and internalization into cancer cells. Consequently, they have a significant impact on drug delivery and therapeutic efficacy. However, it remains unclear whether and how macrophages alter their biological function in response to nanoparticle elasticity. Here, we report on the nano-mechanical biological effects resulting from the interactions between elastic silica nanoparticles (SNs) and macrophages. The SNs with variational elasticity Young's moduli ranging from 81 to 837 MPa were synthesized, and it was demonstrated that M2 [tumor-associated macrophages (TAMs)] could be repolarized to M1 by the soft SNs. Additionally, our findings revealed that cell endocytosis, membrane tension, the curvature protein Baiap2, and the cytoskeleton were all influenced by the elasticity of SNs. Moreover, the mechanically sensitive protein Piezo1 on the cell membrane was activated, leading to calcium ion influx, activation of the NF-κB pathway, and the initiation of an inflammatory response. In vivo experiments demonstrated that the softest 81 MPa SNs enhanced tumor penetration and accumulation and repolarized TAMs in intratumoral hypoxic regions, ultimately resulting in a significant inhibition of tumor growth. Taken together, this study has established a cellular feedback mechanism in response to nanoparticle elasticity, which induces plasma membrane deformation and subsequent activation of mechanosensitive signals. This provides a distinctive "nano-mechanical immunoengineering" strategy for reprogramming TAMs to enhance cancer immunotherapy.

Atmospheric brown carbon in China haze is dominated by secondary formation.

Brown carbon (BrC) is a class of light-absorbing organic aerosols (OA) and has significant influence on atmospheric radiative forcing. However, the current limited understanding of the physicochemical properties of BrC restricts the accurate evaluation of its environmental effects. Here the optical characteristics and chemical composition of BrC during wintertime in the Yangtze River Delta (YRD) region, China were measured by using high-resolution aerosol mass spectrometry (HR-AMS) and UV-vis spectrometry. Our results showed that BrC in PM2.5 during the campaign was dominated by water-soluble organics, which consist of less oxidized oxygenated OA (LO-OOA), more oxidized oxygenated OA (MO-OOA), fossil fuel OA (FFOA) and biomass burning OA (BBOA). MO-OOA and BBOA were the strongest light absorbing BrC at 365 nm (Abs365), followed by LO-OOA and FFOA with a mass absorption coefficient (MAC) being 0.74 ± 0.04, 0.73 ± 0.03, 0.48 ± 0.04 and 0.39 ± 0.06 m2 g-1 during the campaign, respectively. In the low relative humidity (RH < 80 %) haze periods Abs365 of LO-OOA contributed to 44 % of the total light absorption at 365 nm, followed by MO-OOA (31 %), FFOA (21 %) and BBOA (4 %). In contrast, in the high-RH (RH > 80 %) haze periods Abs365 was dominated by MO-OOA, which accounted for 62 % of the total Abs365, followed by LO-OOA (17 %), BBOA (13 %) and FFOA (8 %). Chemical composition analysis further showed that LO-OOA and MO-OOA are produced from gas-phase photooxidation of VOCs and aerosol aqueous reactions, respectively, in which ammonia significantly enhanced the formation and light absorption of BrC in the high RH haze period. On average, >75 % of the total Abs365nm in the YRD region during the haze events was contributed by LO-OOA and MO-OOA, suggesting that atmospheric BrC in China haze periods is predominantly formed by secondary reactions.

Downregulation of miR-181c-5p in Alzheimer's disease weakens the response of microglia to Aß phagocytosis.

Alzheimer's disease (AD) is an age-associated neurodegenerative disease. Recently, studies have demonstrated the potential involvement of microRNA-181c-5p (miR-181c-5p) in AD. However, the mechanism through which miR-181c-5p is responsible for the onset and progression of this disease remains unclear, and our study aimed to explore this problem. Differential expression analysis of the AD dataset was performed to identify dysregulated genes. Based on hypergeometric analysis, AD differential the upstream regulation genes miR-181c-5p was found. We constructed a model where SH-SY5Y and BV2 cells were exposed to Aß1-42 to simulate AD. Levels of tumor necrosis factor-alpha, interleukin-6, and IL-1ß were determined using enzyme-linked immunosorbent assay or reverse transcription quantitative polymerase chain reaction. Phosphorylation levels of p-P38 and P38 were detected by Western blot. The level of apoptosis in BV2 cells under Aß1-42 stress was exacerbated by miR-181c-5p mimic. Downregulated miR-181c-5p impaired the phagocytosis and degradation of Aß by BV2 cells. The release of proinflammatory cytokines in BV2 cells with Aß1-42 stress was alleviated by miR-181c-5p upregulation. Additionally, miR-181c-5p downregulation alleviated the phosphorylation of P38 in Aß1-42-induced SH-SY5Y cells. In conclusion, miR-181c-5p improves the phagocytosis of Aß by microglial cells in AD patients, thereby reducing neuroinflammation.

Upcycle polyethylene terephthalate waste by photoreforming: Bifunction of Pt cocatalyst.

Upcycle polyethylene terephthalate (PET) waste by photoreforming (PR) is a sustainable and green approach to tackle environmental problems but with challenges to obtain valuable oxidation products and high purity hydrogen simultaneously. Noble metal cocatalysts are essential to enhance the overall PR reaction efficacy. In this work, TiO2 nanotubes (TiO2 NTs) decorated with single Pt atoms (Pt1/TiO2) or Pt nanoparticles (PtNPs/TiO2) are used in the photoreforming reaction (in one batch), and the oxidation products from ethylene glycol (EG, hydrolysed product of PET) in liquid phase and hydrogen are detected. With Pt1/TiO2, EG is oxidized to glyoxal, glyoxylate or lactate, and hydrogen evolution rate (r H2) reaches 51.8 µmol⋅h-1⋅gcat-1, that is 30 times higher than that of TiO2. For PtNPs/TiO2 (size of Pt NPs: 1.97 nm), hydrogen evolution reaches 219.1 µmol⋅h-1⋅gcat-1, but with the oxidation product of acetate only. DFT calculation demonstrates that for Pt NPs, the reaction path for hydrogen evolution is preferred thermodynamically, due to the formation of Schottky junction. On the oxidation of EG, theoretical and spectroscopic analysis suggest that bidentate adsorption of EG at the interface is facile on Pt1/TiO2, compared to that on PtNPs/TiO2 (two Pt sites), but oxidation products, adsorb less strongly, compared to PtNPs/TiO2, that eventually regulates the distribution of oxidation products. The results thus demonstrate the bifunctions of Pt in the PR reaction, i.e., electron transfer mediator for hydrogen evolution and reactive sites for molecules adsorption. The oxidation reaction is dominated by the adsorption-desorption behavior of molecules but the reduction reaction is controlled by the electron transfer. In addition, acidification of pretreated PET alkaline solution achieves separation of pure terephthalic acid (PTA), which further improves the reaction efficiency possibly by offering high density of active sites and acidic environment. Our work thus demonstrates that to upcycle PET plastics, an optimized process can be reached by atomic design of photocatalysts and proper treatment on the plastic wastes.

Acute lymphoblastic leukemia with pseudo-Chediak-Higashi granules in the initial diagnosis and relapse.

Pseudo-Chediak-Higashi granules are large cytoplasmic inclusions commonly encountered in myeloblasts or other myeloid precursors in acute myeloid leukemia and myelodysplastic syndromes. However, pseudo-Chediak-Higashi granules are rarely found in acute lymphoblastic leukemia (ALL). We present the case of an 8-year-old boy who was diagnosed with ALL with pseudo-Chediak-Higashi granules in the initial diagnosis and relapse, acting like a characteristic marker.

SGTR+: End-to-End Scene Graph Generation With Transformer.

Scene Graph Generation (SGG) remains a challenging visual understanding task due to its compositional property. Most previous works adopt a bottom-up, two-stage or point-based, one-stage approach, which often suffers from high time complexity or suboptimal designs. In this paper, we propose a novel SGG method to address the aforementioned issues, formulating the task as a bipartite graph construction problem. To address the issues above, we create a transformer-based end-to-end framework to generate the entity and entity-aware predicate proposal set, and infer directed edges to form relation triplets. Moreover, we design a graph assembling module to infer the connectivity of the bipartite scene graph based on our entity-aware structure, enabling us to generate the scene graph in an end-to-end manner. Based on bipartite graph assembling paradigm, we further propose a new technical design to address the efficacy of entity-aware modeling and optimization stability of graph assembling. Equipped with the enhanced entity-aware design, our method achieves optimal performance and time-complexity. Extensive experimental results show that our design is able to achieve the state-of-the-art or comparable performance on three challenging benchmarks, surpassing most of the existing approaches and enjoying higher efficiency in inference.

An enhanced decision-making framework for predicting future trends of sharing economy.

This work aims to provide a reliable and intelligent prediction model for future trends in sharing economy. Moreover, it presents valuable insights for decision-making and policy development by relevant governmental bodies. Furthermore, the study introduces a predictive system that incorporates an enhanced Harris Hawk Optimization (HHO) algorithm and a K-Nearest Neighbor (KNN) forecasting framework. The method utilizes an improved simulated annealing mechanism and a Gaussian bare bone structure to improve the original HHO, termed SGHHO. To achieve optimal prediction performance and identify essential features, a refined simulated annealing mechanism is employed to mitigate the susceptibility of the original HHO algorithm to local optima. The algorithm employs a mechanism that boosts its global search ability by generating fresh solution sets at a specific likelihood. This mechanism dynamically adjusts the equilibrium between the exploration and exploitation phases, incorporating the Gaussian bare bone strategy. The best classification model (SGHHO-KNN) is developed to mine the key features with the improvement of both strategies. To assess the exceptional efficacy of the SGHHO algorithm, this investigation conducted a series of comparative trials employing the function set of IEEE CEC 2014. The outcomes of these experiments unequivocally demonstrate that the SGHHO algorithm outperforms the original HHO algorithm on 96.7% of the functions, substantiating its remarkable superiority. The algorithm can achieve the optimal value of the function on 67% of the tested functions and significantly outperforms other competing algorithms. In addition, the key features selected by the SGHHO-KNN model in the prediction experiment, including " Form of sharing economy in your region " and " Attitudes to the sharing economy ", are important for predicting the future trends of the sharing economy in this study. The results of the prediction demonstrate that the proposed model achieves an accuracy rate of 99.70% and a specificity rate of 99.38%. Consequently, the SGHHO-KNN model holds great potential as a reliable tool for forecasting the forthcoming trajectory of the sharing economy.

[Leukemia Genotype Analysis of Children with Acute Lymphoblastic Leukemia in Yunnan Area].

OBJECTIVE: To analyze 43 leukemia genes in children with acute lymphoblastic leukemia (ALL) in Yunnan province, and provide the basis for the diagnosis and treatment of children with ALL in this area. METHODS: The clinical data of 428 children with newly diagnosed ALL in Yunnan area from January 2015 to December 2020 were retrospectively analyzed. Multiple nested PCR technology was used to detect 43 common leukemia genes. RESULTS: Among the 428 children with ALL, 159 were positive for leukemia genes, with a positive rate of 37.15% (159/428), and a total of 15 leukemia genes were detected. Among the 159 leukemia gene-positive children, ETV6-RUNX1+ accounted for 25.79% (41/159), followed by E2A-PBX1+ and BCR-ABL+, accounting for 24.53% (39/159) and 23.27% (37/159) respectively. MLL+ accounted for 6.29% (10/159), WT1+ accounted for 4.40% (7/159), IKZF1 gene deletion and CRLF2+ accounted for 3.77% (6/159) respectively. The positive rate of MLL (46.15%) was the highest in ＜1-year old group, the positive rate of ETV6-RUNX1 (10.56%) was the highest in 1-10-year old group, and BCR-ABL+ rate (23.65%) was the highest in >10-year old group. The distribution of leukemia genes in different age groups was statistically significant (P <0.05). CONCLUSION: The most common fusion gene of children with ALL in Yunnan is ETV6-RUNX1, followed by E2A-PBX1 and BCR-ABL.

Applicability of RANS models and pressure drop in edge subchannels for 19-pin wire-wrapped fuel bundle channel in CiADS.

The accelerator-driven subcritical system has a strong transmutation ability and high inherent safety, and it is internationally recognized as the most promising long-life nuclear waste disposal device. This study involves the construction of a Visual Hydraulic ExperimentaL Platform (VHELP) for the purpose of evaluating the applicability of Reynolds-averaged Navier-Stokes (RANS) models and analyzing the pressure distribution within the fuel bundle channel of China initiative accelerator-driven system (CiADS). Measurements of thirty differential pressures in edge subchannels within a 19-pin wire-wrapped fuel bundle channel were obtained under different conditions using deionized water. The pressure distribution in the fuel bundle channel at Reynolds numbers of 5000, 7500, 10,000, 12,500, and 15,000 was simulated using Fluent. The results show that RANS models obtained accurate results, and the shear stress transport k-ω model provided the most accurate prediction of the pressure distribution. The difference between the results of the Shear stress transport (SST) k-ω model and experimental data was the smallest, and the maximum difference was ±5.57%. Moreover, the error between the experimental data and numerical results of the axial differential pressure was smaller than that of the transverse differential pressure. The pressure periodicity in axial and transverse directions (one pitch) and a relatively three-dimensional pressure measurements were studied. The static pressure fluctuated and decreased periodically as the z-axis coordinate increased. These results can facilitate research on the cross-flow characteristics of liquid metal-cooled fast reactors.

Noble-Metal-Free Single- and Dual-Atom Catalysts for Artificial Photosynthesis.

Artificial photosynthesis enables direct solar-to-chemical energy conversion aimed at mitigating environmental pollution and producing solar fuels and chemicals in a green and sustainable approach, and efficient, robust, and low-cost photocatalysts are the heart of artificial photosynthesis systems. As an emerging new class of cocatalytic materials, single-atom catalysts (SACs) and dual-atom catalysts (DACs) have received a great deal of current attention due to their maximal atom utilization and unique photocatalytic properties, whereas noble-metal-free ones impart abundance, availability, and cost-effectiveness allowing for scalable implementation. This review outlines the fundamental principles and synthetic methods of SACs and DACs and summarizes the most recent advances in SACs (Co, Fe, Cu, Ni, Bi, Al, Sn, Er, La, Ba, etc.) and DACs (CuNi, FeCo, InCu, KNa, CoCo, CuCu, etc.) based on non-noble metals, confined on an arsenal of organic or inorganic substrates (polymeric carbon nitride, metal oxides, metal sulfides, metal-organic frameworks, carbon, etc.) acting as versatile scaffolds in solar-light-driven photocatalytic reactions, including hydrogen evolution, carbon dioxide reduction, methane conversion, organic synthesis, nitrogen fixation, hydrogen peroxide production, and environmental remediation. The review concludes with the challenges, opportunities, and future prospects of noble-metal-free SACs and DACs for artificial photosynthesis.

A novel photoelectrochemical sensor based on flower-like SnS2, sea urchin-like AgBiS2 and graphene oxide nanocomposite film for efficient and sensitive detection of acetaminophen in lake water samples.

As an emerging detection technology, photoelectrochemical sensors have been widely noticed for their unique technical features. Among others, the technology has been widely used in the fields of drug, biological antibody or antigen and contaminant detection. Secondly, acetaminophen, as a novel environmental pollutant, is difficult to be degraded in the ecosystem, which in turn causes serious impacts on the ecosystem. Therefore, in this work, we designed a photoelectrochemical sensor based on a composite film of flower-like SnS2, sea urchin-like AgBiS2 and graphene oxide for the detection of acetaminophen in water samples. Among them, graphene oxide, as a two-dimensional carbon-based material, can immobilize other photoelectric materials well. In addition, the flower-like SnS2 and sea urchin-like AgBiS2 can enhance the intensity of the photoelectric response due to their synergistic effect. Notably, the combination of graphene oxide with SnS2 and AgBiS2 revealed an exponential increase in the photoresponse intensity, indicating that SnS2/AgBiS2/GO has a satisfactory photoresponse intensity. At the same time, the photoelectrochemical sensor exhibited sensitive detection performance (LOD = 4 nM) and a wide detection range (0.01-50 µM) for acetaminophen under optimal detection conditions. Moreover, it also showed excellent detection performance in the detection of actual water samples, indicating that it can be applied to the detection of acetaminophen in lakes.

Identification of molecular signatures associated with sleep disorder and Alzheimer's disease.

Background: Alzheimer's disease (AD) and sleep disorders are both neurodegenerative conditions characterized by impaired or absent sleep. However, potential common pathogenetic mechanisms of these diseases are not well characterized. Methods: Differentially expressed genes (DEGs) were identified using publicly available human gene expression profiles GSE5281 for AD and GSE40562 for sleep disorder. DEGs common to the two datasets were used for enrichment analysis, and we performed multi-scale embedded gene co-expression network analysis (MEGENA) for common DEGs. Fast gene set enrichment analysis (fGSEA) was used to obtain common pathways, while gene set variation analysis (GSVA) was applied to quantify those pathways. Subsequently, we extracted the common genes between module genes identified by MEGENA and genes of the common pathways, and we constructed protein-protein interaction (PPI) networks. The top 10 genes with the highest degree of connectivity were classified as hub genes. Common genes were used to perform Metascape enrichment analysis for functional enrichment. Furthermore, we quantified infiltrating immune cells in patients with AD or sleep disorder and in controls. Results: DEGs common to the two disorders were involved in the citrate cycle and the HIF-1 signaling pathway, and several common DEGs were related to signaling pathways regulating the pluripotency of stem cells, as well as 10 other pathways. Using MEGENA, we identified 29 modules and 1,498 module genes in GSE5281, and 55 modules and 1,791 module genes in GSE40562. Hub genes involved in AD and sleep disorder were ATP5A1, ATP5B, COX5A, GAPDH, NDUFA9, NDUFS3, NDUFV2, SOD1, UQCRC1, and UQCRC2. Plasmacytoid dendritic cells and T helper 17 cells had the most extensive infiltration in both AD and sleep disorder. Conclusion: AD pathology and pathways of neurodegeneration participate in processes contributing in AD and sleep disorder. Hub genes may be worth exploring as potential candidates for targeted therapy of AD and sleep disorder.

Identification of Candidate Genes Associated With Prognosis in Glioblastoma.

Background: Glioblastoma (GBM) is the most common malignant primary brain tumor, which associated with extremely poor prognosis. Methods: Data from datasets GSE16011, GSE7696, GSE50161, GSE90598 and The Cancer Genome Atlas (TCGA) were analyzed to identify differentially expressed genes (DEGs) between patients and controls. DEGs common to all five datasets were analyzed for functional enrichment and for association with overall survival using Cox regression. Candidate genes were further screened using least absolute shrinkage and selection operator (LASSO) and random forest algorithms, and the effects of candidate genes on prognosis were explored using a Gaussian mixed model, a risk model, and concordance cluster analysis. We also characterized the GBM landscape of immune cell infiltration, methylation, and somatic mutations. Results: We identified 3,139 common DEGs, which were associated mainly with PI3K-Akt signaling, focal adhesion, and Hippo signaling. Cox regression identified 106 common DEGs that were significantly associated with overall survival. LASSO and random forest algorithms identified six candidate genes (AEBP1, ANXA2R, MAP1LC3A, TMEM60, PRRG3 and RPS4X) that predicted overall survival and GBM recurrence. AEBP1 showed the best prognostic performance. We found that GBM tissues were heavily infiltrated by T helper cells and macrophages, which correlated with higher AEBP1 expression. Stratifying patients based on the six candidate genes led to two groups with significantly different overall survival. Somatic mutations in AEBP1 and modified methylation of MAP1LC3A were associated with GBM. Conclusion: We have identified candidate genes, particularly AEBP1, strongly associated with GBM prognosis, which may help in efforts to understand and treat the disease.

A non-enzymatic photoelectrochemical sensor based on g-C3N4@CNT heterojunction for sensitive detection of antioxidant gallic acid in food.

Gallic acid (GA) is found in a wide range of natural plants and is relevant to the health of human beings. Here, a photoelectrochemical sensing platform based on g-C3N4@CNT heterojunction has been prepared for the highly sensitive and selective detection of GA. Under the light of xenon lamp, the photocurrent of g-C3N4@CNT is 7 times higher than that of g-C3N4. And the sensor generates 4 times more photocurrent in the presence of GA than without GA. This sensor has a wide linear range from 10 nM to 10 µM with a limit of detection as low as 2 nM. Also, the abundant amino groups of g-C3N4 provide excellent selectivity for the sensor. Furthermore, the sensor can be used for the analysis of GA in black tea samples, which provides a novel and rapid method for the detection of GA in food samples.

Resveratrol-Loaded TPGS-Resveratrol-Solid Lipid Nanoparticles for Multidrug-Resistant Therapy of Breast Cancer: In Vivo and In Vitro Study.

Multidrug resistance (MDR) is a serious problem during cancer therapy. The purpose of the present study was to formulate D-α-Tocopheryl polyethylene glycol 1000 succinate-resveratrol-solid lipid nanoparticles (TPGS-Res-SLNs) to improve its therapeutic efficacy against breast cancer. In this study, the solvent injection method was used to prepare the TPGS-Res-SLNs. It was found that the TPGS-Res-SLNs exhibited zeta potential and drug-loading of -25.6 ± 1.3 mV and 32.4 ± 2.6%, respectively. Therefore, it was evident that the TPGS-Res-SLNs can increase cellular uptake of chemotherapeutic drugs, induce mitochondrial dysfunction, and augment tumor treatment efficiency by inducing apoptosis. Moreover, it was found that SKBR3/PR cells treated with TPGS-Res-SLNs exhibited significant inhibition of cell migration and invasion, as compared with free resveratrol. In addition, results from in vivo SKBR3/PR xenograft tumor models revealed that TPGS-Res-SLNs has better efficacy in promoting apoptosis of tumor cells owing to high therapeutic outcomes on tumors when compared with the efficacy of free resveratrol. In conclusion, the findings of the present study indicate significant potential for use of TPGS-Res-SLNs as an efficient drug delivery vehicle to overcome drug resistance in breast cancer therapy.

Corrigendum: Microglia Mediate the Occurrence and Development of Alzheimer's Disease Through Ligand-Receptor Axis Communication.

[This corrects the article DOI: 10.3389/fnagi.2021.731180.].

DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation.

Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics. Herein we introduce a deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. By leveraging in silico libraries generated by DeepPhospho, we establish a DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Our DeepPhospho-empowered workflow substantially expands the phosphoproteome coverage while maintaining high quantification performance, which leads to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach. DeepPhospho is provided as a web server as well as an offline app to facilitate user access to model training, predictions and library generation.

Microglia Mediate the Occurrence and Development of Alzheimer's Disease Through Ligand-Receptor Axis Communication.

Alzheimer's disease (AD) is a common neurodegenerative disease. Its onset is insidious and its progression is slow, making diagnosis difficult. In addition, its underlying molecular and cellular mechanisms remain unclear. In this study, clustering analysis was performed on single-cell RNA sequencing (scRNA-seq) data from the prefrontal cortex of 48 AD patients. Each sample module was identified to be a specific AD cell type, eight main brain cell types were identified, and the dysfunctional evolution of each cell type was further explored by pseudo-time analysis. Correlation analysis was then used to explore the relationship between AD cell types and pathological characteristics. In particular, intercellular communication between neurons and glial cells in AD patients was investigated by cell communication analysis. In patients, neuronal cells and glial cells significantly correlated with pathological features, and glial cells appear to play a key role in the development of AD through ligand-receptor axis communication. Marker genes involved in communication between these two cell types were identified using five types of modeling: logistic regression, multivariate logistic regression, least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). LASSO modeling identified CXCR4, EGFR, MAP4K4, and IGF1R as key genes in this communication. Our results support the idea that microglia play a role in the occurrence and development of AD through ligand-receptor axis communication. In particular, our analyses identify CXCR4, EGFR, MAP4K4, and IGF1R as potential biomarkers and therapeutic targets in AD.

A Liquid Metal Artificial Muscle.

Artificial muscles possess a vast potential in accelerating the development of robotics, exoskeletons, and prosthetics. Although a variety of emerging actuator technologies are reported, they suffer from several issues, such as high driving voltages, large hysteresis, and water intolerance. Here, a liquid metal artificial muscle (LMAM) is demonstrated, based on the electrochemically tunable interfacial tension of liquid metal to mimic the contraction and extension of muscles. The LMAM can work in different solutions with a wide range of pH (0-14), generating actuation strains of up to 87% at a maximum extension speed of 15 mm s-1 . More importantly, the LMAM only needs a very low driving voltage of 0.5 V. The actuating components of the LMAM are completely built from liquids, which avoids mechanical fatigue and provides actuator linkages without mechanical constraints to movement. The LMAM is used for developing several proof-of-concept applications, including controlled displays, cargo deliveries, and reconfigurable optical reflectors. The simplicity, versatility, and efficiency of the LMAM are further demonstrated by using it to actuate the caudal fin of an untethered bionic robotic fish. The presented LMAM has the potential to extend the performance space of soft actuators for applications from engineering fields to biomedical applications.

Identification of an miRNA Regulatory Network and Candidate Markers for Ischemic Stroke Related to Diabetes.

PURPOSE: Type 2 diabetes mellitus (T2DM) increases the risk of ischemic stroke and poor prognosis. This study aimed to identify molecular mechanisms that are dysregulated in T2DM-associated ischemic stroke and candidate genes that might serve as biomarkers. METHODS: The top 25% variance genes in the GSE21321 and GSE22255 datasets were analyzed for coexpression. The differentially expressed mRNAs (DEmRs) between patients with T2DM or ischemic stroke and controls were analyzed. Then, the union of overlapping coexpressed genes and overlapping DEmRs was analyzed. The miRNAs differentially expressed in T2DM-associated ischemic stroke were also analyzed. CIBERSORT was used to evaluate the levels of infiltration by immune cells in T2DM-associated stroke. RESULTS: Thirteen coexpression modules were identified in T2DM and 10 in ischemic stroke, and 594 module genes were shared between the two conditions. A total of 4452 mRNAs differentially expressed between T2DM patients and controls were identified, as were 2390 mRNAs differentially expressed between ischemic stroke and controls. The 771 union genes were enriched mainly in immune-related biological functions and signaling pathways. UBE2N, TGFB3, EXOSC1, and VIM were identified as candidate markers. In addition, we identified miR-576-3p as having the most regulatory roles in both T2DM and ischemic stroke. Mast cell activation was significantly down-regulated in T2DM but up-regulated in ischemic stroke. CONCLUSION: These findings provide numerous testable hypotheses about the pathways underlying T2DM-associated ischemic stroke, which may help identify therapeutic targets.