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The aim of this study was to explore the application value of transvaginal color Doppler ultrasound based on the improved mean shift algorithm in the diagnosis of idiopathic premature ovarian failure (POF). In this study, 80 patients with idiopathic POF were selected and included in the experimental group, and 40 volunteers who underwent health examinations during the same period were selected and included in the control group, who underwent transvaginal Doppler ultrasound examination. At the same time, an improved mean shift algorithm was proposed based on artificial intelligence technology and applied to ultrasound image processing. In addition, the ovarian artery parameters of patients were compared in two groups, including peak systolic flow rate (PSV), diastolic flow rate (EDV), resistance index (RI), and pulsatile index (PI). The results showed that the relative difference degree (RDD) of the segmentation results of the algorithm in this study was significantly lower than that of Snake, Live_wire, and the traditional mean shift algorithm, while the relative overlap degree (ROD) and Dice coefficient were opposite, and the differences were significant (P

As an important attempt towards creating hierarchical structures more like nature, the peptide is employed as a building block to build supramolecular architectures. An emerging question is whether the molecular mechanism of self-assembly obtained from the small molecule system, e.g., the driving forces of assembly are conventionally regarded as pairwise-additive, can be manifested in the self-association of biologically relevant amphiphilic peptides. A peptide, KRT-R, was derived from the 120-144 segment of keratin 14. The single cation-to-cation substitution with KRT-R at the site of 125 from arginine (R) to either lysine (K) or histidine (H) results in the peptide helices, KRT-K and KRT-H, sharing 96% sequence identity. These KRT-derived peptides possess similarities in the folding structures but exhibit divergent self-assembled structures. KRT-R and KRT-K self-assemble into sheets and fibrils, respectively. Whereas KRT-H associates into heterogeneous structures, including sheets, particles, and branched networks. The intrinsic tyrosine fluorescence spectroscopy measurements with the KRT-derived peptides within a temperature range of 25 °C to 95 °C reveal that the heating-triggered structural transitions of KRT-derived peptides are divergent. The alternation of single cationic residue changes the thermodynamic signature of peptide assemblies upon heating. A chemical denaturation experiment with KRT-derived peptides indicates that the intermolecular interactions that govern the supramolecular architectures formed by peptides are distinct. Overall, our work demonstrates the contribution of the interplay among various noncovalent interactions to supramolecular assembly.

Amino acid chirality has been envisioned as an important strategy to regulate structure and function of peptide self-assembled architectures. However, the molecular mechanism of chirality effects in peptide assemblies remains largely elusive. Here, the assembly structures of l-peptide polyphenylalanine F10 (FFFFFFFFFF) and block heterochiral peptide F5f5 (FFFFFfffff) composed of two FFFFF repeat blocks with opposite chirality were characterized at the single-molecule level by using scanning tunneling microscopy. Each peptide formed two distinctively different assembly structures on the HOPG surface, in which peptide chains took parallel and antiparallel β-sheet conformations, respectively. The molecular-level observations revealed that the staggered arrangement of cross-strand side chains achieved in the antiparallel β-sheet structure of the block heterochiral peptide facilitated intimate packing of side chains and maximized inter-residue van der Waals interactions, which led to more residues participating in assembly and greatly stabilized the β-sheet structure of the surface-bound peptide assembly, but such cross-strand nested interactions were not accessible in the heterochiral parallel β-sheet structure and the enantiomerically pure assembly structures. This work could contribute to the molecular insights of stereochemical interactions in peptide assemblies and feasibility of extending this block heterochirality pattern to other peptides with various lengths and amino acid compositions for structural regulations.

As China's economy continues to grow, more and more attention is being paid to environmental pollution issues, including haze pollution, which is the most prominent of the air problems. In order to alleviate the haze pollution problem while ensuring the normal operation of the economy, the government has adopted an environmental regulation policy along with the introduction of foreign direct investment. This study uses a fixed effects model and two-stage least squares to examine the correlation of foreign direct investment and environmental regulation to the haze pollution level by using the Chinese provincial panel data from 2008 to 2019. We found a positive interaction between the foreign direct investment and environmental regulation, where a high-quality investment promotes the haze reduction, and the environmental regulation can enhance this effect. The results approve the possibilities that China can bring the merit investment and reduce its air pollution simultaneously, but how to balance the interaction between environmental regulation and the investment needs to draw attention.

A two-dimensional thin Co-MOF (ZIF-67) nanosheet with high oxidase-like activity was applied for sensitive visual GSH detection.

Activated fibroblasts are thought to play important roles in the progression of many solid tumors, but little is known about the mechanisms responsible for the recruitment of fibroblasts in tumors. Using several methods, we identified platelet-derived growth factor A (PDGFA) as the major fibroblast chemoattractant and mitogen from conditioned medium generated by the Calu-6 lung carcinoma cell line. In addition, we showed that Calu-6 tumors express significant levels of PDGFC, and that the levels of expression of these two PDGFRα ligands correlate strongly with the degree of stromal fibroblast infiltration into the tumor mass. The most intense expression of PDGFRα was observed in fibroblasts in the tumor outer rim. We subsequently showed that disrupting PDGFRα-mediated signaling results in significant inhibition of tumor growth in vivo. Furthermore, analysis of a compendium of microarray data revealed significant expression of PDGFA, PDGFC, and PDGFRα in human lung tumors. We propose that therapies targeting this stromal cell type may be effective in treating certain types of solid tumors.

Supplementary Figure 1 from Tumor-Driven Paracrine Platelet-Derived Growth Factor Receptor α Signaling Is a Key Determinant of Stromal Cell Recruitment in a Model of Human Lung Carcinoma

Cepharanthine (CEP) is a natural remedy that potently inhibits SARS-CoV-2 activity both in vitro and in vivo. We conducted a proof-of-concept, double-blind, randomized, placebo-controlled trial among adults with asymptomatic or mild coronavirus disease 2019 (COVID-19). Patients were stratified randomly to de novo infection or viral rebound, and assigned in a 1:1:1 ratio to receive 60 mg/day or 120 mg/day of CEP or placebo. Primary outcome the time from randomization to negative nasopharyngeal swab, and safety were evaluated. A total of 262 de novo infected and 124 viral rebound patients underwent randomization. In the 188 de novo patients included in modified intention-to-treat (mITT) population, when compared with placebo, 60 mg/day CEP slightly shortened the time to negative (difference=-0.77 days, hazard ratio (HR)=1.40, 95% CI 0.97 to 2.01, p=0.072), and 120 mg/day CEP did not show the trend. Among de novo patients in the per-protocol set (PPS), 60 mg/day CEP significantly shortened the time to negative (difference=-0.87 days, HR=1.56, 95% CI 1.03 to 2.37, p=0.035). Among viral rebound patients in the mITT population, neither 120 mg/day nor 60 mg/day CEP significantly shortened the time to negative compared to placebo. Adverse events were not different among the three groups, and no serious adverse events occurred. Treatment of asymptomatic or mild Covid-19 with 120 mg/day or 60 mg/day CEP did not shorten the time to negative compared with placebo, without evident safety concerns. Among de novo infected patients with good compliance, 60 mg/day CEP significantly shortened the time to negative compared with placebo (NCT05398705).

Heterogeneous nanostructured metals are emerging strategies for achieving both high strength and ductility, which are particularly attractive for high entropy alloys (HEAs) to combine the synergistic enhancements from multielement composition, grain boundaries, and heterogeneity effects. However, the construction of heterogeneous nanostructured HEAs remains elusive and can involve delicate processes that are not practically scalable. Herein we report using composition design (i.e., enthalpy engineering) to create hierarchical, nanostructured HEAs as demonstrated by adding Ni into FeCrCoAlTi0.5 HEA. The strong enthalpic interaction between (Ni,Co) and (Al,Ti) pairs in FeCrCoAlTi0.5Nix (x = 0.5–1.5) induced phase partitions into B2 (ordered phase, hard) matrix and A2 (disordered phase, soft) precipitates, resulting in a hierarchical structure of B2 grains and sub-grains of near-coherent A2 nanodomains (∼ 12.5 nm) divided by A2 interdendritic regions. As a result, the FeCrCoAlTi0.5Ni1.5 HEA with this unique hierarchical nanostructure exhibits the best combination of strength and plasticity, i.e., a 2-fold increase in compressive strength (2.60 GPa) and significant enhancement of plastic strain (15.8%) as compared with the original FeCrCoAlTi0.5 HEA. Enthalpy analysis and simulation study reveal the phase partition process during cooling induced by an enthalpy-driven order-disorder transition while the order parameters illustrate the strong ordering in (Ni,Co)(Al,Ti)-rich B2 phase and high entropy mixing in less interactive FeCrCo-rich A2 phase. Our work therefore provides a strategy for hierarchical nanostructured HEA formation by composition design considering enthalpy and entropy interplay.

Semi-supervised network representation learning is becoming a hotspot in graph mining community, which aims to learn low-dimensional vector representations of vertices using partial label information. In particular, graph neural networks integrate structural information and other side information like vertex attributes to learn node representations. Although the existing semi-supervised graph learning performs well on limited labeled data, it is still often hampered when labeled dataset is quite small. To mitigate this issue, we propose PMNRL, a pseudo-multitask learning framework for semi-supervised network representation learning to boost the expression power of graph networks such as vanilla GCN (Graph Convolutional Networks) and GAT (Graph Attention Networks). In PMNRL, by leveraging the community structures in networks, we create a pseudo task that classifies nodes’ community affiliation, and conduct a joint learning of two tasks (i.e., the original task and the pseudo task). Our proposed scheme can take advantage of the inherent connection between structural proximity and label similarity to improve the performance without the need to resort to more labels. The proposed framework is implemented in two ways: two-stage method and end-to-end method. For two-stage method, communities are first detected and then the community affiliations are used as “labels” along with original labels to train the joint model. In end-to-end method, the unsupervised community learning is combined into the representation learning process by shared layers and task-specific layers, so as to encourage the common features and specific features for different tasks at the same time. The experimental results on three real-world benchmark networks demonstrate the performance improvement of the vanilla models using our framework without any additional labels, especially when there are quite few labels.

Constructing faradaic electrode with superior desalination performance is important for expanding the applications of capacitive deionization (CDI). Herein, a simple one-step alkalized treatment for in situ synthesis of 1D TiO

Natural extracellular vesicles (EVs) play important roles in many life processes such as in the intermolecular transfer of substances and genetic information exchanges. Investigating the origins and working mechanisms of natural EVs may provide an understanding of life activities, especially regarding the occurrence and development of diseases. Additionally, due to their vesicular structure, EVs (in small molecules, nucleic acids, proteins, etc.) could act as efficient drug-delivery carriers. Herein, we describe the sources and biological functions of various EVs, summarize the roles of EVs in disease diagnosis and treatment, and review the application of EVs as drug-delivery carriers. We also assess the challenges and perspectives of EVs in biomedical applications., Graphical abstract In this review, we summarize the mechanism of extracellular vesicles in many physical processes and diseases, and describe the biomedical applications of extracellular vesicles (EV) in disease diagnosis and therapy, drug-delivery carriers.Image 1

Many network analysis tasks like classification over nodes require careful efforts in engineering features used by learning algorithms. Most of recent studies have been made and succeeded in the field of static network representation learning. However, real-world networks are often dynamic and little work has been done on how to describe dynamic networks. In this work, we pose the problem of condensing dynamic networks and introduce SuRep , an encoding-decoding framework which utilizes matrix factorization technique to derive a succinct representation of a dynamic network in any stationary phase. We show that the succinct representation method can uncover the invariant structural properties in the network evolution and derive dense feature representations of the nodes as the byproduct. This method can be easily extended to dynamic attribute networks. For experiments on detecting change points in dynamic networks and network classification with real-world datasets we demonstrate SuRep ’s potential for capturing latent patterns among nodes.

Here, we explore whether PEGylation of antibodies can modulate their biodistribution to the eye, an organ once thought to be immune privileged but has recently been shown to be accessible to IV-administered large molecules, such as antibodies. We chose to PEGylate an anti-MerTK antibody, a target with known potential for ocular toxicity, to minimize biodistribution to retinal pigment epithelial cells (RPEs) in the eye by increasing the hydrodynamic volume of the antibody. We used site-specific conjugation to an engineered cysteine on anti-MerTK antibody to chemically attach 40-kDa branched or linear PEG polymers. Despite reduced binding to MerTK on cells, site-specifically PEGylated anti-MerTK retained similar potency in inhibiting MerTK-mediated macrophage efferocytosis of apoptotic cells. Importantly, we found that PEGylation of anti-MerTK significantly reduced MerTK receptor occupancy in RPE cells in both naïve mice and MC-38 tumor-bearing mice, with the branched PEG exhibiting a greater effect than linear PEG. Furthermore, similar to unconjugated anti-MerTK, PEGylated anti-MerTK antibody triggered type I IFN response and exhibited antitumor effect in syngeneic mouse tumor studies. Our results demonstrate the potential of PEGylation to control ocular biodistribution of antibodies.

Nanozyme with the merit of excellent and adjustable catalytic activity, outstanding stability and low cost is a promising alternative for natural enzymes widely applied in a variety of fields. In the present study, a new two-dimensional cobalt-based MOF nanocomposite designated as MVCM@β-CD was synthesized. Combined with the strategies of increasing the ratio of Co(Ⅲ)/Co(Ⅱ) and modifying with small molecule β-cyclodextrin (β-CD), MVCM@β-CD displayed remarkably enhanced oxidase-mimicking activity, which was attributed to synergistic effect from large surface area of two dimensional Co-MOF nanosheet, numerous exposed active sites, high-proportioned trivalence of cobalt and regulating action of β-cyclodextrin. The addition of aminophenol isomers inhibited the catalytic oxidation process, resulting in different color change of the solution and UV-Vis absorption behaviors, based on which a sensitive ratiometric colorimetry for m-aminophenol (m-Ap) and a simple colorimetric p-aminophenol (p-Ap) detection method were developed with the detection limit of 0.16 μM and 1.01 μM, respectively. This method realized the colorimetric differentiation of aminophenol isomers, which provided a simple, accurate and low-cost approach for visual discrimination without complicated instrument and procedure, especially appropriate for on-site detection.

Rapid antigen detection tests are urgently needed for the early diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The discovery of a binder with high affinity and selectivity for the biomarkers presented by SARS-CoV-2 is crucial to the development of the rapid antigen detection method. We utilized the surface biopanning to identify a peptide binder R1 from a phage-displayed peptide library consisting of 10

By utilizing direct hydrothermal synthesis, catalytic active sites are assembled infibrous framework of phosphosilicate (FPS), controllably. Aluminium (Al) incorporation as a heteroatom at distinct Si/Al ratios is utilized to modify their properties of surface. XRD, XPS, ICP, EDX, TEM, FE-SEM, and BET analyzes are utilized to characteristic catalysts. Converting carbon dioxide (CO2) to cyclic carbonates was suggested as the best approach to produce this C1 building block for the proximity of epoxides. For oxidation carboxylation of styrenes with carbon dioxide, we found that, nanoparticles of Al/FPS are proper catalyst, in one-step reaction. Al/FPS catalytic system in comparison of other reported catalysts can convert various olefins into the synthesis of cyclic carbonates in the highest yields and mild conditions in comparison with the data formerly presented.

Detecting or recognizing event related change points in dynamic networks becomes an increasingly important task, as a change in network's structure may associate with a change in function of the networked system. However, general change point detection methods either fail to extract effective features or do not scale well. In this work, we introduce the probability distribution of nodes’ importance to characterize a network, the profile that allows for comparison between two networks and clustering on snapshots of dynamic networks. Based on this, we develop summarization scheme to detect change points on dynamical networks by segmenting the snapshots into disjoint clusters, which can guarantee the scalability on large dynamical networks. Specifically, we construct a network whose nodes represent the dynamic network snapshots. Then we do community detection on the constructed network and serialize the community detection results in chronological order. The resultant sequence naturally indicates the potential changes. Experiments on both synthetic and real-world networks show the outperformance of our framework compared to the state-of-the-art methods.