Multiorgan dysfunction syndrome due to high-dose cantharidin poisoning: A case report.

BACKGROUND: This report delves into the diagnostic and therapeutic journey undertaken by a patient with high-dose cantharidin poisoning and multiorgan dysfunction syndrome (MODS). Particular emphasis is placed on the comprehensive elucidation of the clinical manifestations of high-dose cantharidin poisoning, the intricate path to diagnosis, and the exploration of potential underlying mechanisms. CASE SUMMARY: A patient taking 10 g of cantharidin powder orally subsequently developed MODS. The patient was treated with supportive care, fluid hydration and antibiotics, and hemoperfusion and hemofiltration therapy for 24 h and successfully recovered 8 d after hospital admission. Cantharidin poisoning can cause life-threatening MODS and is rare clinically. This case underscores the challenge in diagnosis and highlights the need for early clinical differentiation to facilitate accurate assessment and prompt intervention. CONCLUSION: This article has reported and analyzed the clinical data, diagnosis, treatment, and prognosis of a case of high-dose cantharidin poisoning resulting in MODS and reviewed the relevant literature to improve the clinical understanding of this rare condition.

LollipopE: Bi-centered lollipop embedding for complex logic query on knowledge graph.

Answering complex First-Order Logic (FOL) query plays a vital role in multi-hop knowledge graph (KG) reasoning. Geometric methods have emerged as a promising category of approaches in this context. However, existing best-performing geometric query embedding (QE) model is still up against three-fold potential problems: (i) underutilization of embedding space, (ii) overreliance on angle information, (iii) uncaptured hierarchy structure. To bridge the gap, we propose a lollipop-like bi-centered query embedding method named LollipopE. To fully utilize embedding space, LollipopE employs learnable centroid positions to represent multiple entities distributed along the same axis. To address the potential overreliance on angular metrics, we design an angular-based and centroid-based metric. This involves calculating both an angular distance and a centroid-based geodesic distance, which empowers the model to make more informed selections of relevant answers from a wider perspective. To effectively capture the hierarchical relationships among entities within the KG, we incorporate dynamic moduli, which allows for the representation of the hierarchical structure among entities. Extensive experiments demonstrate that LollipopE surpasses the state-of-the-art geometric methods. Especially, on more hierarchical datasets, LollipopE achieves the most significant improvement.

MGFmiRNAloc:predicting miRNA Subcellular Localization Using Molecular Graph Feature and Convolutional Block Attention Module.

MiRNA has distinct physiological functions at various cellular locations. However, few effective computational methods for predicting the subcellular location of miRNA exist, thereby leaving considerable room for improvement. Accordingly, our study proposes the MGFmiRNAloc simplified molecular input line entry system (SMILES) format as a new approach for predicting the subcellular localization of miRNA. Additionally, the graphical convolutional network (GCN) technique was employed to extract the atomic nodes and topological structure of a single base, thereby constructing RNA sequence molecular map features. Subsequently, the channel attention and spatial attention mechanisms (CBAM) were designed to mine deeper for more efficient information. Finally, the prediction module was used to detect the subcellular localization of miRNA. The 10-fold cross-validation and independent test set experiments demonstrate that MGFmiRNAloc outperforms the most sophisticated methods. The results indicate that the new atomic level feature representation proposed in this study could overcome the limitations of small samples and short miRNA sequences, accurately predict the subcellular localization of miRNAs, and be extended to the subcellular localization of other sequences.

MAG-Net : Multi-fusion network with grouped attention for retinal vessel segmentation.

Retinal vessel segmentation plays a vital role in the clinical diagnosis of ophthalmic diseases. Despite convolutional neural networks (CNNs) excelling in this task, challenges persist, such as restricted receptive fields and information loss from downsampling. To address these issues, we propose a new multi-fusion network with grouped attention (MAG-Net). First, we introduce a hybrid convolutional fusion module instead of the original encoding block to learn more feature information by expanding the receptive field. Additionally, the grouped attention enhancement module uses high-level features to guide low-level features and facilitates detailed information transmission through skip connections. Finally, the multi-scale feature fusion module aggregates features at different scales, effectively reducing information loss during decoder upsampling. To evaluate the performance of the MAG-Net, we conducted experiments on three widely used retinal datasets: DRIVE, CHASE and STARE. The results demonstrate remarkable segmentation accuracy, specificity and Dice coefficients. Specifically, the MAG-Net achieved segmentation accuracy values of 0.9708, 0.9773 and 0.9743, specificity values of 0.9836, 0.9875 and 0.9906 and Dice coefficients of 0.8576, 0.8069 and 0.8228, respectively. The experimental results demonstrate that our method outperforms existing segmentation methods exhibiting superior performance and segmentation outcomes.

Surfactant-Free Synthesis of Crystalline Mesoporous Metal Oxides by a Seeds/ NaCl-Mediated Growth Strategy.

Transitional metal oxides (TMOs) with ultra-high specific surface areas (SSAs), large pore volume, and tailored exposed facets appeal to significant interests in heterogeneous catalysis. Nevertheless, synthesizing the metal oxides with all the above features is challenging. Herein, the so-called seeds/NaCl-mediated growth method is successfully developed based on a bottom-up route. First, the (Brunauer-Emmett-Teller) BET SSAs of TMOs prepared with this method are significantly higher, where the BET SSAs of CeO2 , SnO2 , Nb2 O5 , Fe3 O4 , Mn3 O4 , Mg(OH)2 , and ZrO2 reached 187, 275, 518, 212, 147, 186, and 332 m2  g-1 , respectively. Second, these TMOs exhibit unique mesoporous structures, generated mainly by the aggregation of rod-like or other aspherical primary nanoparticles. More importantly, no environmental-unfriendly organic surfactants or expensive metal alkoxides are involved in this method. Therefore, the entire synthesis protocol fully fitted the "green synthesis" definition, and the corresponding TMOs prepares displayed excellent catalytic performance.

Multifunctional Oxidized Dextran-Metformin as a Tissue-Adhesive Hydrogel to Prevent Postoperative Peritoneal Adhesions in Patients with Metabolic Syndrome.

Patients with metabolic syndrome (MetS) undergoing surgery are at high risk of developing peritoneal adhesions and other severe postoperative complications. However, the single shielding function and absence of physiological activity render conventional methods less useful in preventing adhesions in patients with MetS. To address this challenge, a convenient method is introduced for developing a novel tissue-adhesive hydrogel called oxidized dextran-metformin (ODE-ME) via Schiff base linkages. This injectable ODE-ME hydrogel exhibits excellent tissue-adhesive properties and various physiological functions, particularly enhanced antibacterial effects. Furthermore, in vivo experiments demonstrate that the hydrogel can effectively alleviate hyperglycemia, reduce excessive inflammation, and improve fibrinolytic activity in MetS mice, thereby preventing adhesions and promoting incisional healing. The hydrogel concurrently isolates injured tissues and lowers the blood glucose levels immediately after surgery in mice. Therefore, the ODE-ME hydrogel functions as a multifunctional barrier material and has potential for preventing postoperative peritoneal adhesions in patients with MetS in clinical settings.

Causal Association Between Tea Consumption and Gout: A Mendelian Randomization Study.

OBJECTIVE: Evidence from prospective studies on the consumption of tea and risk of gout is conflicting and limited. We aimed to investigate the potential causal effects of tea intake on gout using Mendelian randomization (MR). METHODS: Genome-wide association studies in UK Biobank included 349 376 individuals and successfully discovered single-nucleotide polymorphisms linked to consumption of one cup of tea per day. Summary statistics from the Chronic Kidney Disease Genetics consortium included 13 179 cases and 750 634 controls for gout. Two-sample MR analyses were used to evaluate the relationship between tea consumption and gout risk. The inverse-variance weighted (IVW) method was used for primary analysis, and sensitivity analyses were also conducted to validate the potential causal effect. RESULTS: In this study, the genetically predicted increase in tea consumption per cup was associated with a lower risk of gout in the IVW method (OR: 0.90; 95% CI: 0.82-0.98). Similar results were found in weighted median methods (OR: 0.88; 95% CI: 0.78-1.00), while no significant associations were found in MR-Egger (OR: 0.89; 95% CI: 0.71-1.11), weighted mode (OR: 0.80; 95% CI: 0.65-0.99), and simple mode (OR: 1.01; 95% CI: 0.75-1.36). In addition, no evidence of pleiotropy was detected by MR-Egger regression (P=0.95) or MR-PRESSO analysis (P=0.07). CONCLUSION: This study provides evidence for the daily consumption of an extra cup of tea to reduce the risk of gout.

Tackling higher-order relations and heterogeneity: Dynamic heterogeneous hypergraph network for spatiotemporal activity prediction.

Spatiotemporal activity prediction aims to predict user activities at a particular time and location, which is applicable in city planning, activity recommendations, and other domains. The fundamental endeavor in spatiotemporal activity prediction is to model the intricate interaction patterns among users, locations, time, and activities, which is characterized by higher-order relations and heterogeneity. Recently, graph-based methods have gained popularity due to the advancements in graph neural networks. However, these methods encounter two significant challenges. Firstly, higher-order relations and heterogeneity are not adequately modeled. Secondly, the majority of established methods are designed around the static graph structures that rely solely on co-occurrence relations, which can be imprecise. To overcome these challenges, we propose DyH2N, a dynamic heterogeneous hypergraph network for spatiotemporal activity prediction. Specifically, to enhance the capacity for modeling higher-order relations, hypergraphs are employed in lieu of graphs. Then we propose a set representation learning-inspired heterogeneous hyperedge learning module, which models higher-order relations and heterogeneity in spatiotemporal activity prediction using a non-decomposable manner. To improve the encoding of heterogeneous spatiotemporal activity hyperedges, a knowledge representation-regularized loss is introduced. Moreover, we present a hypergraph structure learning module to update the hypergraph structures dynamically. Our proposed DyH2N model has been extensively tested on four real-world datasets, proving to outperform previous state-of-the-art methods by 5.98% to 27.13%. The effectiveness of all framework components is demonstrated through ablation experiments.

Silane coupling agent in biomedical materials.

Medical devices are becoming more and more significant in our daily life. For implantable medical devices, good biocompatibility is required for further use in vivo. Thus, surface modification of medical devices is really important, which gives a wide application scene for a silane coupling agent. The silane coupling agent is able to form a durable bond between organic and inorganic materials. The dehydration process provides linking sites to achieve condensation of two hydroxyl groups. The forming covalent bond brings excellent mechanical properties among different surfaces. Indeed, the silane coupling agent is a popular component in surface modification. Metals, proteins, and hydrogels are using silane coupling agent to link parts commonly. The mild reaction environment also brings advantages for the spread of the silane coupling agent. In this review, we summarize two main methods of using the silane coupling agent. One is acting as a crosslinker mixed in the whole system, and the other is to provide a bridge between different surfaces. Moreover, we introduce their applications in biomedical devices.

CRBSP:Prediction of CircRNA-RBP Binding Sites Based on Multimodal Intermediate Fusion.

Circular RNA (CircRNA) is widely expressed and has physiological and pathological significance, regulating post-transcriptional processes via its protein-binding activity. However, whereas much work has been done on linear RNA and RNA binding protein (RBP), little is known about the binding sites of CircRNA. The current report is on the development of a medium-term multimodal data fusion strategy, CRBSP, to predict CircRNA-RBP binding sites. CRBSP represents the CircRNA trinucleotide semantic, location, composition and frequency information as the corresponding coding methods of Word to vector (Word2vec), Position-specific trinucleotide propensity (PSTNP), Pseudo trinucleotide composition (PseTNC) and Trinucleotide nucleotide composition (TNC), respectively. CNN (Convolution Neural Networks) was used to extract global information and BiLSTM (bidirectional Long- and Short-Term Memory network) encoder and LSTM (Long- and Short-Term Memory network) decoder for local sequence information. Enhancement of the contributions of key features by the self-attention mechanism was followed by mid-term fusion of the four enhanced features. Logistic Regression (LR) classifier showed that CRBSP gives a mean AUC value of 0.9362 through 5-fold Cross Validation of all 37 datasets, a performance which is superior to five current state-of-the-art models. Similar evaluation of linear RNA-RBP binding sites gave an AUC value of 0.7615 which is also higher than other prediction methods, demonstrating the robustness of CRBSP.

STDP-based adaptive graph convolutional networks for automatic sleep staging.

Automatic sleep staging is important for improving diagnosis and treatment, and machine learning with neuroscience explainability of sleep staging is shown to be a suitable method to solve this problem. In this paper, an explainable model for automatic sleep staging is proposed. Inspired by the Spike-Timing-Dependent Plasticity (STDP), an adaptive Graph Convolutional Network (GCN) is established to extract features from the Polysomnography (PSG) signal, named STDP-GCN. In detail, the channel of the PSG signal can be regarded as a neuron, the synapse strength between neurons can be constructed by the STDP mechanism, and the connection between different channels of the PSG signal constitutes a graph structure. After utilizing GCN to extract spatial features, temporal convolution is used to extract transition rules between sleep stages, and a fully connected neural network is used for classification. To enhance the strength of the model and minimize the effect of individual physiological signal discrepancies on classification accuracy, STDP-GCN utilizes domain adversarial training. Experiments demonstrate that the performance of STDP-GCN is comparable to the current state-of-the-art models.

An Unsupervised Classification Algorithm for Heterogeneous Cryo-EM Projection Images Based on Autoencoders.

Heterogeneous three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is an important but very challenging technique for recovering the conformational heterogeneity of flexible biological macromolecules such as proteins in different functional states. Heterogeneous projection image classification is a feasible solution to solve the structural heterogeneity problem in single-particle cryo-EM. The majority of heterogeneous projection image classification methods are developed using supervised learning technology or require a large amount of a priori knowledge, such as the orientations or common lines of the projection images, which leads to certain limitations in their practical applications. In this paper, an unsupervised heterogeneous cryo-EM projection image classification algorithm based on autoencoders is proposed, which only needs to know the number of heterogeneous 3D structures in the dataset and does not require any labeling information of the projection images or other a priori knowledge. A simple autoencoder with multi-layer perceptrons trained in iterative mode and a complex autoencoder with residual networks trained in one-pass learning mode are implemented to convert heterogeneous projection images into latent variables. The extracted high-dimensional features are reduced to two dimensions using the uniform manifold approximation and projection dimensionality reduction algorithm, and then clustered using the spectral clustering algorithm. The proposed algorithm is applied to two heterogeneous cryo-EM datasets for heterogeneous 3D reconstruction. Experimental results show that the proposed algorithm can effectively extract category features of heterogeneous projection images and achieve high classification and reconstruction accuracy, indicating that the proposed algorithm is effective for heterogeneous 3D reconstruction in single-particle cryo-EM.

A Review of Cu-Ni-Sn Alloys: Processing, Microstructure, Properties, and Developing Trends.

Cu-Ni-Sn alloys have been widely used in the aerospace industry, the electronics industry, and other fields due to their excellent electrical and thermal conductivity, high strength, corrosion and wear resistance, etc., which make Cu-15Ni-8Sn alloys the perfect alternative to Cu-Be alloys. This paper begins with how Cu-Ni-Sn alloys are prepared. Then, the microstructural features, especially the precipitation order of each phase, are described. In addition, the influence of alloying elements, such as Si, Ti, and Nb, on its microstructure and properties is discussed. Finally, the effects of plastic deformation and heat treatment on Cu-Ni-Sn alloys are discussed. This review is able to provide insight into the development of novel Cu-Ni-Sn alloys with a high performance.

CapsNet-LDA: predicting lncRNA-disease associations using attention mechanism and capsule network based on multi-view data.

Cumulative studies have shown that many long non-coding RNAs (lncRNAs) are crucial in a number of diseases. Predicting potential lncRNA-disease associations (LDAs) can facilitate disease prevention, diagnosis and treatment. Therefore, it is vital to develop practical computational methods for LDA prediction. In this study, we propose a novel predictor named capsule network (CapsNet)-LDA for LDA prediction. CapsNet-LDA first uses a stacked autoencoder for acquiring the informative low-dimensional representations of the lncRNA-disease pairs under multiple views, then the attention mechanism is leveraged to implement an adaptive allocation of importance weights to them, and they are subsequently processed using a CapsNet-based architecture for predicting LDAs. Different from the conventional convolutional neural networks (CNNs) that have some restrictions with the usage of scalar neurons and pooling operations. the CapsNets use vector neurons instead of scalar neurons that have better robustness for the complex combination of features and they use dynamic routing processes for updating parameters. CapsNet-LDA is superior to other five state-of-the-art models on four benchmark datasets, four perturbed datasets and an independent test set in the comparison experiments, demonstrating that CapsNet-LDA has excellent performance and robustness against perturbation, as well as good generalization ability. The ablation studies verify the effectiveness of some modules of CapsNet-LDA. Moreover, the ability of multi-view data to improve performance is proven. Case studies further indicate that CapsNet-LDA can accurately predict novel LDAs for specific diseases.

Effect of High-Pressure Micro-Fluidization on the Inactivation of Staphylococcus aureus in Liquid Food.

High-pressure homogenization has been extensively studied for its excellent homogenization effect and the prospect of continuous liquid food production, but its sterilization ability still needs to be improved. In this study, we replaced the homogenization valve with two opposing diamond nozzles (0.05 mm inner diameter) so that the fluid collided at high velocity, corresponding to high-pressure micro-fluidization (HPM). Moreover, HPM treatment significantly inactivated Staphylococcus aureus ~7 log in the liquid with no detectable sub-lethal state at a pressure of 400 MPa and a discharge temperature of 50 °C. The sterilization effect of HPM on S. aureus subsp. aureus was attributed to a significantly disrupted cell structure and increased membrane permeability, which led to the leakage of intracellular proteins, resulting in bacterial death. At the same time, HPM treatment was able to significantly reduce the ability of S. aureus subsp. aureus to form biofilms, which, in turn, reduced its virulence. Finally, compared to the simulated system, more effective sterilization was observed in apple juice, with its color and pH remaining unchanged, which suggested that HPM can be used to process other liquid foods.

RSAP-Net: joint optic disc and cup segmentation with a residual spatial attention path module and MSRCR-PT pre-processing algorithm.

BACKGROUND: Glaucoma can cause irreversible blindness to people's eyesight. Since there are no symptoms in its early stage, it is particularly important to accurately segment the optic disc (OD) and optic cup (OC) from fundus medical images for the screening and prevention of glaucoma. In recent years, the mainstream method of OD and OC segmentation is convolution neural network (CNN). However, most existing CNN methods segment OD and OC separately and ignore the a priori information that OC is always contained inside the OD region, which makes the segmentation accuracy of most methods not high enough. METHODS: This paper proposes a new encoder-decoder segmentation structure, called RSAP-Net, for joint segmentation of OD and OC. We first designed an efficient U-shaped segmentation network as the backbone. Considering the spatial overlap relationship between OD and OC, a new Residual spatial attention path is proposed to connect the encoder-decoder to retain more characteristic information. In order to further improve the segmentation performance, a pre-processing method called MSRCR-PT (Multi-Scale Retinex Colour Recovery and Polar Transformation) has been devised. It incorporates a multi-scale Retinex colour recovery algorithm and a polar coordinate transformation, which can help RSAP-Net to produce more refined boundaries of the optic disc and the optic cup. RESULTS: The experimental results show that our method achieves excellent segmentation performance on the Drishti-GS1 standard dataset. In the OD and OC segmentation effects, the F1 scores are 0.9752 and 0.9012, respectively. The BLE are 6.33 pixels and 11.97 pixels, respectively. CONCLUSIONS: This paper presents a new framework for the joint segmentation of optic discs and optic cups, called RSAP-Net. The framework mainly consists of a U-shaped segmentation skeleton and a residual space attention path module. The design of a pre-processing method called MSRCR-PT for the OD/OC segmentation task can improve segmentation performance. The method was evaluated on the publicly available Drishti-GS1 standard dataset and proved to be effective.

The cytological and electrophysiological effects of silver nanoparticles on neuron-like PC12 cells.

The aim of this study was to investigate the toxic effects and mechanism of silver nanoparticles (SNPs) on the cytological and electrophysiological properties of rat adrenal pheochromocytoma (PC12) cells. Different concentrations of SNPs (20 nm) were prepared, and the effects of different application durations on the cell viability and electrical excitability of PC12 quasi-neuronal networks were investigated. The effects of 200 µM SNPs on the neurite length, cell membrane potential (CMP) difference, intracellular Ca2+ content, mitochondrial membrane potential (MMP) difference, adenosine triphosphate (ATP) content, and reactive oxygen species (ROS) content of networks were then investigated. The results showed that 200 µM SNPs produced grade 1 cytotoxicity at 48 h of interaction, and the other concentrations of SNPs were noncytotoxic. Noncytotoxic 5 µM SNPs significantly increased electrical excitability, and noncytotoxic 100 µM SNPs led to an initial increase followed by a significant decrease in electrical excitability. Cytotoxic SNPs (200 µM) significantly decreased electrical excitability. SNPs (200 µM) led to decreases in neurite length, MMP difference and ATP content and increases in CMP difference and intracellular Ca2+ and ROS levels. The results revealed that not only cell viability but also electrophysiological properties should be considered when evaluating nanoparticle-induced neurotoxicity. The SNP-induced cytotoxicity mainly originated from its effects on ATP content, cytoskeletal structure and ROS content. The decrease in electrical excitability was mainly due to the decrease in ATP content. ATP content may thus be an important indicator of both cell viability and electrical excitability in PC12 quasi-neuronal networks.

SIL-Net: A Semi-Isotropic L-shaped network for dermoscopic image segmentation.

BACKGROUND: Dermoscopic image segmentation using deep learning algorithms is a critical technology for skin cancer detection and therapy. Specifically, this technology is a spatially equivariant task and relies heavily on Convolutional Neural Networks (CNNs), which lost more effective features during cascading down-sampling or up-sampling. Recently, vision isotropic architecture has emerged to eliminate cascade procedures in CNNs as well as demonstrates superior performance. Nevertheless, it cannot be used for the segmentation task directly. Based on these discoveries, this research intends to explore an efficient architecture which not only preserves the advantages of the isotropic architecture but is also suitable for clinical dermoscopic diagnosis. METHODS: In this work, we introduce a novel Semi-Isotropic L-shaped network (SIL-Net) for dermoscopic image segmentation. First, we propose a Patch Embedding Weak Correlation (PEWC) module to address the issue of no interaction between adjacent patches during the standard Patch Embedding process. Second, a plug-and-play and zero-parameter Residual Spatial Mirror Information (RSMI) path is proposed to supplement effective features during up-sampling and optimize the lesion boundaries. Third, to further reconstruct deep features and get refined lesion regions, a Depth Separable Transpose Convolution (DSTC) based up-sampling module is designed. RESULTS: The proposed architecture obtains state-of-the-art performance on dermoscopy benchmark datasets ISIC-2017, ISIC-2018 and PH2. Respectively, the Dice coefficient (DICE) of above datasets achieves 89.63%, 93.47%, and 95.11%, where the Mean Intersection over Union (MIoU) are 82.02%, 88.21%, and 90.81%. Furthermore, the robustness and generalizability of our method has been demonstrated through additional experiments on standard intestinal polyp datasets (CVC-ClinicDB and Kvasir-SEG). CONCLUSION: Our findings demonstrate that SIL-Net not only has great potential for precise segmentation of the lesion region but also exhibits stronger generalizability and robustness, indicating that it meets the requirements for clinical diagnosis. Notably, our method shows state-of-the-art performance on all five datasets, which highlights the effectiveness of the semi-isotropic design mechanism.

Self-healing, injectable hydrogel based on dual dynamic covalent cross-linking against postoperative abdominal cavity adhesion.

Clinically, increasing the peritoneal barrier is an effective adjunct to reducing postoperative peritoneal adhesion. This study presents a facile template for preparing a supramolecular hybrid hydrogel through dynamic covalent cross-linking between carboxymethyl chitosan (CMCS), 2-formylphenylboronic acid (2-FPBA), and quercetin (Que). The as-prepared complex CMCS/2-FPBA/Que (CFQ) hydrogel exhibited favorable antibacterial, anti-inflammatory, and antioxidant effects. A L929 cytotoxicity evaluation confirmed the favorable cytocompatibility of the CFQ hydrogel. The postoperative anti-adhesion ability of the CFQ hydrogel was further evaluated in rats with lateral wall defects and cecal abrasions. Compared with control groups, the tissue adhesion rate was significantly reduced by increasing the Que concentration in all the hydrogel-treated groups. Additionally, the sustained-release time of the C3F0.8Q0.08 hydrogel can exceed 14 days, which is highly desirable for clinical wound treatment. STATEMENT OF SIGNIFICANCE: Postoperative adhesions are a very common postoperative complication that seriously affects the quality of life of patients. The currently commonly used methods for preventing adhesion mainly use degradable barrier materials for physical separation. In this study, we prepared a dual dynamic covalently cross-linked CFQ hydrogel, which is not only degradable and injectable, but also has multiple properties such as antibacterial, antioxidant and anti-inflammatory, which can effectively prevent postoperative adhesion and promote wound healing.

Alkali Metal Cations Modulate the Energy Level of SnO2 via Micro-agglomerating and Anchoring for Perovskite Solar Cells.

N-type tin oxide (SnO2) films are commonly used as an electron transport layer (ETL) in perovskite solar cells (PSCs). However, SnO2 films are of poor quality due to facile agglomeration under a low-temperature preparation method. In addition, energy level mismatch between the SnO2 and perovskite (PVK) layer as well as interfacial charge recombination would cause open-circuit voltage loss. In this work, alkali metal oxalates (M-Oxalate, M = Li, Na, and K) are doped into the SnO2 precursor to solve these problems. First, it is found that the hydrolyzed alkali metal cations tend to change colloid size distribution of SnO2, in which Na-Oxalate with suitable basicity leads to most uniform colloid size distribution and high-quality SnO2-Na films. Second, the electron conductivity is enhanced by slightly agglomerated SnO2-Na, which facilitates the transmission of electrons. Third, alkali metal cations increase the conduction band level of SnO2 in the sequence of K+, Na+, and Li+ to promote band alignment between ETLs and perovskite. Based on the optimized film quality and energy states of SnO2-Na, the best PSC efficiency of 20.78% is achieved with a significantly enhanced open-circuit voltage of 1.10 V. This work highlights the function of alkali metal salts on the colloid particle distribution and energy level modulation of SnO2.