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An integrated asymmetric hydrogel electrolyte with a tailored composition and chemical structure on the cathode/anode-electrolyte interface is designed to boost the cost-effective, high-energy Zn-I2 battery. Such a configuration concurrently addresses the parasitic reactions on the Zn anode side and the polyiodide shuttle issue afflicting the cathode. Specifically, the Zn2+-cross-linked sodium alginate and carrageenan dual network (Carra-Zn-Alg) is adopted to guide the Zn2+ transport, achieving a dendrite-free morphology on the Zn surface and ensuring long-term stability. For the cathode side, the poly(vinyl alcohol)-strengthened poly(3,4-ethylenedioxythiophene)polystyrenesulfonate hydrogel (PVA-PEDOT) with high conductivity is employed to trap polyiodide and accelerate electron transfer for mitigating the shuttle effect and facilitating I2/I- redox kinetics. Attributing to the asymmetrical architecture with a customized interfacial chemistry, the optimized Zn-I2 cell exhibits a superior Coulombic efficiency of 99.84% with a negligible capacity degradation at 0.1 A g-1 and an enhanced stability of 10â¯000 cycles at 5 A g-1. The proposed asymmetric hydrogel provides a promising route to simultaneously resolve the distinct challenges encountered by the cathode and anode interfaces in rechargeable batteries.
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Objective: Knee osteoarthritis (KOA) is a chronic condition characterized by persistent pain that can lead to severe disability. In this study, we primarily investigated the analgesic effect of Huojing decoction on MIA-induced knee arthritis. Methods: The network pharmacology method was employed to acquire target information of Huojing decoction and KOA. MIA was intratibially injected to induce KOA pain in rats. Huojing decoction was then administered once daily via intragastric administration for 14 days. Pain level was assessed by paw withdrawal threshold (PWT) and paw withdrawal latency (PWL). The levels of inflammatory cytokines were determined by ELISA and PCR. TRPV1 and CGRP were detected through immunohistochemistry. The protein expression of TrkA, MKK3/6 and p38 was assessed by Western blot. Results: Mechanical allodynia and thermal hyperalgesia were observed in KOA rats. The expression levels of inflammatory cytokines were significantly decreased after Huojing decoction infusion of KOA rats. TRPV1 and CGRP were reduced with treatment. Furthermore, the protein expressions of TrkA, MKK3/6 and p38 in the DRG of rats were significantly decreased. Conclusion: Our data suggested that Huojing decoction can alleviate inflammation in KOA pain rats. Additionally, it can inhibit the expression of TrKA, MKK3/6 and p38 signaling pathways, indicating its analgesic effect on KOA pain rats.
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Potassium metal batteries (PMBs) are promising candidates for large-scale energy storage. Conventional carbonate electrolytes exhibit unsatisfactory thermodynamic stability against potassium (K) metal anode. Linear ether is widely adopted because of its compatibility with K metal, but the poor oxidation stability restricts the application with high-voltage cathodes. Herein, a weakly solvating cyclic ether is proposed as a solvent to stabilize the K-electrolyte interface and build a robust solid-electrolyte interphase (SEI). This weakly solvating electrolyte (WSE) possesses an anion-dominated solvation structure, which facilitates the anion decomposition for constructing an inorganic-rich SEI. The superior mechanical properties of the SEI, as examined by atomic force microscopy, prevent the SEI from fracture. Consequently, this WSE achieves highly reversible plating/stripping behavior of K metal for 1300 h with a high average Coulombic efficiency of 99.20%. Stable full cells are also demonstrated with a high-voltage cathode at harsh conditions. This work complements the design of WSEs for advanced PMBs by cyclic ether solvents.
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Macrophages, as the largest immune cell group in tumour tissues, play a crucial role in influencing various malignant behaviours of tumour cells and tumour immune evasion. As the research on macrophages and cancer immunotherapy develops, the importance of appropriate research models becomes increasingly evident. The development of organoids has bridged the gap between traditional two-dimensional (2D) cultures and animal experiments. Recent studies have demonstrated that organoids exhibit similar physiological characteristics to the source tissue and closely resemble the in vivo genome and molecular markers of the source tissue or organ. However, organoids still lack an immune component. Developing a co-culture model of organoids and macrophages is crucial for studying the interaction and mechanisms between tumour cells and macrophages. This paper presents an overview of the establishment of co-culture models, the current research status of organoid macrophage interactions, and the current status of immunotherapy. In addition, the application prospects and shortcomings of the model are explained. Ultimately, it is hoped that the co-culture model will offer a preclinical testing platform for maximising a precise cancer immunotherapy strategy.
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Hyperglycemia provides a favorable breeding ground for bacteria, resulting in repeated and persistent inflammation of wounds and prolonged healing processes. In this study, platinum (Pt) nanoparticles (NPs) and glucose oxidase (GOx) were decorated on the surface of camelina lipid droplets (OB) linked with hFGF2, forming PGOB through in situ reduction of Pt ions and electrostatic adsorption, respectively. PGOB exhibits cascade enzyme catalytic activity, which can be activated by glucose in diabetic wound tissues. Specifically, GOx on PGOB catalyzes glucose into hydrogen peroxide, which can further decompose into hydroxyl radicals that have higher toxicity for bacterial inactivation. Additionally, glucose decomposition creates a low pH microenvironment, facilitating the cascade catalytic activity that ensures better bacterial suppression within the wound tissues. Furthermore, hFGF2 promotes the proliferation and migration of fibroblasts. Both in vitro and in vivo experiments confirm that PGOB effectively accelerates wound healing processes through bacteria inactivation and tissue regeneration. This study has developed an alternative strategy for glucose-triggered synergistic cascade therapy for diabetic wounds.
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Skin-like stretchable electronics emerge as promising human-machine interfaces but are challenged by the paradox between superior electronic property and reliable mechanical deformability. Here, a general strategy is reported for establishing robust large-scale deformable electronics by effectively isolating strains and strengthening interfaces. A copolymer substrate is designed to consist of mosaic stiff and elastic areas with nearly four orders of magnitudes modulus contrast and cross-linked interfaces. Electronic functional devices and stretchable liquid metal (LM) interconnects are conformally attached at the stiff and elastic areas, respectively, through hydrogen bonds. As a result, functional devices are completely isolated from strains, and resistances of LM conductors change by less than one time when the substrate is deformed by up to 550%. By this strategy, solar cells, wireless charging antenna, supercapacitors, and light-emitting diodes are integrated into a self-powered electronic skin that can laminate on the human body and exhibit stable performances during repeated multimode deformations, demonstrating an efficient path for realizing highly deformable energy autonomous soft electronics.
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One of the greatest frequent types of malignancy is gastric cancer (GC). Metastasis, an essential feature of stomach cancer, results in a high rate of mortality and a poor prognosis. However, metastasis biological procedures are not well recognized. Long non-coding RNAs (lncRNAs) have a role in numerous gene regulation pathways via epigenetic modification as well as transcriptional and post-transcriptional control. LncRNAs have a role in a variety of disorders, such as cardiovascular disease, Alzheimer's, and cancer. LncRNAs are substantially related to GC incidence, progression, metastasis and drug resistance. Several research released information on the molecular processes of lncRNAs in GC pathogenesis. By interacting with a gene's promoter or enhancer region to influence gene expression, lncRNAs can operate as an oncogene or a tumor suppressor. This review includes the lncRNAs associated with metastasis of GC, which may give insights into the processes as well as potential clues for GC predicting and tracking.
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The extracellular matrix (ECM) is critical for drug resistance in colorectal cancer (CRC). The abundant collagen within the ECM significantly influences tumor progression and matrix-mediated drug resistance (MMDR) by binding to discoidin domain receptor 1 (DDR1), but the specific mechanisms by which tumor cells modulate ECM via DDR1 and ultimately regulate TME remain poorly understand. Furthermore, overcoming drug resistance by modulating the tumor ECM remains a challenge in CRC treatment. In this study, a novel mechanism is elucidated by which DDR1 mediates the interactions between tumor cells and collagen, enhances collagen barriers, inhibits immune infiltration, promotes drug efflux, and leads to MMDR in CRC. To address this issue, a multistage drug delivery system carrying DDR1-siRNA and chemotherapeutic agents is employed to disrupt collagen barriers by silencing DDR1 in tumor, enhancing chemotherapy drugs diffusion and facilitating immune infiltration. These findings not only revealed a novel role for collagen-rich matrix mediated by DDR1 in tumor resistance, but also introduced a promising CRC treatment strategy.
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Secondary structure prediction is a key step in understanding protein function and biological properties and is highly important in the fields of new drug development, disease treatment, bioengineering, etc. Accurately predicting the secondary structure of proteins helps to reveal how proteins are folded and how they function in cells. The application of deep learning models in protein structure prediction is particularly important because of their ability to process complex sequence information and extract meaningful patterns and features, thus significantly improving the accuracy and efficiency of prediction. In this study, a combined model integrating an improved temporal convolutional network (TCN), bidirectional long short-term memory (BiLSTM), and a multi-head attention (MHA) mechanism is proposed to enhance the accuracy of protein prediction in both eight-state and three-state structures. One-hot encoding features and word vector representations of physicochemical properties are incorporated. A significant emphasis is placed on knowledge distillation techniques utilizing the ProtT5 pretrained model, leading to performance improvements. The improved TCN, achieved through multiscale fusion and bidirectional operations, allows for better extraction of amino acid sequence features than traditional TCN models. The model demonstrated excellent prediction performance on multiple datasets. For the TS115, CB513 and PDB (2018-2020) datasets, the prediction accuracy of the eight-state structure of the six datasets in this paper reached 88.2%, 84.9%, and 95.3%, respectively, and the prediction accuracy of the three-state structure reached 91.3%, 90.3%, and 96.8%, respectively. This study not only improves the accuracy of protein secondary structure prediction but also provides an important tool for understanding protein structure and function, which is particularly applicable to resource-constrained contexts and provides a valuable tool for understanding protein structure and function.
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Estructura Secundaria de Proteína , Proteínas , Proteínas/química , Aprendizaje Profundo , Redes Neurales de la Computación , Biología Computacional/métodos , Bases de Datos de Proteínas , Modelos MolecularesRESUMEN
The increasing utilization of mouse models in human neuroscience research places higher demands on computational methods to translate findings from the mouse brain to the human one. In this study, we develop BrainAlign, a self-supervised learning approach, for the whole brain alignment of spatial transcriptomics (ST) between humans and mice. BrainAlign encodes spots and genes simultaneously in two separated shared embedding spaces by a heterogeneous graph neural network. We demonstrate that BrainAlign could integrate cross-species spots into the embedding space and reveal the conserved brain regions supported by ST information, which facilitates the detection of homologous regions between humans and mice. Genomic analysis further presents gene expression connections between humans and mice and reveals similar expression patterns for marker genes. Moreover, BrainAlign can accurately map spatially similar homologous regions or clusters onto a unified spatial structural domain while preserving their relative positions.
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Encéfalo , Transcriptoma , Animales , Humanos , Ratones , Encéfalo/metabolismo , Perfilación de la Expresión Génica/métodos , Biología Computacional/métodos , AlgoritmosRESUMEN
Ionogels are promising material candidates for ionotronics due to their excellent ionic conductivity, stretchability, and thermal stability. However, it is challenging to develop 3D printable ionogels with both excellent electrical and mechanical properties. Here, we report a highly conductive and stretchable nanostructured (CSN) ionogel for 3D printing ionotronic sensors. We propose the photopolymerization-induced microphase separation strategy to prepare the CSN ionogels comprising continuous conducting nanochannels intertwined with cross-linked polymeric framework. The resultant CSN ionogels simultaneously achieves high ionic conductivity (over 3 S m-1), high stretchability (over 1500%), low degree of hysteresis (0.4% at 50% strain), wide-temperature-range thermostability (-72 to 250 °C). Moreover, its high compatible with DLP 3D printing enables the fabrication of complex ionogel micro-architectures with high resolution (up to 5 µm), which allows us to manufacture capacitive sensors with superior sensing performances. The proposed CSN ionogel paves an efficient way to manufacture the next-generation capacitive sensors with enhanced performance.
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Accumulated evidence emerges that dynamic changes in human gut microbiota and microbial metabolites can alter the ecological balance of symbiotic hosts. The gut microbiota plays a role in various diseases through different mechanisms. More and more attention has been paid to the effects that human microbiota extends beyond the gut. This review summarized the current understanding of the roles that gut microbiota plays in hematopoietic regulation and the occurrence and development of benign and malignant hematologic diseases. The progress of the application of microbiota in treatment was discussed in order to provide new insights into clinical diagnosis and treatment in the future.
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The utilization of a nanoporous (NP) GaN fabricated by electrochemical etching has been demonstrated to be effective in the fabrication of a high-performance ultraviolet (UV) photodetector (PD). However, the NP-GaN PD typically exhibits a low light-dark current ratio and slow light response speed. In this study, we present three types of UV PDs based on an unetched GaN, NP-GaN distributed Bragg reflector (DBR), and NP-GaN-DBR with a Ga2O3 single-crystal film (Ga2O3/NP-GaN-DBR). The unetched GaN PD does not exhibit a significant photoresponse. Compared to the NP-GaN-DBR PD device, the Ga2O3/NP-GaN-DBR PD demonstrates a larger light-dark current ratio (6.14 × 103) and higher specific detectivity (8.9 × 1010 Jones) under 365 nm at 5 V bias due to its lower dark current (3.0 × 10-10 A). This reduction in the dark current can be attributed to the insertion of the insulating Ga2O3 between the metal and the NP-GaN-DBR, which provides a thicker barrier thickness and higher barrier height. Additionally, the Ga2O3/NP-GaN-DBR PD device exhibits shorter rise/decay times (0.33/0.23 s) than the NP-GaN-DBR PD, indicating that the growth of a Ga2O3 layer on the DBR effectively reduces the trap density within the NP-GaN DBR structure. Although the device with a Ga2O3 layer presents low photoresponsivity (0.1 A/W), it should be feasible to use Ga2O3 as a dielectric layer based on the above-mentioned reasons.
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Cancer is one of the major public health challenges in the world, which is characterized by rapid progression and high mortality. Immunotherapy, represented by PD-1 monoclonal antibody, has significantly improved the efficacy of malignant tumors and has become one of the most popular immunotherapy methods at present. Therefore, there is an increasing demand for novel detection methods for PD-1 monoclonal antibodies. The aim of this work was to establish a rapid, simple, and sensitive immunochromatographic test strip (ICTS) based on the AuNPs enlargement for both visual and instrumental detection of the PD-1 monoclonal antibody concentration. The mixed solution of NH2OH·HCl and HAuCl4 was used as an enhancement solution to lower the detection limit and achieve higher sensitivity. A test strip reader was used to construct a visualized quantitative detection standard curve for the PD-1 monoclonal antibody concentration. The LOD was 1.58 ng/mL through a triple signal-to-noise ratio. The detection time was within 10 min. The constructed test strips can rapidly, accurately, and efficiently detect the concentration of PD-1 monoclonal antibody in real samples.
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Anticuerpos Monoclonales , Cromatografía de Afinidad , Nanopartículas del Metal , Receptor de Muerte Celular Programada 1 , Anticuerpos Monoclonales/inmunología , Anticuerpos Monoclonales/química , Receptor de Muerte Celular Programada 1/inmunología , Cromatografía de Afinidad/métodos , Nanopartículas del Metal/química , Humanos , Oro/química , Tiras Reactivas , Límite de DetecciónRESUMEN
BACKGROUND: X-ray radiography is a widely used imaging technique worldwide, and its image quality directly affects diagnostic accuracy. Therefore, X-ray image quality control (QC) is essential. However, subjectively assessing image quality is inefficient and inconsistent, especially when large amounts of image data are being evaluated. Thus, subjective assessment cannot meet current QC needs. PURPOSE: To meet current QC needs and improve the efficiency of image quality assessment, a complete set of quality assessment criteria must be established and implemented using artificial intelligence (AI) technology. Therefore, we proposed a multi-criteria AI system for automatically assessing the image quality of knee radiographs. METHODS: A knee radiograph QC knowledge graph containing 16 "acquisition technique" labels representing 16 image quality defects and five "clarity" labels representing five grades of clarity were developed. Ten radiographic technologists conducted three rounds of QC based on this graph. The single-person QC results were denoted as QC1 and QC2, and the multi-person QC results were denoted as QC3. Each technologist labeled each image only once. The ResNet model structure was then used to simultaneously perform classification (detection of image quality defects) and regression (output of a clarity score) tasks to construct an image QC system. The QC3 results, comprising 4324 anteroposterior and lateral knee radiographs, were used for model training (70% of the images), validation (10%), and testing (20%). The 865 test set data were used to evaluate the effectiveness of the AI model, and an AI QC result, QC4, was automatically generated by the model after training. Finally, using a double-blind method, the senior QC expert reviewed the final QC results of the test set with reference to the results QC3 and QC4 and used them as a reference standard to evaluate the performance of the model. The precision and mean absolute error (MAE) were used to evaluate the quality of all the labels in relation to the reference standard. RESULTS: For the 16 "acquisition technique" features, QC4 exhibited the highest weighted average precision (98.42% ± 0.81%), followed by QC3 (91.39% ± 1.35%), QC2 (87.84% ± 1.68%), and QC1 (87.35% ± 1.71%). For the image clarity features, the MAEs between QC1, QC2, QC3, and QC4 and the reference standard were 0.508 ± 0.021, 0.475 ± 0.019, 0.237 ± 0.016, and 0.303 ± 0.018, respectively. CONCLUSIONS: The experimental results show that our automated quality assessment system performed well in classifying the acquisition technique used for knee radiographs. The image clarity quality evaluation accuracy of the model must be further improved but is generally close to that of radiographic technologists. Intelligent QC methods using knowledge graphs and convolutional neural networks have the potential for clinical applications.
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The combination of all-inorganic perovskites (PVSKs) and polymers allows for free-standing flexible optoelectronic devices. However, solubility difference of the PVSK precursors and concerns over the compatibility between polymer carriers and PVSKs imply a great challenge to incorporate different kinds of PVSKs into polymer matrices by the same manufacturing process. In this work, PVSK precursors are introduced into poly(2-hydroxyethyl acrylate) (PHEA) hydrogels in sequence, in which the PVSK-gel composites are achieved with full-color emissions by simply varying the precursor species. Moreover, it is found that CsBr has a higher interaction energy with the (111) plane of CsPbBr3 than the (110) plane; thus, the CsPbBr3 crystals with a shape of truncated cube and tetragon are observed during the CsPbBr3-Cs4PbBr6 phase transition over time. The PVSK-gel composites feature excellent bendability, elasticity, and stretchable deformation (tensile strain > 500%), which allows for 3D printing emissive customized stereoscopic architectures with shape-memory features.
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The relationship between infectious agents and autoimmune diseases is a complex issue. In recent years, increasing clinical cases have indicated that infectious agents play an important role in the development of autoimmune diseases. Molecular mimicry is currently widely regarded as the primary pathogenic mechanism of various autoimmune diseases in humans. Components of infectious agents can undergo molecular mimicry with components in patients' bodies, leading to the development of various autoimmune diseases. In this article, we provide a brief overview of current research of the current research status on the relationship between infectious agents and autoimmune diseases, and describe our current understanding of their mechanisms of action in order to better understand the pathogenesis, diagnosis, and treatment of autoimmune diseases.
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Enfermedades Autoinmunes , Humanos , Imitación Molecular , Enfermedades TransmisiblesRESUMEN
A stable stripping/plating process of the zinc anode is extremely critical for the practical application of aqueous zinc metal batteries. However, obstacles, including parasitic reactions and dendrite growth, notoriously deteriorate the stability and reversibility of zinc anode. Herein, Methyl l-α-aspartyl-l-phenylalaninate (Aspartame) is proposed as an effective additive in the ZnSO4 system to realize high stability and reversibility. Aspartame molecule with rich polar functional groups successfully participates in the solvation sheath of Zn2+ to suppress water-induced side reactions. The self-driven adsorption of Aspartame on zinc anode improves uniform deposition with a dose of 10 mm. These synergetic functions endow the zinc anode with a significantly long cycling lifespan of 4500 h. The cell coupled with a vanadium-based cathode also exhibited a high-capacity retention of 71.8% after 1000 cycles, outperforming the additive-free counterparts.
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Microemulsions are thermodynamically stable, optically isotropic, transparent, or semi-transparent mixed solutions composed of two immiscible solvents stabilized by amphiphilic solutes. This comprehensive review explores state-of-the-art techniques for characterizing microemulsions, which are versatile solutions essential across various industries, such as pharmaceuticals, food, and petroleum. This article delves into spectroscopic methods, nuclear magnetic resonance, small-angle scattering, dynamic light scattering, conductometry, zeta potential analysis, cryo-electron microscopy, refractive index measurement, and differential scanning calorimetry, examining each technique's strengths, limitations, and potential applications. Emphasizing the necessity of a multi-technique approach for a thorough understanding, it underscores the importance of integrating diverse analytical methods to unravel microemulsion structures from molecular to macroscopic scales. This synthesis provides a roadmap for researchers and practitioners, fostering advancements in microemulsion science and its wide-ranging industrial applications.