Identification and evaluation of candidate COVID-19 critical genes and medicinal drugs related to plasma cells.

The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus, represents one of the most significant global health crises in recent history. Despite extensive research into the immune mechanisms and therapeutic options for COVID-19, there remains a paucity of studies focusing on plasma cells. In this study, we utilized the DESeq2 package to identify differentially expressed genes (DEGs) between COVID-19 patients and controls using datasets GSE157103 and GSE152641. We employed the xCell algorithm to perform immune infiltration analyses, revealing notably elevated levels of plasma cells in COVID-19 patients compared to healthy individuals. Subsequently, we applied the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm to identify COVID-19 related plasma cell module genes. Further, positive cluster biomarker genes for plasma cells were extracted from single-cell RNA sequencing data (GSE171524), leading to the identification of 122 shared genes implicated in critical biological processes such as cell cycle regulation and viral infection pathways. We constructed a robust protein-protein interaction (PPI) network comprising 89 genes using Cytoscape, and identified 20 hub genes through cytoHubba. These genes were validated in external datasets (GSE152418 and GSE179627). Additionally, we identified three potential small molecules (GSK-1070916, BRD-K89997465, and idarubicin) that target key hub genes in the network, suggesting a novel therapeutic approach. These compounds were characterized by their ability to down-regulate AURKB, KIF11, and TOP2A effectively, as evidenced by their low free binding energies determined through computational analyses using cMAP and AutoDock. This study marks the first comprehensive exploration of plasma cells' role in COVID-19, offering new insights and potential therapeutic targets. It underscores the importance of a systematic approach to understanding and treating COVID-19, expanding the current body of knowledge and providing a foundation for future research.

Atom-glue stabilized Pt-based intermetallic nanoparticles.

Pt-based nanoparticles (NPs) have been widely used in catalysis. However, this suffers from aggregation and/or sintering at working conditions. We demonstrate a robust strategy for stabilizing PtCo NPs under high temperature with strong interaction between M-N-C and PtCo NPs with Pt-M-N coordination, namely, "atom glue." Such atom glue for stabilizing Pt-based NPs can be extended to Zn, Mn, Fe, Ni, Co, and Cu, being a versatile strategy for stabilizing PtCo NPs, which substantially promotes the performance toward oxygen reduction reaction (ORR) and fuel cell. Impressively, the mass activity (MA) reaches 2.99 A mgPt-1 for ORR over g-Zn-N-C/PtCo, and 79.3% of the initial MA is maintained after 90K cycles in fuel cell. This work provides a versatile strategy for stabilizing Pt-based NPs via atom glue, which is likely to spark widespread interest across various fields.

Bis-benzylisoquinoline alkaloids inhibit flavivirus entry and replication by compromising endolysosomal trafficking and autophagy.

Flaviviruses, such as dengue virus (DENV), Zika virus (ZIKV), and Japanese encephalitis virus (JEV), represent a substantial public health challenge as there are currently no approved treatments available. Here, we investigated the antiviral effects of bis-benzylisoquinoline alkaloids (BBAs) on flavivirus infections. We evaluated five specific BBAs-berbamine, tetrandrine, iso-tetrandrine, fangchinoline, and cepharanthine-and found that they effectively inhibited infections by ZIKV, DENV, or JEV by blocking virus entry and genome replication stages in the flavivirus life cycle. Furthermore, we synthesized a fluorophore-conjugated BBA and showed that BBAs targeted endolysosomes, causing lysosomal pH alkalization. Mechanistic studies on inhibiting ZIKV infection by BBAs revealed that these compounds blocked TRPML channels, leading to lysosomal dysfunction and reducing the expression of NCAM1, a key receptor for the entry of ZIKV into cells, thereby decreasing cells susceptibility to ZIKV infection. Additionally, BBAs inhibited the fusion of autophagosomes and lysosomes, significantly reducing viral RNA replication. Collectively, our results suggest that BBAs inhibit flavivirus entry and replication by compromising endolysosomal trafficking and autophagy, respectively, underscoring the potential of BBAs as therapeutic agents against flavivirus infections.

TP-LMMSG: a peptide prediction graph neural network incorporating flexible amino acid property representation.

Bioactive peptide therapeutics has been a long-standing research topic. Notably, the antimicrobial peptides (AMPs) have been extensively studied for its therapeutic potential. Meanwhile, the demand for annotating other therapeutic peptides, such as antiviral peptides (AVPs) and anticancer peptides (ACPs), also witnessed an increase in recent years. However, we conceive that the structure of peptide chains and the intrinsic information between the amino acids is not fully investigated among the existing protocols. Therefore, we develop a new graph deep learning model, namely TP-LMMSG, which offers lightweight and easy-to-deploy advantages while improving the annotation performance in a generalizable manner. The results indicate that our model can accurately predict the properties of different peptides. The model surpasses the other state-of-the-art models on AMP, AVP and ACP prediction across multiple experimental validated datasets. Moreover, TP-LMMSG also addresses the challenges of time-consuming pre-processing in graph neural network frameworks. With its flexibility in integrating heterogeneous peptide features, our model can provide substantial impacts on the screening and discovery of therapeutic peptides. The source code is available at https://github.com/NanjunChen37/TP_LMMSG.

A Biphasic Strategy to Synergistically Accelerate Activation and CO Spillover in Formic Acid Oxidation Catalysis.

Developing highly efficient and carbon monoxide (CO)-tolerant platinum (Pt) catalysts for the formic acid oxidation reaction (FAOR) is vital for direct formic acid fuel cells (DFAFCs), yet it is challenging due to the high energy barrier of direct intermediates (HCOO* and COOH*) as well as the CO poisoning issues associated with Pt alloy catalysts. Here we present a versatile biphasic strategy by creating a hexagonal/cubic crystalline-phase-synergistic PtPb/C (h/c-PtPb/C) catalyst to tackle the aforementioned issues. Detailed investigations reveal that h/c-PtPb/C can simultaneously facilitate the adsorption of direct intermediates while inhibiting CO adsorption, thereby significantly improving the activation and CO spillover. As a result, h/c-PtPb/C showcases an outstanding FAOR activity of 8.1 A mgPt-1, which is 64.5 times higher than that of commercial Pt/C and significantly surpasses monophasic PtPb. Moreover, the h/c-PtPb/C-based membrane electrode assembly exhibits an exceptional peak power density of 258.7 mW cm-2 for practical DFAFC applications.

TransPTM: a transformer-based model for non-histone acetylation site prediction.

Protein acetylation is one of the extensively studied post-translational modifications (PTMs) due to its significant roles across a myriad of biological processes. Although many computational tools for acetylation site identification have been developed, there is a lack of benchmark dataset and bespoke predictors for non-histone acetylation site prediction. To address these problems, we have contributed to both dataset creation and predictor benchmark in this study. First, we construct a non-histone acetylation site benchmark dataset, namely NHAC, which includes 11 subsets according to the sequence length ranging from 11 to 61 amino acids. There are totally 886 positive samples and 4707 negative samples for each sequence length. Secondly, we propose TransPTM, a transformer-based neural network model for non-histone acetylation site predication. During the data representation phase, per-residue contextualized embeddings are extracted using ProtT5 (an existing pre-trained protein language model). This is followed by the implementation of a graph neural network framework, which consists of three TransformerConv layers for feature extraction and a multilayer perceptron module for classification. The benchmark results reflect that TransPTM has the competitive performance for non-histone acetylation site prediction over three state-of-the-art tools. It improves our comprehension on the PTM mechanism and provides a theoretical basis for developing drug targets for diseases. Moreover, the created PTM datasets fills the gap in non-histone acetylation site datasets and is beneficial to the related communities. The related source code and data utilized by TransPTM are accessible at https://www.github.com/TransPTM/TransPTM.

Discovering DNA shape motifs with multiple DNA shape features: generalization, methods, and validation.

DNA motifs are crucial patterns in gene regulation. DNA-binding proteins (DBPs), including transcription factors, can bind to specific DNA motifs to regulate gene expression and other cellular activities. Past studies suggest that DNA shape features could be subtly involved in DNA-DBP interactions. Therefore, the shape motif annotations based on intrinsic DNA topology can deepen the understanding of DNA-DBP binding. Nevertheless, high-throughput tools for DNA shape motif discovery that incorporate multiple features altogether remain insufficient. To address it, we propose a series of methods to discover non-redundant DNA shape motifs with the generalization to multiple motifs in multiple shape features. Specifically, an existing Gibbs sampling method is generalized to multiple DNA motif discovery with multiple shape features. Meanwhile, an expectation-maximization (EM) method and a hybrid method coupling EM with Gibbs sampling are proposed and developed with promising performance, convergence capability, and efficiency. The discovered DNA shape motif instances reveal insights into low-signal ChIP-seq peak summits, complementing the existing sequence motif discovery works. Additionally, our modelling captures the potential interplays across multiple DNA shape features. We provide a valuable platform of tools for DNA shape motif discovery. An R package is built for open accessibility and long-lasting impact: https://zenodo.org/doi/10.5281/zenodo.10558980.

Construction of Immune Infiltration-Related LncRNA Signatures Based on Machine Learning for the Prognosis in Colon Cancer.

Colon cancer is one of the malignant tumors with high morbidity, lethality, and prevalence across global human health. Molecular biomarkers play key roles in its prognosis. In particular, immune-related lncRNAs (IRL) have attracted enormous interest in diagnosis and treatment, but less is known about their potential functions. We aimed to investigate dysfunctional IRL and construct a risk model for improving the outcomes of patients. Nineteen immune cell types were collected for identifying house-keeping lncRNAs (HKLncRNA). GSE39582 and TCGA-COAD were treated as the discovery and validation datasets, respectively. Four machine learning algorithms (LASSO, Random Forest, Boruta, and Xgboost) and a Gaussian mixture model were utilized to mine the optimal combination of lncRNAs. Univariate and multivariate Cox regression was utilized to construct the risk score model. We distinguished the functional difference in an immune perspective between low- and high-risk cohorts calculated by this scoring system. Finally, we provided a nomogram. By leveraging the microarray, sequencing, and clinical data for immune cells and colon cancer patients, we identified the 221 HKLncRNAs with a low cell type-specificity index. Eighty-seven lncRNAs were up-regulated in the immune compared to cancer cells. Twelve lncRNAs were beneficial in improving performance. A risk score model with three lncRNAs (CYB561D2, LINC00638, and DANCR) was proposed with robust ROC performance on an independent dataset. According to immune-related analysis, the risk score is strongly associated with the tumor immune microenvironment. Our results emphasized IRL has the potential to be a powerful and effective therapy for enhancing the prognostic of colon cancer.

Identification of a small chemical as a lysosomal calcium mobilizer and characterization of its ability to inhibit autophagy and viral infection.

We previously identified glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as one of the cyclic adenosine diphosphoribose (cADPR)'s binding proteins and found that GAPDH participates in cADPR-mediated Ca2+ release from endoplasmic reticulum via ryanodine receptors (RyRs). Here, we aimed to chemically synthesise and pharmacologically characterise novel cADPR analogues. Based on the simulated cADPR-GAPDH complex structure, we performed the structure-based drug screening, identified several small chemicals with high docking scores to cADPR's binding pocket in GAPDH and showed that two of these compounds, C244 and C346, are potential cADPR antagonists. We further synthesised several analogues of C346 and found that its analogue, G42, also mobilised Ca2+ release from lysosomes. G42 alkalised lysosomal pH and inhibited autophagosome-lysosome fusion. Moreover, G42 markedly inhibited Zika virus (ZIKV, a flavivirus) or murine hepatitis virus (MHV, a ß-coronavirus) infections of host cells. These results suggest that G42 inhibits virus infection, likely by triggering lysosomal Ca2+ mobilisation and inhibiting autophagy.

Chromothripsis detection with multiple myeloma patients based on deep graph learning.

MOTIVATION: Chromothripsis, associated with poor clinical outcomes, is prognostically vital in multiple myeloma. The catastrophic event is reported to be detectable prior to the progression of multiple myeloma. As a result, chromothripsis detection can contribute to risk estimation and early treatment guidelines for multiple myeloma patients. However, manual diagnosis remains the gold standard approach to detect chromothripsis events with the whole-genome sequencing technology to retrieve both copy number variation (CNV) and structural variation data. Meanwhile, CNV data are much easier to obtain than structural variation data. Hence, in order to reduce the reliance on human experts' efforts and structural variation data extraction, it is necessary to establish a reliable and accurate chromothripsis detection method based on CNV data. RESULTS: To address those issues, we propose a method to detect chromothripsis solely based on CNV data. With the help of structure learning, the intrinsic relationship-directed acyclic graph of CNV features is inferred to derive a CNV embedding graph (i.e. CNV-DAG). Subsequently, a neural network based on Graph Transformer, local feature extraction, and non-linear feature interaction, is proposed with the embedding graph as the input to distinguish whether the chromothripsis event occurs. Ablation experiments, clustering, and feature importance analysis are also conducted to enable the proposed model to be explained by capturing mechanistic insights. AVAILABILITY AND IMPLEMENTATION: The source code and data are freely available at https://github.com/luvyfdawnYu/CNV_chromothripsis.

Fluorinated Poly(aryl piperidinium) Membranes for Anion Exchange Membrane Fuel Cells.

Anion-exchange-membrane fuel cells (AEMFCs) are a cost-effective alternative to proton-exchange-membrane fuel cells (PEMFCs). The development of high-performance and durable AEMFCs requires highly conductive and robust anion-exchange membranes (AEMs). However, AEMs generally exhibit a trade-off between conductivity and dimensional stability. Here, a fluorination strategy to create a phase-separated morphological structure in poly(aryl piperidinium) AEMs is reported. The highly hydrophobic perfluoroalkyl side chains augment phase separation to construct interconnected hydrophilic channels for anion transport. As a result, these fluorinated PAP (FPAP) AEMs simultaneously possess high conductivity (>150 mS cm-1 at 80 °C) and high dimensional stability (swelling ratio <20% at 80 °C), excellent mechanical properties (tensile strength >80 MPa and elongation at break >40%) and chemical stability (>2000 h in 3 m KOH at 80 °C). AEMFCs with a non-precious Co-Mn spinel cathode using the present FPAP AEMs achieve an outstanding peak power density of 1.31 W cm-2 . The AEMs remain stable over 500 h of fuel cell operation at a constant current density of 0.2 A cm-2 .

Identification of triazolopyridine derivatives as a new class of AhR agonists and evaluation of anti-psoriasis effect in a mouse model.

The aryl hydrocarbon receptor (AhR), a ligand-dependent transcription factor, can regulate the immune balance of Th17/22 and Treg cells, which plays an important role in the development and maintenance of the skin barrier. We herein report the discovery of triazolopyridine derivatives as a new class of AhR agonists. Structure-activity relationship analyses led to the identification of the most active compound, 6-bromo-2-(4-bromophenyl)-[1,2,4]triazolo[1,5-a]pyridine (12a), with an EC50 (50% effective concentration) value of 0.03 nM. Compound 12a could induce rapid nuclear enrichment of AhR, trigger the transcription of downstream genes and promote skin barrier repair. Topical or oral administration of 12a could significantly alleviate imiquimod (IMQ)-induced psoriasis-like skin lesion. Considering the excellent in vivo anti-psoriasis activity as well as good pharmacokinetic properties, 12a could be a promising lead compound for drug discovery against psoriasis, and deserving further investigation.

Branched Poly(Aryl Piperidinium) Membranes for Anion-Exchange Membrane Fuel Cells.

Anion-exchange membrane fuel cells (AEMFCs) are a promising, next-generation fuel cell technology. AEMFCs require highly conductive and robust anion-exchange membranes (AEMs), which are challenging to develop due to the tradeoff between conductivity and water uptake. Here we report a method to prepare high-molecular-weight branched poly(aryl piperidinium) AEMs. We show that branching reduces water uptake, leading to improved dimensional stability. The optimized membrane, b-PTP-2.5, exhibits simultaneously high OH- conductivity (>145 mS cm-1 at 80 °C), high mechanical strength and dimensional stability, good processability, and excellent alkaline stability (>1500 h) in 1 M KOH at 80 °C. AEMFCs based on b-PTP-2.5 reached peak power densities of 2.3 W cm-2 in H2 -O2 and 1.3 W cm-2 in H2 -air at 80 °C. The AEMFCs can run stably under a constant current of 0.2 A cm-2 over 500 h, during which the b-PTP-2.5 membrane remains stable.

Insight into the Alkaline Stability of N-Heterocyclic Ammonium Groups for Anion-Exchange Polyelectrolytes.

The alkaline stability of N-heterocyclic ammonium (NHA) groups is a critical topic in anion-exchange membranes (AEMs) and AEM fuel cells (AEMFCs). Here, we report a systematic study on the alkaline stability of 24 representative NHA groups at different hydration numbers (λ) at 80 °C. The results elucidate that γ-substituted NHAs containing electron-donating groups display superior alkaline stability, while electron-withdrawing substituents are detrimental to durable NHAs. Density-functional-theory calculations and experimental results suggest that nucleophilic substitution is the dominant degradation pathway in NHAs, while Hofmann elimination is the primary degradation pathway for NHA-based AEMs. Different degradation pathways determine the alkaline stability of NHAs or NHA-based AEMs. AEMFC durability (from 1 A cm-2 to 3 A cm-2 ) suggests that NHA-based AEMs are mainly subjected to Hofmann elimination under 1 A cm-2 current density for 1000 h, providing insights into the relationship between current density, λ value, and durability of NHA-based AEMs.

Poly(fluorenyl aryl piperidinium) membranes and ionomers for anion exchange membrane fuel cells.

Low-cost anion exchange membrane fuel cells have been investigated as a promising alternative to proton exchange membrane fuel cells for the last decade. The major barriers to the viability of anion exchange membrane fuel cells are their unsatisfactory key components-anion exchange ionomers and membranes. Here, we present a series of durable poly(fluorenyl aryl piperidinium) ionomers and membranes where the membranes possess high OH- conductivity of 208 mS cm-1 at 80 °C, low H2 permeability, excellent mechanical properties (84.5 MPa TS), and 2000 h ex-situ durability in 1 M NaOH at 80 °C, while the ionomers have high water vapor permeability and low phenyl adsorption. Based on our rational design of poly(fluorenyl aryl piperidinium) membranes and ionomers, we demonstrate alkaline fuel cell performances of 2.34 W cm-2 in H2-O2 and 1.25 W cm-2 in H2-air (CO2-free) at 80 °C. The present cells can be operated stably under a 0.2 A cm-2 current density for ~200 h.

Poly(Alkyl-Terphenyl Piperidinium) Ionomers and Membranes with an Outstanding Alkaline-Membrane Fuel-Cell Performance of 2.58†W cm-2.

Aryl-ether-free anion-exchange ionomers (AEIs) and membranes (AEMs) have become an important benchmark to address the insufficient durability and power-density issues associated with AEM fuel cells (AEMFCs). Here, we present aliphatic chain-containing poly(diphenyl-terphenyl piperidinium) (PDTP) copolymers to reduce the phenyl content and adsorption of AEIs and to increase the mechanical properties of AEMs. Specifically, PDTP AEMs possess excellent mechanical properties (storage modulus>1800 MPa, tensile strength>70 MPa), H2 fuel-barrier properties (<10 Barrer), good ion conductivity, and ex-situ stability. Meanwhile, PDTP AEIs with low phenyl content and high-water permeability display excellent peak power densities (PPDs). The present AEMFCs reach outstanding PPDs of 2.58 W cm-2 (>7.6 A cm-2 current density) and 1.38 W cm-2 at 80 °C in H2 /O2 and H2 /air, respectively, along with a specific power (PPD/catalyst loading) over 8 W mg-1 , which is the highest record for Pt-based AEMFCs so far.

Analytical bond-order potential for silver, palladium, ruthenium and iodine bulk diffusion in silicon carbide.

The analytical bond-order potential has been developed for simulating fission product (Ag, Pd, Ru, and I) behavior in SiC, especially for their diffusion. We have proposed adding experimentally available elastic constants and physical properties of the elements as well as important defect formation energies calculated from density functional theory simulation to the list of typical properties as the extensive fitting database. The results from molecular dynamics simulations are in a reasonable agreement with defect properties and energy barriers of their experimental/computational counterparts. The successful validation of the new potential has established a good reliability and transferability of the potentials, which enables the ability of simulation in extended scale. The kinetic behavior such as diffusion of different interstitials is then realized by applying the new interatomic potentials. The bulk diffusion is less likely to dominate the transport of the four fission products under pure thermal condition, when we refer to their extremely small values of the effective diffusion coefficients. The interstitial mechanism is hard for Pd, Ru, and I to access due to the high formation energy and high migration energy. However, it is found that the migration energy of silver is relatively low, which indicates Ag diffusion via an interstitial mechanism being feasible, especially under irradiation condition, where massive interstitials can be formed in high-temperature nuclear reactors.

The design of a multifunctional separator regulating the lithium ion flux for advanced lithium-ion batteries.

Herein, we design a controllable approach for preparing multifunctional polybenzimidazole porous membranes with superior fire-resistance, excellent thermo-stability, and high wettability. Specifically, the recyclable imidazole is firstly utilized as the eco-friendly template for micropores formation, which is an interesting finding and has tremendous potential for low-cost industrial production. The unique backbone structure of the as-prepared polybenzimidazole porous membrane endows the separator with superb thermal dimensional stability at 300 °C. Most significantly, the inherent flame retardancy of polybenzimidazole can ensure the high security of lithium-ion batteries, and the existence of polar groups of imidazole can regulate the Li+ flux and improve the ionic conductivity of lithium ions. Notably, the cell with a polybenzimidazole porous membrane presents higher capability (131.7 mA h g-1) than that of a commercial Celgard membrane (95.4 mA h g-1) at higher charge-discharge density (5C), and it can work normally at 120 °C. The fascinating comprehensive properties of the polybenzimidazole porous membrane with excellent thermal-stability, satisfying wettability, superb flame retardancy and good electrochemical performance indicate its promising application for high-safety and high-performance lithium-ion batteries.

Magnetic field-oriented ferroferric oxide/poly(2,6-dimethyl-1,4-phenylene oxide) hybrid membranes for anion exchange membrane applications.

Concentrating on the ion conductivity of anion exchange membranes (AEMs), we present a magnetic-field-oriented strategy to address the insufficient ion conductivity and the lifetime problem of AEMs used in alkali membrane fuel cells (AMFCs). Magnetic ferroferric oxide (Fe3O4) is functionalized with quaternary ammonium (QA) groups to endow the QA-Fe3O4 with ion-exchange ability. A series of aligned QA-Fe3O4/poly(2,6-dimethyl-1,4-phenylene oxide) (PPO) hybrid membranes were fabricated by doping QA-Fe3O4 in a triple-ammonium-functionalized PPO (TA-PPO) solution in an applied magnetic field. The structure of aligned QA-Fe3O4 in the TA-PPO membrane is clearly observed by using a scanning electron microscope (SEM). More importantly, the aligned QA-Fe3O4 constructs successive and effective ion-transport channels in the QA-Fe3O4/TA-PPO membrane, which dramatically improves the ion conductivity of the membranes. Notably, the magnetic-field-induced ion channels (MICs) are different from microscopic phase-induced ion channels (PICs). These MICs display much shorter ion transport distances and broader water channels than traditional PICs in AEMs. The aligned QA-Fe3O4/TA-PPO hybrid membrane displays a further 55% increase in ion conductivity after magnetic-field orientation compared to the normal QA-Fe3O4/TA-PPO membrane. Surprisingly, the aligned QA-Fe3O4 also improves the alkali stability and fuel cell performance of the hybrid membrane. The aligned 6%-QA-Fe3O4/TA-PPO hybrid membrane realizes a maximal power density of 224 mW cm-2. In summary, this work provides a novel and effective method to prepare high-performance AEMs.

Three-Decker Strategy Based on Multifunctional Layered Double Hydroxide to Realize High-Performance Hydroxide Exchange Membranes for Fuel Cell Applications.

Herein, we present a three-decker layered double hydroxide (LDH)/poly(phenylene oxide) (PPO) for hydroxide exchange membrane (HEM) applications. Hexagonal LDH is functionalized with highly stable 3-hydroxy-6-azaspiro [5.5] undecane (OH-ASU) cations to promote it's ion-exchange capacity. The ASU-LDH is combined with triple-cations functionalized PPO (TC-PPO) to fabricate a three-decker ASU-LDH/TC-PPO hybrid membrane by an electrostatic-spraying method. Notably, the ASU-LDH layer with a porous structure shows many valuable properties for the ASU-LDH/TC-PPO hybrid membranes, such as improving hydroxide conductivity, dimensional stability, and alkaline stability. The maximum OH- conductivity of ASU-LDH/TC-PPO hybrid membranes achieves 0.113 S/cm at 80 °C. Only 11.5% drops in OH- conductivity was detected after an alkaline stability test in 1 M NaOH at 80 °C for 588 h, prolonging the lifetime of the TC-PPO membrane. Furthermore, the ASU-LDH/TC-PPO hybrid membrane realizes a maximum power density of 0.209 W/cm2 under a current density of 0.391 A/cm2. In summary, the ASU-LDH/TC-PPO hybrid membranes provide a reliable method for preparing high-performance HEMs.