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Antibodies play a pivotal role in immune defense and serve as key therapeutic agents. The process of affinity maturation, wherein antibodies evolve through somatic mutations to achieve heightened specificity and affinity to target antigens, is crucial for effective immune response. Despite their significance, assessing antibody-antigen binding affinity remains challenging due to limitations in conventional wet lab techniques. To address this, we introduce AntiFormer, a graph-based large language model designed to predict antibody binding affinity. AntiFormer incorporates sequence information into a graph-based framework, allowing for precise prediction of binding affinity. Through extensive evaluations, AntiFormer demonstrates superior performance compared with existing methods, offering accurate predictions with reduced computational time. Application of AntiFormer to severe acute respiratory syndrome coronavirus 2 patient samples reveals antibodies with strong neutralizing capabilities, providing insights for therapeutic development and vaccination strategies. Furthermore, analysis of individual samples following influenza vaccination elucidates differences in antibody response between young and older adults. AntiFormer identifies specific clonotypes with enhanced binding affinity post-vaccination, particularly in young individuals, suggesting age-related variations in immune response dynamics. Moreover, our findings underscore the importance of large clonotype category in driving affinity maturation and immune modulation. Overall, AntiFormer is a promising approach to accelerate antibody-based diagnostics and therapeutics, bridging the gap between traditional methods and complex antibody maturation processes.
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SARS-CoV-2 , Humanos , SARS-CoV-2/imunologia , SARS-CoV-2/genética , COVID-19/virologia , COVID-19/imunologia , Afinidade de Anticorpos , Anticorpos Antivirais/imunologia , Anticorpos Neutralizantes/imunologia , Biologia Computacional/métodos , Ligação ProteicaRESUMO
Impaired wound healing in diabetes results from a complex interplay of factors that disrupt epithelialization and wound closure. MG53, a tripartite motif (TRIM) family protein, plays a key role in repairing cell membrane damage and facilitating tissue regeneration. In this study, bone marrow-derived mesenchymal stem cells (BMSCs) were transduced with lentiviral vectors overexpressing MG53 to investigate their efficacy in diabetic wound healing. Using a db/db mouse wound model, we observed that BMSCs-MG53 significantly enhanced diabetic wound healing. This improvement was associated with marked increase in re-epithelialization and vascularization. BMSCs-MG53 promoted recruitment and survival of BMSCs, as evidenced by an increase in MG53/Ki67-positive BMSCs and their improved response to scratch wounding. The combination therapy also promoted angiogenesis in diabetic wound tissues by upregulating the expression of angiogenic growth factors. MG53 overexpression accelerated the differentiation of BMSCs into endothelial cells, manifested as the formation of mature vascular network structure and a remarkable increase in DiI-Ac-LDL uptake. Our mechanistic investigation revealed that MG53 binds to caveolin-3 (CAV3) and subsequently increases phosphorylation of eNOS, thereby activating eNOS/NO signaling. Notably, CAV3 knockdown reversed the promoting effects of MG53 on BMSCs endothelial differentiation. Overall, our findings support the notion that MG53 binds to CAV3, activates eNOS/NO signaling pathway, and accelerates the therapeutic effect of BMSCs in the context of diabetic wound healing. These insights hold promise for the development of innovative strategies for treating diabetic-related impairments in wound healing.
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Transplante de Células-Tronco Mesenquimais , Células-Tronco Mesenquimais , Óxido Nítrico Sintase Tipo III , Óxido Nítrico , Transdução de Sinais , Cicatrização , Animais , Células-Tronco Mesenquimais/metabolismo , Óxido Nítrico Sintase Tipo III/metabolismo , Camundongos , Óxido Nítrico/metabolismo , Masculino , Camundongos Endogâmicos C57BL , Neovascularização Fisiológica , Células Cultivadas , Humanos , Diabetes Mellitus Experimental/terapia , Diabetes Mellitus Experimental/metabolismo , Diferenciação Celular , Proteínas de MembranaRESUMO
In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.
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Ciclo Celular , RNA-Seq , Análise de Célula Única , Análise de Célula Única/métodos , Ciclo Celular/genética , RNA-Seq/métodos , Sequenciamento de Cromatina por Imunoprecipitação/métodos , Camundongos , Análise de Sequência de RNA/métodos , Humanos , Animais , Biologia Computacional/métodos , Análise da Expressão Gênica de Célula ÚnicaRESUMO
OBJECTIVES: Precise literature recommendation and summarization are crucial for biomedical professionals. While the latest iteration of generative pretrained transformer (GPT) incorporates 2 distinct modes-real-time search and pretrained model utilization-it encounters challenges in dealing with these tasks. Specifically, the real-time search can pinpoint some relevant articles but occasionally provides fabricated papers, whereas the pretrained model excels in generating well-structured summaries but struggles to cite specific sources. In response, this study introduces RefAI, an innovative retrieval-augmented generative tool designed to synergize the strengths of large language models (LLMs) while overcoming their limitations. MATERIALS AND METHODS: RefAI utilized PubMed for systematic literature retrieval, employed a novel multivariable algorithm for article recommendation, and leveraged GPT-4 turbo for summarization. Ten queries under 2 prevalent topics ("cancer immunotherapy and target therapy" and "LLMs in medicine") were chosen as use cases and 3 established counterparts (ChatGPT-4, ScholarAI, and Gemini) as our baselines. The evaluation was conducted by 10 domain experts through standard statistical analyses for performance comparison. RESULTS: The overall performance of RefAI surpassed that of the baselines across 5 evaluated dimensions-relevance and quality for literature recommendation, accuracy, comprehensiveness, and reference integration for summarization, with the majority exhibiting statistically significant improvements (P-values <.05). DISCUSSION: RefAI demonstrated substantial improvements in literature recommendation and summarization over existing tools, addressing issues like fabricated papers, metadata inaccuracies, restricted recommendations, and poor reference integration. CONCLUSION: By augmenting LLM with external resources and a novel ranking algorithm, RefAI is uniquely capable of recommending high-quality literature and generating well-structured summaries, holding the potential to meet the critical needs of biomedical professionals in navigating and synthesizing vast amounts of scientific literature.
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Algoritmos , Armazenamento e Recuperação da Informação , PubMed , Armazenamento e Recuperação da Informação/métodos , Processamento de Linguagem NaturalRESUMO
Pushing intercalation-type cathode materials to their theoretical capacity often suffers from fragile Li-deficient frameworks and severe lattice strain, leading to mechanical failure issues within the crystal structure and fast capacity fading. This is particularly pronounced in layered oxide cathodes because the intrinsic nature of their structures is susceptible to structural degradation with excessive Li extraction, which remains unsolved yet despite attempts involving elemental doping and surface coating strategies. Herein, a mechanochemical strengthening strategy is developed through a gradient disordering structure to address these challenges and push the LiCoO2 (LCO) layered cathode approaching the capacity limit (256 mAh g-1, up to 93% of Li utilization). This innovative approach also demonstrates exceptional cyclability and rate capability, as validated in practical Ah-level pouch full cells, surpassing the current performance benchmarks. Comprehensive characterizations with multiscale X-ray, electron diffraction, and imaging techniques unveil that the gradient disordering structure notably diminishes the anisotropic lattice strain and exhibits high fatigue resistance, even under extreme delithiation states and harsh operating voltages. Consequently, this designed LCO cathode impedes the growth and propagation of particle cracks, and mitigates irreversible phase transitions. This work sheds light on promising directions toward next-generation high-energy-density battery materials through structural chemistry design.
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Transitioning from polycrystalline to single-crystalline nickel-rich cathodes has garnered considerable attention in both academia and industry, driven by advantages of high tap density and enhanced mechanical properties. However, cathodes with high nickel content (>70%) suffer from substantial capacity degradation, which poses a challenge to their commercial viability. Leveraging multiscale spatial resolution diffraction and imaging techniques, we observe that lattice rotations occur universally in single-crystalline cathodes and play a pivotal role in the structure degradation. These lattice rotations prove unrecoverable and govern the accumulation of adverse lattice distortions over repeated cycles, contributing to structural and mechanical degradation and fast capacity fade. These findings bridge the previous knowledge gap that exists in the mechanistic link between fast performance failure and atomic-scale structure degradation.
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Understanding disease progression and sophisticated tumor ecosystems is imperative for investigating tumorigenesis mechanisms and developing novel prevention strategies. Here, we dissected heterogeneous microenvironments during malignant transitions by leveraging data from 1396 samples spanning 13 major tissues. Within transitional stem-like subpopulations highly enriched in precancers and cancers, we identified 30 recurring cellular states strongly linked to malignancy, including hypoxia and epithelial senescence, revealing a high degree of plasticity in epithelial stem cells. By characterizing dynamics in stem-cell crosstalk with the microenvironment along the pseudotime axis, we found differential roles of ANXA1 at different stages of tumor development. In precancerous stages, reduced ANXA1 levels promoted monocyte differentiation toward M1 macrophages and inflammatory responses, whereas during malignant progression, upregulated ANXA1 fostered M2 macrophage polarization and cancer-associated fibroblast transformation by increasing TGF-ß production. Our spatiotemporal analysis further provided insights into mechanisms responsible for immunosuppression and a potential target to control evolution of precancer and mitigate the risk for cancer development.
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PURPOSE: The recent findings from the DESTINY-Breast04 trial highlighted the clinical importance of distinguishing between HER2 immunohistochemistry (IHC) scores 0 and 1 + in metastatic breast cancer (BC). However, pathologist interpretation of HER2 IHC scoring is subjective, and standardized methodology is needed. We evaluated the consistency of HER2 IHC scoring among pathologists and the accuracy of digital image analysis (DIA) in interpreting HER2 IHC staining in cases of HER2-low BC. METHODS: Fifty whole-slide biopsies of BC with HER2 IHC staining were evaluated, comprising 25 cases originally reported as IHC score 0 and 25 as 1 +. These slides were digitally scanned. Six pathologists with breast expertise independently reviewed and scored the scanned images, and DIA was applied. Agreement among pathologists and concordance between pathologist scores and DIA results were statistically analyzed using Kendall coefficient of concordance (W) tests. RESULTS: Substantial agreement among at least five of the six pathologists was found for 18 of the score 0 cases (72%) and 15 of the score 1 + cases (60%), indicating excellent interobserver agreement (W = 0.828). DIA scores were highly concordant with pathologist scores in 96% of cases (47/49), indicating excellent concordance (W = 0.959). CONCLUSION: Although breast subspecialty pathologists were relatively consistent in evaluating BC with HER2 IHC scores of 0 and 1 +, DIA may be a reliable supplementary tool to enhance the standardization and quantification of HER2 IHC assessment, especially in challenging cases where results may be ambiguous (i.e., scores 0-1 +). These findings hold promise for improving the accuracy and consistency of HER2 testing.
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Neoplasias da Mama , Imuno-Histoquímica , Variações Dependentes do Observador , Receptor ErbB-2 , Humanos , Neoplasias da Mama/patologia , Neoplasias da Mama/metabolismo , Receptor ErbB-2/metabolismo , Feminino , Imuno-Histoquímica/métodos , Reprodutibilidade dos Testes , Biomarcadores Tumorais/metabolismo , Biomarcadores Tumorais/análise , Processamento de Imagem Assistida por Computador/métodosRESUMO
Single-crystal Ni-rich cathodes offer promising prospects in mitigating intergranular microcracks and side reaction issues commonly encountered in conventional polycrystalline cathodes. However, the utilization of micrometer-sized single-crystal particles has raised concerns about sluggish Li+ diffusion kinetics and unfavorable structural degradation, particularly in high Ni content cathodes. Herein, we present an innovative in situ doping strategy to regulate the dominant growth of characteristic planes in the single-crystal precursor, leading to enhanced mechanical properties and effectively tackling the challenges posed by ultrahigh-nickel layered cathodes. Compared with the traditional dry-doping method, our in situ doping approach possesses a more homogeneous and consistent modifying effect from the inside out, ensuring the uniform distribution of doping ions with large radius (Nb, Zr, W, etc). This mitigates the generally unsatisfactory substitution effect, thereby minimizing undesirable coating layers induced by different solubilities during the calcination process. Additionally, the uniformly dispersed ions from this in situ doping are beneficial for alleviating the two-phase coexistence of H2/H3 and optimizing the Li+ concentration gradient during cycling, thus inhibiting the formation of intragranular cracks and interfacial deterioration. Consequently, the in situ doped cathodes demonstrate exceptional cycle retention and rate performance under various harsh testing conditions. Our optimized in situ doping strategy not only expands the application prospects of elemental doping but also offers a promising research direction for developing high-energy-density single-crystal cathodes with extended lifetime.
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The use of trivalent erbium (Er3+), typically embedded as an atomic defect in the solid-state, has widespread adoption as a dopant in telecommunication devices and shows promise as a spin-based quantum memory for quantum communication. In particular, its natural telecom C-band optical transition and spin-photon interface make it an ideal candidate for integration into existing optical fiber networks without the need for quantum frequency conversion. However, successful scaling requires a host material with few intrinsic nuclear spins, compatibility with semiconductor foundry processes, and straightforward integration with silicon photonics. Here, we present Er-doped titanium dioxide (TiO2) thin film growth on silicon substrates using a foundry-scalable atomic layer deposition process with a wide range of doping controls over the Er concentration. Even though the as-grown films are amorphous after oxygen annealing, they exhibit relatively large crystalline grains, and the embedded Er ions exhibit the characteristic optical emission spectrum from anatase TiO2. Critically, this growth and annealing process maintains the low surface roughness required for nanophotonic integration. Finally, we interface Er ensembles with high quality factor Si nanophotonic cavities via evanescent coupling and demonstrate a large Purcell enhancement (≈300) of their optical lifetime. Our findings demonstrate a low-temperature, nondestructive, and substrate-independent process for integrating Er-doped materials with silicon photonics. At high doping densities this platform can enable integrated photonic components such as on-chip amplifiers and lasers, while dilute concentrations can realize single ion quantum memories.
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In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity and confounding factors. As we know, cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it's not clear how it will work on the integrated single-cell multi-omics data. Here, we developed a Cell Cycle-Aware Network (CCAN) to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the out-standing performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.
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Microwave-absorbing materials with regulatable absorption frequency and optical camouflage hold great significance in intelligent electronic devices and advanced stealth technology. Herein, we present an innovative microwave-absorbing foam that can dynamically tune microwave absorption frequencies via a simple mechanical compression while in parallel enabling optical camouflage over broad spectral ranges by adjusting the structural colors. The vivid colors spanning different color categories generated from thin-film interference can be precisely regulated by adjusting the thickness of the conformal TiO2 coatings on Ni/melamine foam. Enhanced interfacial and defect-induced polarizations resulting from the introduction of TiO2 coating synergistically contribute to the dielectric attenuation performance. Consequently, such a foam exhibits exceptional microwave absorption capabilities, and the absorption frequency can be dynamically tuned from the S band to the Ku band by manipulating its compression ratio. Additionally, simulation calculations validate the adjustable electromagnetic wave loss behavior, offering valuable insights for the development of next-generation intelligent electromagnetic devices across diverse fields.
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The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, has resulted in the loss of millions of lives and severe global economic consequences. Every time SARS-CoV-2 replicates, the viruses acquire new mutations in their genomes. Mutations in SARS-CoV-2 genomes led to increased transmissibility, severe disease outcomes, evasion of the immune response, changes in clinical manifestations and reducing the efficacy of vaccines or treatments. To date, the multiple resources provide lists of detected mutations without key functional annotations. There is a lack of research examining the relationship between mutations and various factors such as disease severity, pathogenicity, patient age, patient gender, cross-species transmission, viral immune escape, immune response level, viral transmission capability, viral evolution, host adaptability, viral protein structure, viral protein function, viral protein stability and concurrent mutations. Deep understanding the relationship between mutation sites and these factors is crucial for advancing our knowledge of SARS-CoV-2 and for developing effective responses. To fill this gap, we built COV2Var, a function annotation database of SARS-CoV-2 genetic variation, available at http://biomedbdc.wchscu.cn/COV2Var/. COV2Var aims to identify common mutations in SARS-CoV-2 variants and assess their effects, providing a valuable resource for intensive functional annotations of common mutations among SARS-CoV-2 variants.
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Bases de Dados Genéticas , SARS-CoV-2 , Humanos , Mutação , SARS-CoV-2/genética , Anotação de Sequência Molecular , Variação GenéticaRESUMO
Among the diverse sources of neoantigens (i.e. single-nucleotide variants (SNVs), insertions or deletions (Indels) and fusion genes), fusion gene-derived neoantigens are generally more immunogenic, have multiple targets per mutation and are more widely distributed across various cancer types. Therefore, fusion gene-derived neoantigens are a potential source of highly immunogenic neoantigens and hold great promise for cancer immunotherapy. However, the lack of fusion protein sequence resources and knowledge prevents this application. We introduce 'FusionNeoAntigen', a dedicated resource for fusion-specific neoantigens, accessible at https://compbio.uth.edu/FusionNeoAntigen. In this resource, we provide fusion gene breakpoint crossing neoantigens focused on â¼43K fusion proteins of â¼16K in-frame fusion genes from FusionGDB2.0. FusionNeoAntigen provides fusion gene information, corresponding fusion protein sequences, fusion breakpoint peptide sequences, fusion gene-derived neoantigen prediction, virtual screening between fusion breakpoint peptides having potential fusion neoantigens and human leucocyte antigens (HLAs), fusion breakpoint RNA/protein sequences for developing vaccines, information on samples with fusion-specific neoantigen, potential CAR-T targetable cell-surface fusion proteins and literature curation. FusionNeoAntigen will help to develop fusion gene-based immunotherapies. We will report all potential fusion-specific neoantigens from all possible open reading frames of â¼120K human fusion genes in future versions.
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Antígenos de Neoplasias , Bases de Dados Genéticas , Neoplasias , Proteínas de Fusão Oncogênica , Humanos , Antígenos de Neoplasias/genética , Antígenos HLA , Mutação INDEL , Mutação , Neoplasias/genética , Proteínas de Fusão Oncogênica/genéticaRESUMO
Drug resistance poses a significant challenge in cancer treatment. Despite the initial effectiveness of therapies such as chemotherapy, targeted therapy and immunotherapy, many patients eventually develop resistance. To gain deep insights into the underlying mechanisms, single-cell profiling has been performed to interrogate drug resistance at cell level. Herein, we have built the DRMref database (https://ccsm.uth.edu/DRMref/) to provide comprehensive characterization of drug resistance using single-cell data from drug treatment settings. The current version of DRMref includes 42 single-cell datasets from 30 studies, covering 382 samples, 13 major cancer types, 26 cancer subtypes, 35 treatment regimens and 42 drugs. All datasets in DRMref are browsable and searchable, with detailed annotations provided. Meanwhile, DRMref includes analyses of cellular composition, intratumoral heterogeneity, epithelial-mesenchymal transition, cell-cell interaction and differentially expressed genes in resistant cells. Notably, DRMref investigates the drug resistance mechanisms (e.g. Aberration of Drug's Therapeutic Target, Drug Inactivation by Structure Modification, etc.) in resistant cells. Additional enrichment analysis of hallmark/KEGG (Kyoto Encyclopedia of Genes and Genomes)/GO (Gene Ontology) pathways, as well as the identification of microRNA, motif and transcription factors involved in resistant cells, is provided in DRMref for user's exploration. Overall, DRMref serves as a unique single-cell-based resource for studying drug resistance, drug combination therapy and discovering novel drug targets.
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Bases de Dados Factuais , Resistência a Medicamentos , MicroRNAs , Neoplasias , Humanos , Resistência a Medicamentos/genética , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica , MicroRNAs/genética , Neoplasias/tratamento farmacológico , Neoplasias/genética , InternetRESUMO
StemDriver is a comprehensive knowledgebase dedicated to the functional annotation of genes participating in the determination of hematopoietic stem cell fate, available at http://biomedbdc.wchscu.cn/StemDriver/. By utilizing single-cell RNA sequencing data, StemDriver has successfully assembled a comprehensive lineage map of hematopoiesis, capturing the entire continuum from the initial formation of hematopoietic stem cells to the fully developed mature cells. Extensive exploration and characterization were conducted on gene expression features corresponding to each lineage commitment. At the current version, StemDriver integrates data from 42 studies, encompassing a diverse range of 14 tissue types spanning from the embryonic phase to adulthood. In order to ensure uniformity and reliability, all data undergo a standardized pipeline, which includes quality data pre-processing, cell type annotation, differential gene expression analysis, identification of gene categories correlated with differentiation, analysis of highly variable genes along pseudo-time, and exploration of gene expression regulatory networks. In total, StemDriver assessed the function of 23 839 genes for human samples and 29 533 genes for mouse samples. Simultaneously, StemDriver also provided users with reference datasets and models for cell annotation. We believe that StemDriver will offer valuable assistance to research focused on cellular development and hematopoiesis.
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Hematopoese , Células-Tronco Hematopoéticas , Animais , Humanos , Camundongos , Redes Reguladoras de Genes , Hematopoese/genética , Células-Tronco Hematopoéticas/metabolismo , Reprodutibilidade dos Testes , Bases de Conhecimento , Linhagem da CélulaRESUMO
Membranes incorporating two-dimensional (2D) materials have shown great potential for water purification and energy storage and conversion applications. Their ordered interlayer galleries can be modified for their tunable chemical and structural properties. Montmorillonite (MMT) is an earth-abundant phyllosilicate mineral that can be exfoliated into 2D flakes and reassembled into membranes. However, the poor water stability and random interlayer spacing of MMT caused by weak interlamellar interactions pose challenges for practical membrane applications. Herein, we demonstrate a facile approach to fabricating 2D MMT membranes with alkanediamines as cross-linkers. The incorporation of diamine molecules of different lengths enables controllable interlayer spacing and strengthens interlamellar connections, leading to tunable ion transport properties and boosted membrane stability in aqueous environments.
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Premelting of ice, a quasi-liquid layer (QLL) at the surface below the melting temperature, was first postulated by Michael Faraday 160 y ago. Since then, it has been extensively studied theoretically and experimentally through many techniques. Existing work has been performed predominantly on hexagonal ice, at conditions close to the triple point. Whether the same phenomenon can persist at much lower pressure and temperature, where stacking disordered ice sublimates directly into water vapor, remains unclear. Herein, we report direct observations of surface premelting on ice nanocrystals below the sublimation temperature using transmission electron microscopy (TEM). Similar to what has been reported on hexagonal ice, a QLL is found at the solid-vapor interface. It preferentially decorates certain facets, and its thickness increases as the phase transition temperature is approached. In situ TEM reveals strong diffusion of the QLL, while electron energy loss spectroscopy confirms its amorphous nature. More significantly, the premelting observed in this work is thought to be related to the metastable low-density ultraviscous water, instead of ambient liquid water as in the case of hexagonal ice. This opens a route to understand premelting and grassy liquid state, far away from the normal water triple point.
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Galvanic exchange seeds the growth of Pt nanostructures on the Ni foam monolith. Subsequent atomic layer deposition of ultrathin Al2O3 followed by annealing under air affords supported Pt catalysts with ultralow loading (0.020 ppm). In addition to the expected enhancement of the stability of the Pt particles on the surface, the â¼2 nm Al2O3 overcoat appears to also play a crucial role in the overall structural integrity of the NiOx nanoplates that grow on the Ni foam surface as a result of the preparative route. The resulting material is physically robust toward repeated handling and showcases retention of catalytic activity over 10 standard catalyst recycling trials, standing in marked contrast to the uncoated samples. Catalyst activity was tested via the hydrogenation of various functionalized styrenes at low temperatures and low hydrogen pressure in ethanol as a solvent, with a TOF as high as 9.5 × 106 h-1 for unfunctionalized styrene. Notably, the catalysts show excellent tolerance toward F, Cl, and Br substituents and no hydrogenation of the aromatic ring.
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OBJECTIVE: To prospectively investigate the nocturnal bladder function and sleep in children with refractory primary monosymptomatic nocturnal enuresis (RPMNE). MATERIALS AND METHODS: Fifty-three children diagnosed with RPMNE and 30 controls who had upper urinary tract abnormality but without any voiding problems were included in the study. RPMNE patients underwent a standardized investigation protocol, including the Pittsburgh Sleep Quality Index (PSQI) questionnaire, a 7-day bladder diary, and the simultaneous ambulatory urodynamic monitoring and polysomnography (PSG); controls were evaluated using the PSQI questionnaire and PSG. RESULTS: The children with RPMNE were subdivided into the nocturnal detrusor overactivity (NDO) case group and the non-NDO case group. The children in the NDO case group had a higher percentage of total sleep time in light sleep and a lower percentage in the N3 sleep stage than those in the non-NDO case group and control group (P <.05). The cortical arousal index and PSQI scores of both RPMNE subgroups were higher compared to the control group (P <.05). The incidences of reduced nocturnal bladder capacity (NBC) in the NDO case group were higher than in the non-NDO case group (P <.05). The frequency of involuntary detrusor contractions during sleep was positively correlated with cortical arousal index in the NDO case group (r = 0.811, P <.0001). CONCLUSION: In addition to the reduced NBC, the RPMNE is related to abnormal NDO, increased light sleep period, and cortical arousal dysfunction. Moreover, there is a certain correlation between the abnormal degrees of NDO and cortical arousal dysfunction.