3D genomic mapping reveals multifocality of human pancreatic precancers.

Pancreatic intraepithelial neoplasias (PanINs) are the most common precursors of pancreatic cancer, but their small size and inaccessibility in humans make them challenging to study1. Critically, the number, dimensions and connectivity of human PanINs remain largely unknown, precluding important insights into early cancer development. Here, we provide a microanatomical survey of human PanINs by analysing 46 large samples of grossly normal human pancreas with a machine-learning pipeline for quantitative 3D histological reconstruction at single-cell resolution. To elucidate genetic relationships between and within PanINs, we developed a workflow in which 3D modelling guides multi-region microdissection and targeted and whole-exome sequencing. From these samples, we calculated a mean burden of 13 PanINs per cm3 and extrapolated that the normal intact adult pancreas harbours hundreds of PanINs, almost all with oncogenic KRAS hotspot mutations. We found that most PanINs originate as independent clones with distinct somatic mutation profiles. Some spatially continuous PanINs were found to contain multiple KRAS mutations; computational and in situ analyses demonstrated that different KRAS mutations localize to distinct cell subpopulations within these neoplasms, indicating their polyclonal origins. The extensive multifocality and genetic heterogeneity of PanINs raises important questions about mechanisms that drive precancer initiation and confer differential progression risk in the human pancreas. This detailed 3D genomic mapping of molecular alterations in human PanINs provides an empirical foundation for early detection and rational interception of pancreatic cancer.

The Cross-Regulation Between Set1, Clr4, and Lsd1/2 in Schizosaccharomyces pombe.

Eukaryotic chromatin is organized into either silenced heterochromatin or relaxed euchromatin regions, which controls the accessibility of transcriptional machinery and thus regulates gene expression. In fission yeast, Schizosaccharomyces pombe, Set1 is the sole H3K4 methyltransferase and is mainly enriched at the promoters of actively transcribed genes. In contrast, Clr4 methyltransferase initiates H3K9 methylation, which has long been regarded as a hallmark of heterochromatic silencing. Lsd1 and Lsd2 are two highly conserved H3K4 and H3K9 demethylases. As these histone-modifying enzymes perform critical roles in maintaining histone methylation patterns and, consequently, gene expression profiles, cross-regulations among these enzymes are part of the complex regulatory networks. Thus, elucidating the mechanisms that govern their signaling and mutual regulations remains crucial. Here, we demonstrated that C-terminal truncation mutants, lsd1-ΔHMG and lsd2-ΔC, do not compromise the integrity of the Lsd1/2 complex but impair their chromatin-binding capacity at the promoter region of target genomic loci. We identified protein-protein interactions between Lsd1/2 and Raf2 or Swd2, which are the subunits of the Clr4 complex (CLRC) and Set1-associated complex (COMPASS), respectively. We showed that Clr4 and Set1 modulate the protein levels of Lsd1 and Lsd2 in opposite ways through the ubiquitin-proteasome-dependent pathway. During heat stress, the protein levels of Lsd1 and Lsd2 are upregulated in a Set1-dependent manner. The increase in protein levels is crucial for differential gene expression under stress conditions. Together, our results support a cross-regulatory model by which Set1 and Clr4 methyltransferases control the protein levels of Lsd1/2 demethylases to shape the dynamic chromatin landscape.

xSiGra: explainable model for single-cell spatial data elucidation.

Recent advancements in spatial imaging technologies have revolutionized the acquisition of high-resolution multichannel images, gene expressions, and spatial locations at the single-cell level. Our study introduces xSiGra, an interpretable graph-based AI model, designed to elucidate interpretable features of identified spatial cell types, by harnessing multimodal features from spatial imaging technologies. By constructing a spatial cellular graph with immunohistology images and gene expression as node attributes, xSiGra employs hybrid graph transformer models to delineate spatial cell types. Additionally, xSiGra integrates a novel variant of gradient-weighted class activation mapping component to uncover interpretable features, including pivotal genes and cells for various cell types, thereby facilitating deeper biological insights from spatial data. Through rigorous benchmarking against existing methods, xSiGra demonstrates superior performance across diverse spatial imaging datasets. Application of xSiGra on a lung tumor slice unveils the importance score of cells, illustrating that cellular activity is not solely determined by itself but also impacted by neighboring cells. Moreover, leveraging the identified interpretable genes, xSiGra reveals endothelial cell subset interacting with tumor cells, indicating its heterogeneous underlying mechanisms within complex cellular interactions.

AntiFormer: graph enhanced large language model for binding affinity prediction.

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.

Structural basis of a small monomeric Clivia fluorogenic RNA with a large Stokes shift.

RNA-based fluorogenic modules have revolutionized the spatiotemporal localization of RNA molecules. Recently, a fluorophore named 5-((Z)-4-((2-hydroxyethyl)(methyl)amino)benzylidene)-3-methyl-2-((E)-styryl)-3,5-dihydro-4H-imidazol-4-one (NBSI), emitting in red spectrum, and its cognate aptamer named Clivia were identified, exhibiting a large Stokes shift. To explore the underlying molecular basis of this unique RNA-fluorophore complex, we determined the tertiary structure of Clivia-NBSI. The overall structure uses a monomeric, non-G-quadruplex compact coaxial architecture, with NBSI sandwiched at the core junction. Structure-based fluorophore recognition pattern analysis, combined with fluorescence assays, enables the orthogonal use of Clivia-NBSI and other fluorogenic aptamers, paving the way for both dual-emission fluorescence and bioluminescence imaging of RNA molecules within living cells. Furthermore, on the basis of the structure-based substitution assay, we developed a multivalent Clivia fluorogenic aptamer containing multiple minimal NBSI-binding modules. This innovative design notably enhances the recognition sensitivity of fluorophores both in vitro and in vivo, shedding light on future efficient applications in various biomedical and research contexts.

DRMref: comprehensive reference map of drug resistance mechanisms in human cancer.

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.

Structure-based characterization and compound identification of the wild-type THF class-II riboswitch.

Riboswitches are conserved regulatory RNA elements participating in various metabolic pathways. Recently, a novel RNA motif known as the folE RNA motif was discovered upstream of folE genes. It specifically senses tetrahydrofolate (THF) and is therefore termed THF-II riboswitch. To unravel the ligand recognition mechanism of this newly discovered riboswitch and decipher the underlying principles governing its tertiary folding, we determined both the free-form and bound-form THF-II riboswitch in the wild-type sequences. Combining structural information and isothermal titration calorimetry (ITC) binding assays on structure-based mutants, we successfully elucidated the significant long-range interactions governing the function of THF-II riboswitch and identified additional compounds, including alternative natural metabolites and potential lead compounds for drug discovery, that interact with THF-II riboswitch. Our structural research on the ligand recognition mechanism of the THF-II riboswitch not only paves the way for identification of compounds targeting riboswitches, but also facilitates the exploration of THF analogs in diverse biological contexts or for therapeutic applications.

SpaRx: elucidate single-cell spatial heterogeneity of drug responses for personalized treatment.

Spatial cellular authors heterogeneity contributes to differential drug responses in a tumor lesion and potential therapeutic resistance. Recent emerging spatial technologies such as CosMx, MERSCOPE and Xenium delineate the spatial gene expression patterns at the single cell resolution. This provides unprecedented opportunities to identify spatially localized cellular resistance and to optimize the treatment for individual patients. In this work, we present a graph-based domain adaptation model, SpaRx, to reveal the heterogeneity of spatial cellular response to drugs. SpaRx transfers the knowledge from pharmacogenomics profiles to single-cell spatial transcriptomics data, through hybrid learning with dynamic adversarial adaption. Comprehensive benchmarking demonstrates the superior and robust performance of SpaRx at different dropout rates, noise levels and transcriptomics coverage. Further application of SpaRx to the state-of-the-art single-cell spatial transcriptomics data reveals that tumor cells in different locations of a tumor lesion present heterogenous sensitivity or resistance to drugs. Moreover, resistant tumor cells interact with themselves or the surrounding constituents to form an ecosystem for drug resistance. Collectively, SpaRx characterizes the spatial therapeutic variability, unveils the molecular mechanisms underpinning drug resistance and identifies personalized drug targets and effective drug combinations.

spaCI: deciphering spatial cellular communications through adaptive graph model.

Cell-cell communications are vital for biological signalling and play important roles in complex diseases. Recent advances in single-cell spatial transcriptomics (SCST) technologies allow examining the spatial cell communication landscapes and hold the promise for disentangling the complex ligand-receptor (L-R) interactions across cells. However, due to frequent dropout events and noisy signals in SCST data, it is challenging and lack of effective and tailored methods to accurately infer cellular communications. Herein, to decipher the cell-to-cell communications from SCST profiles, we propose a novel adaptive graph model with attention mechanisms named spaCI. spaCI incorporates both spatial locations and gene expression profiles of cells to identify the active L-R signalling axis across neighbouring cells. Through benchmarking with currently available methods, spaCI shows superior performance on both simulation data and real SCST datasets. Furthermore, spaCI is able to identify the upstream transcriptional factors mediating the active L-R interactions. For biological insights, we have applied spaCI to the seqFISH+ data of mouse cortex and the NanoString CosMx Spatial Molecular Imager (SMI) data of non-small cell lung cancer samples. spaCI reveals the hidden L-R interactions from the sparse seqFISH+ data, meanwhile identifies the inconspicuous L-R interactions including THBS1-ITGB1 between fibroblast and tumours in NanoString CosMx SMI data. spaCI further reveals that SMAD3 plays an important role in regulating the crosstalk between fibroblasts and tumours, which contributes to the prognosis of lung cancer patients. Collectively, spaCI addresses the challenges in interrogating SCST data for gaining insights into the underlying cellular communications, thus facilitates the discoveries of disease mechanisms, effective biomarkers and therapeutic targets.

DeepAlloDriver: a deep learning-based strategy to predict cancer driver mutations.

Driver mutations can contribute to the initial processes of cancer, and their identification is crucial for understanding tumorigenesis as well as for molecular drug discovery and development. Allostery regulates protein function away from the functional regions at an allosteric site. In addition to the known effects of mutations around functional sites, mutations at allosteric sites have been associated with protein structure, dynamics, and energy communication. As a result, identifying driver mutations at allosteric sites will be beneficial for deciphering the mechanisms of cancer and developing allosteric drugs. In this study, we provided a platform called DeepAlloDriver to predict driver mutations using a deep learning method that exhibited >93% accuracy and precision. Using this server, we found that a missense mutation in RRAS2 (Gln72 to Leu) might serve as an allosteric driver of tumorigenesis, revealing the mechanism of the mutation in knock-in mice and cancer patients. Overall, DeepAlloDriver would facilitate the elucidation of the mechanisms underlying cancer progression and help prioritize cancer therapeutic targets. The web server is freely available at: https://mdl.shsmu.edu.cn/DeepAlloDriver.

Integration of multiomics features for blood-based early detection of colorectal cancer.

BACKGROUND: Early detection of colorectal cancer (CRC) significantly enhances patient outcomes. Conventional CRC screening tools, like endoscopy and stool-based tests, have constraints due to their invasiveness or suboptimal patient adherence. Recently, liquid biopsy employing plasma cell-free DNA (cfDNA) has emerged as a potential noninvasive screening technique for various malignancies. METHODS: In this research, we harnessed the Mutation Capsule Plus (MCP) technology to profile an array of genomic characteristics from cfDNA procured from a single blood draw. This profiling encompassed DNA methylation, the 5' end motif, copy number variation (CNV), and genetic mutations. An integrated model built upon selected multiomics biomarkers was trained using a cohort of 93 CRC patients and 96 healthy controls. RESULTS: This model was subsequently validated in another cohort comprising 89 CRC patients and 95 healthy controls. Remarkably, the model achieved an area under the curve (AUC) of 0.981 (95% confidence interval (CI), 0.965-0.998) in the validation set, boasting a sensitivity of 92.1% (95% CI, 84.5%-96.8%) and a specificity of 94.7% (95% CI, 88.1%-98.3%). These numbers surpassed the performance of any single genomic feature. Importantly, the sensitivities reached 80% for stage I, 89.2% for stage II, and were 100% for stages III and IV. CONCLUSION: Our findings underscore the clinical potential of our multiomics liquid biopsy test, indicating its prospective role as a noninvasive method for early-stage CRC detection. This multiomics approach holds promise for further refinement and broader clinical application.

A noninvasive multianalytical approach establishment for risk assessment and gastric cancer screening.

Effective screening and early detection are critical to improve the prognosis of gastric cancer (GC). Our study aims to explore noninvasive multianalytical biomarkers and construct integrative models for preliminary risk assessment and GC detection. Whole genomewide methylation marker discovery was conducted with CpG tandems target amplification (CTTA) in cfDNA from large asymptomatic screening participants in a high-risk area of GC. The methylation and mutation candidates were validated simultaneously using one plasma from patients at various gastric lesion stages by multiplex profiling with Mutation Capsule Plus (MCP). Helicobacter pylori specific antibodies were detected with a recomLine assay. Integrated models were constructed and validated by the combination of multianalytical biomarkers. A total of 146 and 120 novel methylation markers were found in CpG islands and promoter regions across the genome with CTTA. The methylation markers together with the candidate mutations were validated with MCP and used to establish a 133-methylation-marker panel for risk assessment of suspicious precancerous lesions and GC cases and a 49-methylation-marker panel as well as a 144-amplicon-mutation panel for GC detection. An integrated model comprising both methylation and specific antibody panels performed better for risk assessment than a traditional model (AUC, 0.83 and 0.63, P < .001). A second model for GC detection integrating methylation and mutation panels also outperformed the traditional model (AUC, 0.82 and 0.68, P = .005). Our study established methylation, mutation and H. pylori-specific antibody panels and constructed two integrated models for risk assessment and GC screening. Our findings provide new insights for a more precise GC screening strategy in the future.

Modifying Polyvinylidene Chloride Resin with Fluorine Monomer and Cross-Linking Monomers.

Modified polyvinylidene chloride (PVDC) resin was prepared using octafluoropentyl methacrylate and trimethylolpropane trimethacrylate as modifying monomers through seeded emulsion polymerization. The successful incorporation of octafluoropentyl methacrylate into the PVDC resin was confirmed by Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS) analyses. Scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and XPS were utilized to investigate the element distribution in the modified monomer emulsion and the mechanism of monomer modification. The results demonstrated that the fluorine monomer was reacted in the resin, and mainly concentrated on the surface of the resin. The addition of octafluoropentyl methacrylate and trimethylolpropane trimethacrylate improved the water resistance of the resin. Compared to unmodified PVDC resin, the contact angle of the modified PVDC resin increased from 89.46° to 109.51°, and the water resistance at room temperature increased from 120 to 500 h. Furthermore, the modified resin exhibited excellent mechanical properties, thermal stability, and storage stability.

Proapoptotic effect of WS-299 induced by NOXA accumulation and NRF2-counterbalanced oxidative stress damage through targeting RBX1-UBE2M interaction in gastric cancers.

The abnormal activation of Cullin RING E3 Ligases (CRLs) is closely associated with the occurrence and development of various cancers. Targeting the neddylation pathway represents an effective approach for cancer treatment. In this work, we reported that WS-299, structurally featuring a coumarin moiety attached to the triazolopyrimidine, exhibited excellent anti-proliferative activity in MGC-803 and HGC-27 cells. WS-299 exerted potent anticancer effects by inhibiting clone formation, EdU incorporation and inducing cell cycle arrest. WS-299 inhibited CUL3/5 neddylation and caused an obvious accumulation of Nrf2 and NOXA, substrates of CRL3 and CRL5, respectively. Biochemical studies showed that WS-299 inhibited CUL3 neddylation by inhibiting RBX1-UBE2M interaction. The anti-proliferative effect of WS-299 was mainly induced by NOXA-mediated apoptosis. Of note, Nrf2 attenuated WS-299-induced reactive oxygen species (ROS) levels. Furthermore, Nrf2 accumulation also had an antagonistic effect on NOXA-induced apoptosis. Therefore, WS-299 and siNrf2 synergistically increased ROS levels, apoptotic cells and suppressed tumor growth in vivo. Taken together, our research clarified the anti-cancer mechanisms of WS-299 through targeting the RBX1-UBE2M protein-protein interaction and inhibiting the neddylation modification of CUL3 and CUL5. More importantly, our studies also demonstrated that combination of WS-299 with shNrf2 could be an effective strategy for treating gastric cancers.

Targeting DNA polymerase to DNA double-strand breaks reduces DNA deletion size and increases templated insertions generated by CRISPR/Cas9.

Most insertions or deletions generated by CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9) endonucleases are short (<25 bp), but unpredictable on-target long DNA deletions (>500 bp) can be observed. The possibility of generating long on-target DNA deletions poses safety risks to somatic genome editing and makes the outcomes of genome editing less predictable. Methods for generating refined mutations are desirable but currently unavailable. Here, we show that fusing Escherichia coli DNA polymerase I or the Klenow fragment to Cas9 greatly increases the frequencies of 1-bp deletions and decreases >1-bp deletions or insertions. Importantly, doing so also greatly decreases the generation of long deletions, including those >2 kb. In addition, templated insertions (the insertion of the nucleotide 4 nt upstream of the protospacer adjacent motif) were increased relative to other insertions. Counteracting DNA resection was one of the mechanisms perturbing deletion sizes. Targeting DNA polymerase to double-strand breaks did not increase off-targets or base substitution rates around the cleavage sites, yet increased editing efficiency in primary cells. Our strategy makes it possible to generate refined DNA mutations for improved safety without sacrificing efficiency of genome editing.

Structure-based insights into fluorogenic RNA aptamers.

Fluorogenic RNA aptamers are in vitro-selected RNA molecules capable of binding to specific fluorophores, significantly increasing their intrinsic fluorescence. Over the past decade, the color palette of fluorescent RNA aptamers has greatly expanded. The emergence and development of these fluorogenic RNA aptamers has introduced a powerful approach for visualizing RNA localization and transport with high spatiotemporal resolution in live cells. To date, a variety of tertiary structures of fluorogenic RNA aptamers have been determined using X-ray crystallography or NMR spectroscopy. Many of these fluorogenic RNA aptamers feature base quadruples or base triples in their fluorophore-binding sites. This review summarizes the structure-based investigations of fluorogenic RNA aptamers, with a focus on their overall folds, ligand-binding pockets and fluorescence activation mechanisms. Additionally, the exploration of how structures guide rational optimization to enhance RNA visualization techniques is discussed.

The MarR family regulator RmaH mediates acid tolerance of Lactococcus lactis through regulating peptidoglycan modification genes.

Lactococcus lactis, widely used in the food fermentation industry, has developed various ways to regulate acid adaptation in the process of evolution. The investigation into how peptidoglycan (PG) senses and responds to acid stress is an expanding field. Here, we addressed the regulation of murT-gatD genes which are responsible for the amidation of PG D-Glu. We found that lactic acid stress reduced murT-gatD expression, and overexpressing these genes notably decreased acid tolerance of L. lactis NZ9000, possibly due to a reduction in PG's negative charge, facilitating the influx of extracellular protons into the cell. Subsequently, using a combination of DNA pull-down assay and electrophoretic mobility shift assay (EMSA), we identified a novel MarR family regulator, RmaH, as an activator of murT-gatD transcription. Further MEME motif prediction, EMSA verification and fluorescent protein reporter assay showed that RmaH directly bound to the DNA motif 5'-KGVAWWTTTTGCT-3' located in the upstream region of murT-gatD. Beyond the mechanistic investigation of RmaH activation of murT-gatD, this study provides new insight into how peptidoglycan modification is regulated and responds to lactic acid stress.

AcrR1, a novel TetR/AcrR family repressor, mediates acid and antibiotic resistance and nisin biosynthesis in Lactococcus lactis F44.

Lactococcus lactis, widely used in the manufacture of dairy products, encounters various environmental stresses both in natural habitats and during industrial processes. It has evolved intricate machinery of stress sensing and defense to survive harsh stress conditions. Here, we identified a novel TetR/AcrR family transcription regulator, designated AcrR1, to be a repressor for acid and antibiotic tolerance that was derepressed in the presence of vancomycin or under acid stress. The survival rates of acrR1 deletion strain ΔAcrR1 under acid and vancomycin stresses were about 28.7-fold (pH 3.0, HCl), 8.57-fold (pH 4.0, lactic acid) and 2.73-fold (300 ng/mL vancomycin) greater than that of original strain F44. We also demonstrated that ΔAcrR1 was better able to maintain intracellular pH homeostasis and had a lower affinity to vancomycin. No evident effects of AcrR1 deletion on the growth and morphology of strain F44 were observed. Subsequently, we characterized that the transcription level of genes associated with amino acids biosynthesis, carbohydrate transport and metabolism, multidrug resistance, and DNA repair proteins significantly upregulated in ΔAcrR1 using transcriptome analysis and quantitative reverse transcription-PCR assays. Additionally, AcrR1 could repress the transcription of the nisin post-translational modification gene, nisC, leading to a 16.3% increase in nisin yield after AcrR1 deletion. Our results not only refined the knowledge of the regulatory mechanism of TetR/AcrR family regulator in L. lactis, but presented a potential strategy to enhance industrial production of nisin.

Association between burnout and post-traumatic stress disorder among frontline nurse during COVID-19 pandemic: A moderated mediation analysis.

AIMS AND OBJECTIVES: The aim of this study was to investigate the relationship between burnout and post-traumatic stress disorder (PTSD) among frontline nurses who went to assist the epidemic situation in Wuhan, China, during the outbreak in 2020. The study also explored the mediating role of depression and the moderating role of age in the main relationship. BACKGROUND: The relationship between burnout and PTSD in nurse has rarely been investigated in the context of the COVID-19 pandemic. Understand the relationship between these variables can provide empirical evidence for developing interventions and protocols that improve the health of nurses in future public health emergencies. DESIGN: An online cross-sectional survey of targeted local 327 nurses who went to assist the COVID-19 epidemic situation in Wuhan during the initial outbreak. METHODS: This study was conducted in August 2020, the burnout scale, the PTSD scale and the depression scale were used to survey participants. The moderated mediation model was used to test research hypotheses. RESULTS: Burnout could affect the PTSD symptoms in nursing staffs and depression could mediate this relationship. Age moderated the relationship between burnout/depression and PTSD, and the effects was strong and significant among younger participants in the relationship between burnout and PTSD. CONCLUSIONS: Burnout was identified as a core risk factor of PTSD in nurses. Depression and age played significant roles in the relationship between burnout and PTSD. RELEVANCE TO CLINICAL PRACTICE: PTSD, as a symptom that manifests after experiencing a stressful event, should be a key concern among frontline healthcare professionals. This study suggests that PTSD in nurses can be further reduced by reducing burnout. Attention should also be paid to the PTSD status of nurses of different age groups. PATIENT OR PUBLIC CONTRIBUTION: Patients and the public were not involved in the design and implementation of this study. Frontline nurses completed an online questionnaire for this study.

Oxygen-Activated Boron Nitride for Selective Photocatalytic Coupling of Methanol to Ethylene Glycol.

The controllable photocatalytic C-C coupling of methanol to produce ethylene glycol (EG) is a highly desirable but challenging objective for replacing the current energy-intensive thermocatalytic process. Here, we develop a metal-free porous boron nitride catalyst that demonstrates exceptional selectivity in the photocatalytic production of EG from methanol under mild conditions. Comprehensive experiments and calculations are conducted to thoroughly investigate the reaction mechanism, revealing that the OB3 unit in the porous BN plays a critical role in the preferential activation of C-H bond in methanol to form ⋅CH2OH via a concerted proton-electron transfer mechanism. More prominent energy barriers are observed for the further dehydrogenation of the ⋅CH2OH intermediate on the OB3 unit, inhibiting the formation of some other by-products during the catalytic process. Additionally, a small downhill energy barrier for the coupling of ⋅CH2OH in the OB3 unit promotes the selective generation of EG. This study provides valuable insights into the underlying mechanisms and can serve as a guide for the design and optimization of photocatalysts for efficient and selective EG production under mild conditions.