HiTE: a fast and accurate dynamic boundary adjustment approach for full-length transposable element detection and annotation.

Recent advancements in genome assembly have greatly improved the prospects for comprehensive annotation of Transposable Elements (TEs). However, existing methods for TE annotation using genome assemblies suffer from limited accuracy and robustness, requiring extensive manual editing. In addition, the currently available gold-standard TE databases are not comprehensive, even for extensively studied species, highlighting the critical need for an automated TE detection method to supplement existing repositories. In this study, we introduce HiTE, a fast and accurate dynamic boundary adjustment approach designed to detect full-length TEs. The experimental results demonstrate that HiTE outperforms RepeatModeler2, the state-of-the-art tool, across various species. Furthermore, HiTE has identified numerous novel transposons with well-defined structures containing protein-coding domains, some of which are directly inserted within crucial genes, leading to direct alterations in gene expression. A Nextflow version of HiTE is also available, with enhanced parallelism, reproducibility, and portability.

Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks.

MOTIVATION: Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited. RESULTS: Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of employing hypergraphs for representing annotated gene sets. Second, we validate the accuracy of DISHyper-predicted cancer genes using functional validation results and multiple independent functional genomics data. Third, our model predicts 44 novel cancer genes, and subsequent analysis shows their significant associations with multiple types of cancers. Overall, our study provides a new perspective for discovering cancer genes and reveals previously undiscovered cancer genes. AVAILABILITY AND IMPLEMENTATION: DISHyper is freely available for download at https://github.com/genemine/DISHyper.

DDAffinity: predicting the changes in binding affinity of multiple point mutations using protein 3D structure.

MOTIVATION: Mutations are the crucial driving force for biological evolution as they can disrupt protein stability and protein-protein interactions which have notable impacts on protein structure, function, and expression. However, existing computational methods for protein mutation effects prediction are generally limited to single point mutations with global dependencies, and do not systematically take into account the local and global synergistic epistasis inherent in multiple point mutations. RESULTS: To this end, we propose a novel spatial and sequential message passing neural network, named DDAffinity, to predict the changes in binding affinity caused by multiple point mutations based on protein 3D structures. Specifically, instead of being on the whole protein, we perform message passing on the k-nearest neighbor residue graphs to extract pocket features of the protein 3D structures. Furthermore, to learn global topological features, a two-step additive Gaussian noising strategy during training is applied to blur out local details of protein geometry. We evaluate DDAffinity on benchmark datasets and external validation datasets. Overall, the predictive performance of DDAffinity is significantly improved compared with state-of-the-art baselines on multiple point mutations, including end-to-end and pre-training based methods. The ablation studies indicate the reasonable design of all components of DDAffinity. In addition, applications in nonredundant blind testing, predicting mutation effects of SARS-CoV-2 RBD variants, and optimizing human antibody against SARS-CoV-2 illustrate the effectiveness of DDAffinity. AVAILABILITY AND IMPLEMENTATION: DDAffinity is available at https://github.com/ak422/DDAffinity.

RNA m6A detection using raw current signals and basecalling errors from Nanopore direct RNA sequencing reads.

MOTIVATION: Nanopore direct RNA sequencing (DRS) enables the detection of RNA N6-methyladenosine (m6A) without extra laboratory techniques. A number of supervised or comparative approaches have been developed to identify m6A from Nanopore DRS reads. However, existing methods typically utilize either statistical features of the current signals or basecalling-error features, ignoring the richer information of the raw signals of DRS reads. RESULTS: Here, we propose RedNano, a deep-learning method designed to detect m6A from Nanopore DRS reads by utilizing both raw signals and basecalling errors. RedNano processes the raw-signal feature and basecalling-error feature through residual networks. We validated the effectiveness of RedNano using synthesized, Arabidopsis, and human DRS data. The results demonstrate that RedNano surpasses existing methods by achieving higher area under the ROC curve (AUC) and area under the precision-recall curve (AUPRs) in all three datasets. Furthermore, RedNano performs better in cross-species validation, demonstrating its robustness. Additionally, when detecting m6A from an independent dataset of Populus trichocarpa, RedNano achieves the highest AUC and AUPR, which are 3.8%-9.9% and 5.5%-13.8% higher than other methods, respectively. AVAILABILITY AND IMPLEMENTATION: The source code of RedNano is freely available at https://github.com/Derryxu/RedNano.

RGCNPPIS: A Residual Graph Convolutional Network for Protein-Protein Interaction Site Prediction.

Accurate identification of protein-protein interaction (PPI) sites is crucial for understanding the mechanisms of biological processes, developing PPI networks, and detecting protein functions. Currently, most computational methods primarily concentrate on sequence context features and rarely consider the spatial neighborhood features. To address this limitation, we propose a novel residual graph convolutional network for structure-based PPI site prediction (RGCNPPIS). Specifically, we use a GCN module to extract the global structural features from all spatial neighborhoods, and utilize the GraphSage module to extract local structural features from local spatial neighborhoods. To the best of our knowledge, this is the first work utilizing local structural features for PPI site prediction. We also propose an enhanced residual graph connection to combine the initial node representation, local structural features, and the previous GCN layer's node representation, which enables information transfer between layers and alleviates the over-smoothing problem. Evaluation results demonstrate that RGCNPPIS outperforms state-of-the-art methods on three independent test sets. In addition, the results of ablation experiments and case studies confirm that RGCNPPIS is an effective tool for PPI site prediction.

High-throughput screening of 222 pesticides in road environments in a megacity of northern China: A new approach to urban population exposure.

A large number of pesticides have been widely manufactured and applied, and are released into the environment with negative impact on human health. Pesticides are largely used in densely populated urban environments, in green zones, along roads and on private properties. In order to characterize the potential exposure related health effects of pesticide and their occurrence in the urban environment, 222 pesticides were screened and quantified in 228 road dust and 156 green-belt soil samples in autumn and spring from Harbin, a megacity in China, using GC-MS/MS base quantitative trace analysis. The results showed that a total of 33 pesticides were detected in road dust and green-belt soil, with the total concentrations of 650 and 236 ng/g (dry weight = dw), respectively. The concentrations of pesticides in road dust were significantly higher than that in green-belt soil. Pesticides in the environment were influenced by the seasons, with the highest concentrations of insecticides in autumn and the highest levels of herbicides in spring. In road dust, the concentrations of highways in autumn and spring (with the mean values of 94.1 and 68.2 ng/g dw) were much lower than that of the other road classes (arterial roads, sub-arterial roads and branch ways). Whereas in the green-belt soil, there was no significant difference in the concentration of pesticides between the different road classes. A first risk assessment was conducted to evaluate the potential adverse health effects of the pesticides, the results showed that the highest hazard index (HI) for a single pesticide in dust and soil was 0.12, the hazard index for children was higher than that for adults, with an overall hazard index of less than 1. Our results indicated that pesticide levels do not have a significant health impact on people.

The G143S mutation in cytochrome b confers high resistance to pyraclostrobin in Fusarium pseudograminearum.

BACKGROUND: Wheat crown rot (WCR), primarily caused by Fusarium pseudograminearum has become more and more prevalent in winter wheat areas in China. However, limited fungicides have been registered for the control of WCR in China so far. Pyraclostrobin is a representative quinone outside inhibitor (QoI) with excellent activity against Fusarium spp. There is currently limited research on the resistance risk and resistance mechanism of F. pseudograminearum to pyraclostrobin. RESULTS: Here, we determined the activity of pyraclostrobin against F. pseudograminearum. The EC50 values ranged from 0.022 to 0.172 µg mL-1 with an average EC50 value of 0.071 ± 0.030 µg mL-1. Four highly pyraclostrobin-resistant mutants were obtained from two sensitive strains by ultraviolet (UV) mutagenesis in the laboratory. The mutants showed decreased mycelial growth rate and virulence as compared with the corresponding wild-type strains, indicating that pyraclostrobin resistance suffered a fitness penalty in F. pseudograminearum. It was found that the high resistance of four mutants was caused by the G143S mutation in Cytb. Molecular docking analysis also further confirms that the G143S mutation in Cytb decreased the binding affinity between pyraclostrobin and Cytb. CONCLUSION: The resistance risk of F. pseudograminearum to pyraclostrobin could be low to medium. Although a mutation at the G143S position of Cytb could potentially occur, this mutation decreases the fitness of the mutant, which may reduce its survival in the environment. Therefore, the negative consequences of a possible mutation are lower. This makes pyraclostrobin a good candidate for controlling crown rot in wheat. © 2024 Society of Chemical Industry.

Prediction of Protein-Protein Interactions Based on Integrating Deep Learning and Feature Fusion.

Understanding protein-protein interactions (PPIs) helps to identify protein functions and develop other important applications such as drug preparation and protein-disease relationship identification. Deep-learning-based approaches are being intensely researched for PPI determination to reduce the cost and time of previous testing methods. In this work, we integrate deep learning with feature fusion, harnessing the strengths of both approaches, handcrafted features, and protein sequence embedding. The accuracies of the proposed model using five-fold cross-validation on Yeast core and Human datasets are 96.34% and 99.30%, respectively. In the task of predicting interactions in important PPI networks, our model correctly predicted all interactions in one-core, Wnt-related, and cancer-specific networks. The experimental results on cross-species datasets, including Caenorhabditis elegans, Helicobacter pylori, Homo sapiens, Mus musculus, and Escherichia coli, also show that our feature fusion method helps increase the generalization capability of the PPI prediction model.

The synergy of recombinant NSP4 and VP4 from porcine rotavirus elicited a strong mucosal response.

Porcine rotavirus (PoRV) is one of the main pathogens causing diarrhea in piglets, and multiple genotypes coexist. However, an effective vaccine is currently lacking. Here, the potential adjuvant of nonstructural protein 4 (NSP4) and highly immunogenic structural protein VP4 prompted us to construct recombinant NSP486-175aa (NSP4*) and VP426-476aa (VP4*) proteins, combine them as immunogens to evaluate their efficacy. Results indicated that NSP4* enhanced systemic and local mucosal responses induced by VP4*. The VP4*-IgG, VP4*-IgA in feces and IgA-secreting cells in intestines induced by the co-immunization were significantly higher than those induced by VP4* alone. Co-immunization of NSP4* and VP4* also induced strong cellular immunity with significantly increased IFN-λ than the single VP4*. Summarily, the NSP4* as a synergistical antigen exerted limited effects on the PoRV NAbs elevation, but conferred strong VP4*-specific mucosal and cellular efficacy, which lays the foundation for the development of a more effective porcine rotavirus subunit vaccine.

A novel approach to alleviate acetaminophen-induced hepatotoxicity with hybrid balloon flower root-derived exosome-like nanoparticles (BDEs) with silymarin via inhibition of hepatocyte MAPK pathway and apoptosis.

INTRODUCTION: Balloon flower root-derived exosome-like nanoparticles (BDEs) have recently been proposed as physiologically active molecules with no cytotoxicity. However, the therapeutic effects of drug-induced hepatotoxicity of BDEs have not been elucidated. BDEs contain a large amount of platycodin D, which is widely known to be effective in regulating inflammation and ameliorating systemic toxicity. Thus, the main therapeutic activity of BDEs is attributed to inhibiting the inflammatory response and alleviating toxicity. In this study, we fabricated the hybrid BDEs fused with liposomes containing silymarin (SM) to enhance the synergistic effect on inhibition of acetaminophen-induced hepatotoxicity (APAP). OBJECTIVE: Considering the potential therapeutic effects of BDEs, and the potential to achieve synergistic effects to improve therapeutic outcomes, we constructed hybrid BDEs with a soy lecithin-based liposome loaded with SM. Since liposomes can provide higher thermal stability and have greater structural integrity, these might be more resistant to clearance and enzymatic degradation of drug molecules. METHODS: Hybrid BDEs with liposome-loaded SM (BDEs@lipo-SM) were fabricated by thin-film hydration and extrusion. BDEs@lipo-SM were characterized using dynamic light scattering and high-performance liquid chromatography. After confirmation of the physical properties of BDEs@lipo-SM, various therapeutic properties were evaluated. RESULTS: BDEs@lipo-SM were internalized by hepatocytes and immune cells and significantly decreased mRNA expression of apoptosis and inflammation-relevant cytokines by inhibiting the hepatocyte MAPK pathway. BDEs@lipo-SM significantly induced an increase in glutathione levels and inhibited APAP-induced hepatotoxicity. CONCLUSION: From this study, we know that BDEs are reliable and safe nanovesicles containing natural metabolites derived from balloon flower, and they can facilitate intercellular communication. BDEs are also easily modified to enhance drug loading capacity, targeting effects, and long-term accumulation in vivo. BDEs@lipo-SM have therapeutic benefits for acute liver injury and can alleviate cell death and toxicity. They can be efficiently delivered to the liver and effectively inhibit APAP-induced hepatotoxicity by inhibiting the MAPK signaling pathway and apoptosis, which accelerates liver recovery in the APAP-induced acute liver injury model. These findings highlight that BDEs represent an attractive delivery vehicle for drug delivery.

The safety, immunogenicity, and efficacy of heterologous boosting with a SARS-CoV-2 mRNA vaccine (SYS6006) in Chinese participants aged 18 years or more: a randomized, open-label, active-controlled phase 3 trial.

Continuous emergence of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), enhanced transmissibility, significant immune escape, and waning immunity call for booster vaccination. We evaluated the safety, immunogenicity, and efficacy of heterologous booster with a SARS-CoV-2 mRNA vaccine SYS6006 versus an active control vaccine in a randomized, open-label, active-controlled phase 3 trial in healthy adults aged 18 years or more who had received two or three doses of SARS-CoV-2 inactivated vaccine in China. The trial started in December 2022 and lasted for 6 months. The participants were randomized (overall ratio: 3:1) to receive one dose of SYS6006 (N = 2999) or an ancestral receptor binding region-based, alum-adjuvanted recombinant protein SARS-CoV-2 vaccine (N = 1000), including 520 participants in an immunogenicity subgroup. SYS6006 boosting showed good safety profiles with most AEs being grade 1 or 2, and induced robust wild-type and Omicron BA.5 neutralizing antibody response on Days 14 and 28, demonstrating immunogenicity superiority versus the control vaccine and meeting the primary objective. The relative vaccine efficacy against COVID-19 of any severity was 51.6% (95% CI, 35.5-63.7) for any variant, 66.8% (48.6-78.5) for BA.5, and 37.7% (2.4-60.3) for XBB, from Day 7 through Month 6. In the vaccinated and infected hybrid immune participants, the relative vaccine efficacy was 68.4% (31.1-85.5) against COVID-19 of any severity caused by a second infection. All COVID-19 cases were mild. SYS6006 heterologous boosting demonstrated good safety, superior immunogenicity and high efficacy against BA.5-associated COVID-19, and protected against XBB-associated COVID-19, particularly in the hybrid immune population.Trial registration: Chinese Clinical Trial Registry: ChiCTR2200066941.

High-sensitivity strain sensor based on an asymmetric tapered air microbubble Fabry-Pérot interferometer with an ultrathin wall.

A Fabry-Pérot interferometer (FPI) with an asymmetric tapered structure and air microbubble with an ultrathin wall is designed for high-sensitivity strain measurement. The sensor contains an air microbubble formed by two single-mode fibers (SMF) prepared by fusion splicer arc discharge, and a taper is applied to one side of the air microbubble with a wall thickness of 3.6 µm. In this unique asymmetric structure, the microbubble is more easily deformed under stress, and the strain sensitivity of the sensor is up to 15.89 pm/µÉ› as evidenced by experiments.The temperature sensitivity and cross-sensitivity of the sensor are 1.09 pm/°C and 0.069 µÉ›/°C in the temperature range of 25-200°C, respectively, thus reducing the measurement error arising from temperature variations. The sensor has notable virtues such as high strain sensitivity, low-temperature sensitivity, low-temperature cross-sensitivity, simple and safe process preparation, and low cost. Experiments confirm that the sensor has good stability and repeatability, and it has high commercial potential, especially strain measurements in complex environments.

Translational PET Imaging of Nectin-4 Expression in Multiple Different Cancers with 68Ga-N188.

Nectin cell adhesion molecule 4 (nectin-4) is a transmembrane protein overexpressed on a variety of cancers and plays an important role in oncogenic and metastatic processes. The nectin-4-targeted antibody-drug conjugate enfortumab vedotin has been approved for treating locally advanced or metastatic urothelial cancer, but the efficacy in other types of cancer remains to be explored. The aim of this study was to evaluate the feasibility of nectin-4-targeted PET imaging with 68Ga-N188 as a noninvasive method to quantify membranous nectin-4 expression in multiple tumor types-an approach that may provide insight for patient stratification and treatment selection. Methods: Sixty-two patients with 16 types of cancer underwent head-to-head 68Ga-N188 and 18F-FDG PET/CT imaging for initial staging or detection of recurrence and metastases. Correlation between lesion SUVmax and nectin-4 expression determined by immunohistochemistry staining was analyzed in 36 of 62 patients. Results: The SUVmax of 68Ga-N188 had a positive correlation with membranous nectin-4 expression in the various tumor types tested (r = 0.458; P = 0.005), whereas no association was observed between the SUVmax and cytoplasmic nectin-4 expression. The detection rates for patient-based analysis of 68Ga-N188 and 18F-FDG PET/CT examinations were comparable (95.00% [57/60] vs. 93.33% [56/60]). In patients with pancreatic cancer, 68Ga-N188 exhibited a potential advantage for detecting residual or locally recurrent tumors; this advantage may assist in clinical decision-making. Conclusion: The correlation between nectin-4-targeted 68Ga-N188 PET imaging and membranous nectin-4 expression indicates the potential of 68Ga-N188 as an effective tool for selecting patients who may benefit from enfortumab vedotin treatment. The PET imaging results provided evidence to explore nectin-4-targeted therapy in a variety of tumors. 68Ga-N188 may improve the restaging of pancreatic cancer but requires further evaluation in a powered, prospective setting.

[Fertility preservation through natural and artificial ovaries: a review].

Ovarian tissue cryopreservation (OTC) is currently the exclusive choice for preserving fertility in both young girls before reaching puberty and young women who require immediate chemotherapy. Ovarian tissue transplantation has proven to be effective in restoring hormonal cycles and fertility. However, in certain cancer cases, there is a potential risk of inadvertently reintroducing malignant cells when transplanting cryopreserved ovarian tissue. Therefore, the use of an artificial ovary as an innovative and complementary approach allows for the development of isolated follicles, facilitates oocyte maturation and ovulation, and can partially restore endocrine function. This paper presents a comprehensive overview of techniques used to preserve fertility in natural ovarian tissues, including slow freezing, vitrification and hydrogel encapsulation methods. Additionally, it reviews fertility preservation techniques for artificial ovarian tissues, such as strategies involving hydrogel-encapsulated follicle, scaffolding for constructing ovarian microtissues, and 3D printing engineering. Lastly, this article explores current challenges and difficulties encountered in preserving ovarian tissue fertility, while also anticipating future trends in development, making it a valuable reference for the implementation of ovarian tissue fertility preservation.

GSScore: a novel Graphormer-based shell-like scoring method for protein-ligand docking.

Protein-ligand interactions (PLIs) are essential for cellular activities and drug discovery. But due to the complexity and high cost of experimental methods, there is a great demand for computational approaches to recognize PLI patterns, such as protein-ligand docking. In recent years, more and more models based on machine learning have been developed to directly predict the root mean square deviation (RMSD) of a ligand docking pose with reference to its native binding pose. However, new scoring methods are pressingly needed in methodology for more accurate RMSD prediction. We present a new deep learning-based scoring method for RMSD prediction of protein-ligand docking poses based on a Graphormer method and Shell-like graph architecture, named GSScore. To recognize near-native conformations from a set of poses, GSScore takes atoms as nodes and then establishes the docking interface of protein-ligand into multiple bipartite graphs within different shell ranges. Benefiting from the Graphormer and Shell-like graph architecture, GSScore can effectively capture the subtle differences between energetically favorable near-native conformations and unfavorable non-native poses without extra information. GSScore was extensively evaluated on diverse test sets including a subset of PDBBind version 2019, CASF2016 as well as DUD-E, and obtained significant improvements over existing methods in terms of RMSE, $R$ (Pearson correlation coefficient), Spearman correlation coefficient and Docking power.

Real-World Experience among Elderly Metastatic Breast Cancer Patients Treated with CDK4/6 Inhibitor-Based Therapy.

The largest portion of breast cancer patients diagnosed after 70 years of age present with hormone receptor-positive (HR+) breast cancer subtypes. Cyclin-dependent kinase (CDK) 4/6 inhibitor treatment, in conjunction with endocrine therapy, has become standard-of-care for metastatic HR+ breast cancer. In total, 320 patients with metastatic breast cancer receiving CDK4/6 inhibitor combined with fulvestrant or an aromatase inhibitor were enrolled in an ongoing observational study or were included in an IRB-approved retrospective study. All patients receiving CDK4/6 inhibitor-based therapy that were ≥70 years of age (n = 111) displayed prolonged progression-free survival (27.6 months) as compared to patients <70 years of age (n = 209, 21.1 months, HR = 1.38, p < 0.05). Specifically, patients receiving a CDK4/6 inhibitor with an aromatase inhibitor who were ≥70 years of age (n = 79) displayed exceptionally prolonged progression-free survival (46.0 months) as compared to patients receiving the same treatment who were <70 years of age (n = 161, 21.8 months, HR = 1.71, p < 0.01). However, patients ≥70 years of age also experienced more frequent adverse responses to CDK4/6 inhibitor-based treatment leading to dose reduction, hold, or discontinuation than the younger cohort (69% and 53%, respectively). Treatment strategies that may decrease toxicity without affecting efficacy (such as dose titration) are worth further exploration.

DLP: Duplex Link Prediction Via Subspace Segmentation for Predicting drug-MiRNA Associations.

The arduous and costly journey of drug discovery is increasingly intersecting with computational approaches, which promise to accelerate the analysis of bioassays and biomedical literature. The critical role of microRNAs (miRNAs) in disease progression has been underscored in recent studies, elevating them as potential therapeutic targets. This emphasizes the need for the development of sophisticated computational models that can effectively identify promising drug targets, such as miRNAs. Herein, we present a novel method, termed Duplex Link Prediction (DLP), rooted in subspace segmentation, to pinpoint potential miRNA targets. Our approach initiates with the application of the Network Enhancement (NE) algorithm to refine the similarity metric between miRNAs. Thereafter, we construct two matrices by pre-loading the association matrix from both the drug and miRNA perspectives, employing the K Nearest Neighbors (KNN) technique. The DLSR algorithm is then applied to predict potential associations. The final predicted association scores are ascertained through the weighted mean of the two matrices. Our empirical findings suggest that the DLP algorithm outperforms current methodologies in the realm of identifying potential miRNA drug targets. Case study validations further reinforce the real-world applicability and effectiveness of our proposed method. The code of DLP is freely available at https://github.com/kaizheng-academic/DLP.

Calcitonin gene­related peptide alleviates hyperoxia­induced human alveolar cell injury via the CGRPR/TRPV1/Ca2+ axis.

Although exogenous calcitonin gene­related peptide (CGRP) protects against hyperoxia­induced lung injury (HILI), the underlying mechanisms remain unclear. The present study attempted to elucidate the molecular mechanism by which CGRP protects against hyperoxia­induced alveolar cell injury. Human alveolar A549 cells were treated with 95% hyperoxia to establish a hyperoxic cell injury model. ELISA was performed to detect the CGRP secretion. Immunofluorescence, quantitative (q)PCR, and western blotting were used to detect the expression and localization of CGRP receptor (CGRPR) and transient receptor potential vanilloid 1 (TRPV1). Cell counting kit­8 and flow cytometry were used to examine the proliferation and apoptosis of treated cells. Digital calcium imaging and patch clamp were used to analyze the changes in intracellular Ca2+ signaling and membrane currents induced by CGRP in A549 cells. The mRNA and protein expression levels of Cyclin D1, proliferating cell nuclear antigen (PCNA), Bcl­2 and Bax were detected by qPCR and western blotting. The expression levels of CGRPR and TRPV1 in A549 cells were significantly downregulated by hyperoxic treatment, but there was no significant difference in CGRP release between cells cultured under normal air and hyperoxic conditions. CGRP promoted cell proliferation and inhibited apoptosis in hyperoxia, but selective inhibitors of CGRPR and TRPV1 channels could effectively attenuate these effects; TRPV1 knockdown also attenuated this effect. CGRP induced Ca2+ entry via the TRPV1 channels and enhanced the membrane non­selective currents through TRPV1 channels. The CGRP­induced increase in intracellular Ca2+ was reduced by inhibiting the phospholipase C (PLC)/protein kinase C (PKC) pathway. Moreover, PLC and PKC inhibitors attenuated the effects of CGRP in promoting cell proliferation and inhibiting apoptosis. In conclusion, exogenous CGRP acted by inversely regulating the function of TRPV1 channels in alveolar cells. Importantly, CGRP protected alveolar cells from hyperoxia­induced injury via the CGRPR/TRPV1/Ca2+ axis, which may be a potential target for the prevention and treatment of the HILI.

Preparing a Mice Model of Severe Acute Pancreatitis via a Combination of Caerulein and Lipopolysaccharide Intraperitoneal Injection.

The treatment of severe acute pancreatitis (SAP), with high mortality rates, poses a significant clinical challenge. Investigating the pathological changes associated with SAP using animal models can aid in identifying potential therapeutic targets and exploring novel treatment approaches. Previous studies primarily induced pancreatic injury through retrograde bile duct injection of sodium taviaurocholate, but the impact of surgical damage on the quality of the animal model remains unclear. In this study, we employed various frequencies of intraperitoneal Caerulein injections combined with different doses of LPS to induce pancreatic injury in C57BL/6J mice and compared the extent of injury across five intraperitoneal injection protocols. Regarding inducing acute pancreatitis in mice, an intraperitoneal injection protocol is proposed that results in a mortality rate as high as 80% within 5 days. Specifically, mice received ten daily intraperitoneal injections of Caerulein (50 µg/kg), followed by an injection of LPS (15 mg/kg) one hour after the last Caerulein administration. By adjusting the frequency and dosage of injected medications, one can manipulate the severity of pancreatic injury effectively. This model exhibits strong controllability and has a short replication cycle, making it feasible for completion by a single researcher without requiring expensive equipment. It conveniently and accurately simulates key disease characteristics observed in human SAP while demonstrating a high degree of reproducibility.

Modulating CO2 Electrocatalytic Conversion to the Organics Pathway by the Catalytic Site Dimension.

Electrochemical reduction of carbon dioxide to organic chemicals provides a value-added route for mitigating greenhouse gas emissions. We report a family of carbon-supported Sn electrocatalysts with the tin size varying from single atom, ultrasmall clusters to nanocrystallites. High single-product Faradaic efficiency (FE) and low onset potential of CO2 conversion to acetate (FE = 90% @ -0.6 V), ethanol (FE = 92% @ -0.4 V), and formate (FE = 91% @ -0.6 V) were achieved over the catalysts of different active site dimensions. The CO2 conversion mechanism behind these highly selective, size-modulated p-block element catalysts was elucidated by structural characterization and computational modeling, together with kinetic isotope effect investigation.