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Abstract Background A growing body of researches indicate that the disrupted expression of long non-coding RNA (lncRNA) is linked to a range of human disorders. Therefore, the effective prediction of lncRNA-disease association (LDA) can not only suggest solutions to diagnose a condition but also save significant time and labor costs. Method In this work, we proposed a novel LDA predicting algorithm based on graph convolutional network and transformer, named GCNFORMER. Firstly, we integrated the intraclass similarity and interclass connections between miRNAs, lncRNAs and diseases, and built a graph adjacency matrix. Secondly, to completely obtain the features between various nodes, we employed a graph convolutional network for feature extraction. Finally, to obtain the global dependencies between inputs and outputs, we used a transformer encoder with a multiheaded attention mechanism to forecast lncRNA-disease associations. Results The results of fivefold cross-validation experiment on the public dataset revealed that the AUC and AUPR of GCNFORMER achieved 0.9739 and 0.9812, respectively. We compared GCNFORMER with six advanced LDA prediction models, and the results indicated its superiority over the other six models. Furthermore, GCNFORMER's effectiveness in predicting potential LDAs is underscored by case studies on breast cancer, colon cancer and lung cancer. Conclusions The combination of graph convolutional network and transformer can effectively improve the performance of LDA prediction model and promote the in-depth development of this research filed.

Abstract Background Many biological studies have shown that lncRNAs regulate the expression of epigenetically related genes. The study of lncRNAs has helped to deepen our understanding of the pathogenesis of complex diseases at the molecular level. Due to the large number of lncRNAs and the complex and time-consuming nature of biological experiments, applying computer techniques to predict potential lncRNA-disease associations is very effective. To explore information between complex network structures, existing methods rely mainly on lncRNA and disease information. Metapaths have been applied to network models as an effective method for exploring information in heterogeneous graphs. However, existing methods are dominated by lncRNAs or disease nodes and tend to ignore the paths provided by intermediate nodes. Methods We propose a deep learning model based on hierarchical graphical attention networks to predict unknown lncRNA-disease associations using multiple types of metapaths to extract features. We have named this model the MMHGAN. First, the model constructs a lncRNA-disease–miRNA heterogeneous graph based on known associations and two homogeneous graphs of lncRNAs and diseases. Second, for homogeneous graphs, the features of neighboring nodes are aggregated using a multihead attention mechanism. Third, for the heterogeneous graph, metapaths of different intermediate nodes are selected to construct subgraphs, and the importance of different types of metapaths is calculated and aggregated to obtain the final embedded features. Finally, the features are reconstructed using a fully connected layer to obtain the prediction results. Results We used a fivefold cross-validation method and obtained an average AUC value of 96.07% and an average AUPR value of 93.23%. Additionally, ablation experiments demonstrated the role of homogeneous graphs and different intermediate node path weights. In addition, we studied lung cancer, esophageal carcinoma, and breast cancer. Among the 15 lncRNAs associated with these diseases, 15, 12, and 14 lncRNAs were validated by the lncRNA Disease Database and the Lnc2Cancer Database, respectively. Conclusion We compared the MMHGAN model with six existing models with better performance, and the case study demonstrated that the model was effective in predicting the correlation between potential lncRNAs and diseases.

Increasing evidence indicates that mutations and dysregulation of long non-coding RNA (lncRNA) play a crucial role in the pathogenesis and prognosis of complex human diseases. Computational methods for predicting the association between lncRNAs and diseases have gained increasing attention. However, these methods face two key challenges: obtaining reliable negative samples and incorporating lncRNA-disease association (LDA) information from multiple perspectives. This paper proposes a method called NDMLDA, which combines multi-view feature extraction, unsupervised negative sample denoising, and stacking ensemble classifier. Firstly, an unsupervised method (K-means) is used to design a negative sample denoising module to alleviate the imbalance of samples and the impact of potential noise in the negative samples on model performance. Secondly, graph attention networks are employed to extract multi-view features of both lncRNAs and diseases, thereby enhancing the learning of association information between them. Finally, lncRNA-disease association prediction is implemented through a stacking ensemble classifier. Existing research datasets are integrated to evaluate performance, and 5-fold cross-validation is conducted on this dataset. Experimental results demonstrate that NDMLDA achieves an AUC of 0.9907and an AUPR of 0.9927, with a 5-fold cross-validation variance of less than 0.1%. These results outperform the baseline methods. Additionally, case studies further illustrate the model’s potential in cancer diagnosis and precision medicine implementation.

Diabetic neuropathy is regarded as one of the most debilitating outcomes of diabetes. It can affect both the peripheral and central nervous systems, leading to pain, decreased motility, cognitive decline, and dementia. S-palmitoylation is a reversible posttranslational lipid modification, and its dysregulation has been implicated in metabolic syndrome, cancers, neurological disorders, and infections. However, the role of S-palmitoylation in diabetic neuropathy remains unclear. Here we demonstrate a potential association between activating protein palmitoylation and diabetic neuropathy. We compared the proteomic data of lumbar dorsal root ganglia (DRG) of diabetes mice and palmitoylome profiling data of the HUVEC cell line. The mapping results identified peroxiredoxin-6 (PRDX6) as a novel target in diabetic neuropathy, whose biological mechanism was associated with S-palmitoylation. Bioinformatic prediction revealed that PRDX6 had two palmitoylation sites, Cys47 and Cys91. Immunofluorescence results indicated PRDX6 translocating between the cytoplasm and cell membrane. Protein function analysis proposed that increased palmitoylation could competitively inhibit the formation of disulfide-bond between Cys47 and Cys91 and change the spatial topology of PRDX6 protein. Cl–HCO3- anion exchanger 3 (AE3) was one of the AE family members, which was proved to express in DRG. AE3 activity evoked Cl- influx in neurons which was generally associated with increased excitability and susceptibility to pain. We demonstrated that the S-palmitoylation status of Cys47 could affect the interaction between PRDX6 and the C-terminal domain of AE3, thereby regulating the activity of AE3 anion exchanger enzyme in the nervous system. The results highlight a central role for PRDX6 palmitoylation in protection against diabetic neuropathy.

More and more evidences have showed that the unnatural expression of long non-coding RNA (lncRNA) is relevant to varieties of human diseases. Therefore, accurate identification of disease-related lncRNAs can help to understand lncRNA expression at the molecular level and to explore more effective treatments for diseases. Plenty of lncRNA-disease association prediction models have been raised but it is still a challenge to recognize unknown lncRNA-disease associations. In this work, we have proposed a computational model for predicting lncRNA-disease associations based on geometric complement heterogeneous information and random forest. Firstly, geometric complement heterogeneous information was used to integrate lncRNA-miRNA interactions and miRNA-disease associations verified by experiments. Secondly, lncRNA and disease features consisted of their respective similarity coefficients were fused into input feature space. Thirdly, an autoencoder was adopted to project raw high-dimensional features into low-dimension space to learn representation for lncRNAs and diseases. Finally, the low-dimensional lncRNA and disease features were fused into input feature space to train a random forest classifier for lncRNA-disease association prediction. Under five-fold cross-validation, the AUC (area under the receiver operating characteristic curve) is 0.9897 and the AUPR (area under the precision-recall curve) is 0.7040, indicating that the performance of our model is better than several state-of-the-art lncRNA-disease association prediction models. In addition, case studies on colon and stomach cancer indicate that our model has a good ability to predict disease-related lncRNAs.

Abstract Background Accumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA-disease association (LDA) prediction models have been implemented by integrating multiple kinds of data resources. However, most of the existing models ignore the interference of noisy and redundancy information among these data resources. Results To improve the ability of LDA prediction models, we implemented a random forest and feature selection based LDA prediction model (RFLDA in short). First, the RFLDA integrates the experiment-supported miRNA-disease associations (MDAs) and LDAs, the disease semantic similarity (DSS), the lncRNA functional similarity (LFS) and the lncRNA-miRNA interactions (LMI) as input features. Then, the RFLDA chooses the most useful features to train prediction model by feature selection based on the random forest variable importance score that takes into account not only the effect of individual feature on prediction results but also the joint effects of multiple features on prediction results. Finally, a random forest regression model is trained to score potential lncRNA-disease associations. In terms of the area under the receiver operating characteristic curve (AUC) of 0.976 and the area under the precision-recall curve (AUPR) of 0.779 under 5-fold cross-validation, the performance of the RFLDA is better than several state-of-the-art LDA prediction models. Moreover, case studies on three cancers demonstrate that 43 of the 45 lncRNAs predicted by the RFLDA are validated by experimental data, and the other two predicted lncRNAs are supported by other LDA prediction models. Conclusions Cross-validation and case studies indicate that the RFLDA has excellent ability to identify potential disease-associated lncRNAs.

Metabolic syndrome is a cluster of the most dangerous heart attack risk factors (diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure), and has become a major global threat to human health. A number of studies have demonstrated that hundreds of non-coding RNAs, including miRNAs and lncRNAs, are involved in metabolic syndrome-related diseases such as obesity, type 2 diabetes mellitus, hypertension, etc. However, these research results are distributed in a large number of literature, which is not conducive to analysis and use. There is an urgent need to integrate these relationship data between metabolic syndrome and non-coding RNA into a specialized database. To address this need, we developed a metabolic syndrome-associated non-coding RNA database (ncRNA2MetS) to curate the associations between metabolic syndrome and non-coding RNA. Currently, ncRNA2MetS contains 1,068 associations between five metabolic syndrome traits and 627 non-coding RNAs (543 miRNAs and 84 lncRNAs) in four species. Each record in ncRNA2MetS database represents a pair of disease-miRNA (lncRNA) association consisting of non-coding RNA category, miRNA (lncRNA) name, name of metabolic syndrome trait, expressive patterns of non-coding RNA, method for validation, specie involved, a brief introduction to the association, the article referenced, etc. We also developed a user-friendly website so that users can easily access and download all data. In short, ncRNA2MetS is a complete and high-quality data resource for exploring the role of non-coding RNA in the pathogenesis of metabolic syndrome and seeking new treatment options. The website is freely available at http://www.biomed-bigdata.com:50020/index.html

Abstract Ribosome biogenesis plays a pivotal role in maintaining stem cell homeostasis, yet the precise regulatory mechanisms governing this process in mouse embryonic stem cells (mESCs) remain largely unknown. In this investigation, we ascertain that DEAD-box RNA helicase 10 (DDX10) is indispensable for upholding cellular homeostasis and the viability of mESCs. Positioned predominantly at the nucleolar dense fibrillar component (DFC) and granular component (GC), DDX10 predominantly binds to 45S ribosomal RNA (rRNA) and orchestrates ribosome biogenesis. Degradation of DDX10 prevents the release of U3 snoRNA from pre-rRNA, leading to perturbed pre-rRNA processing and compromised maturation of the 18S rRNA, thereby disrupting the biogenesis of the small ribosomal subunit. Moreover, DDX10 participates in the process of liquid-liquid phase separation (LLPS), which is necessary for efficient ribosome biogenesis. Notably, the NUP98-DDX10 fusion associated with acute myelocytic leukemia (AML) alters the cellular localization of DDX10 and results in loss of ability to regulate pre-rRNA processing. Collectively, this study reveals the critical role of DDX10 as a pivotal regulator of ribosome biogenesis in mESCs.

In this paper, we investigated the nonlinear vibration characteristics and time-delay feedback controllability of a fractional horizontal roll system, which is described by a fractional Duffing-van der Pol oscillator under an external harmonic excitation. We focused on the resonance of fractional roller systems and conducted corresponding vibration control. The amplitude-frequency equations of primary resonance and superharmonic resonance were obtained using the multiple scale method. The amplitude-frequency characteristic curves of the system with different parameters were presented, and the influence of system parameters on the curves was analyzed. In addition, the time-delay feedback controller was designed to control the parameter excitation vibration. The numerical simulation results have verified the effectiveness of the time-delay controller in eliminating the jumping and hysteresis phenomena of the rolling system. The comparisons of approximate analytical solution and numerical solution was fulfilled, and the result certifies the correctness and satisfactory precision of the approximately analytical solution. The analysis results provide certain theoretical guidance for the vibration reduction of the horizontal nonlinear roller system.

Abstract Estrogen excess in females has been linked to a diverse array of chronic and acute diseases. Emerging research shows that exposure to estrogen-like compounds such as bisphenol S leads to increases in 17β-estradiol levels, but the mechanism of action is unclear. The aim of this study was to reveal the underlying signaling pathway-mediated mechanisms, target site and target molecule of action of bisphenol S causing excessive estrogen synthesis. Human ovarian granulosa cells SVOG were exposed to bisphenol S at environmentally relevant concentrations (1 μg/L, 10 μg/L, and 100 μg/L) for 48 h. The results confirms that bisphenol S accumulates mainly on the cell membrane, binds to follicle stimulating hormone receptor (FSHR) located on the cell membrane, and subsequently activates the downstream cyclic adenosine monophosphate/protein kinase A (cAMP/PKA) signaling pathway, leading to enhanced conversion of testosterone to 17β-estradiol. This study deepens our knowledge of the mechanisms of environmental factors in pathogenesis of hyperestrogenism.

Abstract Background The Porcine Epidemic Diarrhea Virus (PEDV) has caused significant economic losses in the global swine industry. As a potential drug for treating diarrhea, the antiviral properties of attapulgite deserve further study. Methods In this study, various methods such as RT-qPCR, Western blot, viral titer assay, Cytopathic Effect, immunofluorescence analysis and transmission electron microscopy were used to detect the antiviral activity of attapulgite and to assess its inhibitory effect on PEDV. Results When exposed to the same amount of virus, there was a significant decrease in the expression of the S protein, resulting in a viral titer reduction from 10-5.613 TCID50/mL to 10-2.90 TCID50/mL, which represents a decrease of approximately 102.6 folds. Results of cytopathic effect and indirect immunofluorescence also indicate a notable decrease in viral infectivity after attapulgite treatment. Additionally, it was observed that modified materials after acidification had weaker antiviral efficacy compared to powdered samples that underwent ultrasonic disintegration, which showed the strongest antiviral effects. Conclusion As a result, Attapulgite powders can trap and adsorb viruses to inhibit PEDV in vitro, leading to loss of viral infectivity. This study provides new materials for the development of novel disinfectants and antiviral additives.

Abstract Background Infectious laryngotracheitis (ILT) is a highly infectious upper respiratory tract disease of chickens caused by infectious laryngotracheitis virus or Gallid herpesvirus 1 (GaHV-1). ILT is an important respiratory disease of chickens and annually causes significant economic losses in the chicken industry. Although numerous relevant studies have been published, the overall prevalence of ILT infection among chicken in mainland China is still unknown, and associated risk factors need to be evaluated to establish preventive measures. Results The present study reviewed the literature on the prevalence of ILT in chickens in China as of December 20, 2022, retrieved from six databases—CNKI, Wanfang, VIP, PubMed, Web of Science, and ScienceDirect—were used to retrieve relevant studies published between January 1, 1981 and December 20, 2022. The literature quality of studies was assessed, and 20 studies with a total of 108,587 samples were included in the meta-analysis. Results of the meta-analysis showed that the overall prevalence of ILT was 10% (95% confidence interval: 8 –12%) through the random-effects model, which showed high heterogeneity, I2 = 99.4%. Further subgroup analyses showed that the prevalence of ILT decreased over time; furthermore, the prevalence in Northwest China was slightly lower than that in North China and South China, and the prevalence estimated using the diagnostic technique AGP was higher than that reported using other diagnostic techniques. Conclusions ILT is prevalent to some extent in mainland China. Given that the ILT attenuated live vaccine has a certain level of virulence and the prevalence differences between regions, we recommend controlling breeding density, improving immunization programs and continuously monitoring viruses and to prevent ILT prevailing in mainland China.

Despite a surge of studies on the construction of researcher identities among English as a Foreign Language (EFL) teachers, insufficient attention has been paid to their ongoing identity development after establishing a researcher identity. Using narrative inquiry, this study investigated how an EFL academic transitioned from being a rising star in research to becoming a teaching-focused academic midcareer with emotional flux in situated socio-institutional contexts. Data were collected from semi-structured interviews, narrative frame, institutional documents, and the participant's academic profile. The data analysis revealed that while the participant started her research journey as a confident novice researcher, she faced negative emotions arising from encounters with potential bias in academia, institutional managerial practices, and diminished self-agency with waned research motivation. Such negative emotions gradually escalated, posing severe impediments to her researcher identity. Eventually, these impediments resulted in her research stagnation and subsequent transformation of her identity into that of a teaching-focused EFL academic midcareer. The findings provide a nuanced understanding of the complexities involved in the continuous development of EFL academics' researcher identities in the changing landscape of higher education. The study also provides implications for supporting EFL teachers in constructing and maintaining a robust researcher identity to facilitate their ongoing professional development.

In the process of printed matter quality inspection, the influence of lighting conditions leads to a large color difference between the captured image and the actual image. Therefore, this paper proposes an illumination correction model based on Remora Optimization Algorithm for Regularized Extreme Learning Machine (ROA-RELM). First, the algorithm extracts the statistical features of the printed matter image using the Grey-Edge framework. Next, the statistical features are used as inputs to the Regularized Extreme Learning Machine (RELM). Then, the remora algorithm (ROA) is used to optimize the weights w and bias b of the input layer of the RELM, which improves the prediction accuracy. At last, the image is corrected by color constancy algorithm based on the output. In order to verify the effectiveness of the algorithm proposed in this paper, the method of color difference detection is used in this paper to evaluate the effect of the illumination correction algorithm on the detection of the color quality of printed matter. The experimental results show that the algorithm mentioned in this paper can effectively improve the presentation of printed matter images, and the angle root-mean-square error is reduced by 11.04% compared with the control group’s illumination-corrected model.

ABSTRACTSilibinin is a natural flavonoid with anti-diabetic activity. Glucagon-like peptide−1 (GLP−1), an intestinal hormone mainly secreted from L cells, which regulates insulin production and sensitivity, appears to be a potential therapeutic strategy for T2DM. The current study aims to determine the protective effect of silibinin against palmitic acid (PA)-induced damage in GLUTag cells. The results revealed that PA triggered endoplasmic reticulum (ER) stress and apoptosis in GLUTag cells, while silibinin attenuated PA-induced lipotoxicity. Based on the estrogen-like effects of silibinin and the role of estrogen receptors in regulating glycolipid metabolism, the involvement of estrogen receptors in the protective effects of silibinin in GLUTag cells was further investigated. The results showed that estrogen receptor α- and β-specific inhibitors reversed the inhibitory impact of silibinin on ER stress. Our study demonstrated that silibinin protects GLUTag cells from PA-induced injury by decreasing ER stress under the regulation of estrogen receptors α and β.

In this paper, the concept of incorporating core–shell structured units as secondary phases to toughen Al2O3 ceramics is proposed. Al2O3 composite ceramics toughened by B4C@TiB2 core–shell units are successfully synthesized using a combination of molten salt methodology and spark plasma sintering. The synthesis of B4C@TiB2 core–shell toughening units stems from the prior production of core–shell structural B4C@TiB2 powders, and this core–shell structure is effectively preserved within the Al2O3 matrix after sintering. The B4C@TiB2 core–shell toughening unit consists of a micron-sized B4C core enclosed by a shell approximately 500 nm in thickness, composed of numerous nanosized TiB2 grains. The regions surrounding these core–shell units exhibit distinct geometric structures and encompass multidimensional variations in phase composition, grain dimensions, and thermal expansion coefficients. Consequently, intricate stress distributions emerge, fostering the propagation of cracks in multiple dimensions. This behavior consumes a considerable amount of crack propagation energy, thereby enhancing the fracture toughness of the Al2O3 matrix. The resulting Al2O3 composite ceramics display relative density of 99.7%±0.2%, Vickers hardness of 21.5±0.8 GPa, and fracture toughness 6.92±0.22 MPa·m1/2.

Gestational diabetes mellitus (GDM) is a condition of diabetes with onset or first recognition in pregnancy. Its incidence is increasing, and GDM deleteriously affects both mother and the fetus during and even after pregnancy. Prev ious studies in mice have shown that during pregnancy, ß-cell proliferation increases in the middle and late stages of pregnancy and returns to normal levels after delivery. Hormones, such as prolactin, estradiol, and progesterone as well as protein kinas es, play important roles in regulating gestation-mediated ß-cell proliferation; however, the regulatory relationship between them is uncertain. We previously found that protein kin ase Pbk was crucial for basal proliferation of mouse islet cells. Herein we show that P bk is upregulated during pregnancy in mice and Pbk kinase activity is required for enhan ced ß-cell proliferation during pregnancy. Notably, knock-in (KI) of a kinase-inactivati ng Pbk mutation leads to impaired glucose tolerance and reduction of ß-cell proliferation and islet mass in mice during pregnancy. Prolactin upregulates the expression of Pbk, but the upregulation is diminished by knockdown of the prolactin receptor and by the inhibitors of JAK and STAT5, which mediate prolactin receptor signaling, in ß-cells. Treatment of ß-cells with prolactin increases STAT5 binding to the Pbk locus, as well as the recruitment of RNA polymerase II, resulting in increased Pbk transcription. These results demonstrate that Pbk is upregulated during pregnancy, at least partly by prolact in-induced and STAT5-mediated enhancement of gene transcription, and Pbk is essentia l for pregnancyinduced ß-cell proliferation, increase in islet mass, and maintenance of normal blood glucose during pregnancy in preclinical models. These findings p rovide new insights into the interplay between hormones and protein kinases that ul timately prevent the development of GDM. [ABSTRACT FROM AUTHOR]

Infectious bronchitis (IB) is a highly contagious acute viral disease that leads to substantial economic losses in the poultry industry. Previous research conducted in our laboratory has indicated that Nsp2 may serve as a key virulence factor within the IBV genome, as evidenced by its pronounced divergence between the field strain and its attenuated counterpart. Understanding the interaction between Nsp2 and host proteins is crucial to elucidating the role of the Nsp2 protein in the pathogenesis and proliferation of IBV. Currently, much remains to be uncovered regarding the host proteins that interact with the IBV Nsp2 protein. In this study, 10 host proteins, including COX1, COX3, NFIA, ITGA1, ATP1B1, ATP1B3, ABCB1, ISCA1, DNAJA1, and IREB2, were screened to interact with IBV Nsp2 through yeast two-hybrid experiments and molecular docking simulations. Furthermore, the interaction of Nsp2 with ATP1B3, DNAJA1, and ISCA1 proteins was further validated through co-immunoprecipitation and confocal experiments. The GO, KEGG, and PPI databases revealed that the host proteins interacting with Nsp2 are primarily associated with ATPase activation, Fe-S cluster binding, ion homeostasis, and innate immune regulation. The examination of the expression levels of these Nsp2-interacting host proteins during IBV infection demonstrated the significant downregulation of COX3, COX1, ATP1B1, and ATP1B3, while NFIA, DNAJA1, and IREB2 showed significant upregulation. Moreover, our study identified that IBV enhances viral replication by upregulating DNAJA1 expression, although the underlying mechanism requires further investigation. These findings provide valuable insights into the potential role of the Nsp2 protein in the pathogenesis of IBV.

This study systematically investigated the effects of temperature changes on the degradation of antibiotics in soil, as well as the alterations in microbial community structure and aggregation, through a field warming experiment in a greenhouse. Compared to non-warming soil, the warming treatment significantly accelerated the degradation rate of tetracyclines during soil freezing and mitigated the impact of environmental fluctuations on soil microbial communities. The greenhouse environment promoted the growth and reproduction of a wide range of microbial taxa, but the abundance of Myxococcota was positively correlated with antibiotic concentrations in both treatments, suggesting a potential specific association with antibiotic degradation processes. Long-term warming in the greenhouse led to a shift in the assembly process of soil microbial communities, with a decrease in dispersal limitation and an increase in the drift process. Furthermore, co-occurrence network analysis revealed a more loosely structured microbial community in the greenhouse soil, along with the emergence of new characteristic taxa. Notably, more than 60% of the key taxa that connected the co-occurrence networks in both groups belonged to rare taxa, indicating that rare taxa play a crucial role in maintaining community structure and function.

Abstract Amidst progressive advancements in tissue engineering, there has been a significant enhancement in the efficacy of anti-inflammatory hydrogel dressings, addressing a myriad of clinical challenges on wound healing. A frequent complication during the initial stages of deep second-degree burn wound healing is the onset of an inflammatory storm, typically occurring without effective intervention. This event disrupts normal biological healing sequences, leading to undesirable regression. In response, we have customized a tunable, multidimensional anti-inflammatory hydrogel platform based on sulfated alginates (Algs), loaded with Prussian blue (PB) nanozymes. This platform competently eliminates surplus reactive oxygen species (ROS) present in the wound bed. Algs, functioning as a mimic of sulfated glycosaminoglycans (including heparin, heparan sulfate, and chondroitin sulfate) in the extracellular matrices (ECM), demonstrate a high affinity towards inflammatory chemokines such as interleukin-8 (IL-8) and monocyte chemotactic protein-1 (MCP-1). This affinity effectively impedes the infiltration of inflammatory cells into the wound. Concurrently, Algs markedly modulate the macrophage phenotype transition from M1 to M2. Ultimately, our potent anti-inflammatory hydrogels, which strategically target inflammatory chemokines, M1 macrophages, and ROS, successfully attenuate dysregulated hyperinflammation in wound sites. Precise immunomodulation administered to deep second-degree burn wounds in mice has demonstrated promotion of neovascular maturation, granulation tissue formation, collagen deposition, and wound closure. Our biomimetic hydrogels, therefore, represent a significant expansion in the repertoire of anti-inflammatory strategies available for clinical practice.

Summary: Dysregulated macrophage polarization from pro-inflammatory M1 to anti-inflammatory M2 phenotypes underlies impaired cutaneous wound healing. This study reveals Vγ4+ γδ T cells spatiotemporally calibrate macrophage trajectories during skin repair via sophisticated interferon-γ (IFN-γ) conditioning across multiple interconnected tissues. Locally within wound beds, infiltrating Vγ4+ γδ T cells directly potentiate M1 activation and suppress M2 polarization thereby prolonging local inflammation. In draining lymph nodes, infiltrated Vγ4+ γδ T cells expand populations of IFN-γ-competent lymphocytes which disseminate systemically and infiltrate into wound tissues, further enforcing M1 macrophages programming. Moreover, Vγ4+γδ T cells flushed into bone marrow stimulate increased IFN-γ production, which elevates the output of pro-inflammatory Ly6C+monocytes. Mobilization of these monocytes continually replenishes the M1 macrophage pool in wounds, preventing phenotypic conversion to M2 activation. Thus, multi-axis coordination of macrophage activation trajectories by trafficking Vγ4+ γδ T cells provides a sophisticated immunological mechanism regulating inflammation timing and resolution during skin repair.

Porcine deltacoronavirus (PDCoV) is a novel enteric coronavirus that can cause vomiting, watery diarrhea in pigs and the death of piglets. The open reading frame (ORF) 5 is one of the accessory genes in PDCoV genome and encodes an accessory protein NS6. To date, the function of NS6 is still unclear. In this study, the recombinant NS6 was successfully expressed in prokaryotic expression system and purified. To prepare monoclonal antibody (mAb), six-week-old female BALB/c mice were primed subcutaneously with purified NS6. A novel mouse mAb against NS6 was obtained and designated as 3D5. The isotype of 3D5 is IgG2b with kappa (κ) light chain. 3D5 can specifically recognizes the natural NS6 in swine testis (ST) cells infected with PDCoV and expressed NS6 in human embryonic kidney 293T (HEK 293T) cells transfected with mammalian vector. The minimal linear B cell epitope recognised by 3D5 on NS6 was 25VPELIDPLVK34 determined by peptide scanning and named EP-3D5. The sequence of EP-3D5 is completely conserved among PDCoV strains. Moreover, six to nine residues of EP-3D5 were identified to be conserved in non-PDCoV strains. These results provide valuable insights into the antigenic structure and function of NS6 in virus pathogenesis, and aid for the development of PDCoV epitope-associated diagnostics and vaccine design.

Vascular dementia (VD) is a prevalent cognitive disorder among the elderly. Its pathological mechanism encompasses neuronal damage, synaptic dysfunction, vascular abnormalities, neuroinflammation, and oxidative stress, among others. In recent years, extracellular vesicles (EVs) derived from mesenchymal stem cells (MSCs) have garnered significant attention as an emerging therapeutic strategy. Current research indicates that MSC-derived extracellular vesicles (MSC-EVs) play a pivotal role in both the diagnosis and treatment of VD. Thus, this article delves into the recent advancements of MSC-EVs in VD, discussing the mechanisms by which EVs influence the pathophysiological processes of VD. These mechanisms form the theoretical foundation for their neuroprotective effect in VD treatment. Additionally, the article highlights the potential applications of EVs in VD diagnosis. In conclusion, MSC-EVs present a promising innovative treatment strategy for VD. With rigorous research and ongoing innovation, this concept can transition into practical clinical treatment, providing more effective options for VD patients.

Abstract Suppressing persistent multidrug-resistant (MDR) bacterial infections and excessive inflammation is the key for treating chronic wounds. Therefore, developing a microenvironment-responsive material with good biodegradability, drug-loading, anti-infection, and anti-inflammatory properties is desired to boost the chronic wounds healing process; however, using ordinary assembly remains a defect. Herein, we propose a pH/enzyme dual-responsive polymyxin B (PMB) spatiotemporal-release hydrogel (GelMA/OSSA/PMB), namely, the amount of OSSA and PMB released from GelMA/OSSA/PMB was closely related the wound pH and the enzyme concentration changing. The GelMA/OSSA/PMB showed better biosafety than equivalent free PMB, owing to the controlled release of PMB, which helped kill planktonic bacteria and inhibit biofilm activity in vitro. In addition, the GelMA/OSSA/PMB exhibited excellent antibacterial and anti-inflammatory properties. A MDR Pseudomonas aeruginosa caused infection was effectively resolved by the GelMA/OSSA/PMB hydrogel in vivo, thereby significantly boosting wound closure during the inflammatory phase. Furthermore, GelMA/OSSA/PMB accelerated the sequential phases of wound repair.

The aggregate-to-binders ratio (A/Bs) is an important parameter for the design and preparation of repair mortars. In this paper, the influences of A/Bs on the physical and mechanical properties of red-mud-modified magnesium phosphate repair mortar (RMPM) were systematically investigated. By exploring the capillary absorption characteristics of RMPM, the effect mechanism of A/Bs on its water resistance and mechanical properties was further clarified. The results indicated that the fluidity of fresh RMPM reduced with an increase in A/Bs, and its setting time was first shortened and then prolonged. The compressive strength, flexural strength, interfacial bonding strength, and water resistance of RMPM increased and then decreased with the increasing A/Bs and reached the maximum when the A/Bs was 1.0. The capillary absorption of RMPM was a linear correlation with the square root of the immersion time, and whose slope, that is, the capillary absorption coefficient, and capillary porosity decreased and then increased with the increase in A/Bs. Capillary porosity had a linear relationship with the strength retention rate, which indicated that A/Bs produced a significant effect on the water resistance of RMPM by modifying its capillary pore characteristics. When the A/Bs was 1.0, RMPM had the lowest capillary absorption coefficient and capillary porosity, and thus possesses appropriate mechanical properties and water resistance.

Three-dimensional reconstruction from point clouds is an important research topic in computer vision and computer graphics. However, the discrete nature, sparsity, and noise of the original point cloud contribute to the results of 3D surface generation based on global features often appearing jagged and lacking details, making it difficult to describe shape details accurately. We address the challenge of generating smooth and detailed 3D surfaces from point clouds. We propose an adaptive octree partitioning method to divide the global shape into local regions of different scales. An iterative loop method based on GRU is then used to extract features from local voxels and learn local smoothness and global shape priors. Finally, a moving least-squares approach is employed to generate the 3D surface. Experiments demonstrate that our method outperforms existing methods on benchmark datasets (ShapeNet dataset, ABC dataset, and Famous dataset). Ablation studies confirm the effectiveness of the adaptive octree partitioning and GRU modules.

The QXL87 live attenuated vaccine strain for infectious bronchitis represents the first approved QX type (GI-19 lineage) vaccine in China. This strain was derived from the parental strain CK/CH/JS/2010/12 through continuous passage in SPF chicken embryos. To elucidate the molecular mechanism behind its attenuation, whole-genome sequencing was conducted on both the parental and attenuated strains. Analysis revealed 145 nucleotide mutations in the attenuated strain, leading to 48 amino acid mutations in various proteins, including Nsp2 (26), Nsp3 (14), Nsp4 (1), S (4), 3a (1), E (1), and N (1). Additionally, a frameshift mutation caused by a single base insertion in the ORFX resulted in a six-amino-acid extension. Subsequent comparison of post-translational modification sites, protein structure, and protein–protein binding sites between the parental and attenuated strains identified three potential virulence genes: Nsp2, Nsp3, and S. The amino acid mutations in these proteins not only altered their conformation but also affected the distribution of post-translational modification sites and protein–protein interaction sites. Furthermore, three potential functional mutation sites—P106S, A352T, and L472F, all located in the Nsp2 protein—were identified through PROVEAN, PolyPhen, and I-Mutant. Overall, our findings suggest that Nsp2, Nsp3, and S proteins may play a role in modulating IBV pathogenicity, with a particular focus on the significance of the Nsp2 protein. This study contributes to our understanding of the molecular mechanisms underlying IBV attenuation and holds promise for the development of safer live attenuated IBV vaccines using reverse genetic approaches.

Infectious coryza is an acute respiratory infection caused by Avibacterium paragallinarum, which is widely distributed throughout the world. However, there is no effective molecular typing scheme to obtain basic knowledge about the Av. paragallinarum population structure. This study aimed to develop a multilocus sequence typing (MLST) scheme for Av. paragallinarum that allows for the worldwide comparison of sequence data. For this purpose, the genetic variability of 59 Av. paragallinarum strains from different geographical origins and serovars was analyzed to identify correlations. The MLST scheme was developed using seven conserved housekeeping genes, which identified eight STs that clustered all of the strains into three evolutionary branches. The analytical evaluation of the clone group relationship between the STs revealed two clone complexes (CC1 and CC2) and three singletons (ST2, ST5, and ST6). Most of the isolates from China belonged to ST1 and ST3 in CC1. ST8 from Peru and ST7 from North America together formed CC2. Our results showed that the Av. paragallinarum strains isolated from China had a distant genetic relationship with CC2, indicating strong regional specificity. The MLST scheme established in this study can monitor the dynamics and genetic differences of Av. paragallinarum transmission.

Porokeratosis, a keratinizing disorder of unknown etiology, exhibits an autosomal dominant inheritance pattern or manifests as an isolated acquired dermatosis. This condition can occur at any site on the skin; however, scrotal lesions are extremely rare. Only 18 cases of scrotal lesions were identified through a comprehensive review of the relevant literature. Herein, we present a case of a 19-year-old patient with porokeratosis of the scrotum. Additionally, we provide a summary of the etiologies, clinical manifestations, and histopathology of scrotal porokeratosis, and present differential diagnoses by reviewing the related literature.

ABSTRACT Phosphorylation, a significant posttranslational modification, is implicated in nearly two-thirds of all proteins. The involvement of phosphorylation in various cellular responses is known to play a crucial role in the replication of pseudorabies virus (PRV). However, the phosphoproteomic landscape during PRV infection remains unknown. To address this, a tandem mass tag-based liquid chromatography-tandem mass spectrometry was performed. It was found that a total of 7,342 phosphosites were detected; among these, 1,197 phosphosites were significantly upregulated, and 1,120 phosphosites were significantly downregulated when the threshold was set at 1.5. Gene ontology analysis of these significantly changed proteins showed that they mainly participated in mRNA processing, RNA splicing, stress granule assembly, and other pathways. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that these proteins were associated with cell cycle, spliceosome, tight junction, mRNA surveillance, and other pathways. Additionally, an immunoblotting assay confirmed the phosphoproteomic results by detecting the protein level of several upregulated phosphorylated proteins, which included pBRAF, pRPS6KA1, and pSTMN1. Further experiments using siRNA showed that knockdown of BRAF, RPS6KA1, and STMN1 significantly inhibited the replication of PRV. In summary, this study provides insight into the phosphoproteomic landscape during PRV infection and contributes to a better understanding of the mechanisms underlying PRV replication. IMPORTANCE Pseudorabies virus (PRV) is a kind of alpha herpesvirus that infects a wide range of animals and even human beings. Therefore, it is important to explore the mechanisms behind PRV replication and pathogenesis. By conducting a tandem mass tag-based phosphoproteome, this study revealed the phosphorylated proteins and cellular response pathways involved in PRV infection. Findings from this study shed light on the relationship between the phosphorylated cellular proteins and PRV infection, as well as guiding the discovery of targets for the development of antiviral compounds against PRV.

The infectious bursal disease virus (IBDV) is a member of the viruses that can induce immunosuppression in chickens. In recent years, more and more IBDV-infected cases by the novel variant IBDV were reported in China, and it has been demonstrated that currently used vaccines could not provide complete protection against these new IBDV variants. However, a lack of comprehensive analysis of the genomic characteristics of the novel variant strain IBDV has hampered its vaccine development. In this study, a strain of IBDV, designated HB202201, was phylogenetically analyzed, and it was found that the hypervariable region (HVR) of VP2 belonged to the novel variant strain. Furthermore, the 5′- and 3′-ends of segments A and B were analyzed using the rapid amplification of cDNA end (RACE) method. After the full-length of segment A and segment B were determined, the phylogenetic analysis of the segment A and segment B showed that the isolated HB202201 belonged to A2dB1 genotype, which demonstrated the HB202201 belonged to the novel variant strain. In addition, the specific mutations in VP1-VP5 amino acids were analyzed, which showed that there were multiple typical mutations in novel variant IBDV proteins, including VP1 (G24, I141, V163, and E240), VP2 (K221, and I252), VP3 (Q167 and L196), and VP5 (R7, P44, R92, G104, and E147), whereas there was no typical mutation in VP4. This study provides insights into the genomic and antigenic characteristics of the novel variant IBDV, which will promote the development of novel vaccine against the novel variant IBDV.

ABSTRACTSince its discovery, QX-type avian infectious bronchitis virus (IBV) has rapidly spread worldwide and become the most prevalent dominant genotype in Asia and Europe. Currently, although the pathogenicity of QX-type IBV in the reproductive system of hens is widely and deeply understood, its pathogenicity in the reproductive system of roosters remains largely unknown. In this study, 30-week-old specific pathogen-free (SPF) roosters were used to investigate the pathogenicity of QX-type IBV in the reproductive system after infection. The results showed that QX-type IBV infection caused abnormal testicular morphology, moderate atrophy and obvious dilatation of seminiferous tubules, and produced intense inflammation and obvious pathological injuries in the ductus deferens of infected chickens. Immunohistochemistry results showed that QX-type IBV can replicate in spermatogenic cells at various stages and in the mucous layer of the ductus deferens. Further studies showed that QX-type IBV infection affects plasma levels of testosterone, luteinizing hormone, and follicle-stimulating hormone as well as causes changes in transcription levels of their receptors in the testis. Furthermore, the transcription levels of StAR, P450scc, 3βHSD and 17βHSD4 also changed during testosterone synthesis after QX-type IBV infection, indicating that the virus can directly affect steroidogenesis. Finally, we found that QX-type IBV infection leads to extensive germ cell apoptosis in the testis. Collectively, our results suggest that QX-type IBV replicates in the testis and ductus deferens, causing severe tissue damage and disruption of reproductive hormone secretion. These adverse events eventually lead to mass germ cell apoptosis in the testis, affecting the reproductive function of roosters.

Abstract Background Pasteurella multocida (P. multocida) infection can cause a series of diseases in different animals and cause huge economic losses to the breeding industry. P. multocida is considered to be one of the most significant pathogens in rabbits. In order to elucidate the pathogenic mechanism and innate immune response of P. multocida, an infection experiment was carried out in this study. Results Our results showed that the clinical symptoms of rabbits were severe dyspnoea and serous nasal fluid. During the course of the disease, the deaths peaked at 2 days post infection (dpi) and mortality rate was 60%. The pathological changes of the lung, trachea, and thymus were observed. In particular, consolidation and abscesses appeared in lung. Histopathologic changes in rabbits showed edema, hemorrhage, and neutrophil infiltration in the lung. P. multocida can rapidly replicate in a variety of tissues, and the colonization in most of the tested tissues reached the maximum at 2 dpi and then decreased at 3 dpi. The number of P. multocida in lung and thymus remained high level at 3 dpi. Toll-like receptors 2 and 4 signaling pathways were activated after P. multocida infection. The expression of Il1β, Il6, Il8, and Tnf-α was significantly increased. The expression of most proinflammatory cytokines peaked at 2 dpi and decreased at 3 dpi, and the expression trend of cytokines was consistent with the colonization of P. multocida in rabbit tissues. Conclusions The P. multocida can rapidly replicate in various tissues of rabbit and cause bacteremia after infection. TLRs signaling pathways were activated after P. multocida infection, significantly inducing the expression of proinflammatory cytokines, which is might the main cause of respiratory inflammation and septicemia.

The demand for air-to-ground communication has surged in recent years, underscoring the significance of unmanned aerial vehicles (UAVs) in enhancing mobile communication, particularly in emergency scenarios due to their deployment efficiency and flexibility. In situations such as emergency cases, UAVs can function as efficient temporary aerial base stations and enhance communication quality in instances where terrestrial base stations are incapacitated. Trajectory planning and resource allocation of UAVs continue to be vital techniques, while a relatively limited number of algorithms account for the dynamics of ground users. This paper focuses on emergency communication scenarios such as earthquakes, proposing an innovative path planning and resource allocation algorithm. The algorithm leverages a multi-stage subtask iteration approach, inspired by the block coordinate descent technique, to address the challenges presented in such critical environments. In this study, we establish an air-to-ground communication model, subsequently devising a strategy for user dynamics. This is followed by the introduction of a joint scheduling process for path and resource allocation, named ISATR (iterative scheduling algorithm of trajectory and resource). This process encompasses highly interdependent decision variables, such as location, bandwidth, and power resources. For mobile ground users, we employ the cellular automata (CA) method to forecast the evacuation trajectory. This algorithm successfully maintains data communication in the emergency-stricken area and enhances the communication quality through bandwidth division and power control which varies with time. The effectiveness of our algorithm is validated by evaluating the average throughput with different parameters in various simulation conditions and by using several heuristic methods as a contrast.