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Abstract Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Studies have indicated that immune dysfunction plays a central role in the pathogenesis of sepsis. Dendritic cells (DCs) play a crucial role in the emergence of immune dysfunction in sepsis. The major manifestations of DCs in the septic state are abnormal functions and depletion in numbers, which are linked to higher mortality and vulnerability to secondary infections in sepsis. Apoptosis is the most widely studied pathway of number reduction in DCs. In the past few years, there has been a surge in studies focusing on regulated cell death (RCD). This emerging field encompasses various forms of cell death, such as necroptosis, pyroptosis, ferroptosis, and autophagy-dependent cell death (ADCD). Regulation of DC’s RCD can serve as a possible therapeutic focus for the treatment of sepsis. Throughout time, numerous tactics have been devised and effectively implemented to improve abnormal immune response during sepsis progression, including modifying the functions of DCs and inhibiting DC cell death. In this review, we provide an overview of the functional impairment and RCD of DCs in septic states. Also, we highlight recent advances in targeting DCs to regulate host immune response following septic challenge. Graphical Abstract

Abstract Mitochondria are the centers of energy and material metabolism, and they also serve as the storage and dispatch hubs of metal ions. Damage to mitochondrial structure and function can cause abnormal levels and distribution of metal ions, leading to cell dysfunction and even death. For a long time, mitochondrial quality control pathways such as mitochondrial dynamics and mitophagy have been considered to inhibit metal-induced cell death. However, with the discovery of new metal-dependent cell death including ferroptosis and cuproptosis, increasing evidence shows that there is a complex relationship between mitochondrial quality control and metal-dependent cell death. This article reviews the latest research results and mechanisms of crosstalk between mitochondrial quality control and metal-dependent cell death in recent years, as well as their involvement in neurodegenerative diseases, tumors and other diseases, in order to provide new ideas for the research and treatment of related diseases.

Abstract Background Liver ischemia/reperfusion (I/R) injury is usually caused by hepatic inflow occlusion during liver surgery, and is frequently observed during war wounds and trauma. Hepatocyte ferroptosis plays a critical role in liver I/R injury, however, it remains unclear whether this process is controlled or regulated by members of the DEAD/DExH-box helicase (DDX/DHX) family. Methods The expression of DDX/DHX family members during liver I/R injury was screened using transcriptome analysis. Hepatocyte-specific Dhx58 knockout mice were constructed, and a partial liver I/R operation was performed. Single-cell RNA sequencing (scRNA-seq) in the liver post I/R suggested enhanced ferroptosis by Dhx58 hep−/− . The mRNAs and proteins associated with DExH-box helicase 58 (DHX58) were screened using RNA immunoprecipitation-sequencing (RIP-seq) and IP-mass spectrometry (IP-MS). Results Excessive production of reactive oxygen species (ROS) decreased the expression of the IFN-stimulated gene Dhx58 in hepatocytes and promoted hepatic ferroptosis, while treatment using IFN-α increased DHX58 expression and prevented ferroptosis during liver I/R injury. Mechanistically, DHX58 with RNA-binding activity constitutively associates with the mRNA of glutathione peroxidase 4 (GPX4), a central ferroptosis suppressor, and recruits the m6A reader YT521-B homology domain containing 2 (YTHDC2) to promote the translation of Gpx4 mRNA in an m6A-dependent manner, thus enhancing GPX4 protein levels and preventing hepatic ferroptosis. Conclusions This study provides mechanistic evidence that IFN-α stimulates DHX58 to promote the translation of m6A-modified Gpx4 mRNA, suggesting the potential clinical application of IFN-α in the prevention of hepatic ferroptosis during liver I/R injury.

Abstract Background Circular RNAs (circRNAs) can regulate microRNA activity and are related to various diseases, such as cancer. Functional research on circRNAs is the focus of scientific research. Accurate identification of circRNAs is important for gaining insight into their functions. Although several circRNA prediction models have been developed, their prediction accuracy is still unsatisfactory. Therefore, providing a more accurate computational framework to predict circRNAs and analyse their looping characteristics is crucial for systematic annotation. Results We developed a novel framework, CircDC, for classifying circRNAs from other lncRNAs. CircDC uses four different feature encoding schemes and adopts a multilayer convolutional neural network and bidirectional long short-term memory network to learn high-order feature representation and make circRNA predictions. The results demonstrate that the proposed CircDC model is more accurate than existing models. In addition, an interpretable analysis of the features affecting the model is performed, and the computational framework is applied to the extended application of circRNA identification. Conclusions CircDC is suitable for the prediction of circRNA. The identification of circRNA helps to understand and delve into the related biological processes and functions. Feature importance analysis increases model interpretability and uncovers significant biological properties. The relevant code and data in this article can be accessed for free at https://github.com/nmt315320/CircDC.git .

Inferring small molecule-miRNA associations (MMAs) is crucial for revealing the intricacies of biological processes and disease mechanisms. Deep learning, renowned for its exceptional speed and accuracy, is extensively used for predicting MMAs. However, given their heavy reliance on data, inaccuracies during data collection can make these methods susceptible to noise interference. To address this challenge, we introduce the joint masking and self-supervised (JMSS)-MMA model. This model synergizes graph autoencoders with a probability distribution-based masking strategy, effectively countering the impact of noisy data and enabling precise predictions of unknown MMAs. Operating in a self-supervised manner, it deeply encodes the relationship data of small molecules and miRNA through the graph autoencoder, delving into its latent information. Our masking strategy has successfully reduced data noise, enhancing prediction accuracy. To our knowledge, this is the pioneering integration of a masking strategy with graph autoencoders for MMA prediction. Furthermore, the JMSS-MMA model incorporates a node-degree–based decoder, deepening the understanding of the network’s structure. Experiments on two mainstream datasets confirm the model’s efficiency and precision, and ablation studies further attest to its robustness. We firmly believe that this model will revolutionize drug development, personalized medicine, and biomedical research.

Abstract Background Sustained yet intractable immunosuppression is commonly observed in septic patients, resulting in aggravated clinical outcomes. However, due to the substantial heterogeneity within septic patients, precise indicators in deciphering clinical trajectories and immunological alterations for septic patients remain largely lacking. Methods We adopted cross-species, single-cell RNA sequencing (scRNA-seq) analysis based on two published datasets containing circulating immune cell profile of septic patients as well as immune cell atlas of murine model of sepsis. Flow cytometry, laser scanning confocal microscopy (LSCM) imaging and Western blotting were applied to identify the presence of S100A9+ monocytes at protein level. To interrogate the immunosuppressive function of this subset, splenic monocytes isolated from septic wild-type or S100a9 −/− mice were co-cultured with naïve CD4+ T cells, followed by proliferative assay. Pharmacological inhibition of S100A9 was implemented using Paquinimod via oral gavage. Results ScRNA-seq analysis of human sepsis revealed substantial heterogeneity in monocyte compartments following the onset of sepsis, for which distinct monocyte subsets were enriched in disparate subclusters of septic patients. We identified a unique monocyte subset characterized by high expression of S100A family genes and low expression of human leukocyte antigen DR (HLA-DR), which were prominently enriched in septic patients and might exert immunosuppressive function. By combining single-cell transcriptomics of murine model of sepsis with in vivo experiments, we uncovered a similar subtype of monocyte significantly associated with late sepsis and immunocompromised status of septic mice, corresponding to HLA-DR low S100A high monocytes in human sepsis. Moreover, we found that S100A9+ monocytes exhibited profound immunosuppressive function on CD4+ T cell immune response and blockade of S100A9 using Paquinimod could partially reverse sepsis-induced immunosuppression. Conclusions This study identifies HLA-DR low S100A high monocytes correlated with immunosuppressive state upon septic challenge, inhibition of which can markedly mitigate sepsis-induced immune depression, thereby providing a novel therapeutic strategy for the management of sepsis.

Both salt rejection and pressure-bearing properties of the conventional thin film composite (TFC) polyamide reverse osmosis (RO) membrane are easily weakened at high temperature. In order to improve the high temperature resistance, in this work, a polyamide TFC RO membrane with covalent organic frameworks (COFs) intermediate layer was prepared. Firstly, the COFs layer was decorated on polyether sulfone (PES) support membrane by a unidirectional diffusion method and further modified for shrinking the micropore via the chemical crosslinking reaction with 1,3-diamino-2-propanol (DAPL) or ethylenediamine (EDA), and then continued the conventional interfacial polymerization of m-phenylene diamine (MPD) and trimesoyl chloride (TMC) on the resultant COFs layer for preparing the RO membrane. Furthermore, the correlationship between the microstructure of COFs layer and the separation performance of modified RO membrane was systematically investigated. Due to the introduction of the COFTpPa-DAPL intermediate layer with more regular microstructure and specific hydrophilicity, the resultant TFC-COFTpPa-DAPL RO membrane exhibited improvement in water flux by 30 ​% (reached to 50.5 ​L ​m−2 ​h−1) and higher salt rejection (>99.5 ​%) as compared with the conventional polyamide RO membrane and other reported temperature resistance RO membranes. Meanwhile, this TFC-COFTpPa-DAPL membrane showed good long-term separation stability during the RO process for 160 ​h. Especially, its water flux increased to 98.8 ​L ​m−2 ​h−1 without weakening salt rejection (about 99.4 ​%) at 70 ​°C. This study provides an effective way to fabricate the high temperature resistance TFC polyamide RO membrane with good comprehensive separation performance based on COFs intermediate layer.

Abstract Emerged evidence has indicated that immunosuppression is involved in the occurrence and development of sepsis. To provide clinical practice recommendations on the immune function in sepsis, an expert consensus focusing on the monitoring and treatment of sepsis-induced immunosuppression was developed. Literature related to the immune monitoring and treatment of sepsis were retrieved from PubMed, Web of Science, and Chinese National Knowledge Infrastructure to design items and expert opinions were collected through an online questionnaire. Then, the Delphi method was used to form consensus opinions, and RAND appropriateness method was developed to provide consistency evaluation and recommendation levels for consensus opinions. This consensus achieved satisfactory results through two rounds of questionnaire survey, with 2 statements rated as perfect consistency, 13 as very good consistency, and 9 as good consistency. After summarizing the results, a total of 14 strong recommended opinions, 8 weak recommended opinions and 2 non-recommended opinions were produced. Finally, a face-to-face discussion of the consensus opinions was performed through an online meeting, and all judges unanimously agreed on the content of this consensus. In summary, this expert consensus provides a preliminary guidance for the monitoring and treatment of immunosuppression in patients with sepsis.

ObjectiveTo explore the feasibility of breeding cleaning degree mice with micro-barrier cages in conventional environmental facilities.MethodsClean-grade mice were housed and maintained in positive-pressure microbarrier cages in a general environment, during which third-party sampling of the microbial status of the animals in the microbarrier was performed on a quarterly frequency.ResultsFrom June 2020 to July 2022, no microorganisms that must be excluded by the national standard for cleaning degree mice have been detected in two consecutive years.ConclusionMicro-barrier cages can be used to raise and maintain cleaning grade mice in conventional environmental facilities.

Cancer treatments always face challenging problems, particularly drug resistance due to tumor cell heterogeneity. The existing datasets include the relationship between gene expression and drug sensitivities; however, the majority are based on tissue-level studies. Study drugs at the single-cell level are perspective to overcome minimal residual disease caused by subclonal resistant cancer cells retained after initial curative therapy. Fortunately, machine learning techniques can help us understand how different types of cells respond to different cancer drugs from the perspective of single-cell gene expression. Good modeling using single-cell data and drug response information will not only improve machine learning for cell–drug outcome prediction but also facilitate the discovery of drugs for specific cancer subgroups and specific cancer treatments. In this paper, we review machine learning and deep learning approaches in drug research. By analyzing the application of these methods on cancer cell lines and single-cell data and comparing the technical gap between single-cell sequencing data analysis and single-cell drug sensitivity analysis, we hope to explore the trends and potential of drug research at the single-cell data level and provide more inspiration for drug research at the single-cell level. We anticipate that this review will stimulate the innovative use of machine learning methods to address new challenges in precision medicine more broadly.

ObjectiveAs a common yet intractable complication of severe sepsis, acute respiratory distress syndrome (ARDS) is closely associated with poor clinical outcomes and elevated medical expenses. The aim of the current study is to generate a model combining transcriptional biomarkers and clinical parameters to alarm the development of ARDS in septic patients.MethodsGene expression profile (GSE66890) was downloaded from the Gene Expression Omnibus database and clinical data were extracted. Differentially expressed genes (DEGs) from whole blood leukocytes were identified between patients with sepsis alone and septic patients who develop ARDS. ARDS prediction model was constructed using backward stepwise regression and Akaike Information Criterion (AIC). Meanwhile, a nomogram based on this model was established, with subsequent internal validation.ResultsA total of 57 severe septic patients were enrolled in this study, and 28 (49.1%) developed ARDS. Based on the differential expression analysis, six DEGs (BPI, OLFM4, LCN2, CD24, MMP8 and MME) were screened. According to the outcome prediction model, six valuable risk factors (direct lung injury, shock, tumor, BPI, MME and MMP8) were incorporated into a nomogram, which was used to predict the onset of ARDS in septic patients. The calibration curves of the nomogram showed good consistency between the probabilities and observed values. The decision curve analysis also revealed the potential clinical usefulness of the nomogram. The area under the receiver operating characteristic (AUROC) for the prediction of ARDS occurrence in septic patients by the nomogram was 0.86 (95% CI = 0.767-0.952). A sensitivity analysis showed that the AUROC for the prediction of ARDS development in septic patients without direct lung injury was 0.967 (95% CI = 0.896-1.0).ConclusionsThe nomogram based on transcriptional biomarkers and clinical parameters showed a good performance for the prediction of ARDS occurrence in septic patients.

Glioma-associated macrophage/microglia (GAM) represents a key player in shaping a unique glioma ecosystem to facilitate tumor progression and therapeutic resistance. Numerous studies have been published concerning GAM, but no relevant bibliometric study has been performed yet. Our bibliometric study aimed to comprehensively summarize and analyze the global scientific output, research hotspots, and trendy topics of publications on GAM over time. Data on publications on GAM were collected using the Web of Science (WoS). The search date was 16 January 2022, and the publications were collected from 2002 to 2021. Totally, 1,224 articles and reviews were incorporated and analyzed in the current study. It showed that the annual publications concerning GAM kept increasing over the past 20 years. The United States had the largest number of publications and total citations. Holland, Kettenmann, and Gutmann were the top three authors in terms of citation frequency. Neuro-oncology represented the most influential journal in GAM studies, with the highest H-index, total citations, and publication numbers. The paper published by Hambardzumyan in 2016 had the highest local citations. Additionally, the analysis of keywords implied that “prognosis,” “tumor microenvironment,” and “immunotherapy” might become research hotspots. Furthermore, trendy topics in GAM studies suggested that “immune infiltration,” “immune microenvironment,” “bioinformatics,” “prognosis,” and “immunotherapy” deserved additional attention. In conclusion, this bibliometric study comprehensively analyzed the publication trend of GAM studies for the past 20 years, in which the research hotspots and trendy topics were also uncovered. This information offered scholars critical references for conducting in-depth studies on GAM in the future.