Gum arabic coating alleviates chilling injury of cold-stored peach by regulating reactive oxygen species, phenolic, and sugar metabolism.

In this study, gum arabic (GA) coating was employed to mitigate chilling injury in peach fruit, and it was observed that 10% GA coating exhibited the most favorable effect. GA coating significantly inhibited the decline of AsA content and enhanced antioxidant enzyme activity in peach fruit, thereby enhancing reactive oxygen species (ROS) scavenging rate while reducing its accumulation. Simultaneously, GA coating inhibited the activity of oxidative degradation enzymes for phenolics and enhanced synthase activity, thus maintaining higher levels of total phenolics and flavonoids in fruits. Additionally, compared to the control fruit, GA-coated fruits demonstrated higher concentrations of sucrose and sorbitol, accompanied more robust activity of sucrose synthase and sucrose phosphate synthase, as well as reduced activity of acid invertase and neutral invertase. Our study demonstrates that GA coating can effectively enhance the cold resistance of peach fruit by regulating ROS, phenolics, and sugar metabolism, maintaining high levels of phenolics and sucrose while enhancing antioxidant activity.

Efficient signal sequence of mRNA vaccines enhances the antigen expression to expand the immune protection against viral infection.

The signal sequence played a crucial role in the efficacy of mRNA vaccines against virus pandemic by influencing antigen translation. However, limited research had been conducted to compare and analyze the specific mechanisms involved. In this study, a novel approach was introduced by substituting the signal sequence of the mRNA antigen to enhance its immune response. Computational simulations demonstrated that various signal peptides differed in their binding capacities with the signal recognition particle (SRP) 54 M subunit, which positively correlated with antigen translation efficiency. Our data revealed that the signal sequences of tPA and IL-6-modified receptor binding domain (RBD) mRNA vaccines sequentially led to higher antigen expression and elicited more robust humoral and cellular immune protection against the SARS-CoV-2 compared to the original signal sequence. By highlighting the importance of the signal sequence, this research provided a foundational and safe approach for ongoing modifications in signal sequence-antigen design, aiming to optimize the efficacy of mRNA vaccines.

Shenling Baizhu powder alleviates non-alcoholic fatty liver disease by modulating autophagy and energy metabolism in high-fat diet-induced rats.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) has emerged as a burgeoning health problem worldwide, but no specific drug has been approved for its treatment. Shenling Baizhu powder (SL) is extensively used to treat NAFLD in Chinese clinical practice. However, the therapeutic components and pharmacological mechanisms of SL against NAFLD have not been thoroughly investigated. PURPOSE: This study aimed to investigate the pharmacological impact and molecular mechanism of SL on NAFLD. METHODS: First, we established an animal model of NAFLD by high-fat diet (HFD) feeding, and evaluated the therapeutic efficacy of SL on NAFLD by physiological, biochemical, pathological, and body composition analysis. Next, the effect of SL on autophagic flow in NAFLD rats was evaluated by ultrastructure, immunofluorescence staining, and western blotting. Moreover, an integrated strategy of targeted energy metabolomics and network pharmacology was performed to characterize autophagy-related genes and explore the synergistic effects of SL active compounds. UPLC-MS/MS, molecular docking combined with in vivo and in vitro experiments were conducted to verify the key compounds and genes. Finally, a network was established among SL-herb-compound-genes-energy metabolites-NAFLD, which explains the complicated regulating mechanism of SL on NAFLD. RESULTS: We discovered that SL decreased hepatic lipid accumulation, hepatic steatosis, and insulin resistance, and improved systemic metabolic disorders and pathological abnormalities. Subsequently, an integrated strategy of targeted energy metabolomics and network pharmacology identified quercetin, ellagic acid, kaempferol, formononetin, stigmasterol, isorhamnetin and luteolin as key compounds; catalase (CAT), AKT serine/threonine kinase 1 (AKT), nitric oxide synthase 3 (eNOS), NAD(P)H quinone dehydrogenase 1 (NQO1), heme oxygenase 1 (HO-1) and hypoxia-inducible factor 1 subunit alpha (HIF-1α) were identified as key genes; while nicotinamide adenine dinucleotide phosphate (NADP) and succinate emerged as key energy metabolites. Mechanistically, we revealed that SL may exert its anti-NAFLD effect by inducing autophagy activation and forming a comprehensive regulatory network involving key compounds, key genes, and key energy metabolites, ultimately alleviating oxidative stress, endoplasmic reticulum stress, and mitochondrial dysfunction. CONCLUSION: Our study demonstrated the therapeutic effect of SL in NAFLD models, and establishes a basis for the development of potential products from SL plant materials for the treatment of NAFLD.

Unified Multi-modal Diagnostic Framework with Reconstruction Pre-training and Heterogeneity-combat Tuning.

Medical multi-modal pre-training has revealed promise in computer-aided diagnosis by leveraging large-scale unlabeled datasets. However, existing methods based on masked autoencoders mainly rely on data-level reconstruction tasks, but lack high-level semantic information. Furthermore, two significant heterogeneity challenges hinder the transfer of pre-trained knowledge to downstream tasks, i.e., the distribution heterogeneity between pre-training data and downstream data, and the modality heterogeneity within downstream data. To address these challenges, we propose a Unified Medical Multi-modal Diagnostic (UMD) framework with tailored pre-training and downstream tuning strategies. Specifically, to enhance the representation abilities of vision and language encoders, we propose the Multi-level Reconstruction Pre-training (MR-Pretrain) strategy, including a feature-level and data-level reconstruction, which guides models to capture the semantic information from masked inputs of different modalities. Moreover, to tackle two kinds of heterogeneities during the downstream tuning, we present the heterogeneity-combat downstream tuning strategy, which consists of a Task-oriented Distribution Calibration (TD-Calib) and a Gradient-guided Modality Coordination (GM-Coord). In particular, TD-Calib fine-tunes the pre-trained model regarding the distribution of downstream datasets, and GM-Coord adjusts the gradient weights according to the dynamic optimization status of different modalities. Extensive experiments on five public medical datasets demonstrate the effectiveness of our UMD framework, which remarkably outperforms existing approaches on three kinds of downstream tasks.

Diagnostic Accuracy of Computerized Bowel Sound Analysis with Non-Invasive Devices for Irritable Bowel Syndrome: A Systematic Review and Meta-Analysis.

Objective To assess the diagnostic accuracy of bowel sound analysis for irritable bowel syndrome (IBS) with a systematic review and meta-analysis. Methods We searched MEDLINE, EMBASE, the Cochrane Library, Web of Science, and IEEE Xplore databases until September 2023. Cross-sectional and case-control studies on diagnostic accuracy of bowel sound analysis for IBS were identified. We estimated the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio with a 95% confidence interval (CI), and plotted a summary receiver operating characteristic curve and evaluated the area under the curve. Results Four studies were included. The pooled diagnostic sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.94 (95% CI, 0.87－0.97), 0.89 (95% CI, 0.81－0.94), 8.43 (95% CI, 4.81－14.78), 0.07 (95% CI, 0.03－0.15), and 118.86 (95% CI, 44.18－319.75), respectively, with an area under the curve of 0.97 (95% CI, 0.95－0.98). Conclusions Computerized bowel sound analysis is a promising tool for IBS. However, limited high-quality data make the results' validity and applicability questionable. There is a need for more diagnostic test accuracy studies and better wearable devices for monitoring and analysis.

Dual-targeting tigecycline nanoparticles for treating intracranial infections caused by multidrug-resistant Acinetobacter baumannii.

Multidrug-resistant (MDR) Acinetobacter baumannii (A. baumannii) is a formidable pathogen responsible for severe intracranial infections post-craniotomy, exhibiting a mortality rate as high as 71%. Tigecycline (TGC), a broad-spectrum antibiotic, emerged as a potential therapeutic agent for MDR A. baumannii infections. Nonetheless, its clinical application was hindered by a short in vivo half-life and limited permeability through the blood-brain barrier (BBB). In this study, we prepared a novel core-shell nanoparticle encapsulating water-soluble tigecycline using a blend of mPEG-PLGA and PLGA materials. This nanoparticle, modified with a dual-targeting peptide Aß11 and Tween 80 (Aß11/T80@CSs), was specifically designed to enhance the delivery of tigecycline to the brain for treating A. baumannii-induced intracranial infections. Our findings demonstrated that Aß11/T80@CSs nanocarriers successfully traversed the BBB and effectively delivered TGC into the cerebrospinal fluid (CSF), leading to a significant therapeutic response in a model of MDR A. baumannii intracranial infection. This study offers initial evidence and a platform for the application of brain-targeted nanocarrier delivery systems, showcasing their potential in administering water-soluble anti-infection drugs for intracranial infection treatments, and suggesting promising avenues for clinical translation.

An improved hierarchical variational autoencoder for cell-cell communication estimation using single-cell RNA-seq data.

Analysis of cell-cell communication (CCC) in the tumor micro-environment helps decipher the underlying mechanism of cancer progression and drug tolerance. Currently, single-cell RNA-Seq data are available on a large scale, providing an unprecedented opportunity to predict cellular communications. There have been many achievements and applications in inferring cell-cell communication based on the known interactions between molecules, such as ligands, receptors and extracellular matrix. However, the prior information is not quite adequate and only involves a fraction of cellular communications, producing many false-positive or false-negative results. To this end, we propose an improved hierarchical variational autoencoder (HiVAE) based model to fully use single-cell RNA-seq data for automatically estimating CCC. Specifically, the HiVAE model is used to learn the potential representation of cells on known ligand-receptor genes and all genes in single-cell RNA-seq data, respectively, which are then utilized for cascade integration. Subsequently, transfer entropy is employed to measure the transmission of information flow between two cells based on the learned representations, which are regarded as directed communication relationships. Experiments are conducted on single-cell RNA-seq data of the human skin disease dataset and the melanoma dataset, respectively. Results show that the HiVAE model is effective in learning cell representations, and transfer entropy could be used to estimate the communication scores between cell types.

Self-paced graph memory for learner GPA prediction and it's application in learner multiple evaluation.

A scientific and rational evaluation of teaching is essential for personalized learning. In the current teaching assessment model that solely relies on Grade Point Average (GPA), learners with different learning abilities may be classified as the same type of student. It is challenging to uncover the underlying logic behind different learning patterns when GPA scores are the same. To address the limitations of pure GPA evaluation, we propose a data-driven assessment strategy as a supplement to the current methodology. Firstly, we integrate self-paced learning and graph memory neural networks to develop a learning performance prediction model called the self-paced graph memory network. Secondly, inspired by outliers in linear regression, we use a t-test approach to identify those student samples whose loss values significantly differ from normal samples, indicating that these students have different inherent learning patterns/logic compared to the majority. We find that these learners' GPA levels are distributed across different levels. Through analyzing the learning process data of learners with the same GPA level, we find that our data-driven strategy effectively addresses the shortcomings of the GPA evaluation model. Furthermore, we validate the rationality of our method for student data modeling through protein classification experiments and student performance prediction experiments, it ensuring the rationality and effectiveness of our method.

Federated Discriminative Representation Learning for Image Classification.

Acquiring big-size datasets to raise the performance of deep models has become one of the most critical problems in representation learning (RL) techniques, which is the core potential of the emerging paradigm of federated learning (FL). However, most current FL models concentrate on seeking an identical model for isolated clients and thus fail to make full use of the data specificity between clients. To enhance the classification performance of each client, this study introduces the FDRL, a federated discriminative RL model, by partitioning the data features of each client into a global subspace and a local subspace. More specifically, FDRL learns the global representation for federated communication between those isolated clients, which is to capture common features from all protected datasets via model sharing, and local representations for personalization in each client, which is to preserve specific features of clients via model differentiating. Toward this goal, FDRL in each client trains a shared submodel for federated communication and, meanwhile, a not-shared submodel for locality preservation, in which the two models partition client-feature space by maximizing their differences, followed by a linear model fed with combined features for image classification. The proposed model is implemented with neural networks and optimized in an iterative manner between the server of computing the global model and the clients of learning the local classifiers. Thanks to the powerful capability of local feature preservation, FDRL leads to more discriminative data representations than the compared FL models. Experimental results on public datasets demonstrate that our FDRL benefits from the subspace partition and achieves better performance on federated image classification than the state-of-the-art FL models.

The predictive value of the systemic inflammatory response index for the occurrence of multiple organ dysfunction syndrome in patients with wasp sting injury.

Wasp sting injuries pose a significant threat to public health, especially in areas with abundant trees. Mortality rates are alarmingly high, with reports indicating that up to 70% or more of cases result in multiple organ dysfunction syndrome (MODS). It is crucial for emergency and critical care physicians to promptly identify and implement effective measures to reduce the occurrence of MODS in patients who have been stung by wasps. Therefore, finding a reliable predictive indicator is of utmost importance. We conducted a retrospective study, and clinical data of 226 hospitalized patients with wasp sting from July 2013 to April 2023 in the First College of Clinical Medicine Science, China Three Gorges University were collected. The patients were divided into MODS group and non-MODS (NMODS) group, and the general data, clinical symptoms, laboratory indexes, and prognosis were compared between the two groups. The indicators with significant differences in univariate analysis were included in the multivariate Logistic regression analysis to analyze the independent risk factors for MODS. The value of systemic inflammatory response index (SIRI) in predicting the occurrence of MODS in wasp sting was analyzed by using the receiver operating characteristic curve (ROC curve). Of the 214 patients with wasp sting, 109 cases were in the NMODS group, and 105 cases were in the MODS group. The SIRI was 1.6 (0.7, 3.6) and 12.2 (5.2, 23.3) in the NMODS group and MODS group, respectively, with a significant difference between the two groups (P ＜ 0.001). SIRI was an independent risk factor for MODS in patients with wasp sting; the AUC of SIRI in predicting MODS in wasp sting was 0.886 (P ＜ 0.001), and the optimal cutoff value was 6.39, with a sensitivity of 71.43% and a specificity of 94.5%, which had prediction value. Allowing for early identification and enabling doctors to intervene and provide timely treatment. SIRI was defined as follows: SIRI = neutrophil count × monocyte count/lymphocyte count.

Propagation of noninteger cylindrical vector vortex beams in a gradient-index fiber.

The characteristics of two noninteger cylindrical vector vortex beams (NCVVBs) propagating through a radial gradient-index (GRIN) fiber are analyzed on the basis of the generalized Huygens-Fresnel principle. The NCVVBs exhibit periodic and stable transmission characteristics in the radial GRIN fiber. Polarization changes, the presence of spin angular momentum (SAM), and changes in the orbital angular momentum (OAM) of the NCVVBs are observed at the focal plane of the radial GRIN fiber. Spin-orbit interactions of NCVVBs are verified in the radial GRIN fiber for the first time, to the best of our knowledge.

Facial Micro-Expression Recognition Using Double-Stream 3D Convolutional Neural Network with Domain Adaptation.

Humans show micro-expressions (MEs) under some circumstances. MEs are a display of emotions that a human wants to conceal. The recognition of MEs has been applied in various fields. However, automatic ME recognition remains a challenging problem due to two major obstacles. As MEs are typically of short duration and low intensity, it is hard to extract discriminative features from ME videos. Moreover, it is tedious to collect ME data. Existing ME datasets usually contain insufficient video samples. In this paper, we propose a deep learning model, double-stream 3D convolutional neural network (DS-3DCNN), for recognizing MEs captured in video. The recognition framework contains two streams of 3D-CNN. The first extracts spatiotemporal features from the raw ME videos. The second extracts variations of the facial motions within the spatiotemporal domain. To facilitate feature extraction, the subtle motion embedded in a ME is amplified. To address the insufficient ME data, a macro-expression dataset is employed to expand the training sample size. Supervised domain adaptation is adopted in model training in order to bridge the difference between ME and macro-expression datasets. The DS-3DCNN model is evaluated on two publicly available ME datasets. The results show that the model outperforms various state-of-the-art models; in particular, the model outperformed the best model presented in MEGC2019 by more than 6%.

Landmark tracking in liver US images using cascade convolutional neural networks with long short-term memory.

Accurate tracking of anatomic landmarks is critical for motion management in liver radiation therapy. Ultrasound (US) is a safe, low-cost technology that is broadly available and offer real-time imaging capability. This study proposed a deep learning-based tracking method for the US image-guided radiation therapy. The proposed cascade deep learning model is composed of an attention network, a mask region-based convolutional neural network (mask R-CNN), and a long short-term memory (LSTM) network. The attention network learns a mapping from an US image to a suspected area of landmark motion in order to reduce the search region. The mask R-CNN then produces multiple region-of-interest proposals in the reduced region and identifies the proposed landmark via three network heads: bounding box regression, proposal classification, and landmark segmentation. The LSTM network models the temporal relationship among the successive image frames for bounding box regression and proposal classification. To consolidate the final proposal, a selection method is designed according to the similarities between sequential frames. The proposed method was tested on the liver US tracking datasets used in the medical image computing and computer assisted interventions 2015 challenges, where the landmarks were annotated by three experienced observers to obtain their mean positions. Five-fold cross validation on the 24 given US sequences with ground truths shows that the mean tracking error for all landmarks is 0.65 ± 0.56 mm, and the errors of all landmarks are within 2 mm. We further tested the proposed model on 69 landmarks from the testing dataset that have the similar image pattern with the training pattern, resulting in a mean tracking error of 0.94 ± 0.83 mm. The proposed deep-learning model was implemented on a graphics processing unit (GPU), tracking 47-81 frames s-1. Our experimental results have demonstrated the feasibility and accuracy of our proposed method in tracking liver anatomic landmarks using US images, providing a potential solution for real-time liver tracking for active motion management during radiation therapy.

A novel heterologous receptor-binding domain dodecamer universal mRNA vaccine against SARS-CoV-2 variants.

There are currently approximately 4,000 mutations in the SARS-CoV-2 S protein gene and emerging SARS-CoV-2 variants continue to spread rapidly worldwide. Universal vaccines with high efficacy and safety urgently need to be developed to prevent SARS-CoV-2 variants pandemic. Here, we described a novel self-assembling universal mRNA vaccine containing a heterologous receptor-binding domain (HRBD)-based dodecamer (HRBDdodecamer) against SARS-CoV-2 variants, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (B.1.1.28.1), Delta (B.1.617.2) and Omicron (B.1.1.529). HRBD containing four heterologous RBD (Delta, Beta, Gamma, and Wild-type) can form a stable dodecameric conformation under T4 trimerization tag (Flodon, FD). The HRBDdodecamer -encoding mRNA was then encapsulated into the newly-constructed LNPs consisting of a novel ionizable lipid (4N4T). The obtained universal mRNA vaccine (4N4T-HRBDdodecamer) presented higher efficiency in mRNA transfection and expression than the approved ALC-0315 LNPs, initiating potent immune protection against the immune escape of SARS-CoV-2 caused by evolutionary mutation. These findings demonstrated the first evidence that structure-based antigen design and mRNA delivery carrier optimization may facilitate the development of effective universal mRNA vaccines to tackle SARS-CoV-2 variants pandemic.

Amelioration of Chilling Injury by Fucoidan in Cold-Stored Cucumber via Membrane Lipid Metabolism Regulation.

Cucumber fruit is very sensitive to chilling injury, which rapidly depreciates their commodity value. Herein, the effect of fucoidan treatment on cucumber under cold stress were investigated. Fucoidan treatment of cold-stored cucumber alleviated the occurrence of chilling injury, delayed weight loss, lowered electrolyte leakage and respiration rate, and retarded malondialdehyde accumulation. Different from the control fruit, fucoidan treated fruit showed a high level of fatty acid unsaturated content, fatty acid unsaturation, and unsaturation index and increased ω-FDAS activity, along with upregulated expression levels of CsSAD and CsFAD genes. Fucoidan reduced the phosphatidic acid content and membrane lipid peroxidation, lowered the phospholipase D (PLD) and lipoxygenase (LOX) activity, and downregulated the expression levels of CsPLD and CsLOX genes. Collectively, fucoidan treatment maintained the integrity of cell membrane in cold-stress cucumbers. The results provide a new prospect for the development of fucoidan as a preservative agent in the low-temperature postharvest storage of cucumbers.

GLassonet: Identifying Discriminative Gene Sets Among Molecular Subtypes of Breast Cancer.

Breast cancer is a heterogeneous disease caused by various alterations in the genome or transcriptome. Molecular subtypes of breast cancer have been reported, but useful biomarkers remain to be identified to uncover underlying biological mechanisms and guide clinical decisions. Towards biomarker discovery, several studies focus on genomic alterations that provide differences, while few works concern transcriptomic characterizations that mediate tumor progression. Rather than using differential expression (DE) or weighted network analysis, we propose a feature selection method, dubbed GLassonet, to identify discriminative biomarkers from transcriptome-wide expression profiles by embedding the relationship graph of high-dimensional expressions into the Lassonet model. GLassonet comprises a nonlinear neural network for identifying cancer subtypes, a skipping fully connected layer for canceling the connections of hidden layers from input features to output categories, and a graph enhancement for preserving the discriminative graph into the selected subspace. First, an iterative optimization algorithm learns model parameters on the TCGA breast cancer dataset to investigate the classification performance. Then, we probe the distribution patterns of GLassonet-selected gene sets across the cancer subtypes and compare them to gene sets outputted from the state-of-the-art. More profoundly, we conduct the overall survival analysis on three GLassonet-selected new marker genes, i.e., SOX10, TPX2, and TUBA1C, to investigate their expression changes and assess their prognostic impacts. Finally, we perform the enrichment analysis to discover the functional associations of the GLassonet-selected genes with GO terms and KEGG pathways. Experimental results show that GLassonet has a powerful ability to select the discriminative genes, which improve cancer subtype classification performance and provide potential biomarkers for cancer personalized therapy.

Impact of treatments before allogeneic hematopoietic stem cell transplantation in patients with higher-risk myelodysplastic syndrome.

OBJECTIVE: The study aimed to evaluate pre-allogeneic hematopoietic stem cell transplantation (allo-HSCT) treatment, compare the endpoints related to disease management between pre-HSCT cytoreduction patients and upfront transplantation patients with higher-risk myelodysplastic syndrome (MDS). METHODS: A total of 90 higher-risk MDS patients administered allo-HSCT in the Hematology Department of the First Affiliated Hospital of Zhengzhou University were retrospectively analyzed, which included 28 patients with upfront transplantation and 62 patients with pre-transplant cytoreduction, including 30 patients received hypomethylating agents (HMA) and 32 patients received hypomethylating agents and induction chemotherapy (HMA+IC). Difference between the two groups regarding hematopoietic reconstruction, graft-versus-host disease (GVHD), relapse rate, non-relapse death (NRM), overall survival (OS) and relapse-free survival (RFS) was compared. RESULTS: No significant differences in OS, DFS and NRM were found between the upfront transplantation and pre-transplant cytoreduction groups, and cumulative cGVHD occurrence and relapse rates were 35.7 % and 14.5 % (P = 0.029), and 10.7 % and 12.9 % (p = 0.535), respectively. Survival rates were significantly higher in the upfront transplantation and HMA+IC groups compared with the HMA group (3-year OS: 67.9 %, 68.8 %, 43.3 %, P = 0.039; 3-year RFS: 64.3 %, 62.5 %, 43.3 %, P = 0.107; 3-year NRM: 25.0 %, 21.9 %, 50.0 %, P = 0.025). Compared with the upfront transplantation group, overall response to cytoreductive therapy (OR) and non-response to cytoreductive therapy (NR), 3-year OS were 67.9 %, 73.0 % and 32.0 % (P < 0.001), 3-year RFS were 64.3 %, 73.0 % and 24.0 % (P < 0.001) and 3-year NRM were 25.0 %, 21.6 %, and 56.0 %, respectively (P < 0.001). Upfront transplantation (n = 11) had better OS and RFS compared with the cytoreductive group (n = 10) in patients with ≥ 10 % bone marrow blast cells before transplantation (3-year OS: 63.64 %, 22.22 %, p = 0.010; 3-year DFS: 63.64 %, 20.00 %, p = 0.012, respectively). CONCLUSION: The pre-transplant treatment regimen was an independent prognostic factor of OS and NRM. If the donor is suitable, upfront transplantation may provide longer survival in higher-risk MDS patients, which, however, may also increase the incidence of cGVHD. Even in patients with bone marrow blast cells ≥ 10 % before transplantation, upfront transplantation was not worse than transplantation after cytoreductive therapy. While waiting for a transplant, HMA+IC therapy may be a good pre-transplant treatment option.

Manganese-coordinated mRNA vaccines with enhanced mRNA expression and immunogenicity induce robust immune responses against SARS-CoV-2 variants.

It is urgent to develop more effective mRNA vaccines against the emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants owing to the immune escape. Here, we constructed a novel mRNA delivery system [IC8/Mn lipid nanoparticles (IC8/Mn LNPs)]with high immunogenicity, via introducing a stimulator of interferon genes (STING) agonist [manganese (Mn)] based on a newly synthesized ionizable lipid (IC8). It was found that Mn can not only promote maturation of antigen-presenting cells via activating STING pathway but also improve mRNA expression by facilitating lysosomal escape for the first time. Subsequently, IC8/Mn LNPs loaded with mRNA encoding the Spike protein of SARS-CoV-2 Delta or Omicron variant (IC8/Mn@D or IC8/Mn@O) were prepared. Both mRNA vaccines induced substantial specific immunoglobulin G responses against Delta or Omicron. IC8/Mn@D displayed strong pseudovirus neutralization ability, T helper 1-biased immune responses, and good safety. It can be concluded that IC8/Mn LNPs have great potential for developing Mn-coordinated mRNA vaccines with robust immunogenicity and good safety.

Facile synthesis of super-thermal insulating polyimide aerogel-like films.

Thermal superinsulation materials play a key role in reducing energy consumption. In this article, flexible polyimide aerogel-like films are developed by a facile non-solvent-induced phase separation combined with ambient drying method. The pore structure and insulation properties are well controlled by changing the compositions of the coagulation bath. Polyimide films with macro-nano hierarchical pore structure and uniform nanopores are prepared by adjusting the content of water and alcohol as the non-solvent. The relationship between the insulation performance and textured structure of polyimide was studied. After optimization, the produced film achieved a low thermal conductivity of 0.019 W⋅m-1·K-1 but good tensile strength of 89.6 MPa, compared favorably with literature results. Hence, this article demonstrates that application of the facile phase inversion method to prepare porous polymers can be expanded from desalination or gas separation fields to insulation for energy-saving purposes.

mRNA Vaccines Against SARS-CoV-2 Variants Delivered by Lipid Nanoparticles Based on Novel Ionizable Lipids.

SARS-CoV-2 variants are now still challenging all the approved vaccines, including mRNA vaccines. There is an urgent need to develop new generation mRNA vaccines with more powerful efficacy and better safety against SARS-CoV-2 variants. In this study, a new set of ionizable lipids named 4N4T are constructed and applied to form novel lipid nanoparticles called 4N4T-LNPs. Leading 4N4T-LNPs exhibit much higher mRNA translation efficiency than the approved SM-102-LNPs. To test the effectiveness of the novel delivery system, the DS mRNA encoding the full-length S protein of the SARS-CoV-2 variant is synthesized and loaded in 4N4T-LNPs. The obtained 4N4T-DS mRNA vaccines successfully trigger robust and durable humoral immune responses against SARS-CoV-2 and its variants including Delta and Omicron. Importantly, the novel vaccines have higher RBD-specific IgG titers and neutralizing antibody titers than SM-102-based DS mRNA vaccine. Besides, for the first time, the types of mRNA vaccine-induced neutralizing antibodies are found to be influenced by the chemical structure of ionizable lipids. 4N4T-DS mRNA vaccines also induce strong Th1-skewed T cell responses and have good safety. This work provides a novel vehicle for mRNA delivery that is more effective than the approved LNPs and shows its application in vaccines against SARS-CoV-2 variants.