Based on Molecular Docking, Molecular Dynamics Simulation and MM/PB(GB)SA to Study Potential Inhibitors of PRRSV-Nsp4.

Porcine reproductive and respiratory syndrome (PRRS) is one of the most serious infectious immunosuppressive diseases in the world. The nonstructural protein Nsp4 can be used as an ideal target for anti-PRRSV replication inhibitors. However, little is known about potential inhibitors that target Nsp4 to affect PRRSV replication. The purpose of this study was to screen potential natural inhibitors that affect PRRSV replication by inhibiting Nsp4. Five compounds with strong binding affinity to Nsp4 were selected by structure-based molecular docking method. The complexes of naringin dihydrochalcone (NDC), agathisflavone (AGT), and amentoflavone (AMF) with Nsp4 were stable throughout the molecular dynamics simulation. According to MM/PBSA analysis, the free energies of binding of NDC, AGT, and AMF to Nsp4 were less than-30 Kcal/mol. In conclusion, these three compounds are worthy of further investigation as novel inhibitors of PRRSV. This study provides a theoretical basis for the development of anti-PRRSV natural drugs.

Indole-3-acetic acid improves periphyton's resistance to ultraviolet-B: From physiological-biochemical properties and bacteria community to livestock-polluted water purification.

Livestock-polluted water is a pressing water environmental issue in plateau pastoral regions, necessitating the adoption of eco-friendly solutions. Despite periphyton being a promising alternative, its efficacy is limited by the prevalence of intense ultraviolet radiation, particularly ultraviolet-B (UVB), in these regions. Therefore, this study employs molecular tools and small-scale trials to explore the crucial role of indole-3-acetic acid (IAA) in modulating periphyton characteristics and mediating nutrient removal from livestock-polluted water under UVB exposure. The results revealed that IAA augments periphyton's resilience to UVB stress through several pathways, including increasing periphyton's biomass, producing more extracellular polymeric substances (EPS), and enhancing antioxidant enzyme activities and photosynthetic activity of periphyton. Moreover, IAA addition increased periphyton's bacterial diversity, reshaped bacterial community structure, enhanced community stability, and elevated the R2 value of neutral processes in bacterial assembly from 0.257 to 0.651 under UVB. Practically, an IAA concentration of 50 mg/L was recommended. Small-scale trials confirmed the effectiveness of IAA in assisting UVB-stressed periphyton to remove nitrogen and phosphorus from livestock-polluted water, without the risk of nitrogen accumulation. These findings offer valuable insights into the protection of aquatic ecosystems in plateau pastoral regions based on periphyton property in an eco-friendly manner.

Vanishing nematic order beyond the pseudogap phase in overdoped cuprate superconductors.

During the last decade, translational and rotational symmetry-breaking phases-density wave order and electronic nematicity-have been established as generic and distinct features of many correlated electron systems, including pnictide and cuprate superconductors. However, in cuprates, the relationship between these electronic symmetry-breaking phases and the enigmatic pseudogap phase remains unclear. Here, we employ resonant X-ray scattering in a cuprate high-temperature superconductor [Formula: see text] (Nd-LSCO) to navigate the cuprate phase diagram, probing the relationship between electronic nematicity of the Cu 3d orbitals, charge order, and the pseudogap phase as a function of doping. We find evidence for a considerable decrease in electronic nematicity beyond the pseudogap phase, either by raising the temperature through the pseudogap onset temperature T* or increasing doping through the pseudogap critical point, p*. These results establish a clear link between electronic nematicity, the pseudogap, and its associated quantum criticality in overdoped cuprates. Our findings anticipate that electronic nematicity may play a larger role in understanding the cuprate phase diagram than previously recognized, possibly having a crucial role in the phenomenology of the pseudogap phase.

Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review.

Bitter peptides are small molecular peptides produced by the hydrolysis of proteins under acidic, alkaline, or enzymatic conditions. These peptides can enhance food flavor and offer various health benefits, with attributes such as antihypertensive, antidiabetic, antioxidant, antibacterial, and immune-regulating properties. They show significant potential in the development of functional foods and the prevention and treatment of diseases. This review introduces the diverse sources of bitter peptides and discusses the mechanisms of bitterness generation and their physiological functions in the taste system. Additionally, it emphasizes the application of bioinformatics in bitter peptide research, including the establishment and improvement of bitter peptide databases, the use of quantitative structure-activity relationship (QSAR) models to predict bitterness thresholds, and the latest advancements in classification prediction models built using machine learning and deep learning algorithms for bitter peptide identification. Future research directions include enhancing databases, diversifying models, and applying generative models to advance bitter peptide research towards deepening and discovering more practical applications.

iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms.

Diabetes mellitus is a chronic metabolic disease, which causes an imbalance in blood glucose homeostasis and further leads to severe complications. With the increasing population of diabetes, there is an urgent need to develop drugs to treat diabetes. The development of artificial intelligence provides a powerful tool for accelerating the discovery of antidiabetic drugs. This work aims to establish a predictor called iPADD for discovering potential antidiabetic drugs. In the predictor, we used four kinds of molecular fingerprints and their combinations to encode the drugs and then adopted minimum-redundancy-maximum-relevance (mRMR) combined with an incremental feature selection strategy to screen optimal features. Based on the optimal feature subset, eight machine learning algorithms were applied to train models by using 5-fold cross-validation. The best model could produce an accuracy (Acc) of 0.983 with the area under the receiver operating characteristic curve (auROC) value of 0.989 on an independent test set. To further validate the performance of iPADD, we selected 65 natural products for case analysis, including 13 natural products in clinical trials as positive samples and 52 natural products as negative samples. Except for abscisic acid, our model can give correct prediction results. Molecular docking illustrated that quercetin and resveratrol stably bound with the diabetes target NR1I2. These results are consistent with the model prediction results of iPADD, indicating that the machine learning model has a strong generalization ability. The source code of iPADD is available at https://github.com/llllxw/iPADD.

Multi-Lane Differential Variable Speed Limit Control via Deep Neural Networks Optimized by an Adaptive Evolutionary Strategy.

In advanced transportation-management systems, variable speed limits are a crucial application. Deep reinforcement learning methods have been shown to have superior performance in many applications, as they are an effective approach to learning environment dynamics for decision-making and control. However, they face two significant difficulties in traffic-control applications: reward engineering with delayed reward and brittle convergence properties with gradient descent. To address these challenges, evolutionary strategies are well suited as a class of black-box optimization techniques inspired by natural evolution. Additionally, the traditional deep reinforcement learning framework struggles to handle the delayed reward setting. This paper proposes a novel approach using covariance matrix adaptation evolution strategy (CMA-ES), a gradient-free global optimization method, to handle the task of multi-lane differential variable speed limit control. The proposed method uses a deep-learning-based method to dynamically learn optimal and distinct speed limits among lanes. The parameters of the neural network are sampled using a multivariate normal distribution, and the dependencies between the variables are represented by a covariance matrix that is optimized dynamically by CMA-ES based on the freeway's throughput. The proposed approach is tested on a freeway with simulated recurrent bottlenecks, and the experimental results show that it outperforms deep reinforcement learning-based approaches, traditional evolutionary search methods, and the no-control scenario. Our proposed method demonstrates a 23% improvement in average travel time and an average of a 4% improvement in CO, HC, and NOx emission.Furthermore, the proposed method produces explainable speed limits and has desirable generalization power.

Employing synergetic effect of ZnSe quantum dots and layered Ni(OH)2to boost the performance of lithium-sulfur cathodes.

The low sulfur utilization, cycling instability, and sluggish kinetics are the critical obstructions to practical applications of lithium-sulfur batteries (LSBs). Constructing sulfur hosts with high conductivity, suppressed shuttle effect, and rapid kinetics is essential for their practical application in LSBs. Here, we synthetically utilized the merits of ZnSe quantum dots (QDs) and layered Ni(OH)2to boost the performance of LSBs. A novel core-shell ZnSe-CNTs/S@Ni(OH)2was constructed using the ZnSe-CNTs network as framework to load sulfur and following with Ni(OH)2encapsulation. The CNT network decorated with ZnSe QDs not only serves as a conductive framework providing fast electron/ion transfer channels, but also limits polysulfide diffusion physically and chemically. Layered Ni(OH)2, the wrinkled encapsulation, not only permits fast electron/ion transfer, but also buffers the expansion, confines active materials, and limits the polysulfide dissolution chemically. When used as a cathode, ZnSe-CNTs/S@Ni(OH)2presents enhanced electrochemistry performance compared with ZnSe-CNTs/S and CNTs/S. The average specific capacity decreases from 1021.9 mAh g-1at 0.2 C to 665.0 mAh g-1at 2 C, showing rate capacity much higher than ZnSe-CNTs/S and CNTs/S. After 150 cycles, the capacity at 0.5 C slowly reduces from 926.7 to 789.0 mAh g-1, showing high retention of 85.1%. Therefore, our investigation provides a new strategy to construct a promising sulfur cathode for LSBs.

Incorporation ZnS quantum dots into carbon nanotubes for high-performance lithium-sulfur batteries.

Constructing sulfur hosts with high electronic conductivity, large void space, strong chemisorption, and rapid redox kinetics is critically important for their practical applications in lithium-sulfur batteries (LSBs). Herein, by coupling ZnS quantum dots (QDs) with carbon nanotubes (CNTs), one multifunctional sulfur host CNT/ZnS-QDs is designed via a facile one-step hydrothermal method. SEM and TEM analyses reveal that small ZnS-QDs (<5 nm) are uniformly anchored on the CNT surface as well as encapsulated into CNT channels. This special architecture ensures sulfur direct contacting with highly conductive CNTs; meanwhile, the catalytic effect of anchored ZnS-QDs improves the chemisorption and confinement to polysulfides. Benefiting from these merits, when used as sulfur hosts, this special architecture manifests a high specific capacity, superior rate capability, and long-term cycling stability. The ZnS-QDs dependent electrochemical performance is also evaluated by adjusting the mass ratio of ZnS-QDs, and the host of CNT/ZnS-QDs 27% owns the optimal cell performance. The specific capacity decreases from 1051 mAh g-1 at 0.2 C to 544 mAh g-1 at 2.0 C, showing rate capability much higher than CNT/S and other CNT/ZnS-QDs/S samples. After 150 cycles, the cyclic capacity at 0.5 C exhibits a slow reduction from 1051 mAh g-1 to 771 mAh g-1, showing a high retention of 73.4% with a coulombic efficiency of over 99%. The electrochemical impedance spectroscopy analyses demonstrate that this special architecture juggles high conductivity and excellent confinement of polysulfides, which can significantly suppress the notorious shuttle effect and accelerate the redox kinetics. The strategy in this study provides a feasible approach to design efficient sulfur hosts for realizing practically usable LSBs.

The clinical value of cytokines in chronic fatigue syndrome.

Chronic fatigue syndrome (CFS) is a heterogeneous disorder with uncertain pathogenesis. Without effective therapy, CFS is characterized by disabling fatigue, depression, memory loss, and somatic discomfort. This comprehensive and impartial review aimed to assess the available evidence and examined the potential clinical value of using cytokines for the monitoring of CFS and as targets for the treatment of CFS. Inflammatory reactions and immune modulation are considered to contribute to the pathophysiology of CFS, and it is well documented that cytokines present in both blood and cerebrospinal fluid (CSF) are closely associated with the progression and severity of CFS. However, pathophysiological and methodological limitations prevent using circulating cytokines as independent diagnostic indices. Moreover, there is no evidence to support the use of CSF cytokines as independent diagnostic indices. Nevertheless, a comprehensive evaluation of changes in circulating and CSF cytokines may improve clinical understanding of the pathophysiology of patients with CFS, aiding in the establishment of an appropriate diagnosis. Importantly, the available evidence does not support the value of cytokines as therapeutic targets. We believe that an improved understanding of cytokine-related mechanisms will be helpful to explore new cytokine-related therapeutic targets.

Effects of asymptomatic infection on the dynamical interplay between behavior and disease transmission in multiplex networks.

Multiplex network theory is widely introduced to deepen the understanding of the dynamical interplay between self-protective behavior and epidemic spreading. Most of the existing studies assumed that all infected individuals can transmit disease- related information or can be perceived by their neighbors. However, owing to lack of distinct symptoms for patients in the initial stage of infection, the disease information cannot be transmitted in the population, which may lead to the wrong perception of infection risk and inappropriate behavior response. In this work, we divide infected individuals into Exposed-state (without obvious clinical symptoms) individuals and Infected-state (with evident clinical symptoms) individuals, both of whom can spread disease, but only Infected-state individuals can diffuse disease information. Then, in this work we establish UAU-SEIS (Unaware-Aware-Unaware-Susceptible-Exposed-Infected-Susceptible) model in multiplex networks and analyze the effect of asymptomatic infection and the isolation of Infected-state individuals on the density of infection and the epidemic threshold. Furthermore, we extend the UAU-SEIS model by taking the individual heterogeneity into consideration. Combined Markov chain approach and Monte-Carlo Simulations, we find that asymptomatic infection has an effect on the density of infected individuals and the epidemic threshold, and the extent of the effect is influenced by whether Infected-state individuals are isolated or treated. In addition, results show that the individual heterogeneity can lower the density of infected individuals, but cannot enhance the epidemic threshold.

A Novel Self-Assembly Nanocrystal as Lymph Node-Targeting Delivery System: Higher Activity of Lymph Node Targeting and Longer Efficacy Against Lymphatic Metastasis.

Mitoxantrone (MTO) is used to treat certain types of cancer, mostly metastatic cancer. While the drug has poor aqueous solubility and high side effects. Self-assembly nanocrystal is a novel lymphatic targeting delivery system. In our study, MTO self-assembly nanocrystal (MTO NC) was successfully prepared to improve lymphatic targeting ability and reduce its toxicity. MTO NCs had small size, stable potential, and uniform distribution. The average particle size of MTO NCs was less than 100 nm with the 0.218 PDI and - 6.6 mV the Zeta potential value. TEM images showed that MTO NCs had a sphere-like morphology with smooth surface and uniform distribution; Atomic force microscopy (AFM) images gave a 3D surface of MTO NCs. Polarizing microscope micrograph (PLM) of MTO NCs in lymph nodes demonstrated the crystal structure of MTO NCs when it was exposed to physiological condition. Transmission electron microscopy showed the presence of MTO NCs in mice lymph nodes. Pharmacokinetic parameters of MTO strongly demonstrated that MTO NCs could target the lymph nodes after subcutaneous injection. Moreover, tissue distribution results indicated that MTO NCs were mainly absorbed by the lymphatics and reduced system toxicity. Finally, a lymphatic metastasis mice model was established to precede the pharmacodynamics of MTO NCs, and using MTO liposomes as a reference preparation, the inhibitory effect of MTO NCs on lymphatic metastasis was markedly higher. Briefly, MTO NCs, as a novel self-assembled lymphatic targeting system, can accumulate in the metastatic lymph nodes and lead anticancer drug to kill cancer cells and control lymphatic metastasis with extremely low systemic toxicity.

Can rewiring strategy control the epidemic spreading?

Relation existed in the social contact network can affect individuals' behaviors greatly. Considering the diversity of relation intimacy among network nodes, an epidemic propagation model is proposed by incorporating the link-breaking threshold, which is normally neglected in the rewiring strategy. The impact of rewiring strategy on the epidemic spreading in the weighted adaptive network is explored. The results show that the rewiring strategy cannot always control the epidemic prevalence, especially when the link-breaking threshold is low. Meanwhile, as well as strong links, weak links also play a significant role on epidemic spreading.

Controlled atmosphere storage drive proteomic change in Chinese Daohuaxiang.

Although the use of a controlled atmosphere is one of the most successful storage techniques, the mechanism thereof in rice storage remains unclear. We stored aromatic rice cultivar Daohuaxiang in a package filled with 98 % N2 and 35 % CO2 for 3 months. We investigated 2-acetyl-1-pyrroline loss, enzyme activities, and proteomics changes of rice during storage. The results showed that the content of 2-acetyl-1-pyrroline was reduced by 37.40 %, 25.65 %, and 43.89 % during storage using 98 % N2, 35 % CO2 controlled atmosphere storage, and conventional storage. Controlled atmosphere storage slowed down the increase of malondialdehyde content in Daohuaxiang. The results showed that 26S proteasome regulatory particle triple-A ATPase subunit 6, superoxide dismutase, glutathione transferase, and other key proteins were upregulated during 35 % CO2 regulation. This study provided a meaningful basis for exploring the regulation strategy of aromatic rice quality and strengthening the quality control of aromatic rice industry.

Non-destructive detection of defective maize kernels using hyperspectral imaging and convolutional neural network with attention module.

Rapid, effective and non-destructive detection of the defective maize kernels is crucial for their high-quality storage in granary. Hyperspectral imaging (HSI) coupled with convolutional neural network (CNN) based on spectral and spatial attention (Spl-Spal-At) module was proposed for identifying the different types of maize kernels. The HSI data within 380-1000 nm of six classes of sprouted, heat-damaged, insect-damaged, moldy, broken and healthy kernels was collected. The CNN-Spl-At, CNN-Spal-At and CNN-Spl-Spal-At models were established based on the spectra, images and their fusion features as inputs for the recognition of different kernels. Further compared the performances of proposed models and conventional models were built by support vector machine (SVM) and extreme learning machine (ELM). The results indicated that the recognition ability of CNN with attention series models was significantly better than that of SVM and ELM models and fused features were more conducive to expressing the appearance of different kernels than single features. And the CNN-Spl-Spal-At model had an optimal recognition result with high average classification accuracy of 98.04 % and 94.56 % for the training and testing sets, respectively. The recognition results were visually presented on the surface image of kernels with different colors. The CNN-Spl-Spal-At model was built in this study could effectively detect defective maize kernels, and it also had great potential to provide the analysis approaches for the development of non-destructive testing equipment based on HSI technique for maize quality.

Potent antitumor activity of a bispecific T-cell engager antibody targeting the intracellular antigen KRAS G12V.

Kirsten Rat Sarcoma viral oncogene homolog (KRAS) is one of the most frequent oncogenes. However, there are limited treatment options due to its intracellular expression. To address this, we developed a novel bispecific T-cell engager (BiTE) antibody targeting HLA-A2/KRAS G12V complex and CD3 (HLA-G12V/CD3 BiTE). We examined its specific binding to tumor cells and T cells, as well as its anti-tumor effects in vivo. HLA-G12V/CD3 BiTE was expressed in Escherichia coli and its binding affinities to CD3 and HLA-A2/KRAS G12V were measured by flow cytometry, along with T-cell activation. In a xenograft pancreatic tumor model, the HLA-G12V/CD3 BiTE's anti-tumor effects were assessed through tumor growth, survival time, and safety. Our results demonstrated specific binding of HLA-G12V/CD3 BiTE to tumor cells with an HLA-A2/KRAS G12V mutation and T cells. The HLA-G12V/CD3 BiTE also activated T-cells in the presence of tumor cells in vitro. HLA-G12V/CD3 BiTE in vivo testing showed delayed tumor growth without severe toxicity to major organs and prolonged mouse survival. This study highlights the potential of constructing BiTEs recognizing an HLA-peptide complex and providing a novel therapy for cancer treatment targeting the intracellular tumor antigen.

Cellulose-based materials in environmental protection: A scientometric and visual analysis review.

As sustainable materials, cellulose-based materials have attracted significant attention in the field of environmental protection, resulting in the publication of numerous academic papers. However, there is a scarcity of literature that involving scientometric analysis within this specific domain. This review aims to address this gap and highlight recent research in this field by utilizing scientometric analysis and a historical review. As a result, 21 highly cited articles and 10 mostly productive journals were selected out. The scientometric analysis reveals that recent studies were objectively clustered into five interconnected main themes: extraction of cellulose from raw materials and its degradation, adsorption of pollutants using cellulose-based materials, cellulose-acetate-based membrane materials, nanocellulose-based materials, and other cellulose-based materials such as carboxymethyl cellulose and bacterial cellulose for environmental protection. Analyzing the distribution of author keywords and thoroughly examining relevant literature, the research focuses within these five themes were summarized. In the future, the development of eco-friendly and cost-effective methods for extracting and preparing cellulose and its derivatives, particularly nanocellulose-based materials, remains an enduring pursuit. Additionally, machine learning techniques holds promise for the advancement and application of cellulose-based materials. Furthermore, there is potential to expand the research and application scope of cellulose-based materials for environmental protection.

A Novel Brain Network Analysis Method for Pediatric ADHD Using RFE-GA Feature Selection Strategy.

Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent childhood disorder, and related research has been increasing in recent years. However, it remains a challenging issue to accurately identify individuals with ADHD. The research proposes a method for ADHD detection using Recursive Feature Elimination-Genetic Algorithm (RFE-GA) for the feature selection of EEG data. Firstly, this study employed Transfer Entropy (TE) to construct brain networks from the EEG data of the ADHD and Normal groups, conducting an analysis of effective connectivity to unveil causal relationships in the brain's information exchange activities. Subsequently, a dual-layer feature selection method combining Recursive Feature Elimination (RFE) and Genetic Algorithm (GA) was proposed. Using the global search capability of GA and the feature selection ability of RFE, the performance of each feature subset is evaluated to find the optimal feature subset. Finally, a Support Vector Machine (SVM) classifier was employed to classify the ultimate feature set. The results revealed the control group exhibited lower connectivity strength in the left temporal alpha and beta bands, but higher frontal connectivity strength compared to the ADHD group. Additionally, in the gamma frequency band, the control group had higher top lobe connectivity strength than the ADHD group. Through the RFE-GA feature selection method, the optimized feature set was more concise, achieving classification accuracies of 91.3%, 94.1%, and 90.7% for the alpha, beta, and gamma frequency bands, respectively. The proposed RFE-GA feature selection method significantly reduced the number of features, thereby improving classification accuracy. .

Predicting the role of the human gut microbiome in type 1 diabetes using machine-learning methods.

Gut microbes is a crucial factor in the pathogenesis of type 1 diabetes (T1D). However, it is still unclear which gut microbiota are the key factors affecting T1D and their influence on the development and progression of the disease. To fill these knowledge gaps, we constructed a model to find biomarker from gut microbiota in patients with T1D. We first identified microbial markers using Linear discriminant analysis Effect Size (LEfSe) and random forest (RF) methods. Furthermore, by constructing co-occurrence networks for gut microbes in T1D, we aimed to reveal all gut microbial interactions as well as major beneficial and pathogenic bacteria in healthy populations and type 1 diabetic patients. Finally, PICRUST2 was used to predict Kyoto Encyclopedia of Genes and Genomes (KEGG) functional pathways and KO gene levels of microbial markers to investigate the biological role. Our study revealed that 21 identified microbial genera are important biomarker for T1D. Their AUC values are 0.962 and 0.745 on discovery set and validation set. Functional analysis showed that 10 microbial genera were significantly positively associated with D-arginine and D-ornithine metabolism, spliceosome in transcription, steroid hormone biosynthesis and glycosaminoglycan degradation. These genera were significantly negatively correlated with steroid biosynthesis, cyanoamino acid metabolism and drug metabolism. The other 11 genera displayed an inverse correlation. In summary, our research identified a comprehensive set of T1D gut biomarkers with universal applicability and have revealed the biological consequences of alterations in gut microbiota and their interplay. These findings offer significant prospects for individualized management and treatment of T1D.

Strategies to Mitigate Age-Related Bias in Machine Learning: Scoping Review.

BACKGROUND: Research suggests that digital ageism, that is, age-related bias, is present in the development and deployment of machine learning (ML) models. Despite the recognition of the importance of this problem, there is a lack of research that specifically examines the strategies used to mitigate age-related bias in ML models and the effectiveness of these strategies. OBJECTIVE: To address this gap, we conducted a scoping review of mitigation strategies to reduce age-related bias in ML. METHODS: We followed a scoping review methodology framework developed by Arksey and O'Malley. The search was developed in conjunction with an information specialist and conducted in 6 electronic databases (IEEE Xplore, Scopus, Web of Science, CINAHL, EMBASE, and the ACM digital library), as well as 2 additional gray literature databases (OpenGrey and Grey Literature Report). RESULTS: We identified 8 publications that attempted to mitigate age-related bias in ML approaches. Age-related bias was introduced primarily due to a lack of representation of older adults in the data. Efforts to mitigate bias were categorized into one of three approaches: (1) creating a more balanced data set, (2) augmenting and supplementing their data, and (3) modifying the algorithm directly to achieve a more balanced result. CONCLUSIONS: Identifying and mitigating related biases in ML models is critical to fostering fairness, equity, inclusion, and social benefits. Our analysis underscores the ongoing need for rigorous research and the development of effective mitigation approaches to address digital ageism, ensuring that ML systems are used in a way that upholds the interests of all individuals. TRIAL REGISTRATION: Open Science Framework AMG5P; https://osf.io/amg5p.

An Immunomodulator-Boosted Lactococcus Lactis Platform For Enhanced In Situ Tumor Vaccine.

In situ vaccination is an attractive type of cancer immunotherapy, and methods of persistently dispersing immune agonists throughout the entire tumor are crucial for maximizing their therapeutic efficacy. Based on the probiotics usually used for dietary supplements, an immunomodulator-boosted Lactococcus lactis (IBL) strategy is developed to enhance the effectiveness of in situ vaccination with the immunomodulators. The intratumoral delivery of OX40 agonist and resiquimod-modified Lactococcus lactis (OR@Lac) facilitates local retention and persistent dispersion of immunomodulators, and dramatically modulates the key components of anti-tumor immune response. This novel vaccine activated dendritic cells and cytotoxic T lymphocytes in the tumor and tumor-draining lymph nodes, and ultimately significantly inhibited tumor growth and prolonged the survival rate of tumor-bearing mice. The combination of OR@Lac and ibrutinib, a myeloid-derived suppressor cell inhibitor, significantly alleviated or even completely inhibited tumor growth in tumor-bearing mice. In conclusion, IBL is a promising in situ tumor vaccine approach for clinical application and provides an inspiration for the delivery of other drugs.