Inverse Identification of Constitutive Model for GH4198 Based on Genetic-Particle Swarm Algorithm.

A precise Johnson-Cook (J-C) constitutive model is the foundation for precise calculation of finite-element simulation. In order to obtain the J-C constitutive model accurately for a new cast and forged alloy GH4198, an inverse identification of J-C constitutive model was proposed based on a genetic-particle swarm algorithm. Firstly, a quasi-static tensile test at different strain rates was conducted to determine the initial yield strength A, strain hardening coefficient B, and work hardening exponent n for the material's J-C model. Secondly, a new method for orthogonal cutting model was constructed based on the unequal division shear theory and considering the influence of tool edge radius. In order to obtain the strain-rate strengthening coefficient C and thermal softening coefficient m, an orthogonal cutting experiment was conducted. Finally, in order to validate the precision of the constitutive model, an orthogonal cutting thermo-mechanical coupling simulation model was established. Meanwhile, the sensitivity of J-C constitutive model parameters on simulation results was analyzed. The results indicate that the parameter m significantly affects chip morphology, and that the parameter C has a notable impact on the cutting force. This study addressed the issue of missing constitutive parameters for GH4198 and provided a theoretical reference for the optimization and identification of constitutive models for other aerospace materials.

Reconfigurable, Transformable Soft Pneumatic Actuator with Tunable Three-Dimensional Deformations for Dexterous Soft Robotics Applications.

Numerous soft actuators based on pneumatic network (PneuNet) design have already been proposed and extensively employed across various soft robotics applications in recent years. Despite their widespread use, a common limitation of most existing designs is that their action is predetermined during the fabrication process, thereby restricting the ability to modify or alter their function during operation. To address this shortcoming, in this article the design of a Reconfigurable, Transformable Soft Pneumatic Actuator (RT-SPA) is proposed. The working principle of the RT-SPA is analogous to the conventional PneuNet. The key distinction between the two lies in the ability of the RT-SPA to undergo controlled transformations, allowing for more versatile bending and twisting motions in various directions. Furthermore, the unique reconfigurable design of the RT-SPA enables the selection of actuation units with different sizes to achieve a diverse range of three-dimensional deformations. This versatility enhances the RT-SPA's potential for adaptation to a multitude of tasks and environments, setting it apart from traditional PneuNet. The article begins with a detailed description of the design and fabrication of the RT-SPA. Following this, a series of experiments are conducted to evaluate the performance of the RT-SPA. Finally, the abilities of the RT-SPA for locomotion, gripping, and object manipulation are demonstrated to illustrate the versatility of the RT-SPA across different aspects.

Improvement of DC Performance and RF Characteristics in GaN-Based HEMTs Using SiNx Stress-Engineering Technique.

In this work, the DC performance and RF characteristics of GaN-based high-electron-mobility transistors (HEMTs) using the SiNx stress-engineered technique were systematically investigated. It was observed that a significant reduction in the peak electric field and an increase in the effective barrier thickness in the devices with compressive SiNx passivation contributed to the suppression of Fowler-Nordheim (FN) tunneling. As a result, the gate leakage decreased by more than an order of magnitude, and the breakdown voltage (BV) increased from 44 V to 84 V. Moreover, benefiting from enhanced gate control capability, the devices with compressive stress SiNx passivation showed improved peak transconductance from 315 mS/mm to 366 mS/mm, along with a higher cutoff frequency (ft) and maximum oscillation frequency (fmax) of 21.15 GHz and 35.66 GHz, respectively. Due to its enhanced frequency performance and improved pinch-off characteristics, the power performance of the devices with compressive stress SiNx passivation was markedly superior to that of the devices with stress-free SiNx passivation. These results confirm the substantial potential of the SiNx stress-engineered technique for high-frequency and high-output power applications, which are crucial for future communication systems.

First-principles study on pressure-induced superconductivity and structural design on transition metal diborides.

Recent experiments on α-MoB2 with MgB2-type structure achieved superconductivity at ∼32 K under 90 GPa, the highest among transition-metal diborides, rekindling interest in their superconducting properties. Our study systematically investigates the band structures of AlB2-type transition metal diborides. We found that the superior superconductivity of MoB2, WB2, and TcB2 correlates with their von Hove singularities near the Fermi level (EF), potentially linked to electron-phonon coupling. These three diborides exhibit similar critical temperature (Tc) trends under pressure: rising initially, peaking around 60 GPa, and then declining. While unstable at ambient pressure, their thermodynamic and dynamical stability limits vary significantly, possibly explaining experimental discrepancies. To stabilize MoB2 at ambient pressure, we designed MoXB4 compounds (X = other transition metals) by substituting every other Mo layer in MoB2 with an X layer. This modification aims to stabilize the structure and enhance superconductivity by reducing d-electron concentration at EF. This principle extends to other potential superconducting diborides, such as WB2 and TcB2. Using Nb as an example, we found that Nb atoms in AlB2-type MoNbB4 may exhibit random occupancy, potentially explaining disparities between theoretical predictions and experimental results. Our study offers valuable insights into superconductivity in transition metal diborides, paving the way for future research and applications.

CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution.

Predicting the strength of promoters and guiding their directed evolution is a crucial task in synthetic biology. This approach significantly reduces the experimental costs in conventional promoter engineering. Previous studies employing machine learning or deep learning methods have shown some success in this task, but their outcomes were not satisfactory enough, primarily due to the neglect of evolutionary information. In this paper, we introduce the Chaos-Attention net for Promoter Evolution (CAPE) to address the limitations of existing methods. We comprehensively extract evolutionary information within promoters using merged chaos game representation and process the overall information with modified DenseNet and Transformer structures. Our model achieves state-of-the-art results on two kinds of distinct tasks related to prokaryotic promoter strength prediction. The incorporation of evolutionary information enhances the model's accuracy, with transfer learning further extending its adaptability. Furthermore, experimental results confirm CAPE's efficacy in simulating in silico directed evolution of promoters, marking a significant advancement in predictive modeling for prokaryotic promoter strength. Our paper also presents a user-friendly website for the practical implementation of in silico directed evolution on promoters. The source code implemented in this study and the instructions on accessing the website can be found in our GitHub repository https://github.com/BobYHY/CAPE.

Structural Phase Transition and Disproportionation of MnF3 Under High Pressure and High Temperature.

Materials that once suffered under high-pressure and high-temperature conditions often display unusual phenomena that challenge traditional understanding. MnF3, an intermediate valence state Mn-F compound, exhibits a distorted octahedral crystal structure influenced by the Jahn-Teller effect. Here we report the structural phase transition and self-disproportionation of MnF3 under high pressure and high temperature. The initial octahedra phase I2/a-MnF3 transforms into the hendecahedra Pnma phase under high pressure. Subsequently, we found that molten Pnma-MnF3 self-disproportionate into MnF2 and MnF4 with the aid of laser heating at a pressure above 57.1 GPa. Raman spectra and UV-vis absorption experiments confirmed these changes that were ultimately confirmed by synchrotron radiation XRD. The equation of states for the volume with the pressure of these Mn-F compounds was also given. This work expands the study of Mn-F systems and provides guidance for the behavior of transition metal fluorides under high pressures and high temperatures.

SnapFISH-IMPUTE: an imputation method for multiplexed DNA FISH data.

Chromatin spatial organization plays a crucial role in gene regulation. Recently developed and prospering multiplexed DNA FISH technologies enable direct visualization of chromatin conformation in the nucleus. However, incomplete data caused by limited detection efficiency can substantially complicate and impair downstream analysis. Here, we present SnapFISH-IMPUTE that imputes missing values in multiplexed DNA FISH data. Analysis on multiple published datasets shows that the proposed method preserves the distribution of pairwise distances between imaging loci, and the imputed chromatin conformations are indistinguishable from the observed conformations. Additionally, imputation greatly improves downstream analyses such as identifying enhancer-promoter loops and clustering cells into distinct cell types. SnapFISH-IMPUTE is freely available at https://github.com/hyuyu104/SnapFISH-IMPUTE .

Integrating PointNet-Based Model and Machine Learning Algorithms for Classification of Rupture Status of IAs.

BACKGROUND: The rupture of intracranial aneurysms (IAs) would result in subarachnoid hemorrhage with high mortality and disability. Predicting the risk of IAs rupture remains a challenge. METHODS: This paper proposed an effective method for classifying IAs rupture status by integrating a PointNet-based model and machine learning algorithms. First, medical image segmentation and reconstruction algorithms were applied to 3D Digital Subtraction Angiography (DSA) imaging data to construct three-dimensional IAs geometric models. Geometrical parameters of IAs were then acquired using Geomagic, followed by the computation of hemodynamic clouds and hemodynamic parameters using Computational Fluid Dynamics (CFD). A PointNet-based model was developed to extract different dimensional hemodynamic cloud features. Finally, five types of machine learning algorithms were applied on geometrical parameters, hemodynamic parameters, and hemodynamic cloud features to classify and recognize IAs rupture status. The classification performance of different dimensional hemodynamic cloud features was also compared. RESULTS: The 16-, 32-, 64-, and 1024-dimensional hemodynamic cloud features were extracted with the PointNet-based model, respectively, and the four types of cloud features in combination with the geometrical parameters and hemodynamic parameters were respectively applied to classify the rupture status of IAs. The best classification outcomes were achieved in the case of 16-dimensional hemodynamic cloud features, the accuracy of XGBoost, CatBoost, SVM, LightGBM, and LR algorithms was 0.887, 0.857, 0.854, 0.857, and 0.908, respectively, and the AUCs were 0.917, 0.934, 0.946, 0.920, and 0.944. In contrast, when only utilizing geometrical parameters and hemodynamic parameters, the accuracies were 0.836, 0.816, 0.826, 0.832, and 0.885, respectively, with AUC values of 0.908, 0.922, 0.930, 0.884, and 0.921. CONCLUSION: In this paper, classification models for IAs rupture status were constructed by integrating a PointNet-based model and machine learning algorithms. Experiments demonstrated that hemodynamic cloud features had a certain contribution weight to the classification of IAs rupture status. When 16-dimensional hemodynamic cloud features were added to the morphological and hemodynamic features, the models achieved the highest classification accuracies and AUCs. Our models and algorithms would provide valuable insights for the clinical diagnosis and treatment of IAs.

New Virus Variant Detection Based on the Optimal Natural Metric.

The highly variable SARS-CoV-2 virus responsible for the COVID-19 pandemic frequently undergoes mutations, leading to the emergence of new variants that present novel threats to public health. The determination of these variants often relies on manual definition based on local sequence characteristics, resulting in delays in their detection relative to their actual emergence. In this study, we propose an algorithm for the automatic identification of novel variants. By leveraging the optimal natural metric for viruses based on an alignment-free perspective to measure distances between sequences, we devise a hypothesis testing framework to determine whether a given viral sequence belongs to a novel variant. Our method demonstrates high accuracy, achieving nearly 100% precision in identifying new variants of SARS-CoV-2 and HIV-1 as well as in detecting novel genera in Orthocoronavirinae. This approach holds promise for timely surveillance and management of emerging viral threats in the field of public health.

Investigating the antiviral activity of Erigeron annuus (L.) pers extract against RSV and examining its active components.

ETHNOPHARMACOLOGICAL RELEVANCE: The plants in the genus Erigeron are known to exhibit antiviral activities, including those against the respiratory syncytial virus (RSV). In traditional medicine Erigeron annuus (L.) Pers (EA) has been used in the treatment of pulmonary diseases and acute infectious hepatitis. AIM OF THIS STUDY: The aim of this study is to determine the optimum extraction method to produce the most potent anti-RSV extract, elucidate its mode and mechanisms of antiviral activity in both in vitro and in vivo models, and identify the chemical structures of the bioactive compounds. MATERIALS AND METHODS: The whole plant of EA was extracted with ethyl acetate, methanol, ethanol, water, aqueous methanol (60, 80% and 100%) and aqueous ethanol (50, 75% and 95%) using maceration, reflux, and ultrasound-assisted extraction methods. The antiviral activities of the extracts were determined in vitro. The in vitro antiviral activities of the extracts were determined using Hep-2 cells. Four in vitro experiments were performed to determine the mode of antiviral activity of the most active extract, ethyl acetate fraction (EAE) of Erigeron annuus whole plant extract prepared by refluxing with 50% ethanol, by examining its ability to inactivate the virus directly, inhibit viral adsorption and penetration, inhibit viral replication and preventive effect. The effect of temperature and duration of treatment on these modes of action was also determined. The antiviral activity of the EAE was also assessed in vivo in a mouse model. The lung index, viral load, and lung tissue histology were measured. qRT-PCR and ELISA studies were performed to determine the expression of key genes (TLR-3 and TLR-4) and proteins (IL-2, IFN-γ, and TNF-α) related to RSV infection. The most active antiviral compound was isolated using chromatography techniques, and its chemical structure was identified through electrospray triple quadrupole mass spectroscopy and nuclear magnetic resonance spectroscopy. RESULTS: The EAE was the most active on RSV. In vitro experiments showed that the antiviral activity of EAE is via direct inactivation, inhibition of entry, and inhibition of the proliferation of the virus. In vivo experiments showed that the EAE effectively inhibited the proliferation of RSV in the lungs and alleviated the lung tissue lesions in RSV-infected mice. The antiviral activity of the EAE is mediated by downregulating the expression of TLR3 and TLR4 in the lung, upregulating the expression of IL-2 and IFN-γ, and downregulating the expression of TNF-α. Apigenin 7-O-methylglucuronide was found to be a major bioactive compound in EAE. CONCLUSIONS: The results of this study confirmed the antiviral activity of EA by inactivating, inhibiting the entry, and inhibiting the proliferation of RSV. The activity is mediated by regulating the immunity and inflammatory mediators. Apigenin 7-O-methylglucuronide is the bioactive compound present in EA.

Structural Phase Transition and Decomposition of XeF2 under High Pressure and Its Formation of Xe-Xe Covalent Bonds.

Noble gases with inert chemical properties have rich bonding modes under high pressure. Interestingly, Xe and Xe form covalent bonds, originating from the theoretical simulation of the pressure-induced decomposition of XeF2, which has yet to be experimentally confirmed. Moreover, the structural phase transition and metallization of XeF2 under high pressure have always been controversial. Therefore, we conducted extensive experiments using a laser-heated diamond anvil cell technique to investigate the above issues of XeF2. We propose that XeF2 undergoes a structural phase transition and decomposition above 84.1 GPa after laser heating, and the decomposed product Xe2F contains Xe-Xe covalent bonds. Neither the pressure nor temperature alone could bring about these changes in XeF2. With our UV-vis absorption experiment, I4/mmm-XeF2 was metalized at 159 GPa. This work confirms the existence of Xe-Xe covalent bonds and provides insights into the controversy surrounding XeF2, enriching the research on noble gas chemistry under high pressure.

MEMS Oscillators-Network-Based Ising Machine with Grouping Method.

Combinatorial optimization (CO) has a broad range of applications in various fields, including operations research, computer science, and artificial intelligence. However, many of these problems are classified as nondeterministic polynomial-time (NP)-complete or NP-hard problems, which are known for their computational complexity and cannot be solved in polynomial time on traditional digital computers. To address this challenge, continuous-time Ising machine solvers have been developed, utilizing different physical principles to map CO problems to ground state finding. However, most Ising machine prototypes operate at speeds comparable to digital hardware and rely on binarizing node states, resulting in increased system complexity and further limiting operating speed. To tackle these issues, a novel device-algorithm co-design method is proposed for fast sub-optimal solution finding with low hardware complexity. On the device side, a piezoelectric lithium niobate (LiNbO3) microelectromechanical system (MEMS) oscillator network-based Ising machine without second-harmonic injection locking (SHIL) is devised to solve Max-cut and graph coloring problems. The LiNbO3 oscillator operates at speeds greater than 9 GHz, making it one of the fastest oscillatory Ising machines. System-wise, an innovative grouping method is used that achieves a performance guarantee of 0.878 for Max-cut and 0.658 for graph coloring problems, which is comparable to Ising machines that utilize binarization.

A combination influenza mRNA vaccine candidate provided broad protection against diverse influenza virus challenge.

Influenza viruses present a significant threat to global health. The production of a universal vaccine is considered essential due to the ineffectiveness of current seasonal influenza vaccines against mutant strains. mRNA technology offers new prospects in vaccinology, with various candidates for different infectious diseases currently in development and testing phases. In this study, we encapsulated a universal influenza mRNA vaccine. The vaccine encoded influenza hemagglutinin (HA), nucleoprotein (NP), and three tandem repeats of matrix protein 2 (3M2e). Twice-vaccinated mice exhibited strong humoral and cell-mediated immune responses in vivo. Notably, these immune responses led to a significant reduction in viral load of the lungs in challenged mice, and also conferred protection against future wild-type H1N1, H3N2, or H5N1 influenza virus challenges. Our findings suggest that this mRNA-universal vaccine strategy for influenza virus may be instrumental in mitigating the impact of future influenza pandemics.

Development and validation of a risk prediction model for mild cognitive impairment in elderly patients with type 2 diabetes mellitus.

BACKGROUND: The prevalence of mild cognitive impairment (MCI) is steadily increasing among elderly people with type 2 diabetes (T2DM). This study aimed to create and validate a predictive model based on a nomogram. METHODS: This cross-sectional study collected sociodemographic characteristics, T2DM-related factors, depression, and levels of social support from 530 older adults with T2DM. We used LASSO regression and multifactorial logistic regression to determine the predictors of the model. The performance of the nomogram was evaluated using calibration curves, receiver operating characteristics (ROC), and decision curve analysis (DCA). RESULTS: The nomogram comprised age, smoking, physical activity, social support, depression, living alone, and glycosylated hemoglobin. The AUC for the training and validation sets were 0.914 and 0.859. The DCA showed good clinical applicability. CONCLUSIONS: This predictive nomogram has satisfactory accuracy and discrimination. Therefore, the nomogram can be intuitively and easily used to detect MCI in elderly adults with T2DM.

The optimal metric for viral genome space.

Understanding the structural similarity between genomes is pivotal in classification and phylogenetic analysis. As the number of known genomes rockets, alignment-free methods have gained considerable attention. Among these methods, the natural vector method stands out as it represents sequences as vectors using statistical moments, enabling effective clustering based on families in biological taxonomy. However, determining an optimal metric that combines different elements in natural vectors remains challenging due to the absence of a rigorous theoretical framework for weighting different k-mers and orders. In this study, we address this challenge by transforming the determination of optimal weights into an optimization problem and resolving it through gradient-based techniques. Our experimental results underscore the substantial improvement in classification accuracy achieved by employing these optimal weights, reaching an impressive 92.73% on the testing set, surpassing other alignment-free methods. On one hand, our method offers an outstanding metric for virus classification, and on the other hand, it provides valuable insights into feature integration within alignment-free methods.

GaN integrated optical devices for glycerol viscosity measurement.

This Letter presents the fabrication and characterization of a chip-scale GaN optical device for measuring glycerol viscosity. The monolithically integrated GaN chip with a size of 1 × 1 mm2 comprises a light-emitting diode (LED) and a photodiode (PD) on a transparent sapphire substrate. The glycerol droplet applied to the device acts as a medium for coupling light from the LED to the PD. When a mechanical impulse is applied, the droplet undergoes a damped vibration that depends on its viscosity, causing a change in its shape and altering the path of light propagation. The viscosity of the glycerol sample can be determined by obtaining the rate of attenuation of the measured photocurrent signals. The proposed unit offers a fast time response in microseconds and requires only a small sample volume of 5 µl. The developed device is highly suitable for the practical measurement of glycerol viscosity due to its miniaturization, low cost, and ease of operation without the need for external optical components.

Factors associated with the level of self-management in elderly patients with chronic diseases: a pathway analysis.

BACKGROUND: To analyze the effects and pathways of factors such as psychological capital, family functioning, and sources of meaning in life on the level of self-management in elderly patients with chronic diseases and to provide a basis for the development of relevant nursing interventions in the future. METHODS: Convenience sampling was used to select elderly patients with chronic diseases who underwent medical checkups and consultations at three community hospitals in Jinzhou city from March 2023 to October 2023, and the self-designed General Information Questionnaire (GIS), Psychological Capital of the Elderly Scale (PCE), Family Functioning Index Questionnaire (APGAR), Sources of Meaning of Life Scale for Older Adults(SMSE), and Self-Management Behavior of Chronic Patients Scale (SMCS) were used. SPSS 26.0 was used for data entry, one-way analysis, Pearson correlation analysis, and multiple linear regression were used to analyze the data, and Amos 17.0 was used to construct the structural equation model. RESULTS: A total of 355 elderly patients with chronic diseases were included, and their self-management score was 74.75 ± 12.93, which was moderate. The results of the influencing factor analysis showed that the influencing factors of the self-management level of elderly chronic disease patients were age, years of illness, psychological capital, family functioning, and sources of meaning in life (p < 0.05). Path analysis revealed that sources of meaning in life were a partial mediator of the relationship between psychological capital and self-management, with an effect value of 0.166 (95% CI: 0.042,0.391), accounting for 37.6% of the total effect; life meaning was a partial mediator of family functioning and self-management level, with an effect value of 0.231 (95% CI: 0.040,0.452), accounting for 54.0% of the total effect. accounting for 54.0% of the total effect. CONCLUSION: The self-management of elderly patients with chronic diseases is intermediate. Healthcare professionals should actively implement holistic healthcare management measures from the family aspect to help patients understand the meaning of life and improve the level of patients' psychological capital to improve the self-management level of elderly patients with chronic diseases.

Automated recognition of chromosome fusion using an alignment-free natural vector method.

Chromosomal fusion is a significant form of structural variation, but research into algorithms for its identification has been limited. Most existing methods rely on synteny analysis, which necessitates manual annotations and always involves inefficient sequence alignments. In this paper, we present a novel alignment-free algorithm for chromosomal fusion recognition. Our method transforms the problem into a series of assignment problems using natural vectors and efficiently solves them with the Kuhn-Munkres algorithm. When applied to the human/gorilla and swamp buffalo/river buffalo datasets, our algorithm successfully and efficiently identifies chromosomal fusion events. Notably, our approach offers several advantages, including higher processing speeds by eliminating time-consuming alignments and removing the need for manual annotations. By an alignment-free perspective, our algorithm initially considers entire chromosomes instead of fragments to identify chromosomal structural variations, offering substantial potential to advance research in this field.

Serum cell division cycle 42 reflects the development and progression of diabetic nephropathy in patients with diabetes mellitus.

Cell division cycle 42 (CDC42) regulates podocyte apoptosis to take part in the development and progression of diabetic nephropathy (DN), but currently the clinical evidence is limited. The aim of the present study was to investigate the capability of serum CDC42 expression level to estimate the development and progression of DN in patients with diabetes mellitus (DM). Patients with type 2 DM (n=306) were enrolled and divided into normoalbuminuria (n=185), microalbuminuria (n=72) and macroalbuminuria (n=49) groups based on the urinary albumin-to-creatinine ratio. Serum CDC42 was measured in all subjects using enzyme-linked immunosorbent assay. The median (interquartile range) CDC42 in patients with DM was 0.461 (0.314-0.690) ng/ml (range, 0.087-1.728 ng/ml). CDC42 was positively associated with the estimated glomerular filtration rate (P<0.001), but negatively correlated with body mass index, systolic blood pressure, hemoglobin A1c, serum creatine, serum uric acid and C reactive protein (all P<0.050). CDC42 levels were lowest in the macroalbuminuria group, followed by the microalbuminuria group, and were highest in the normoalbuminuria group (P<0.001). CDC42 indicated that it was a favorable estimator for the presence of albuminuria [area under the curve (AUC), 0.792; 95% confidence interval (CI), 0.736-0.848] and macroalbuminuria (AUC, 0.845; 95% CI, 0.775-0.915). By analyses in four different multivariate logistic regression models, increased CDC42 was independently associated with the presence of microalbuminuria (all P<0.001), macroalbuminuria (most P<0.001) and microalbuminuria + macroalbuminuria (all P<0.001). Serum CDC42 level negatively correlated with microalbuminuria and macroalbuminuria in patients with DM, suggesting its ability for estimating the development and progression of DN.

Combination Immunotherapy of Oncolytic Flu-Vectored Virus and Programmed Cell Death 1 Blockade Enhances Antitumor Activity in Hepatocellular Carcinoma.

Oncolytic viruses (OVs) are appealing anti-tumor agents. But it is limited in its effectiveness. In this study, we used combination therapy with immune checkpoint inhibitor to enhance the antitumor efficacy of OVs. Using reverse genetics technology, we rescued an oncolytic influenza virus with the name delNS1-GM-CSF from the virus. After identifying the hemagglutination and 50% tissue culture infectivedose (TCID50) of delNS1-GM-CSF, it was purified, and the viral morphology was observed under electron microscopy. Reverse transcription quantitative-polymerase chain reaction (RT-qPCR) was used to identify the level of GM-CSF expression in delNS1-GM-CSF, and the GM-CSF expression level was determined after infection with delNS1-GM-CSF by enzyme linked immunosorbent assay (ELISA). To study the tumor-killing effect of delNS1-GM-CSF, we utilized the hepatocellular carcinoma (HCC) tumor-bearing mouse model. To examine signaling pathways, we performed transcriptome sequencing on mouse tumor tissue and applied western blotting to confirm the results. Changes in T-cell infiltration in HCC tumors following treatment were analyzed using flow cytometry and immunohistochemistry. DelNS1-GM-CSF can target and kill HCCs without damaging normal hepatocytes. DelNS1-GM-CSF combined with programmed cell death 1 blockade therapy enhanced anti-tumor effects and significantly improved mouse survival. Further, we found that combination therapy had an antitumor impact via the janus kinase-signal transducer and activator of transcription (JAK2-STAT3) pathway as well as activated CD4+ and CD8+T cells. Interestingly, combined therapy also showed promising efficacy in distant tumors. DelNS1-GM-CSF is well targeted. Mechanistic investigation revealed that it functions through the JAK2-STAT3 pathway. Combination immunotherapies expected to be a novel strategy for HCC immunotherapy.