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ObjectiveTo comprehensively analyze the ADRs associated with Denosumab (Prolia) in the treatment of osteoporosis using data from the FAERS database, and gain a better understanding of the potential risks and side effects of Denosumab (Prolia) therapy.MethodsData of Denosumab (Prolia) were collected from the FAERS database covering the period from first quarter of 2010 to the third quarter of 2023. Disproportionality analysis was performed by calculating the reporting odds ratios (ROR), proportional reporting ratio (PRR), and Bayesian analysis confidence propagation neural network (BCPNN) to detect positive signals.ResultsTotally, 17,985,365 reports were collected from the FAERS database, 1,97,807 reports of Denosumab (Prolia) were identified as the “primary suspected (PS)” ADRs. Denosumab (Prolia) induced ADRs occurred in 27 organ systems. 38 significant disproportionality PTs satisfying with the three algorithms were retained at the same time. Unexpected significant ADRs such as bone density abnormal and immobile also occur. The majority of the ADRs occurred within the first 30 days after Denosumab (Prolia) initiation.ConclusionBased on the American FAERS database, the high frequency ADRs of Denosumab (Prolia) were hypocalcaemia, bone density abnormal, eczema, rebound effect, spinal deformity, etc. Clinical use of this drug should focus on this part of ADRs. Attention should also be paid to newly discovered ADRs, such as immobile, menopausal symptoms, etc., to avoid more serious consequences. Cohort studies, more detailed and comprehensive case information, and long-term clinical investigations are needed to confirm these results and to further understand the safety profile of Denosumab (Prolia).

Building segmentation has extensive research value and application prospects in high-resolution remote sensing image (HRSI) processing. However, complex architectural contexts, varied building morphologies, and non-building occlusions make building segmentation challenging. Compared with traditional methods, deep learning-based methods present certain advantages in terms of accuracy and intelligence. At present, the most popular option is to first apply a single neural network to encode an HRSI, then perform a decoding process through up-sampling or using a transposed convolution operation, and then finally obtain the segmented building image with the help of a loss function. Although effective, this approach not only tends to lead to a loss of detail information, but also fails to fully utilize the contextual features. As an alternative, we propose a novel network called NPSFF-Net. First, using an improved pseudo-Siamese network composed of ResNet-34 and ResNet-50, two sets of deep semantic features of buildings are extracted with the support of transfer learning, and four encoded features at different scales are obtained after fusion. Then, information from the deepest encoded feature is enriched using a feature enhancement module, and the resolutions are recovered via the operations of skip connections and transposed convolutions. Finally, the discriminative features of buildings are obtained using the designed feature fusion algorithm, and the optimal segmentation model is obtained by fitting a cross-entropy loss function. Our method obtained intersection-over-union values of 89.45% for the Aerial Imagery Dataset, 71.88% for the Massachusetts Buildings Dataset, and 68.72% for the Satellite Dataset I.

The ability of wheat to adapt to a wide range of environmental conditions is determined mostly by allelic diversity among genes regulating vernalization requirement. Vrn-1 is a major regulator of this requirement. In this study, two novel alleles of Vrn-A1 were discovered in Chinese cultivars: vrn-A1n was identified in two landraces, Jiunong 2 and Ganchun 16, and Vrn-A1o was detected in Duanhongmangmai. Both novel alleles showed a linked duplication in the promoter region. The common copy of these two alleles was identical to the recessive allele vrn-A1. Compared with the recessive allele vrn-A1, the other copy of vrn-A1n contained a 54-bp deletion in the promoter region and the distinct copy of Vrn-A1o contained an 11-bp deletion in the promoter region. In segregating populations in the greenhouse under nonvernalizing (20–25 °C) and long-day (16 h light) conditions, plants with the novel vrn-A1n allele did not head earlier than those with the recessive vrn-A1 allele. However, plants that were either homozygous or heterozygous for the novel Vrn-A1o allele headed earlier than those with the recessive vrn-A1 allele. To identify the novel allele with the small-sized product and facilitate screening, a DNA marker for the novel dominant allele Vrn-A1o was designed. Analysis of the novel-allele distribution showed that two cultivars carrying the vrn-A1n allele were dispersed in the northwestern spring wheat zone, and 12 cultivars carrying the dominant Vrn-A1o allele were widely distributed in the northwestern spring wheat zone, Xinjiang winter and spring wheat zone, Yellow and Huai River valley winter wheat zone, and Qinghai-Tibetan Plateau spring and winter wheat zone. Our study identifies useful germplasm and a DNA marker for wheat breeding.

Android has been the most widely used operating system for mobile phones over the past few years. Malicious attacks against android are a major privacy and security concern. Malware detection techniques for android applications are therefore significant. A class of methods using Function Call Graphs (FCGs) for android malware detection has shown great potential. The relationships between functions are limited to simple binary relationships (i.e., graphs) in these methods. However, one function often calls several other functions to produce specific effects in android applications, which cannot be captured with FCGs. In this paper, we propose to formalize android malware detection as a hypergraph-level classification task. A hypergraph is a topology capable of portraying complex relationships between multiple vertices, which can better characterize the functional behavior of android applications. We model android applications using hypergraphs and extract the embedded features of android applications using hypergraph neural networks to represent the functional behavior of android applications. Hypergraph neural networks can encode high-order data correlation in a hypergraph structure for data representation learning. In experiments, we validate the gaining effect of hypergraphs on detection performance across two open-source android application datasets. Especially, HGNNP obtains the best classification performance of 91.10% on the Malnet-Tiny dataset and 97.1% on the Drebin dataset, which outperforms all baseline methods.

A shale reservoir is a complex system with lots of nanoscale pore throat structures and variable permeability. Even though shale reservoirs contain both organic and inorganic matter, the slip effect and phase behavior complicate the two-phase flow mechanism. As a result, understanding how microscale effects occur is critical to effectively developing shale reservoirs. In order to explain the experimental phenomena that are difficult to describe using classical two-phase flow theory, this paper proposes a new simulation method for two-phase shale oil reservoirs that takes into account the microscale effects, including the phase change properties of oil and gas in shale micro- and nanopores, as well as the processes of dissolved gas escape, nucleation, growth and aggregation. The presented numerical simulation framework, aimed at comprehending the dynamics of the two-phase flow within fractured horizontal wells situated in macroscale shale reservoirs, is subjected to validation against real-world field data. This endeavor serves the purpose of enhancing the theoretical foundation for predicting the production capacity of fractured horizontal wells within shale reservoirs. The impact of capillary forces on the fluid dynamics of shale oil within micro- and nanoscale pores is investigated in this study. The investigation reveals that capillary action within these micro- and nanoscale pores of shale formations results in a reduction in the actual bubble point pressure within the oil and gas system. Consequently, the reservoir fluid persists in a liquid monophasic state, implying a constrained mobility and diminished flow efficiency of shale oil within the reservoir. This constrained mobility is further characterized by a limited spatial extent of pressure perturbation and a decelerated pressure decline rate, which are concurrently associated with a relatively elevated oil saturation level.

Trauma-induced osteonecrosis of the femoral head (TI-ONFH) is a pathological process in which the destruction of blood vessels supplying blood to the femoral head causes the death of bone tissue cells. Vascular cell adhesion molecule 1 (VCAM-1) has been shown to have potent proangiogenic activity, but the role in angiogenesis of TI-ONFH is unclear. In this work, we discovered that VCAM-1 was significantly downregulated in the bone marrow mesenchymal stem cells (BMSCs) derived from patients with TI-ONFH. Subsequently, we constructed BMSCs overexpressing VCAM-1 using a lentiviral vector. VCAM-1 enhances the migration and angiogenesis of BMSCs. We further performed mRNA transcriptome sequencing to explore the mechanisms by which VCAM-1 promotes angiogenesis. Gene ontology biological process enrichment analysis demonstrated that upregulated differentially expressed genes (DEGs) were related to blood vessel development. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that upregulated DEGs were engaged in the Apelin signaling pathway. Apelin-13 is the endogenous ligand of the APJ receptor and activates this G protein-coupled receptor. Treatment with Apelin-13 activated the Apelin signaling pathway and suppressed the expression of cellular communication network factor 2 in BMSCs. Furthermore, Apelin-13 also inhibits the migration and angiogenesis of VCAM-1-BMSCs. In summary, VCAM-1 plays an important role in vascular microcirculation disorders of TI-ONFH, which provides a new direction for the molecular mechanism and treatment of TI-ONFH.

Abstract Background Studies have shown that medial subluxation of the tibia occurs after anterior cruciate ligament (ACL) rupture. However, it is unclear whether anterior cruciate ligament reconstruction (ACLR) can correct tibial coronal subluxation. Purpose To determine whether the tibia is medially subluxated after ACL rupture, and whether ACLR can correct medial subluxation of the tibia. Study design Case series; Level of evidence, 4, Retrospective clinical study. Methods The distance of tibial coronal subluxation before and after ACLR surgery was measured in 48 patients with ACL rupture and meniscus injury. Tibiofemoral subluxation was defined as the perpendicular distance between the long axis of the tibia and a second parallel line originating at the most proximal aspect of the femoral intercondylar notch. To determine the long axis of the tibia, two circles separated by 5 cm were centered on the proximal tibia. The proximal circle is 5 cm from the tibial plateau, and the distal circle is 5 cm from the proximal circle. The line passing through the center of the two circles was considered the long axis of the proximal tibia. Care was taken to ensure that each patient lied on the back with their patellae facing upward, to minimize rotational variation among the radiographs. At the same time, 30 patients with simple meniscus injury who underwent arthroscopy during the same period were selected to determine the degree of tibiofemoral coronal subluxation as the baseline value. The changes before and after operation were compared, as well as the differences with the baseline data. Result The average follow-up period was 21.2 ± 5.8 months. The average distance of tibial coronal subluxation before ACLR was 5.5 ± 2.1 mm, which was significantly different from that of baseline group (7.3 ± 2.1 mm) (P

A low-profile, wideband, and high-gain antenna array, based on a novel double-H-shaped slot microstrip patch radiating element and robust against high temperature variations, is proposed in this work. The antenna element was designed to operate in the frequency range between 12 GHz and 18.25 GHz, with a 41.3% fractional bandwidth (FBW) and an obtained peak gain equal to 10.2 dBi. The planar array, characterized by a feed network with a flexible 1 to 16 power divider, comprised 4 × 4 antenna elements and generated a pattern with a peak gain of 19.1 dBi at 15.5 GHz. An antenna array prototype was fabricated, and the measurements showed good agreement with the numerical simulations as the manufactured antenna operated in the range of 11.4–17 GHz, with a 39.4% FBW, and the peak gain at 15.5 GHz was 18.7 dBi. The high-temperature simulated and experimental results, performed in a temperature chamber, demonstrated that the array performance was stable in a wide temperature range, from −50 °C to 150 °C.

Phage therapy is widely being reconsidered as an alternative to antibiotics for the treatment of multidrug-resistant bacterial infections, including salmonellosis caused by Salmonella. As facultative intracellular parasites, Salmonella could spread by vertical transmission and pose a great threat to both human and animal health; however, whether phage treatment might provide an optional strategy for controlling bacterial vertical infection remains unknown. Herein, we explored the effect of phage therapy on controlling the vertical transmission of Salmonella enterica serovar Gallinarum biovar Pullorum (S. Pullorum), a poultry pathogen that causes economic losses worldwide due to high mortality and morbidity. A Salmonella phage CKT1 with lysis ability against several S. enterica serovars was isolated and showed that it could inhibit the proliferation of S. Pullorum in vitro efficiently. We then evaluated the effect of phage CKT1 on controlling the vertical transmission of S. Pullorum in an adult broiler breeder model. The results demonstrated that phage CKT1 significantly alleviated hepatic injury and decreased bacterial load in the liver, spleen, heart, ovary, and oviduct of hens, implying that phage CKT1 played an active role in the elimination of Salmonella colonization in adult chickens. Additionally, phage CKT1 enabled a reduction in the Salmonella-specific IgG level in the serum of infected chickens. More importantly, the decrease in the S. Pullorum load on eggshells and in liquid whole eggs revealed that phage CKT1 effectively controlled the vertical transmission of S. Pullorum from hens to laid eggs, indicating the potential ability of phages to control bacterial vertical transmission.

Land surface temperature (LST) is a vital parameter in the surface energy budget and water cycle. One of the most important foundations for LST studies is a theory to understand how to model LST with various influencing factors, such as canopy structure, solar radiation, and atmospheric conditions. Both physical-based and empirical methods have been widely applied. However, few studies have compared these two categories of methods. In this paper, a physical-based method, soil canopy observation of photochemistry and energy fluxes (SCOPE), and two empirical methods, random forest (RF) and long short-term memory (LSTM), were selected as representatives for comparison. Based on a series of measurements from meteorological stations in the Heihe River Basin, these methods were evaluated in different dimensions, i.e., the difference within the same surface type, between different years, and between different climate types. The comparison results indicate a relatively stable performance of SCOPE with a root mean square error (RMSE) of approximately 2.0 K regardless of surface types and years but requires many inputs and a high computational cost. The empirical methods performed relatively well in dealing with cases either within the same surface type or changes in temporal scales individually, with an RMSE of approximately 1.50 K, yet became less compatible in regard to different climate types. Although the overall accuracy is not as stable as that of the physical method, it has the advantages of fast calculation speed and little consideration of the internal structure of the model.

Antibiotic resistance genes of Escherichia coli (E. coli) from companion animals were still poorly understood. Here, we investigated the extended-spectrum β-lactamases (ESBLs) resistance genes of E. coli from companion animals in Shandong, China. A total of 79 isolates (80.6%) were recovered from 98 healthy or diarrheal companion animals in 2021, among which ESBLs-producing isolates accounted for 43.0% (34/79), and more than half of ESBL E. coli (ESBL-EC) strains (n = 19) were isolated from healthy companion animals. Diarrheagenic E. coli isolates (45.6%, n = 36) were represented by enterotoxigenic (ETEC) (32.9%), enteropathogenic (EPEC) (10.1%) and enteroinvasive (EIEC) (2.6%), 20 isolates of which were from healthy pets. Among tested antibiotics, resistance to tetracycline (64.6%) was the most commonly observed, followed by doxycycline (59.5%) and ampicillin (53.2%). Notably, all isolates were susceptible to meropenem. The multidrug-resistant (MDR) rate was 49.4%, 20 isolates of which were ESBLs producers; moreover, 23.4%, 16.4% of ESBL-EC strains were resistant to 5 or more, 7 or more antibiotics, respectively. Among the 5 β-lactamase resistance genes, the most frequent gene was blaCTX-M (60.76%), followed by blaSHV (40.51%). The plasmid-mediated quinolone resistance (PMQR) gene aac(6’)-Ib-cr was detected in 35 isolates. Additionally, ESBL-associated genes (i.e., blaCTX-M, blaSHV) were found in 76.5% ESBL-EC strains, with six isolates carrying blaCTX-M and blaSHV. The marker gene of high-pathogenicity island gene irp2 (encoding iron capture systems) was the most frequency virulence gene. Our results showed that ESBL-EC were widespread in healthy or diarrhea companion animals, especially healthy pets, which may be a potential reservoir of antibiotic resistance, therefore, enhancing a risk to public and animal health.

With the continuous increase in wind power penetration, doubly fed wind turbines can quickly respond to changes in grid frequency, and have particularly important inertia-response characteristics. This article starts with the excitation control principle of a doubly fed induction generator, compares the transient frequency characteristics of the synchronous generator under fault, and proposes that the doubly fed induction generator can control the speed or active power of the generator through excitation. It proves the unique “active power transient frequency characteristics” of doubly fed induction generators. Under different wind speeds, the inertia response capability of the wind turbine is quantified, and the degrees of influence of the inertia time constant and frequency characteristic slope of the doubly fed induction generator on the transient change process are analyzed. Finally, simulation and experiments verify the correctness of the above theory, which provides a basis for significantly improving the transient stability of the power system and realizing the controllability of the transient stability of the power system.

To acquire high-resolution wide-swath (HRWS) imaging capacity, the displaced phase center multichannel azimuth beam (DPCMAB) technology is usually adopted in spaceborne synthetic aperture radar (SAR), while multichannel reconstruction must be carried out before imaging process due to azimuth nonuniform sampling. Up to now, almost all azimuth multichannel reconstruction algorithms have been mainly based on conventional hyperbolic range equation (CHRE), but the accuracy of the CHRE model is usually not suitable for the HRWS mode, especially for high resolution and large squint observation cases. In this study, the azimuth multichannel signal model based on the advanced hyperbolic range equation (AHRE) is established and analyzed. The major difference between multichannel signal models based on CHRE and AHRE is the additional time-varying phase error between azimuth channels. The time-varying phase error is small and can be ignored in the monostatic DPCMAB SAR system, but it must be considered and compensated in the distributed DPCMAB SAR system. In addition to the time-varying phase error, additional Doppler spectrum shift and extended Doppler bandwidth should be considered in the squint case during azimuth multichannel reconstruction. The azimuth multichannel reconstruction algorithm based on AHRE is proposed in this paper. Before multichannel reconstruction and combination, time-varying phase errors between azimuth channels were first compensated, and the range-frequency-dependent de-skewing function was derived to remove the two-dimension (2D) spectrum tilt to avoid azimuth under-sampling. Then, azimuth multichannel data were reconstructed according to the azimuth multichannel impulse response based on AHRE. Finally, the range-frequency dependent re-skewing function was introduced to recover the tilted 2D spectrum. Simulation results on both point and distributed targets validated the proposed azimuth multichannel reconstruction approach.

The low accuracy of satellite cloud fraction (CF) data over the Arctic seriously restricts the accurate assessment of the regional and global radiative energy balance under a changing climate. Previous studies have reported that no individual satellite CF product could satisfy the needs of accuracy and spatio-temporal coverage simultaneously for long-term applications over the Arctic. Merging multiple CF products with complementary properties can provide an effective way to produce a spatiotemporally complete CF data record with higher accuracy. This study proposed a spatiotemporal statistical data fusion framework based on cumulative distribution function (CDF) matching and the Bayesian maximum entropy (BME) method to produce a synthetic 1°×1° CF dataset in the Arctic during 2000-2020. The CDF matching was employed to remove the systematic biases among multiple passive sensor datasets through the constraint of using CF from an active sensor. The BME method was employed to combine adjusted satellite CF products to produce a spatiotemporally complete and accurate CF product. The advantages of the presented fusing framework are that it not only uses the spatiotemporal autocorrelations but also explicitly incorporates the uncertainties of passive sensor products benchmarked with reference data, i.e., active sensor product and ground-based observations. The inconsistencies of Arctic CF between passive sensor products and the reference data were reduced by about 10-20% after fusing. Compared with ground-based observations, R2 increased by about 0.20-0.48 and the root mean square error (RMSE) and bias reductions averaged about 6.09% and 4.04% for land regions, respectively; these metrics for ocean regions were about 0.05-0.31, 2.85%, and 3.15%, respectively. Compared with active sensor data, R2 increased by nearly 0.16, and RMSE and bias declined by about 3.77% and 4.31%, respectively, in land; meanwhile, improvements in ocean regions were about 0.3 for R2, 4.46% for RMSE and, 3.92% for bias. The comparison with the ERA5 reanalysis and CMIP6 CF datasets shows that the proposed fusion algorithm effectively corrected the CF data with differences greater than 30%. Moreover, the fused product effectively supplements the temporal gaps of AVHRR36 based products caused by satellite faults and the data missing from MODIS-based products prior to the launch of Aqua, and extends the temporal range better than the active product; it addresses the spatial insufficiency of the active sensor data and the AVHRR-based products acquired at latitudes greater than 82.5°N. A continuous monthly 1-degree CF product covering the entire Arctic during 2000-2020 was generated and is freely available to the public at https://doi.org/10.5281/zenodo. 7624605 (Liu et al., 2022). This is of great importance for reducing the uncertainty in the estimation of surface radiation parameters and thus helps researchers to better understand the earth's energy imbalance. [ABSTRACT FROM AUTHOR]

ObjectivesThis study aimed to develop a deep learning radiomic model using multimodal imaging to differentiate benign and malignant breast tumours.MethodsMultimodality imaging data, including ultrasonography (US), mammography (MG), and magnetic resonance imaging (MRI), from 322 patients (112 with benign breast tumours and 210 with malignant breast tumours) with histopathologically confirmed breast tumours were retrospectively collected between December 2018 and May 2023. Based on multimodal imaging, the experiment was divided into three parts: traditional radiomics, deep learning radiomics, and feature fusion. We tested the performance of seven classifiers, namely, SVM, KNN, random forest, extra trees, XGBoost, LightGBM, and LR, on different feature models. Through feature fusion using ensemble and stacking strategies, we obtained the optimal classification model for benign and malignant breast tumours.ResultsIn terms of traditional radiomics, the ensemble fusion strategy achieved the highest accuracy, AUC, and specificity, with values of 0.892, 0.942 [0.886–0.996], and 0.956 [0.873–1.000], respectively. The early fusion strategy with US, MG, and MRI achieved the highest sensitivity of 0.952 [0.887–1.000]. In terms of deep learning radiomics, the stacking fusion strategy achieved the highest accuracy, AUC, and sensitivity, with values of 0.937, 0.947 [0.887–1.000], and 1.000 [0.999–1.000], respectively. The early fusion strategies of US+MRI and US+MG achieved the highest specificity of 0.954 [0.867–1.000]. In terms of feature fusion, the ensemble and stacking approaches of the late fusion strategy achieved the highest accuracy of 0.968. In addition, stacking achieved the highest AUC and specificity, which were 0.997 [0.990–1.000] and 1.000 [0.999–1.000], respectively. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity of 1.000 [0.999–1.000] under the early fusion strategy.ConclusionThis study demonstrated the potential of integrating deep learning and radiomic features with multimodal images. As a single modality, MRI based on radiomic features achieved greater accuracy than US or MG. The US and MG models achieved higher accuracy with transfer learning than the single-mode or radiomic models. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity under the early fusion strategy, showed higher diagnostic performance, and provided more valuable information for differentiation between benign and malignant breast tumours.

Atherosclerosis, a highly prevalent cardiovascular disease, has significantly contributed to morbidity and mortality worldwide. Despite the availability of various treatment options in clinical practice, complete reversal of the atherosclerotic pathological process remains challenging. Traditional anti-arteriosclerosis drugs often suffer from poor solubility, limited targeting ability and obvious adverse effects. Additionally, novel therapeutics such as genetic and protein-based drugs inherently suffer from instability issues, thereby compromising their actual curative efficacy and impeding further clinical application. Recently, nanomedicine-based drug delivery strategies have emerged as promising approaches for treating atherosclerosis due to their ability to specifically transport various drugs to target cells, microstructures and specific molecules within lesion sites. These advanced features enhance drug properties and bioavailability while minimizing off-target effects. Moreover, numerous studies have demonstrated that nanomedicine not only inhibits plaque progression but also reduces lesional areas and eventually reverses atherosclerosis. The utilization of nanomedicine is considered an important therapeutic tactic for further mitigating residual risks associated with atherosclerosis. This review provides a comprehensive overview of emerging nanomedicine-based strategies for targeted delivery of anti-atherosclerosis drugs, elucidating their distinct mechanisms and highlighting their remarkable efficacy in combating atherosclerosis. Additionally, critical challenges and future perspectives associated with the development and application of anti-arteriosclerosis nanomedicine are thoroughly discussed.

Vaccines has long been the focus of antiviral immunotherapy research. Viral epitopes are thought to be useful biomarkers for immunotherapy (both antibody-based and cellular). In this study, we designed a novel vaccine molecule, the Hantaan virus (HTNV) glycoprotein (GP) tandem Th epitope molecule (named the Gnc molecule), in silico. Subsequently, computer analysis was used to conduct a comprehensive and in-depth study of the various properties of the molecule and its effects as a vaccine molecule in the body. The Gnc molecule was designed for DNA vaccines and optimized with a lysosomal-targeting membrane protein (LAMP) strategy. The effects of GP-derived Th epitopes and multiepitope vaccines were initially verified in animals. Our research has resulted in the design of two vaccines based on effective antiviral immune targets. The effectiveness of molecular therapies has also been preliminarily demonstrated in silico and in laboratory animals, which lays a foundation for the application of a vaccines strategy in the field of antivirals.

(1) Background and Purpose: Ebola virus (EBOV) is the causative agent of Ebola virus disease (EVD), which causes extremely high mortality and widespread epidemics. The only glycoprotein (GP) on the surface of EBOV particles is the key to mediating viral invasion into host cells. DNA vaccines for EBOV are in development, but their effectiveness is unclear. The lack of immune characteristics resides in antigenic MHC class II reactivity. (2) Methods: We selected MHC-II molecules from four human leukocyte antigen II (HLA-II) superfamilies with 98% population coverage and eight mouse H2-I alleles. IEDB, NetMHCIIpan, SYFPEITHI, and Rankpep were used to screen MHC-II-restricted epitopes with high affinity for EBOV GP. Further immunogenicity and conservation analyses were performed using VaxiJen and BLASTp, respectively. EpiDock was used to simulate molecular docking. Cluster analysis and binding affinity analysis of EBOV GP epitopes and selected MHC-II molecules were performed using data from NetMHCIIpan. The selective GP epitopes were verified by the enzyme-linked immunospot (ELISpot) assay using splenocytes of BALB/c (H2d), C3H, and C57 mice after DNA vaccine pVAX-GPEBO immunization. Subsequently, BALB/c mice were immunized with Protein-GPEBO, plasmid pVAX-GPEBO, and pVAX-LAMP/GPEBO, which encoded EBOV GP. The dominant epitopes of BALB/c (H-2-I-AdEd genotype) mice were verified by the enzyme-linked immunospot (ELISpot) assay. It is also used to evaluate and explore the advantages of pVAX-LAMP/GPEBO and the reasons behind them. (3) Results: Thirty-one HLA-II-restricted and 68 H2-I-restricted selective epitopes were confirmed to have high affinity, immunogenicity, and conservation. Nineteen selective epitopes have cross-species reactivity with good performance in MHC-II molecular docking. The ELISpot results showed that pVAX-GPEBO could induce a cellular immune response to the synthesized selective peptides. The better immunoprotection of the DNA vaccines pVAX-LAMP/GPEBO coincides with the enhancement of the MHC class II response. (4) Conclusions: Promising MHC-II-restricted candidate epitopes of EBOV GP were identified in humans and mice, which is of great significance for the development and evaluation of Ebola vaccines.