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Building extraction is significant for the intelligent interpretation of high-resolution remote sensing images (HRSIs). However, in some complex scenarios where the features of the building and its adjacent ground objects are similar, the current segmentation model cannot distinguish them effectively. Therefore, we propose a complex scenarios adaptive network (CSA-Net) for building extraction. CSA-Net is comprised of the hierarchical-context feature extraction (HFE) module, the global-local feature interaction (GFI) module, and the multiscale-adaptive feature fusion (MFF) structure. The HFE obtains high-level semantic information at different levels and fuses it with low-level detailed information by skipping connections to enhance the reasoning and perception ability of building structure in complex scenes. Then, the GFI acquires global-local features of buildings and their surrounding environment via dense multiscale dilated convolution. The information can be shared through efficient interaction among features, and irrelevant backgrounds can be suppressed. Then, in the up-sampling process, the MFF alleviates the feature loss and enhances the robustness of the network by using feature fusion after layer-by-layer adaptive weight allocation. Experiments show that CSA-Net outperforms other comparable methods, with intersection over union values of 79.99%, 89.75%, and 73.59%, respectively, on the Google Arlinton, WHU, and Massachusetts building datasets. The visual comparison results demonstrate that our method can enhance the accuracy of building extraction in complex scenes. Meanwhile, the efficiency results indicate our approach strikes a balance between calculation parameters and time and achieves high levels of efficiency.

The complexity of indoor point cloud and information loss during point cloud acquisition are the main challenges for accurate and complete indoor buildings 3D wireframe construction. This paper presents a 3D wireframe construction method for indoor structure point clouds based on Gestalt rules. First, boundary points of indoor structures are used for structural plane segmentation, including ceilings, floors, walls, beams, etc. Then, point clouds of each structural plane are projected onto its best-fit plane to obtain a 2D projection, followed by boundary points extraction and line fitting to get the 2D line segments. Finally, an accurate and complete 3D wireframe is constructed by optimizing anomalous line segments based on the Gestalt rules. In our method, Gestalt rules are used to deal with problems in line segments, including cluttered distribution, duplicate extraction, non-regularization (non-parallel and non-perpendicular), topological errors (non-closed and discontinuous), and missing information. Experiments demonstrate that our proposed method significantly improves the accuracy and completeness of the results.

Hyperspectral image (HSI) classification aims to recognize categories of objects based on spectral–spatial features and has been used in a wide range of real-world application areas. Attention mechanisms are widely used in HSI classification for their ability to focus on important information in images automatically. However, due to the approximate spectral–spatial features in HSI, mainstream attention mechanisms are difficult to accurately distinguish the small difference, which limits the classification accuracy. To overcome this problem, a spectral–spatial-sensorial attention network (S3AN) with controllable factors is proposed to efficiently recognize different objects. Specifically, two controllable factors, dynamic exponential pooling (DE-Pooling) and adaptive convolution (Adapt-Conv), are designed to enlarge the difference in approximate features and enhance the attention weight interaction. Then, attention mechanisms with controllable factors are utilized to build the redundancy reduction module (RRM), feature learning module (FLM), and label prediction module (LPM) to process HSI spectral–spatial features. The RRM utilizes the spectral attention mechanism to select representative band combinations, and the FLM introduces the spatial attention mechanism to highlight important objects. Furthermore, the sensorial attention mechanism extracts location and category information in a pseudo label to guide the LPM for label prediction and avoid details from being ignored. Experimental results on three public HSI datasets show that the proposed method is able to accurately recognize different objects with an overall accuracy (OA) of 98.69%, 98.89%, and 97.56%, respectively.

Abstract Background Personalized immunotherapy utilizing cancer vaccines tailored to the tumors of individual patients holds promise for tumors with high genetic heterogeneity, potentially enabling eradication of the tumor in its entirety. Methods Here, we demonstrate a general strategy for biological nanovaccines that trigger tailored tumor-specific immune responses for hepatocellular carcinoma (HCC). Dendritic cell (DC)-derived exosomes (DEX) are painted with a HCC-targeting peptide (P47-P), an α-fetoprotein epitope (AFP212-A2) and a functional domain of high mobility group nucleosome-binding protein 1 (N1ND-N), an immunoadjuvant for DC recruitment and activation, via an exosomal anchor peptide to form a “trigger” DEX vaccine (DEXP&A2&N). Results DEXP&A2&N specifically promoted recruitment, accumulation and activation of DCs in mice with orthotopic HCC tumor, resulting in enhanced cross-presentation of tumor neoantigens and de novo T cell response. DEXP&A2&N elicited significant tumor retardation and tumor-specific immune responses in HCC mice with large tumor burdens. Importantly, tumor eradication was achieved in orthotopic HCC mice when antigenic AFP peptide was replaced with the full-length AFP (A) to form DEXP&A&N. Supplementation of Fms-related tyrosine kinase 3 ligand greatly augmented the antitumor immunity of DEXP&A&N by increasing immunological memory against tumor re-challenge in orthotopic HCC mice. Depletion of T cells, cross-presenting DCs and other innate immune cells abrogated the functionality of DEXP&A&N. Conclusions These findings demonstrate the capacity of universal DEX vaccines to induce tumor-specific immune responses by triggering an immune response tailored to the tumors of each individual, thus presenting a generalizable approach for personalized immunotherapy of HCC, by extension of other tumors, without the need to identify tumor antigens.

As clouds of different thicknesses block sunlight, large areas of cloud shadows with varying brightness can appear on the ground. Cloud shadows in high-resolution remote sensing images lead to uneven loss of image feature information. However, cloud shadows still retain feature information, and how to compensate for and restore unbalanced cloud shadow occlusion is of great significance in improving image quality. Though traditional shadow compensation methods can enhance the shaded brightness, the results are inconsistent in a single shadow region with over-compensated or insufficient compensation problems. Thus, this paper proposes a shadow-balanced compensation method combined with multi-level information. Multi-level information comprising the information of a shadow pixel, a local super-pixel centered with the pixel, the global cloud shadow region, and the global non-shadow region information, to comply with the cloud shadow’s internal difference. First, the original image is detected via the cloud shadow detection method and post-processing. The initial shadow is detected combined with designed complex shadow features and morphological shadow index features with threshold methods. Then, post-processing considering shadow area and morphological operation is applied to remove the small, non-cloud-shadow objects. Meanwhile, the initial image is also divided into super-pixel homogeneity regions using the super-pixel segmentation principle. A super-pixel region is between the pixel and the shadow area. Different from pixel and other window regions, it can provide a different measurement levels considering object homogeneity. Thus, a balanced compensation model is designed by combining the feature value of a shadow pixel and the mean and variance of a super-pixel, shadow region, and non-shadow region on the basis of the linear correlation correction principle. The super-pixel around the shadow pixel provides a local reliable homogenous region. It can reflect the internal difference inside the shadow region. Therefore, introducing a super-pixel in the proposed model can effectively compensate for the shaded information in a balanced way. Compared to those of only using pixel and shadow region information, the compensated results introduce super-pixel information, can deal with the homogenous region as a global one, and can be adaptive to the illustration differences in a cloud shadow. The experimental results show that compared to that of other reference methods, the quality of the proposed compensation result is better. The proposed method can enhance brightness and recover detailed information in shadow regions in a more balanced way. The issue of over-compensation and insufficient compensation inside a single shadow region can be resolved. Thus, the total result is similar to that of a non-shadow region. The proposed method can be used to recover the cloud shadow information more self-adaptively to improve image quality and usage in other applications.

With the improvement of spectral resolution, the redundant information in the hyperspectral imaging (HSI) datasets brings computational, analytical, and storage complexities. Feature selection is a combinatorial optimization problem, which selects a subset of feasible features to reduce the dimensionality of data and decrease the noise information. In recent years, the evolutionary algorithm (EA) has been widely used in feature selection, but the diversity of agents is lacking in the population, which leads to premature convergence. In this paper, a feature selection method based on discarding–recovering and co-evolution mechanisms is proposed with the aim of obtaining an effective feature combination in HSI datasets. The feature discarding mechanism is introduced to remove redundant information by roughly filtering the feature space. To further enhance the agents’ diversity, the reliable information interaction is also designed into the co-evolution mechanism, and if detects the event of stagnation, a subset of discarded features will be recovered using adaptive weights. Experimental results demonstrate that the proposed method performs well on three public datasets, achieving an overall accuracy of 92.07%, 92.36%, and 98.01%, respectively, and obtaining the number of selected features between 15% and 25% of the total.

Cloud detection is a crucial step in remote sensing image preprocessing. Deep learning (DL) methods are currently preferred for cloud detection, and the original images need to be cropped into smaller patches for DL training and testing. However, in very-high-resolution (VHR) remote sensing cloud-snow coexistence scenes, the cropped patches have low feature richness and high inter-class feature similarity. As a result, the common DL methods are prone to misclassifying clouds in complex regions. Therefore, based on the expanded cloud detection dataset of VHR cloud-snow coexistence regions, this paper proposes a multi-task driven and reconfigurable network (MTDR-Net) to better perform the cloud detection work in the scenes. There are four modules in MTDR-Net: the high-resolution backbone with multiple projection heads module (HRB-MPH), the re-parametrizable multi-scale feature fusion module (RP-MFF), the lightweight and adaptive feature fusion module (LAFF), and the multi-task gradient flow guidance module (MTGFG). HRB-MPH facilitates global information interaction, enhances the multi-level granularity feature representation, and supports feature sharing between pixel-level and superpixel-level segmentation tasks. RP-MFF is used to capture the local multi-scale cloud features in the training stage and simplify losslessly the structure in the testing stage. LAFF is employed to reconstruct meaningful cloud features. MTGFG can provide unbiased guidance on dividing gradient flow between the multi-tasks. The experimental results show that the MTDR-Net obtains the greatest accuracy performance due to its anti-interference capacity for confusing ground objects and its excellent cloud boundary and thin cloud extraction ability. In addition, MTDR-Net has the fewest parameters, forward-reasoning floating point operations, memory footprint, and memory access volume. With the coarse-to-fine strategy, MTDR-Net can be further sped up by avoiding inference resource consumption in cloud-free/all-cloud scenes.

The joint utilization of multi-source data is of great significance in geospatial observation applications, such as urban planning, disaster assessment, and military applications. However, this approach is confronted with challenges including inconsistent data structures, irrelevant physical properties, scarce training data, insufficient utilization of information and an imperfect feature fusion method. Therefore, this paper proposes a novel binary-tree Transformer network (BTRF-Net), which is used to fuse heterogeneous information and utilize complementarity among multi-source remote sensing data to enhance the joint classification performance of hyperspectral image (HSI) and light detection and ranging (LiDAR) data. Firstly, a hyperspectral network (HSI-Net) is employed to extract spectral and spatial features of hyperspectral images, while the elevation information of LiDAR data is extracted using the LiDAR network (LiDAR-Net). Secondly, a multi-source transformer complementor (MSTC) is designed that utilizes the complementarity and cooperation among multi-modal feature information in remote sensing images to better capture their correlation. The multi-head complementarity attention mechanism (MHCA) within this complementor can effectively capture global features and local texture information of images, hence achieving full feature fusion. Then, to fully obtain feature information of multi-source remote sensing images, this paper designs a complete binary tree structure, binary feature search tree (BFST), which fuses multi-modal features at different network levels to obtain multiple image features with stronger representation abilities, effectively enhancing the stability and robustness of the network. Finally, several groups of experiments are designed to compare and analyze the proposed BTRF-Net with traditional methods and several advanced deep learning networks using two datasets: Houston and Trento. The results show that the proposed network outperforms other state-of-the-art methods even with small training samples.

The use of tumour exosome-activated dendritic cell (DC) immunotherapy shows promise for the treatment of large established tumours. Here, the authors generate alarmin HMGN1-attached tumour exosomes which significantly improve therapy efficacy by boosting DC activation in several preclinical mouse models.

Insufficient delivery of oligonucleotides to muscle and heart remains a barrier for clinical implementation of antisense oligonucleotide (AO)-mediated exon-skipping therapeutics in Duchenne muscular dystrophy (DMD), a lethal monogenic disorder caused by frame-disrupting mutations in the DMD gene. We previously demonstrated that hexose, particularly an equal mix of glucose:fructose (GF), significantly enhanced oligonucleotide delivery and exon-skipping activity in peripheral muscles of mdx mice; however, its efficacy in the heart remains limited. Here we show that co-administration of GF with peptide-conjugated phosphorodiamidate morpholino oligomer (PPMO, namely, BMSP-PMO) induced an approximately 2-fold higher level of dystrophin expression in cardiac muscles of adult mdx mice compared to BMSP-PMO in saline at a single injection of 20 mg/kg, resulting in evident phenotypic improvement in dystrophic mdx hearts without any detectable toxicity. Dystrophin expression in peripheral muscles also increased. However, GF failed to potentiate BMSP-PMO efficiency in aged mdx mice. These findings demonstrate that GF is applicable to both PMO and PPMO. Furthermore, GF potentiates oligonucleotide activity in mdx mice in an age-dependent manner, and, thus, it has important implications for its clinical deployment for the treatment of DMD and other muscular disorders. Keywords: hexose, peptide-conjugated morpholino oligomer, Duchenne muscular dystrophy, exon skipping, cardiac functions

Accurate semantic segmentation results of the overhead catenary system (OCS) are significant for OCS component extraction and geometric parameter detection. Actually, the scenes of OCS are complex, and the density of point cloud data obtained through Light Detection and Ranging (LiDAR) scanning is uneven due to the character difference of OCS components. However, due to the inconsistent component points, it is challenging to complete better semantic segmentation of the OCS point cloud with the existing deep learning methods. Therefore, this paper proposes a point cloud multi-scale feature fusion refinement structure neural network (PMFR-Net) for semantic segmentation of the OCS point cloud. The PMFR-Net includes a prediction module and a refinement module. The innovations of the prediction module include the double efficient channel attention module (DECA) and the serial hybrid domain attention (SHDA) structure. The point cloud refinement module (PCRM) is used as the refinement module of the network. DECA focuses on detail features; SHDA strengthens the connection of contextual semantic information; PCRM further refines the segmentation results of the prediction module. In addition, this paper created and released a new dataset of the OCS point cloud. Based on this dataset, the overall accuracy (OA), F1-score, and mean intersection over union (MIoU) of PMFR-Net reached 95.77%, 93.24%, and 87.62%, respectively. Compared with four state-of-the-art (SOTA) point cloud deep learning methods, the comparative experimental results showed that PMFR-Net achieved the highest accuracy and the shortest training time. At the same time, PMFR-Net segmentation performance on S3DIS public dataset is better than the other four SOTA segmentation methods. In addition, the effectiveness of DECA, SHDA structure, and PCRM was verified in the ablation experiment. The experimental results show that this network could be applied to practical applications.

Approval of antisense oligonucleotide eteplirsen highlights the promise of exon-skipping therapeutics for Duchenne muscular dystrophy patients. However, the limited efficacy of eteplirsen underscores the importance to improve systemic delivery and efficacy. Recently, we demonstrated that a glucose and fructose (GF) delivery formulation effectively potentiates phosphorodiamidate morpholino oligomer (PMO). Considering the clinical potential of GF, it is important to determine the long-term compatibility and efficacy with PMO in mdx mice prior to clinical translation. Here, we report that yearlong administration of a clinically applicable PMO dose (50 mg/kg/week for 3 weeks followed by 50 mg/kg/month for 11 months) with GF elicited sustainably high levels of dystrophin expression in mdx mice, with up to 45% of the normal level of dystrophin restored in most peripheral muscles without any detectable toxicity. Importantly, PMO-GF resulted in phenotypical rescue and mitochondrial biogenesis with functional improvement. Carbohydrate metabolites measurements revealed improved metabolic and energetic conditions after PMO-GF treatment in mdx mice without metabolic anomaly. Collectively, our study shows PMO-GF’s ability to elicit long-lasting therapeutic effects with tolerable toxicity and represents a new treatment modality for Duchenne muscular dystrophy, and provides guidelines for antisense oligonucleotides with GF in clinical use. Keywords: Duchenne muscular dystrophy, exon skipping, GF, morpholino oligomers, mitochondria

Building extraction from RGB VHR images is an important and popular topic for mapping, disaster emergency responding, and city management. The automation of most methodologies cannot meet the need for applications. In this paper, based on classification and optimization, we propose a novel methodology using shadows to automatically extract building samples and verify buildings accurately to improve automation and accuracy. On one hand, in order to acquire various and reliable building samples automatically for classification, detected shadows first are shifted opposite to the direction of illumination to extract building shadows. Furthermore, each building shadow will be shifted again in the same way. Then according to the distribution of classes in these customized shifted regions, building samples can be filtered out by removing those recognized objects. On the other hand, besides the common measures to optimize the initial building during post-processing; a new, original, and an efficient shadow-based index for building verification is also designed. Shadow rate on the intersect boundary between the expanding edge of candidate regions and their shifted regions following the illumination direction can efficiently recognize buildings. When the proposed method is compared to other sample acquisition methods based on shadow, experimental results show that the approach for building samples acquisition is helpful to get accurate initial building results. Moreover, in comparison with other building extraction methods, the proposed building verification method can distinguish buildings from non-buildings. This significantly improves the accuracy of the final results. Numerical assessments performed on a series of test images indicate that our proposed approach for building extraction is efficient and feasible, especially in suburban areas.

Clouds and snow in remote sensing imageries cover underlying surface information, reducing image availability. Moreover, they interact with each other, decreasing the cloud and snow detection accuracy. In this study, we propose a convolutional neural network for cloud and snow detection, named the cloud and snow detection network (CSD-Net). It incorporates the multi-scale feature fusion module (MFF) and the controllably deep supervision and feature fusion structure (CDSFF). MFF can capture and aggregate features at various scales, ensuring that the extracted high-level semantic features of clouds and snow are more distinctive. CDSFF can provide a deeply supervised mechanism with hinge loss and combine information from adjacent layers to gain more representative features. It ensures the gradient flow is more oriented and error-less, while retaining more effective information. Additionally, a high-resolution cloud and snow dataset based on WorldView2 (CSWV) was created and released. This dataset meets the training requirements of deep learning methods for clouds and snow in high-resolution remote sensing images. Based on the datasets with varied resolutions, CSD-Net is compared to eight state-of-the-art deep learning methods. The experiment results indicate that CSD-Net has an excellent detection accuracy and efficiency. Specifically, the mean intersection over the union (MIoU) of CSD-Net is the highest in the corresponding experiment. Furthermore, the number of parameters in our proposed network is just 7.61 million, which is the lowest of the tested methods. It only has 88.06 GFLOPs of floating point operations, which is less than the U-Net, DeepLabV3+, PSPNet, SegNet-Modified, MSCFF, and GeoInfoNet. Meanwhile, CSWV has a higher annotation quality since the same method can obtain a greater accuracy on it.

Deep learning approaches have been widely used in building automatic extraction tasks and have made great progress in recent years. However, the missing detection and wrong detection causing by spectrum confusion is still a great challenge. The existing fully convolutional networks (FCNs) cannot effectively distinguish whether the feature differences are from one building or the building and its adjacent non-building objects. In order to overcome the limitations, a building multi-feature fusion refined network (BMFR-Net) was presented in this paper to extract buildings accurately and completely. BMFR-Net is based on an encoding and decoding structure, mainly consisting of two parts: the continuous atrous convolution pyramid (CACP) module and the multiscale output fusion constraint (MOFC) structure. The CACP module is positioned at the end of the contracting path and it effectively minimizes the loss of effective information in multiscale feature extraction and fusion by using parallel continuous small-scale atrous convolution. To improve the ability to aggregate semantic information from the context, the MOFC structure performs predictive output at each stage of the expanding path and integrates the results into the network. Furthermore, the multilevel joint weighted loss function effectively updates parameters well away from the output layer, enhancing the learning capacity of the network for low-level abstract features. The experimental results demonstrate that the proposed BMFR-Net outperforms the other five state-of-the-art approaches in both visual interpretation and quantitative evaluation.

Building boundary optimization is an essential post-process step for building extraction (by image classification). However, current boundary optimization methods through smoothing or line fitting principles are unable to optimize complex buildings. In response to this limitation, this paper proposes an object-oriented building contour optimization method via an improved generalized gradient vector flow (GGVF) snake model and based on the initial building contour results obtained by a classification method. First, to reduce interference from the adjacent non-building object, each building object is clipped via their extended minimum bounding rectangles (MBR). Second, an adaptive threshold Canny edge detection is applied to each building image to detect the edges, and the progressive probabilistic Hough transform (PPHT) is applied to the edge result to extract the line segments. For those cases with missing or wrong line segments in some edges, a hierarchical line segments reconstruction method is designed to obtain complete contour constraint segments. Third, accurate contour constraint segments for the GGVF snake model are designed to quickly find the target contour. With the help of the initial contour and constraint edge map for GGVF, a GGVF force field computation is executed, and the related optimization principle can be applied to complex buildings. Experimental results validate the robustness and effectiveness of the proposed method, whose contour optimization has higher accuracy and comprehensive value compared with that of the reference methods. This method can be used for effective post-processing to strengthen the accuracy of building extraction results.

Exon-skipping therapies such as systemic i.v. administration of morpholino are being explored as a means of treating Duchenne muscular dystrophy. Here the authors show that adding a glucose-fructose mix can enhance uptake of phosphorodiamidate morpholino oligomer and its therapeutic effect in mdxmice.

Current automatic shadow compensation methods often suffer because their contrast improvement processes are not self-adaptive and, consequently, the results they produce do not adequately represent the real objects. The study presented in this paper designed a new automatic shadow compensation framework based on improvements to the Wallis principle, which included an intensity coefficient and a stretching coefficient to enhance contrast and brightness more efficiently. An automatic parameter calculation strategy also is a part of this framework, which is based on searching for and matching similar feature points around shadow boundaries. Finally, a final compensation combination strategy combines the regional compensation with the local window compensation of the pixels in each shadow to improve the shaded information in a balanced way. All these strategies in our method work together to provide a better measurement for customizing suitable compensation depending on the condition of each region and pixel. The intensity component I also is automatically strengthened through the customized compensation model. Color correction is executed in a way that avoids the color bias caused by over-compensated component values, thereby better reflecting shaded information. Images with clouds shadows and ground objects shadows were utilized to test our method and six other state-of-the-art methods. The comparison results indicate that our method compensated for shaded information more effectively, accurately, and evenly than the other methods for customizing suitable models for each shadow and pixel with reasonable time-cost. Its brightness, contrast, and object color in shaded areas were approximately equalized with non-shaded regions to present a shadow-free image.

[This corrects the article DOI: 10.1371/journal.pone.0148263.].

Texture classification is an important topic for many applications in machine vision and image analysis, and Gabor filter is considered one of the most efficient tools for analyzing texture features at multiple orientations and scales. However, the parameter settings of each filter are crucial for obtaining accurate results, and they may not be adaptable to different kinds of texture features. Moreover, there is redundant information included in the process of texture feature extraction that contributes little to the classification. In this paper, a new texture classification technique is detailed. The approach is based on the integrated optimization of the parameters and features of Gabor filter, and obtaining satisfactory parameters and the best feature subset is viewed as a combinatorial optimization problem that can be solved by maximizing the objective function using hybrid ant lion optimizer (HALO). Experimental results, particularly fitness values, demonstrate that HALO is more effective than the other algorithms discussed in this paper, and the optimal parameters and features of Gabor filter are balanced between efficiency and accuracy. The method is feasible, reasonable, and can be utilized for practical applications of texture classification.

Mouse models are commonly used for studying hepatocellular carcinoma (HCC) biology and exploring new therapeutic interventions. Currently three main modalities of HCC mouse models have been extensively employed in pre-clinical studies including chemically induced, transgenic and transplantation models. Among them, transplantation models are preferred for evaluating in vivo drug efficacy in pre-clinical settings given the short latency, uniformity in size and close resemblance to tumors in patients. However methods used for establishing orthotopic HCC transplantation mouse models are diverse and fragmentized without a comprehensive comparison. Here, we systemically evaluate four different approaches commonly used to establish HCC mice in preclinical studies, including intravenous, intrasplenic, intrahepatic inoculation of tumor cells and intrahepatic tissue implantation. Four parameters--the latency period, take rates, pathological features and metastatic rates--were evaluated side-by-side. 100% take rates were achieved in liver with intrahepatic, intrasplenic inoculation of tumor cells and intrahepatic tissue implantation. In contrast, no tumor in liver was observed with intravenous injection of tumor cells. Intrahepatic tissue implantation resulted in the shortest latency with 0.5 cm (longitudinal diameter) tumors found in liver two weeks after implantation, compared to 0.1cm for intrahepatic inoculation of tumor cells. Approximately 0.1cm tumors were only visible at 4 weeks after intrasplenic inoculation. Uniform, focal and solitary tumors were formed with intrahepatic tissue implantation whereas multinodular, dispersed and non-uniform tumors produced with intrahepatic and intrasplenic inoculation of tumor cells. Notably, metastasis became visible in liver, peritoneum and mesenterium at 3 weeks post-implantation, and lung metastasis was visible after 7 weeks. T cell infiltration was evident in tumors, resembling the situation in HCC patients. Our study demonstrated that orthotopic HCC mouse models established via intrahepatic tissue implantation authentically reflect clinical manifestations in HCC patients pathologically and immunologically, suggesting intrahepatic tissue implantation is a preferable approach for establishing orthotopic HCC mouse models.

Antisense oligonucleotide (AO)-mediated exon-skipping therapeutics shows great promise in correcting frame-disrupting mutations in the DMD gene for Duchenne muscular dystrophy. However, insufficient systemic delivery limits clinical adoption. Previously, we showed that a glucose/fructose mixture augmented AO delivery to muscle in mdx mice. Here, we evaluated if fructose alone could enhance the activities of AOs with different chemistries in mdx mice. The results demonstrated that fructose improved the potency of AOs tested with the greatest effect on phosphorodiamidate morpholino oligomer (PMO), resulted in a 4.25-fold increase in the number of dystrophin-positive fibres, compared to PMO in saline in mdx mice. Systemic injection of lissamine-labeled PMO with fructose at 25 mg/kg led to increased uptake and elevated dystrophin expression in peripheral muscles, compared to PMO in saline, suggesting that fructose potentiates PMO by enhancing uptake. Repeated intravenous administration of PMO in fructose at 50 mg/kg/week for 3 weeks and 50 mg/kg/month for 5 months restored up to 20% of wild-type dystrophin levels in skeletal muscles with improved functions without detectable toxicity, compared to untreated mdx controls. Collectively, we show that fructose can potentiate AOs of different chemistries in vivo although the effect diminished over repeated administration.

Antisense oligonucleotide (AO)-mediated exon-skipping therapeutics shows great promise for Duchenne muscular dystrophy (DMD) patients. However, recent failure with drisapersen, an AO candidate drug in phase 3 trial, highlights the importance of exploring other effective AO chemistries for DMD. Previously, we demonstrated the appreciable biological activity of peptide nucleic acid (PNA) AOs in restoring dystrophin expression in dystrophin-deficient mdx mice intramuscularly. Here, we further explore the systemic potential and feasibility of PNA AOs in mediating exon skipping in mdx mice as a comprehensive systemic evaluation remains lacking. Systemic delivery of PNA AOs resulted in therapeutic level of dystrophin expression in body-wide peripheral muscles and improved dystrophic pathology in mdx mice without any detectable toxicity. Up to 40% of dystrophin restoration was achieved in gastrocnemius, to a less extent with other skeletal muscles, with no dystrophin in heart. Notably, comparable systemic activity was obtained between PNA AOs and phosphorodiamidate morpholino oligomer, a DMD AO chemistry in phase 3 clinical trial, under an identical dosing regimen. Overall, our data demonstrate that PNA is viable for DMD exon-skipping therapeutics with 20 mer showing the best combination of activity, solubility, and safety and further modifications to increase PNA aqueous solubility can enable longer, more effective therapeutics without the associated toxicity.

Antisense oligonucleotide (AO)-mediated exon-skipping therapy is one of the most promising therapeutic strategies for Duchenne Muscular Dystrophy (DMD) and several AO chemistries have been rigorously investigated. In this report, we focused on the effect of 2'-O-methoxyethyl oligonucleotides (MOE) on exon skipping in cultured mdx myoblasts and mice. Efficient dose-dependent skipping of targeted exon 23 was achieved in myoblasts with MOE AOs of different lengths and backbone chemistries. Furthermore, we established that 25-mer MOE phosphorothioate (PS) AOs provided the greatest exon-skipping efficacy. When compared with 2'O methyl phosphorothioate (2'OmePS) AOs, 25-mer MOE (PS) AOs also showed higher exon-skipping activity in vitro and in mdx mice after intramuscular injections. Characterization of uptake in vitro corroborated with exon-skipping results, suggesting that increased uptake of 25-mer MOE PS AOs might partly contribute to the difference in exon-skipping activity observed in vitro and in mdx mice. Our findings demonstrate the substantial potential for MOE PS AOs as an alternative option for the treatment of DMD.

Single chain variable fragments (scFvs) against diethylstilbestrol (DES) were selected from the splenocytes of non-immunized mice by ribosome display technology. A naive library was constructed and engineered to allow in vitro transcription and translation using an E. coli lysate system. Alternating selection in solution and immobilization in microtiter wells was used to pan mRNA-ribosome-antibody (ARM) complexes. After seven rounds of ribosome display, the expression vector pTIG-TRX containing the selected specific scFv DNAs were transformed into Escherichia coli BL21 (DE3) for expression. Twenty-six positive clones were screened and five clones had high antibody affinity and specificity to DES as evidenced by indirect competitive ELISA. Sequence analysis showed that these five DES-specific scFvs had different amino acid sequences, but the CDRs were highly similar. Surface plasmon resonance (SPR) analysis was used to determine binding kinetics of one clone (30-1). The measured K(D) was 3.79 µM. These results indicate that ribosome display technology can be used to efficiently isolate hapten-specific antibody (Ab) fragments from a naive library; this study provides a methodological framework for the development of novel immunoassays for multiple environmental pollutants with low molecular weight detection using recombinant antibodies.

[This corrects the article DOI: 10.1371/journal.pone.0033186.].