Epithelial cells derived exosomal miR-203a-3p facilitates stromal inflammation of type IIIA chronic prostatitis/chronic pelvic pain syndrome by targeting DUSP5 and increasing MCP-1 generation.

Increased proinflammatory cytokines and infiltration of inflammatory cells in the stroma are important pathological features of type IIIA chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS-A), and the interaction between stromal cells and other cells in the inflammatory microenvironment is closely related to the inflammatory process of CP/CPPS-A. However, the interaction between stromal and epithelial cells remains unclear. In this study, inflammatory prostate epithelial cells (PECs) released miR-203a-3p-rich exosomes and facilitated prostate stromal cells (PSCs) inflammation by upregulating MCP-1 expression. Mechanistically, DUSP5 was identified as a novel target gene of miR-203a-3p and regulated PSCs inflammation through the ERK1/2/MCP-1 signaling pathway. Meanwhile, the effect of exosomes derived from prostatic fluids of CP/CPPS-A patients was consistent with that of exosomes derived from inflammatory PECs. Importantly, we demonstrated that miR-203a-3p antagomirs-loaded exosomes derived from PECs targeted the prostate and alleviated prostatitis by inhibiting the DUSP5-ERK1/2 pathway. Collectively, our findings provide new insights into underlying the interaction between PECs and PSCs in CP/CPPS-A, providing a promising therapeutic strategy for CP/CPPS-A.

Target Functionalized Carbon Dot Nanozymes with Dual-Model Photoacoustic and Fluorescence Imaging for Visual Therapy in Atherosclerosis.

Multifunctional nanomedicines have been used in atherosclerosis theranostics. Herein, phosphatidylserine-specific peptide CLIKKPF-functionalized carbon-dots nanozymes (pep-CDs) are reported for specific and efficient noninvasive theranostic of atherosclerosis. Surprisingly, pep-CDs are discovered to not only inherit the inherent properties of carbon dots (CDs), including deep-red fluorescence emission, photoacoustic response, and superoxide dismutase-like antioxidant, and anti-inflammatory activities but also possess the ability to target recognition on foam cells and target localization on plaques due to the specific interaction of CLIKKPF with phosphatidylserine on the membrane outer surface of foam cells. Furthermore, the target localization effect of pep-CDs vastly promotes the efficient accumulation of CDs in plaque, thus maximizing AS theranostic of CDs. Interestingly, pep-CDs could be developed to image plaque for monitoring atherosclerosis pathological progression in real-time resulting from the different content of foam cells. This work on the one hand proposes a simple and feasible strategy to construct theranostic nanoplatform employing only a single functional unit (i.e., multifunctional CDs) to simplify the fabrication procedure, on the other hand, highlights the advantages of the active target auxiliary mode for atherosclerosis theranostic applications.

MiR-216a-3p inhibits the proliferation and invasion of fibroblast-like synoviocytes by targeting dual-specificity phosphatase 5.

Dual-specificity phosphatase 5 (DUSP5) is a novel anti-inflammatory modulator in many inflammatory diseases. However, the role of DUSP5 in fibroblast-like synoviocytes (FLS) of rheumatoid arthritis (RA) remains unknown. In this study, we aimed to explore the biological function and regulation of DUSP5 in FLS. We found that lower DUSP5 expression level was detected in collagen-induced arthritis (CIA) and synoviocyte MH7A. Overexpression of DUSP5 markedly decreased the proliferation, migration, and invasion of MH7A, which correlated with suppressing the phosphorylation of extracellular signal-regulated kinase (ERK). Moreover, DUSP5 was identified as a novel target gene of miR-216a-3p, which was upregulated in FLS. Therefore, DUSP5 expression was negatively regulated by miR-216a-3p, and the effect of DUSP5 overexpression on FLS was reversed by miR-216a-3p mimics. Overall, our study demonstrates that DUSP5 is a miR-216a-3p target gene and its anti-inflammatory function in FLS via inactivation of ERK. These results revealed that the miR-216a-3p/DUSP5 pathway may play a crucial role in the malignant behavior of FLS, which may serve as a new target for the treatment of RA.

Carvedilol ameliorates persistent erythema of erythematotelangiectatic rosacea by regulating the status of anxiety/depression.

The treatment of persistent erythema and rosacea flushing is extremely challenging, especially for patients with anxiety. The aim of this study was to verify the efficacy of carvedilol in rosacea patients with persistent erythema and flushing. A total of 156 patients were randomized to use oral carvedilol 5 mg bid (twice per day) (n = 105) or topical brimonidine (n = 51) for a 10-week period with 6 weeks of follow-up. Both the efficacy of carvedilol and the status of anxiety/depression were analyzed by patient self-assessment (PSA), clinician erythema assessment (CEA), generalized anxiety disorder (GAD-7), and patient health questionnaire-9 (PHQ-9). Our study found that carvedilol exerted a dramatic reduction in CEA/PSA scores and sting/burning sensation scores in comparison to topical brimonidine. Additionally, carvedilol treatment dramatically improved telangiectasia, erythema, and pigmentation with no obvious side effects. Patients with carvedilol treatment showed an improvement of depression/anxiety, as reflected by lower GAD-7 and PHQ-9 scores than patients with topical brimonidine. Notably, we found carvedilol treatment had better outcomes among patients under 30 years of age with rosacea younger than 30 years old. Conclusively, our findings reveal that carvedilol could quickly and effectively improve facial erythema, which might stem from the improved the status of anxiety/depression.

MFAN: Multi-Level Features Attention Network for Fake Certificate Image Detection.

Up to now, most of the forensics methods have attached more attention to natural content images. To expand the application of image forensics technology, forgery detection for certificate images that can directly represent people's rights and interests is investigated in this paper. Variable tampered region scales and diverse manipulation types are two typical characteristics in fake certificate images. To tackle this task, a novel method called Multi-level Feature Attention Network (MFAN) is proposed. MFAN is built following the encoder-decoder network structure. In order to extract features with rich scale information in the encoder, on the one hand, we employ Atrous Spatial Pyramid Pooling (ASPP) on the final layer of a pre-trained residual network to capture the contextual information at different scales; on the other hand, low-level features are concatenated to ensure the sensibility to small targets. Furthermore, the resulting multi-level features are recalibrated on channels for irrelevant information suppression and enhancing the tampered regions, guiding the MFAN to adapt to diverse manipulation traces. In the decoder module, the attentive feature maps are convoluted and unsampled to effectively generate the prediction mask. Experimental results indicate that the proposed method outperforms some state-of-the-art forensics methods.

LncRNA-H19 silencing suppresses synoviocytes proliferation and attenuates collagen-induced arthritis progression by modulating miR-124a.

OBJECTIVES: Long non-coding RNA H19 (lncRNA-H19) is highly expressed in fibroblast-like synoviocytes (FLS) from patients with RA. The present study aimed to clarify the pathological significance and regulatory mechanisms of lncRNA-H19 in FLS. METHODS: Mice with CIA were locally injected with LV-shH19. The progression of CIA was explored by measuring arthritic index (AI), paw thickness (PT) and histologic analysis. The growth and cell cycle of human synoviocyte MH7A were assessed by CCK-8 and flow cytometric analysis. The putative binding sites between lncRNA-H19 and miR-124a were predicted online, and the binding was identified by luciferase assay. RT-qPCR, Western blot and luciferase assay were performed to explore the molecular mechanisms between liver X receptor (LXR), lncRNA-H19, miR-124a and its target genes. RESULTS: The expression of lncRNA-H19 was closely associated with the proliferation of synoviocytes and knockdown of lncRNA-H19 significantly ameliorated the progression of CIA, reflected by decreased AI, PT and cartilage destruction. Notably, lncRNA-H19 competitively bound to miR-124a, which directly targets CDK2 and MCP-1. It was confirmed that lncRNA-H19 regulates the proliferation of synoviocytes by acting as a sponge of miR-124a to modulate CDK2 and MCP-1 expression. Furthermore, the agonists of LXR inhibited lncRNA-H19-mediated miR-124a-CDK2/MCP-1 signalling pathway in synoviocytes. The 'lncRNA-H19-miR-124a-CDK2/MCP-1' axis plays an important role in LXR anti-arthritis. CONCLUSION: Regulation of the miR-124a-CDK2/MCP-1 pathway by lncRNA-H19 plays a crucial role in the proliferation of FLS. Targeting this axis has therapeutic potential in the treatment of RA and may represent a novel strategy for RA treatment.

Reactive oxygen species-responsive dexamethasone-loaded nanoparticles for targeted treatment of rheumatoid arthritis via suppressing the iRhom2/TNF-α/BAFF signaling pathway.

Rheumatoid arthritis (RA) is an immune-mediated inflammatory disease that results in synovitis, cartilage destruction, and even loss of joint function. The frequent and long-term administration of anti-rheumatic drugs often leads to obvious adverse effects and patient non-compliance. Therefore, to specifically deliver dexamethasone (Dex) to inflamed joints and reduce the administration frequency of Dex, we developed Dex-loaded reactive oxygen species (ROS)-responsive nanoparticles (Dex/Oxi-αCD NPs) and folic acid (FA) modified Dex/Oxi-αCD NPs (Dex/FA-Oxi-αCD NPs) and validated their anti-inflammatory effect in vitro and in vivo. In vitro study demonstrated that these NPs can be effectively internalized by activated macrophages and the released Dex from NPs significantly downregulated the expression of iRhom2, TNF-α, and BAFF in activated Raw264.7. In vivo experiments revealed that Dex/Oxi-αCD NPs, especially Dex/FA-Oxi-αCD NPs significantly accumulated at inflamed joints in collagen-induced arthritis (CIA) mice and alleviated the joint swelling and cartilage destruction. Importantly, the expression of iRhom2, TNF-α, and BAFF in the joint was inhibited by intravenous injection of Dex/Oxi-αCD NPs and Dex/FA-Oxi-αCD NPs. Collectively, our data revealed that Dex-loaded ROS-responsive NPs can target inflamed joints and attenuate arthritis, and the 'iRhom2-TNF-α-BAFF' pathway plays an important role in the treatment of RA with the NPs, suggesting that this pathway may be a novel target for RA therapy.

A Survey of Deep Learning-Based Source Image Forensics.

Image source forensics is widely considered as one of the most effective ways to verify in a blind way digital image authenticity and integrity. In the last few years, many researchers have applied data-driven approaches to this task, inspired by the excellent performance obtained by those techniques on computer vision problems. In this survey, we present the most important data-driven algorithms that deal with the problem of image source forensics. To make order in this vast field, we have divided the area in five sub-topics: source camera identification, recaptured image forensic, computer graphics (CG) image forensic, GAN-generated image detection, and source social network identification. Moreover, we have included the works on anti-forensics and counter anti-forensics. For each of these tasks, we have highlighted advantages and limitations of the methods currently proposed in this promising and rich research field.

A New Dataset for Source Identification of High Dynamic Range Images.

Digital source identification is one of the most important problems in the field of multimedia forensics. While Standard Dynamic Range (SDR) images are commonly analyzed, High Dynamic Range (HDR) images are a less common research subject, which leaves space for further analysis. In this paper, we present a novel database of HDR and SDR images captured in different conditions, including various capturing motions, scenes and devices. As a possible application of this dataset, the performance of the well-known reference pattern noise-based source identification algorithm was tested on both kinds of images. Results have shown difficulties in source identification conducted on HDR images, due to their complexity and wider dynamic range. It is concluded that capturing conditions and devices themselves can have an impact on source identification, thus leaving space for more research in this field.

Salient region detection by fusing bottom-up and top-down features extracted from a single image.

Recently, some global contrast-based salient region detection models have been proposed based on only the low-level feature of color. It is necessary to consider both color and orientation features to overcome their limitations, and thus improve the performance of salient region detection for images with low-contrast in color and high-contrast in orientation. In addition, the existing fusion methods for different feature maps, like the simple averaging method and the selective method, are not effective sufficiently. To overcome these limitations of existing salient region detection models, we propose a novel salient region model based on the bottom-up and top-down mechanisms: the color contrast and orientation contrast are adopted to calculate the bottom-up feature maps, while the top-down cue of depth-from-focus from the same single image is used to guide the generation of final salient regions, since depth-from-focus reflects the photographer's preference and knowledge of the task. A more general and effective fusion method is designed to combine the bottom-up feature maps. According to the degree-of-scattering and eccentricities of feature maps, the proposed fusion method can assign adaptive weights to different feature maps to reflect the confidence level of each feature map. The depth-from-focus of the image as a significant top-down feature for visual attention in the image is used to guide the salient regions during the fusion process; with its aid, the proposed fusion method can filter out the background and highlight salient regions for the image. Experimental results show that the proposed model outperforms the state-of-the-art models on three public available data sets.

Pairwise prediction-error expansion for efficient reversible data hiding.

In prediction-error expansion (PEE) based reversible data hiding, better exploiting image redundancy usually leads to a superior performance. However, the correlations among prediction-errors are not considered and utilized in current PEE based methods. Specifically, in PEE, the prediction-errors are modified individually in data embedding. In this paper, to better exploit these correlations, instead of utilizing prediction-errors individually, we propose to consider every two adjacent prediction-errors jointly to generate a sequence consisting of prediction-error pairs. Then, based on the sequence and the resulting 2D prediction-error histogram, a more efficient embedding strategy, namely, pairwise PEE, can be designed to achieve an improved performance. The superiority of our method is verified through extensive experiments.

LDFT-based watermarking resilient to local desynchronization attacks.

Up to now, a watermarking scheme that is robust against desynchronization attacks (DAs) is still a grand challenge. Most image watermarking resynchronization schemes in literature can survive individual global DAs (e.g., rotation, scaling, translation, and other affine transforms), but few are resilient to challenging cropping and local DAs. The main reason is that robust features for watermark synchronization are only globally invariable rather than locally invariable. In this paper, we present a blind image watermarking resynchronization scheme against local transform attacks. First, we propose a new feature transform named local daisy feature transform (LDFT), which is not only globally but also locally invariable. Then, the binary space partitioning (BSP) tree is used to partition the geometrically invariant LDFT space. In the BSP tree, the location of each pixel is fixed under global transform, local transform, and cropping. Lastly, the watermarking sequence is embedded bit by bit into each leaf node of the BSP tree by using the logarithmic quantization index modulation watermarking embedding method. Simulation results show that the proposed watermarking scheme can survive numerous kinds of distortions, including common image-processing attacks, local and global DAs, and noninvertible cropping.

Forensic identification of resampling operators: A semi non-intrusive approach.

Recently, several new resampling operators have been proposed and successfully invalidate the existing resampling detectors. However, the reliability of such anti-forensic techniques is unaware and needs to be investigated. In this paper, we focus on the forensic identification of digital image resampling operators including the traditional type and the anti-forensic type which hides the trace of traditional resampling. Various resampling algorithms involving geometric distortion (GD)-based, dual-path-based and postprocessing-based are investigated. The identification is achieved in the manner of semi non-intrusive, supposing the resampling software could be accessed. Given an input pattern of monotone signal, polarity aberration of GD-based resampled signal's first derivative is analyzed theoretically and measured by effective feature metric. Dual-path-based and postprocessing-based resampling can also be identified by feeding proper test patterns. Experimental results on various parameter settings demonstrate the effectiveness of the proposed approach.

Geometrically robust image watermarking by sector-shaped partitioning of geometric-invariant regions.

In a feature-based geometrically robust watermarking system, it is a challenging task to detect geometric-invariant regions (GIRs) which can survive a broad range of image processing operations. Instead of commonly used Harris detector or Mexican hat wavelet method, a more robust corner detector named multi-scale curvature product (MSCP) is adopted to extract salient features in this paper. Based on such features, disk-like GIRs are found, which consists of three steps. First, robust edge contours are extracted. Then, MSCP is utilized to detect the centers for GIRs. Third, the characteristic scale selection is performed to calculate the radius of each GIR. A novel sector-shaped partitioning method for the GIRs is designed, which can divide a GIR into several sector discs with the help of the most important corner (MIC). The watermark message is then embedded bit by bit in each sector by using Quantization Index Modulation (QIM). The GIRs and the divided sector discs are invariant to geometric transforms, so the watermarking method inherently has high robustness against geometric attacks. Experimental results show that the scheme has a better robustness against various image processing operations including common processing attacks, affine transforms, cropping, and random bending attack (RBA) than the previous approaches.

Pinpoint authentication watermarking based on a chaotic system.

Watermarking technique is one of the active research fields in recent ten years, which can be used in copyright management, content authentication, and so on. For the authentication watermarking, tamper localization and detection accuracy are two important performances. However, most methods in literature cannot obtain precise localization. In addition, few researchers pay attention to the problem of detection accuracy. In this paper, a pinpoint authentication watermarking is proposed based on a chaotic system, which is sensitive to the initial value. The approach can not only exactly localize the malicious manipulations but reveal block substitutions when Holliman-Memon attack (VQ attack) occurs. An image is partitioned into non-overlapped regions according to the requirement on precision. In each region, a chaotic model is iteratively applied to produce the chaotic sequences based on the initial values, which are determined by combining the prominent luminance values of pixels, position information and an image key. Subsequently, an authentication watermark is constructed using the binary chaotic sequences and embedded in the embedding space. At the receiver, a detector extracts the watermark and localizes the tampered regions without access to the host image or the original watermark. The precision of spatial localization can attain to one pixel, which is valuable to the images observed at non-ordinary distance, such as medical images and military images. The detection accuracy rate is defined and analyzed to present the probability of a detector making right decisions. Experimental results demonstrate the effectiveness and advantages of our algorithm.