Delineating molecular regulatory network of meat quality of longissimus dorsi indicated by transcriptomic, proteomic, and metabolomics analysis in rabbit.

This study aims to investigate the potential regulatory network responsible for the meat quality using multi-omics to help developing better varieties. Slaughter performance and meat quality of Shuxing No.1 rabbit outperformed IRA rabbit according to the tested rabbit parameters. Differentially expressed genes (DEGs) and differentially abundance proteins (DAPs) were involved in meat quality-related pathways, such as PI3K - Akt and MAPK signaling pathways. Only SMTNL1 and PM20D2 shared between DEGs and DAPs. Olfactory-sensitive undecanal, a differentially abundant metabolite (DAM) in volatilomics (vDAMs), correlated with all of the remaining 11 vDAMs, and most of 12 vDAMs were associated with amino acid metabolism. Integration revealed that 829 DEGs/DAPs were associated with 15 DAMs in four KEGG pathways, such as melatonin (a DAM in widely targeted metabolomics) was significantly positively correlated with ALDH and negatively correlated with RAB3D and CAT in the tryptophan metabolism pathway. This study sheds light on the potential mechanisms that contribute to the improved meat quality and flavor. SIGNIFICANCE: Shuxing No.1 rabbit is a new breed of meat rabbit in the Chinese market. In meat marketing, meat quality usually determines the purchase intention of consumers. Determining the biological and molecular mechanisms of meat quality in meat rabbit is essential for developing strategies to improve meat quality. According to the tested rabbit parameters, this study ascertained that the slaughter performance and meat quality of Shuxing No.1 rabbit surpasses that of IRA rabbit. The present study profiled the transcriptome, proteome, widely targeted metabolome, and volatilome of longissimus dorsi from Shuxing No.1 rabbit and IRA rabbit. The study found that meat quality and flavor-related tryptophan metabolism pathway is enriched with many DEGs/DAPs (including ALDH, RAB3D, and CAT), as well as a DAM, melatonin. This study sheds light on the potential mechanisms that contribute to the improved meat quality and flavor.

Secure cross-chain interaction solution in multi-blockchain environment.

In the context of the increasingly diversified blockchain technology, interoperability among heterogeneous blockchains has become key to further advancing this field. Existing cross-chain technologies, while facilitating data and asset exchange between different blockchains to some extent, have exposed issues such as insufficient security, low efficiency, and inconsistent standards. Consequently, these issues give rise to significant obstacles in terms of both scalability and seamless communication among blockchains within a multi-chain framework. To address this, this paper proposes an efficient method for cross-chain interaction in a multi-chain environment. Building upon the traditional sidechain model, this method employs smart contracts and hash time-locked contracts (HTLCs) to design a cross-chain interaction scheme. This approach decentralizes the execution of locking, verifying, and unlocking stages in cross-chain transactions, effectively avoiding centralization risks associated with third-party entities in the process. It also greatly enhances the efficiency of fund transfers between the main chain and sidechains, while ensuring the security of cross-chain transactions to some extent. Additionally, this paper innovatively proposes a cross-chain data interaction strategy. Through smart contracts on the main chain, data from sidechains can be uploaded, verified, and stored on the main chain, achieving convenient and efficient cross-chain data sharing. The contribution of this paper is the development of a decentralized protocol that coordinates the execution of cross-chain interactions without the need to trust external parties, thereby reducing the risk of centralization and enhancing security. Experimental results validate the effectiveness of our solution in increasing transaction security and efficiency, with significant improvements over existing models. Our experiments emphasize the system's ability to handle a variety of transaction scenarios with improved throughput and reduced latency, highlighting the practical applicability and scalability of our approach.

IoT data security in outsourced databases: A survey of verifiable database.

With the swift advancement of cloud computing and the Internet of Things (IoT), to address the issue of massive data storage, IoT devices opt to offload their data to cloud servers so as to alleviate the pressure of resident storage and computation. However, storing local data in an outsourced database is bound to face the danger of tampering. To handle the above problem, a verifiable database (VDB), which was initially suggested in 2011, has garnered sustained interest from researchers. The concept of VDB enables resource-limited clients to securely outsource extremely large databases to untrusted servers, where users can retrieve database records and modify them by allocating new values, and any attempts at tampering will be detected. This paper provides a systematic summary of VDB. First, a definition of VDB is given, along with correctness and security proofs. And the VDB based on commitment constructions is introduced separately, mainly divided into vector commitments and polynomial commitments. Then VDB schemes based on delegated polynomial functions are introduced, mainly in combination with Merkle trees and forward-secure symmetric searchable encryption. We then classify the current VDB schemes relying on four different assumptions. Besides, we classify the established VDB schemes built upon two different groups. Finally, we introduce the applications and future development of VDB. To our knowledge, this is the first VDB review paper to date.

A Multitask Dynamic Graph Attention Autoencoder for Imbalanced Multilabel Time Series Classification.

Graph learning is widely applied to process various complex data structures (e.g., time series) in different domains. Due to multidimensional observations and the requirement for accurate data representation, time series are usually represented in the form of multilabels. Accurately classifying multilabel time series can provide support for personalized predictions and risk assessments. It requires effectively capturing complex label relevance and overcoming imbalanced label distributions of multilabel time series. However, the existing methods are unable to model label relevance for multilabel time series or fail to fully exploit it. In addition, the existing multilabel classification balancing strategies suffer from limitations, such as disregarding label relevance, information loss, and sampling bias. This article proposes a dynamic graph attention autoencoder-based multitask (DGAAE-MT) learning framework for multilabel time series classification. It can fully and accurately model label relevance for each instance by using a dynamic graph attention-based graph autoencoder to improve multilabel classification accuracy. DGAAE-MT employs a dual-sampling strategy and cooperative training approach to improve the classification accuracy of low-frequency classes while maintaining the classification accuracy of high-frequency and mid-frequency classes. It avoids information loss and sampling bias. DGAAE-MT achieves a mean average precision (mAP) of 0.955 and an F1 score of 0.978 on a mixed medical time series dataset. It outperforms state-of-the-art works in the past two years.

Comparison of Callisphere Drug-Eluting Beads Transarterial Chemoembolization and Conventional Transarterial Chemoembolization for the treatment of Hepatocellular Carcinoma.

Objective: To compare the efficacy of CalliSphere drug-eluting beads (DEBs) and conventional (c) transarterial chemoembolization (TACE) in the treatment of hepatocellular carcinoma (HCC). Methods: We retrospectively reviewed the clinical data of 125 patients with HCC who had received treatment in Affiliated Hospital of North Sichuan Medical College from January 2018 to February 2019. Sixty-one patients underwent DEB-TACE (observation group) and 64 patients underwent cTACE (control group). The clinical efficacies, overall survivals, and incidence of postoperative adverse reactions between the two groups were compared. Results: The objective response rate in the observation group (85.25%) was higher than that in the control group (70.31%; P<0.05). The disease control in the observation group (96.72%) was higher than that in the control group (85.94%; P<0.05). The median survival time of the observation group (24.85 months) was significantly higher than that in the control group (18.18 months; P<0.05). The incidence of adverse reactions in the observation group (4.92%) was lower than that in the control group (17.19%, P<0.05). Conclusions: In the treatment of HCC, Callisphere DEB-TACE has better efficacy and longer patient survival with fewer adverse reactions compared to cTACE.

Deep Learning-Enhanced Internet of Things for Activity Recognition in Post-Stroke Rehabilitation.

Wearable sensors provide a more effective means of activity monitoring and management by recording patients' daily activity data for assessing their daily function and rehabilitation progress, as well as providing a convenient and practical solution for human activity recognition (HAR). However, during the motor rehabilitation of stroke patients, sensors provide vast amounts of high-dimensional data that are large and complex. To enhance the accuracy of activity monitoring and identification, as well as address the limitations of real-time processing, data visualization, and tracking in conventional monitoring approaches, it is essential to perform valid data processing and analysis. This paper combines deep learning models to explore the potential relationships and patterns between data to build an intelligent post-stroke rehabilitation system. This paper proposes a novel framework aimed at accurately recognizing activities performed by stroke patients. Our approach leverages a data fusion mechanism based on multiple sensors to construct a fusion tensor and employs a bidirectional long and short-term memory (BiLSTM) network enhanced with an attention mechanism. This network effectively captures temporal patterns and long-term dependencies within the data, resulting in improved performance for wearable sensor-based activity classification. Furthermore, we introduce an enhanced loss function to optimize the learning process. To assess the performance of the proposed model algorithm, two benchmark datasets were employed. These datasets served as the basis for evaluating and comparing the baseline method as well as other proposed methods. The experimental results clearly demonstrated that the proposed model outperformed the compared methods, indicating its superior performance in activity recognition.

Protective efficacy of recombinant proteins AMA1 and IMP1 in rabbits infected with Eimeria intestinalis.

Eimeria intestinalis is one of the most pathogenic rabbit coccidia species causing severe intestinal damage and increased risk of secondary infection from opportunistic pathogens, which results in huge economic losses to the rabbit industry. Anticoccidial drugs are currently the main method to control coccidiosis; however, increasing resistance and drug residues have fueled research on anticoccidial vaccines. Apical membrane antigen 1 (AMA1) and immune mapped protein 1 (IMP1), as surface proteins, are associated with host invasion and might have the potential as candidate vaccine antigens. In the present study, recombinant IMP1 (rEiIMP1) and AMA1 (rEiAMA1) from E. intestinalis were expressed using Escherichia coli BL21. The immunoreactivity and immunoprotective effects of rEiIMP1 and rEiAMA1 were then analyzed. Fifty rabbits were grouped randomly (n = 10 per group): The unimmunized-unchallenged control group (sterilized phosphate-buffered saline (PBS)), the unimmunized-challenged control group (sterilized PBS), the vector protein-challenged control group (100 µg of pET-32a vector protein per rabbit), the rEiIMP1 immunized group (100 µg of rEiIMP1 per rabbit), and the rEiAMA1 immunized group (100 µg of rEiAMA1 per rabbit). After two immunizations, the rabbits were challenged with homologous oocysts (except for the unimmunized-unchallenged group). Serum specific antibody levels were assessed weekly throughout the experimental period; and the levels of different cytokines in the serum before the challenge were detected. The clinical symptoms, oocysts output, weight gain, feed conversion ratio (FCR), and lesion scores were recorded after experimental infection, and the anticoccidial indexes (ACIs) were calculated. The results showed that both rEiIMP1 and rEiAMA1 had good immunoreactivity. Rabbits immunized with rEiIMP1 and rEiAMA1 displayed 66.74 % and 63.14 % oocyst reduction, respective land 81.79 % and 78.87 % body weight gain, respectively. The rEiIMP1 and rEiAMA1 groups had lower FCRs (3.77:1 and 4.06:1, respectively) and lesion scores (P = 0.00). The rEiIMP1 and rEiAMA1 showed moderate effects, with an ACI of 152.09 and 147.17, respectively. Immunization induced high levels of anti-rEiIMP1 and -rEiAMA1 antibodies. Rabbits immunized with rEiIMP1 and rEiAMA1 displayed significantly increased interleukin (IL)- 2 (P = 0.00), interferon gamma (IFN)- γ (P = 0.00), and IL- 4 (P = 0.00) levels. Therefore, this study provided potential candidate vaccine antigens for E. intestinalis.

NVAS: A non-interactive verifiable federated learning aggregation scheme for COVID-19 based on game theory.

The continued spread of COVID-19 seriously endangers the physical and mental health of people in all countries. It is an important method to establish inter agency COVID-19 detection and prevention system based on game theory through wireless communication and artificial intelligence. Federated learning (FL) as a privacy preserving machine learning framework has received extensive attention. From the perspective of game theory, FL can be regarded as a process in which multiple participants play games against each other to maximize their own interests. This requires that the user's data is not leaked during the training process. However, existing studies have proved that the privacy protection capability of FL is insufficient. In addition, the existing way of realizing privacy protection through multiple rounds of communication between participants increases the burden of wireless communication. To this end, this paper considers the security model of FL based on game theory, and proposes our scheme, NVAS, a non-interactive verifiable privacy-preserving FL aggregation scheme in wireless communication environments. The NVAS can protect user privacy during FL training without unnecessary interaction between participants, which can better motivate more participants to join and provide high-quality training data. Furthermore, we designed a concise and efficient verification algorithm to ensure the correctness of model aggregation. Finally, the security and feasibility of the scheme are analyzed.

Distributed Medical Data Storage Mechanism Based on Proof of Retrievability and Vector Commitment for Metaverse Services.

The metaverse is a unified, persistent, and shared multi-user virtual environment with a fully immersive, hyper-temporal, and diverse interconnected network. When combined with healthcare, it can effectively improve medical services and has great potential for development in realizing medical training, enhanced teaching, and remote surgical treatment. The metaverse provides immersive services for users through massive and multimodal data, and its data scale and data growth rate are bound to show exponential growth. Blockchain-based distributed storage is a fundamental way to keep the metaverse running continuously; however, many blockchains, such as Ethereum and Filecoin, suffer from low transaction throughput and high latency, which seriously affect the efficiency of distributed storage services and make it difficult to apply them to the metaverse environment. To this end, this paper first proposes a network architecture for distributed storage systems based on proof of retrievability to address the problem of centralized decision making and single point of access in centralized storage. The secure data storage of the metaverse health system is ensured. Secondly, we designed two data transmission protocols through vector commitment and encoding functions to achieve the transfer of time cost from the critical path to storage nodes and improve the efficiency of data verification between nodes as well as the scalability of the metaverse health system. Finally, this paper also conducts security analysis and performance analysis of the proposed scheme, and the results show that our scheme is secure and efficient.

Data storage mechanism of industrial IoT based on LRC sharding blockchain.

With the rapid development of Industry 4.0, the data security of Industrial Internet of Things in the Industry 4.0 environment has received widespread attention. Blockchain has the characteristics of decentralization and tamper-proof. Therefore, it has a natural advantage in solving the data security problem of Industrial Internet of Things. However, current blockchain technologies face challenges in providing consistency, scalability and data security at the same time in Industrial Internet of Things. To address the scalability problem and data security problem of Industrial Internet of Things, this paper constructs a highly scalable data storage mechanism for Industrial Internet of Things based on coded sharding blockchain. The mechanism uses coded sharding technology for data processing to improve the fault tolerance and storage load of the blockchain to solve the scalability problem. Then a cryptographic accumulator-based data storage scheme is designed which connects the cryptographic accumulator with the sharding nodes to save storage overhead and solve the security problem of data storage and verification. Finally, the scheme is proved to be security and the performance of the scheme is evaluated.

Evaluation of the immune protective effects of rEmMIC2 and rEmMIC3 from Eimeria magna in rabbits.

Eimeria magna is a common pathogen in rabbits, which results in lethargy, weight loss, diarrhea, and even death in severe cases after infection. The current method for preventing rabbit coccidiosis is to add anticoccidial drugs to the diet. However, there are many concerns about drug resistance and drug residues. In our study, the rEmMIC2 and rEmMIC3 proteins were cloned and expressed to evaluate potential as recombinant subunit vaccine candidate antigens. The protective effects of rEmMIC2 and rEmMIC3 were evaluated by the relative weight gain ratio, oocyst decrease rate, anticoccidial index, feed conversion ratio, pathological alterations, clinical symptoms, specific IgG antibody, and cytokine levels in rabbits. The molecular weights of rEmMIC2 and rEmMIC3 were 18.69 kDa and 17.47 kDa, respectively. After the coccidia challenge, the control groups showed anorexia and soft poop, whereas the experimental group showed few anorexia symptoms. Significantly different from the control group, the relative weight gain ratios of the immunized rEmMIC2 and rEmMIC3 groups were 78.37% and 75.29%, respectively, and the oocyst reduction was 77.95% and 76.09%, respectively, and the anticoccidial index was 171.12 and 169.29, respectively. IgG antibody, IFN-γ, IL-4, IL-10, and IL-17 levels were significantly increased in the experimental group. The results showed that rEmMIC2 and rEmMIC3 have potential as vaccine candidate antigens.

Verifiable Delay Function and Its Blockchain-Related Application: A Survey.

The concept of verifiable delay functions has received attention from researchers since it was first proposed in 2018. The applications of verifiable delay are also widespread in blockchain research, such as: computational timestamping, public random beacons, resource-efficient blockchains, and proofs of data replication. This paper introduces the concept of verifiable delay functions and systematically summarizes the types of verifiable delay functions. Firstly, the description and characteristics of verifiable delay functions are given, and weak verifiable delay functions, incremental verifiable delay functions, decodable verifiable delay functions, and trapdoor verifiable delay functions are introduced respectively. The construction of verifiable delay functions generally relies on two security assumptions: algebraic assumption or structural assumption. Then, the security assumptions of two different verifiable delay functions are described based on cryptography theory. Secondly, a post-quantum verifiable delay function based on super-singular isogeny is introduced. Finally, the paper summarizes the blockchain-related applications of verifiable delay functions.

Enabling secure mutual authentication and storage checking in cloud-assisted IoT.

Internet of things (IoT) is a technology that can collect the data sensed by the devices for the further real-time services. Using the technique of cloud computing to assist IoT devices in data storing can eliminate the disadvantage of the constrained local storage and computing capability. However, the complex network environment makes cloud servers vulnerable to attacks, and adversaries pretend to be legal IoT clients trying to access the cloud server. Hence, it is necessary to provide a mechanism of mutual authentication for the cloud system to enhance the storage security. In this paper, a secure mutual authentication is proposed for cloud-assisted IoT. Note that the technique of chameleon hash signature is used to construct the authentication. Moreover, the proposed scheme can provide storage checking with the assist of a fully-trusted entity, which highly improves the checking fairness and efficiency. Security analysis proves that the proposed scheme in this paper is correct. Performance analysis demonstrates that the proposed scheme can be performed with high efficiency.

Trait Analysis in Domestic Rabbits (Oryctolagus cuniculus f. domesticus) Using SNP Markers from Genotyping-by-Sequencing Data.

The domestic rabbit (Oryctolagus cuniculus f. domesticus) is a very important variety in biomedical research and agricultural animal breeding. Due to the different geographical areas in which rabbit breeds originated, and the long history of domestication/artificial breeding, rabbits have experienced strong selection pressure, which has shaped many traits of most rabbit varieties, such as color and weight. An efficient genome-wide single-nucleotide polymorphism (SNP) detection strategy is genotyping-by-sequencing (GBS), which has been widely used in many organisms. This study attempted to explore bi-allelic SNPs associated with fur color and weight-related traits using GBS in five rabbit breeds. The data consisted of a total 831,035 SNPs in 150 individuals from Californian rabbits (CF), German Zika rabbits (ZK), Qixing rabbits (QX), Sichuan grey rabbits (SG), and Sichuan white rabbits (SW). In addition, these five breeds of rabbits were obviously independent populations, with high genetic differentiation among breeds and low genetic diversity within breeds. A total of 32,144 SNP sites were identified by selective sweep among the different varieties. The genes that carried SNP loci in these selected regions were related to important traits (fur color and weight) and signal pathways, such as the MAPK/ERK signaling pathway and the Hippo signaling pathway. In addition, genes related to fur color and weight were identified, such as ASIPs, MITFs and KITs, ADCY3s, YAPs, FASs, and ACSL5s, and they had more SNP sites. The research offers the foundation for further exploration of molecular genetic markers of SNPs that are related to traits.

An Algorithm for Precipitation Correction in Flood Season Based on Dendritic Neural Network.

In recent years, the National Climate Center has developed a dynamic downscaling prediction technology based on the Climate-Weather Research and Forecasting (CWRF) regional climate model and used it for summer precipitation prediction, but there are certain deviations, and it is difficult to predict more accurately. The CWRF model simulates the summer precipitation forecast data from 1996 to 2019 and uses a combination of dendrite net (DD) and artificial neural networks (ANNs) to conduct a comparative analysis of summer precipitation correction techniques. While summarizing the characteristics and current situation of summer precipitation in the whole country, the meteorological elements related to precipitation are analyzed. CWRF is used to simulate summer precipitation and actual observation precipitation data to establish a model to correct the precipitation. By comparing with the measured data of the ground station after quality control, the relevant evaluation index analysis is used to determine the best revised model. The results show that the correction effect based on the dendritic neural network algorithm is better than the CWRF historical return, in which, the anomaly correlation coefficient (ACC) and the temporal correlation coefficient (TCC) both increased by 0.1, the mean square error (MSE) dropped by about 26%, and the overall trend anomaly (Ps) test score was also improved, showing that the machine learning algorithms can correct the summer precipitation in the CWRF regional climate model to a certain extent and improve the accuracy of weather forecasts.

RPARP-AS1/miR125a-5p Axis Promotes Cell Proliferation, Migration and Invasion in Colon Cancer.

BACKGROUND: It was reported that long-noncoding RNAs (lncRNAs) had been identified as a novel class of regulators related to various cancers. RPARP-AS1, a differentially-expressed gene, was found in analysis of the gene expression profile of CRC from GEO database. However, its function has not been clear. METHODS: RPARP-AS1 expression was determined by qPCR and Startbase3 analysis. Knockdown of RPARP-AS1 in CRC cell lines was performed by RNAi technology, named si-RPARP-AS1 HCT116 and si-RPARP-AS1 LoVo. Cell proliferation was examined by CCK8 and colony formation assay. RNA pull-down and Luciferase reporter assay were performed to confirm the interaction between RPARP-AS1 and miR-125a-5p. RESULTS: In the study, we found that the expression of RPARP-AS1 was significantly up-regulated in CRC tissues and multiple CRC cell lines, which was closely related to poor prognosis of CRC patients. Loss-of-function studies indicated that knockdown of RPARP-AS1 inhibited CRC cell proliferation, migration and invasion in HCT116 and LoVo cell lines. Results of research on the mechanisms showed that RPARP-AS1 functioned as a competitive endogenous RNA (ceRNA) to sponge miR-125a-5p, therefore promoting CRC procession. CONCLUSION: In summary, these results indicated that RPARP-AS1/miR-125a-5p axis played a positive role in promoting cell proliferation, migration and invasion in CC. It may be as a biomarker used to evaluate CRC prognosis.

LINC01287 facilitates proliferation, migration, invasion and EMT of colon cancer cells via miR-4500/MAP3K13 pathway.

BACKGROUND: Accumulated studies indicate that aberrant expression of long noncoding RNAs (lncRNAs) is associated with tumorigenesis and progression of colon cancer. In the present study, long intergenic non-protein coding RNA 1287 (LINC01287) was identified to up-regulate in colon cancer by transcriptome RNA-sequencing, but the exact function remained unclear. METHODS: Transcriptome RNA-sequencing was conducted to identify dysregulated lncRNAs. Expression of LINC01287 was evaluated by real-time quantitative PCR. The downstream targets of LINC01287 and miR-4500 were verified by luciferase reporter assay, pull down assay and western blot. The potential functions of LINC01287 were evaluated by cell viability assay, colony formation assay, soft agar assay, flow cytometry, transwell migration and invasion assay, and tumor xenograft growth in colon cancer cells. RESULTS: Our results indicated that LINC01287 was up-regulated in colon cancer patients. High LINC01287 expression was associated with advanced TNM stage, lymph node metastasis, distant metastasis and shorter overall survival. Knockdown of LINC01287 inhibited cell growth, colony formation in plates and soft agar, transwell cell migration and invasion, and epithelial-mesenchymal transition (EMT) of colon cancer cells, while LINC01287 overexpression had contrary effects. In addition, LINC01287 mediated MAP3K13 expression by sponging miR-4500, thus promoted NF-κB p65 phosphorylation. Restored MAP3K13 expression or miR-4500 knockdown partially abrogated the effects of silencing LINC01287 in colon cancer cells. CONCLUSION: Our findings demonstrated that the LINC01287/miR-4500/MAP3K13 axis promoted progression of colon cancer. Therefore, LINC01287 might be a potential therapeutic target and prognostic marker for colon cancer patients.

Privacy-Preserving Smart Road-Pricing System with Trustworthiness Evaluation in VANETs.

With the advanced development of the intelligent transportation system, vehicular ad hoc networks have been observed as an excellent technology for the development of intelligent traffic management in smart cities. Recently, researchers and industries have paid great attention to the smart road-tolling system. However, it is still a challenging task to ensure geographical location privacy of vehicles and prevent improper behavior of drivers at the same time. In this paper, a reliable road-tolling system with trustworthiness evaluation is proposed, which guarantees that vehicle location privacy is secure and prevents malicious vehicles from tolling violations at the same time. Vehicle route privacy information is encrypted and uploaded to nearby roadside units, which then forward it to the traffic control center for tolling. The traffic control center can compare data collected by roadside units and video surveillance cameras to analyze whether malicious vehicles have behaved incorrectly. Moreover, a trustworthiness evaluation is applied to comprehensively evaluate the multiple attributes of the vehicle to prevent improper behavior. Finally, security analysis and experimental simulation results show that the proposed scheme has better robustness compared with existing approaches.

Circular RNA, microRNA and Protein Profiles of the Longissimus Dorsi of Germany ZIKA and Sichuan White Rabbits.

Due to the dietetic properties and remarkable nutritive value of rabbit meat, its industry is increasing rapidly. However, the association between circular RNAs, microRNAs, and proteins and muscle fiber type, and meat quality of rabbit is still unknown. Here, using deep sequencing and iTRAQ proteomics technologies we first identified 3159 circRNAs, 356 miRNAs, and 755 proteins in the longissimus dorsi tissues from Sichuan white (SCWrabs) and Germany great line ZIKA rabbits (ZIKArabs). Next, we identified 267 circRNAs, 3 miRNAs, and 29 proteins differentially expressed in the muscle tissues of SCWrabs and ZIKArabs. Interaction network analysis revealed some key regulation relationships between noncoding RNAs and proteins that might be associated with the muscle fiber type and meat quality of rabbit. Further, miRNA isoforms and gene variants identified in SCWrabs and ZIKArabs revealed some pathways and biological processes related to the muscle development. This is the first study of noncoding RNA and protein profiles for the two rabbit breeds. It provides a valuable resource for future studies in rabbits and will improve our understanding of the molecular regulation mechanisms in the muscle development of livestock. More importantly, the output of our study will benefit the researchers and producers in the rabbit breeding program.

Application of interventional embolization in the treatment of iatrogenic pseudoaneurysms.

The present study aimed to investigate the clinical effectiveness and safety of endovascular embolization for the treatment of pseudoaneurysm secondary to previous abdominal and pelvic surgery or radiological percutaneous abdominal procedure. A retrospective review was performed on all patients with abdominal and pelvic pseudoaneurysm confirmed by CT angiography or digital subtraction angiography and treated with endovascular embolization. Different techniques of embolization with coils were applied and the outcomes, including clinical effectiveness and safety, were assessed. A total of 31 patients with a total of 32 pseudoaneurysms were included in the present study. Of these pseudoaneurysms, 23 were from the main trunks and branches of the gastroduodenal artery, 5 were from the splenic artery, 2 were from the common hepatic artery, 1 was from the right hepatic artery and 1 was from the right internal iliac artery. There were no serious complications observed and there was no occurrence of re-bleeding following embolization. The embolization of the pseudoaneurysms was successful in all patients. In conclusion, endovascular embolization is a safe and effective method for the treatment of secondary iatrogenic pseudoaneurysm in the abdomen and pelvis.