Diallyl trisulfide regulates PGK1/Nrf2 expression and reduces inflammation to alleviate neurological damage in mice after traumatic brain injury.

BACKGROUND: Diallyl trisulfide (DATS) has a direct antioxidant capacity and emerges as a promising neuroprotective agent. This study was designed to investigate the role of DATS in traumatic brain injury (TBI). METHODS: TBI mouse models were established using the controlled weight-drop impact, followed by DATS administration. The effects of DATS on neurological deficit, brain damage, inflammation and phosphoglycerate kinase 1 (PGK1) expression were detected using mNSS test, histological analysis, TUNEL assay, enzyme-linked immunosorbent assay and immunofluorescence. PC12 cells were subjected to H2O2-induced oxidative injury after pre-treatment with DATS, followed by cell counting kit-8 assay, flow cytometry and ROS production detection. Apoptosis-related proteins and the PGK1/nuclear factor erythroid-2 related factor 2 (Nrf2) pathway were examined using Western blot. RESULTS: DATS ameliorated the cerebral cortex damage, neurological dysfunction and apoptosis, as well as decreased PGK1 positivity and expressions of pro-inflammatory cytokines (IL-6, IL-1ß, TNF-α) in mice after TBI. DATS also enhanced viability, blocked apoptosis and inhibited ROS production in H2O2-induced PC12 cells. DATS downregulated Cleaved-Caspase3, Bax and PGK1 levels, and upregulated Bcl-2 and Nrf2 levels in TBI mouse models and the injured cells. CONCLUSION: DATS regulates PGK1/Nrf2 expression and inflammation to alleviate neurological damage in mice after TBI.

Enhancing cancer therapy: the integration of oncolytic virus therapy with diverse treatments.

As one of the significant challenges to human health, cancer has long been a focal point in medical treatment. With ongoing advancements in the field of medicine, numerous methodologies for cancer therapy have emerged, among which oncolytic virus therapy has gained considerable attention. However, oncolytic viruses still exhibit limitations. Combining them with various therapies can further enhance the efficacy of cancer treatment, offering renewed hope for patients. In recent research, scientists have recognized the promising prospect of amalgamating oncolytic virus therapy with diverse treatments, potentially surmounting the restrictions of singular approaches. The central concept of this combined therapy revolves around leveraging oncolytic virus to incite localized tumor inflammation, augmenting the immune response for immunotherapeutic efficacy. Through this approach, the patient's immune system can better recognize and eliminate cancer cells, simultaneously reducing tumor evasion mechanisms against the immune system. This review delves deeply into the latest research progress concerning the integration of oncolytic virus with diverse treatments and its role in various types of cancer therapy. We aim to analyze the mechanisms, advantages, potential challenges, and future research directions of this combination therapy. By extensively exploring this field, we aim to instill renewed hope in the fight against cancer.

K-Anonymity Privacy Protection Algorithm for Multi-Dimensional Data against Skewness and Similarity Attacks.

Currently, a significant focus has been established on the privacy protection of multi-dimensional data publishing in various application scenarios, such as scientific research and policy-making. The K-anonymity mechanism based on clustering is the main method of shared-data desensitization, but it will cause problems of inconsistent clustering results and low clustering accuracy. It also cannot defend against several common attacks, such as skewness and similarity attacks at the same time. To defend against these attacks, we propose a K-anonymity privacy protection algorithm for multi-dimensional data against skewness and similarity attacks (KAPP) combined with t-closeness. Firstly, we propose a multi-dimensional sensitive data clustering algorithm based on improved African vultures optimization. More specifically, we improve the initialization, fitness calculation, and solution update strategy of the clustering center. The improved African vultures optimization can provide the optimal solution with various dimensions and achieve highly accurate clustering of the multi-dimensional dataset based on multiple sensitive attributes. It ensures that multi-dimensional data of different clusters are different in sensitive data. After the dataset anonymization, similar sensitive data of the same equivalence class will become less, and it eventually does not satisfy the premise of being theft by skewness and similarity attacks. We also propose an equivalence class partition method based on the sensitive data distribution difference value measurement and t-closeness. Namely, we calculate the sensitive data distribution's difference value of each equivalence class and then combine the equivalence classes with larger difference values. Each equivalence class satisfies t-closeness. This method can ensure that multi-dimensional data of the same equivalence class are different in multiple sensitive attributes, and thus can effectively defend against skewness and similarity attacks. Moreover, we generalize sensitive attributes with significant weight and all quasi-identifier attributes to achieve anonymous protection of the dataset. The experimental results show that KAPP improves clustering accuracy, diversity, and anonymity compared to other similar methods under skewness and similarity attacks.

Knowledge-Aware Multispace Embedding Learning for Personalized Recommendation.

Recommender systems help users filter items they may be interested in from massive multimedia content to alleviate information overload. Collaborative filtering-based models perform recommendation relying on users' historical interactions, which meets great difficulty in modeling users' interests with extremely sparse interactions. Fortunately, the rich semantics hidden in items may be promising in helping to describing users' interests. In this work, we explore the semantic correlations between items on modeling users' interests and propose knowledge-aware multispace embedding learning (KMEL) for personalized recommendation. KMEL attempts to model users' interests across semantic structures to leverage valuable knowledge. High-order semantic collaborative signals are extracted in multiple independent semantic spaces and aggregated to describe users' interests in each specific semantic. The semantic embeddings are adaptively integrated with a target-aware attention mechanism to learn cross-space multisemantic embeddings for users and items, which are fed to the subsequent pairwise interaction layer for personalized recommendation. Experiments on real-world datasets demonstrate the effectiveness of the proposed KMEL model.

Application of CNN Algorithm Based on Chaotic Recursive Diagonal Model in Medical Image Processing.

With the gradual improvement of people's living standards, the production and drinking of all kinds of food is increasing. People's disease rate has increased compared with before, which leads to the increasing number of medical image processing. Traditional technology cannot meet most of the needs of medicine. At present, convolutional neural network (CNN) algorithm using chaotic recursive diagonal model has great advantages in medical image processing and has become an indispensable part of most hospitals. This paper briefly introduces the use of medical science and technology in recent years. The hybrid algorithm of CNN in chaotic recursive diagonal model is mainly used for technical research, and the application of this technology in medical image processing is analysed. The CNN algorithm is optimized by using chaotic recursive diagonal model. The results show that the chaotic recursive diagonal model can improve the structure of traditional neural network and improve the efficiency and accuracy of the original CNN algorithm. Then, the application research and comparison of medical image processing are performed according to CNN algorithm and optimized CNN algorithm. The experimental results show that the CNN algorithm optimized by chaotic recursive diagonal model can help medical image automatic processing and patient condition analysis.

Long noncoding RNA H19 promotes the apoptosis of corpus cavernsum smooth muscle cells after cavernosal nerve injury via JNK signalling pathway.

JNK/ Bcl-2/ Bax pathway participates in corpus cavernosal smooth muscle cells apoptosis during early period after cavernosal nerve (CN) crush injury (CNCI). Nevertheless, the regulation mechanisms of long noncoding RNA H19 in apoptosis during early stage after CN injury are still poorly understood. The rats in sham group were not direct injury to the CNs. The rats in CNCI group were performed to bilateral CN crush injury. The ICP/MAP rate and smooth muscle content were significantly lower than that in the sham group. Primary CCSMCs were prepared from the tissues samples after completing erectile function detection. Phosphorylated-JNK level was increased significantly, and the expression of Bax and Bcl-2 was elevated and declined in CNCI group respectively. Except for Bcl-2, the mRNA levels of H19, JNK and Bax were significantly increased in CNCI group. After H19 siRNA transfection, for the mRNA and protein levels, JNK and Bax were declined, while Bcl-2 was enhanced. LncRNA H19 might be involved in regulation of Bcl-2, Bax via JNK signalling pathway in CCSMCs apoptosis after CN injury.

Two-level incremental checkpoint recovery scheme for reducing system total overheads.

Long-running applications are often subject to failures. Once failures occur, it will lead to unacceptable system overheads. The checkpoint technology is used to reduce the losses in the event of a failure. For the two-level checkpoint recovery scheme used in the long-running tasks, it is unavoidable for the system to periodically transfer huge memory context to a remote stable storage. Therefore, the overheads of setting checkpoints and the re-computing time become a critical issue which directly impacts the system total overheads. Motivated by these concerns, this paper presents a new model by introducing i-checkpoints into the existing two-level checkpoint recovery scheme to deal with the more probable failures with the smaller cost and the faster speed. The proposed scheme is independent of the specific failure distribution type and can be applied to different failure distribution types. We respectively make analyses between the two-level incremental and two-level checkpoint recovery schemes with the Weibull distribution and exponential distribution, both of which fit with the actual failure distribution best. The comparison results show that the total overheads of setting checkpoints, the total re-computing time and the system total overheads in the two-level incremental checkpoint recovery scheme are all significantly smaller than those in the two-level checkpoint recovery scheme. At last, limitations of our study are discussed, and at the same time, open questions and possible future work are given.