Early on-treatment plasma interleukin-18 as a promising indicator for long-term virological response in patients with HIV-1 infection

Background and aimsIt is necessary to identify simple biomarkers that can efficiently predict the efficacy of long-term antiretroviral therapy (ART) against human immunodeficiency virus (HIV), especially in underdeveloped countries. We characterized the dynamic changes in plasma interleukin-18 (IL-18) and assessed its performance as a predictor of long-term virological response.MethodsThis was a retrospective cohort study of HIV-1-infected patients enrolled in a randomized controlled trial with a follow-up of 144  weeks of ART. Enzyme-linked immunosorbent assay was performed to evaluate plasma IL-18. Long-term virological response was defined as HIV-1 RNA <20 copies/mL at week 144.ResultsAmong the 173 enrolled patients, the long-term virological response rate was 93.1%. Patients with a long-term virological response had significantly lower levels of week 24 IL-18 than non-responders. We defined 64  pg./mL, with a maximum sum of sensitivity and specificity, as the optimal cutoff value of week 24 IL-18 level to predict long-term virological response. After adjusting for age, gender, baseline CD4+ T-cell count, baseline CD4/CD8 ratio, baseline HIV-1 RNA level, HIV-1 genotype and treatment strategy, we found that lower week 24 IL-18 level (≤64 vs. >64 pg./mL, a OR 19.10, 95% CI: 2.36–154.80) was the only independent predictor of long-term virological response.ConclusionEarly on-treatment plasma IL-18 could act as a promising indicator for long-term virological response in patients with HIV-1 infection. Chronic immune activation and inflammation may represent a potential mechanism; further validation is necessary

The Double Burdens of Mental Health Among AIDS Patients With Fully Successful Immune Restoration: A Cross-Sectional Study of Anxiety and Depression in China

Background: Anxiety and depression continue to be significant comorbidities for people with HIV infection. We investigated the prevalence of and factors associated with anxiety and depression among adult HIV-infected patients across China.Methods: In this cross-sectional study, we described clinical and psychosocial variables related to depression and anxiety in 4103 HIV-infected persons. Doctors assessed anxiety and depression by asking patients whether they had experienced anxiety or depression in the prior month. Patients also self-administered the Hospital Anxiety and Depression (HAD) scale; those with score ≥8 on HAD-A/D were considered to be at high risk of anxiety or depression.Results: Associations between socio-demographic, psychosocial, and ART-related clinical factors and risk of depression or anxiety were investigated using multivariable logistic regression. Among patients assessed between 9/2014 and 11/2015, 27.4% had symptoms of anxiety, 32.9% had symptoms of depression, and 19.0% had both. Recentness of HIV diagnoses (P = 0.046) was associated with elevated odds of anxiety. Older age (P = 0.004), higher educational attainment (P < 0.001), employment (P = 0.001), support from family / friends (P < 0.001), and sleep disturbance (P < 0.001), and number of ART regimen switches (P = 0.046) were associated with risk of depression, while neither sex nor transmission route showed any associations. There were no significant associations with HIV-specific clinical factors including current CD4+ T cell count and current viral load.Conclusions: Prevalence of symptoms of anxiety and depression is high in this cohort of treatment-experienced HIV patients. Psychological and social-demographic factors, rather than HIV disease status, were associated with risk of depression and anxiety. This finding highlights the need to deliver interventions to address the mental health issues affecting HIV-infected persons with fully successful immune restoration across China

Balanced Multi-Relational Graph Clustering

Multi-relational graph clustering has demonstrated remarkable success in uncovering underlying patterns in complex networks. Representative methods manage to align different views motivated by advances in contrastive learning. Our empirical study finds the pervasive presence of imbalance in real-world graphs, which is in principle contradictory to the motivation of alignment. In this paper, we first propose a novel metric, the Aggregation Class Distance, to empirically quantify structural disparities among different graphs. To address the challenge of view imbalance, we propose Balanced Multi-Relational Graph Clustering (BMGC), comprising unsupervised dominant view mining and dual signals guided representation learning. It dynamically mines the dominant view throughout the training process, synergistically improving clustering performance with representation learning. Theoretical analysis ensures the effectiveness of dominant view mining. Extensive experiments and in-depth analysis on real-world and synthetic datasets showcase that BMGC achieves state-of-the-art performance, underscoring its superiority in addressing the view imbalance inherent in multi-relational graphs. The source code and datasets are available at https://github.com/zxlearningdeep/BMGC.Comment: Accepted by ACM Multimedia 202

Gray Relevance Algorithm Based Routing Protocol in Ad Hoc Network

The characteristics of nodes moving arbitrarily and the network topology changing frequently lead to AODV routing protocol, which uses minimum hop-count as the metric for route selection, facing intermittent connectivity frequently which would cause QoS of network degradation in Ad Hoc Network. In this paper, we integrate three cross layer infor-mation which consists of the remaining energy of nodes, the remaining queue length and the hop-count from source node to destination node. Then we present the GRA-AODV routing protocol based on the gray relevance algorithm. By comparing the simulation and experimental results, in the case of slightly increase in routing overhead, the improved Gray Relevance Algorithm-AODV routing possesses lower average end to end delay and lower packet loss rate, and it has superior robustness in the mobile Ad Hoc Network with network topology changing frequently

Research on Acoustic Emission Characteristics and Crack Evolution during Rock Failure under Tensile and Tensile- and Compressive-Shear Stress States

Tensile, compressive-shear, and tensile-shear failure are three typical failure modes of rock- and lining-support structural materials in underground engineering. Comparative studies of the acoustic emission (AE) evolution characteristics of specimens of the same shape and size under different stress states are of great significance in determining universal disaster warning guidelines. Based on a self-developed multi-functional test system, direct tensile, compressive-shear, and tensile-shear tests were conducted on intact and jointed rock-like specimens, comparing AE amplitude and peak frequency parameters under different failure modes from the perspectives of crack scale and crack type evolution. All failure tests were monitored with microcrack propagation at an early stage until ultimate rupture occurred at the peak-load moment. As a result, the “quiet-period” could only be observed from the compressive-shear test. The AE signals distributed in three bands can be used as an indicator of failure identifications. Tensile and shear cracks can be identified by strong and weak amplitudes of low- and high-frequency signals. These results enhance the knowledge of the failure modes of rock mechanics for more applications in monitoring disasters in rock engineering

An Explainable Student Performance Prediction Method Based on Dual-Level Progressive Classification Belief Rule Base

Explainable artificial intelligence (XAI) is crucial in education for making educational technologies more transparent and trustworthy. In the domain of student performance prediction, both the results and the processes need to be recognized by experts, making the requirement for explainability very high. The belief rule base (BRB) is a hybrid-driven method for modeling complex systems that integrates expert knowledge with transparent reasoning processes, thus providing good explainability. However, class imbalances in student grades often lead models to ignore minority samples, resulting in inaccurate assessments. Additionally, BRB models face the challenge of losing explainability during the optimization process. Therefore, an explainable student performance prediction method based on dual-level progressive classification BRB (DLBRB-i) has been proposed. Principal component regression (PCR) is used to select key features, and models are constructed based on selected metrics. The BRB’s first layer classifies data broadly, while the second layer refines these classifications for accuracy. By incorporating explainability constraints into the population-based covariance matrix adaptation evolution strategy (P-CMA-ES) optimization process, the explainability of the model is ensured effectively. Finally, empirical analysis using real datasets validates the diagnostic accuracy and explainability of the DLBRB-i model