Explore the vast range of titles available.

Background This study aimed to assess the methodological quality of the systematic reviews/meta-analyses (SRs/MAs) of chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) using A Measurement Tool to Assess systematic Reviews (AMSTAR2) and to explore the potential influencing factors. Methods PubMed, EMBASE and Cochrane Library databases were searched for relevant studies. AMSTAR2 was used for evaluating the methodological quality of eligible SRs/MAs. Differences between methodological characteristics of SRs/MAs were compared using chi-square tests. The intra-class correlation coefficient (ICC) was used to assess reviewer agreement in the pre-experiment. Multivariate regression analysis was used to identify potential factors affecting methodological quality. Results A total of 45 SRs/MAs were included. After AMSTAR2 evaluation, only two (4.4%) of 45 SRs/MAs were moderate, three (6.7%) were rated as low quality, and the remainder 40 (88.9%) were rated as critically low quality. Among the 16 items of AMSTAR2, item 3 and item 10 had the poorest adherence. Item 4 received the most significant number of \"Partial Yes\" responses. Univariable analysis indicated that there were significant differences in methodological quality in SRs between different continents ( P  = 0.027) as well as between preregistered SRs and those that were not ( P  = 0.004). However, in multivariate analysis, there was no significant association between methodological quality and the following research characteristics: publication year, continent, whether reporting followed Preferred Reporting Items for Systematic Reviews (PRISMA), preregistration, funding support, randomized controlled trials (RCT) enrollment, whether SR was published in the Cochrane Database of Systematic Reviews (CDSR), and whether with meta-analysis. Additionally, subgroup analysis based on interventional SRs/MAs showed that continent was independently associated with the methodological quality of SRs/MAs of CP/CPPS via univariable and multivariate analysis. Conclusions Our study demonstrates that the methodological quality of SRs/MAs of CP/CPPS was generally poor. SRs/MAs of CP/CPPS should adopt the AMSTAR2 to enhance their methodological quality.

Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) is one of the most common diseases of the male urological system while the etiology and treatment of CP/CPPS remain a thorny issue. Cumulative research suggested a potentially important role of glial cells in CP/CPPS. This narrative review retrospected literature and grasped the research process about glial cells and CP/CPPS. Three types of glial cells showed a crucial connection with general pain and psychosocial symptoms. Microglia might also be involved in lower urinary tract symptoms. Only microglia and astrocytes have been studied in the animal model of CP/CPPS. Activated microglia and reactive astrocytes were found to be involved in both pain and psychosocial symptoms of CP/CPPS. The possible mechanism might be to mediate the production of some inflammatory mediators and their interaction with neurons. Glial cells provide a new insight to understand the cause of complex symptoms of CP/CPPS and might become a novel target to develop new treatment options. However, the activation and action mechanism of glial cells in CP/CPPS needs to be further explored.

The emerging topic of sequential recommender (SR) has attracted increasing attention in recent years, which focuses on understanding and learning the sequential dependencies of user behaviors hidden in the user-item interactions. Previous methods focus on capturing the point-wise sequential dependencies with considering the time evenly spaced. However, in the real world, the time and semantic irregularities are hidden in the user’s successive actions. Meanwhile, with the tremendous increase of users and items, the hardness of modeling user interests from spare explicit feedback. To this end, we seek to explore the influence of item-aspect reviews sequence with varied time intervals on sequential modeling. We present RTiSR, a review-driven time interval-aware sequential recommendation framework, to predict the user’s next purchase item by jointly modeling the sequence dependencies from aspect-aware reviews. The main idea is twofold: (1) explicitly learning user and item representation from reviews by assigning different weights, and (2) leveraging a hybrid neural network to capture the collective sequence patterns with a flexible order from aspect-aware review sequences. We conduct extensive experiments on industrial datasets to evaluate the effectiveness of RTiSR. Experimental results demonstrate the superior performance of RTiSR in different evaluation metrics, compared to the state-of-the-art competitors.

As a native tree species in China, is highly responsive to nitrogen fertilization. However, related studies are scarce. This research seeks to elucidate how different nitrogen fertilizers affect its growth and nitrogen metabolism across different growth stages, thereby determining the most suitable type and establishing a scientific foundation for its fertilization. This study aimed to investigate the effects of topdressing with different nitrogen fertilizers on the growth and nitrogen metabolism of seedlings, with the goal of providing a scientific basis for optimized nitrogen fertilization management in cultivation. The study used 3-year-old seedlings as the material, and the fertilization rate was 3 g per seedling. The experiment was conducted in a one-way randomized block design with four treatments, including the control treatment and three nitrogen fertilizer treatments: urea (amide nitrogen fertilizer), ammonium sulfate (ammonium nitrogen fertilizer), and sodium nitrate (nitrate nitrogen fertilizer). From the start of the experiment, the branch and leaf morphology, the height growth, and the basal diameter growth of the seedlings in each treatment were monitored periodically. The activities of the nitrogen-metabolizing enzymes, such as nitrate reductase, glutamine synthetase, glutamate synthetase, and glutamate dehydrogenase, in the leaves were also measured. At growth cessation, all treatments were evaluated for biomass production, root morphological characteristics, and total nitrogen content in different plant parts (i.e., roots, stems, and leaves). Due to the high nitrogen content in the cultivation substrate, the application of the different nitrogen fertilizers induced varying levels of fertilizer injury. Temporal analysis revealed that the growth inhibition was not uniform across stages. While all nitrogen treatments ultimately suppressed the overall height and diameter growth compared with the control, the timing and the intensity of these effects varied. For instance, the urea treatment initially showed less inhibition, whereas the sodium nitrate treatment consistently exhibited the strongest inhibitory effect throughout the experiment. Similarly, the promotion of nitrogen metabolism enzyme activity by the different fertilizers also displayed distinct temporal patterns, with peaks occurring at different measurement points. All nitrogen treatments increased the nitrogen content in the root, stem, and leaf parts, but decreased the nitrogen translocation efficiency of seedlings. All nitrogen treatments increased the nitrogen accumulation in the roots and stems of seedlings. Urea treatment enhanced foliar nitrogen accumulation, whereas both the ammonium sulfate and sodium nitrate treatments reduced foliar nitrogen accumulation. All three nitrogen treatments significantly influenced both the growth and physiological indices of seedlings. While generally enhancing the nitrogen metabolism and accumulation, improper selection of fertilizer types or excessive application rates elevated the tissue nitrogen concentration, inducing phytotoxic effects that ultimately inhibited seedling growth. In this research, sodium nitrate had the greatest toxic effect on seedlings, followed by ammonium sulfate and urea. Among the nitrogen fertilizers tested, urea proved superior at an application rate of 3 g per plant for 3-year-old seedlings.

Machilus thunbergii Siebold & Zucc. is recognized as an excellent tree species for landscaping and shelter forest. Excessive drought can affect the changes of physiological and biochemical substances in plants. However, little is known at present regarding the drought stress of M. thunbergii seedlings. In this paper, matrix water content, the anatomical structure of leaves, relative water content of leaves, and physiological characteristics index of leaves under droughting stress were dynamically observed. Droughting stress led to the wilting of M. thunbergii leaves, gradual closure of stomata on leaf epidermis, increases in stomatal density, gradual loosening of leaf cell structure arrangement, a thickening in leaf palisade tissue, and reductions in spongy tissue. Droughting stress caused the relative water content of the cultivation substrate to decline, the cultivation substrate reached the moderate drought level, and the seedlings began to die. Droughting stress led to the destruction of activity and balance of the leaf protective enzyme system, excessive accumulation of free radicals, the destruction of enzyme structure and function, and the production of lipid peroxidation product MDA. Droughting stress reduced the relative water content of leaves as a whole, the content of osmotic adjustment substances proline and soluble protein continued to decline, and a large number of electrolyte leakage in cells, causing serious damage to seedlings.

N6-methyladenosine (m6A) is the most abundant internal messenger RNA modification in eukaryotes, influencing various physiological and pathological processes by regulating RNA metabolism. Numerous studies have investigated the role of m6A in inflammatory responses and inflammatory diseases. In this study, VOSviewer and Citespace were used to perform bibliometric analysis to systematically evaluating the current landscape of research on the association between m6A and inflammation. The literature was sourced from the Web of Science Core Collection, with characteristics including year, country/region, institution, author, journal, citation, and keywords. According to the bibliometric analysis results of keywords, we present a narrative summary of the potential mechanisms by which m6A regulates inflammation. The results showed that the key mechanisms by which m6A modulates inflammation include apoptosis, autophagy, oxidative stress, immune cell dysfunction, and dysregulation of signaling pathways.

Sequential recommender systems play a pivotal role in modern recommendation scenarios by capturing users’ dynamic interests through their historical interactions. While existing methods often rely on sophisticated deep models to enhance recommendation quality, they suffer from performance degradation due to sparse supervision signals and popularity bias in the training data. In this paper, we propose TCA4Rec, a robust sequential recommendation framework that addresses these challenges via a novel two-stage contrastive learning approach. Our framework incorporates an additional memory module to aggregate sequence embeddings, thereby providing flexible and generalized representations of user preferences. To mitigate popularity bias, we derive an Asymmetric Multi-instance Noise Contrastive Estimation (AMINCE) loss function that supplies rich, bias-aware training signals, while our two-stage training strategy significantly reduces the over-dominance of popular items during optimization. Extensive experiments are conducted on three real-world datasets. The experimental results demonstrate that TCA4Rec achieves significant improvements over state-of-the-art baselines, in terms of recommendation accuracy and robustness against sparse and noisy data. It attains relatively gains of 19.26% in HR@5 and 17.97% in NDCG@5 on the Amazon-sports dataset. The code is available at https://github.com/shixiaoyu0216/TCA4Rec/tree/main .

Brain-inspired navigation technologies combine environmental perception, spatial cognition, and target navigation to create a comprehensive navigation research system. Researchers have used various sensors to gather environmental data and enhance environmental perception using multimodal information fusion. In spatial cognition, a neural network model is used to simulate the navigation mechanism of the animal brain and to construct an environmental cognition map. However, existing models face challenges in achieving high navigation success rate and efficiency. In addition, the limited incorporation of navigation mechanisms borrowed from animal brains necessitates further exploration. On the basis of the brain-inspired navigation process, this paper launched a systematic study on brain-inspired environment perception, brain-inspired spatial cognition, and goal-based navigation in brain-inspired navigation, which provides a new classification of brain-inspired cognition and navigation techniques and a theoretical basis for subsequent experimental studies. In the future, brain-inspired navigation technology should learn from more perfect brain-inspired mechanisms to improve its generalization ability and be simultaneously applied to large-scale distributed intelligent body cluster navigation. The multidisciplinary nature of brain-inspired navigation technology presents challenges, and multidisciplinary scholars must cooperate to promote the development of this technology.

Hepatocellular carcinoma (HCC) is a key global health issue, marked by poor clinical outcomes and lower survival rates. Icaritin (ICT), a bioactive compound derived from traditional Chinese medicine, has shown promising multi-target antitumor properties and potential clinical benefits in the treatment of HCC; however, its precise mechanisms of action remain insufficiently understood. Therefore, this study adopted an integrative strategy that combined bioinformatics analysis, experimental validation, and network pharmacology to systematically explore the prognostic and therapeutic relevance of ICT-associated genes. Initially, potential targets of ICT and HCC-associated genes were identified through extensive database screening, and the overlapping candidates were further determined using WGCNA and differential expression analysis. These core intersecting genes were subsequently refined via four complementary machine learning algorithms, KM survival analysis and LASSO Cox regression to establish a prognostic risk score model with predictive value. Additionally, molecular docking and dynamics simulations were performed to evaluate the binding stability between ICT and these targets. Finally, experiments were conducted to evaluate the effects of ICT on the proliferation and migration, as well as the expression of core target genes. We identified thirty-five overlapping targets between ICT and HCC, and functional enrichment analysis showed that these genes are primarily implicated in cell cycle regulation and glycolytic pathways, highlighting potential mechanisms through which ICT exerts its antitumor effects. By integrating multiple machine learning approaches, KM survival analysis and LASSO Cox regression, we developed a four-gene prognostic model that successfully stratified HCC patients into higher- and lower-risk groups. Molecular docking and molecular dynamics simulations demonstrated that ICT binds stably to core targets, supporting its potential role in modulating disease progression. validation confirmed that ICT suppresses HepG2 and Huh7 cells proliferation and migration in a dose-dependent manner, while molecular analyses demonstrated that ICT treatment significantly downregulates CA9, UCK2, and FABP5 expression and simultaneously upregulates CYP2C9, thereby supporting its role in modulating critical oncogenic pathways. Modulation of ICT-targeted genes was found to effectively suppress HCC progression, underscoring their potential value as prognostic biomarkers and ideal therapeutic targets for the treatment of HCC.

The emerging topic of privacy-preserving deep learning as a service has attracted increasing attention in recent years, which focuses on building an efficient and practical neural network prediction framework to secure client and model-holder data privately on the cloud. In such a task, the time cost of performing the secure linear layers is expensive, where matrix multiplication is the atomic operation. Most existing mix-based solutions heavily emphasized employing BGV-based homomorphic encryption schemes to secure the linear layer on the CPU platform. However, they suffer an efficiency and energy loss when dealing with a larger-scale dataset, due to the complicated encoded methods and intractable ciphertext operations. To address it, we propose cuSCNN, a secure and efficient framework to perform the privacy prediction task of a convolutional neural network (CNN), which can flexibly perform on the GPU platform. Its main idea is 2-fold: (1) To avoid the trivia and complicated homomorphic matrix computations brought by BGV-based solutions, it adopts GSW-based homomorphic matrix encryption to efficiently enable the linear layers of CNN, which is a naive method to secure matrix computation operations. (2) To improve the computation efficiency on GPU, a hybrid optimization approach based on CUDA (Compute Unified Device Architecture) has been proposed to improve the parallelism level and memory access speed when performing the matrix multiplication on GPU. Extensive experiments are conducted on industrial datasets and have shown the superior performance of the proposed cuSCNN framework in terms of runtime and power consumption compared to the other frameworks.