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•This study provides the first dual-modality (visual and auditory) evidence of musicians’ enhanced inhibitory control.•Musicians show stronger early auditory encoding (larger N1) and more efficient conflict monitoring and motor inhibition.•EEG results reveal smaller N2, larger P3, and reduced low-beta power in musicians compared to non-musicians.•Findings extend music training beyond domain-specific expertise, with implications for education, therapy, and social adaptation. Musicians' perceptual advantages are often concentrated in the auditory rather than the visual modality. However, whether the relationship between musical training and the core cognitive ability of inhibitory control exhibits auditory modality specificity remains unclear. To address this gap, the present study employed matched visual and auditory Go/No-go tasks combined with electroencephalography (EEG) to compare differences in inhibitory control between university students with long-term musical training (musical training group) and their untrained peers (control group). The results showed that in both visual and auditory inhibitory control tasks, even after controlling for eight potential confounding variables including age, socioeconomic status, IQ, and the Big Five personality traits, the musical training group not only demonstrated behavioral advantages (higher d′ scores) but also exhibited enhanced neural activity during conflict monitoring (smaller N2 amplitudes and increased theta power) and motor inhibition (larger P3 amplitudes). These findings suggest a modality-general effect in the relationship between musical training and enhanced inhibitory control. Meanwhile, the musically trained group showed specific advantages only in the early processing stage of auditory stimuli, reflecting the potential strengthening effect of the auditory system brought about by musical experience. This study is the first to reveal, from a dual-modality perspective, the relationship between musical training and cross-modal inhibitory control. It contributes to our understanding of the cognitive mechanisms associated with musical training and provides empirical evidence for its potential applications in music therapy and education.

This study investigates ice crystal sedimentation calculation errors arising from three-moment bulk cloud scheme. Both offline tests and one-dimensional cloud model simulations indicate that sedimentation calculation errors are most pronounced at both the cloud bottom and cloud top. At the cloud bottom, the error stems from how the bulk method treats ice crystal sedimentation. Specifically, the method uses three weighted fall velocities (corresponding to the three moments) to represent instantaneous fluxes through a fixed altitude, which inherently assumes that falling ice crystals can only affect the adjacent model layer below. This assumption artificially constrains the falling distance of larger ice crystals. At the cloud top, the differences among these three weighted fall velocities can give rise to physical inconsistencies. This issue is handled by artificial adjustment, which leads to a spurious narrow size distribution shape of ice crystals, especially under model configurations with coarse temporal resolution (large dT) and fine vertical resolution (small dH). If only the sedimentation process is considered, the above calculation errors can be effectively minimized by lowering the dT/dH ratio.

With the growing pressures of modern society, physical and mental health issues have emerged as critical global concerns. Forest therapy (FT), a novel health management model that integrates natural environments with physical and mental healing, has gained increasing attention in recent years. However, mainstream FT approaches often overlook the psychological value of interpersonal interaction. Building upon traditional FT, this study proposes a new framework called companionship-based forest therapy (CBFT), which emphasizes the importance of emotional support within natural settings. CBFT is not intended as a replacement for conventional FT, but rather as an optimized approach that enhances its therapeutic effects by incorporating the element of companionship. This study aims to evaluate the physiological and psychological benefits of a novel intervention model—companion-based forest therapy (CBFT)—compared to conventional forest therapy models. Grounded in psychological theories and supported by empirical analysis, this study presents an applied framework of CBFT grounded in established psychological theories and validates its effectiveness through a comparative intervention involving 30 college students. Interpreted from the perspectives of ecological and humanistic psychology, the results indicate that CBFT significantly improves emotional regulation, reduces physiological stress responses, and enhances overall mental well-being. These findings highlight the value of social connection in FT practices and offer new directions for the development and application of forest therapy.

Pancreatic ductal adenocarcinoma (PDAC) remains among the most lethal gastrointestinal cancers, characterized by rapid progression and resistance to therapy driven by significant metabolic reprogramming. Although alterations in glycolysis, glutamine, and lipid metabolism are well established, recent studies emphasize a more crucial factor: the emergence of context-specific metabolic dependencies within the nutrient-deprived tumor microenvironment. This review highlights that focusing on these dependencies, particularly those resulting from the distinctive metabolic interactions between cancer cells and the surrounding stroma, offers a promising strategy for overcoming treatment challenges. Evidence demonstrating that stromal-driven metabolic pathways supply energy and building blocks and confer resistance to standard chemo- and immune-therapies is examined. Furthermore, innovative approaches to target these vulnerabilities in PDAC metabolic subtypes, including synthetic-lethal interactions and key transporters in metabolic pathways are explored. How functional precision medicine, which uses patient-derived models to identify metabolic vulnerabilities, may convert these insights into personalized therapies is examined. Moving from broad metabolic inhibition to precise targeting of the tumour-stroma metabolic ecosystem could substantially improve the prognosis for PDAC. Graphical abstract

Background: Inhibitory control is an important component of cognitive processing that is influenced by multimodal information processing. Recent research has mainly focused on the influence of visual information on inhibitory control, paying less attention to the impact of auditory training, which limits the exploration of the mechanism and practical training of inhibitory control. The influence of music training on inhibitory control has received considerable attention in recent years. To explore the modality-specificity of inhibitory control, this study compares the behavioral and brain electrical activities of different music training experiences through visual and auditory inhibitory control tasks. Methods: This investigation utilized event-related potential (ERP) and time-frequency analysis methodologies to examine the behavioral and neural patterns of thirty participants with musical expertise alongside thirty individuals without such training while completing both visual and auditory Stroop tasks. Further analysis was conducted to examine the modality-specific effect of music training on inhibitory control. Results: The results showed no significant group differences in behavioral performance or traditional ERP components (N450 and sustained potential (SP)) in either modality. Time-frequency analysis revealed no significant differences in theta power in the visual modality. However, in the auditory modality, the music training group exhibited significantly lower beta power, suggesting that music training may more efficiently recruit neural resources when resolving auditory conflict. Conclusions: These findings indicate that inhibitory control exhibits modality specificity across both visual and auditory modalities. Specifically, music training mainly improves auditory conflict resolution, suggesting its impact on inhibitory control is limited to specific sensory modalities.

Icariin is a biologically active substance in that is used for the treatment of neurologic disorders. However, a comprehensive analysis of the molecular mechanisms of icariin is lacking. In this review, we present a brief history of the use of icariin for medicinal purposes; describe the active chemical components of ; and examine the evidence from experimental studies that have uncovered molecular targets of icariin in different diseases. We also constructed a protein-protein interaction network and carried out Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses to predict the therapeutic actions of icariin in nervous system diseases including Alzheimer disease, Parkinson disease, ischemic stroke, depressive disorder, multiple sclerosis, glioblastoma, and hereditary spastic paraplegias. The results of our analyses can guide future studies on the application of icariin to the treatment of neurologic disorders.

Asthma is a complex, chronic inflammatory condition of the airways that comes in many forms. Because different inflammatory processes drive it, we can generally categorize asthma into two main types: type 2 inflammatory asthma and non-type 2 inflammatory asthma. Type 2 inflammation is usually the culprit in most folks grappling with severe asthma. There is a noticeable difference in the treatment approaches for different phenotypes of severe asthma. The main reason is that patients suffering from type 2 inflammatory asthma can respond well to treatment with biological agents. Several well-verified biological agents, such as anti-immunoglobulin E (IgE) monoclonal antibodies, anti-interleukin (IL)-4 monoclonal antibodies, anti-IL-5 monoclonal antibodies, and anti-thymic stromal lymphopoietin (TSLP) monoclonal antibodies, have shown outstanding effectiveness. They can significantly alleviate asthma exacerbations, lower the number of eosinophils, improve pulmonary function, decrease the dependence on oral corticosteroids, and elevate the quality of life for patients with asthma. This discourse meticulously evaluates the therapeutic prowess of biological agents in the treatment and control of severe asthma, concurrently investigating their impact on histological indices, to highlight the crucial role of precision medicine in the strategic concatenation of therapy for this refractory malady.

Background Statistical epistasis, or “gene–gene interaction” in genetic association studies, means the nonadditive effects between the polymorphic sites on two different genes affecting the same phenotype. In the genetic association analysis of complex traits, nevertheless, the researchers haven’t found enough clues of statistical epistasis so far. Methods We developed a statistical model where the statistical epistasis was presented as an extra linkage disequilibrium between the polymorphic sites of different risk genes. The power of statistical test for identifying the gene–gene interaction was calculated and then compared in different hypothesis scenarios. Results Our results show the statistical power increases with the increasing of interaction coefficient, relative risk, and linkage disequilibrium with genetic markers. However, the power of interaction discovery is much lower than that of regular single-site association test. When rigorous criteria were employed in statistical tests, the identification of gene–gene interaction became a very difficult task. Since the criterion of significance was given to be p -value ≤ 5.0 × 10 –8 , the same as that of many genome-wide association studies, there is little chance to identify the gene–gene interaction in all kind of circumstances. Conclusions The lack of epistasis tends to be an inevitable result caused by the statistical principles of methods in the genetic association studies and therefore is the inherent characteristic of the research itself.

Baseline IL-6 levels have been found to be non-predictive of subsequent outcomes following tocilizumab treatment, highlighting the need for more reliable predictive markers. To address this, a retrospective analysis was conducted on the clinical profiles, diagnostic tests, and follow-up prognoses of 60 patients with severe or critical COVID-19, all of whom were identified as experiencing a cytokine storm and subsequently received tocilizumab treatment. Among the patients, the overall survival rate during follow-up was 80%, with further analysis revealing that advanced age was an independent risk factor for adverse outcomes. Following tocilizumab administration, a statistically significant increase in IL-6 and D-dimer levels was observed, while markers such as C-reactive protein (CRP), procalcitonin (PCT), and fibrinogen demonstrated reductions compared to pre-treatment values. Specifically, IL-6 levels initially surged briefly after tocilizumab intervention before gradually diminishing. To assess the prognostic utility of IL-6, the Receiver Operating Characteristic (ROC) curve was employed, which yielded an area under the curve (AUC) of 0.812, indicating strong predictive capability, with a sensitivity of 100% and a specificity of 53.49%. The optimal cut-off value for IL-6 was identified at 147.79 pg/mL. In conclusion, IL-6 levels tend to rise transiently following tocilizumab therapy, before gradually declining. These post-treatment IL-6 measurements may serve as a valuable biomarker for assessing prognosis in patients undergoing this treatment.

This paper introduces an improved sequential convex programming algorithm using adaptive non-uniform discretization for the hypersonic entry problem. In order to ensure real-time performance, an inverse-free precise discretization based on first-order hold discretization is adopted to obtain a high-accuracy solution with fewer temporal nodes, which would lead to constraint violation between the temporal nodes due to the sparse time grid. To deal with this limitation, an adaptive non-uniform discretization is developed, which provides a search direction for purposeful clustering of discrete points by adding penalty terms in the problem construction process. Numerical results show that the proposed method has fast convergence with high accuracy while all the path constraints are satisfied over the time horizon, thus giving potential to real-time trajectory planning.