Targeting the ELF1/EGFR/ERK positive feedback loop overcomes resistance to androgen receptor inhibition in AR-Vs positive prostate cancer.

Bidirectional interactions between neuromodulatory systems and the cerebellum in brain function.

miR-to-miR interactions: Investigating the effects of miR-21 down-regulation and bisphenol exposure in COCs, arrested 8-cell embryos and blastocysts.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a dismal prognosis, characterized by an immunosuppressive tumor microenvironment and rapid development of chemoresistance. The present study investigated the histamine receptor H1 (HRH1), a G protein-coupled receptor, and its functional interplay with the transcriptional coactivator YAP1, a known driver of PDAC progression. Through integrated multi-omics analysis of data from TCGA, ICGC and GEO cohorts, the present study found that HRH1 was consistently overexpressed in PDAC, and its expression correlated with poor prognosis. Mendelian randomization analysis further suggested a causal relationship between the use of fexofenadine, an HRH1 antagonist, and a reduced risk of PDAC. Mechanistically, the present study identified a positive feedback loop in which HRH1 stabilizes and activates YAP1 signaling, while YAP1 knockdown downregulates HRH1 transcription, potentially through direct binding to the HRH1 promoter region. The present study constructed a prognostic model based on the HRH1-YAP1 axis by integrating a panel of 101 machine learning algorithms. The model, which includes HRH1, YAP1, ECT2, ITGB5, SHCBP1, MAML2, YWHAZ and ITGA2, effectively stratified patients into groups with differential risk. Patients in the high-risk group exhibited characteristics associated with worse outcomes, including KRAS mutations, chemoresistance, an immunosuppressive microenvironment and reduced responsiveness to immunotherapy. The present study establishes HRH1 as a novel therapeutic target in PDAC and proposes the repurposing of fexofenadine as a promising strategy to disrupt the oncogenic HRH1-YAP1 feedback loop.

Andrograpanin attenuates secondary neuroinflammation after spinal cord injury by modulating microglial glycolysis via HIF-1α.

• A dynamic risk assessment method involving system dynamics simulation and Bayesian network is proposed • The system dynamics model enables dynamic probability prediction under the coupling mechanism of faults • A Bayesian network-based consequence model is established to assess the effect of safety barriers • A case study demonstrates the effectiveness of the proposed methodology for methanol bunkering under SIMOPs • The societal risks associated with methanol leakage are assessed across varying population densities This paper proposes a dynamic risk assessment method based on system dynamics (SD) simulation to deal with both the complexity of systems involving simultaneous operations (SIMOPs) and their dynamic evolution over time. A fault tree model is constructed and transformed into an SD model comprising four feedback loops, enabling dynamic probability prediction under coupling mechanisms while accounting for each loss of containment (LOC). Critical hazard factors are identified and ranked based on mutual information (MI), allowing prioritization of safety interventions. Consequence-probability modelling is carried out by establishing a Bayesian network (BN)-based event tree considering safety barriers (SBs). The severity of potential consequences is evaluated using fire modelling in ALOHA. Individual and societal risk values are subsequently calculated, followed by the development of risk mitigation measures at a bunkering station in the Port of Shanghai, China, as a practical case study. The proposed mitigation strategies effectively reduce leakage probability and associated risks. The proposed framework effectively characterizes the temporal evolution of risk, provides enhanced representation of time-dependent risk dynamics compared with traditional static quantitative risk assessment models, and offers a transferable basis for alternative marine fuels and port operations. To the best of our knowledge, the combination of bow-tie analysis, dynamic simulation, and Bayesian networks for temporal methanol bunkering risk assessment remains unexplored in the current academic literature.

Hybrid membrane nanovesicles reprogram macrophages to resolve oxidative stress in intervertebral disc degeneration.

PIT-Net: Physics-informed transformer with differentiable diffusion constraints for quantitative photoacoustic tomography of deep tissues.

Autonomous soft robotics: Revolutionizing motion with intelligence and flexibility.

Baicalein alleviates hepatic lipid metabolism disorders via the PPARα-FGF21-adiponectin axis: Regulating crosstalk between the liver and adipose tissue.

Oxytocin moderates father-infant dynamics during triadic family interaction: A dynamic structural equation modeling approach.

Autophagy-centered regulation of PI3K/Akt/mTOR and MAPK signaling by traditional Chinese medicine in gastric cancer.

Macrophytes and Emerging Contaminants: Insights on Removal and Toxicological Responses.

Psychosocial stress and chronic pain: A threshold model of biological lock-in.

RNA-based cancer immunotherapy: Harnessing the power of RNA nanoparticles for melanoma treatment.

Reshaping psoriasis topical therapy: Continuous hydrogenation orchestrates 'redox-metabolic homeostasis' for efficient remission and relapse intervention.

• Analytical model for computing acoustic flow in a fan intake • Suitable for flutter bite and noise studies at low and high frequencies • Supports arbitrary number of acoustic treatments in the model • Well-conditioned formulation enables use of direct solvers • Application to the contribution of liners to fan flutter Acoustic treatments are used in modern aeroengines to reduce community noise. Recent studies have highlighted an acoustic-driven instability known as flutter bite. This phenomenon arises at frequencies lower than those associated with noise, where an upstream acoustic wave, generated by fan vibration, is reflected at the duct inlet, forming a feedback loop. To study acoustic behavior over a wide frequency range, we introduce a new model. It consists of an annular duct with an arbitrary number of sections, each potentially featuring different acoustic treatments. The acoustic wave propagation inside the intake is solved analytically, and a mode-matching technique is employed to handle transitions between sections. Unlike previous approaches, we formulate the problem to obtain a numerically well-conditioned linear system, enabling the use of direct solvers instead of iterative ones. We discuss the convergence with respect to the number of radial modes and improve it through an appropriate choice of projection functions, even for low impedance values. Two illustrative applications are presented: the analysis of flutter bite in a fan intake with a single liner, and a study of the effect of multiple acoustic treatments.

PRC1 promotes MAFLD progression by regulating glycolysis/lactylation axis and forming a positive feedback loop.

• The SAFT-GT toolchain enables semi-automatic Attack-Fault Tree generation for enhanced safety and security assessment in self-adaptive systems. • The toolchain efficiently integrates into the feedback loop of self-adaptive systems, allowing for dynamic updates based on security assessments. • A user study with domain experts confirms the toolchain’s relevance and practical applicability in real-world scenarios. • Performance experiments demonstrate that the Attack-Fault Tree generation pipeline operates within feasible time constraints, supporting real-time applications. • The complete toolchain and resources are provided for download, fostering further research and collaboration in the field. In the rapidly evolving landscape of software engineering, the demand for robust and secure systems has become increasingly critical. This is especially true for self-adaptive systems due to their complexity and the dynamic environments in which they operate. To address this issue, we designed and developed the SAFT-GT toolchain that tackles the multifaceted challenges associated with ensuring both safety and security. This paper provides a comprehensive description of the toolchain’s architecture and functionalities, including the Attack-Fault Trees generation and model combination approaches. We emphasize the toolchain’s ability to integrate seamlessly with existing systems, allowing for enhanced safety and security analyses without requiring extensive modifications and domain knowledge. Our proposed approach can address evolving security threats, including both known vulnerabilities and emerging attack vectors that could compromise the system. As a use case for the toolchain, we integrate it into the feedback loop of self-adaptive systems. Finally, to validate the practical applicability of the toolchain, we conducted an extensive user study involving domain experts, whose insights and feedback underscore the toolchain’s relevance and usability in real-world scenarios. Our findings demonstrate the toolchain’s effectiveness in real-world applications while highlighting areas for future improvements. The toolchain and associated resources are available in an open-source repository to promote reproducibility and encourage further research in this field.

Targeting the lactate-lactylation-glucose uptake axis: Cryptotanshinone reprograms metabolic-epigenetic crosstalk in gastric cancer.