Qingdao Municipal Hospital

Cervical cancer (CC) remains a significant global health issue, accounting for approximately 7% of all cancer cases in women. This study investigated the anti-cancer potential of pectolinarigenin (PEC), a bioactive compound derived from plants, aiming to explore its therapeutic effects and underlying mechanisms against CC. By integrating network pharmacology analysis with cellular assays, we identified 13 key targets of PEC related to CC, with molecular docking highlighting AKT as a primary target. Experimentally, PEC demonstrated strong anti-cancer effects on cervical cancer both in vivo and in vitro. Western blotting analysis revealed that PEC treatment led to a dose-dependent decrease in Bcl-2 protein levels, coupled with increased activation of pro-apoptotic markers Bax and cleaved caspase-3 in both cell lines. PEC also elevated the levels of LC3B II protein, indicating the induction of autophagy. Notably, this autophagic response was inhibited by 3-MA, an autophagy inhibitor, suggesting that PEC played a regulatory role in activating autophagy. Mechanistic studies confirmed that PEC effectively suppressed the AKT/mTOR signaling pathway, a critical regulator of both autophagy and apoptosis in cancer cells. Overall, this is the first study to demonstrate that PEC exerted potent anti-cancer effects against CC by concurrently inducing autophagy and apoptosis through targeted inhibition of the AKT/mTOR pathway. These findings highlighted the potential of PEC as a promising natural therapeutic agent for CC, paving the way for new treatment strategies. Further comprehensive research is warranted to fully explore PEC’s therapeutic capabilities and to develop innovative anti-cancer therapies.

Various artificial intelligence-based medical technologies have been used for diagnosing and treating diseases. Although moyamoya disease (MMD) poses a significant threat to the lives of patients, artificial intelligence (AI) use in MMD is still limited. This study aims to review the literature on predicting, diagnosing, and treating MMD with AI, as well as to discuss its current status, challenges, and future directions. Using AI in treating MMD can enhance diagnostic accuracy and treatment outcomes. AI-driven imaging analysis aids in the early detection of vascular abnormalities linked to MMD, facilitating timely intervention and management. In addition, machine learning (ML) algorithms can analyze large data sets from clinical trials and patient records to identify patterns and predict outcomes, informing personalized treatment plans. AI has shown great potential in the diagnosis and treatment of MMD. The application of AI in treating MMD represents a promising frontier that may revolutionize clinical practices.

Previous studies have shown that single symptoms are associated with self-management, and symptom clusters are still a sparsely investigated field. This study aimed to explore the possible symptom clusters after renal transplantation and investigate the correlation between symptom clusters and self-management. A cross-sectional survey was conducted on 381 renal transplant recipients (RTRs) from 2 tertiary hospitals in different regions. Sociodemographic and clinical characteristics, Symptom Experience Scale, and RTRs Self-Management Scale were used. Symptom clusters were created with exploratory factor analysis. Multiple regression analysis was used to explore the relationship between self-management and symptom clusters. Four symptom clusters were extracted, which were related to activity endurance, emotion, neuro-gastrointestinal, and hormone. Symptom clusters were negatively correlated with self-management (p < .001), and the activity endurance symptom cluster was the influencing factor of self-management (β = -.209, p < .001). The impact of symptom clusters on self-management among RTRs exhibits heterogeneity. Clinical therapies should prioritize the activity endurance symptom cluster since it is a substantial risk factor for impaired self-management ability.

Cotyledons serve dual roles as nutrient reserves and physical protectors during early seedling establishment. However, they are vulnerable to diverse granivorous and environmental stressors. Previous studies have predominantly focused on cotyledon loss magnitude, leaving a significant knowledge gap regarding how post-emergence timing of damage interacts with environmental factors such as nutrient availability to influence seedling establishment. We conducted a controlled factorial experiment manipulating three cotyledon removal timings (1, 2, and 4 weeks post-emergence) and two nutrient supplementation levels (i.e., high and low nutrient level) treatment with a widely distributed oak species, Quercus dentata. Morphological (height, basal diameter, and biomass allocation, etc.) and physiological traits (photosynthetic rate, leaf nutrients, etc.) were measured after treatment for five months. Cotyledon removal induced severe and irreversible reductions in seedling height, basal stem diameter, and root biomass, showing that the first week after emergence is a critical vulnerability window. Nutrient supplementation exhibited time-dependent effectiveness. The compensatory effect became progressively weaker with increasingly earlier removal of cotyledons. High nutrient conditions significantly enhanced photosynthetic rates and leaf nutrient content across all timing removal treatments. Structural equation modeling revealed that nutrient influenced biomass primarily through indirect pathways, mediated by physiological traits rather than direct effects. Our model explained 90–94% of organ biomass variation. Our findings demonstrate that cotyledon damage timing determines the success of Q. dentata seedling establishment. The identification of ontogenetic windows for vulnerability and compensatory capacity challenges the traditional view of cotyledons as static nutrient pools. If cotyledon removal is anticipated in Q. dentata restoration practices, implementing protective measures during the first post-emergence week and timing nutrient interventions to coincide with periods of maximum compensatory capacity could significantly enhance seedling establishment.