Liaoning Jianzhu Vocational University

The immunosuppressive tumor microenvironment and poor drug targeting remain major obstacles in bladder cancer (BC) therapy. To address this, a combination strategy integrating chemotherapy and immunotherapy was employed by co-delivering epirubicin (EPI) and the immune modulator imiquimod (IMQ) using a folate-modified nanocarrier. A hydroxyethyl starch-based epirubicin prodrug modified with folic acid (FA-HES-EPI) was first synthesized to improve tumor selectivity. FA-HES-EPI/IMQ micelles were then fabricated via nanoprecipitation by encapsulating IMQ into the hydrophobic core, aiming to achieve synergistic therapeutic efficacy. Folate modification conferred tumor-targeting capability to the micelles and promoted efficient cellular uptake via folate receptor-mediated endocytosis. The acid-sensitive hydrazone bond enabled controlled release of both EPI and IMQ in the acidic tumor microenvironment, thereby enhancing their combined chemo-immunotherapeutic effects. In an orthotopic BC model, FA-HES-EPI/IMQ micelles significantly enhanced drug accumulation at the tumor site, repolarized M2-type tumor-associated macrophages (TAMs) toward the M1 phenotype, remodeled the tumor stroma, and achieved a tumor inhibition rate of 96.7%, markedly surpassing that of FA-HES-EPI micelles (86.6%) and free EPI (62.3%), with negligible systemic toxicity. This pH-responsive co-delivery system represents a promising approach to improve both efficacy and safety in bladder cancer treatment.

Abstract To address the problems of high mechanical sensitivity and high storage risk of traditional energetic materials, a non-energetic substitute system based on cellulose acetate propionate (CAP)/α-cellulose/dibutyl phthalate (DBP) was proposed. Eight groups of samples with different ratios were prepared by the semi-solvent method. Their rheological properties were tested by a capillary rheometer and compared with those of traditional propellant ZY-11. The results show that when DBP=25 wt%, CAP=22.5-32.5 wt%, and α-cellulose=42.5-52.5 wt%, the viscosity behavior of the substitute system at 70-90 ºC deviates by <5% from that of ZY-11, and the shear thinning characteristic is more significant. The π-π bond interaction between CAP and DBP, together with the hydrogen-bond network of α-cellulose, synergistically regulates the processing performance of the system. This study provides technical support for the development of low-risk propellant substitutes.

Background Paediatric cancer survivors often experience cognitive late effects, including impairments in working memory, processing speed, and executive function, which can adversely affect academic performance, psychosocial development, and long-term functional outcomes. Traditional educational interventions may be limited by stigma, accessibility, and inconsistent engagement. Artificial intelligence (AI)-driven virtual tutors represent a promising, personalised approach to cognitive rehabilitation in this population. Methods A mixed-methods RCT with 150 survivors compared AI Tutor, Traditional Tutoring, and Control groups. Assessments included neurocognitive tests (WISC-V, Flanker, N-Back), academic metrics, psychosocial scales, and participant/parent/educator interviews. Subgroup analyses and ethical considerations (bias, equity) were explored. The study aimed to support and measure cognitive rehabilitation outcomes in this population. Results Participants in the AI Tutor group demonstrated significantly greater cognitive rehabilitation gains, particularly in working memory, processing speed, and maths performance compared to both the Traditional Tutoring and Control groups (p < 0.001). Flanker task error reduction and N-Back task performance indicated enhanced executive function, particularly among adolescents. Engagement metrics (e.g., session completion rates) were higher in the AI group, with younger children showing increased consistency and participation. Qualitative findings highlighted the AI tutor’s non-judgmental nature, reduced perceived stigma, and effective use of gamification to foster motivation and routine. Conclusion AI-driven virtual tutors provide a scalable, personalised, and stigma-reducing approach for cognitive rehabilitation in paediatric cancer survivors, effectively complementing traditional methods across settings. Addressing ethical concerns like algorithmic fairness and access is crucial for equitable implementation. This study supports integrating AI tutors into survivorship care.

To elucidate the effect of cold stress on Dendroctonus valens larvae, a study was conducted under controlled laboratory conditions to examine the physiological and biochemical mechanisms associated with cold stress, coupled with transcriptome sequencing. Physiological and biochemical assessments indicated stable water content in larvae during cold stress initiation, with triglycerides and fats serving as primary energy reserves that decreased over cold stress progression. Glycogen and trehalose were identified as energy sources for larval energy metabolism, with their levels increasing as cold stress duration extended. Superoxide dismutase (SOD) activity exhibited an initial decline followed by an increase, while peroxidase (POD) activity initially rose before decreasing over induction time, and catalase (CAT) activity decreased during cold stress induction. Transcriptome sequencing at various time points revealed 4630 upregulated and 1554 downregulated genes, predominantly involved in metabolic pathways such as carbohydrate, amino acid, and lipid metabolism. Quantitative polymerase chain reaction (qPCR) results validated the transcriptome data accuracy. This investigation delineated the physiological, biochemical, and transcriptome alterations during cold stress, offering a theoretical framework for the rational prediction of Dendroctonus valens outbreaks.

<strong class="journal-contentHeaderColor">Abstract.</strong> Manure is widely applied in greenhouses to enhance soil organic carbon (SOC) and improve fertility. However, how SOC fractions and their chemical stability change under different soil textures and long-term manure inputs remains unclear. We investigated greenhouse soils with 2&ndash;50 years of manure application in loam and sandy loam. SOC, easily oxidizable carbon (EOC), microbial biomass carbon (MBC), dissolved organic carbon (DOC), particulate organic carbon (POC), and mineral-associated organic carbon (MAOC) were quantified. The molecular structures of SOC were analyzed via ¹³C NMR spectroscopy. Results showed that SOC in loam stabilized after about 20 years of manure application, whereas sandy loam reached equilibrium within 2 years. In loam, aromatic C and carbonyl C in SOC increased, raising the aromaticity index (ARM); in sandy loam, alkyl C increased, elevating A/OA and the hydrophobicity index (HI). Loam contained higher SOC, EOC, POC, and MAOC contents than sandy loam, with SOC positively correlated with EOC, POC, and MAOC, whereas in sandy loam SOC was positively correlated only with EOC and MAOC, and negatively with fPOC. In loam, ARM and HI promoted SOC accumulation by stimulating EOC and POC, which enhanced MAOC formation. In sandy loam, HI mainly promoted SOC through increasing EOC, which enhanced MAOC formation. In loam, MAOC formation was mainly associated with POC, whereas in sandy loam it was driven by EOC. Overall, in greenhouses, long-term stability of SOC depends on the transformation of labile carbon into stable fractions, with fine-textured soils exhibiting greater sequestration efficiency due to higher structural stability and greater MAOC accumulation.

Personalizing mechanical ventilation to prevent lung injury requires continuous and accurate assessment of respiratory mechanics, a task that remains a significant challenge in modern ICUs. Current methods are often invasive or rely on disruptive clinical maneuvers. To address this, we introduce a novel data-driven architecture capable of non-invasively predicting patient-specific pulmonary elastance and resistance. Our model combines a Bidirectional LSTM (BiLSTM) backbone, which captures the complex temporal dynamics of ventilator waveforms and chest-wall motion, with a Liquid Attention mechanism. This approach advances upon prior attention-based models in physiological signal analysis, which typically apply more static or uniform weighting, by allowing our model to dynamically learn to flow its focus towards the most salient physiological events within each breath. This dynamic allocation of attention allows our model to generate highly accurate estimations by adaptively weighting critical moments such as peak pressures or flow reversals. Evaluated on a diverse dataset encompassing multiple PEEP settings and breathing patterns, the proposed model significantly outperforms conventional approaches. This work not only presents a powerful tool for safer ventilation management but also highlights the potential of dynamic, context-aware attention mechanisms in decoding complex physiological time-series data.