［Abstract］　Objective　To investigate the effects of artificial intelligence(AI)-assisted multidimensional self-efficacy improving nursing care on coping styles, self-efficacy, and treatment adherence in lung cancer patients with chemotherapy-induced myelosuppression. Methods　A total of 68 lung cancer patients with chemotherapy-induced myelosuppression, who were admitted to Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen, were recruited and divided into observation group and control group by using random number table method, with 34 patients in each group. The control group received routine nursing care related to myelosuppression, while the observation group received the same routine nursing care as the control group plus AI-assisted multidimensional self-efficacy improving nursing care. The AI tools were mainly used for the patients′ risk stratification and nursing decision support. Before and after the intervention, the Simple Coping Style Questionnaire(SCSQ) and the General Self-Efficacy Scale(GSES) were used to evaluate the coping styles and self-efficacy levels of the patients, and the treatment adherence was compared between the patients in the two groups. Results　After the intervention, the SCSQ-positive coping style scores and GSES scores in the observation group were higher than those in the control group, while the SCSQ-negative coping style scores in the observation group were lower than those in the control group, with statistically significant differences between the two groups(P<0.05). The complete adherence rate of the treatment in the observation group was higher than that in the control group, with statistically significant difference between the two groups(P<0.05). Conclusion　AI-assisted multidimensional self-efficacy improving nursing care is conducive to improving the coping styles and self-efficacy levels in lung cancer patients with chemotherapy-induced myelosuppression, and enhances the patients′ treatment adherence.]]> 2026/1/31 0:00:00 1, LI Siqin¹, WANG Yu¹, NIU Niu¹, NING Yanting²]]> FU Jianghong¹, LI Siqin¹, WANG Yu¹, NIU Niu¹, NING Yanting² 3true ［Abstract］　Objective　To decode the temporal transcriptional responses of endothelial cells to distinct shear stress patterns, and to construct a low fluid shear stress(LFSS) molecular features, and to assess its clinical value in pan-cancer prognosis, and to explore the application potential of artificial intelligence(AI) for dynamic mechanobiological prediction. Methods　Three independent human endothelial transcriptome datasets related to shear stress were integrated by using the Gene Expression Omnibus(GEO). Robust differentially expressed genes(DEGs) were identified using cross-cohort meta-analysis by weighted Stouffer′s Z method. Temporal expression patterns under steady shear stress(ST), oscillatory shear stress(OS), and pulsatile shear stress(PS) were decoded by using Theil-Sen regression and k-means clustering. LFSS scoring was constructed based on LFSS-specific upregulated genes and was evaluated in The Cancer Genome Atlas(TCGA) pan-cancer atlas cohort(n=11 160) by using Kaplan-Meier survival analysis and multivariable Cox proportional hazards regression models. A long short-term memory(LSTM) network model was further constructed to predict shear stress-induced gene expression dynamics and compared with random forest(RF) and support vector regression(SVR) models. Results　A total of 811 robust DEGs were identified across the datasets. Temporal analysis revealed that OS induced aberrant cell cycle and DNA replication programs via the sustained activation of the YAP/TAZ signaling axis, whereas PS predominantly triggered a physiological protective response via the KLF2/KLF4 pathway. The LFSS scores were significantly correlated with overall survival in 13 types of cancers［false discovery rate(FDR)<0.05］ and were positively correlated with hypoxia and pathological angiogenesis pathways in the tumor microenvironment. The LSTM model outperformed RF and SVR models, with a coefficient of determination(R²) of 0.842 and a mean absolute error(MAE) of 0.068, demonstrating that the LSTM model had superior generalization performance. Conclusion　LFSS can induce specific endothelial transcriptional remodeling and has significant prognostic implications in a variety of cancers. LSTM-based AI models can effectively capture the shear stress-related temporal dynamics, providing a novel medico-engineering framework for decoding vascular mechanical abnormalities and supporting the assessment of precise treatment for tumors.]]> 2026/1/31 0:00:00 1, WU Bin², LIU Hu³, WANG Ning⁴, ZHANG Ke⁴, HUANG Sizhao⁴]]> NIU Niu¹, WU Bin², LIU Hu³, WANG Ning⁴, ZHANG Ke⁴, HUANG Sizhao⁴ 2true ［Abstract］　Objective　To construct a prediction model for the survival prognosis of bladder cancer patients based on deep learning features extracted from tumor regions in hematoxylin and eosin(HE)-stained slides. Methods　The clinical data of 379 patients with bladder cancer were collected from The Cancer Genome Atlas(TCGA) database［including 450 slices of whole-slide images(WSI)］, and the clinical data of 179 patients with bladder cancer who were admitted to the Affiliated Guangdong Second Provincial General Hospital of Jinan University from September 2017 to May 2024 were collected(including 244 slices of WSI). The ResNet50 model was applied for transfer learning to identify tumor regions. The RetCCL model was applied for extracting deep learning features. The extracted deep learning features were screened via univariate Cox regression and LASSO regression, and a risk scoring model was constructed. The maximally selected rank statistics method(MSRSM) was adopted to determine of the risk scores of deep learning features. According to the optimal cut-off values, the patients were divided into high-risk group and low-risk group, and the survival prognosis was compared between the two groups by using Kaplan-Meier survival curve. The factors influencing the survival prognosis of the bladder cancer patients were analyzed by using Cox regression, and a nomogram model was constructed based on the screened risk factor indicators. The calibration accuracy and net clinical benefit of the model were comprehensively evaluated by using calibration curve and decision curve analysis(DCA). Results　The RetCCL model was applied to extract the features from all the WSI, and each slice of WSI had 14 336 features. Twenty-two features with prognostic predictive value were obtained by using univariate Cox regression analysis. Furthermore, LASSO regression analysis was performed on these 22 features to obtain 16 features with non-zero regression coefficients. Based on this, 16 pathological deep learning features and the corresponding regression coefficients were used to construct a risk scoring model for the bladder cancer patients. The results of analyses of TCGA database and clinical data from the Affiliated Guangdong Second Provincial General Hospital of Jinan University showed that based on the risk scores of deep learning features, the overall survival of the high-risk group was significantly shorter than that of the low-risk group(P<0.05). The results of multivariate Cox regression analysis showed that the risk level of deep learning features and M stage were independent risk factors affecting the survival prognosis of the patients with bladder cancer(P<0.05). Based on these two indicators, a nomogram model for predicting the survival prognosis of the bladder cancer patients was constructed. The calibration curve showed that the model exhibited a good consistency between the predicted survival rates of the patients at 1 year, 3 years and 5 years after surgery and the actual survival rates. The results of DCA showed that the decision-making of the prediction model could achieve good net clinical benefits in terms of survival prognosis at 1 year, 3 years and 5 years after surgery. Conclusion　The survival prognosis prediction model for bladder cancer patients constructed according to deep learning features extracted from tumor regions in HE-stained slides has good predictive efficacy and can provide precise individualized prognosis assessment tools for clinical practice.]]> 2026/1/31 11:24:05 1, HE Yifeng², ZHENG Zongtai³, ZENG Deqin²]]> LUO Guanshui¹, HE Yifeng², ZHENG Zongtai³, ZENG Deqin² 1true