19 cinnamamide/ester-triazole compounds were designed, synthesized and evaluated for their anti-Alzheimer's disease (AD) activity. Among them, compound 4f displayed excellent anti-β-amyloid protein (Aβ)-mediated cytotoxicity (EC₅₀ = 2.03 ± 2.45 μmol·L^-1) in APPswe cells and acetylcholinesterase inhibiton (IC₅₀ = 4.88 ± 4.70 μmol·L^-1). Further study indicated that, at dosages of (1, 5 and 25 mg·kg^-1), compound 4f was effective in improving spatial learning and memory deficits in Aβ_1-42-impaired mice, which was achieved by promoting the nonamyloidogenic signaling and inhibiting the amyloidogenic pathway, along with the suppression of Aβ-induced Tau phosphorylation. All animal experiments in this study were approved by the Experimental Animal Care and Use Committee of the Institute of Medicinal Biotechnology (IMB-20220908D701). In conclusion, compound 4f holds promise as a lead candidate for AD treatment, and the present study lays the foundation for its subsequent development.

RNA modifications constitute a crucial class of post-transcriptional chemical alterations that profoundly influence RNA stability and translational efficiency, thereby shaping cellular protein expression profiles. These diverse chemical marks are ubiquitously involved in key biological processes, including cell proliferation, differentiation, apoptosis, and metastatic potential, and they exert precise regulatory control over these functions. A major advance in the field is the recognition that RNA modifications do not act in isolation. Instead, they participate in complex, dynamic interactions—through synergistic enhancement, antagonism, competitive binding, and functional crosstalk—forming what is now termed the “RNA modification interactome” or “RNA modification interaction network.” The formation and functional operation of this interactome rely on a multilayered regulatory framework orchestrated by RNA-modifying enzymes—commonly referred to as “writers,” “erasers,” and “readers.” These enzymes exhibit hierarchical organization within signaling cascades, often functioning in upstream-downstream sequences and converging at critical regulatory nodes. Their integration is further mediated through shared regulatory elements or the assembly into multi-enzyme complexes. This intricate enzymatic network directly governs and shapes the interdependent relationships among various RNA modifications. This review systematically elucidates the molecular mechanisms underlying both direct and indirect interactions between RNA modifications. Building upon this foundation, we introduce novel quantitative assessment frameworks and predictive disease models designed to leverage these interaction patterns. Importantly, studies across multiple disease contexts have identified core downstream signaling axes driven by specific constellations of interacting RNA modifications. These findings not only deepen our understanding of how RNA modification crosstalk contributes to disease initiation and progression, but also highlight its translational potential. This potential is exemplified by the discovery of diagnostic biomarkers based on interaction signatures and the development of therapeutic strategies targeting pathogenic modification networks. Together, these insights provide a conceptual framework for understanding the dynamic and multidimensional regulatory roles of RNA modifications in cellular systems. In conclusion, the emerging concept of RNA modification crosstalk reveals the extraordinary complexity of post-transcriptional regulation and opens new research avenues. It offers critical insights into the central question of how RNA-modifying enzymes achieve substrate specificity—determining which nucleotides within specific RNA transcripts are selectively modified during defined developmental or pathological stages. Decoding these specificity determinants, shaped in large part by the modification interactome, is essential for fully understanding the biological and pathological significance of the epitranscriptome.

Aim To investigate the effect of cinobufo-talin(CB)on the small ubiquitin-like modifier(SU-MO)modification of hypoxia inducible factor 1 alpha(HIF1α)on ferroptosis of gastric cancer cells.Meth-ods Human normal gastric mucosal epithelial cells(GES-1)and gastric cancer cells(MGC803)were treated with various concentrations of CB.MTT assay was employed to determine cell viability and calculate the IC50 value of CB in gastric cancer cells.Cell inva-sion was evaluated using Transwell assay.Cancer cells were subjected to treatment with ferroptosis agonists(erastin)or inhibitors(ferrostatin-1)to assess the levels of lipid reactive oxygen species(ROS),malon-dialdehyde(MDA),cell apoptosis,intracellular total iron,and Fe2+in gastric cancer cells.Western blot a-nalysis was conducted to detect the expression of SU-MO1 and HIF1α,while immunoprecipitation(IP)was utilized to investigate the interaction between SUMO1 and HIF1 α.An allograft tumor model was established and treated with CB or erastin to assess the impact of CB on tumor growth and ferroptosis in gastric cancer cells in vivo.Results CB at concentrations below 2μmol·L-1 had no impact on the viability of GES-1 cells.Compared to the control group,CB treatment dose-dependently inhibited the viability and invasion of MGC803 cells.CB treatment increased the levels of lipid reactive oxygen species(ROS),malondialdehyde(MDA),total iron,and Fe2+in gastric cancer cells,and promoted cell apoptosis(all P＜0.05),compared to the control group.The combination of CB and eras-tin enhanced ferroptosis,while ferrostatin-1 treatment suppressed CB-induced ferroptosis in gastric cancer cells.Mechanistically,CB inhibited the expression of SUMO1,reduced the SUMOylation of HIF1α,and consequently suppressed its expression.The ferroptosis induced by CB in gastric cancer cells could be reversed by overexpression of SUMO1.In vivo experiments con-firmed that CB inhibited tumor growth and induced fer-roptosis in gastric cancer cells.Conclusion CB in-duces ferroptosis in gastric cancer cells by inhibiting the SUMOylation modification of HIF1α.

Objective:This study explored the designs of physicians'compensation incentives that were compatible with the reform of Diagnosis-related groups(DRG)payment,so as to provide a reference for optimizing policies related to medical insurance payment reform.Methods:We designed seven different physicians'compensation schemes that converted DRG payment incentives into salary incentives,using economic experiments.The total of 210 medical students and 65 doctors were recruited as subjects.We tested the quantity of medical services for patients that participants provided and the corresponding patient health benefits under different incentive schemes.Results:The two designs of feedback of DRG payment to physicians and linking DRG payment surplus to physicians'performance wages both could transmit the incentive of the payment methods to the service providers.On this basis,a quality-based pay-for-performance payment was introduced,and the deviation between the quantity of services provided by subjects and the optimal quantity of services decreased,and the loss ratio of patient health benefits also decreased.Conclusion:When transmitting DRG payment incentives to medical service providers,the physician compensation design combined with quality-based pay-for-performance payments is more conducive to improving patient health benefits.

Objective:This study aims to develop a validated instrument for assessing the degree of integration perceived by patients in the services provided by general practitioner(GP)teams,and to evaluate the current state of integrated medical-prevention services.Methods:The survey tool was initially created through a review of existing literature and semi-structured interviews.It was subsequently refined and enhanced following consultations with experts.The utility of the instrument was confirmed through empirical research,which also facilitated an analysis of the status quo regarding the integration of medical and prevention services.Results:The devised instrument encompasses 18 items distributed across six dimensions:service comprehensiveness,service continuity,depth of service,continuity of relationship,patient-centricity,and service coordination.The cumulative score reflecting patients'perception of the integration level in GP teams'medical-prevention services was 54.30±12.45.Conclusion:The survey instrument developed in this research is characterized by its rationality,novelty,and practical relevance.It offers a patient-centered perspective for assessing the quality of integrated medical and prevention services for primary chronic diseases.While the overall perception of integration among patients is positive,there is a notable deficiency in the depth of services provided.There is a need for optimization of the service process,clarification of the service path,refinement of service management,and development of an integrated incentive mechanism for medical and preventive care,aiming for a deeper integration between medical services and preventive care.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective:This study aimed at investigating the efficacy and safety of eltrombopag in the treatment of adult primary immune thrombocytopenia (ITP) and evaluated the factors influencing its efficacy and side effects.Methods:A total of 198 patients with adult ITP who were admitted to Tianjin Medical University General Hospital between January 2018 and March 2022 were retrospectively analyzed. The efficacy of each starting dose of eltrombopag was evaluated, and adverse events were analyzed. The factors influencing efficacy were investigated, including sex, age, adult ITP type, platelet antibodies, and combined drug treatments.Results:Of the 198 patients, 70 males and 128 females with a median age of 45 years (18-88 years) were included; 130 (65.7%) had newly diagnosed adult ITP, 25 (12.6%) had persistent adult ITP, and 43 (21.7%) had chronic adult ITP. The bleeding event scores at baseline were assessed; 84.3% had scores of<4 and 15.7% had scores of ≥4. The eltrombopag response rate (initial response) at 6 weeks was 78.8% (complete response [CR]: 49.0%; CR1: 14.6%; CR2: 15.2%). The median response time to eltrombopag was 7 (7, 14) days. The initial response rates to 25, 50, and 75 mg eltrombopag were 74.1%, 85.9%, and 60.0%, respectively ( P=0.031). The initial response rate to the 50 mg dose was significantly higher than that of the 25-mg and 75-mg doses. Two patients received 100 mg as the starting dose, and their initial response was 0. Regarding dose adjustment, 70.7% of the patients remained on the starting dose, 8.6% underwent dose adjustment to 50 mg, and 6.1% underwent dose adjustment to 75 mg. Another two patients underwent dose adjustment to 100 mg. After dose adjustment, the persistent response rates were 83.6%, 85.3%, and 85.7% for the 25-, 50-, and 75-mg doses, respectively, with no significant difference. After dose adjustment, the sustained efficacy rate for the 100-mg dose (4 patients) was 100.0%. After 6 weeks of treatment with eltrombopag, the overall bleeding score of patients with ITP decreased. The number of patients with a score of ≥4 decreased to 0, the number of patients with a score of<4 decreased, and there was no significant change in the number of patients with a score of 1-2. The most common adverse event associated with eltrombopag was impaired liver function (7.7%). No thrombosis events or other adverse events were observed. ITP type and number of megakaryocytes significantly affected the initial response to eltrombopag. The initial response rates to eltrombopag for newly diagnosed adult ITP, persistent adult ITP, and chronic adult ITP were 85.3%, 56.0%, and 76.2%, respectively ( P=0.003). For megakaryocytes, the initial response rates were 61.8%, 87.1%, and 84.3% ( P=0.009) for the decreased, normal, and increased megakaryocyte groups, respectively. Conclusion:Eltrombopag, as a second-line or higher treatment for adult ITP, has a rapid onset of action and good safety. The initial response rate is significantly higher with a dose of 50 mg than with a dose of 25 mg. Patients with newly diagnosed ITP and those with normal or increased megakaryocyte numbers have a higher initial response rate to eltrombopag.

Objective:To explore the impact of rehabilitation exercise intervention mode based on cardiac function classification on clinical effect and quality of life(QOL)in patients with chronic heart failure(CHF).Methods:A total of 160 CHF patients who visited our hospital from Dec 2021 to Jan 2023 were selected,and 154 cases were fi-nally enrolled.According to the random number table method,patients were divided into study group and control group with 77 cases in each group.Control group received routine nursing program,while the study group received rehabilitation exercise intervention based on cardiac function classification on the basis of control group,both groups were intervened for three months.Clinical total effective rate,and cardiopulmonary function,serum oxidative stress indicators and MLHFQ score before and after intervention were compared between two groups.Results:Total effective rates of study subgroups of class Ⅱ and Ⅲ were significantly higher than those of control group(class Ⅱ:100.00％vs.83.78％;class Ⅲ:97.37％vs.80.00％)(P＜0.05 both).Compared with control subgroup of classⅢ after intervention,there were significant rise in peak VO2[(16.98±2.03)ml·min-1·kg-1 vs.(18.61±2.41)ml·min-1·kg-1],LVEF[(41.73±4.53)％vs.(48.03±5.22)％]and 6MWD[(351.34±61.00)m vs.(391.53±64.42)m](P＜0.01 all);and significant reductions in LVEDd[(57.55±3.91)mm vs.(53.18±3.07)mm],LVESd[(35.90±2.91)mm vs.(30.50±2.67)mm],levels of LPO[(6.00±0.99)mg/L vs.(3.95±0.61)mg/L],MPO[(3.83±0.58)mg/L vs.(2.03±0.28)mg/L],and MLHFQ total score[(57.05±4.57)points vs.(45.29±3.94)points]in study subgroup of class Ⅲ(P=0.001 all).Compared with control subgroup of class Ⅱ after intervention,there were significant rise in peak VO2,LVEF and 6MWD,and significant reductions in LVEDd,LVESd,levels of LPO,MPO and MLHFQ score in study subgroup of class Ⅱ,P＜0.05 or＜0.01.There was no significant difference in the incidence rate of adverse events during follow-up between two groups(3.90％vs.6.49％,P=0.717).Conclusion:Rehabilitation exercise intervention based on cardiac function classifi-cation can significantly improve cardiopulmonary function,inhibit oxidative stress response in vivo and improve quality of life in CHF patients,which is worthy of promotion and application in clinical practice.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.