ObjectiveThis paper aims to conduct a secondary analysis of a randomized controlled trial on the treatment of myelodysplastic syndrome （MDS） with deficiency of both the spleen and kidney and blockage of toxin and blood stasis with an arsenic-containing traditional Chinese medicine compound， by applying the mixed model for repeated measure （MMRM） and the method of stratified composite outcome with win ratio. The analysis includes the assessment of hematological efficacy and the composite outcome evaluation of adverse reactions， so as to more comprehensively assess the therapy of this regimen. MethodsThe MMRM and win ratio methods were used to evaluate the efficacy of a prospective，multi-center，double-blind，randomized controlled study. The blood routine （hemoglobin concentration，neutrophil count， and platelet count） and biochemical indexes （aspartate aminotransferase，alanine aminotransferase，serum creatinine，and serum ferritin） of the patients were detected at the time of enrollment and at the end of each course of treatment in the laboratory department of Xiyuan Hospital. The patients' syndromes at the time of enrollment and after treatment were recorded and scored according to the therapy standard of traditional Chinese medicine for diseases and syndromes. MMRM was used to analyze the blood routine indexes of the experimental group and the control group. This method has the advantages of high data reliability and dynamic efficacy under intervention and time. The win ratio method was used to evaluate the composite outcome of traditional Chinese medicine syndrome scores and biochemical indexes according to the priority and to verify the clinical safety of arsenic-containing traditional Chinese medicine compound. ResultsThe results of MMRM analysis showed that the hemoglobin concentration of patients in the group with arsenic-containing traditional Chinese medicine compound increased significantly compared with that before treatment in the group，while that in the placebo group decreased significantly （P<0.01）. When compared with that after treatment in the placebo group，the hemoglobin concentration of patients in the group with arsenic-containing traditional Chinese medicine compound increased significantly，and the mean difference of least squares （LS） was statistically significant （P<0.01）. When compared with those before treatment in the group，there were no statistically significant differences in the neutrophil count and platelet count in both groups. After treatment，there were no statistically significant differences in the neutrophil count， platelet count， and the mean difference of LS between the two groups. The analysis results of win ratio showed that the group with arsenic-containing traditional Chinese medicine compound had a significant advantage in the comparison of composite outcomes，with a win ratio （95% CI） of 2.01 （1.24-3.27） （P<0.01），and that the possibility of "winning" in terms of safety was 2.01 times that of the placebo group. The safety advantage of the group with arsenic-containing traditional Chinese medicine compound mainly came from the traditional Chinese medicine syndrome scores，renal function indexes， and iron reserve capacity indexes，and the number of winning times was less than that of losing times in the comparison of liver function outcomes. ConclusionThe MMRM analysis proves that the arsenic-containing traditional Chinese medicine compound can significantly improve the hemoglobin concentration of patients with myelodysplastic syndrome with refractory cytopenia and multilineage dysplasia （MDS-RCMD） of the type of deficiency of both the spleen and kidney and blockage of toxin and blood stasis. This conclusion is not interfered with by time trends and individual relationships and methodologically improves the credibility of the therapy of the arsenic-containing traditional Chinese medicine compound in treating MDS. Four outcomes are evaluated by the win ratio method，namely traditional Chinese medicine syndromes，liver function，renal function， and iron reserve capacity，proving that the arsenic-containing traditional Chinese medicine compound has the comprehensive advantages of improving the survival quality of the patients and reducing adverse reactions. The win ratio outcome provides clear comparative indexes for the evaluation of adverse reactions，making it easier for regulatory authorities，medical staff， and patients to understand the safety of the arsenic-containing traditional Chinese medicine compound in clinical application.

OBJECTIVE To investigate the influence of CYP2C19 gene polymorphism on platelet function and inflammatory cytokines in elderly patients with ischemic stroke， and to analyze potential factors associated with poor prognosis. METHODS A retrospective study was conducted on elderly patients with ischemic stroke admitted to our hospital from June 2024 to June 2025， wh o underwent CYP2C19 genotype testing and received antiplatelet therapy with clopidogrel. The levels of platelet function indicators and inflammatory cytokines before and after treatment were compared among patients with different metabolic phenotypes. Based on the prognosis at 6 months post-treatment， patients were divided into poor prognosis group and good prognosis group. Univariate analysis was performed on general data， metabolic phenotype， the levels of platelet function indicators and inflammatory cytokines. Variables with P ＜0.05 and the levels of inflammatory cytokines before treatment were included in a multivariate Logistic regression analysis to identify independent risk factors for poor prognosis. Multiple linear regression was used to further analyze the relationship between metabolic phenotypes and inflammatory cytokines. RESULTS A total of 448 elderly patients with ischemic stroke were included； among them， 162 cases were normal metabolic phenotype， 218 were intermediate metabolic phenotype， and 68 were poor metabolic phenotype. No rapid or ultrarapid metabolic phenotypes were observed. After treatment， platelet aggregation rate， the levels of P-selectin and platelet activated complex-1 （PAC-1）， high-sensitivity C-reactive Protein （hs-CRP）， interleukin-1β （IL-1β）， IL-6 and tumor necrosis factor-α （TNF-α） in the normal metabolic phenotype group， intermediate metabolic phenotype group， and poor metabolic phenotype group （except for platelet aggregation rate， and the levels of P-selectin and PAC-1 in the poor metabolic phenotype group） were significantly lower than those before treatment in the same group. Moreover， the above indicators in the normal metabolic phenotype group were significantly lower than those in the intermediate and poor metabolic phenotype groups at the corresponding time， and the levels of platelet function indicators in the intermediate metabolic phenotype group were significantly lower than those in the poor metabol ic phenotype group at the corresponding time （ P ＜0.05）. Univariate and multivariate Logistic regression analyses showed that combined with hypertension， combined with diabetes mellitus， and intermediate or poor metabolic genotypes were independent risk factors for poor prognosis in elderly patients with ischemic stroke （ P ＜0.05）. Multiple linear regression analysis showed that serum levels of hs-CRP， IL-1β， IL-6 and TNF-α before treatment were significantly higher in patients with intermediate and poor metabolic genotypes compared to those with normal metabolic genotype （ P ＜0.05）， with a greater magnitude of increase in inflammatory cytokines observed in the patients with poor metabolic genotype. CONCLUSIONS The elderly ischemic stroke patients with CYP2C19 intermediate and poor metabolic genotypes have poor inhibition effect on platelet and higher levels of inflammatory cytokines than normal metabolic genotype； CYP2C19 gene polymorphism， and in combination with hypertension and diabetes， can be used as independent predictors of poor prognosis.

ObjectiveThis paper aims to investigate the material basis of the anti-inflammatory efficacy and mechanism of action of Bushen Tongdu prescription （BSTDP）. MethodsThe chemical components of BSTDP and its blood-absorbed components in vivo were systematically identified by using ultra-performance liquid chromatography-linear ion trap-electrostatic field orbitrap high-resolution mass spectrometry （UPLC-LIT-Orbitrap-MS）. Network pharmacology was employed to screen blood-absorbed bioactive components and potential targets of this formula. A protein-protein interaction （PPI） network of core targets was constructed to conduct enrichment analysis. Molecular docking was further utilized to verify the binding affinity between key components and targets. The inflammatory model was established and verified in vivo by using a transgenic zebrafish Tg （mpx： GFP）. At three days post-fertilization （3 dpf）， larvae of zebrafish were randomly assigned to blank group， model group， positive drug dexamethasone acetate group （75 μmol·L^-1）， and BSTDP groups with low， medium， and high doses （500， 1 000， and 2 000 mg·L^-1）. The distribution and quantity of neutrophils in the yolk sac region were observed under a fluorescence microscope. The mRNA expression levels of key genes in the toll-like receptor 4 （TLR4）/myeloid differentiation factor 88 （MyD88）/nuclear factor kappa-B （NF-κB） signaling pathway and inflammatory factors including interleukin （IL）-1β， IL-6， and tumor necrosis factor-α （TNF-α） were detected by Real-time quantitative polymerase chain reaction （Real-time PCR）. ResultsA total of 120 chemical components were identified in BSTDP， among which 26 original components were confirmed by using serum pharmacochemical methods. A total of 227 common targets linking rheumatoid arthritis （RA） and the blood-absorbed components were screened by network pharmacology. It is suggested that pseudobrucine， vomicine， sinapine， rehmannioside， cinnamyl alcohol glycoside， and methylephedrine exert anti-inflammatory effects by acting on core targets including protein kinase B1 （Akt1）， signal transducer and activator of transcription 3 （STAT3）， tumor necrosis factor （TNF）， TLR4， mitogen-activated protein kinase 14 （MAPK14）， and phosphatidylinositol-4，5-bisphosphate 3-kinase catalytic subunit α （PIK3CA）， thereby modulating multiple signaling pathways such as TLR4 and NF-κB. In vivo verification in zebrafish demonstrates that the maximum tolerable concentration of Bushen Tongdu Formula is 2 000 mg·L^-1. Compared to those in the blank group， zebrafish in the model group showed a significantly higher number of neutrophils in the yolk sac region （P<0.01） and rising mRNA levels of TLR4， MyD88， NF-κB， TNF-α， IL-6， and IL-1β （P<0.01）. Compared to that in the model group， the number of neutrophils was significantly reduced in BSTDP groups with medium and high doses， as well as the dexamethasone acetate group （P<0.05， P<0.01）. There was no statistically significant difference in the low dose group. The mRNA expression levels of TLR4， MyD88， NF-κB， TNF-α， IL-6， and IL-1β were significantly down-regulated （P<0.05， P<0.01）. ConclusionThis paper identifies the material basis of the efficacy of BSTDP， demonstrating that the formula can exert an anti-inflammatory effect through the TLR4/MyD88/NF-κB signaling pathway. The results provide scientific experimental evidence for its further clinical application.

ObjectiveThis paper aims to investigate the material basis of the anti-inflammatory efficacy and mechanism of action of Bushen Tongdu prescription （BSTDP）. MethodsThe chemical components of BSTDP and its blood-absorbed components in vivo were systematically identified by using ultra-performance liquid chromatography-linear ion trap-electrostatic field orbitrap high-resolution mass spectrometry （UPLC-LIT-Orbitrap-MS）. Network pharmacology was employed to screen blood-absorbed bioactive components and potential targets of this formula. A protein-protein interaction （PPI） network of core targets was constructed to conduct enrichment analysis. Molecular docking was further utilized to verify the binding affinity between key components and targets. The inflammatory model was established and verified in vivo by using a transgenic zebrafish Tg （mpx： GFP）. At three days post-fertilization （3 dpf）， larvae of zebrafish were randomly assigned to blank group， model group， positive drug dexamethasone acetate group （75 μmol·L^-1）， and BSTDP groups with low， medium， and high doses （500， 1 000， and 2 000 mg·L^-1）. The distribution and quantity of neutrophils in the yolk sac region were observed under a fluorescence microscope. The mRNA expression levels of key genes in the toll-like receptor 4 （TLR4）/myeloid differentiation factor 88 （MyD88）/nuclear factor kappa-B （NF-κB） signaling pathway and inflammatory factors including interleukin （IL）-1β， IL-6， and tumor necrosis factor-α （TNF-α） were detected by Real-time quantitative polymerase chain reaction （Real-time PCR）. ResultsA total of 120 chemical components were identified in BSTDP， among which 26 original components were confirmed by using serum pharmacochemical methods. A total of 227 common targets linking rheumatoid arthritis （RA） and the blood-absorbed components were screened by network pharmacology. It is suggested that pseudobrucine， vomicine， sinapine， rehmannioside， cinnamyl alcohol glycoside， and methylephedrine exert anti-inflammatory effects by acting on core targets including protein kinase B1 （Akt1）， signal transducer and activator of transcription 3 （STAT3）， tumor necrosis factor （TNF）， TLR4， mitogen-activated protein kinase 14 （MAPK14）， and phosphatidylinositol-4，5-bisphosphate 3-kinase catalytic subunit α （PIK3CA）， thereby modulating multiple signaling pathways such as TLR4 and NF-κB. In vivo verification in zebrafish demonstrates that the maximum tolerable concentration of Bushen Tongdu Formula is 2 000 mg·L^-1. Compared to those in the blank group， zebrafish in the model group showed a significantly higher number of neutrophils in the yolk sac region （P<0.01） and rising mRNA levels of TLR4， MyD88， NF-κB， TNF-α， IL-6， and IL-1β （P<0.01）. Compared to that in the model group， the number of neutrophils was significantly reduced in BSTDP groups with medium and high doses， as well as the dexamethasone acetate group （P<0.05， P<0.01）. There was no statistically significant difference in the low dose group. The mRNA expression levels of TLR4， MyD88， NF-κB， TNF-α， IL-6， and IL-1β were significantly down-regulated （P<0.05， P<0.01）. ConclusionThis paper identifies the material basis of the efficacy of BSTDP， demonstrating that the formula can exert an anti-inflammatory effect through the TLR4/MyD88/NF-κB signaling pathway. The results provide scientific experimental evidence for its further clinical application.

There are various complicated wired connections in traditional endoscopic systems, which have disadvantages such as prolonging the preoperative setup time, restricting the movement of laparoscope, hindering the intraoperative manipulation, potentially contaminating the operation area and causing safety hazards.Our team has developed a wireless intelligent ultra-high-definition endoscopic system for the first time, which has been widely applied in many urological surgeries, such as tumors, stones, transurethral prostate enucleation, laparoscopic pyeloureteroplasty, laparoscopic renal cyst top decompression and so on.This essay reviews the applications and advantages of this system in urology, and forecasts its prospects.

ObjectiveFunctional near-infrared spectroscopy (fNIRS), a novel non-invasive technique for monitoring cerebral activity, can be integrated with upper limb rehabilitation robots to facilitate the real-time assessment of neurological rehabilitation outcomes. The rehabilitation robot is designed with 3 training modes: passive, active, and resistance. Among these, the resistance mode has been demonstrated to yield superior rehabilitative outcomes for patients with a certain level of muscle strength. The control modes in the resistance mode can be categorized into dynamic and static control. However, the effects of different control modes in the resistance mode on the motor function of patients with upper limb hemiplegia in stroke remain unclear. Furthermore, the effects of force, an important parameter of different control modes, on the activation of brain regions have rarely been reported. This study investigates the effects of dynamic and static resistance modes under varying resistance levels on cerebral functional alterations during motor rehabilitation in post-stroke patients. MethodsA cohort of 20 stroke patients with upper limb dysfunction was enrolled in the study, completing preparatory adaptive training followed by 3 intensity-level tasks across 2 motor paradigms. The bilateral prefrontal cortices (PFC), bilateral primary motor cortices (M1), bilateral primary somatosensory cortices (S1), and bilateral premotor and supplementary motor cortices (PM) were examined in both the resting and motor training states. The lateralization index (LI), phase locking value (PLV), network metrics were employed to examine cortical activation patterns and topological properties of brain connectivity. ResultsThe data indicated that both dynamic and static modes resulted in significantly greater activation of the contralateral M1 area and the ipsilateral PM area when compared to the resting state. The static patterns demonstrated a more pronounced activation in the contralateral M1 in comparison to the dynamic patterns. The results of brain network analysis revealed significant differences between the dynamic and resting states in the contralateral PFC area and contralateral M1 area (F=4.709, P=0.038), as well as in the contralateral PM area and ipsilateral M1 area (F=4.218, P=0.049). Moreover, the findings indicated a positive correlation between the activation of the M1 region and the increase in force in the dynamic mode, which was reversed in the static mode. ConclusionBoth dynamic and static resistance training modes have been demonstrated to activate the corresponding brain functional regions. Dynamic resistance modes elicit greater oxygen changes and connectivity to the region of interest (ROI) than static resistance modes. Furthermore, the effects of increasing force differ between the two modes. In patients who have suffered a stroke, dynamic modes may have a more pronounced effect on the activation of exercise-related functional brain regions.

OBJECTIVE To evaluate the cost-effectiveness of finerenone combined with standard of care （SoC） in the treatment of heart failure with mildly reduced ejection fraction （HFmrEF） or preserved ejection fraction （HFpEF）. METHODS Based on a phase Ⅲ clinical trial， a Markov model was constructed from the perspective of China’s healthcare system to compare the treatment outcomes of finerenone combined with SoC regimen versus SoC regimen alone in the treatment of different cardiac functional statuses of HFmrEF/HFpEF. Using quality-adjusted life year （QALY） as the health output index， 3 times China’s per capita GDP in 2023 as the willingness-to-pay （WTP） threshold， a simulation was conducted with a 3-month cycle length and a 10- year time horizon， incorporating an annual discount rate of 5%. The dynamic changes across various stages of HFmrEF/HFpEF treated with finerenone combined with SoC versus SoC alone were simulated to evaluate the long-term effectiveness and costs of the two treatment strategies. Additionally， one-way sensitivity analysis and probabilistic sensitivity analysis were performed， to test the robustness of the results. RESULTS The incremental cost-effectiveness ratio （ICER） of the finerenone combined with SoC regimen versus SoC regimen alone was 179 504.75 yuan/QALY， which was below the WTP threshold set in this study， indicating that the finerenone combined with SoC regimen possessed certain economic advantages. The results of one-way sensitivity analysis showed that the utility value of NYHA Ⅱ status， the drug price of finerenone， the discount rate， and the probability of hospital transfer for both groups had a great influence on ICER， but did not affect the robustness of the model. The probabilistic sensitivity analysis also confirmed the robustness of the model. CONCLUSIONS Under the WTP threshold set in this study， finerenone combined with SoC is cost-effective in the treatment of HFmrEF/HFpEF， compared with the SoC regimen.

Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods Clinical data of patients with clinical stageⅠ IAC admitted to Yantaishan Hospital Affiliated to Binzhou Medical University from October 2018 to May 2023 were retrospectively analyzed. Based on the 2021 WHO pathological grading criteria for lung adenocarcinoma, IAC was divided into gradeⅠ, grade Ⅱ, and grade Ⅲ. The differences in parameters among the groups were compared, and logistic regression analysis was used to evaluate the predictive efficacy of AI quantitative parameters for grade Ⅲ IAC patients. Parameters were screened using least absolute shrinkage and selection operator (LASSO) regression analysis. Three machine learning models were constructed based on these parameters to predict grade Ⅲ IAC and were internally validated to assess their efficacy. Nomograms were used for visualization. Results A total of 261 IAC patients were included, including 101 males and 160 females, with an average age of 27-88 (61.96±9.17) years. Six patients had dual primary lesions, and different lesions from the same patient were analyzed as independent samples. There were 48 patients of gradeⅠ IAC, 89 patients of grade Ⅱ IAC, and 130 patients of grade Ⅲ IAC. There were statitical differences in the AI quantitive parameters such as consolidation/tumor ratio (CTR), ect among the three goups. (P<0.05). Univariate analysis showed that the differences in all variables except age were statistically significant (P<0.05) between the group gradeⅠ+grade Ⅱand the group grade Ⅲ . Multivariate analysis suggested that CTR and CT standard deviation were independent risk factors for identifying grade Ⅲ IAC, and the two were negatively correlated. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade ⅡIAC. CTR and CT median value were selected by using LASSO regression. Logistic regression, random forest, and XGBoost models were constructed and validated, among which, the XGBoost model demonstrated the best predictive performance. Conclusion Cautious consideration should be given to grade Ⅲ IAC when CTR is higher than 39.48% and CT standard deviation is less than 122.75 HU. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.

OBJECTIVE To investigate the relationship of long non-coding RNA （LncRNA） metastasis-associated lung adenocarcinoma transcript 1 （MALAT1） and doxorubicin （DOX） resistance in osteosarcoma （OS） cells. METHODS MG-63 and MG-63/DOX cells were treated with different concentrations of DOX （0， 0.01， 0.05， 0.1， 1 μmol/L）， and survival rates and half maximal inhibitory concentration were determined using CCK-8 assay. The expressions of LncRNA MALAT1 in MG-63 and MG-63/ DOX cells were detected by real-time quantitative fluorescence PCR. MG-63/DOX cells were divided into Control group， knocking down LncRNA MALAT1 negative control （sh-NC） group， sh-MALAT1 group， sh-MALAT1+anti-NC group， and sh-MALAT1+ anti-miR-154-5p group. The expressions of LncRNA MALAT1， miR-154-5p and cyclin D1 （CCND1） mRNA in MG-63/DOX cells of each group were detected. The effects of knocking down LncRNA MALAT1 on the proliferation， migration， invasion， and apoptosis of MG-63/DOX cells were detected by CCK-8 assay， scratch test， Transwell experiment and flow cytometry， respectively. The expression of proliferating cell nuclear protein （PCNA） and CCND1 protein in MG-63/DOX cells was detected by Western blot assay. Interactions between LncRNA MALAT1 and miR-154-5p， miR-154-5p and CCND1 were detected by dual luciferase reporter gene experiment. RESULTS Compared with 0 μmol/L DOX， 0.01， 0.05， 0.1 and 1 μmol/L DOX could reduce the survival rates of MG-63 and MG-63/DOX cells （except for 0.01 μmol/L DOX） （P＜0.05）， IC50 were 0.07 and 0.13 μmol/L， respectively. The survival rate， cell migration number and invasion number of MG-63/DOX cells， scratch closure rate， mRNA expressions of LncRNA MALAT1， mRNA and protein expressions of CCND1， and PCNA protein expression in sh-MALAT1 group were significantly lower than sh-NC group and Control group； the apoptosis rate and miR-154-5p expression were significantly higher than sh-NC group and Control group （P＜0.05）. sh-MALAT1+anti-miR-154-5p group was able to reverse the aforementioned biological effects in sh-MALAT1 group （P＜0.05）. In MG-63/DOX cells transfected with both MALAT1-wild type （WT） and CCND1-WT， the luciferase activity in the miR-154-5p mimic group was significantly lower than mimic negative control group （P＜ 0.05）. CONCLUSIONS Knocking down LncRNA MALAT1 can inhibit the DOX resistance of OS cells， and its mechanism may be targeting the miR-154-5p/CCND1 axis.

Stroke ranks as the third leading cause of death and the fourth leading cause of disability worldwide. Its high disability rate and prolonged recovery period not only severely impact patients' quality of life but also impose a significant burden on families and society. Primary prevention is the cornerstone of stroke control, as early intervention on risk factors can effectively reduce its incidence. Therefore, the development of predictive models for first-ever stroke risk holds substantial clinical value. In recent years, advancements in big data and artificial intelligence technologies have opened new avenues for stroke risk prediction. This article reviews the current research status of traditional methods and machine learning models in predicting first-ever stroke risk and outlines future development trends from three perspectives: First, emphasis should be placed on technological innovation by incorporating advanced algorithms such as deep learning and large models to further enhance the accuracy of predictive models. Second, there is a need to diversify data types and optimize model architectures to construct more comprehensive and precise predictive models. Lastly, particular attention should be given to the clinical validation of models in real-world settings. This not only enhances the robustness and generalizability of the models but also promotes physicians' understanding of predictive models, which is crucial for their application and dissemination.