Objective To establish the therapeutic effect prediction model of platelet transfusion in hematological patients,and receiver operating characteristic(ROC)curve and clinical cases are used to evaluate the clinical application value of the predic-tion model.Methods A total of 485 patients with hematological diseases who received platelet transfusion therapy in Kailuan General Hospital from January 2020 to December 2023 were selected,corrected count increment(CCI)was used to divide the patients into platelet transfusion effective group(n=340)and transfusion ineffective group(n=145).Multivariate Logistic regres-sion analysis was used to establish the prediction model of platelet infusion efficacy,and ROC curve was used to evaluate the application effect of the forcasting model.109 clinical cases were used to verify the practical application effect of the model,and the sensitivity,specificity and accuracy were calculated.Results Among 485 patients with hematological diseases,the incidence of ineffective platelet transfusion was 29.90%(145/485).Compated with the effective group,the ineffective group had more previous platelet transfusions was higher,and the difference was statistically significant(t=-4.435,P<0.05).In the ineffective group,there were more cases of hyperplenism,aplastic anemia and lymphoma,higher infection rate and higher positive rate of platelet antibody,and the differences were statistically significant(χ2=6.301～37.522,all P<0.05).Multivariate Logistic regres-sion analysis found that previous platelet infusion times,infection,leukemia,aplastic anemia and platelet antibodies were risk factors for ineffective platelet transfusion in patients with hematological diseases(Wald χ2=5.224～21.548,all P<0.05).Based on these risk factors,platelet infusion effect prediction models 1 and 2 were constructed.ROC curve was used to evaluate the application effect of the prediction model.The area under the curve(AUC),cut-offpoint,sensitivity and specificity of model 1 were 0.884,0.042,82.35%,88.89%.The AUC,cut-offpoint,corresponding sensitivity and specificity of prediction model 2 were 0.910,59.784,81.18%,94.44%,respectively.The Z values of model 1 and model 2 were 12.159 and 13.151,respectively.The prediction effect of model 2 was better than that of model 1.The actual application results showed that the sensitivity,specificity and accuracy of prediction model 1,2 were 85.71%,92.05%,90.89%and 90.48%,93.18%,92.66%,respectively.Conclusion The ineffective rate of platelet transfusion in hematological patients is relatively high.The prediction models 1 and 2 for platelet transfusion effectiveness have good results in predicting ineffective platelet transfusion,and prediction model 2 is better than pre-diction model 1,which can provide reliable basis for hematological patients on accurate platelet transfusion.

Objective To identify potential drug target genes associated with idiopathic pulmonary fibrosis(IPF)and predict therapeutic candidates using a two-sample Mendelian randomization(MR)approach across the druggable genome.Methods Druggable genome data from the DGIdb database and Finan were integrated to identify overlapping genes.A two-sample MR analysis was performed to infer causal relationships between genes and IPF.Functional enrichment analyses,including Gene Ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG),were conducted to explore biological pathways.Drug-target interactions were predicted via DSigDB database screening,followed by molecular docking simulations to evaluate binding affinities.Results Among the 2588 overlapping druggable genes,thirty exhibited significant causal associations with IPF(P＜0.05).Four hub genes(NOD2,LATS2,LTA,and TCF7L2)were enriched in IPF-related pathways,notably Hippo and TNF signaling.Six potential therapeutics were identified:oxyphenbutazone,moexipril,α-galactosylceramide,GSK429286A,CGP74514A,and JW-7-24-1.Molecular docking confirmed strong binding affinities between these drugs and their targets.Conclusion This study has identified thirty druggable gene targets and six candidate drugs for IPF.The enrichment of hub genes in key pathways and validated drug-target interactions provide insights into IPF therapies.

Objective To establish the therapeutic effect prediction model of platelet transfusion in hematological patients,and receiver operating characteristic(ROC)curve and clinical cases are used to evaluate the clinical application value of the predic-tion model.Methods A total of 485 patients with hematological diseases who received platelet transfusion therapy in Kailuan General Hospital from January 2020 to December 2023 were selected,corrected count increment(CCI)was used to divide the patients into platelet transfusion effective group(n=340)and transfusion ineffective group(n=145).Multivariate Logistic regres-sion analysis was used to establish the prediction model of platelet infusion efficacy,and ROC curve was used to evaluate the application effect of the forcasting model.109 clinical cases were used to verify the practical application effect of the model,and the sensitivity,specificity and accuracy were calculated.Results Among 485 patients with hematological diseases,the incidence of ineffective platelet transfusion was 29.90%(145/485).Compated with the effective group,the ineffective group had more previous platelet transfusions was higher,and the difference was statistically significant(t=-4.435,P<0.05).In the ineffective group,there were more cases of hyperplenism,aplastic anemia and lymphoma,higher infection rate and higher positive rate of platelet antibody,and the differences were statistically significant(χ2=6.301～37.522,all P<0.05).Multivariate Logistic regres-sion analysis found that previous platelet infusion times,infection,leukemia,aplastic anemia and platelet antibodies were risk factors for ineffective platelet transfusion in patients with hematological diseases(Wald χ2=5.224～21.548,all P<0.05).Based on these risk factors,platelet infusion effect prediction models 1 and 2 were constructed.ROC curve was used to evaluate the application effect of the prediction model.The area under the curve(AUC),cut-offpoint,sensitivity and specificity of model 1 were 0.884,0.042,82.35%,88.89%.The AUC,cut-offpoint,corresponding sensitivity and specificity of prediction model 2 were 0.910,59.784,81.18%,94.44%,respectively.The Z values of model 1 and model 2 were 12.159 and 13.151,respectively.The prediction effect of model 2 was better than that of model 1.The actual application results showed that the sensitivity,specificity and accuracy of prediction model 1,2 were 85.71%,92.05%,90.89%and 90.48%,93.18%,92.66%,respectively.Conclusion The ineffective rate of platelet transfusion in hematological patients is relatively high.The prediction models 1 and 2 for platelet transfusion effectiveness have good results in predicting ineffective platelet transfusion,and prediction model 2 is better than pre-diction model 1,which can provide reliable basis for hematological patients on accurate platelet transfusion.

Objective To explore the role of clinical pharmacists involved in the case of a patient with acute myeloid leukemia whose QTc interval prolongation was induced by gilteritinib,and to provide reference for drug treatment and monitoring of those patients.Methods The abnormal electrocardiogram(ECG)of a patient with acute myeloid leukemia was found in time by clinical pharmacists,who participated in clinical diagnosis and treatment by analyzing the patient's underlying diseases,diagnosis and treatment process,therapeutic drugs and their potential interactions.Results Clinical pharmacists suspected that the prolonged QTc interval was likely to be an adverse reaction caused by gilteritinib,and recommended immediate discontinuation of the drug and re-examination of the electrocardiogram.The physician took the suggestion to stop the suspected drug therapy with gilteritinib promptly,and ECG was rechecked 3 d later,and the QTc value returned to the normal range.Conclusion Clinical pharmacists participating in clinical diagnosis and treatment could provide better pharmaceutical care for patients.

Stroke is one of the most common cerebrovascular diseases， including hemorrhagic stroke and ischemic stroke. From a modern medical perspective， stroke is caused by cerebrovascular damage or embolism leading to impaired blood circulation. From the traditional Chinese medicine （TCM） perspective， the pathogenesis of this disease is mainly due to the disorder of Qi and blood， which ascend to the brain， causing either blood extravasation or blockage of brain collaterals. Stasis is a pathological factor that runs throughout the entire course of stroke， and the method of promoting blood circulation and resolving stasis has been a core treatment for stroke for a long time. Hirudo， as a traditional insect drug， has shown good effects in promoting blood circulation and resolving stasis. Modern pharmacological research has confirmed that Hirudo contains anticoagulant components， which provide significant advantages in dissolving thrombi in ischemic stroke and facilitating hematoma absorption in hemorrhagic stroke. Hirudo and its related preparations have been proven to exert an anti-stroke effect through anticoagulation， anti-thrombosis， and protection of vascular endothelium. As a result， they have been widely used in the treatment of stroke. This article explored the theoretical basis and research status of using Hirudo for treating stroke based on its main active components and hemostatic properties and summarized the current research status of commonly used Hirudo-based formulations and preparations， aiming to provide references for the involvement of Hirudo in stroke treatment.