UPLC-MS/MS method for the determination of voriconazole plasma concentration from pediatric patients with hematologic tumors: An application toward personalized therapy.

Untreated invasive fungal infection is one of the important risk factors affecting the prognosis of pediatric patients with hematologic tumors. Voriconazole (VOR) is the first-line antifungal drug for the treatment of Aspergillus infections. In order to reduce the risk of adverse drug reactions while producing an ideal antifungal effect, therapeutic drug monitoring was performed to maintain the VOR plasma concentration in a range of 1,000-5,500 ng/ml. In the present study, a reliable, accurate, sensitive and quick ultra-high performance liquid chromatograph-tandem mass spectrometry (UPLC-MS/MS) method was developed for the determination of the VOR level. Protein precipitation was performed using acetonitrile, and then the chromatographic separation was carried out by UPLC using a C18 column with the gradient mobile phases comprising 0.1% methanoic acid in acetonitrile (A) and 0.1% methanoic acid in water (B). In the selective reaction monitor mode, the mass spectrometric detection was carried out using an TSQ Endura triple quadruple mass spectrometer. The performance of this UPLC-MS/MS method was validated as per the National Medical Products Administration for Bioanalytical Method Validation. Additionally, the plasma concentrations of VOR in pediatric patients with hematologic tumors were detected using this method, and the analyzed results were used for personalized therapy.

Exploring the Feasibility of Machine Learning to Predict Risk Stratification Within 3 Months in Chest Pain Patients with Suspected NSTE-ACS.

Objective: We aimed to assess the feasibility and superiority of machine learning (ML) methods to predict the risk of Major Adverse Cardiovascular Events (MACEs) in chest pain patients with NSTE-ACS. Methods: Enrolled chest pain patients were from two centers, Beijing Anzhen Emergency Chest Pain Center Beijing Bo'ai Hospital, China Rehabilitation Research Center. Five classifiers were used to develop ML models. Accuracy, Precision, Recall, F-Measure and AUC were used to assess the model performance and prediction effect compared with HEART risk scoring system. Ultimately, ML model constructed by Naïve Bayes was employed to predict the occurrence of MACEs. Results: According to learning metrics, ML models constructed by different classifiers were superior over HEART (History, ECG, Age, Risk factors, & Troponin) scoring system when predicting acute myocardial infarction (AMI) and all-cause death. However, according to ROC curves and AUC, ML model constructed by different classifiers performed better than HEART scoring system only in prediction for AMI. Among the five ML algorithms, Linear support vector machine (SVC), Naïve Bayes and Logistic regression classifiers stood out with all Accuracy, Precision, Recall and F-Measure from 0.8 to 1.0 for predicting any event, AMI, revascularization and all-cause death ( vs. HEART ≤ 0.78), with AUC from 0.88 to 0.98 for predicting any event, AMI and revascularization ( vs. HEART ≤ 0.85). ML model developed by Naïve Bayes predicted that suspected acute coronary syndrome (ACS), abnormal electrocardiogram (ECG), elevated hs-cTn I, sex and smoking were risk factors of MACEs. Conclusion: Compared with HEART risk scoring system, the superiority of ML method was demonstrated when employing Linear SVC classifier, Naïve Bayes and Logistic. ML method could be a promising method to predict MACEs in chest pain patients with NSTE-ACS.

Stromal cell-derived factor-1α as a novel biomarker for hyperlipidemia.

Stromal cell-derived factor-1 (SDF-1) is expressed in a wide variety of organs, such as heart, and plays a pivotal role in the mobilization of hematopoietic stem and progenitor cells in bone marrow. SDF-1α, a common subtype of SDF-1, may control hematopoiesis and angiogenesis, but its role in the pathogenesis of hyperlipidemia is unknown. The aim of this study was to determine the role of SDF-1α in the pathogenesis of hyperlipidemia. First, log-transformed SDF-1α serum levels (logSDF-1α) were significantly higher in male patients with borderline high lipid profile (BHLP; n=28; 2.15±0.08 ng/ml) compared to control subjects (n=37; 1.94±0.06 ng/ml; P<0.01). The logSDF-1α in male patients with high lipid profile (HLP; n=33; 1.95±0.08 ng/ml) were lower than BHLP patients (P<0.01). The logSDF-1α was positively associated with HDL-C only in female patients (n=125; r=0.379, P=0.016). These results suggest the different pathophysiology in male and female patients with hyperlipidemia. Moreover, flow cytometry analysis showed that expression of the SDF-1α receptor, CXC-chemokine receptor 4, was lower in peripheral blood mononuclear cells of patients with BHLP (n=10) and HLP (n=10), compared to control subjects (n=10; P<0.001). Lastly, peripheral blood leukocyte, neutrophil and lymphocyte counts were higher in BHLP patients (n=62; P<0.05). Taken together, we suggest SDF-1α as a biomarker of hyperlipidemia that may be helpful to uncover the pathogenesis of hyperlipidemia.