A superparamagnetic iron oxide (SPIO) nanoparticle is emerging as an ideal probe for noninvasive cell tracking. However, its low intracellular labeling efficiency has limited the potential usage and has evoked great interest in developing new labeling strategies. We have developed fluorescein isothiocyanate (FITC)-incorporated silica-coated core-shell SPIO nanoparticles, SPIO@SiO2(FITC), with diameters of 50 nm, as a bifunctionally magnetic vector that can efficiently label human mesenchymal stem cells (hMSCs), via clathrin- and actin-dependent endocytosis with subsequent intracellular localization in late endosomes/lysosomes. The uptake process displays a time- and dose-dependent behavior. In our system, SPIO@SiO2(FITC) nanoparticles induce sufficient cell MRI contrast at an incubation dosage as low as 0.5 microg of iron/mL of culture medium with 1.2x105 hMSCs, and the in vitro detection threshold of cell number is about 1x104 cells. Furthermore, 1.2x105 labeled cells can also be MRI-detected in a subcutaneous model in vivo. Labeled hMSCs are unaffected in their viability, proliferation, and differentiation capacities into adipocytes and osteocytes which can still be readily MRI detected. This is the first report that hMSCs can be efficiently labeled with MRI contrast nanoparticles and can be monitored in vitro and in vivo with a clinical 1.5-T MRI imager under low incubation concentration of iron oxide, short incubation time, and low detection cell numbers at the same time.

Mutations of nicotinamide adenine dinucleotide phosphate-dependent isocitrate dehydrogenase gene (IDH1) have been identified in patients with gliomas. Recent genomewide screening also revealed IDH1 mutation as a recurrent event in acute myeloid leukemia (AML), but its clinical implications in AML are largely unknown. We analyzed 493 adult Chinese AML patients in Taiwan and found 27 patients (5.5%) harboring this mutation. IDH1 mutation was strongly associated with normal karyotype (8.4%, P ‫؍‬ .002), isolated monosomy 8 (P ‫؍‬ .043), NPM1 mutation (P < .001), and FrenchAmerican-British M1 subtype (P < .001), but inversely associated with French-AmericanBritish M4 subtype (P ‫؍‬ .030) and expression of HLA-DR, CD13, and CD14 (P ‫؍‬ .002, .003, and .038, respectively). There was no impact of this mutation on patient survival. Sequential analysis of IDH1 mutation was performed in 130 patients during followups. None of the 112 patients without IDH1 mutation at diagnosis acquired this mutation at relapse. In all 18 IDH1-mutated patients studied, the mutation disappeared in complete remission; the same mutation reappeared in all 11 samples obtained at relapse. We conclude that IDH1 is associated with distinct clinical and biologic characteristics and seems to be very stable during disease evolution. (Blood. 2010;115(14): 2749-2754)

Background: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that results in increased morbidity and mortality after cardiac surgery. Most established prediction models are limited to the analysis of nonlinear relationships and fail to fully consider intraoperative variables, which represent the acute response to surgery. Therefore, this study utilized an artificial intelligence-based machine learning approach thorough perioperative data-driven learning to predict CSA-AKI. Methods: A total of 671 patients undergoing cardiac surgery from August 2016 to August 2018 were enrolled. AKI following cardiac surgery was defined according to criteria from Kidney Disease: Improving Global Outcomes (KDIGO). The variables used for analysis included demographic characteristics, clinical condition, preoperative biochemistry data, preoperative medication, and intraoperative variables such as time-series hemodynamic changes. The machine learning methods used included logistic regression, support vector machine (SVM), random forest (RF), extreme gradient boosting (XGboost), and ensemble (RF + XGboost). The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). We also utilized SHapley Additive exPlanation (SHAP) values to explain the prediction model. Results: Development of CSA-AKI was noted in 163 patients (24.3%) during the first postoperative week. Regarding the efficacy of the single model that most accurately predicted the outcome, RF exhibited the greatest AUC (0.839, 95% confidence interval [CI] 0.772-0.898), whereas the AUC (0.843, 95% CI 0.778-0.899) of ensemble model (RF + XGboost) was even greater than that of the RF model alone. The top 3 most influential features in the RF importance matrix plot were intraoperative urine output, units of packed red blood cells (pRBCs) transfused during surgery, and preoperative hemoglobin level. The SHAP summary plot was used to illustrate the positive or negative effects of the top 20 features attributed to the RF. We also used the SHAP dependence plot to explain how a single feature affects the output of the RF prediction model.

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