Glioblastoma multiforme (GBM) is the most common and malignant brain tumor with poor prognosis. The heterogeneous and aggressive nature of GBMs increases the difficulty of current standard treatment. The presence of GBM stem cells and the blood brain barrier (BBB) further contribute to the most important compromise of chemotherapy and radiation therapy. Current suggestions to optimize GBM patients’ outcomes favor controlled targeted delivery of chemotherapeutic agents to GBM cells through the BBB using nanoparticles and monoclonal antibodies. Nanotechnology and nanocarrier-based drug delivery have recently gained attention due to the characteristics of biosafety, sustained drug release, increased solubility, and enhanced drug bioactivity and BBB penetrability. In this review, we focused on recently developed nanoparticles and emerging strategies using nanocarriers for the treatment of GBMs. Current studies using nanoparticles or nanocarrier-based drug delivery system for treatment of GBMs in clinical trials, as well as the advantages and limitations, were also reviewed.
It is unknown whether neonatal ventilator-associated pneumonia (VAP) caused by multidrug-resistant (MDR) pathogens and inappropriate initial antibiotic treatment is associated with poor outcomes after adjusting for confounders. Methods: We prospectively observed all neonates with a definite diagnosis of VAP from a tertiary level neonatal intensive care unit (NICU) in Taiwan between October 2017 and March 2020. All clinical features, therapeutic interventions, and outcomes were compared between the MDR–VAP and non-MDR–VAP groups. Multivariate regression analyses were used to investigate independent risk factors for treatment failure. Results: Of 720 neonates who were intubated for more than 2 days, 184 had a total of 245 VAP episodes. The incidence rate of neonatal VAP was 10.1 episodes/per 1000 ventilator days. Ninety-six cases (39.2%) were caused by MDR pathogens. Neonates with MDR–VAP were more likely to receive inadequate initial antibiotic therapy (51.0% versus 4.7%; p < 0.001) and had delayed resolution of clinical symptoms (38.5% versus 25.5%; p = 0.034), although final treatment outcomes were comparable with the non-MDR–VAP group. Inappropriate initial antibiotic treatment was not significantly associated with worse outcomes. The VAP-attributable mortality rate and overall mortality rate of this cohort were 3.7% and 12.0%, respectively. Independent risk factors for treatment failure included presence of concurrent bacteremia (OR 4.83; 95% CI 2.03–11.51; p < 0.001), septic shock (OR 3.06; 95% CI 1.07–8.72; p = 0.037), neonates on high-frequency oscillatory ventilator (OR 4.10; 95% CI 1.70–9.88; p = 0.002), and underlying neurological sequelae (OR 3.35; 95% CI 1.47–7.67; p = 0.004). Conclusions: MDR–VAP accounted for 39.2% of all neonatal VAP in the neonatal intensive care unit (NICU), but neither inappropriate initial antibiotics nor MDR pathogens were associated with treatment failure. Neonatal VAP with concurrent bacteremia, septic shock, and underlying neurological sequelae were independently associated with final worse outcomes.
Background: Early identification of critically ill neonates with poor outcomes can optimize therapeutic strategies. We aimed to examine whether machine learning (ML) methods can improve mortality prediction for neonatal intensive care unit (NICU) patients on intubation for respiratory failure. Methods: A total of 1734 neonates with respiratory failure were randomly divided into training (70%, n = 1214) and test (30%, n = 520) sets. The primary outcome was the probability of NICU mortality. The areas under the receiver operating characteristic curves (AUCs) of several ML algorithms were compared with those of the conventional neonatal illness severity scoring systems including the NTISS and SNAPPE-II. Results: For NICU mortality, the random forest (RF) model showed the highest AUC (0.939 (0.921–0.958)) for the prediction of neonates with respiratory failure, and the bagged classification and regression tree model demonstrated the next best results (0.915 (0.891–0.939)). The AUCs of both models were significantly better than the traditional NTISS (0.836 (0.800–0.871)) and SNAPPE-II scores (0.805 (0.766–0.843)). The superior performances were confirmed by higher accuracy and F1 score and better calibration, and the superior and net benefit was confirmed by decision curve analysis. In addition, Shapley additive explanation (SHAP) values were utilized to explain the RF prediction model. Conclusions: Machine learning algorithms increase the accuracy and predictive ability for mortality of neonates with respiratory failure compared with conventional neonatal illness severity scores. The RF model is suitable for clinical use in the NICU, and clinicians can gain insights and have better communication with families in advance.
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