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Introduction: Intrawound application of vancomycin is becoming increasingly controversial for the prevention of surgical site infection (SSI). As children undergoing spinal fusion for secondary scoliosis are at high risk for SSIs, evidence regarding the impact of intraoperative vancomycin installation on SSI rates in these patients is of utmost importance. Methodology: A single surgeon cohort of patients under 18 years of age undergoing surgery for secondary scoliosis in 2017 was analyzed with regard to the development of SSIs requiring surgical revision and adverse events. Use of vancomycin was restricted to cases with higher risk of infection. Patients undergoing distraction surgery for growing devices were excluded. Results: After exclusions, 64 patients remained (vancomycin n = 39, control n = 25). The SSI rates were 12.8% in patients receiving vancomycin (n = 5/39) and 4% in the control group (n = 1/25, p = 0.785). None of the patients suffered from adverse events. Univariable logistic regression revealed younger age (p = 0.03) and meningomyelocele as predictors for SSI (p = 0.006), while the high-risk group receiving vancomycin was not at higher odds for SSI, also after adjustment for possible confounders such as age or MMC (p = 0.031; p = 0.009). Discussion: SSI rates were comparable between groups, suggesting a normalization of SSI risk in the vancomycin-treated patients with a preoperatively increased risk of SSI. Future, larger studies in these rare diseases are needed to confirm these results. [ABSTRACT FROM AUTHOR]

Effective treatment for glioblastoma (GBM) is limited by the presence of the blood–brain barrier (BBB) and rapid resistance to single agent therapies. To address these issues, we developed a transferrin-functionalized nanoparticle (Tf-NP) that can deliver dual combination therapies. Using intravital imaging, we show the ability of Tf-NPs to traverse intact BBB in mice as well as achieve direct tumor binding in two intracranial orthotopic models of GBM. Treatment of tumor-bearing mice with Tf-NPs loaded with temozolomide and the bromodomain inhibitor JQ1 leads to increased DNA damage and apoptosis that correlates with a 1.5- to 2-fold decrease in tumor burden and corresponding increase in survival compared to equivalent free-drug dosing. Immunocompetent mice treated with Tf-NP-loaded drugs also show protection from the effects of systemic drug toxicity, demonstrating the preclinical potential of this nanoscale platform to deliver novel combination therapies to gliomas and other central nervous system tumors., Version of Record

Background: 5q-Spinal muscular atrophy (SMA) is now one of the 5% treatable rare diseases worldwide. As disease-modifying therapies alter disease progression and patient phenotypes, paediatricians and consulting disciplines face new unknowns in their treatment decisions. Conclusions made from historical patient data sets are now mostly limited, and new approaches are needed to ensure our continued best standard-of-care practices for this exceptional patient group. Here, we present a data-driven machine learning approach to a rare disease data set to predict spinal muscular atrophy (SMA)-associated scoliosis., Methods: We collected data from 84 genetically confirmed 5q-SMA patients who have received novel SMA therapies. We performed expert domain knowledge-directed feature engineering, correlation and predictive power score (PPS) analyses for feature selection. To test the predictive performance of the selected features, we trained a Random Forest Classifier and evaluated model performance using standard metrics., Results: The SMA data set consisted of 1304 visits and over 360 variables. We performed feature engineering for variables related to 'interventions', 'devices', 'orthosis', 'ventilation', 'muscle contractures' and 'motor milestones'. Through correlation and PPS analysis paired with expert domain knowledge feature selection, we identified relevant features for scoliosis prediction in SMA that included disease progression markers: Hammersmith Functional Motor Scale Expanded 'HFMSE' (PPS = 0.27) and 6-Minute Walk Test '6MWT' scores (PPS = 0.44), 'age' (PPS = 0.41) and 'weight' (PPS = 0.49), 'contractures' (PPS = 0.17), the use of 'assistive devices' (PPS = 0.39, 'ventilation' (PPS = 0.16) and the presence of 'gastric tubes' (PPS = 0.35) in SMA patients. These features were validated using expert domain knowledge and used to train a Random Forest Classifier with an observed accuracy of 0.82 and an average receiver operating characteristic (ROC) area of 0.87., Conclusion: The introduction of disease-modifying SMA therapies, followed by the implementation of SMA in newborn screenings, has presented physicians with never-seen patients. We used feature engineering tools to overcome one of the main challenges when using data-driven approaches in rare disease data sets. Through predictive modelling of this data, we defined disease progression markers, which are easily assessed during patient visits and can help anticipate scoliosis onset. This highlights the importance of progressive features in the drug-induced revolution of this rare disease and further supports the ongoing efforts to update the SMA classification. We advocate for the consistent documentation of relevant progression markers, which will serve as a basis for data-driven models that physicians can use to update their best standard-of-care practices., (© 2024 The Author(s). Journal of Cachexia, Sarcopenia and Muscle published by Wiley Periodicals LLC.)