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Access status: Open Access ,
Human Cytomegalovirus as a Therapeutic Target in Glioma Stem Cells.
(Cells, 2026-03) Bou Dargham, Tarek; Vaios, Eugene J; Lawler, Sean; Batich, Kristen
Glioblastoma is the most aggressive tumor among gliomas, and recurrence remains inevitable despite aggressive therapies. Resistance to existing treatment modalities is attributed in part to the presence of glioma stem cells, which comprise a distinct cell subpopulation that sustains cell renewal and tumor evasion through multiple mechanisms. Therapeutic strategies using herpesviruses have been evaluated following the discovery of differential human cytomegalovirus (HCMV) expression in glioblastoma tumor cells. The absence of expression in normal brain tissue led to multiple clinical trials demonstrating the potential clinical utility of targeted HCMV via herpesvirus-based oncolytic therapeutic strategies. This review provides a comprehensive overview of existing studies evaluating the expression and biological significance of HCMV within glioma stem cells. Targeting HCMV in this cellular compartment may disrupt the continuous cellular support and resilience of glioblastoma stem cells, thereby enhancing the efficacy of current treatments.
Access status: Open Access ,
Association of peri-reperfusion oxygen exposure and early allograft dysfunction after deceased donor orthotopic liver transplantation: a retrospective cohort study
(BMC Anesthesiology) Wilson, Elizabeth A; Patel, Gaurav P; Boorman, David W; Jabaley, Craig S
Access status: Open Access ,
A Rare Case of Gould Syndrome Presenting With Gastrointestinal Bleeding in a Pediatric Patient
(Cureus, 2026-08-16) Gonzalez, Nelson; Pearrow, Peyton; Puri, Sandeep K
Access status: Open Access ,
Development and Temporal Validation of a Machine Learning Model to Predict Clinical Deterioration.
(Hospital pediatrics, 2024-01) Foote, Henry P; Shaikh, Zohaib; Witt, Daniel; Shen, Tong; Ratliff, William; Shi, Harvey; Gao, Michael; Nichols, Marshall; Sendak, Mark; Balu, Suresh; Osborne, Karen; Kumar, Karan R; Jackson, Kimberly; McCrary, Andrew W; Li, Jennifer S
<h4>Objectives</h4>Early warning scores detecting clinical deterioration in pediatric inpatients have wide-ranging performance and use a limited number of clinical features. This study developed a machine learning model leveraging multiple static and dynamic clinical features from the electronic health record to predict the composite outcome of unplanned transfer to the ICU within 24 hours and inpatient mortality within 48 hours in hospitalized children.<h4>Methods</h4>Using a retrospective development cohort of 17 630 encounters across 10 388 patients, 2 machine learning models (light gradient boosting machine [LGBM] and random forest) were trained on 542 features and compared with our institutional Pediatric Early Warning Score (I-PEWS).<h4>Results</h4>The LGBM model significantly outperformed I-PEWS based on receiver operating characteristic curve (AUROC) for the composite outcome of ICU transfer or mortality for both internal validation and temporal validation cohorts (AUROC 0.785 95% confidence interval [0.780-0.791] vs 0.708 [0.701-0.715] for temporal validation) as well as lead-time before deterioration events (median 11 hours vs 3 hours; P = .004). However, LGBM performance as evaluated by precision recall curve was lesser in the temporal validation cohort with associated decreased positive predictive value (6% vs 29%) and increased number needed to evaluate (17 vs 3) compared with I-PEWS.<h4>Conclusions</h4>Our electronic health record based machine learning model demonstrated improved AUROC and lead-time in predicting clinical deterioration in pediatric inpatients 24 to 48 hours in advance compared with I-PEWS. Further work is needed to optimize model positive predictive value to allow for integration into clinical practice.
Access status: Open Access ,
Association Between Dead Space to Tidal Volume Ratio and Duration of Respiratory Support After Extubation in Critically Ill Children.
(Respiratory care, 2023-11) Feldman, Alexandra C; Miller, Andrew G; Rotta, Alexandre T; Rehder, Kyle J; Kumar, Karan R
<h4>Background</h4>The dead-space-to-tidal-volume ratio (V<sub>D</sub>/V<sub>T</sub>) has been used to successfully predict extubation failure in children who are critically ill. However, a singular reliable measure to predict the level and duration of respiratory support after liberation from invasive mechanical ventilation has remained elusive. The objective of this study was to evaluate the association between V<sub>D</sub>/V<sub>T</sub> and the duration of postextubation respiratory support.<h4>Methods</h4>This was a retrospective cohort study of subjects who were mechanically ventilated and admitted to a single-center pediatric ICU between March 2019 and July 2021, and who had been extubated with a recorded V<sub>D</sub>/V<sub>T</sub>. A cutoff of 0.30 was chosen a priori, with subjects divided into 2 groups, V<sub>D</sub>/V<sub>T</sub> < 0.30 and V<sub>D</sub>/V<sub>T</sub> ≥ 0.30, and postextubation respiratory support was recorded at specified time intervals (24 h, 48 h, 72 h, 7 d, and 14 d).<h4>Results</h4>We studied 54 subjects. Those with V<sub>D</sub>/V<sub>T</sub> ≥ 0.30 had a significantly longer median (interquartile range) duration of respiratory support after extubation (6 [3-14] d vs 2 [0-4] d; <i>P</i> = .001) and longer median (interquartile range) ICU stay (14 [12-19] d vs 8 [5-22] d; <i>P</i> = .046) versus the subjects with V<sub>D</sub>/V<sub>T</sub> < 0.30. The distribution of respiratory support did not differ significantly between V<sub>D</sub>/V<sub>T</sub> at the time of extubation (<i>P</i> = .13) or at 14 d after extubation (<i>P</i> = .21) but was significantly different during the intervening time points after extubation (24 h [<i>P</i> = .01], 48 h [<i>P</i> < .001], 72 h [<i>P</i> < .001], and 7 d [<i>P</i> = .02]).<h4>Conclusions</h4>V<sub>D</sub>/V<sub>T</sub> was associated with the duration and level of respiratory support needed after extubation. Prospective studies are needed to establish if V<sub>D</sub>/V<sub>T</sub> can successfully predict the level of respiratory support after extubation.