OBJECTIVEModern surgical planning and prognostication requires the most accurate outcomes data to practice evidence-based medicine. For clinicians treating children following traumatic brain injury (TBI) these data are severely lacking. The first aim of this study was to assess published CT classification systems in the authors’ pediatric cohort. A pediatric-specific machine-learning algorithm called an artificial neural network (ANN) was then created that robustly outperformed traditional CT classification systems in predicting TBI outcomes in children.METHODSThe clinical records of children under the age of 18 who suffered a TBI and underwent head CT within 24 hours after TBI (n = 565) were retrospectively reviewed.RESULTS“Favorable” outcome (alive with Glasgow Outcome Scale [GOS] score ≥ 4 at 6 months postinjury, n = 533) and “unfavorable” outcome (death at 6 months or GOS score ≤ 3 at 6 months postinjury, n = 32) were used as the primary outcomes. The area under the receiver operating characteristic (ROC) curve (AUC) was used to delineate the strength of each CT grading system in predicting survival (Helsinki, 0.814; Rotterdam, 0.838; and Marshall, 0.781). The AUC for CT score in predicting GOS score ≤ 3, a measure of overall functionality, was similarly predictive (Helsinki, 0.717; Rotterdam, 0.748; and Marshall, 0.663). An ANN was then constructed that was able to predict 6-month outcomes with profound accuracy (AUC = 0.9462 ± 0.0422).CONCLUSIONSThis study showed that machine-learning can be leveraged to more accurately predict TBI outcomes in children.

BackgroundIncreased knowledge of the temporal patterns in the distribution of trauma admissions could be beneficial to staffing and resource allocation efforts. However, little work has been done to understand how this distribution varies based on patient acuity, trauma mechanism or need for intervention. We hypothesize that temporal patterns exist in the distribution of trauma admissions, and that deep patterns exist when traumas are analyzed by their type and severity.Study designWe conducted a cross-sectional observational study of adult patient flow at a level one trauma center over three years, 7/1/2013–6/30/2016. Primary thermal injuries were excluded. Frequency analysis was performed for patients grouped by ED disposition and mechanism against timing of admission; in subgroup analysis additional exclusion criteria were imposed.Results10,684 trauma contacts were analyzed. Trauma contacts were more frequent on Saturdays and Sundays than on weekdays (p<0.001). Peak arrival time was centered around evening shift change (6-7pm), but differed based on ED disposition: OR and ICU or Step-Down admissions (p = 0.0007), OR and floor admissions (p<0.0001), and ICU or Step-Down and floor admissions (p<0.0001). Step-Down and ICU arrival times (p = 0.42) were not different. Penetrating injuries peaked later than blunt (p<0.0001). Trauma varies throughout the year; we establish a high incidence trauma season (April to late October). Different mechanisms have varying dependence upon season; Motorcycle crashes (MCCs) have the greatest dependence.ConclusionWe identify new patterns in the temporal and seasonal variation of trauma and of specific mechanisms of injury, including the novel findings that 1) penetrating trauma tends to present at later times than blunt, and 2) critically ill patients requiring an OR tend to present later than those who are less acute and require an ICU or Step-Down unit. These patients present later than those who are admitted to the floor. Penetrating trauma patients arriving later than blunt may be the underlying reason why operative patients arrive later than other patients.

Wimmer RJ, Liu Y, Schachter TN, Stonko DP, Peercy BE, Schneider MF. Mathematical modeling reveals modulation of both nuclear influx and efflux of Foxo1 by the IGF-I/PI3K/Akt pathway in skeletal muscle fibers. Am J Physiol Cell Physiol 306: C570-C584, 2014. First published January 22, 2014; doi:10.1152/ajpcell.00338.2013.-Foxo family transcription factors contribute to muscle atrophy by promoting transcription of the ubiquitin ligases muscle-specific RING finger protein and muscle atrophy F-box/atrogin-1. Foxo transcriptional effectiveness is largely determined by its nuclear-cytoplasmic distribution, with unphosphorylated Foxo1 transported into nuclei and phosphorylated Foxo1 transported out of nuclei. We expressed the fluorescent fusion protein Foxo1-green fluorescent protein (GFP) in cultured adult mouse flexor digitorum brevis muscle fibers and tracked the time course of the nuclear-to-cytoplasmic Foxo1-GFP mean pixel fluorescence ratio (N/C) in living fibers by confocal imaging. We previously showed that IGF-I, which activates the Foxo kinase Akt/PKB, caused a rapid marked decline in N/C, whereas inhibition of Akt caused a modest increase in N/C. Here we develop a two-state mathematical model for Foxo1 nuclear-cytoplasmic redistribution, where Foxo phosphorylation/dephosphorylation is assumed to be fast compared with nuclear influx and efflux. Cytoplasmic Foxo1-GFP mean pixel fluorescence is constant due to the much larger cytoplasmic than nuclear volume. Analysis of N/C time courses reveals that IGF-I strongly increased unidirectional nuclear efflux, indicating similarly increased fractional phosphorylation of Foxo1 within nuclei, and decreased unidirectional nuclear influx, indicating increased cytoplasmic fractional phosphorylation of Foxo1. Inhibition of Akt increased Foxo1 unidirectional nuclear influx, consistent with block of Foxo1 cytoplasmic phosphorylation, but did not decrease Foxo1 unidirectional nuclear efflux, indicating that Akt may not be involved in Foxo1 nuclear efflux under control conditions. New media change experiments show that cultured fibers release IGF-I-like factors, which maintain low nuclear Foxo1 in the medium. This study demonstrates the power of quantitative modeling of observed nuclear fluxes.Akt1; Foxo1; IGF-I; skeletal muscle TRANSCRIPTION OF A GIVEN GENE is controlled by numerous regulatory proteins, including transcription factors, which are primary activators of transcription, as well as numerous other activators, coactivators, repressors, and corepressors (12, 15a). These factors combine to form macromolecular transcriptional regulatory complexes assembled on the promoter regions of the gene in question (16). To participate in such regulatory complexes, the regulatory molecules must be resident in the nucleus. Thus an important aspect of transcriptional regulation is control of the nuclear-cytoplasmic distribution of these regulatory molecules, and the mechanisms controlling nuclearcytoplasmic movements of transcriptional regulatory molecules can thus be important dete...

ACDF, anterior cervical discectomy and fusionBMI, body mass indexEQ-5D, EuroQol-5DMCID, minimal clinically important differenceMCS, mental component scalemJOA, modified Japanese Orthopaedic AssociationNDI, Neck Disability IndexNRS, Numerical Rating ScalePCS, physical component scalePROs, patient-reported outcomesSF-12, Short Form 12.

OBJECTIVEPrognostication and surgical planning for WHO grade I versus grade II meningioma requires thoughtful decision-making based on radiographic evidence, among other factors. Although conventional statistical models such as logistic regression are useful, machine learning (ML) algorithms are often more predictive, have higher discriminative ability, and can learn from new data. The authors used conventional statistical models and an array of ML algorithms to predict atypical meningioma based on radiologist-interpreted preoperative MRI findings. The goal of this study was to compare the performance of ML algorithms to standard statistical methods when predicting meningioma grade.METHODSThe cohort included patients aged 18–65 years with WHO grade I (n = 94) and II (n = 34) meningioma in whom preoperative MRI was obtained between 1998 and 2010. A board-certified neuroradiologist, blinded to histological grade, interpreted all MR images for tumor volume, degree of peritumoral edema, presence of necrosis, tumor location, presence of a draining vein, and patient sex. The authors trained and validated several binary classifiers: k-nearest neighbors models, support vector machines, naïve Bayes classifiers, and artificial neural networks as well as logistic regression models to predict tumor grade. The area under the curve–receiver operating characteristic curve was used for comparison across and within model classes. All analyses were performed in MATLAB using a MacBook Pro.RESULTSThe authors included 6 preoperative imaging and demographic variables: tumor volume, degree of peritumoral edema, presence of necrosis, tumor location, patient sex, and presence of a draining vein to construct the models. The artificial neural networks outperformed all other ML models across the true-positive versus false-positive (receiver operating characteristic) space (area under curve = 0.8895).CONCLUSIONSML algorithms are powerful computational tools that can predict meningioma grade with great accuracy.

When an unusual intraplacental lesion is identified during pathologic examination, it becomes of substantial import to determine whether it represents a normal structure, metastasis from the mother, or a primary benign tumor, including those secondary to abnormal embryologic development versus a primary malignant placental tumor. In this case report, we identified an incidental nest of intraplacental cells with nondiagnostic morphology and negative initial Glypican-3 stain in a healthy 35-wk gestation. This negative result prompted a broadening of the differential before ultimately determining this lesion was indeed ectopic liver with positive Arginase-1 and HepPar-1 staining. This may represent the mature hepatocyte phenotype within the lesional cells of this near-term birth, a dichotomy not previously discussed in the literature, which focuses on the fetal hepatocyte phenotype, also rarely seen. In this report, we summarize the previous literature regarding intraplacental ectopic liver, and we propose a sensitive approach to suspected ectopic liver of the placenta that may be sufficient to capture both the fetal and mature hepatocyte immunophenotypes. This approach may extend to other related pathologies including assessment of suspected intraumbilical hepatocytes.

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