Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are progressive neurodegenerative disorders marked in most cases by the nuclear exclusion and cytoplasmic deposition of the RNA binding protein TDP43. We previously demonstrated that ALS–associated mutant TDP43 accumulates within the cytoplasm, and that TDP43 mislocalization predicts neurodegeneration. Here, we sought to prevent neurodegeneration in ALS/FTD models using selective inhibitor of nuclear export (SINE) compounds that target exportin-1 (XPO1). SINE compounds modestly extend cellular survival in neuronal ALS/FTD models and mitigate motor symptoms in an in vivo rat ALS model. At high doses, SINE compounds block nuclear egress of an XPO1 cargo reporter, but not at lower concentrations that were associated with neuroprotection. Neither SINE compounds nor leptomycin B, a separate XPO1 inhibitor, enhanced nuclear TDP43 levels, while depletion of XPO1 or other exportins had little effect on TDP43 localization, suggesting that no single exporter is necessary for TDP43 export. Supporting this hypothesis, we find overexpression of XPO1, XPO7 and NXF1 are each sufficient to promote nuclear TDP43 egress. Taken together, our results indicate that redundant pathways regulate TDP43 nuclear export, and that therapeutic prevention of cytoplasmic TDP43 accumulation in ALS/FTD may be enhanced by targeting several overlapping mechanisms.
Study Design: Narrative Summary Review for Navigation & Robotics Focus Issue. Objective: To discuss the challenges and complications of S2-Alar-Iliac (S2AI) spinopelvic fixation using freehand techniques, and to introduce the utility of navigation & robotics to enhance patient safety. Methods: This study involved search of literature using the PubMed database, including retrospective clinical studies, anatomic reports, and surgical reports. The intention was to find literature that discussed complications regarding screw malfunction from manual S2AI placement, anatomical complexity of the sacroiliac joint, and outcomes of S2AI procedures conducted with robotic guidance systems. Results: The sacroiliac joint presents numerous complexities that can lead to challenges in free-hand placement of the S2-alar-iliac screw. Anatomic considerations of the S2AI screw involve close proximity to vital neurovascular structures, including: superior gluteal vessels, external iliac vessels, pudendal vessels, superior gluteal nerves, sciatic nerve, sympathetic chain ganglia, and pudendal nerves. The complications associated with manual S2AI screw installation include screw misplacement, breach of cortical bone, and injury to neurovascular structures. Robotic techniques for establishing S2AI screws involve greater accuracy of screw placement and reduced complications. Conclusions: Accurate placement of S2AI screws is compromised by variation in pelvic anatomy and by a pathway that traverses dense cortical bone of the sacroiliac joint. Accurate placement of S2AI screws is important for patient safety regarding neurovascular structures, and for effective, stable fixation across the SI joint. Robotic navigation of S2AI fixation offers significant utility in improving the accuracy of screw placement and patient safety.
Study Design. A retrospective study at a single academic institution.Objective. The purpose of this study is to utilize machine learning to predict hospital length of stay (LOS) and discharge disposition following adult elective spine surgery, and to compare performance metrics of machine learning models to the American College of Surgeon's National Surgical Quality Improvement Program's (ACS NSQIP) prediction calculator. Summary of Background Data. A total of 3678 adult patients undergoing elective spine surgery between 2014 and 2019, acquired from the electronic health record. Methods. Patients were divided into three stratified cohorts: cervical degenerative, lumbar degenerative, and adult spinal deformity groups. Predictive variables included demographics, body mass index, surgical region, surgical invasiveness, surgical approach, and comorbidities. Regression, classification trees, and least absolute shrinkage and selection operator (LASSO) were used to build predictive models. Validation of the models was conducted on 16% of patients (N = 587), using area under the receiver operator curve (AUROC), sensitivity, specificity, and correlation. Patient data were manually entered into the ACS NSQIP online risk calculator to compare performance. Outcome variables were discharge disposition (home vs. rehabilitation) and LOS (days). Results. Of 3678 patients analyzed, 51.4% were male (n = 1890) and 48.6% were female (n = 1788). The average LOS was 3.66 days. In all, 78% were discharged home and 22% discharged to rehabilitation. Compared with NSQIP (Pearson R 2 = 0.16), the predictions of poisson regression (R 2 = 0.29) and LASSO (R 2 = 0.29) models were significantly more correlated with observed LOS (P = 0.025 and 0.004, respectively). Of the models generated to predict discharge location, logistic regression yielded an AUROC of 0.79, which was statistically equivalent to the AUROC of 0.75 for NSQIP (P = 0.135). Conclusion. The predictive models developed in this study can enable accurate preoperative estimation of LOS and risk of rehabilitation discharge for adult patients undergoing elective spine surgery. The demonstrated models exhibited better performance than NSQIP for prediction of LOS and equivalent performance to NSQIP for prediction of discharge location.
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