Biodegradable polymeric nanoparticles have the potential to be safer alternatives to viruses for gene delivery; however, their use has been limited by poor efficacy in vivo. In this work, we synthesize and characterize polymeric gene delivery nanoparticles and evaluate their efficacy for DNA delivery of herpes simplex virus type I thymidine kinase (HSVtk) combined with the prodrug ganciclovir (GCV) in a malignant glioma model. We investigated polymer structure for gene delivery in two rat glioma cell lines, 9L and F98, to discover nanoparticle formulations more effective than the leading commercial reagent Lipofectamine 2000. The lead polymer structure, poly(1,4-butanediol diacrylate-co-4-amino-1-butanol) end-modified with 1-(3-aminopropyl)-4-methylpiperazine, is a poly(β-amino ester) (PBAE) and formed nanoparticles with HSVtk DNA that were 138 ± 4 nm in size and 13 ± 1 mV in zeta potential. These nanoparticles containing HSVtk DNA showed 100% cancer cell killing in vitro in the two glioma cell lines when combined with GCV exposure, while control nanoparticles encoding GFP maintained robust cell viability. For in vivo evaluation, tumor-bearing rats were treated with PBAE/HSVtk infusion via convection-enhanced delivery (CED) in combination with systemic administration of GCV. These treated animals showed a significant benefit in survival (p = 0.0012 vs control). Moreover, following a single CED infusion, labeled PBAE nanoparticles spread completely throughout the tumor. This study highlights a nanomedicine approach that is highly promising for the treatment of malignant glioma.

OBJECTIVE Augmented reality (AR) is a novel technology which, when applied to spine surgery, offers the potential for efficient, safe, and accurate placement of spinal instrumentation. The authors report the accuracy of the first 205 pedicle screws consecutively placed at their institution by using AR assistance with a unique head-mounted display (HMD) navigation system. METHODS A retrospective review was performed of the first 28 consecutive patients who underwent AR-assisted pedicle screw placement in the thoracic, lumbar, and/or sacral spine at the authors’ institution. Clinical accuracy for each pedicle screw was graded using the Gertzbein-Robbins scale by an independent neuroradiologist working in a blinded fashion. RESULTS Twenty-eight consecutive patients underwent thoracic, lumbar, or sacral pedicle screw placement with AR assistance. The median age at the time of surgery was 62.5 (IQR 13.8) years and the median body mass index was 31 (IQR 8.6) kg/m2. Indications for surgery included degenerative disease (n = 12, 43%); deformity correction (n = 12, 43%); tumor (n = 3, 11%); and trauma (n = 1, 4%). The majority of patients (n = 26, 93%) presented with low-back pain, 19 (68%) patients presented with radicular leg pain, and 10 (36%) patients had documented lower extremity weakness. A total of 205 screws were consecutively placed, with 112 (55%) placed in the lumbar spine, 67 (33%) in the thoracic spine, and 26 (13%) at S1. Screw placement accuracy was 98.5% for thoracic screws, 97.8% for lumbar/S1 screws, and 98.0% overall. CONCLUSIONS AR depicted through a unique HMD is a novel and clinically accurate technology for the navigated insertion of pedicle screws. The authors describe the first 205 AR-assisted thoracic, lumbar, and sacral pedicle screws consecutively placed at their institution with an accuracy of 98.0% as determined by a Gertzbein-Robbins grade of A or B.

No abstract

OBJECTIVE Treatment of primary spinal infection includes medical management with or without surgical intervention. The objective of this study was to identify risk factors for the eventual need for surgery in patients with primary spinal infection on initial presentation. METHODS From January 2010 to July 2019, 275 patients presented with primary spinal infection. Demographic, infectious, imaging, laboratory, treatment, and outcome data were retrospectively reviewed and collected. Thirty-three patients were excluded due to insufficient follow-up (≤ 90 days) or death prior to surgery. RESULTS The mean age of the 242 patients was 58.8 ± 13.6 years. The majority of the patients were male (n = 130, 53.7%), White (n = 150, 62.0%), and never smokers (n = 132, 54.5%). Fifty-four patients (22.3%) were intravenous drug users. One hundred fifty-four patients (63.6%) ultimately required surgery while 88 (36.4%) never needed surgery during the duration of follow-up. There was no significant difference in age, gender, race, BMI, or comorbidities between the surgery and no-surgery groups. On univariate analysis, the presence of an epidural abscess (55.7% in the no-surgery group vs 82.5% in the surgery group, p < 0.0001), the median spinal levels involved (2 [interquartile range (IQR) 2–3] in the no-surgery group vs 3 [IQR 2–5] in the surgery group, p < 0.0001), and active bacteremia (20.5% in the no-surgery vs 35.1% in the surgery group, p = 0.02) were significantly different. The cultured organism and initial laboratory values (erythrocyte sedimentation rate, C-reactive protein, white blood cell count, creatinine, and albumin) were not significantly different between the groups. On multivariable analysis, the final model included epidural abscess, cervical or thoracic spine involvement, and number of involved levels. After adjusting for other variables, epidural abscess (odds ratio [OR] 3.04, 95% confidence interval [CI] 1.64–5.63), cervical or thoracic spine involvement (OR 2.03, 95% CI 1.15–3.61), and increasing number of involved levels (OR 1.16, 95% CI 1.01–1.35) were associated with greater odds of surgery. Fifty-two surgical patients (33.8%) underwent decompression alone while 102 (66.2%) underwent decompression with fusion. Of those who underwent decompression alone, 2 (3.8%) of 52 required subsequent fusion due to kyphosis. No patient required hardware removal due to persistent infection. CONCLUSIONS At time of initial presentation of primary spinal infection, the presence of epidural abscess, cervical or thoracic spine involvement, as well as an increasing number of involved spinal levels were potential risk factors for the eventual need for surgery in this study. Additional studies are needed to assess for risk factors for surgery and antibiotic treatment failure.

No abstract

No abstract

No abstract

OBJECTIVE Damage to the thoracolumbar spine can confer significant morbidity and mortality. The Thoracolumbar Injury Classification and Severity Score (TLICS) is used to categorize injuries and determine patients at risk of spinal instability for whom surgical intervention is warranted. However, calculating this score can constitute a bottleneck in triaging and treating patients, as it relies on multiple imaging studies and a neurological examination. Therefore, the authors sought to develop and validate a deep learning model that can automatically categorize vertebral morphology and determine posterior ligamentous complex (PLC) integrity, two critical features of TLICS, using only CT scans. METHODS All patients who underwent neurosurgical consultation for traumatic spine injury or degenerative pathology resulting in spine injury at a single tertiary center from January 2018 to December 2019 were retrospectively evaluated for inclusion. The morphology of injury and integrity of the PLC were categorized on CT scans. A state-of-the-art object detection region-based convolutional neural network (R-CNN), Faster R-CNN, was leveraged to predict both vertebral locations and the corresponding TLICS. The network was trained with patient CT scans, manually labeled vertebral bounding boxes, TLICS morphology, and PLC annotations, thus allowing the model to output the location of vertebrae, categorize their morphology, and determine the status of PLC integrity. RESULTS A total of 111 patients were included (mean ± SD age 62 ± 20 years) with a total of 129 separate injury classifications. Vertebral localization and PLC integrity classification achieved Dice scores of 0.92 and 0.88, respectively. Binary classification between noninjured and injured morphological scores demonstrated 95.1% accuracy. TLICS morphology accuracy, the true positive rate, and positive injury mismatch classification rate were 86.3%, 76.2%, and 22.7%, respectively. Classification accuracy between no injury and suspected PLC injury was 86.8%, while true positive, false negative, and false positive rates were 90.0%, 10.0%, and 21.8%, respectively. CONCLUSIONS In this study, the authors demonstrate a novel deep learning method to automatically predict injury morphology and PLC disruption with high accuracy. This model may streamline and improve diagnostic decision support for patients with thoracolumbar spinal trauma.