BACKGROUND A dramatic improvement in obliteration rates of large, wide-necked aneurysms has been observed after the FDA approved the Pipeline Embolization Device (PED) in 2011. OBJECTIVE To assess the predictors of complications, morbidity, and unfavorable outcomes in a large cohort of patients with aneurysms treated with PED. METHODS A retrospective chart review of a prospectively maintained database for subjects treated with flow diversion from 2010 to 2019. RESULTS A total of 598 aneurysms were treated during a period extending from 2010 to 2019 (84.28% females, mean age 55.5 yr, average aneurysm size 8.49 mm). Morbidity occurred at a rate of 5.8% and mortality at a rate of 2.2%. Ischemic stroke occurred at a rate of 3%, delayed aneurysmal rupture (DAR) at 1.2%, and distal intraparenchymal hemorrhage (DIPH) at 1.5%. On multivariate analysis, the predictor of stroke was aneurysm size >15 mm. Predictors of DAR were previous subarachnoid hemorrhage (SAH), increasing aneurysm size, and posterior circulation aneurysm. Predictors of DIPH were using more than 1 PED and baseline P2Y12 value. Predictors of in-stent stenosis were the increasing year of treatment and balloon angioplasty, whereas increasing age and previous treatment were negatively associated with in-stent stenosis. Predictors of morbidity were posterior circulation aneurysms, increasing aneurysm size, and hypertension, and incidental aneurysm diagnosis was protective for morbidity. CONCLUSION Flow diversion is a safe and effective treatment option for aneurysms. A better understanding of predictive factors of complications, morbidity, and functional outcomes is of high importance for a more accurate risk assessment.

Visceral pain (VP) is a global problem with complex etiologies and limited therapeutic options.Guanylyl cyclase C (GUCY2C), an intestinal receptor producing cyclic GMP which regulates luminal fluid secretion, has emerged as a therapeutic target for VP. Indeed, FDA-approved GUCY2C agonists ameliorate VP in patients with chronic constipation syndromes, although analgesic mechanisms remain obscure. Here, we reveal that intestinal GUCY2C is selectively enriched in neuropod cells, a type of enteroendocrine cell that synapses with submucosal neurons in mice and humans. GUCY2C High neuropod cells associate with co-cultured dorsal root ganglia neurons and induce hyperexcitability, reducing the rheobase and increasing the resulting number of evoked action potentials. Conversely, the GUCY2C agonist linaclotide eliminated neuronal hyperexcitability produced by GUCY2C-sufficient, but not GUCY2C-deficient, neuropod cells, an effect independent of bulk epithelial cells or extracellular cGMP. Genetic elimination of intestinal GUCY2C amplified nociceptive signaling and VP that was comparable to chemicallyinduced VP but refractory to linaclotide. Importantly, eliminating GUCY2C selectively in neuropod cells also increased nociceptive signaling and VP that was refractory to linaclotide. In the context of loss of GUCY2C hormones in patients with VP, these observations suggest a specific role for neuropod GUCY2C signaling in the pathophysiology and treatment of these pain syndromes.

Demyelinating disease is a critical neurological disease, and there is still a lack of effective treatment methods. In the past two decades, stem cells have emerged as a novel therapeutic...

BACKGROUND Although World Health Organization (WHO) grade I meningiomas are considered “benign” tumors, an elevated Ki-67 is one crucial factor that has been shown to influence tumor behavior and clinical outcomes. The ability to preoperatively discern Ki-67 would confer the ability to guide surgical strategy. OBJECTIVE In this study, we develop a machine learning (ML) algorithm using radiomic feature analysis to predict Ki-67 in WHO grade I meningiomas. METHODS A retrospective analysis was performed for a cohort of 306 patients who underwent surgical resection of WHO grade I meningiomas. Preoperative magnetic resonance imaging was used to perform radiomic feature extraction followed by ML modeling using least absolute shrinkage and selection operator wrapped with support vector machine through nested cross-validation on a discovery cohort (n = 230), to stratify tumors based on Ki-67 <5% and ≥5%. The final model was independently tested on a replication cohort (n = 76). RESULTS An area under the receiver operating curve (AUC) of 0.84 (95% CI: 0.78-0.90) with a sensitivity of 84.1% and specificity of 73.3% was achieved in the discovery cohort. When this model was applied to the replication cohort, a similar high performance was achieved, with an AUC of 0.83 (95% CI: 0.73-0.94), sensitivity and specificity of 82.6% and 85.5%, respectively. The model demonstrated similar efficacy when applied to skull base and nonskull base tumors. CONCLUSION Our proposed radiomic feature analysis can be used to stratify WHO grade I meningiomas based on Ki-67 with excellent accuracy and can be applied to skull base and nonskull base tumors with similar performance achieved.

No abstract