Citations

AbstractObjectiveMost seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient‐specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts.MethodsThis study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0–2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location‐specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of individual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective.ResultsFor the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any individual feature.SignificanceEnvironmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an individual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.

AbstractInduction therapy for multiple myeloma with bortezomib (Velcade), lenalidomide (Revlimid), and dexamethasone (d) (VRd) was traditionally administered as bortezomib given twice weekly on a 3 week cycle. A modified schedule of weekly bortezomib has been adopted over time to decrease treatment burden for patients and reduce treatment‐emergent neuropathy. This study evaluates the response rates and outcomes with different schedules of bortezomib in VRd administered for first‐line treatment for patients with newly diagnosed MM (NDMM). We retrospectively analyzed patients treated with upfront VRd from June 30th 2008 to December 31st 2018, for variations of bortezomib administration. Five hundred and fifty‐five (555) NDMM patients met inclusion criteria; median age 63 years and 61% men. Bortezomib was administered twice weekly every 21 days in 43%, once weekly every 21 days in 41% and once weekly every 28 days in 16%. Though peripheral sensory neuropathy was more frequent with twice weekly dosing (P = .002), this group achieved shorter time to best response (P = .01). Weekly every 21‐day treatment saw higher VGPR or better rates (P = .02). However, with median follow up time of 37 months (IQR 22–56), we found no difference in PFS or OS among the groups. While small differences in response rates were found among the varying administration schedules of bortezomib administration, there was no significant effect on PFS or OS. Given that VRd remains a first line standard of care option for newly diagnosed MM, in the absence of a large trial comparing bortezomib dosing schedule modifications, these results are helpful in supporting current practices of once weekly administration.

AbstractBackgroundUsing an objective mathematical algorithm to assess corticolimbic involvement of neurofibrillary tangle pathology, we identified three AD subtypes: hippocampal sparing (HpSp) AD, typical AD, and limbic predominant AD. Typical AD brains were representative of the expected patterns of hippocampal and cortical involvement as outlined by Braak tangle stage. In contrast, we discovered an extreme phenotype that exists outside of the Braakian‐concept of neurofibrillary tangle patterns. Hippocampal sparing AD cases demonstrate unexpected sparing of the hippocampus relative to severely involved association cortices and limbic predominant AD cases demonstrate inundation of the hippocampus relative to mildly involved association cortices. Using these AD subtypes, our objective was to leverage our understanding of disease spectrum to uncover transcriptomic changes that underlie selective vulnerability of the hippocampus in AD.MethodWe performed RNA‐sequencing in 40 neuropathologically diagnosed AD cases (10 HpSp, 20 Typical and 10 Limbic) and 15 controls to uncover gene expression changes associated with phenotype differences allowing us to prioritize 44 genes for further exploration. Validation with NanoString and quantification with digital pathology were performed in an expanded cohort of 158 AD cases (36 HpSp, 79 Typical and 35 Limbic) and 32 controls. We quantified digital pathology measures of early tangle pathology (CP13), mature tangle pathology (Ab39), amyloid‐β (33.1.1). In addition, we measured cellular markers for microglia (CD68), endothelia (CD34), and astroglia (GFAP). Deep learning based prediction models were employed, which nominated the SLC38A2 gene as a predictor of AD.ResultThe SLC38A2 gene was originally prioritized in our RNA‐sequencing data based upon differential expression between controls and typical AD. We observed upregulation of SLC38A2 in all AD subtypes. RNA‐seq gene expression measures validated well with NanoString for SLC38A2 (R=0.97, p<0.001). Regression analysis identified early tangle (p=0.011) and late tangle measures (p=0.026) as significant predictors of gene expression, but not amyloid measures (p=0.74). Microgliosis approached significance (p=0.052), but with no contribution from endothelial cell burden (p=0.17) or astrogliosis (p=0.58). Age approached significance (p=0.06), but not sex (p=0.88) or APOE‐ε4 status (p=0.10).ConclusionOur data supports consideration of intra‐disease divergence with regard to case stratification and may reveal genes previously masked by heterogeneity of cohorts.

Germ line mutations in H3F3A and H3F3B cause a previously unidentified neurodevelopmental syndrome.

INTRODUCTION: Using the National Cancer Database, we assessed the relationship between facility overall esophageal adenocarcinoma (EAC) case volume and survival. METHODS: We categorized facilities into volume quintiles based on annual EAC patient volume and performed a multivariable Cox proportional hazards regression between facility patient volume and survival. RESULTS: In a cohort of 116,675 patients, facilities with higher vs lower (≥25 vs 1–4 cases) annual EAC patient volume demonstrated improved survival (adjusted hazard ratio: 0.80. 95% confidence interval: 0.70–0.91). DISCUSSION: This robust volume-outcome effect calls for centralization of care for EAC patients at high annual case volume facilities.