Incidence and clinical significance of epidural fluid signal on spinal magnetic resonance imaging after image-guided lumbar puncture in adults.

PURPOSE: We determined the incidence of epidural fluid signal on spinal magnetic resonance imaging (MRI) after image-guided lumbar puncture (LP) in adults. We correlated those imaging findings with clinical status. METHODS: We searched our institution's medical records from January 2013 through April 2020 to identify adult patients who underwent image-guided LP and postdural puncture MRI. We examined the incidence of epidural fluid signal intensity in adults after image-guided dural puncture, characterized its imaging features, and evaluated its associated clinical factors. RESULTS: Of 91 patients who underwent image-guided dural puncture and subsequent spinal MRI within 7 days, 18 (20%) demonstrated epidural fluid signal on postdural puncture MRI. Univariate analysis showed that larger needle size correlated with signal presence (4/8 [50%] LP with a 20-gauge needle vs 13/82 [16%] with a 22-gauge needle, P = 0.03). The odds of observing epidural fluid signal on postdural puncture MRI decreased by 8% per 1-year increase in age (P < 0.001). Postdural puncture symptoms did not differ between those with and without epidural fluid signal intensity. CONCLUSION: Epidural fluid signal on MRI in adults may be an incidental finding more commonly observed in younger patients after dural puncture with larger needles, without a significant correlation with symptomatology.

Three-way comparative study of endoscopic ultrasound-guided transmural gallbladder drainage using lumen-apposing metal stents versus endoscopic transpapillary drainage versus percutaneous cholecystostomy for gallbladder drainage in high-risk surgical patients with acute cholecystitis: clinical outcomes and success in an International, Multicenter Study.

BACKGROUND: Percutaneous cholecystostomy tube (PTGBD), endoscopic retrograde cholangiopancreatography with transpapillary gallbladder drainage (TP), and endoscopic ultrasound-guided transmural gallbladder drainage (EGBD) using lumen-apposing metal stents (LAMS) have been offered for gallbladder decompression for acute cholecystitis in high-risk surgical patients. Yet, there are limited data comparing these therapies. Our aim was to compare the safety and efficacy of EGBD to TP and PTGBD for gallbladder drainage. METHODS: We retrospectively collected high-risk surgical patients from six centers with acute cholecystitis who underwent gallbladder drainage by EGBD, TP, or PTGBD. Data included technical success (gallbladder drainage), clinical success (acute cholecystitis resolution), adverse events (AE), and follow-up. RESULTS: From 2010 to 2016, 372 patients underwent gallbladder drainage, with 146 by PTGBD, 124 by TP, and 102 drained by EGBD. Technical (98% vs. 88% vs. 94%; p = 0.004) and Clinical (97% vs. 90% vs. 80%; p < 0.001) success rates were significantly higher with PTGBD and EGBD compared to TP. PTGBD group had statistically significantly higher number of complications as compared to EGBD and TP groups (2 0% vs. 2% vs. 5%; p = 0.01). Mean hospital stay in the EGBD group was significantly less than TP and PTGBD (16 vs. 18 vs. 19 days; p = 0.01), while additional surgical intervention was significantly higher in the PTGBD group compared to the EGBD and TP groups (49% vs. 4% vs. 11%; p < 0.0001). CONCLUSIONS: EGBD with LAMS is an effective and safer alternative to TP and PTGBD for treatment of patients with acute cholecystitis who cannot undergo surgery. EGBD with LAMS has significantly lower overall AEs, hospital stay, and unplanned admissions compared to PTGBD. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01522573.

In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images.

Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call "in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.

The roles of visual expertise and visual input in the face inversion effect: behavioral and neurocomputational evidence.

Research has shown that inverting faces significantly disrupts the processing of configural information, leading to a face inversion effect. We recently used a contextual priming technique to show that the presence or absence of the face inversion effect can be determined via the top-down activation of face versus non-face processing systems [Ge, L., Wang, Z., McCleery, J., & Lee, K. (2006). Activation of face expertise and the inversion effect. Psychological Science, 17(1), 12-16]. In the current study, we replicate these findings using the same technique but under different conditions. We then extend these findings through the application of a neural network model of face and Chinese character expertise systems. Results provide support for the hypothesis that a specialized face expertise system develops through extensive training of the visual system with upright faces, and that top-down mechanisms are capable of influencing when this face expertise system is engaged.

Learning grammatical structure with Echo State Networks.

Echo State Networks (ESNs) have been shown to be effective for a number of tasks, including motor control, dynamic time series prediction, and memorizing musical sequences. However, their performance on natural language tasks has been largely unexplored until now. Simple Recurrent Networks (SRNs) have a long history in language modeling and show a striking similarity in architecture to ESNs. A comparison of SRNs and ESNs on a natural language task is therefore a natural choice for experimentation. Elman applies SRNs to a standard task in statistical NLP: predicting the next word in a corpus, given the previous words. Using a simple context-free grammar and an SRN with backpropagation through time (BPTT), Elman showed that the network was able to learn internal representations that were sensitive to linguistic processes that were useful for the prediction task. Here, using ESNs, we show that training such internal representations is unnecessary to achieve levels of performance comparable to SRNs. We also compare the processing capabilities of ESNs to bigrams and trigrams. Due to some unexpected regularities of Elman's grammar, these statistical techniques are capable of maintaining dependencies over greater distances than might be initially expected. However, we show that the memory of ESNs in this word-prediction task, although noisy, extends significantly beyond that of bigrams and trigrams, enabling ESNs to make good predictions of verb agreement at distances over which these methods operate at chance. Overall, our results indicate a surprising ability of ESNs to learn a grammar, suggesting that they form useful internal representations without learning them.