Reliable single unit neuron recordings from chronically implanted microelectrode arrays (MEAs) are essential tools in the field of neural engineering. However, following implantation, MEAs undergo a foreign body response that functionally isolates them from the brain and reduces the useful longevity of the array. We tested a novel electrodeposited platinum-iridium coating (EPIC) on penetrating recording MEAs to determine if it improved recording performance. We chronically implanted the arrays in rats and used electrophysiological and histological measurements to compare quantitatively the single unit recording performance of coated vs. uncoated electrodes over a 12-week period. The coated electrodes had substantially lower impedance at 1 kHz and reduced noise, increased signal-to-noise ratio, and increased number of discernible units per electrode as compared to uncoated electrodes. Post-mortem immunohistochemistry showed no significant differences in the immune response between coated and uncoated electrodes. Overall, the EPIC arrays provided superior recording performance than uncoated arrays, likely due to lower electrode impedance and reduced noise.
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http://dx.doi.org/10.1016/j.biomaterials.2019.03.017 | DOI Listing |
Genet Med
December 2024
Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN; Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN. Electronic address:
Purpose: The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results. We performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) with genetic data to understand which decisions may affect performance.
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December 2024
Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran.
Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke.
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December 2024
Department of Neurology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.
The aim of this study was to evaluate how COVID-19 affected acute stroke care and outcome in patients with acute ischemic or hemorrhagic stroke. We performed a retrospective analysis on patients who were admitted with acute ischemic (AIS) or hemorrhagic (ICH) stroke from September 2020 to May 2021 with and without COVID-19. We recorded demographic and clinical data, imaging parameters, functional outcome and mortality at one year.
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December 2024
Xi'an Shiyou University School of Electronic Engineering, Xi'an, 710065, China.
The expressway green channel is an essential transportation policy for moving fresh agricultural products in China. In order to extract knowledge from various records, this study presents a cutting-edge approach to extract information from textual records of failure cases in the vertical field of expressway green channel. We proposed a hybrid approach based on BIO labeling, pre-trained model, deep learning and CRF to build a named entity recognition (NER) model with the optimal prediction performance.
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December 2024
Department of Radiotherapy & Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, JiangSu Province, China.
This study aims to assess the predictive value of certain markers of inflammation in patients with locally advanced or recurrent/metastatic cervical cancer who are undergoing treatment with anti-programmed death 1 (PD-1) therapy. A total of 105 patients with cervical cancer, who received treatment involving immunocheckpoint inhibitors (ICIs), were included in this retrospective study. We collected information on various peripheral blood indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI).
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