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3,425 results for “Anesthesia”
Visual-Evoked Potential (VEP) Event-Related Files from the General Anesthesia and Brain Activity (GABA) Study and Infant Sibling Project (ISP)
<p>HAPPE+ER software was optimized for developmental data using a subset of EEG files from 4-month and 10-month old infants in the General Anesthesia and Brain Activity (GABA) Study. While medically necessary, 1-2 million infants each year undergo general anesthesia – a process that sedates brain activity and impacts early sensory experiences during a time typically characterized by rapid neurocognitive development. The GABA study examines sensory and socioemotional neurodevelopment longitudinally from infancy through childhood in individuals who have and who have never undergone general anesthesia during different windows in the first year of life. The GABA study was carried out in accordance with the recommendations of the Institutional Review Board at Boston Children’s Hospital. All caregivers provided assent for their child’s participation in the GABA study and for the release of the deidentified data. </p> <p>To facilitate the use and understanding of HAPPE+ER software, we have provided a subset of the validation files from the GABA study to serve as a tutorial dataset for how to run event-related potential (ERP) data through this automated processing pipeline. Five files (a.raw - e.raw) are from four 4-month and one 10-month old infants during a pattern reversal visual-evoked potential (VEP) paradigm. Pattern reversal occurred every 500 milliseconds. The pattern stimulus onset is indicated in each file by the code: vep+. Data was collected using a 128-channel EGI HydroCel Geodesic Sensor Net and EGI Net Amps 400, sampled at 1000Hz with an online reference to channel CZ. </p> <p>We have also included a subset of files from the Infant Sibling Project (ISP), an investigation examining infants at high versus low familial risk for autism spectrum disorder over the first 3 years of life. Baseline EEG data was collected while a young child sat in a parent’s lap watching a research assistant blow bubbles or show toys for several minutes. The Infant Sibling Project was carried out in accordance with the recommendations of the Institutional Review Board at Boston University and Boston Children’s Hospital (#X06-08-0374), with written informed consent from all caregivers prior to their child’s participation in the study. All files here have been deidentified, including alteration of exact acquisition dates. Acquisition times have not been altered. For additional information about data collection paradigms, and sample studies published on the larger ISP data set, please see the following references:</p> <ol> <li>Levin, A. R., Varcin, K. J., O’Leary, H. M., Tager-Flusberg, H., and Nelson, C. A. (2017). EEG power at 3 months in infants at high familial risk for autism. J. Neurodev. Disord. 9, 1–13.</li> <li>Gabard-Durnam, L.J., Wilkinson, C., Kapur, K. et al. Longitudinal EEG power in the first postnatal year differentiates autism outcomes. Nat Commun 10, 4188 (2019). <a href="https://doi.org/10.1038/s41467-019-12202-9">https://doi.org/10.1038/s41467-019-12202-9</a></li> </ol> <p>Here we provide a subset of the full dataset with a simulated VEP signal added into the data, as example files for HAPPE+ER. To create these files, we selected a subset of 39 spatially-distributed channels in the baseline EEG files and created sixteen 30-second files using continuous segments of relatively artifact-free (clean) baseline data from the full-length files. Next, from 30-second sections of the same individuals’ EEG that were artifact-laden, we ran ICA and extracted artifact independent components (identified by an expert and labeled artifact by both ICLabel and MARA automated algorithms). We inserted the artifact ICs into that individual’s clean 30-second data segment to create an additional 16 artifact-added files. We then selected a channel from a simulated VEP dataset (included here as simulated_full.set) with a stereotyped and prominent simulated VEP waveform, in this case Oz, and added its timeseries (included here as simulated_singleChan.set) to each channel of the clean and artifact-added files to create two VEP datasets with a known ERP morphology (sim-artifact_a-p and sim-clean_a-p). For additional information about the creation of this simulated data and VEP data with a known ERP morphology, please refer to Monachino et al., in revision; DOI: https://doi.org/10.1101/2021.07.02.450946.</p> <p>Additional files included below are the HAPPE+ER data and pipeline quality metric output spreadsheets for the five GABA study data files for an example run, the output spreadsheet containing the ERP timeseries from the generateERPs script, the .mat file containing the parameter settings for HAPPE+ER for that run, an Excel file with the bad channels for each file, and a tutorial document illustrating the results of this example run. </p>
Mouse_rest_anesthesia
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Fig. 2. Relationships concentration x in Anesthesia of tambaqui Colossoma macropomum (Characiformes: Serrasalmidae) with the essential oils of Aniba rosaeodora and Aniba parviflora and their major compound, linalool
Fig. 2. Relationships concentration x anesthesia induction or recovery time in tambaqui, Colossoma macropomum, exposed to the linalools. a. synthetic linalool; light sedation: y=4.4+(4281/x), r2=0.716, deep sedation: y=-22.5+(12252/x), r2=0.773, deep anesthesia:y=15.3+(17878/x), r2=0.669, recovery: y=43.9+0.66x+0.0015x2, r2=0.712. b. linalool extracted from Aniba rosaeodora; deep sedation: y=29.0+(6010/x), r2=0.784, deep anesthesia: y=-151.3+(60541/x), r2=0.873. Light sedation and recovery: no significant relationship. y = time to reach stage or recovery (s) and x = concentration (µL L-1).
Fig. 1 in Anesthesia and transport of fat snook Centropomus parallelus with the essential oil of Nectandra megapotamica (Spreng.) Mez
Fig. 1. Mortality after transport of fat snook Centropomus parallelus in plastic bags with essential oil from old leaves of Nectandra megapotamica (15 or 30 µL L-1) or ethanol (E) added to the water. W: control with only water. Data presented as means ± SEM (n = 3). a, freshwater - no significant difference between groups or times was observed and the treatments E and 15µL L-1 are superimposed on the first line; b, seawater - values with different superscripts are significantly different (P <0.05). # Significant difference from arrival (0 h).
FIGURE 5 in Linalool chemotype essential oil from Lippia alba in the anesthesia of fat snook (Centropomus parallelus): ventilatory rate, biochemical, antioxidant, and oxidative status parameters
FIGURE 5 | Antioxidant and oxidative stress parameters in the liver after transferring to recovery aquariums of fat snook (Centropomus parallelus) anesthetized with the essential oil from Lippia alba (EOLA). A = GST (glutathione S-transferase). B = SOD (superoxide dismutase). C = CAT (catalase). D = LPO (lipid peroxidation). Data are presented as the mean ± SEM (n = 5 fish per treatment each time). Capital letters indicate significant differences between time points within the same treatment. Lowercase letters indicate significant differences between treatments at the same time point. Two-way ANOVA and Tukey's tests were used to determine statistical significance (P <0.05).
FIGURE 1 in Linalool chemotype essential oil from Lippia alba in the anesthesia of fat snook (Centropomus parallelus): ventilatory rate, biochemical, antioxidant, and oxidative status parameters
FIGURE 1 | Time (s) required for mild and deep anesthesia and recovery in fat snook angelfish (Centropomus parallelus) with increasingly essential oil from Lippia alba (EOLA) concentrations. Data are presented as the mean ± SEM (n = 10 fish per treatment). Different letters indicate significant differences between treatments. One-way ANOVA and Tukey's tests were used to determine statistical significance (P <0.05). Mild and deep anesthesia times showed regression.
FIGURE 4 in Linalool chemotype essential oil from Lippia alba in the anesthesia of fat snook (Centropomus parallelus): ventilatory rate, biochemical, antioxidant, and oxidative status parameters
FIGURE 4 | Blood glucose (A) and whole-body cortisol (B) levels after transferring to recovery aquariums of anesthetized fat snook (Centropomus parallelus) with essential oil from Lippia alba (EOLA). Data are presented as the mean ± SEM (n = 5 fish per treatment each time). Capital letters indicate significant differences between time points within the same treatment. Lowercase letters indicate significant differences between treatments at the same time point. Two-way ANOVA and Tukey's tests were used to determine statistical significance (P <0.05).
FIGURE 2 in Linalool chemotype essential oil from Lippia alba in the anesthesia of fat snook (Centropomus parallelus): ventilatory rate, biochemical, antioxidant, and oxidative status parameters
FIGURE 2 | Time (s) required for mild and deep anesthesia and recovery in fat snook (Centropomus parallelus) exposed to essential oil from Lippia alba (180 µL L−1). Smaller fish = 6.03 ± 0.09 g; 9.30 ± 0.05 cm. Larger fish = 38.49 ± 2.07 g; 16.55 ± 0.26 cm. Data are presented as the mean ± SEM (n = 10 fish per treatment). Different letters indicate significant differences between fish body size classes. One-way ANOVA and Tukey's tests were used to determine statistical significance (P <0.05).
FIGURE 3 in Linalool chemotype essential oil from Lippia alba in the anesthesia of fat snook (Centropomus parallelus): ventilatory rate, biochemical, antioxidant, and oxidative status parameters
FIGURE 3 | Ventilatory rate (VR) of fat snook (Centropomus parallelus) during exposure to the essential oil from Lippia alba (EOLA). Data are presented as the mean ± SEM (n = 8 fish per treatment). Capital letters indicate significant differences between time points within the same treatment. Lowercase letters indicate significant differences between treatments at the same time point. Two-way ANOVA and Tukey's tests were used to determine statistical significance (P <0.05).
10-Hz cross-spectral matrices from intracranial electrode recordings before and after loss of consciousness during propofol-induced general anesthesia
<p>Companion resources associated with the publication, "Propofol disrupts alpha dynamics in functionally distinct thalamocortical networks during loss of consciousness" (in press) (DOI: 10.1073/pnas.2207831120)</p> <p>https://www.biorxiv.org/content/10.1101/2022.04.05.487190v1.full.pdf</p> <p>Contents:</p> <p>1. 10-Hz cross-spectral matrices computed from pre- and post-loss of consciousness epochs (see manuscript for details)</p> <p>2. Channel labels of matrix rows and columns</p> <p>3. Channel coordinates and Freesurfer structural labels in MNI space.</p> <p>4. Structural segmentations of thalamic nuclei in MNI152 space. (Thanks to Fischl Lab, Martinos Center.)</p> <p>Patient demographics are listed in Supporting Information for the PNAS publication.</p> <p>For diffusion images used in the publication, please refer to the WU-Minn Human Connectome Project, using matches listed in the manuscript's Supporting Information.</p>
An Anesthesia-Centered Bundle to Reduce Postoperative Pulmonary Complications: The PRIME-AIR Study
ClinicalTrials.gov study NCT04108130. IPD Sharing: YES. Countries: 1. Publications: 11.
Opioid-Free vs Opioid-Based Anesthesia in Bariatric Surgery
ClinicalTrials.gov study NCT07337135. IPD Sharing: NO. Countries: 1. Publications: 0.
Data associated with "Up and Down states during slow oscillations in slow wave sleep and different levels of anesthesia"
<p>Data collection uploaded is associated with the publication "Up and Down states during slow oscillations in slow wave sleep and different levels of anesthesia" by the same authors in Frontiers in Systems Neuroscience 2021 DOI: 10.3389/fnsys.2021.609645</p> <p>Raw recordings are in *.smr, readable with Spike 2 (Cambridge Electronics Design <a href="http://ced.co.uk/downloads/latestsoftware">CED Downloads | Latest software</a></p> <p> </p>
Ponseti method under general anesthesia is an effective method of treatment for neglected congenital talipes equino varus
<p>This data is associated with an article titled "Ponseti method under general anesthesia is an effective method of treatment for neglected congenital talipes equino varus" </p>
Endoscopic OCT for epidural anesthesia
<h2><strong>Code</strong> [<a href="https://github.com/thepanlab/Endoscopic_OCT_Epidural" target="_blank" rel="noopener">GitHub</a>] | <strong>Publication</strong> [<a href="https://doi.org/10.1038/s41598-022-12950-7" target="_blank" rel="noopener">Scientific Reports'22</a>]</h2> <div> <h3><strong>Abstract</strong></h3> <div> <p>Epidural anesthesia requires injection of anesthetic into the epidural space in the spine. Accurate placement of the epidural needle is a major challenge. To address this, we developed a forward-view endoscopic optical coherence tomography (OCT) system for real-time imaging of the tissue in front of the needle tip during the puncture. We tested this OCT system in porcine backbones and developed a set of deep learning models to automatically process the imaging data for needle localization. A series of binary classification models were developed to recognize the five layers of the backbone, including fat, interspinous ligament, ligamentum flavum, epidural space, and spinal cord. The classification models provided an average classification accuracy of 96.65%. During puncture, it is important to maintain a safe distance between the needle tip and the dura mater. Regression models were developed to estimate that distance based on the OCT imaging data. Based on the Inception architecture, our models achieved a mean absolute percentage error of 3.05% ± 0.55%. Overall, our results validated the technical feasibility of using this novel imaging strategy to automatically recognize different tissue structures and measure the distances ahead of the needle tip during the epidural needle placement.</p> <h3><strong>Description</strong></h3> </div> </div> <p>Epidural anesthesia is a method used to ease the pain of the patients. It is widely used in delivery and many other surgeries. However, there is a failure rate of around 20% due to the lack of needle guidance. Sometimes the needle is punctured too much, leading to some severe complications. Optical coherence tomography (OCT) is a novel technique widely used in medical imaging. It can provide imaging results of subsurface tissue with very high resolution (~several micrometers) in real time. In order to help the epidural anesthesia guidance, we built an OCT endoscope system to help image and recognize the tissue type in front of the needle during the puncture. We fabricated a gradient-index (GRIN) rod lens in front of the OCT scanner to expand the imaging distance, and make it possible to image the inner tissue of samples. To mimic the whole puncture process of the practical surgery, porcine back bones were utilized in our experiments and we inserted our OCT endoscope into five different tissue types: fat; interspinous ligament; ligamentum flavum; epidural space and spinal cord. OCT images of these types were obtained. Additionally, another dataset for epidural space is obtained in which, the distance to the spinal cord is annotated. We then used deep-learning models to perform classification for 1st dataset and regression for 2nd dataset.</p> <p>The dataset contains two folders:</p> <ul> <li>Epidural Classification: Inside each of the subfolders, the images are organized by subjects <ul> <li>Epidural: fat, flavum, ligament and spinal cord tissues</li> <li>Epidural_new_class: epidural space category (or Empty category)</li> </ul> </li> <li>Epidural Regression: Inside each of the subfolders, the images are organized by subjects and distance to spinal cord.</li> </ul> <p><strong>Changelog</strong></p> <p><a href="https://doi.org/10.5281/zenodo.5018581">v2.0</a>: Regression data added</p> <p><a href="https://doi.org/10.5281/zenodo.4891265">v1.0</a>: Classification data only</p>
Blood pressure monitoring during anesthesia induction using PPG morphology features and machine learning
<p>PPG-BP dataset of forty patients undergoing general anesthesia, as described in the corresponding journal publication at PLOS ONE (10.1371/journal.pone.0279419).</p> <p>When using this data, please cite the corresponding journal publication.</p> <p> </p>
Efficacy of Dexmedetomidine Versus Morphine as an Adjunct to Bupivacaine in Caudal Anesthesia.
ClinicalTrials.gov study NCT04445636. IPD Sharing: NO. Countries: 1. Publications: 3.
Induction of Dreaming With EEG and Anesthesia in Healthy Adults
ClinicalTrials.gov study NCT07198711. IPD Sharing: YES. Countries: 1. Publications: 2.
SEdation Versus General Anesthesia for Endovascular Therapy in Acute Ischemic Stroke
ClinicalTrials.gov study NCT03263117. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Evaluation of Modafinil vs Placebo for Treatment of Anesthesia Delayed Emergence in Obstructive Sleep Apnea
ClinicalTrials.gov study NCT02494102. IPD Sharing: Not stated. Countries: 1. Publications: 9.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.