SCENIC was run on all the datasets using the expression matrices provided by the authors (downloaded from GEO or the authors website), including only the cells that passed their quality control, and the default gene filtering for GENIE3 (which resulted in 12-15k genes). The standard SCENIC workflow was run on all datasets (the version at the time of publication is available as supplementary file, updated versions can be found at http://scenic.aertslab.org ). A more detailed description of the datasets and the any peculiarities for each analysis are available in Supplementary Note 1 . Here we provide a brief description of the datasets:
Mouse cortex and hippocampus (Zeisel et al.9 (link), GSE60361): single-cell RNA-seq of 3005 brain cells of juvenile mice (21-31 days old). It contains the main cell types in hippocampus and somatosensory cortex, namely neurons (pyramidal excitatory neurons, and interneurons), glia (astrocytes, oligodendrocytes, microglia), and endothelial cells. Expression matrix units: UMI counts.
Human neurons (Lake et al. 11 (link)): single-nuclei RNA-seq of 3083 neuronal cells from a normal human brain (retrieved postmortem from a 51-year old female, from six different Brodmann areas). Expression matrix units: TPM.
Human brain (Darmanis et al.36 (link), GSE67835): scRNA-seq from 466 cells from adult and fetal human brains. The fetal samples were taken from four different individuals at 16 to 18 weeks post-gestation. The adult brain samples were taken from healthy temporal lobe tissue from 8 different patients (21 - 63 years old) during temporal lobectomy surgery for refractory epilepsy and hippocampal sclerosis. Expression matrix units: logged CPM.
Mouse oligodendrocytes (Marques et al. 37 (link), GSE75330): scRNA-seq data of 5069 cells from the oligodendrocyte lineage. Cells were obtained from several different mouse strains and isolated from ten different regions of the anterior-posterior and dorsal-ventral axis of the mouse juvenile and adult CNS; including white and grey matter. Expression matrix units: UMI counts.
Oligodendroglioma (Tirosh et al. 38 (link), GSE70630): scRNA-seq expression profiles for 4347 cells from 6 untreated grade II oligodendroglioma tumors with either IDH1 or IDH2 mutation, and 1p/19q co-deletion. Only the tumoral cells were used for the analysis (selected by the authors based on CNV profile). Expression matrix units: log2(TPM+1).
Melanoma (Tirosh et al. 13 (link), GSE72056): scRNA-seq of 1252 melanoma cells from 14 different tumors. These include only the cells that are labeled as malignant by the authors, based on their CNV profiles. Expression matrix units: log2(TPM/10+1).
Mouse retina (Macosko et al. 39 (link), GSE63472): scRNA-seq data of 44808 cells obtained through Drop-seq from mouse retina (14 days post-natal). Expression matrix units: log((UMI counts per gene in a cell/Total UMI counts in cell)*10000)+1)].
Embryonic mouse brain (10X Genomics): Chronium Megacell demonstration dataset containing 1,306,127 cells from cortex, hippocampus and subventricular zone of two E18 mice (strain: C57BL/6).
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Drug Resistant Epilepsy
Drug Resistant Epilepsy
Drug resistant epilepsy is a challenging neurological condition where seizures persist despite optimal treatment with antiepileptic drugs.
This form of epilepsy presents unique challenges, as patients often require specialized care and advanced therapies to manage their symptoms.
The condition is characterized by recurrent seizures that are unresponsive to standard medications, leading to significant impact on quality of life.
Effective management of drug resistant epilepsy requires a comprehensive, multidisciplinary approach, incorporating the latest research and innovations in epilepsy care.
PubCompare.ai is revolutionizing this field by leveraging AI-driven protocol optimization to streamline the identification of the most promising treatments and research protocols, empowering scientists and clinicians to deliver better outcomes for patients struggling with this debilitating form of epilepsy.
This form of epilepsy presents unique challenges, as patients often require specialized care and advanced therapies to manage their symptoms.
The condition is characterized by recurrent seizures that are unresponsive to standard medications, leading to significant impact on quality of life.
Effective management of drug resistant epilepsy requires a comprehensive, multidisciplinary approach, incorporating the latest research and innovations in epilepsy care.
PubCompare.ai is revolutionizing this field by leveraging AI-driven protocol optimization to streamline the identification of the most promising treatments and research protocols, empowering scientists and clinicians to deliver better outcomes for patients struggling with this debilitating form of epilepsy.
Most cited protocols related to «Drug Resistant Epilepsy»
Adult
Astrocytes
Autopsy
Brain
Cell Nucleus
Cells
Cortex, Cerebral
Deletion Mutation
Drug Resistant Epilepsy
Embryo
Endothelial Cells
Epistropheus
Females
Fetus
Genes
Gray Matter
Hippocampal Sclerosis
Homo sapiens
IDH2, human
Interneurons
Malignant Neoplasms
Melanoma
Microglia
Mus
Mutation
Neoplasms
Neuroglia
Neurons
Oligodendroglia
Oligodendroglioma
Operative Surgical Procedures
Patients
Pyramidal Cells
Retina
Seahorses
Single-Cell RNA-Seq
Somatosensory Cortex
Strains
Subventricular Zone
Temporal Lobe
Tissues
Cortex, Cerebral
Craniotomy
Cranium
Drug Resistant Epilepsy
Dura Mater
Ethics Committees, Research
General Anesthesia
Hemorrhage
Occipital Lobe
Operative Surgical Procedures
Ovum Implantation
Parietal Lobe
Patients
Platinum
Seizures
Silastic
Woman
Craniotomy
Drug Resistant Epilepsy
Electrocorticography
Operative Surgical Procedures
Patients
Subdural Space
Biopsy human brain tissue containing the anterior temporal lobe and the hippocampus were obtained from surgery for medically refractory epilepsy. All specimens were collected with written patient consent and ethical approval from the Northern X Ethics Committee (biopsy tissue) and the University of Auckland Human Participants Ethics Committee. Eleven biopsy specimens (mean age 45 years) were collected from the surgical theatre at the Auckland City Hospital and transported to our laboratory in Ca2+ and Mg2+ free ice-cold Hank’s balanced salt solution (HBSS; Gibco). Tissue containing the periventricular zone and the hippocampus was dissected and dissociated prior to being digested in HBSS containing 2.5 U/mL papain (Worthington) and 100 U/mL DNase 1 (Invitrogen) for 20 minutes at 37°C with gentle rotation, which included a gentle trituration step at 10 minutes. Enzymatic digestion was halted by the addition of NPC proliferation media; DMEM:F12 containing B27 (Invitrogen), Penicillin/Streptomycin (Gibco), GlutaMAX (Invitrogen), 40 ng/mL FGF-2 (Peprotech), 40 ng/mL EGF (Peprotech) and 2 µg/mL Heparin (Sigma). Cells were collected by centrifugation (170 g×10 minutes), resuspended in the NPC proliferation media and plated onto un-coated T25 culture flasks (Nunc). The following day, culture flasks were gently agitated to detach any loosely adhered cells and all the media was collected and replated onto a fresh T25 culture flask. Media was half changed every 2–3 days and cultures were serially passaged every 20–30 days. Out of the 11 specimens, 9 showed sustained growth (>3 passages) in NPC proliferation media and were used for experimentation.
Human lung fibroblasts were cultured as previously published [21] (link) as a monolayer on un-coated tissue culture flasks in DMEM:F12 base medium supplemented with 10% fetal bovine serum (Gibco), Penicillin/Streptomycin and GlutaMAX.
Human lung fibroblasts were cultured as previously published [21] (link) as a monolayer on un-coated tissue culture flasks in DMEM:F12 base medium supplemented with 10% fetal bovine serum (Gibco), Penicillin/Streptomycin and GlutaMAX.
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Anterior Temporal Lobe
Biopsy
Brain
Cells
Centrifugation
Common Cold
Deoxyribonucleases
Digestion
Drug Resistant Epilepsy
Enzymes
Ethics Committees
Fetal Bovine Serum
Fibroblast Growth Factor 2
Fibroblasts
Hemoglobin, Sickle
Heparin
Homo sapiens
Lung
Operative Surgical Procedures
Papain
Patients
Penicillins
Seahorses
Sodium Chloride
Streptomycin
Tissues
Action Potentials
Amygdaloid Body
Brain
Conditioning, Psychology
Cortex, Cerebral
Drug Resistant Epilepsy
Lobe, Frontal
Operative Surgical Procedures
Parietal Lobe
Patients
Seahorses
Seizures
Most recents protocols related to «Drug Resistant Epilepsy»
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Drug Resistant Epilepsy
Ethics Committees, Research
Females
Patients
Seizures
Seizures, Focal
Woman
We sought institutional approval (Federal Center of Neurosurgery, Tyumen, Russia) for the use of IoUS as our neuronavigational tool during epilepsy surgery. The study model was to retrospectively analyze and descriptively report the patients who had been diagnosed with refractive epilepsy and underwent IoUS-guided epileptogenic tissue resection from January 2015 to June 31, 2020 with subsequent postoperative histological confirmation of FCD type I. In instances where histological results were inconclusive, specimens were sent to an independent histology laboratory for confirmation.
Drug Resistant Epilepsy
Epilepsy
Neuronavigation
Neurosurgical Procedures
Operative Surgical Procedures
Patients
Tissues
Patients included had to satisfy the following criteria: (1) be confirmed with drug-resistant epilepsy, following correctly prescribed and adherently taken two AEDs; (2) seizures, depicted by either scalp or invasive electro-encephalogram (EEG), the latter employing either subdural or invasive electrodes; (3) have had brain MRI imaging; (4) histologically confirmed FCD type I; and (5) postoperative follow-up ≥6 months.
Automated External Defibrillators
Brain
Drug Resistant Epilepsy
Patients
Scalp
Seizures
Subdural Space
The in vivo portion of this study was approved by the Institutional Review Board of the Children’s Hospital of Orange County (CHOC). Informed consent was obtained prior to involvement in the study. Three human subjects with medically intractable epilepsy were each implanted with a high-density 8 × 8 subdural grid of intracerebral EEG electrodes (Ad-Tech FG64C-MP03X-000) in the clinically determined seizure onset zone (SOZ) as part of phase 2 pre-surgical invasive monitoring. Patient information is given in table 1 . Each electrode had an exposed surface area of 1.08 mm2 and electrode spacing was 3 mm center-to-center (this is the same grid used for the in vitro experiment in section 3.1 , so we will similarly refer to these as the ‘small’ electrodes). The effective surface area was changed by electrically shorting adjacent electrodes in groups of two and four, thereby mimicking larger surface areas of 2.16 mm2 (‘pair’ electrodes) and 4.32 mm2 (‘quad’ electrodes), respectively (figure 3 (A)). This was done by connecting jumper wires to the paired electrodes at the jack box outside the patient’s body (figure 3 (B)). The jack box combines the individual electrode wires into an integrated cable before connecting to the amplifier. A quick-release connector enabled rapid reconfiguration of the electrode shorting, minimizing disruption to the patient’s recording. The jack box and jumper wires were placed in a Faraday cage to minimize electrical interference.
We collected 20 min iEEG recordings for each of three different electrode surface areas (small, pair, quad) from a grid in a static brain location while the subjects were sleeping. The sampling rate was 5 kHz, and the data were referenced to the common average of the 8 × 8 grid. The iEEG data were high pass filtered using a zero phase FIR filter at 1 Hz and notch filtered at 60 Hz, 120 Hz, and 180 Hz to remove electrical line noise before analysis. All analysis was done using custom code in MATLAB 2018b.
Similar to the in vitro study, we compared the results to the theoretical circuit model by generating ‘simulated pair’ electrode signals (consisting of the mathematical average of adjacent pairs of small electrodes) and ‘simulated quad’ electrodes (the mathematical average of four adjacent small electrodes). The averaging of signals was done on the raw iEEG data and the data were then re-referenced and filtered as described above. We compared the pair and quad electrode recordings to their associated simulated signals.
We collected 20 min iEEG recordings for each of three different electrode surface areas (small, pair, quad) from a grid in a static brain location while the subjects were sleeping. The sampling rate was 5 kHz, and the data were referenced to the common average of the 8 × 8 grid. The iEEG data were high pass filtered using a zero phase FIR filter at 1 Hz and notch filtered at 60 Hz, 120 Hz, and 180 Hz to remove electrical line noise before analysis. All analysis was done using custom code in MATLAB 2018b.
Similar to the in vitro study, we compared the results to the theoretical circuit model by generating ‘simulated pair’ electrode signals (consisting of the mathematical average of adjacent pairs of small electrodes) and ‘simulated quad’ electrodes (the mathematical average of four adjacent small electrodes). The averaging of signals was done on the raw iEEG data and the data were then re-referenced and filtered as described above. We compared the pair and quad electrode recordings to their associated simulated signals.
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Brain
Child
Drug Resistant Epilepsy
Electricity
Ethics Committees, Research
Homo sapiens
Human Body
Operative Surgical Procedures
Patients
Seizures, Focal
Subdural Space
We also wanted to characterize the impact of electrode size on the morphology of transient electrographic events. Because the study subjects had refractory epilepsy, we focused on interictal epileptiform discharges, i.e. interictal spikes. For this analysis, 20 min segments of data were used, each one clipped from the long-term recording while the patient was sleeping, between midnight and 12:30 am. Interictal spikes were manually marked in the iEEG data in the small electrode configuration for each subject under the supervision of a board-certified epilepsy specialist (Daniel Shrey). We then simulated each spike in the pair and quad electrode configurations by mathematically averaging the corresponding small electrode data. We defined the SNR of each spike as the signal to background amplitude ratio. The amplitude of the spike was measured as the difference between the minimum and maximum voltages recorded over the duration of the spike. To calculate the background amplitude, a one-second interval around the spike, not containing the spike, was considered. The signal in this window was rectified, and the average of the rectified signal was defined as the baseline amplitude. The SNRs were compared across the three electrode configurations and each spike was classified into one of three types: type S, in which a small electrode had the highest SNR, type P, in which a pair electrode had the highest SNR, and type Q, in which a quad electrode had the highest SNR. The spatial spread, defined as the combined area of the electrodes in which the SNR of the signal exceeded 1.5 during the time of the spike, was also calculated for each spike.
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Drug Resistant Epilepsy
Epilepsy
Patients
Supervision
Transients
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More about "Drug Resistant Epilepsy"
Drug Resistant Epilepsy (DRE) is a challenging neurological condition where seizures persist despite optimal treatment with antiepileptic drugs (AEDs).
This form of refractory epilepsy presents unique challenges, as patients often require specialized care and advanced therapies to manage their symptoms.
The condition is characterized by recurrent seizures that are unresponsive to standard medications, leading to significant impact on quality of life.
Effective management of DRE requires a comprehensive, multidisciplinary approach, incorporating the latest research and innovations in epilepsy care.
This includes the use of advanced recording systems, such as the Nicolet ONE clinical amplifier, the 128-channel Harmonie system (Stellate), and the 256-channel amplifier and Z-series digital signal processor board, to accurately diagnose and monitor seizure activity.
Additionally, the use of cell culture media like DMEM, along with agents like Collagenase type II, Penicillin, and Streptomycin, may be employed in research to better understand the underlying mechanisms of DRE.
Genetic factors, such as mutations in ion channels or neurotransmitter receptors, can also contribute to the development of DRE.
Molecular techniques, including Primer Premier V6.0 and SYBR Green I, may be utilized to identify these genetic biomarkers and develop personalized treatment strategies.
Furthermore, advanced therapies like deep brain stimulation (DBS) using specialized DBS leads are being explored to manage drug-resistant seizures.
PubCompare.ai is revolutionizing the field of DRE research by leveraging AI-driven protocol optimization to streamline the identification of the most promising treatments and research protocols.
This empowers scientists and clinicians to deliver better outcomes for patients struggling with this debilitating form of epilepsy.
This form of refractory epilepsy presents unique challenges, as patients often require specialized care and advanced therapies to manage their symptoms.
The condition is characterized by recurrent seizures that are unresponsive to standard medications, leading to significant impact on quality of life.
Effective management of DRE requires a comprehensive, multidisciplinary approach, incorporating the latest research and innovations in epilepsy care.
This includes the use of advanced recording systems, such as the Nicolet ONE clinical amplifier, the 128-channel Harmonie system (Stellate), and the 256-channel amplifier and Z-series digital signal processor board, to accurately diagnose and monitor seizure activity.
Additionally, the use of cell culture media like DMEM, along with agents like Collagenase type II, Penicillin, and Streptomycin, may be employed in research to better understand the underlying mechanisms of DRE.
Genetic factors, such as mutations in ion channels or neurotransmitter receptors, can also contribute to the development of DRE.
Molecular techniques, including Primer Premier V6.0 and SYBR Green I, may be utilized to identify these genetic biomarkers and develop personalized treatment strategies.
Furthermore, advanced therapies like deep brain stimulation (DBS) using specialized DBS leads are being explored to manage drug-resistant seizures.
PubCompare.ai is revolutionizing the field of DRE research by leveraging AI-driven protocol optimization to streamline the identification of the most promising treatments and research protocols.
This empowers scientists and clinicians to deliver better outcomes for patients struggling with this debilitating form of epilepsy.