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Carisoprodol

Carisoprodol is a centrally acting muscle relaxant used to treat acute musculoskeletal pain.
It works by reducing muscle spasm and relieving pain.
Carisoprodol is often prescribed along with rest, physical therapy, and other measures to provide relief from injuries or other musculoskeletal conditions.
While generally safe when used as directed, Carisoprodol can be habit-forming and should be used with caution.
Researchers can leverage PubCompare.ai to effortlessly locate and compare relevant protocols from literature, pre-prints, and patents, enhancing the reproducibilty and accuracy of their Carisoprodol research findings.

Most cited protocols related to «Carisoprodol»

Constructs used to produce AAV included pGP-AAV-syn-GCaMP-WPRE and the Cre recombinase-activated construct pGP-AAV-syn-flex-GCaMP-WPRE. Virus was injected slowly (30 nL in 5 minutes) at a depth of 250 μm into the primary visual cortex (two sites, 2.5 and 2.9 mm lateral from the lambda suture). For population imaging and electrophysiology (Fig 2-3), AAV2/1-syn-GCaMP-WPRE virus (titer: ∼1011 (link) -1012 (link) genomes/mL) was injected into the visual cortex of C57BL/6J mice (1.5-2 months old)6 (link). For dendritic imaging (Fig 4, 5 and 6a-f), sparse labeling was achieved by injecting a mixture of diluted AAV2/1-syn-Cre particles (titer: ∼1012 (link) genomes/mL, diluted 8000-20,000 fold in PBS) and high titer, Cre-dependent GCaMP6s virus (∼8×1011 (link) genomes/mL). This produces strong GCaMP6 expression in a small subset of neurons (∼3-5 cells in a 250 μm × 250 μm × 250 μm volume), defined by Cre expression56 (link). Both pyramidal (Fig. 4-5) and GABAergic (Fig. 6) neurons were labeled using this approach, but they could be distinguished based on the presence or absence of dendritic spines. Post hoc immunolabeling further identified the imaged cells. For specific labeling of parvalbumin interneurons (Fig. 6g and Supplementary Fig. 12), Cre-dependent GCaMP6s AAV was injected into the visual cortex of PV-IRES-Cre mice57 (link). Individual somata (Supplementary Fig. 12) and dendritic segments could be recognized (Fig. 6 g, h, total length of imaged dendrite: 2.86 mm), but the high labeling density made it difficult to track individual dendrites over long distances.
Publication 2013
Cells Cre recombinase Dendrites Dendritic Spines Genome Internal Ribosome Entry Sites Interneurons Mice, Inbred C57BL Neurons Parvalbumins Striate Cortex Sutures TCL1B protein, human Virus Visual Cortex
Mice were placed on a warm blanket (37°C) and kept anesthetized with 0.5% isoflurane and sedated with chlorprothixene (20-40 μL at 0.33 mg/ml, i.m.)30 (link). Imaging was performed using a custom-built two-photon microscope (designs available at research.janelia.org/Svoboda) equipped with a resonant galvo scanning module (Thorlabs), controlled by ScanImage (scanimage.org)60 (link). The light source was a Mai Tai femtosecond pulsed laser (Spectra-Physics) running at 940 nm. The objective was a 16× water immersion lens (Nikon, 0.8 NA, 3 mm working distance). The power used was 35-50 mW for full field imaging (Fig. 2) and 20-40 mW for higher zoom imaging (Fig. 3-6).
Images were collected at 15 Hz (512 × 512 pixels, 250 μm × 250 μm; Fig. 2) or 60 Hz (256 × 256 pixels, 30 μm × 30 μm; Fig. 3), or 15 Hz (512 × 512 pixels, 30 μm × 30 μm; Fig. 4-5), or 15 Hz (512 × 512 pixels, 30 μm × 30 μm - 100 μm × 100 μm; Fig. 6). For dendritic imaging experiments (Fig. 4-6), fields of view were chosen so that extended dendritic segments were in one focal plane. At the end of each imaging session, z-stacks (1 μm step size) of the recorded cells were acquired. The coordinates of the imaged dendrites relative to the parent somata were recorded. The orientation, curvature, and the branching pattern of the dendrites together with the constellation of spines, helped to precisely identify the same field of view in long-term imaging experiments.
Publication 2013
Carisoprodol Cells Chlorprothixene Dendrites Isoflurane Lens, Crystalline Light Microscopy Mus Submersion Vertebral Column
Adult mice (P42–P56) were deeply anesthetized with isofluorane and transcardially perfused with 10 ml 1× Dulbecco's phosphate-buffered saline (DPBS, Life Technologies), followed by 50 ml 4% paraformaldehyde in 0.1 M phosphate buffer. After perfusion, the brains were removed and post-fixed overnight at 4°C. The brains were embedded in 5% agarose in DPBS, and cut into 50 µm thick coronal sections with a vibratome (Leica VT 1200S). Since DPBS contains a saturating concentration of calcium (0.9 mM) GCaMP brightness will be maximal. Every other section was dehydrated with DPBS and coverslipped with Vectashield mounting medium (H-1400, Vector laboratories). The coverslipped sections were imaged using a slide scanner (Nanozoomer, Hamamatsu). Confocal images (LSM 710, Zeiss) were collected for selected brain regions (Fig. 1 and 2, Fig. S1 and S3) [26] , using an 20× 0.8 NA objective and standard GFP imaging filters. Individual images were tiled and stitched using commercial software (Zeiss).
For a subset of mouse lines (GP4.3, GP4.12, GP5.5, GP5.11, and GP5.17) we visualized neurons using NeuN to measure the fraction of neurons expressing GCaMP. Staining was performed on sections that were not used for quantification of expression. Sections were blocked with 2% BSA and 0.4% Triton X-100 solution for 1 hour at room temperature to prevent nonspecific antibody binding, followed by incubation overnight at 4°C with mouse anti-NeuN primary antibody (1∶500; Millipore, MAB 377) and incubation with Alexa594-conjugated goat-anti-mouse secondary antibody (1∶ 500; Life Technologies, A11032) for 4 hours at room temperature. Sections were mounted on microscope slides with Vectashield mounting medium (H-1400, Vector laboratories).
We analyzed primary motor cortex (M1), primary somatosensory cortex (S1), primary visual cortex (V1) and hippocampus (CA1, CA3, and Dentate Gyrus, DG) using confocal microscopy. For sample images in each area we identified all labeled cells, segmented their somata, and calculated the somatic GCaMP fluorescence brightness for each cell. For cortical regions, cells were grouped into layer 2/3 (L2/3) and layer 5 (L5) cells. We also counted the fraction of GCaMP labeled cells (green channel) as a fraction of the NeuN stained cells (red channel). To compensate for variations of imaging conditions across time (e.g. changes in the excitation light source intensity), images of a fluorescence standard, 3.8 µm fluorescent beads (Ultra Rainbow Fluorescent Particles, Bangs Laboratories), were acquired. The average bead brightness was used to normalize the GCaMP signal.
In addition we performed a coarse analysis of expression levels across numerous brain regions (Table 1; Data S1).
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Publication 2014
Adult Alexa594 anti-c antibody Antibodies, Anti-Idiotypic Brain Buffers Calcium Carisoprodol Cells Cloning Vectors Cortex, Cerebral Diploid Cell Fluorescence Goat Gyrus, Dentate Immunoglobulins Light Microscopy Microscopy, Confocal Motor Cortex, Primary Mus Neurons paraform Perfusion Phosphates Saline Solution Seahorses Sepharose Somatosensory Cortex, Primary Triton X-100
A skeleton analysis method was developed to quantify microglia morphology in immunofluorescent images of fixed brain tissue. Confocal images (21-μm z-stack at 3-μm intervals, Zeiss 510, 40×/1.3 oil objective) were acquired at each ipsilateral and contralateral region as identified in Figure 1A. For skeleton analysis, the maximum intensity projection of the iba-1 positive channel was enhanced to visualize all microglia processes followed by noise de-speckling to eliminate single-pixel background fluorescence. The resulting image was converted to a binary and then skeletonized using Image J software (Figure 1B). The AnalyzeSkeleton plugin (http://imagejdocu.tudor.lu/) was then applied to all skeletonized images to collect data on the number of endpoints per frame (Figure 1B, blue) and process length (Figure 1B, orange). These data were used as measures of microglia morphology based on previous reports showing reduced microglia process branching complexity and process length in response to injury [14 (link)-16 (link)]. In addition, others have assessed the microglia process length of single cells using a similar type of analysis [16 (link)]. The number of cell somas per frame was used to normalize all process endpoints and process lengthes.
Confocal images were acquired from an additional cohort of slices as described above in ipsilateral and matching contralateral regions. Using Image J, the minimum threshold (0–255) was adjusted for each contralateral image to exclude background fluorescence (average minimum across all images was 18.5 ± 5); thresholding values were constant between matching contralateral and ipsilateral regions. The percent area and mean fluorescence intensity for each threshold image were multiplied to result in the total fluorescence intensity (TFI) for each image. Cells were counted in each image to result in TFI/cell.
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Publication 2013
Brain Carisoprodol Fluorescence Fluorescent Antibody Technique Injuries Microglia Physiology, Cell Reading Frames Skeleton Tudor
C57BL/6J mice were trained to run for rewards in real and virtual environments29 (link) while recordings were obtained from MEC. Tetrode recordings were performed with a microdrive and headplate assembly that could be used interchangeably for navigation in a real two-dimensional (2D) arena and a virtual 1D track38 . Whole-cell recordings were obtained as described previously29 (link), while mice ran along virtual 1D tracks. Biocytin fills were used to recover cell morphology and determine soma location. A complete description of the methods is available as Supplementary Information.
Publication 2013
biocytin Carisoprodol Mice, Inbred C57BL Mus

Most recents protocols related to «Carisoprodol»

Tip tracking was performed using Manual Tracking in NIH ImageJ (FIJI build [Schindelin et al., 2012 (link)]). DF were only tracked if they met the following conditions: no contact with axons, neighboring DF, or debris during the time course; emanated from dendrites at least 50 µm away from the center of the soma; clearly visible by brightfield during time course; if they initiated or retracted during imaging, non-existent timepoints were removed from further analysis; buckling and wagging DF were included in tracking. Using the manually tracked positions of the DF base and tip, the image files were then further analyzed with a custom MATLAB script to determine the centerline path along each DF (Mendeley data hyperlink). This script used the fluorescent intensity in either the LifeAct or GFP space-filler channel in the vicinity of the tip and base coordinates to define the average tangent direction of the long axis of the DF by computing the tangent angle q at pixel i using θi=12tan1(yyixxix2xi2y2yi2), where brackets denote the intensity-weighted average over a 15 × 15 pixel domain centered on the ith pixel. The centerline curve (x(s),y(s)) was then determined by solving 2xs2=sinθθs;2ys2=cosθθs
subject to the constraint that the starting and ending positions were the tracked positions of the base and tip of the DF. Using the centerline curves for each DF at each time point, we then calculated the absolute tip displacement, DF length, and mean tip fluorescence intensity and were able to extract the following metrics: average filopodial tip speed calculated as the average of the instantaneous speeds (absolute tip displacement per 5 s interval) between successive timepoints; percent motile, percent of total DF population with average tip speeds greater than 0.0128 µm/s (motile; one pixel displacement or greater per 5 s interval) or less than 0.0128 µm/s (non-motile); percent time motile, the percent of time per DF in which instantaneous speed was greater than 0.0128 µm/s; average length, the distance from base to tip along the centerline curve, median protrusion or retraction rate, the positive or negative change in length between successive timepoints, when instantaneous change in length was greater than ±0.0128 µm/s (motile); mean fluorescence intensity for a circular area of 384 nm radius surrounding the distal DF tip with non-cell background omitted; fluorescence intensity variance, a measure of the spread of intensity values compared to the mean. Fluorescence intensity values were normalized for expression by the minimum local intensity during the duration of imaging. For defining motile versus non-motile filopodia, or substantiative protrusion/retraction rates, a threshold of 0.0128 µm/s was chosen as it represents one pixel (effective size at 100X = 0.064 µm) displacement per 5-s interval and undistinguishable from tracking error. Neurite morphology was measured using the ImageJ plug-in Simple Neurite Tracer (Longair et al., 2011 (link)). Tracings were used to determine the number and length of primary and higher order neurites, and length of the axon (the longest Tau-positive process). Protrusion and spine density was determined by counting proturbences or dendritic spines along a length of dendrite. PSD95 foci analysis was performed by generating a binary mask of foci, and using the automated 2D tracking module in NIS-Elements (Nikon) to follow their trajectories.
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Publication 2023
Axon Dendrites Dendritic Spines Epistropheus Filopodia Fluorescence Neurites Radius Vertebral Column
All methods have been described previously (Hoose et al. 2012 (link); Soma et al. 2014 (link)). Briefly, after early G1 cells were collected, they were monitored at regular time intervals for cell size, budding, or DNA content. Samples were also assayed in downstream procedures, such as nuclear staining, as described in the relevant sections.
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Publication 2023
Carisoprodol Cells
Live imaging of microglial processes was performed on 250 µm coronal brain slices from Nf1 ± or Nf1flox/wt mice and their WT littermates using a custom-built two-photon laser-scanning microscope (Till Photonics, Gräfelfing, Germany). EGFP or eYFP was excited by a Chameleon Ultra II laser (Coherent, Dieburg, Germany) at a wavelength of 940 nm. A 40X water-immersion objective (NA 0.8, Olympus, Hamburg, Germany) was used, with scanned 60 µm thick z-stacks and a step size of 3 µm covering a field of 320 × 320 µm. Laser lesions were set to 40 µm under the slice surface in the cortex by focusing the laser beam, set to a wavelength of 810 nm and to maximum power in the selected imaging volume, and scanned until autofluorescence of the injured tissue was visible. This procedure resulted in lesions of ~ 20 µm in diameter in the middle of the observed region. For the recording of microglia surveillance, no laser lesion was performed. IGOR Pro 6.37 (Lake Oswego, USA) was used for data analysis as in Davalos et al. [13 (link)] and Madry et al. [42 (link)]. The sequences of 3D image stacks were converted into sequences of 2D images by a maximum intensity projection algorithm. Grayscale images were first converted into binary form using a threshold. For quantification of laser lesion-induced movements, microglial response to focal lesion was defined as EGFP + pixel count in a proximal circular region 45 µm around the lesion site over time (Rx(t)). Distal fluorescence of the first time point was determined within a diameter of 45 µm to 90 µm around the lesion site for normalization (Ry(0)). Microglial responses were represented as R(t) = (Rx(t)-Rx(0))/Ry(0). For the quantification of baseline surveillance, cells of interest were individually selected by manually drawing a region of interest (ROI) around an area including all their process extensions throughout the 20 min movie and erasing data around that ROI. Starting with the second frame, we subtracted from each binarized frame the preceding frame and counted the number of pixels < 0 (retracting = PR) and > 0 (extending = PE). The surveillance index for each frame is then given by the sum of PR ad PE. The surveillance index of a given cell was then calculated by averaging the indices of the first 20 images in the movie. For ramification index (RI), we used the equation RI = (peri/area)/(2*sqrt(pi/area)), where peri and area are respectively the perimeter and area of a given cell in pixels. For the quantification of these two parameters, the ImageAnalyzeParticles operation in IGOR Pro 6.37 was applied on binarized images in which all analyzed microglia were manually examined and, if necessary, somata and processes connected.
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Publication 2023
Brain Carisoprodol Cells Chameleons Cortex, Cerebral Fluorescence Laser Scanning Microscopy Microglia Movement Mus Perimetry Reading Frames Submersion Tissues
Morphological analysis of microglia was performed on 3-dimensional fluorescence images using Imaris × 64 version 9.6–9.9 (Bitplane, Zurich, Switzerland) algorithms. Microglial cells in which the nucleus was at least 15 µm away from the image border were selected for analysis. The modules "Filament tracer" and "Surface" were used for microglia reconstruction. A total of 50 cells from 3 different mice were analyzed for each group. The background was minimized with an appropriate filter width (20–40 µm) and the region of the analyzed cell was selected manually. The parameters Filament Length, Filament No, Dendrite Branch Pts, Filament No, Sholl Intersections, and Soma Volume were obtained from the specific values calculated by Imaris. Although tracing was performed automatically by the algorithm, we individually verified that processes originated from one defined cell. False connections were removed manually which were commonly less than 1%. The number of Sholl intersections was defined as the number of process intersecting concentric spheres, defining the spatial distribution of segments as a function of distance from the soma (Sholl analysis). All spheres have their center at the soma (beginning point) with a 5 µm step resolution for the spheres.
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Publication 2023
Carisoprodol Cell Nucleus Cells Cytoskeletal Filaments Dendrites Fluorescence Intersectional Framework Microglia Mus Reconstructive Surgical Procedures
SH-SY5Y cells were seeded 24 h prior to differentiation in 24-well plate to a density of 1 × 103 cells per well. Cells were differentiated in B-27™ Plus neuronal culture system (Life Technologies) supplemented with 20 µM retinoic acid (Merck Life Science), 1% L-Glutamine (Gibco), and 1% penicillin/streptomycin (Gibco). Medium was refreshed every other day. Phase-contrast imaging was done on a Zeiss Axiovert 200 M microscope equipped with a Zeiss AxioCam MR3 camera and 20× phase contrast objective. Three images per well were captured at day 8 of differentiation. To extract the total area covered with neurites and soma in each image, we used a custom-developed ImageJ script to automatically segment both neurites and soma, using combinations of simple image operations that can i) remove noise (noise reduction filters), ii) separate fine from coarse structures (rolling ball algorithm or Fast Fourier Transform), iii) separate bright from dark regions (automatic intensity thresholding) and iv) exclude segmented regions based on size or shape (morphological operations). The resulting segmentation masks were used to calculate the ratio of skeletonized neurites per cell body area. In addition, we manually traced individual neurite structures. To this end, images were converted to 8-bit and analyzed with NeuronJ plugin in ImageJ, a commonly used tool for semiautomatic tracings and measurements of neurites67 (link). Any projection from SH-SY5Y cell body was considered a “primary neurite”, whereas projections branching from primary neurites were considered a “secondary neurite”. Three wells per genotype and three images per well were analyzed, and data from two experiments (repetition) was pooled, resulting in over 600 tracings in total. The overall distribution of primary and secondary neurites lengths was plotted. For each image, the fraction of secondary neurites (over the total) was also calculated. One-Way Anova was used for statistical analysis.
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Publication 2023
Carisoprodol Cell Body Cells Genotype Glutamine M-200 Microscopy Microscopy, Phase-Contrast Neurites neuro-oncological ventral antigen 2, human Neurons Penicillins Streptomycin Tretinoin

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More about "Carisoprodol"

Carisoprodol is a centrally acting muscle relaxant that is often prescribed to treat acute musculoskeletal pain.
It works by reducing muscle spasm and providing relief from injuries or other musculoskeletal conditions.
While generally safe when used as directed, Carisoprodol can be habit-forming and should be used with caution.
Researchers can leverage PubCompare.ai, an AI-powered platform, to effortlessly locate and compare relevant protocols from literature, preprints, and patents, enhancing the reproducibility and accuracy of their Carisoprodol research findings.
The platform can help identify the most effective protocols and products, elevating the quality of Carisoprodol research.
Leveraging tools like MATLAB, FD Rapid GolgiStain Kit, LSM 510, Neurolucida software, Neurolucida, Prism 8, LSM 710, Neurolucida 360, and GraphPad Prism 5 can further enhance the analysis and visualization of Carisoprodol-related data, leading to more insightful findings.
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With the power of intelligent comparison and a user-friendly platform, PubCompare.ai can be a valuable tool in advancing Carisoprodol research and understanding.