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> Anatomy > Cell Component > Cytoskeletal Filaments

Cytoskeletal Filaments

Cytoskeletal Filaments are structural components within cells that provide shape, support, and facilitate intracellular transport.
These filamentous structures include actin filaments, intermediate filaments, and microtubules, which play crucial roles in cellular processes like cell division, motility, and signaling.
Explore the world of these dynamic cytoskeletal elements with PubCompare.ai, the leading AI-driven platform for optimizing scientific protocols.
Leveragee powerful search and comparison tools to identify the most effective methods and products for your cytoskeletal filaments research, taking your study to new heights.

Most cited protocols related to «Cytoskeletal Filaments»

1
Negative Staining Procedure for EM
Unless stated otherwise, specimens were prepared for EM using the conventional negative staining procedure. Briefly, a 2.5 ml drop of sample solution was adsorbed to a glow-discharged carbon-coated copper grid, washed with two drops of deionized water, and stained with two drops of freshly prepared 0.75% uranyl formate.
Unless stated otherwise, samples were imaged at room temperature using a Philips Tecnai T12 electron microscope equipped with an LaB6 filament and operated at an acceleration voltage of 120 kV. Images were taken at a magnification of 52,000x and a defocus value of 1.5 mm on Kodak SO-163 film using low-dose procedures. Films were developed for 12 minutes with fullstrength Kodak D-19 developer at 20°C. All micrographs were visually inspected with a laser diffractometer, and only drift-free images were selected for digitization with a Zeiss SCAI scanner using a step size of 7 mm. Micrographs were binned over 3 ¥ 3 pixels to yield a pixel size of 4.04 Å on the specimen level.
Ohi M., Li Y., Cheng Y, & Walz T. (2004). Negative Staining and Image Classification – Powerful Tools in Modern Electron Microscopy. Biological Procedures Online, 6, 23-34.
Publication 2004
Acceleration Carbon Copper Cytoskeletal Filaments Electron Microscopy uranyl formate
2
Comparative Assessment of Mechanosensory Thresholds
All prospective mechanosensitivity experiments with animals were conducted in accordance with the guidelines from the Canadian Council on Animal Care and approval of the Laval University Animal Care Committee. PWT was measured in adult male C57BL/6 mice using von Frey filaments 2 through 9. Mice were placed in acrylic chambers (5.5 × 10 cm) suspended above a wire mesh grid and allowed to acclimatize to the testing apparatus for 1 hour prior to experiments. When the mouse was not moving the von Frey filaments were pressed against the plantar surface of the paw until the filament buckled and held for a maximum of 3 seconds. A positive response was noted if the paw was sharply withdrawn on application of the filament. Flinching immediately upon removal of the filament was also considered a positive response as previously described [3 (link)]. Testing began with filament number 5 and progressed according to an up-down method. Mice were randomly assigned to be tested either with the method of Chaplan et al. [3 (link)] or SUDO. After the first measurement of PWT, a second measurement of PWT was conducted using the alternate test. In some prospective experiments, hyperalgesia was induced by intraplantar injection of capsaicin (0.5% w/v, 5 μl) or complete Freund’s adjuvant (CFA; 10 μl) in one hind paw under brief anesthesia with isoflurane (<3 minutes, 4% isoflurane). PWT was measured 3 hours after intraplantar injection of either compound and again 3 days after injection of CFA. The PWT estimates derived using SUDO or the method of Chaplan et al. [3 (link)] for each condition were compared using a paired t-test in Graphpad Prism 5.
Bonin R.P., Bories C, & De Koninck Y. (2014). A simplified up-down method (SUDO) for measuring mechanical nociception in rodents using von Frey filaments. Molecular Pain, 10, 26.
Publication 2014
Adult Anesthesia Animal Care Committees Animals ARID1A protein, human Capsaicin Cytoskeletal Filaments Freund's Adjuvant Hyperalgesia Isoflurane Males Mice, House Mice, Inbred C57BL prisma
3
Masking Techniques for Helical Structures
Masks are used during refinement on both the 2D experimental images and on the 3D reconstruction. Masking the 2D experimental images helps to reduce the influence of noise or the neighbouring particles on the alignment in the expectation step, and the same mask is also used to determine the mean and standard deviation in the background area for the normalisation of boxed segments. Masking the 3D reconstruction reduces the noise in the solvent region, and thus leads to a better reference for the next iteration. For the analysis of helical structures, a circular 2D mask from standard single-particle analysis is multiplied with an additional rectangular mask across the entire particle image with a width according to the outer_tube_diameter parameter (Fig.3A). This mask is no longer rotationally invariant, but is rotated according to the most likely in-plane rotation angle for each experimental image. Prior to any refinement, this in-plane rotation is estimated from the manual or template-based particle picking procedures (also see below).

Masks for 2D images (A) and 3D maps (B). Areas of a filament inside the mask are shown in grey, areas outside the mask in white. In panel A, the helical axis runs at a 45° angle with the horizontal direction, which represents an arbitrary in-plane rotation angle. The helical axis (i.e. the z-axis) runs parallel to the vertical direction in B. Panels C-F illustrate the need for the circular (or spherical) component of these masks. Rotation of an unmasked segment (C) leads to artefacts (top and bottom in D), due to wrapping effects caused by rotations in the Fourier domain. The application of a circular mask to the original image (E) allows rotation without artefacts (F).

Likewise, a spherical solvent mask on the 3D reconstruction from standard single-particle analysis is multiplied with a hollow cylinder mask with the same outer_tube_diameter parameter describing the width of the 2D mask. An optional inner_tube_diameter parameter also allows to mask away densities inside hollow helices (Fig.3B).
One could argue that because helical segments extend all the way across the image, one would not need the circular or spherical components of these masks. However, as all reference projections are taken as 2D slices from the 3D Fourier transform of the reference map, real-space artefacts may again arise from non-empty corners of the reference upon rotation. To make comparisons between the reference projections and the experimental images consistent, the corners are removed from both the 3D map and the 2D images (Fig.3C-F).
He S, & Scheres S.H. (2017). Helical reconstruction in RELION. Journal of Structural Biology, 198(3), 163-176.
Publication 2017
Cytoskeletal Filaments Epistropheus Helix (Snails) Microtubule-Associated Proteins Reconstructive Surgical Procedures Single Molecule Analysis Solvents
4
Automated Helical Particle Picking and Extraction
Apart from modifications to the refinement program that underlies the 2D/3D classification and 3D auto-refinement options of RELION, we also modified the programs for manual and template-based particle picking and particle extraction. The program for manual picking allows the user to identify the start and end points of helices by subsequent left-mouse clicks in the micrographs. For the template-based picking algorithm, the figure-of-merit calculation, which expresses the fit between the template and any local area in the micrograph, remains unchanged from the single-particle approach (Scheres, 2015 (link)). The adaptation of the algorithm lies in the detection of filaments and cross-overs between filaments. The latter requires additional parameters for the helix diameter, the inter-box distance (in multiples of the estimated helical rise), and the maximum curvature of the filaments (which is expressed as the fraction, between 0 and 1, of the curvature of the circular mask in 2D). The algorithm to detect filaments searches for peaks in the figure-of-merit map for the entire micrograph, and considers neighbouring peaks within a distance of half the template box size. Peaks that have too few neighbours, or neighbours that do not satisfy the maximum curvature requirement, are discarded. Then, the algorithm iteratively extends candidates for helical tracks in both directions, starting from the peak with the highest figure-of-merit, until no new segments can be at the ends. Once all helical tracks have been identified, the program calculates output segment coordinates according to the specified inter-box distance. At this stage, regions in the micrographs where two different helical filaments overlap are excluded.
Upon extraction of the segments from the micrographs, apart from the corresponding X-Y coordinates and the figure-of-merit, the particle extraction program also outputs for each segment its in-plane rotation along the helical track, a distinct number for the helix it belongs to, and its position within that helix. The latter information is then used to construct the relevant priors on the orientations of each segment as described above. This process does not require any additional user input when RELION is used for all steps from particle picking to refinement. However, if users provide extracted segments from outside the RELION workflow, then the additional metadata should be provided through an imported STAR file in RELION format. In that case, it may be more convenient to re-pick inside RELION. Alternatively, RELION can read coordinate files in EMAN(2) BOX-format (Tang et al., 2007 (link)), or as plain ASCII files with X and Y coordinates in the first two columns. Therefore, one can also conveniently provide the start and end coordinates of helices in such formats, and re-extract the individual segments inside RELION.
He S, & Scheres S.H. (2017). Helical reconstruction in RELION. Journal of Structural Biology, 198(3), 163-176.
Publication 2017
Acclimatization Crossing Over, Genetic Cytoskeletal Filaments Helix (Snails) Mental Orientation Mice, Laboratory
5
Tau Protein Filament Structure Determination
The PHF and SF maps from the PT and FL data sets have complementary regions for model building. The β-helix motif at the tips of the C-shaped protofilaments is better ordered in the PHFs than in SFs, whereas the amino- and carboxy-terminal ends of the protofilament cores are best ordered in the SF map from the FL data set. Moreover, density for the cross-β structures near the termini of the PHFs is better defined in the PT reconstruction than in the FL reconstruction. Combined, the different maps allow for unambiguous main-chain tracing of the atomic models (Figure 2; Extended Data Figures 4 and 6). Initial model building started by fitting the L-type β-helix from the magic-angle spinning NMR-derived structure of filaments of the prion-forming domain HET-s218-289 (PDB-entry 2RNM) in the tip of the protofilaments of the PT PHF reconstruction. The sequence of only one of the four microtubule-binding repeats of Tau had a sequence that was compatible with the observed density in both PHFs and SFs (Extended Data Figure 4). Using the β-helix region as a starting point, we then built the remainder of the amino- and carboxy-terminal regions by manually adding amino acids and targeted real-space refinement in COOT54 (link).
Fourier-space refinement of the complete atomic model against the PHF and SF maps was performed in REFMAC55 (link). A stack of three consecutive monomers from each of the protofilaments was refined in order to preserve nearest-neighbour interactions for the middle chain. Local symmetry restraints were imposed in REFMAC to keep all β-strand rungs identical. Since most of the structure adopts a β-strand conformation, hydrogen-bond restraints were imposed to preserve a parallel, in-register hydrogen-bonding pattern in earlier stages of the model building process. In addition, ϕ/ψ angle restraints were imposed for the polyglycine II region, 333GGG335, and the three-residue β-arc, 347KDR349. The dihedral angles for polyglycine II were taken from Crick’s proposed fold27 (link) (ϕ = −80, ψ =150) and those for the three-residue β-arc from the crystal structure of PDB-entry 1QRE56 (link). Side-chain clashes were detected using MOLPROBITY57 (link) and corrected by iterative cycles of real-space refinement in COOT and Fourier-space refinement in REFMAC. Refinements of atomic models were performed for the FL and PT PHFs and for the FL SF. The refined model from the FL SF was rigid-body fitted into the PT SF map. For each refined structure, separate model refinements were performed against a single half-map, and the resulting model was compared to the other half-map to confirm the absence of overfitting. The final models were stable in refinements without additional restraints. Statistics for the final models are shown in Extended Data Table 1.
Fitzpatrick A.W., Falcon B., He S., Murzin A.G., Murshudov G., Garringer H.J., Crowther R.A., Ghetti B., Goedert M, & Scheres S.H. (2017). Cryo-EM structures of Tau filaments from Alzheimer’s disease brain. Nature, 547(7662), 185-190.
Publication 2017
Amino Acids Cytoskeletal Filaments Helix (Snails) Human Body Hydrogen Bonds Microtubule-Associated Proteins Microtubules Muscle Rigidity polyglycine Prions Reconstructive Surgical Procedures Repetitive Region

Most recents protocols related to «Cytoskeletal Filaments»

1
Ultra-High Molecular Weight Polyethylene Webbing
Not available on PMC !

Example 1

A double cloth, plain weave webbing was produced on a needle loom. Each side of the webbing was constructed of 48 ends of 1600 d, 1000 filament ultra-high molecular weight polyethylene yarns and 24 ends of 1000 d, 192 filament polyester yarns along the edges of the webbing, and 12±2 ppi of 1600 d, 1000 filament ultra-high molecular weight polyethylene yarns. The stuffer yarns were 1500 d, 3×4 Kevlar® cord, and 14 cords (168 yarns) were positioned between the front and back sides of the webbing. Binder yarns of 1600 d, 1000 filament ultra-high molecular weight polyethylene yarn binder were woven between the front and back to secure the sides together. A polyester catch cord (1000 d/192/1.5 z) was used to bind the edges of the webbing.

The webbing had a width of approximately 1.0 inches, a thickness of approximately 0.14 inches and a weight of approximately 58 g/linear yard. The tensile strength of the webbing was approximately 8,000 lbs.

US11872419B1. Webbing for fall protection device (2024-01-16). OTEX Specialty Narrow Fabrics, Inc. [US]. Inventors: Mark M. Morgis [US], Kevin W. Raup [US].
Patent 2024
Cone-Rod Dystrophy 2 Cytoskeletal Filaments Forehead Needles Polyesters ultra-high molecular weight polyethylene
2
3D Printed Conductive Materials for Circuits and Sensors
Not available on PMC !

Example 6

Stearic acid was mixed with silver powder (5 g SA:40 g silver) or graphite powder (20 g SA:25 g graphite), these mixtures were deposited as a simple circuits, as rectangular electrodes (20 mm×30 mm×2 mm) or as straight lines (20 mm long, 2 mm wide) (FIG. 14).

The printed silver powder had a particle size in the range 0.5 micrometer to 2 micrometer.

Some of the silver samples were sintered 1 hour at 400° C. and 2 hours at 700° C. The 3D printed circuit was tested using two LEDs that were placed in series and lit by electricity passed through the circuit. Conductivity was measured on the straight lines using a multimeter with standard copper wire and commercial conductive 3D printable filament as controls. ECG signals were collected through 3D printed electrodes or standard electrodes and were recorded using a Powerlab 26T unit and labchart software provided by ADInstruments (FIG. 15).

In sum, electrically conductive materials may be 3D printed and conductivity may be achieved in non-sintered implants. Two uses for these materials may be for circuits or sensors.

US11877900B2. Process for 3D printing (2024-01-23). Ossiform ApS [DK]. Inventors: Morten Østergaard Andersen [DK], Martin Bonde Jensen [DK], Casper Slots [DK].
Patent 2024
Copper Cytoskeletal Filaments Electric Conductivity Electricity Graphite Powder Silver stearic acid
3
Proton Exchange Membrane Fuel Cell Gas Diffusion Layer
Not available on PMC !

Example 22

A method for preparing a gas diffusion layer for proton exchange membrane fuel cell, includes steps as follows:

    • (1) preparing the carbon fiber suspension;
    • mixing the carbon fiber dispersion with the fibrous binder dispersion, then adding the ceramic fiber of 1 mm length (zirconia fiber), and then shearing and dispersing at a high-speed rate of 1500 r/min to obtain the carbon fiber suspension;
    • wherein the carbon fiber dispersion consists of the carbon fiber, the dispersant and water;
    • wherein the fibrous binder dispersion consists of the fibrous binder, the dispersant and water;
    • wherein the viscosity of dispersion composed of the dispersant and water is 2000 Pa·s in the carbon fiber suspension;
    • wherein the dispersant is Tween 60; wherein the amount of the dispersant in the carbon fiber suspension is 1.5 wt % of the amount of water;
    • wherein the fibrous binder is the composite filament numbered F-4 in Table 1;
    • wherein the length of the carbon fiber is 10-20 mm, the aspect ratio of the carbon fiber is 100-3000, and the mass of carbon fibers with the aspect ratio in the interval [100, 500) accounts for 10 wt % of the total mass of carbon fibers, the mass of carbon fibers with the aspect ratio in the interval [500, 1000) accounts for 60 wt % of the total mass of carbon fibers, the mass of carbon fibers with the aspect ratio in the interval [1000, 2000) accounts for 25 wt % of the total mass of carbon fibers, and the mass of carbon fibers with the aspect ratio in the interval [2000, 3000] accounts for 5 wt % of the total mass of carbon fibers; wherein the amount of the carbon fiber in the carbon fiber suspension is 5 wt % of the amount of water;
    • wherein the amount of the ceramic fiber is 5 wt % of the amount of the carbon fiber;
    • (2) papermaking and drying the carbon fiber suspension to obtain the carbon fiber base paper;
    • wherein the drying temperature is 140° C. and the drying time is 5 min;
    • in the prepared carbon fiber base paper, wherein the content of the fibrous binder is 30 wt %;
    • (3) cross-linking and curing of the carbon fiber base paper (hot-pressing cross-linking);
    • wherein the temperature of hot-pressing cross-linking is 300° C., the time of hot-pressing cross-linking is 5 min, and the pressure applied to the carbon fiber base paper is 5 MPa;
    • (4) carbonizing and graphitizing the cross-linked carbon fiber base paper under the protection of argon to obtain a gas diffusion layer for proton exchange membrane fuel cell;
    • wherein the carbonization temperature is 1250° C. and the carbonization time is 15 min; wherein the graphitization temperature is 2000° C. and the graphitization time is 5 min.

The prepared gas diffusion layer for proton exchange membrane fuel cell has hydrophilic channels composed of the ceramic fiber, and the pore gradient (that is, the pore size increases or decreases along the thickness direction), and the layer with the smallest pore size is the intrinsic microporous layer; wherein the gas diffusion layer for proton exchange membrane fuel cell has a thickness of 100 μm, a porosity of 70%, a contact angle with water of 145°, a tensile strength of 30 Ma, a normal resistivity of 70 mΩ·cm, an in-plane resistivity of 7 mΩ·cm, and a permeability of 2060 (mL·mm)/(cm2·h·mmAq).

US11876232B2. Gas diffusion layer for proton exchange membrane fuel cell and preparation method thereof (2024-01-16). DONGHUA UNIVERSITY [CN]. Inventors: Biao Wang [CN], Tongqing Qu [CN], Xiyi Huang [CN].
Patent 2024
A 145 Argon Carbon Fiber Cytoskeletal Filaments Diffusion Fibrosis Permeability Pressure Protons Tween 60 Viscosity zirconium oxide
4
Stiffness Enhancement of Polyurethane Filaments
Not available on PMC !

Example 2

The stiffness of a polyurethane based filament was increased by overlaying a shell of a similar monomer chemistry over the core material. The elastomeric core was Tecothane TT-1077A having a flexural modulus of 1,100 psi and the shell material was Tecoplast TP-470 having have a flexural modulus of 300,000 psi. The filament was assumed to have a diameter of 0.12 inches and the core was assumed to have a diameter of 0.11 inches.

The area of the shell was calculated to be 0.0018 in.2 and the area of the core was calculated to be 0.0095 in.2. The area moment of inertia for the shell was calculated to be 2.99e−6 and the area moment of inertia for the core was calculated to be 1.76e−8. The area moment of inertia for a cylindrical filament of elastomeric material was calculated to be 1.02e−5.

The calculated stiffness of the shell was 0.897 and the stiffness of the core was calculated to be 0.00002, resulting in the core-shell elastomer filament having a composite stiffness of 0.897. In comparison, the stiffness of a neat elastomeric filament was calculated to be 0.011 lbf per in2.

As such, adding 16 volume percent of the shell material increases the stiffness by a factor of over 80.

US11878461B2. Core-shell filament for use in extrusion-based additive manufacturing systems and method of printing parts (2024-01-23). Stratasys, Inc. [US]. Inventors: William J. Swanson [US], William R. Priedeman, Jr. [US].
Patent 2024
A 300 Cytoskeletal Filaments Elastomers factor A Polyurethanes
5
Solvent Extraction of Xerogel Yarn
Not available on PMC !

Example 12

To determine the index of clean n-hexane consumption to produce 1 kg of xerogel yarn, a bobbin containing 90 filaments of gel yarn was continually fed into the extraction unit. The Experiment was carried out by changing the feed flow rate of clean solvent in the fourth unit, such that it was possible to measure the residual oil content in the xerogel yarn as a function of the flow rate, awaiting for the stabilization time of the method. Data to the xerogel sample containing about 4% oil is set forth in Table XI.

TABLE XI
Values/Values/Values/Values/
Extractor 1Extractor 2Extractor 3Extractor 4
Input
Yarn Speed (m/min.)14.514.815.115.4
Contact Time (min.)4444
Oil-In-Yarn Inlet (wt %)86***
Titre Yarn Inlet (dtex)57,410***
Solvent Flow Rate (L/h)15.015.015.015.0
Partition coefficient~1~1~1~1
(—)
Extraction Temperature35353535
(° C.)
Output
Recirculation Phase31.89.962.960.72
Oil-In-Solvent (wt %)
Residual Oil-In-xerogel***4.3
Yarn Outlet (wt %)
Kg Solvent/Kg xerogel14.9
Yarn (—)
* It wasn't determined

US11866849B2. System and method of dosing a polymer mixture with a first solvent, device, system and method of extracting solvent from at least one polymeric yarn, system and method of mechanical pre-recovery of at least one liquid in at least one polymeric yarn, and continuous system and method for producing at least one polymeric yarn (2024-01-09). Braskem America, Inc. [US]. Inventors: Marcos Roberto Paulino Bueno [BR], Andre Penaquioni [BR], Alessandro Bernardi [BR], Sergio Luiz Dias Almeida [BR], Leandro Ohara Oliveira Santa Rosa [BR], Patricia Freitas Oliveira Fialho [BR], Daniela Zaira Rauber [BR].
Patent 2024
Cytoskeletal Filaments n-hexane Solvents

Top products related to «Cytoskeletal Filaments»

1
Von frey filament by Stoelting
Sourced in United States, Israel, Japan
Von Frey filaments are calibrated nylon monofilaments used to measure mechanical sensitivity thresholds. They provide a standardized, objective method for evaluating mechanical pain perception.
2
Von frey filament by North Coast Medical
Sourced in United States
Von Frey filaments are a set of calibrated nylon monofilaments used to assess tactile sensitivity and mechanical pain thresholds. They provide a standardized method for quantifying sensory perception.
3
Dynamic plantar aesthesiometer by Ugo Basile
Sourced in Italy
The Dynamic Plantar Aesthesiometer is a lab equipment used to measure the mechanical sensitivity threshold of the hind paw in rodents. It applies a linearly increasing mechanical force to the animal's paw until a response is elicited.
4
Von frey filament by Ugo Basile
Sourced in Italy
The Von Frey filaments are a set of calibrated nylon monofilaments used to measure mechanical sensory thresholds. They are designed to apply a standardized amount of force to the skin, allowing for the assessment of tactile sensitivity and mechanical pain thresholds.
5
Fusion 360 by Autodesk
Sourced in United States, United Kingdom
Fusion 360 is a cloud-based 3D computer-aided design (CAD), computer-aided manufacturing (CAM), and computer-aided engineering (CAE) software tool. It provides integrated modeling, simulation, collaboration, and machining capabilities.
6
Vitrobot mark 4 by Thermo Fisher Scientific
Sourced in United States, Germany, Netherlands, United Kingdom, Czechia, Israel
The Vitrobot Mark IV is a cryo-electron microscopy sample preparation instrument designed to produce high-quality vitrified specimens for analysis. It automates the process of blotting and plunge-freezing samples in liquid ethane, ensuring consistent and reproducible sample preparation.
7
Von frey filament by Bioseb
Sourced in France, United States
Von Frey filaments are a set of calibrated nylon filaments used to assess mechanical sensitivity. Each filament applies a specific reproducible force when pressed against the skin. They are a standardized tool for quantifying mechanical sensory thresholds.
8
Calibrated von frey filament by Stoelting
Sourced in United States
Calibrated von Frey filaments are a set of standardized nylon monofilaments used to measure mechanical sensitivity and detect thresholds of touch perception. Each filament has a specific calibrated force value that is applied to the skin to elicit a response, providing an objective assessment of somatosensory function.
9
Touch test sensory evaluator by North Coast Medical
Sourced in United States
The Touch-Test Sensory Evaluator is a tool used to assess tactile sensation. It consists of a set of monofilaments with varying thicknesses that apply a precise amount of pressure when applied to the skin. This device is used to evaluate sensory perception and nerve function.
10
Von frey hair by Stoelting
Sourced in United States
Von Frey hairs are a set of calibrated nylon filaments used to assess mechanical sensitivity and pain perception in laboratory settings. These filaments apply a standardized amount of force when pressed against the skin, allowing for the measurement of sensory thresholds.

More about "Cytoskeletal Filaments"

Cytoskeletal filaments, also known as cytoskeleton filaments, are crucial structural components within cells that provide shape, support, and facilitate intracellular transport.
These filamentous structures include actin filaments, intermediate filaments, and microtubules, which play pivotal roles in cellular processes such as cell division, motility, and signaling.
The cytoskeleton is a dynamic and versatile network that undergoes constant remodeling to adapt to the changing needs of the cell.
Actin filaments, for instance, are involved in cell migration, muscle contraction, and the formation of specialized structures like lamellipodia and filopodia.
Intermediate filaments, on the other hand, provide mechanical support and help maintain the integrity of the cell.
Microtubules, the third major component of the cytoskeleton, act as 'highways' for intracellular transport, facilitating the movement of organelles, vesicles, and other cellular cargo.
They also play a crucial role in cell division, forming the mitotic spindle that segregates chromosomes during mitosis.
Researchers studying cytoskeletal filaments often utilize specialized tools and techniques, such as Von Frey filaments, Dynamic Plantar Aesthesiometer, Fusion 360, and the Vitrobot Mark IV, to visualize and analyze these structures.
Calibrated von Frey filaments and Touch-Test Sensory Evaluators are also commonly used to assess mechanical sensitivity and sensory perception.
By exploring the world of cytoskeletal filaments with the help of advanced AI-driven platforms like PubCompare.ai, researchers can optimize their scientific protocols, identify the most effective methods and products, and take their studies to new heights.
With powerful search and comparison tools, researchers can easily locate the best protocols from literature, preprints, and patents, leveraging AI-driven insights to enhance their cytoskeletal filaments research.