Log In Sign up
The largest database of trusted experimental protocols
> Anatomy > Cell Component > Axon Initial Segment

Axon Initial Segment

The axon initial segment (AIS) is a specialized region of the neuron located at the junction between the soma and the axon.
It plays a crucial role in the initiation and propagation of action potentials, ensuring efficient signal transmission.
The AIS is characterized by a high density of voltage-gated sodium channels, which facilitate the generation of action potentials.
It also serves as a barrier, selectively regulating the entry of molecules and organelles into the axon.
Research on the AIS is crucial for understanding neuronal function and pathologies related to axonal excitability and signal propagation.
PubCompare.ai offers a platform to optimize your AIS research by provideing access to peer-reivewed literature, pre-prints, and patents, as well as advanced comparison tools to identify the most reproducible and effective methodologies.

Most cited protocols related to «Axon Initial Segment»

1
Astrocyte Ca2+ Fluctuation Analysis
Detection of astrocyte regions of interest (ROI) containing Ca2+ fluctuations was performed in a semiautomated manner using the GECIquant program developed using the open source ImageJ analyses platform. The same procedure was followed for brain slice and in vivo data. The GECIquant program is implemented in Java based ImageJ script language and runs as a plugin on ImageJ. The input to GECIquant is a confocal 2D fluorescence image stack (8 or 16 or 32-bit) of arbitrary frame size, a user defined sampling rate and with time as the third dimension (t-stack). Data outputs of GECIquant include ROI intensity changes in time, ROI areas and centroid distances of each ROI from a reference ROI. Graphical outputs of GECIquant include ROI intensity kymographs and sub-stacks consisting of fluctuations. Supplementary Information 1 provides the script and Supplementary Information 2 provides a user manual for GECIquant.
Having analyzed all the data shown in this study, we observed three distinct types of spontaneous subcellular Ca2+ fluctuations within astrocytes, which we describe below and then clarify how they were detected within GECIquant. We classify Ca2+ fluctuations as: (1) somatic fluctuations that occur within the somata (these are restricted to somata and initial segments of processes arising from somata), (2) waves that occur exclusively within astrocyte processes and display repeated wave-like expansions and contractions of Ca2+, and (3) microdomain Ca2+ fluctuations that display highly restricted areas in astrocyte processes. These do not expand or contract as waves and remain restricted. The distinct areas covered by these three types of fluctuations are reported in the main text and in Supplementary Fig 5. In this section, we describe a semi-automated method to accurately capture regions of interest (ROIs) for somatic, wave and microdomain Ca2+ fluctuations within astrocytes.
After an image series was acquired (e.g. Fig. 1), the x-y axis drift in the image stacks was stabilized using the Turboreg plugin in ImageJ. All ROIs were then detected using GECIquant. A scale was first assigned to image stacks, based on the confocal digital zoom setting. For most images, we used a 3x digital zoom, which corresponds to a scale of 0.23 μm per pixel. Briefly, a temporal projection of the movie stack was thresholded and the soma was detected with an area criterion of 30 μm2 to infinity within GECIquant. To do this, a temporal maximum intensity projection image was first generated by GECIquant from the image stack. The projection image was manually thresholded by the user with the default setting in ImageJ. Following thresholding, a polygon selection was manually drawn around the approximate astrocyte territory of interest, and the selection was added to the ImageJ ROI manager. Note that the assignment of territory was approximate and was not used for analysis except for the specific data set shown in Fig 2b. All ROIs falling within the range of 30 μm2 to infinity inside the polygon selection were detected by GECIquant and added to the ROI manager. An area range of 30 μm2 to infinity allowed detection of the astrocyte somata in all cases. The resulting detection was visually checked in every case.
To detect wave and microdomain ROIs, we first demarcated and deleted the soma from original image stacks using the clear selection feature in ImageJ. This was done because the increased basal fluorescence from the astrocyte soma relative to the processes prevented accurate thresholding of images for detection of ROIs within astrocyte processes. The ROI detection module in GECIquant was launched and the microdomain ROI option was selected. Microdomains and expanding wave ROIs were detected in separate analysis sessions. We used an area range of 0.5 to 4 μm2 to detect microdomains and an area range of 5 to 2000 μm2 for waves. These values were chosen after initial examination of the movie frames and by using several initial “best guess” test values as a guide. Other researchers who use GECIquant will also need to invest time initially to try several “best guess” values as a way to know what values will work best for the particular cell and fluctuation they are interested in measuring. The values we report here were appropriate for our experiments. For ROI detection, GECIquant generated a temporal maximum intensity projection image from the provided image stack with the deleted cell body. The projection image was manually thresholded by the user and a polygonal selection was manually drawn around the astrocyte of interest. GECIquant automatically detected microdomain and expanding wave ROIs based on the provided area criteria and the ROIs were added to the ImageJ ROI manager. Intensity values for each ROI were extracted in ImageJ and converted to dF/F values. For each ROI, basal F was determined during 50 s periods with no fluctuations. MiniAnalysis 6.0.07 (Synaptosoft) software was used to detect and measure amplitude, half width and frequency values for the somatic, wave and microdomain transients.
We comment on how we analyzed data for the experiments shown in Fig 2. First, for the analyses shown in Fig 2c, we made approximate ROIs that encompassed whole territories and then plotted the intensity of these regions over 300 s. From these traces, we measured the mean fluorescence intensity values over the 300 s period for each cell, and then averaged these values across all cells to generate the graphs in Fig 2c for WT and IP3R2−/− mice. In the case of the graphs shown in Fig 2d, we pooled the individual microdomain and wave Ca2+ fluctuations per cell, obtained the average value per cell of these pooled fluctuations and repeated this procedure for all cells. Then we averaged across all cells to generate the graphs that are shown in Fig 2c for WT and IP3R2−/− mice.
Graphs were made with Origin 8.1 and the figures assembled in CorelDraw 12 (Corel Corporation). No statistical methods were used to pre-determine sample sizes but our sample sizes are similar to those generally employed in the field. Randomization and blinding was not employed. Statistical comparisons were made using unpaired non parametric Mann-Whitney or unpaired parametric Student’s t tests as deemed appropriate after analyzing the raw data to ascertain whether they were normally-distributed using the Dallal and Wilkinson approximation to Lilliefors' method within Instat. When a statistical test was used, the precise P value and the test employed are reported in the text and/or figures legends. If the P was less than 0.00001, then it is reported as P < 0.00001. Otherwise, precise P values are provided in each case.
A methods checklist is available with the supplementary materials.
Srinivasan R., Huang B.S., Venugopal S., Johnston A.D., Chai H., Zeng H., Golshani P, & Khakh B.S. (2015). Ca2+ signaling in astrocytes from IP3R2−/− mice in brain slices and during startle responses in vivo. Nature neuroscience, 18(5), 708-717.
Publication 2015
Astrocytes Axon Initial Segment Brain Carisoprodol Cell Body Cells Diploid Cell Epistropheus Fingers Fluorescence Kymography Mus Reading Frames Student Transients
2
Neural Model Modifications for Electrical Stimulation
The neural models were modified versions of the multi-compartmental, conductance-based models implemented by the Blue Brain Project [29 (link),30 ] in the NEURON v7.4 simulation software [31 (link)]. The original model morphologies were obtained from 3D digital reconstructions of biocytin-filled neurons in all 6 cortical layers of slices of somatosensory cortex from P14 male Wistar rats. To increase morphological diversity amongst cells of the same type, Markram et al. generated virtual “clones” by adding random variation to the branch lengths and rotations of the exemplar models [29 (link)]. A feature-based multi-objective optimization method [32 (link)] was used to fit to electrophysiological data the conductances of up to 13 different published Hodgkin-Huxley-like ion channel models in soma, basal dendrites, apical dendrites, and axon initial segment. The ion channels included transient sodium, persistent sodium, transient potassium, persistent potassium, M-current (Kv7), H-current, high-voltageactivated calcium, low-voltage-activated calcium, A-type potassium (Kv3.1), D-type potassium (Kv1), stochastic potassium, and SK calcium-activated potassium [29 (link)]. From this library of 207 cell types, we selected cells based on their relative abundance and refined our selection within each layer based on cell types exhibiting the lowest thresholds for stimulation with extracellular electric fields. The final set of cell types included layer 1 (L1) neurogliaform cell with a dense axonal arbor (NGC-DA), L2/3 pyramidal cell (L2/3 PC), L4 large basket cell (L4 LBC), L5 thick-tufted pyramidal cell with an early bifurcating apical tuft (L5 TTPC), and L6 tufted pyramidal cell with its dendritic tuft terminating in L4 (L6 TPC-L4). Figure 1 depicts the morphologies of the adult rat model neurons, and the adult human versions are included in the supplemental materials (Figure S1).
Markram et al. used automated “repair” algorithms to regrow computationally axons that were cut by the slice sectioning, thereby generating axonal morphologies that matched the statistics of intact axons for each neuron model [14 ]. The full axonal arbors were used to estimate synaptic connectivity statistics but were not included in the parameter optimizations or network simulations in the original study [29 (link)]. Since axons are likely the lowest threshold elements for most forms of electrical stimulation [12 (link),33 ,34 (link)], these axonal arbors were included in our models after the modifications described below. The NEURON code to generate these modified neuron models is available on ModelDB (https://senselab.med.yale.edu/modeldb/ShowModel.cshtml?model=241165).
Aberra A.S., Peterchev A.V, & Grill W.M. (2018). Biophysically Realistic Neuron Models for Simulation of Cortical Stimulation. Journal of neural engineering, 15(6), 066023.
Publication 2018
Adult Antigen-Presenting Cells Axon Axon Initial Segment biocytin Brain Calcium cDNA Library Cells Clone Cells Cortex, Cerebral Dendrites Electricity Ion Channel Males Nervousness Neurons Potassium Pyramidal Cells Rats, Wistar Reconstructive Surgical Procedures Sodium Somatosensory Cortex Stimulations, Electric Transients
3
Realistic CA1 Neuron Model with Electrophysiological Validation
A morphologically realistic reconstruction of a CA1 neuron (n123) was obtained from the NeuroMorpho database (Pyapali et al. 1998; Ascoli et al. 2007) and passive and active properties for the base model were adopted from an earlier model (Rathour & Narayanan, 2014) that matched several somatodendritic functional maps (Narayanan & Johnston, 2012) through physiologically established channel localization profiles (Fig. 1A–G). The specific membrane capacitance was set uniformly at 1 μF cm–2. Rm and Ra were set in a gradient along the trunk as a function of the radial distance of the compartment from the soma according to the equations and parametric values in Tables 1 and 2 (Fig. 1B). The basal dendrites and the axonal compartments had somatic Rm and Ra, and the apical obliques had the same Rm and Ra as the trunk compartment from which they originated. The model was compartmentalized according to the dλ rule (Carnevale & Hines, 2006) to ensure isopotentiality in each compartment. Specifically, each compartment was smaller than 0.1 × λ100, with λ100 representing the space constant of the section computed at 100 Hz. The five different ion channels used in the model were Hodgkin–Huxley‐type delayed rectifier potassium (KDR), fast sodium (NaF), T‐type calcium (CaT), hyperpolarization‐activated cyclic‐nucleotide‐gated (HCN) non‐specific cation and A‐type potassium (KA) channels (Table 1). Currents through the NaF, KDR, KA and HCN channels employed an Ohmic formulation with reversal potentials for Na+, K+ and h channels set at 55, –90 and –30 mV, respectively. The CaT current was modelled using the Goldman–Hodgkin–Katz (GHK) convention (Shah et al. 2008).
NaF and KDR conductances were distributed uniformly in the soma and across the dendritic arbor with respective maximal conductances set at g¯ Na = 16 mS cm–2 and g¯ KDR = 10 mS cm–2 (Magee & Johnston, 1995; Hoffman et al. 1997). The g¯ Na in the axonal initial segment was five‐fold higher compared to the somatic value. The rest of the axon was considered to be passive. Because the recovery of dendritic sodium channels from inactivation is slower (Colbert et al. 1997), an additional inactivation gating variable was included in the model for Na+ channels that expressed in the apical dendrites (Migliore et al. 1999). The three subthreshold channel conductances (CaT, HCN and KA) were distributed with increasing somato‐apical gradients (Fig. 1B and Table 1), as dictated by corresponding electrophysiological findings (Magee & Johnston, 1995; Hoffman et al. 1997; Magee, 1998). When incorporating electrophysiological observations on differences between the activation parameters of the KA channels in CA1 pyramidal cells (Hoffman et al. 1997), two different KA conductance models were employed for proximal (≤100 μm radial distance from soma) and distal (>100 μm) apical dendritic compartments (Migliore et al. 1999). The half‐maximal activation voltage for HCN channels was –82 mV for proximal apical compartments (radial distance ≤ 100 μm), linearly the voltage varied from –82 mV to –90 mV for compartments between 100 and 300 μm, and the voltage was set at –90 mV for compartments with distances larger than 300 μm (Magee, 1998). All active and passive properties of basal dendritic compartments were set to their respective somatic values.
All somatodendritic active and passive parameters and their gradients were tuned to match distance‐dependent electrophysiological measurements (Fig. 1C–G) of back‐propagating action potentials (bAP), input resistance (Rin), resonance frequency (fR) and total inductive phase (ΦL) from CA1 pyramidal neurons (Spruston et al. 1995; Hoffman et al. 1997; Narayanan & Johnston, 2007, 2008; Rathour & Narayanan, 2014).
Basak R, & Narayanan R. (2018). Spatially dispersed synapses yield sharply‐tuned place cell responses through dendritic spike initiation. The Journal of Physiology, 596(17), 4173-4205.
Full text: Click here
Publication 2018
Action Potentials Axon Axon Initial Segment Calcium Carisoprodol Conferences Dendrites Diploid Cell Hyperpolarization-Activated Cyclic Nucleotide-Gated Channels Ion Channel Microtubule-Associated Proteins Neurons Nucleotides, Cyclic Potassium Pyramidal Cells Reconstructive Surgical Procedures Sodium Sodium Channel Tissue, Membrane Vibration
4
Biologically Plausible Connectome Pruning
The modeled volume of neural tissue contained a large number of axo-dendritic, axo-somatic and axo-axonic appositions that we considered as locations of potential synapses. A preparatory filtering step eliminated all potential synapses, except those that were located on biologically plausible parts of the postsynaptic cell: dendrites in the case of pyramidal to pyramidal connections (Somogyi et al., 1998 (link); Feldmeyer et al., 2002 (link); Kawaguchi et al., 2006 (link); Kubota et al., 2007 (link)); the axon initial segment for connections in the case of chandelier cells (ChCs) (Somogyi, 1977 (link); Somogyi et al., 1982 (link); Howard et al., 2005 (link); Szabadics et al., 2006 (link)) and dendrite or axon for others.
To derive a biologically plausible connectome, we employed a three step pruning algorithm, a modified version of the algorithm proposed in (Fares and Stepanyants, 2009 (link)):
In the first step—general pruning—for each synapse we drew an independent random number R in the interval [0,1) and compared it against a parameter f1.
If R < f1 the potential synapse was admitted to the second step or else kept inactive in a pool accessible to structural plasticity mechanisms.
In the second step—multi-synapse pruning—we drew random numbers R ∈ [0, 1] for every connection. A connection was defined as the set of all potential synapses between a pre- and a postsynaptic cell. The connection was admitted to the next step only if
where Ns was the number of potential synapses forming the connection, and μ2 a parameter to this second step. Thus, the probability of admitting a connection is a rising sigmoidal function of the number of potential synapses contributing to the connection. In this simplified version of the criterion described by (Fares and Stepanyants, 2009 (link)), the width of the transition of the sigmoidal is set to its offset from the origin (here: μ2) multiplied by 0.25. In the results presented in (Fares and Stepanyants, 2009 (link)), the 95% confidence region for this parameter was very wide compared to that of the offset. This suggests that this parameter is relatively unimportant for achieving a good fit to the biological data. The value of 0.25 used to calculate the width of the transition was chosen to ensure that the fraction of connections with only one synapse was < 1%.
In the third step—plasticity pruning—whole connections were again removed randomly and independently. This time, however, potential connections were converted into active connections whenever R < a3 (a3 being the parameter of the third step), guaranteeing that connection pruning was independent of the number of potential synapses. Connections removed during this process were placed in a pool of viable multi-synaptic connections for future use by structural plasticity mechanisms.
Reimann M.W., King J.G., Muller E.B., Ramaswamy S, & Markram H. (2015). An algorithm to predict the connectome of neural microcircuits. Frontiers in Computational Neuroscience, 9, 120.
Full text: Click here
Publication 2015
Axon Axon Initial Segment Biopharmaceuticals Cells Connectome Dendrites Diploid Cell Nerve Tissue Strains Synapses Synaptic Potentials
5
Visualizing Synaptic Dynamics in Dendritic Spines

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2016
Animals Axon Initial Segment Canes Carisoprodol Cells Dendrites Dendritic Spines gephyrin Germ Cells Head Psychological Inhibition Reading Frames Squamous Intraepithelial Lesion Synapses Transients Vertebral Column

Most recents protocols related to «Axon Initial Segment»

1
Upward positive lightning flashes
On 24 July, 2021 the L2 event was observed with the two high-speed cameras: one operating at 24,000 fps, installed on the Kronberg mountain; and the second operating at 10,000 fps, installed at Säntis Das Hotel (Schwaegalp). Note that high-speed camera records of upward positive flashes are very rare and there are only a few reported in the literature47 (link),48 (link).
Figure 2 displays two representative frames taken from the two fast cameras. Several comparative procedures were performed to precisely calibrate the position of the laser: images from the fast cameras in daylight to identify the position of the tower and the surrounding topography; high resolution pictures at night with a D810 Nikon next to the fast cameras when the laser was operating; and reconstruction of the laser direction and position using precise GPS data. In the two pictures in Fig. 2, which depict an upward positive flash, an initial segment of about 70 m from Schwaegalp and 120 m from Kronberg is observed, following the path of the laser beam.
For events without a laser, individual images from one of the cameras were available, allowing us to plot the histograms of the distances to the laser beam as projected in the plane perpendicular to the camera line of sight (Extended Data Fig. 3). The difference in behaviour between the event with (L2) and without a laser (N05, N08) is apparent.
Houard A., Walch P., Produit T., Moreno V., Mahieu B., Sunjerga A., Herkommer C., Mostajabi A., Andral U., André Y.B., Lozano M., Bizet L., Schroeder M.C., Schimmel G., Moret M., Stanley M., Rison W.A., Maurice O., Esmiller B., Michel K., Haas W., Metzger T., Rubinstein M., Rachidi F., Cooray V., Mysyrowicz A., Kasparian J, & Wolf J.P. (2023). Laser-guided lightning. Nature Photonics, 17(3), 231-235.
Full text: Click here
Publication 2023
Axon Initial Segment Reading Frames Reconstructive Surgical Procedures Vision
2
Cryo-EM Structural Characterization of β2m Amyloid Fibrils
For β2m-D76N, each of the movie stacks was grouped from 1477 frames into 54 fractions (~0.8 e−/Å2 per fraction) and aligned and summed using motion correction in RELION3.1.165 (link). CTF parameters were estimated for each micrograph using CTFFIND v4.14. Fibrils from 61 micrographs were manually picked in RELION and the extracted segments used to train automated filament picking in crYOLO. Using an inter-box spacing of 5 Å, a total of 1,117,062 helical segments were extracted 3x binned in RELION3.1 with box dimensions of 660 Å. This large particle dataset was split in 4 subsets, each containing 279,264 particles. Each subset was separately subjected to 2D classification to remove picking artefacts and unfeatured objects, leaving a total of 974,932 fibril segments. A second round of 2D classification facilitated the separation of segments into two main categories according to the apparent fibril width, resulting in a combined total of 574,659 thin fibril segments (1PF) and 400,273 wide fibril segments (2PF) (Supplementary Fig. 3a). A subclass of wide-fibril segments displaying a helical cross-over of 96 nm was used to generate an initial model using relion_helix_inimodel2d66 (link). This model was employed as a template for 3D classification, which allowed separation of the wide fibrils into 2 classes (Supplementary Fig. 3g). The particles corresponding to each of these classes were individually selected and re-extracted unbinned with a box size of 220 Å for further 3D classification rounds, using the corresponding 3D-class model as a starting template. One of these classes (2PFb) was not solvable (Supplementary Fig. 3h). The second class was equivalent to the β2m-ΔN6 2PFa fibril structure and, after multiple refinements with helical parameter searches, the 78,097 segments refined to a resolution of 3.6 Å. After CTF refinement, Bayesian polishing and 3D refinement, the final β2m-D76N 2PFa map was solved at a resolution of 3.0 Å and deposited with a sharpening B-factor of −30 Å2 (Supplementary Fig. 3i). The refined helical parameters were a twist of 359.01˚ and rise of 4.80 Å respectively.
A similar strategy was followed for the initial 3D-classification of the β2m-ΔN6 thin fibril segments, from which two different classes were obtained and labelled as 1PFa and 1PFb respectively (Supplementary Fig. 3b). The particles corresponding to each of these classes were selected individually and re-extracted unbinned in 220 Å boxes. For D76N-1PFa, two subsequent rounds of 3D classification with searches of the helical parameters led to selection of 44,011 ordered segments with a helical twist of 358.57˚ and rise of 4.80 Å (Supplementary Fig. 3c). For D76N-1PFa, a single additional round of 3D classification with helical searches led to the selection of 72,815 ordered segments with a helical twist of 358.49˚ and rise of 4.80 Å (Supplementary Fig. 3e). However, even after CTF refinement and Bayesian polishing of both subsets, the refined 1PF maps continually showed regions of the backbone density where the layers were artificially fused (Supplementary Fig. 3d, f) and so the maps could not be deposited as resolved fibril structures. The fold of the repeating fibril cores for 1PFa and 1PFb clearly showed single protofilament structures resembling the core fold of the other β2m structures in this study, but with varying distinct positions of the N- and C-termini.
Wilkinson M., Gallardo R.U., Martinez R.M., Guthertz N., So M., Aubrey L.D., Radford S.E, & Ranson N.A. (2023). Disease-relevant β2-microglobulin variants share a common amyloid fold. Nature Communications, 14, 1190.
Full text: Click here
Publication 2023
Axon Initial Segment Complement Factor B Crossing Over, Genetic Cytoskeletal Filaments Helix (Snails) Microtubule-Associated Proteins Reading Frames Vertebral Column
3
Reduced Multicompartment Neuron Model
Following the methodology of Elbasiouny (2014) (link) in reducing 3D models into reduced models that still demonstrate highly accurate firing behaviors, we reduced the high-fidelity computer model of Mousa and Elbasiouny (2020) (link) into a six-compartment (6C) model (Figure 1A, 6C model morphology, and electrical parameters are shown in Tables 1, 2, respectively). This 6C model was able to simulate experimental data and the 3D model behaviors with acceptable accuracy (Table 3). In the reduction process, the simplified 6C model was designed to resemble the 3D model in area distribution (Figure 1C), electrical distance distribution (Figure 1B), and electrical properties, which were optimized to fall within the 95% confidence interval of experimental data [passive and active membrane properties, rheobase, action potential (AP) and afterhyperpolarization (AHP) properties, frequency-current (FI) relationship properties] and to match the 3D model properties as much as possible.
Similar to the 3D model of Mousa and Elbasiouny (2020) (link), the 6C model was formed of initial segment/axon hillock (IS/AH), soma, and four dendrites (Figure 1A). All compartments of the 6C model had passive leak channels. The soma had the following additional channels: fast sodium (Naf), delayed rectifier potassium (Kdr), N-type calcium (CaN), and small conductance calcium-activated potassium (SK_AHP) channels. The IS/AH had the following channels (in addition to leak channels): Naf, persistent sodium (Nap), and Kdr. Dendrites 0 and 1 had only leak channels, whereas dendrites 2 and 3 also contained L-type Ca2+ (CaL) and small conductance calcium-activated potassium (SK_L) channels. Importantly, the location and conductance of dendritic CaL and SK_L channels were retained in the 6C model, as in the 3D model of Mousa and Elbasiouny (2020) (link), to generate comparable Ca persistent inward currents (Ca2+ PIC). Specifically, the 3D model had dendritic regions of high (26% of the dendritic area located between 0.44λ and 0.6λ from the soma) and low (10% of the dendritic area located between 0.6λ and 1.1λ from the soma) Ca2+ PIC and SK conductances. To mimic that, the distal dendrites of the 6C model were simulated with two compartments, one with high Ca2+ PIC and SK conductances (dendrite 2, with 26% of the dendritic surface area and distance between 0.45λ and 0.55λ from the soma) and another with low Ca2+ PIC and SK conductances (dendrite 3, with 10% of the dendritic surface area and distance between 0.55λ and 0.93λ from the soma). In this way, the reduced 6C model had dendritic conductances very similar to those of the 3D model of Mousa and Elbasiouny (2020) (link).
All ion channels were conductance-based, following the Hodgkin and Huxley formalism (Hodgkin and Huxley, 1952 (link)), such that activation and inactivation states are voltage-dependent, except for SK channels which use saturation function that depends on the calcium concentration in the compartment (Mousa and Elbasiouny, 2020 (link)). The cell membrane specific capacitance was 1 μF/cm2. All model equations are listed in the Supplementary material.
Moustafa M., Mousa M.H., Saad M.S., Basha T, & Elbasiouny S.M. (2023). Bifurcation analysis of motoneuronal excitability mechanisms under normal and ALS conditions. Frontiers in Cellular Neuroscience, 17, 1093199.
Full text: Click here
Publication 2023
Action Potentials Axon Hillock Axon Initial Segment Calcium Dendrites Electricity Ion Channel Plasma Membrane Potassium Sodium Tissue, Membrane
4
Evaluating COVID-19 Preparedness in Hospitals
Three components made up the questionnaire. The initial segment of the study concentrated on the participants’ backgrounds and demographic data (age, gender, educational level, and job position). The second segment entailed questions that describe and categorize the characteristics of the hospital (sector, geographic location, accreditation status and accreditation cycle, and scope of service pertinent to COVID-19). With regards to the scope of service, the focus was on whether the hospital was authorized to admit and deal with COVID-19 cases, or if the hospital was authorized to quarantine suspected cases and whether the hospital’s laboratory was authorized to deal with COVID-19 samples, or if the hospital dealt with suspected emergency department (ER) cases and referred them to other hospitals. The third segment dealt with emergency response focus areas. In that section, a three-point Likert scale was employed, and the questions were further divided into six focus areas. Each category addressed key emergency response domains. The selection of the questions was based on a literature review and the previously used WHO tool for the assessment of COVID-19 readiness of hospitals. Within the third category, emergency response focus areas included: Emergency preparedness, infection prevention and control, capacity building, case management, communication, and laboratory services. Section 3 was made up of 23 questions, each question was based on a three-point Likert scale (low—1 mark, medium—2 marks, and high—3 marks). The questions in Section 3 represented important steps in accordance with international criteria for emergency preparedness [1 ,9 ]. The questions addressed practices in relation to emergency preparedness during the pandemic. The tool asked the participants about the degree with which they agreed with the impact of each question on their readiness to provide care in the COVID-19 pandemic. The participants’ responses ranged from 1 to 3, and the average number of participants certified as having completed each portion was determined. The cut-off score for good preparedness and hence the quality of care provided during the pandemic was set at 83.5% (which was the average score value for the whole study sample).
Dahmash E.Z., Madi T., Hassan S.K., Oroud Y., Shatat A., Jalabneh R., Rashideh H.A., Aljayyousi A, & Iyire A. (2023). Upholding Quality and Patient Safety during COVID-19 Pandemic—A Jordanian Case Study. Healthcare, 11(4), 523.
Full text: Click here
Publication 2023
Axon Initial Segment Case Management COVID 19 Emergencies Gender Infection Pandemics Quality of Health Care Quarantine
5
Investigating MCF-7 Cell Mechanics
MCF-7 cells were obtained from the American Type Culture Collection (ATCC) and grown as previously described61 (link). Cells were seeded at a concentration of 200,000 cells/mL on plasma-treated glass slides in DMEM medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin at 37 °C in 5% CO2. Cells were either treated for 48 hours with DMSO (0.05 %), 100 nM estrogen (E2), 50 µM resveratrol (RESV) or 5 µM tamoxifen (TAM). For mechanical measurements, a JPK Nanowizard III with a CellHesion extension was used. Pyramidal cantilevers (DNP-S, B, Bruker), with nominal stiffness of 0.12 N/m, a resonance frequency of 23 kHz in air, an opening angle of 22° and a nominal tip radius of 10 nm were used. Calibration by thermal noise making use of the equipartition theorem was performed for each cantilever62 (link). Measurements were done with a constant approach and retract rate of 5 µm/s, a maximum load of 1 nN, curve lengths of 50 µm, a constant deformation 10 s pause segment at an initial load of 1 nN and a sampling rate of 1024 Hz. Measurements were done in L15 medium at 37 °C and performed in the central region of the cell above the nucleus.
Weber A., Vivanco M.D, & Toca-Herrera J.L. (2023). Application of self-organizing maps to AFM-based viscoelastic characterization of breast cancer cell mechanics. Scientific Reports, 13, 3087.
Full text: Click here
Publication 2023
AH 22 Axon Initial Segment Cell Nucleus Cells Estrogens Fetal Bovine Serum MCF-7 Cells Penicillins Plasma Radius Resveratrol Streptomycin Sulfoxide, Dimethyl Tamoxifen Vibration

Top products related to «Axon Initial Segment»

1
Matlab by MathWorks
Sourced in United States, United Kingdom, Germany, Canada, Japan, Sweden, Austria, Morocco, Switzerland, Australia, Belgium, Italy, Netherlands, China, France, Denmark, Norway, Hungary, Malaysia, Israel, Finland, Spain
MATLAB is a high-performance programming language and numerical computing environment used for scientific and engineering calculations, data analysis, and visualization. It provides a comprehensive set of tools for solving complex mathematical and computational problems.
2
Dma q800 by TA Instruments
Sourced in United States, Germany, Belgium, United Kingdom, Ireland
The DMA Q800 is a dynamic mechanical analyzer that measures the viscoelastic properties of materials. It provides quantitative data on a material's stiffness, damping, and other mechanical characteristics.
3
F 7000 by Hitachi
Sourced in Japan, United States, China
The Hitachi F-7000 is a high-performance fluorescence spectrophotometer designed for versatile laboratory applications. It features advanced optics and a sensitive detector to provide accurate and reliable measurements of fluorescence intensity.
4
Eclipse 80i by Nikon
Sourced in Japan, United States, Germany, China, France, United Kingdom, Netherlands, Italy
The Eclipse 80i is a microscope designed for laboratory use. It features an infinity-corrected optical system and offers a range of illumination options. The Eclipse 80i is capable of various imaging techniques, including phase contrast and brightfield microscopy.
5
Axoporator 800 by Molecular Devices
The Axoporator 800 is a high-performance electrophysiology instrument designed for precise intracellular and patch-clamp recording. It features advanced digital signal processing capabilities and intuitive software control. The Axoporator 800 is capable of delivering accurate and reliable electrical stimulation and measurement in a wide range of experimental applications.
6
Rneasy mini kit by Qiagen
Sourced in Germany, United States, United Kingdom, Netherlands, Spain, Japan, Canada, France, China, Australia, Italy, Switzerland, Sweden, Belgium, Denmark, India, Jamaica, Singapore, Poland, Lithuania, Brazil, New Zealand, Austria, Hong Kong, Portugal, Romania, Cameroon, Norway
The RNeasy Mini Kit is a laboratory equipment designed for the purification of total RNA from a variety of sample types, including animal cells, tissues, and other biological materials. The kit utilizes a silica-based membrane technology to selectively bind and isolate RNA molecules, allowing for efficient extraction and recovery of high-quality RNA.
7
Eclipse ti by Nikon
Sourced in Japan, United States, Germany, United Kingdom, China, France, Canada, Netherlands, Italy, Belgium
The Eclipse Ti is an inverted research microscope system designed for advanced live-cell imaging. It features a high-stability stage and optical components optimized for sensitive fluorescence imaging and high-resolution imaging. The Eclipse Ti is equipped with motorized components for automated control of various microscope functions.
8
2100 bioanalyzer by Agilent Technologies
Sourced in United States, Germany, Canada, United Kingdom, France, China, Japan, Spain, Ireland, Switzerland, Singapore, Italy, Australia, Belgium, Denmark, Hong Kong, Netherlands, India
The 2100 Bioanalyzer is a lab equipment product from Agilent Technologies. It is a microfluidic platform designed for the analysis of DNA, RNA, and proteins. The 2100 Bioanalyzer utilizes a lab-on-a-chip technology to perform automated electrophoretic separations and detection.
9
Ankyrin g by Santa Cruz Biotechnology
Sourced in United States
Ankyrin G is a protein that plays a crucial role in the organization and function of the voltage-gated sodium channels in the plasma membrane. It is involved in anchoring and stabilizing these channels at specific sites within the cell, ensuring efficient signal transmission.
10
Alexa fluor 488 goat anti rat by Thermo Fisher Scientific
Sourced in United States, United Kingdom, France
Alexa Fluor 488 goat anti-rat is a fluorescently labeled secondary antibody used in various immunodetection techniques. It is designed to specifically bind and detect rat primary antibodies, allowing for the visualization and localization of target proteins or cells in samples.

More about "Axon Initial Segment"

Axon Initila Segment, AIS, Neuronal Function, Signal Transmission, Action Potentials, Voltage-gated Sodium Channels, Axonal Excitability, Signal Propagation, PubCompare.ai, MATLAB, DMA Q800, F-7000, Eclipse 80i, Axoporator 800, RNeasy Mini Kit, Eclipse Ti, 2100 Bioanalyzer, Ankyrin G, Alexa Fluor 488 goat anti-rat