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REVIEW 4 major objections 6 minor 56 references

Modeling the prion protein-mediated transport of extracellular vesicles on the neuron surface

T0 review · 4 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read The paper claims that extracellular vesicle transport along neuronal surfaces is captured by a flashing Brownian ratchet with an asymmetric sawtooth potential for passive, cytoskeleton-driven motion and a symmetric sawtooth potential for…

desk verdict A mostly honest, useful modeling paper for EV transport whose central drift signal may be an artifact of sign-flipping the data before the skewness test. read the letter →

arxiv 2502.03610 v2 pith:KEO45I7L submitted 2025-02-05 physics.bio-ph

Giulia Pozzi , Giulia Mazzilli , Giulia D'Arrigo , Claudia Verderio , Giuseppe Legname , Stefano Turzi , Pasquale Ciarletta This is my paper · ORCID
classification physics.bio-ph MSC 92C3760H1082C31
keywords extracellularvesiclesprionproteinflashingBrownianratchetsawtoothpotentialcytochalasinDactinpolymerizationmeansquareddisplacementneurodegenerativedisease
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to establish that the movement of prion-bearing extracellular vesicles along neuron surfaces can be described as a combination of two physical ratchet mechanisms: a passive one, in which the neuron's cytoskeleton drags the vesicle through a receptor, and an active one, in which the vesicle's own actin filaments make it roll from receptor to receptor. The authors build a stochastic model of these mechanisms, calibrate it to video tracking data of vesicles under three conditions, and show that the simulations reproduce the key experimental signature: a directed drifting component in control and vesicle-treated samples, and no drift when the neurons' actin is disabled. If correct, the model provides a quantitative framework for interpreting different transport regimes and a basis for asking how vesicle movement contributes to the spread of misfolded proteins in prion-like diseases.

What carries the argument

The central object is the flashing Brownian ratchet, a stochastic process that alternates between free diffusion and motion in a periodic potential. For passive transport the potential is an asymmetric sawtooth $V_1$ of period $L_1$, which generates a directed drift; for active transport the potential is a symmetric sawtooth $V_2$, which by construction produces no net drift and models receptor-to-receptor rolling. The vesicle and receptor are coupled by a linear elastic spring, interpreted as the prion protein, and a four-state Markov chain controls switching between bound and unbound states and between passive and active mechanisms. This machinery carries the argument because the asymmetry of $V_1$ is the sole source of directionality in the model, while the symmetric $V_2$ accounts for undirected active motion.

What would settle it

Track vesicles after treating neurons with Cytochalasin D and simultaneously blocking PrP–receptor interactions with a competing antibody: the passive-ratchet model predicts that the drift seen in CytoD-EV should vanish, and a persistent directed component would falsify the claim. Alternatively, a long-time measurement of CytoD-HN trajectories would falsify the model's assignment if it reveals a net drift, since the symmetric active potential cannot generate one.

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Extended reading notes

Core claim

The central claim is that a data-driven stochastic model, built from a flashing Brownian ratchet with an asymmetric sawtooth potential for passive transport and a flashing ratchet with a symmetric sawtooth potential for active rolling, captures the key features of extracellular vesicle motion on neuronal surfaces. The model assigns each experimental condition a distinct mechanism: untreated control combines passive and active transport; vesicles treated with Cytochalasin D move purely passively; neurons treated with Cytochalasin D support purely active rolling. Under this assignment, simulations reproduce the experimental skewness finding that Ctrl and CytoD-EV displacements contain a drift component while CytoD-HN displacements do not, and the simulated mean squared displacement curves and displacement histograms agree qualitatively with experiment.

Load-bearing premise

The load-bearing premise is that Cytochalasin D cleanly separates the two mechanisms—vesicle treatment disabling only the active one and neuron treatment disabling only the passive one—so each fitted parameter keeps its mechanistic meaning; if the drug also perturbs receptor diffusion or adhesion, the agreement between simulation and experiment becomes curve-fitting.

Editorial extensions

If this is right

  • If the model is correct, the directed component of vesicle motion on neurons comes from the passive, cytoskeleton-driven ratchet, so treatments that disable the neuronal actin network should remove drift while treatments that disable vesicle actin should not.
  • The model predicts that increasing the stiffness of the PrP–receptor link increases vesicle displacements, because the receptor can pull the vesicle more effectively against thermal fluctuations.
  • The model predicts that lowering the surface density of neuronal receptors impairs vesicle motility, because the vesicle must diffuse farther to reach the next binding site.
  • The model predicts that in the purely active regime, vesicle motion is enhanced diffusion without net direction, consistent with the experimentally observed absence of a drift component in CytoD-HN.
  • Because the control condition combines both mechanisms, the model attributes the higher zero-velocity rate and lower mobility of control vesicles to the passive state dominating their motion.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An implicit consequence is that 'active transport' in this model is not directional: the symmetric active potential can only enhance undirected motion, so any net displacement in the control must be attributed entirely to the passive ratchet.
  • A testable extension: treating neurons with Cytochalasin D and simultaneously blocking PrP–receptor binding should abolish the drift seen in CytoD-EV; if a directed component survives, the passive mechanism is not the sole source of directionality.
  • The model's one-mechanism-per-treatment mapping suggests sorting individual vesicle trajectories by kinematic indicators to check whether control trajectories naturally cluster into a passive-like and an active-like subpopulation, as the two-state switching would predict.
  • Because the control condition requires switching between passive and active states, the fitted transition rates imply a prediction for the statistics of pauses and jumps in single trajectories, which could be checked against longer recordings.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes a data-driven stochastic model for the motion of prion-protein-bearing extracellular vesicles (EVs) on neuronal surfaces. It combines an overdamped Langevin description of the EV with Markov-switching flashing ratchet potentials: an asymmetric sawtooth potential V1 for passive, cytoskeleton-driven transport and a symmetric sawtooth potential V2 for active, actin-based rolling. The model is specialized to three experimental conditions, Ctrl (both mechanisms), CytoD-HN (active only), and CytoD-EV (passive only), and its parameters are calibrated to experimental trajectories from D'Arrigo et al. The authors report qualitative agreement between simulations and experiments in terms of mean squared displacement curves, displacement histograms, kinematic indicators, and a skewness-based classification of which conditions display directed transport, with only CytoD-HN lacking a drifting component.

Significance. If the central claim holds, the paper would provide a useful quantitative framework for decomposing EV motion on neurons into passive and active mechanisms, with potential implications for how prion-like pathogenic cargo spreads in neurodegenerative disease. The manuscript is clearly written in terms of stochastic differential equations and Markov processes, and it has strengths worth acknowledging: the code and data are promised on Zenodo, the one-dimensional reduction is empirically motivated (about 80% of the motion is tangent to the neuron surface), and a sensitivity analysis is included. However, the significance is currently conditional because the experimental evidence for directed transport rests on a per-trajectory sign-flipping procedure that may itself create the reported skewness, and because the model validation is largely in-sample, with mechanism assignments assumed rather than independently tested.

major comments (4)
  1. [Section 2 (Data post-processing); Section 3 (Normality and Skewness tests)] The per-trajectory sign standardization described in Section 2 invalidates the skewness tests used as evidence for directed transport. The authors state that 'we standardized the preferential direction of the EVs towards positive values by inverting the sign of the displacements in samples where the linear regression slope computed on the tangent displacement was negative.' A purely diffusive finite trajectory almost always has a nonzero regression slope by chance, and flipping every trajectory with a negative slope mechanically creates a pooled displacement distribution with positive skew. The p-values reported in Section 3 (Ctrl p=0.00296, CytoD-EV p=0.0275, CytoD-HN p=0.731) therefore cannot be interpreted as evidence of a physical drift component. Because the model's central success criterion is reproducing the presence and absence of drift across conditions, the drift analysis must be rerun on unflipped tangent displacements, or with a reflection-invariant statistical test, and the paper must report whether the skewness result survives this correction.
  2. [Section 2 (Numerical method); Section 3 (Numerical vs in-vitro results)] The assignment of transport mechanisms to experimental conditions is assumed rather than tested. The numerical model hard-codes CytoD-HN as purely active and CytoD-EV as purely passive, with the text stating that 'these scenarios are distinguished by the presence of different vesicle transport mechanisms,' while the parameters in Table 4 are calibrated to the same experimental data used for comparison. Consequently the skewness agreement in 'Numerical vs in-vitro results' is partly manufactured: the drift in simulated CytoD-EV arises from the fitted asymmetry alpha=0.2 in V1, and the absence of drift in CytoD-HN is built into the spatially symmetric V2. The paper should validate the mechanism assignments against independent data, for example through the ATP-depletion or myosin-inhibition experiments already mentioned in the Conclusions, or explicitly reframe the results as a conditional demonstration of a hypothesis rather than a confirmation.
  3. [Section 3 (Parameters settings, Sensitivity analysis)] No identifiability or uncertainty analysis is provided for the calibrated parameters in Tables 3 and 4. The sensitivity analysis is one-at-a-time, so it does not address whether different parameter combinations could produce similar skewness, MSD, and histogram outputs, nor the effect of the limited sample sizes (n=15, 13, 14 after quality selection). Without confidence intervals, profile likelihoods, or an identifiability check, the mechanistic interpretation attached to individual fitted values, especially alpha, L1, and the switching rates, is not quantitatively supported. Please add an identifiability analysis and report parameter uncertainty, or clearly label the fitted values as representative rather than inferred.
  4. [Section 2 (Mathematical model, Eq. (3b))] The active transport equation (3b) is asserted rather than derived from a physical balance; the manuscript itself notes that 'despite its appearance, Eq.(3b) is not a momentum balance equation.' Since active transport is one of the two central mechanisms, the specific form of the symmetric sawtooth potential and the use of the effective friction xi_eff need a mechanistic derivation or, at minimum, a clear statement of which microphysical ingredients (for example, actin filament elongation rates or receptor detachment kinetics) produce each term. Moreover, because V2 is symmetric, the active mechanism cannot generate net drift by construction; the CytoD-HN case is therefore only a model of undirected rolling, and the absence of drift in CytoD-HN does not discriminate this active mechanism from a passive symmetric diffusive process.
minor comments (6)
  1. [Table 2] The unit for net mean velocity is listed as 'microm/s^-1' or 'um^-1' in the printed table; it should be microm/s.
  2. [Figure 1 caption] The caption writes 'Cyto-EV' while the text consistently uses 'CytoD-EV'; please unify the terminology.
  3. [Main text and Supplementary Information] Several cross-references appear as unresolved 'Section ??', both in the main text and in the supplementary material; these need to be fixed before publication.
  4. [Section 2 (Mathematical model, Eq. (3b))] The quantity Dr in Eq. (3b) is called a 'rotational diffusion coefficient' but it is assigned the translational value k_B T / xi_eff; please correct the terminology or use a distinct symbol.
  5. [Figure 9] The sensitivity axes for h2 are labeled in units of k_B T (for example, 'h2 = 1e6 kB T') while Table 4 reports h2 in joules; please make the units consistent or clearly state the conversion.
  6. [Table 3] The entry for the cytosol viscosity, 'x[10 - 1500] eta_w depending on the protein size', is garbled and should be rewritten as a clear range with a supporting reference.

Circularity Check

2 steps flagged · score 6.0 of 10

The paper's drift 'prediction' is built from fitted inputs: the experimental skewness evidence follows a sign-flipping preprocessing step, the simulated drift in Ctrl/CytoD-EV is produced by the calibrated asymmetric ratchet α=0.2, and the absence of drift in CytoD-HN is guaranteed by the symmetric active potential.

  1. self definitional [Section 2, 'Data post-processing'; used in Section 3, 'Normality and Skewness tests']
    "As last step before the data analysis, in order to identify directed transport phenomena, we standardized the preferential direction of the EVs towards positive values by inverting the sign of the displacements in samples where the linear regression slope computed on the tangent displacement was negative."

    The skewness test that later establishes a 'directed transport component' (Ctrl p=0.00296, CytoD-EV p=0.0275, CytoD-HN p=0.731) is applied to displacements that were sign-flipped whenever the trajectory's regression slope was negative. Since even a purely Brownian trajectory has a nonzero regression slope with high probability, this sign standardization makes the pooled displacement distribution positively biased by construction. The drift signal that the model is then designed to reproduce is therefore at least partly manufactured by the preprocessing step, rather than being independent evidence of a physical directed component.

  2. fitted input called prediction [Section 2, 'Mathematical model' (Eq. 2), Table 4, and Section 3, 'Sensitivity analysis' / 'Numerical vs in-vitro results']
    "On the other hand, other parameters of the model, such as those that determine the shape of the ratchet potentials or the transition rates of the Markov processes, cannot be directly related to experimentally measured physical quantities and are therefore at best calibrated to match the experimental data, see Table 4. [...] We observe that for α <1/2, the histograms are shifted to the right, meaning that the EV transport is biased towards positive values. On the contrary, for α >1/2 the drift shifts towards negative values."

    Table 4 calibrates α=0.2 for the passive ratchet potential V1, i.e. an asymmetric potential that produces positive drift by the flashing-ratchet mechanism cited in the paper. CytoD-HN, by contrast, is simulated with the spatially symmetric potential V2, which cannot produce net drift by symmetry. The subsequent numerical skewness 'agreement' — drift in Ctrl/CytoD-EV and no drift in CytoD-HN — therefore follows from the chosen fitted potential shapes and mechanism assignments rather than from an independent prediction. The sensitivity analysis explicitly shows that α controls the direction of drift, confirming that the simulated skewness is an output of a fitted input, not a free-of-fit result.

full rationale

The central validation claim — that the model reproduces the presence of a drifting component in Ctrl and CytoD-EV and its absence in CytoD-HN — reduces to fitted inputs by construction. On the experimental side, the skewness test is run after sign-flipping trajectories with negative regression slopes, which biases the pooled displacement distribution toward positive skew. On the modeling side, the simulated drift pattern is dictated by the calibrated asymmetric potential (α=0.2) for the passive ratchet and by the symmetric potential V2 for the active mechanism. The paper itself describes the relevant parameters as 'calibrated to match the experimental data' and acknowledges that 'further experimental validation with larger datasets would be necessary to fully confirm the proposed mechanistic interpretations.' This is the classic pattern of a fitted input being presented as a successful prediction, so the paper earns a partial-circularity score rather than a clean bill. There is independent content in the model — literature-based physical constants, comparisons of kinematic indicators, and the sensitivity analysis — and the mechanism assignment borrowed from D'Arrigo et al. (2021) is experimental rather than a self-defined uniqueness theorem. Those features prevent a higher score, but the load-bearing drift/no-drift comparison is not an independent test of the model.

Assumptions & free parameters 12 free parameters · 6 assumptions · 0 invented entities

The model rests on standard stochastic physics plus several biological modeling choices. The most consequential choices are the treatment-to-mechanism mapping and the ad hoc symmetric active potential, both of which are load-bearing for the claimed agreement. No new physical entities are introduced.

free parameters (12)
  • k = 8e-9 N/m
    Spring stiffness of the PrP-receptor elastic linker, calibrated to match experimental data (Table 4).
  • h1 = 1.7128e-14 J
    Height of the asymmetric saw-tooth potential V1, calibrated to match experimental trajectories.
  • h2 = 2.141e-14 J
    Height of the symmetric saw-tooth potential V2, calibrated to match experimental trajectories.
  • alpha = 0.2
    Asymmetry factor of V1; controls direction and magnitude of the passive drift, calibrated to data.
  • pi1_off = 500 Hz
    Passive unbinding rate, calibrated to match experimental data.
  • pi1_on = 180 Hz
    Passive binding rate, calibrated to match experimental data.
  • pi2_off = 10 Hz
    Active unbinding rate, calibrated to match experimental data.
  • pi2_on = 600 Hz
    Active binding rate, calibrated to match experimental data.
  • eta_PA = 100 Hz
    Passive-to-active transition rate for the Ctrl condition, calibrated to data.
  • eta_AP = 200 Hz
    Active-to-passive transition rate for the Ctrl condition, calibrated to data.
  • L1 = 5e-6 m
    Period of the passive potential V1, estimated as the average observed jump length in trajectories; still a model input.
  • L2 = 6.9e-7 m
    Receptor spacing estimate used as the period of V2, close to EV size; a model input.
assumptions (6)
  • domain assumption EV and receptor dynamics are overdamped Brownian with friction from Stokes law and Einstein relation.
    Eqs. (1a), (1b), (3a), (3b); standard physics but assumes spherical particles and ignores hydrodynamic interactions and membrane viscoelasticity.
  • domain assumption EV and receptor are coupled by a Hookean spring force fel = k(y-x) with constant k.
    Section 2, Mathematical model; the PrP-receptor complex is treated as a linear elastic linker.
  • ad hoc to paper CytoD-EV is purely passive, CytoD-HN is purely active, Ctrl combines both mechanisms.
    Section 2, Numerical method, first paragraph; this mapping is assumed from D'Arrigo et al. and not independently tested.
  • ad hoc to paper Active rolling can be represented by a flashing symmetric sawtooth potential V2 with zero mean force.
    Eq. (4) and Eq. (3b); no derivation from actin mechanics, and a symmetric potential cannot rectify, so the active mechanism produces no net drift by construction.
  • domain assumption EV motion is essentially one-dimensional along the neurite axis.
    Post-processing section; tangential component accounts for about 80 percent of motion, and linear regression defines the axis, but the approximation affects all kinematic indicators.
  • domain assumption Binding, unbinding, and passive-active switching are Markov processes with constant rates.
    Fig. 3 and transition step in Section 2; the authors acknowledge in the Discussion that this is a simplification.

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Pith. "Pith review of Modeling the prion protein-mediated transport of extracellular vesicles on the neuron surface." pith.science (2026). https://pith.science/paper/KEO45I7L

@misc{pith2026250203610,
  author       = {Pith},
  title        = {Pith review of: Modeling the prion protein-mediated transport of extracellular vesicles on the neuron surface},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KEO45I7L}},
  note         = {Machine review of arXiv:2502.03610}
}
read the original abstract

Neurodegenerative diseases are among the leading causes of global mortality, characterized by the progressive deterioration of specific neuron populations, ultimately leading to cognitive decline and dementia. Extracellular vesicles (EVs) are believed to play a role in the early stages of these diseases, acting as carriers of pathogens and contributing to neuroinflammation and disease propagation. This study presents a mathematical model aimed at characterizing the movement of EVs bearing prion protein (PrP) on their surface along neuronal surfaces. The model, informed by experimental data, investigates the influence of PrP and actin polymerization on EV transport dynamics and explores the possible interplay between passive and active mechanisms. EVs isolated from non-human astrocytes were analyzed under three conditions: untreated control (Ctrl), neurons treated with Cytochalasin D (CytoD-HN), and EVs treated with Cytochalasin D (CytoD-EV). The mathematical model is data-driven, testing different hypotheses regarding the underlying transport mechanisms. In the CytoD-EV dataset, EV movement was modeled using a flashing Brownian ratchet to represent directed motion. For active transport in the CytoD-HN set, a symmetric periodic potential was used to describe EV rolling along the neuron surface. The Ctrl scenario incorporates both mechanisms, reflecting a more complex transport behavior. A sensitivity analysis and comparison between numerical predictions and experimental data suggest that the model effectively captures key features of EV motion, providing a quantitative framework to interpret different transport regimes. While some variability remains, the approach offers a promising basis for future investigations into the role of cytoskeletal dynamics in EV-mediated disease propagation.

Figures

Figures reproduced from arXiv: 2502.03610 by the authors.

Figure 1
Figure 1. Size distribution of EVs in the 10,000 × g pellet according to TRPS. Image from ref. [3]. [3] G. D’Arrigo, M. Gabrielli, F. Scaroni, P. Swuec, L. Amin, A. Pegoraro, E. Adinolfi, F. Di Virgilio, D. Cojoc, G. Legname, et al. Astrocytes-derived extracellular vesi￾cles in motion at the neuron surface: Involvement of the prion protein. Journal of Extracellular Vesicles, 10(9):e12114, 2021. [4] Thomas C S¨udhof. Neuroligi… view at source ↗
Figure 1
Figure 1. Data acquisition and post processing. Top row: example of EVs trajectories (red trace) [PITH_FULL_IMAGE:figures/full_fig_p011_1.png] view at source ↗
Figure 2
Figure 2. Schematic representation of the modelled EVs transport mechanisms: (left) passive and (right) [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗
Figures from the paper (7 more)
Figure 3
Figure 3. Figure 3: Graph describing the transition probabilities in a small time-step ∆ [PITH_FULL_IMAGE:figures/full_fig_p016_3.png]
Figure 4
Figure 4. Figure 4: Scatter plot illustrating the relation between couples of kinematic indicators for the three [PITH_FULL_IMAGE:figures/full_fig_p020_4.png]
Figure 5
Figure 5. Figure 5: (Left) Violin plot of the displacements absolute value for each dataset: Ctrl (blue), CytoD-EV [PITH_FULL_IMAGE:figures/full_fig_p021_5.png]
Figure 6
Figure 6. Figure 6: Comparison between the averaged Mean Square Displacement (MSD) obtained from the ex [PITH_FULL_IMAGE:figures/full_fig_p024_6.png]
Figure 7
Figure 7. Figure 7: Comparison between the histogram of the non-zero displacement (black continuous profile) and [PITH_FULL_IMAGE:figures/full_fig_p025_7.png]
Figure 8
Figure 8. Figure 8: Comparison between the experimental (dashed) and numerical (shaded) ranges for the main [PITH_FULL_IMAGE:figures/full_fig_p025_8.png]
Figure 9
Figure 9. Figure 9: Sensitivity analysis performed varying a subset of the model parameters: the spring stifness [PITH_FULL_IMAGE:figures/full_fig_p027_9.png]

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Works this paper leans on

56 extracted references · 55 canonical work pages

  1. [1]

    Ait-Haddou and W

    R. Ait-Haddou and W. Herzog. Brownian ratchet models of molecular motors. Cell biochemistry and biophysics , 38:191–213, 2003

    work page 2003

  2. [2]

    Ajdari and J

    A. Ajdari and J. Prost. Drift induced by a spatially periodic potential of low symmetry-pulsed dielectrophoresis. Comptes rendus de l academie des sciences serie II , 315(13):1635–1639, 1992

    work page 1992

  3. [3]

    Andrini, V

    D. Andrini, V. Balbi, G. Bevilacqua, G. Lucci, G. Pozzi, and D. Riccobelli. Mathematical modelling of axonal cortex contractility. Brain Multiphysics, 3:100060, 2022

    work page 2022

  4. [4]

    2012 alzheimer’s disease facts and figures

    Alzheimer’s Association et al. 2012 alzheimer’s disease facts and figures. Alzheimer’s & dementia , 8(2):131–168, 2012

    work page 2012

  5. [5]

    R. D. Astumian and M. Bier. Fluctuation driven ratchets: molecular motors. Physical review letters, 72(11):1766, 1994

    work page 1994

  6. [6]

    J. J. Blum and M. C. Reed. A model for slow axonal transport and its application to neurofilamentous neuropathies. Cell motility and the cytoskeleton , 12(1):53–65, 1989

    work page 1989

  7. [7]

    P. C. Bressloff. Stochastic processes in cell biology , volume 41. Springer, 2014

    work page 2014

  8. [8]

    A. Brown. Slow axonal transport: stop and go traffic in the axon. Nature Reviews Molecular Cell Biology, 1(2):153–156, 2000

    work page 2000

Show all 56 references
  1. [9]

    A. Brown. Slow axonal transport. In Larry R. Squire, editor, Encyclopedia of Neuroscience , pages 1–9. Academic Press, Oxford, 2009

    2009

  2. [10]

    stop-and-go

    A. Brown, L. Wang, and P. Jung. Stochastic simulation of neurofilament transport in axons: the “stop-and-go” hypothesis. Molecular biology of the cell , 16(9):4243–4255, 2005

    2005

  3. [11]

    F. F. Damberger, B. Christen, D. R. P´ erez, S. Hornemann, and K. W¨ uthrich. Cellular prion protein conformation and function. Proceedings of the National Academy of Sciences , 108(42):17308–17313, 2011

    2011

  4. [12]

    D’Arrigo, M

    G. D’Arrigo, M. Gabrielli, F. Scaroni, P. Swuec, L. Amin, A. Pegoraro, E. Adinolfi, F. Di Virgilio, D. Cojoc, G. Legname, et al. Astrocytes-derived extracellular vesicles in motion at the neuron surface: Involvement of the prion protein. Journal of Extracellular Vesicles , 10(...

    2021

  5. [13]

    De Rooij and E

    R. De Rooij and E. Kuhl. Physical biology of axonal damage. Frontiers in cellular neuroscience , 12:144, 2018

    2018

  6. [14]

    Drago, M

    F. Drago, M. Lombardi, I. Prada, M. Gabrielli, P. Joshi, D. Cojoc, J. Franck, I. Fournier, J. Vizioli, and C. Verderio. Atp modifies the proteome of extracellular vesicles released by microglia and influences their action on astrocytes. Frontiers in pharmacology, 8:910, 2017

    2017

  7. [15]

    B. N. Dugger and D. W. Dickson. Pathology of neurodegenerative diseases. Cold Spring Harbor perspectives in biology, 9(7):a028035, 2017

    2017

  8. [16]

    S. N. Ethier and J. Lee. The flashing brownian ratchet and parrondo’s paradox. Royal Society Open Science, 5(1):171685, 2018

    2018

  9. [17]

    R. P. Feynman, R. B. Leighton, M. Sands, et al. The Feynman lectures on physics , volume 1. Addison-Wesley Reading, MA, 1971

    1971

  10. [18]

    Frost and M.I

    B. Frost and M.I. Diamond. Prion-like mechanisms in neurodegenerative diseases. Nature Reviews Neuroscience, 11(3):155–159, 2010

    2010

  11. [19]

    Gabrielli, I

    M. Gabrielli, I. Prada, P. Joshi, C. Falcicchia, G. D’Arrigo, G. Rutigliano, E. Battocchio, R. Zenatelli, F. Tozzi, A. Radeghieri, et al. Microglial large extracellular vesicles propagate early synaptic dys- function in alzheimer’s disease. Brain, 145(8):2849–2868, 2022

    2022

  12. [20]

    Gallart-Palau, X

    X. Gallart-Palau, X. Guo, A. Serra, and S. K. Sze. Alzheimer’s disease progression characterized by alterations in the molecular profiles and biogenesis of brain extracellular vesicles. Alzheimer’s research & therapy, 12(1):1–15, 2020

    2020

  13. [21]

    Garc´ ıa-Garc´ ıa, P

    R. Garc´ ıa-Garc´ ıa, P. Collet, and L. Truskinovsky. Guided active particles. Physical Review E , 100(4):042608, 2019

    2019

  14. [22]

    Goychuk, V

    I. Goychuk, V. O. Kharchenko, and R. Metzler. Molecular motors pulling cargos in the viscoelastic cytosol: how power strokes beat subdiffusion. Physical Chemistry Chemical Physics , 16(31):16524– 16535, 2014

    2014

  15. [23]

    S.V. Gupta. Units of measurement . Springer, 2020. 30

    2020

  16. [24]

    Hardy and H

    J. Hardy and H. Orr. The genetics of neurodegenerative diseases. Journal of neurochemistry , 97(6):1690–1699, 2006

    2006

  17. [25]

    A. F. Hill. Extracellular vesicles and neurodegenerative diseases. Journal of Neuroscience , 39(47):9269–9273, 2019

    2019

  18. [26]

    M. M. Holm, J. Kaiser, and M. E. Schwab. Extracellular vesicles: multimodal envoys in neural maintenance and repair. Trends in Neurosciences, 41(6):360–372, 2018

    2018

  19. [27]

    Matlab version: 9.13.0 (r2022b), 2022

    The MathWorks Inc. Matlab version: 9.13.0 (r2022b), 2022

    2022

  20. [28]

    Jucker and L

    M. Jucker and L. C. Walker. Propagation and spread of pathogenic protein assemblies in neurode- generative diseases. Nature neuroscience, 21(10):1341–1349, 2018

    2018

  21. [29]

    Keller and C

    D. Keller and C. Bustamante. The mechanochemistry of molecular motors. Biophysical journal , 78(2):541–556, 2000

    2000

  22. [30]

    Korson, W

    L. Korson, W. Drost-Hansen, and F. J. Millero. Viscosity of water at various temperatures. The Journal of Physical Chemistry , 73(1):34–39, 1969

    1969

  23. [31]

    G. Legname. Elucidating the function of the prion protein. PLoS pathogens, 13(8):e1006458, 2017

    2017

  24. [32]

    Lombardi, M

    M. Lombardi, M. Gabrielli, E. Adinolfi, and C. Verderio. Role of atp in extracellular vesicle biogenesis and dynamics. Frontiers in Pharmacology, 12:654023, 2021

    2021

  25. [33]

    M. O. Magnasco. Forced thermal ratchets. Physical Review Letters , 71(10):1477, 1993

    1993

  26. [34]

    Marbach, J

    S. Marbach, J. A. Zheng, and M. Holmes-Cerfon. The nanocaterpillar’s random walk: diffusion with ligand–receptor contacts. Soft Matter , 18(16):3130–3146, 2022

    2022

  27. [35]

    McKinney et al

    W. McKinney et al. Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference , volume 445, pages 51–56. Austin, TX, 2010

    2010

  28. [36]

    C. S. Peskin, G. B. Ermentrout, and G. F. Oster. The correlation ratchet: a novel mechanism for generating directed motion by atp hydrolysis. In Cell Mechanics and Cellular Engineering , pages 479–489. Springer, 1994. 31

    1994

  29. [37]

    J. C. Polanco, C. Li, N. Durisic, R. Sullivan, and J. G¨ otz. Exosomes taken up by neurons hijack the endosomal pathway to spread to interconnected neurons. Acta neuropathologica communications, 6(1):1–14, 2018

    2018

  30. [38]

    S. B. Prusiner. Biology and genetics of prions causing neurodegeneration. Annual review of genetics , 47:601–623, 2013

    2013

  31. [39]

    H. Qian. The mathematical theory of molecular motor movement and chemomechanical energy transduction. Journal of Mathematical Chemistry , 27:219–234, 2000

    2000

  32. [40]

    Riccobelli

    D. Riccobelli. Active elasticity drives the formation of periodic beading in damaged axons. Physical Review E, 104(2):024417, 2021

    2021

  33. [41]

    Riesner, K

    D. Riesner, K. Kellings, K. Post, H. Wille, H. Serban, D. Groth, M. A. Baldwin, and S. B. Prusiner. Disruption of prion rods generates 10-nm spherical particles having high alpha-helical content and lacking scrapie infectivity. Journal of virology , 70(3):1714–1722, 1996

    1996

  34. [42]

    Ruan, J.C

    Z. Ruan, J.C. Delpech, S. Venkatesan Kalavai, A. A. Van Enoo, J. Hu, S. Ikezu, and T. Ikezu. P2rx7 inhibitor suppresses exosome secretion and disease phenotype in p301s tau transgenic mice. Molecular neurodegeneration, 15:1–14, 2020

    2020

  35. [43]

    Sampaoli, A

    S. Sampaoli, A. Agosti, G. Pozzi, and P. Ciarletta. A toy model of misfolded protein aggregation and neural damage propagation in neurodegenerative diseases. International Journal of Non-Linear Mechanics, 144:104083, 2022

    2022

  36. [44]

    Sardar Sinha, A

    M. Sardar Sinha, A. Ansell-Schultz, L. Civitelli, C. Hildesj¨ o, M. Larsson, L. Lannfelt, M. Ingelsson, and M. Hallbeck. Alzheimer’s disease pathology propagation by exosomes containing toxic amyloid- beta oligomers. Acta neuropathologica, 136:41–56, 2018

    2018

  37. [45]

    Sarnataro, A

    D. Sarnataro, A. Pepe, and C. Zurzolo. Cell biology of prion protein. Progress in molecular biology and translational science , 150:57–82, 2017

    2017

  38. [46]

    Stephenson, E

    J. Stephenson, E. Nutma, P. van der Valk, and S. Amor. Inflammation in cns neurodegenerative diseases. Immunology, 154(2):204–219, 2018

    2018

  39. [47]

    Stokes et al

    G.G. Stokes et al. On the effect of the internal friction of fluids on the motion of pendulums. 1851. 32

  40. [48]

    W. K. Surewicz and M. I. Apostol. Prion protein and its conformational conversion: a structural perspective. Prion Proteins, pages 135–167, 2011

    2011

  41. [49]

    Toral and P

    R. Toral and P. Colet. Stochastic numerical methods: an introduction for students and scientists . John Wiley & Sons, 2014

    2014

  42. [50]

    Van den Bedem and E

    H. Van den Bedem and E. Kuhl. Tau-ism: the yin and yang of microtubule sliding, detachment, and rupture. Biophysical journal, 109(11):2215–2217, 2015

    2015

  43. [51]

    Van Rossum and F

    G. Van Rossum and F. L. Drake Jr. Python reference manual . Centrum voor Wiskunde en Infor- matica Amsterdam, 1995

    1995

  44. [52]

    Vilela, N

    M. Vilela, N. Halidi, S. Besson, H. Elliott, K. Hahn, J. Tytell, and G. Danuser. Fluctuation analysis of activity biosensor images for the study of information flow in signaling pathways. In Methods in enzymology, volume 519, pages 253–276. Elsevier, 2013

    2013

  45. [53]

    Virtanen, R

    P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, et al. Scipy 1.0: fundamental algorithms for scientific com- puting in python. Nature methods, 17(3):261–272, 2020

    2020

  46. [54]

    Y. Wang, V. Balaji, S. Kaniyappan, L. Kr¨ uger, S. Irsen, K. Tepper, R. Chandupatla, W. Maetzler, A. Schneider, E. Mandelkow, et al. The release and trans-synaptic transmission of tau via exosomes. Molecular neurodegeneration, 12(1):1–25, 2017

    2017

  47. [55]

    Weickenmeier, M

    J. Weickenmeier, M. Jucker, A. Goriely, and E. Kuhl. A physics-based model explains the prion-like features of neurodegeneration in alzheimer’s disease, parkinson’s disease, and amyotrophic lateral sclerosis. Journal of the Mechanics and Physics of Solids , 124:264–281, 2019

    2019

  48. [56]

    Weickenmeier, E

    J. Weickenmeier, E. Kuhl, and A. Goriely. Multiphysics of prionlike diseases: Progression and atrophy. Physical review letters , 121(15):158101, 2018. 33

    2018

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.