A Soft-Robotic Biomimetic Benchtop Model for Esophageal Motility Simulation

Domain: biology
Published: 2026-03-12
Editorial outcome: minor_revision

Abstract
Large animal models, while valuable, are expensive, time-consuming, and limited to
discrete interventional or terminal timepoints, while existing benchtop models do not offer
an accurate representation of the esophageal environment. Moreover, current pre-clinical
models cannot effectively simulate swallowing dysfunction (dysphagia), restricting
progress in understanding motility disorders like achalasia and hindering evidence-based
dietary recommendations. In response, we present RoboGullet, a biomimetic soft-robotic
model with independent localized longitudinal and circumferential muscle actuation
enabling, for the first time, simulation of both normal and diseased esophageal motility.
We further enhance realism with a biohybrid variant, RoboGullet+, incorporating porcine
esophageal mucosa/submucosa. We demonstrate this platform's versatility through three
key applications: assessing stent migration, simulating achalasia I-III within clinical
diagnostic criteria, and analyzing bolus swallowing. Our findings reveal that: (1) stent
migration increases over fivefold when incorporating longitudinal muscle movement
versus isolated circumferential; (2) using a viscous non-Newtonian bolus improves high-
resolution manometry diagnostic sensitivity of Achalasia III through increasing the Distal
Latency diagnostic metric by 20.83%; and (3) stirring Greek-style yoghurt (common non-
Newtonian dietary recommendation) significantly improves bolus transit versus unstirred
for Achalasia Types I-II patients. This establishes RoboGullet+ as a powerful translational
tool, advancing our understanding of esophageal motility and its therapeutic interventions.

Introduction
The esophagus’s primary function is to facilitate swallowing, the transportation of bolus
from mouth to stomach 1. This is achieved through the coordinated peristalsis of two
muscular layers (an inner circumferential and an outer longitudinal layer) and deglutitive
relaxation of an Upper Esophageal Sphincter (UES) and a Lower Esophageal Sphincter
(LES) which respectively control intake and outflow 2. Dysphagia, or difficulty
swallowing, affects about 20% of the global population and up to 50% of people over the
age of 60 3. This can be for a number of reasons, such as malignancy and motility disorders
such as achalasia 4. Approximately 35% of dysphagia cases are malignant 5, primarily
resulting from esophageal cancer 6 where tumors partially or completely obstruct the
esophageal lumen. Approximately 5% of cases are due to achalasia 5, a major motility
disorder characterized by incomplete LES relaxation during swallows combined with an
absence of peristalsis 4.

The majority of malignant dysphagia cases are incurable at diagnosis 6. These patients
require palliative treatment, most commonly through esophageal self-expanding metal
stents (SEMS) due to their ease of placement, availability, clinical effectiveness, and
potential for removal if necessary 6. SEMS include uncovered (U-SEMs), partially-covered
(PC), and fully-covered (FC) types. However, these designs have limitations: tissue
ingrowth in PC and U-SEMs can re-block the channel and render them unremovable 7.
Conversely, the lack of tissue ingrowth in FC SEMS results in high migration rates (29–
40% 8). Treatment of achalasia, like malignancy, is generally palliative and primarily
targets relaxing the LES to relieve the outflow obstruction. It, however, has excellent
results, seeing >90% success with surgery 9. Notwithstanding, dietary modification is also
often suggested, particularly for patients who failed therapy or those who are poor surgical
candidates for definitive interventions. Despite this, there has been limited research into
evidence-based diet recommendations 10. Achalasia diagnosis is extensively described in
the literature, with the gold standard being high resolution manometry (HRM) 11,12. As a
chronic, progressive disease, early manometric changes, especially in type III achalasia,
may not fulfil established diagnostic criteria and are often misdiagnosed or missed entirely.
It has been suggested that altering the bolus viscosity used for test swallows during HRM
can accentuate diagnostic yield 13,14.

Current in vivo evaluations of dysphagia remain limited to, large animal models for
malignant dysphagia or limited clinical studies for achalasia 15. These studies can be
expensive and time-consuming, provide information only at specific interventional and
terminal timepoints, and lack repeatability across samples. This poses challenges for
functional device assessments and for standardized dietary recommendations. Benchtop
models are used as alternatives or supplements to pre-clinical in vivo models for device
testing, disease evaluation, and surgical training. Highly accurate biomimetic benchtop
models exist for some aspects of the body, such as the heart 16. However, replication of key
physical and functional properties in esophageal or gastrointestinal (GI) tract models has
been limited. Rigid roller systems, the most basic approach 17, simulate peristaltic waves
mechanically. Soft models better replicate the organ’s conformable nature 18. Here, soft
expandable pressure vessels 19, cables 20, and pneumatic cuffs 21, pistons 22, and chambers
23,24 have been employed, with these pneumatic artificial muscles (PAMs) creating the
necessary radial contractile movement. These models, however, lack realistic tissue
interaction and only simulate circumferential muscles. This neglects the longitudinal
muscles, which provide axial movement and account for half the musculature involved in
GI motility 25. Peerlink et al. 26 did introduce an intestinal model with longitudinal
movement but it is without independent PAMs and thus control. Although PAMs for axial
movement exist 27–29, no GI motility model has yet integrated them with radial PAMs. This
is due to the challenges in fabricating two concentric layers with differing independently
actuating directions – typically PAMs achieve singular direction/shape control with a
single soft material and an embedded, less extensible layer 30. Crucially, this lack of
independent circumferential and longitudinal muscle control, on top of not fully describing
normal esophageal motility, means motility disorders, where the muscle layer’s
contractions may be asynchronous or vary in amplitude, cannot be simulated

Here we propose RoboGullet+, a soft-robotic biomimetic benchtop model of the
esophagus, which offers controlled replication of esophageal motility mechanics.
Additionally, it facilitates the creation of bio-hybrid structures to further match the
frictional properties and compliance of native esophagus. We demonstrate that this model
can recreate normal and diseased esophageal motility accurately. Furthermore, we
demonstrate the versatility of the model by evaluating commercial stent migration and the
effects that bolus properties have on achalasia diagnosis and swallowing, showing that the
model can be used to simulate a multitude of conditions relevant to device evaluation,
disease management, and personalized clinical testing.

Results
Study design and workflow
In this work, we propose a biomimetic soft-robotic model that replicates the movement of
circumferential and longitudinal muscles to accurately simulate esophageal motility
(Supplementary Movie 1). The design process is illustrated in Fig. 1a. The esophagus
comprises concentric layers of circumferential (inner) and longitudinal (outer) muscles that
locally contract and relax to achieve radial and axial movement respectively. This
independence allows pathophysiological simulation where muscles are asynchronous 31–34.
Similar to Park et al. 29, we used a biomimetic approach to design the soft-robotic
representation of the muscle orientation and position (Fig. 1a). This consists of two
concentric layers of pneumatically actuated hexagonal rings. The inner layer, fabricated
from a softer silicone (EcoFlex 00-30, SmoothOn), mimics the circumferential muscles
while the outer layer, made of a stiffer silicone (DragonSkin 20), constrains radial
expansion and mimics the longitudinal muscle movement via a bellows-type PAM design
(Supplementary Fig. 1) 35. Similar to other GI motility models 23,26 and the peristaltic
earthworm body 36, the model consists of repeating segments of concentric rings.
Fabrication involved a dual-material lost-wax casting procedure (Supplementary Fig. 2
a,b). A pneumatic control system was set up for the model’s operation (see Supplementary
Note 1 and Supplementary Fig. 3).

Bolus transport through the esophagus (Fig. 1b) has three stages: passive, pre-bolus, and
post-bolus. In the pre-bolus stage longitudinal muscles contract, increasing circumferential
muscle concentration 37. The circumferential muscles then contract radially, forming a
region of high pressure 34. The opposite occurs in the post-bolus stage, creating a region of
low pressure 34. Remaining regions are in a pressure-neutral passive state. The method of
simulating these three stages with the soft-robotic model is shown computationally
(Abaqus, Dassault Systèmes) in Fig. 1b. Pressurization of all chambers to a mid-pressure
(half the pressure used for the pre-bolus stage) replicates the passive stage. This no-load
condition, where the chambers are at half-peak pressures, is referred to as the model
‘passive’ state. Decreasing the pressure in the longitudinal chambers and increasing it in
the circumferential chambers replicates the pre-bolus stage. The opposite then results in
the post-bolus stage.

To demonstrate model versatility, we perform three studies (Fig. 1c): (1) stent migration
under a ‘simple swallowing wave’, (2) the effects of bolus viscosity on HRM diagnosis of
achalasia, and (3) the swallowing efficiency of mixed and unmixed Greek-style yoghurt
under achalasia types II and I. In these studies, porcine esophageal tissue was incorporated
(Supplementary Fig. 2c), running through the model’s inner lumen which, aided by the
model’s hexagonal shape, further mimics the esophageal environment (Fig. 1d). In any
studies without tissue, the model is referred to as RoboGullet as opposed to RoboGullet+.

Computational design of soft-robotic structure
The internal lumen of the esophagus is not a perfect circle. It consists of 4-6 folds 38, along
which it buckles and gives it a polygon-like shape in its passive state 38, which significantly
affects bolus flow 39. To replicate this, we conducted a study to see which shape best
matches the porcine esophagus in its passive state (Fig. 2a). FEA was used to simulate
pneumatic actuation of a soft-robotic model segment with shapes ranging from 0 (circle)
to 6 folds (hexagon). Lumen surface areas at the model ‘passive’ state was mapped
(MATLAB) and compared to porcine esophageal 38 through Dice-Sørensen similarity
coefficient (DSC) for area (Fig. 2b) 40 and the turning distance for shape (Fig. 2c) 41. The
highest turning distances were hexagon (0.77) and triangle (0.82), with hexagon also
scoring highest in RDSC (0.83) and was thus chosen for the design.

The circumferential chambers for radial displacement, based on Chen et al. 42, and the
longitudinal chamber, designed as bellows for axial displacement (Supplementary Fig. 1),
were tested for independent actuation. An FEA model (Abaqus, Dassault Systèmes) was
used to study independence of the proposed chambers and inform material selection (Fig.
2d). The circumferential and longitudinal chambers were inflated to half their peak pressure
(model ‘passive’ state), then the circumferential inflated to peak pressure while
longitudinal held constant, and finally the longitudinal inflated to peak pressure while
circumferential held constant. The displacement in the middle of the lumen surface was
tracked over time. Initially, EcoFlex 00-30 (Fig. 2d) was used for the whole model as this
has similar properties to gastrointestinal tissues 43. The resulting post-decoupling axial
displacement shows a correlation coefficient of 0.64, meaning there is a lack of
independent behavior (Fig. 2e). Replacing the longitudinal chambers with DragonSkin 20
(Fig. 2d) improved independence, with a correlation coefficient of 0.98 indicating the
achievement of independent actuation (Fig. 2f).

Computational and experimental characterization of the soft-robotic model
A scaled version of RoboGullet (10 segments) was created in Abaqus (Dassault Systèmes)
with a HRM catheter (ManoScan ESO 3D, Given Imaging) 44 (Fig. 3a). This setup was
then recreated experimentally (Fig. 3b). For both, pressure data along the manometry
catheter was captured during a swallow (Fig. 3c) (wave settings discussed in
Supplementary Note 2), and the computational data interpolated 45. The resulting
computational HRM topography plots were imported into MATLAB and processed to
match length and timescales. To compare these plots, pressure vs time curves were
extracted at four intervals along the catheter 46 from upper to lower pressure topography
plot peaks (Fig. 3c). Mean amplitude of contraction and wave velocity, commonly used to
describe a swallow wave 47, were chosen as comparison parameters. Wave patterns differ
slightly due to computational simplifications but trends match – the computational model
predicted the experimental mean amplitude of contraction (117.81 ± 12.32 mmHg) and the
wave velocity (4.16 ± 0.04 cm/s) with a computational accuracy of 95.14% and 89.70%
respectively when compared to the experimental data.

Soft-robotic model predicts stent migration in a design dependent manner and the
effect of physiological factors
A simple swallowing wave (Supplementary Fig. 4c), meaning one excluding UES/LES
simulation to allow for linear migration tracking without interference, was simulated for
this study – the HRM plots of the input PID regulated wave, and corresponding PWM input
wave are shown in Fig. 4a. These simulated swallows parameters, detailed in
'Computational and experimental characterization of the soft-robotic model', fit within
those of normal swallowing waves 1 1. Six commercially available stents with either partial
(PC) or full covering (FC) were evaluated (Fig. 4b) from two brands. The type, outer
diameter (OD), and length (L) for each stent are detailed in Supplementary Table 1. Brand
A is a multiple nitinol wire braided construction 48 and Brand B is a dual-gauge nitinol wire
construction 49. Stents were deployed just below the midpoint of the initial model segment
and subjected to 30 simulated swallows (n = 5). Stent displacement was tracked with a
tethered washer (6.4g). Both RoboGullet (Fig. 4c) and RoboGullet+ (Fig. 4d) were used.
Images of stent at start and endpoints are shown in Fig. 4e-f, and intermediate images in
Supplementary Movie 2.

In RoboGullet, Brand A stents displaced significantly more (Stent I = 20. 5 ± 4.3mm, Stent
II = 25.9 ± 1.4mm) than Brand B (Stent III = 4.0 ± 0.9mm, Stent IV = 5.3 ± 4.5mm, Stent
V = 1.7 ± 0.3mm) (p = 0.0383). The regression model in Fig. 4g shows changing brand as
the only statistically significant parameter, with an expected 20.1mm displacement
decrease when switching from Brand A to B.

Adding tissue (RoboGullet+), increased displacement in Stent II by 24.3 ± 11.7mm, Stent
IV by 2.9 ± 4.9mm, and Stent V by 9.1 ± 3.5mm. It decreased in Stent I by 18.9 ± 4.3mm
and in Stent III by 2.0 ± 1.5mm. Stent migration is influenced by multiple factors; our
model helps disentangle these factors to inform improved stent designs. Tissue is
particularly important when comparing the migration of PC, which aim to reduce migration
through tissue interaction 6, and FC stents. Since Stents III and V’s only variation is this
covering, this can be used to illustrate the importance of the biohybrid approach (Fig. 4h),
whereby when tissue is added the FC version’s displacement significantly increases (P <
0.005), while the PC version decreases. Despite this, a regression analysis performed for
RoboGullet+ revealed no statistically significant parameters.

Combining circumferential and longitudinal pneumatic actuation significantly increased
displacement (P < 0.001) (Fig. 4i.). Stent I was evaluated under isolated circumferential
and longitudinal actuation (n = 5). The stent displaced 20.5 ± 4.3mm under the original
combined muscle conditions, compared to the 3.8 ± 5.7mm and -0.1 ± 0.3mm under these
circumferential and longitudinal isolated conditions, respectively.

Soft-robotic simulation of achalasia subtypes and effect of bolus viscosity on their
diagnosis and dietary recommendations
Swallows mimicking normal and three types of achalasia were simulated with
RoboGullet+ using the pneumatic inputs in Supplementary Fig. 5-6. These swallows are
more complex than that used for stent migration studies, as these include the UES and LES
(simulated with 1st and 16th model segment respectively – functional, not anatomical,
simulation was prioritized as there are no major histological differences between UES/LES
and esophageal body 50,51). A ManoScan ESO manometry catheter (Given Imaging) was
inserted in the model and swallows recorded using ManoScan Acquisition. The resulting
HRM plots (Supplementary Fig. 7 – 15) were analyzed in ManoView ESO to extract the
diagnostic metrics 11 (labelled in Fig. 5a): distal latency (DL), integrated relaxation
pressure (IRP), and distal contractile integral (DCI).

The clinical standard for achalasia diagnosis, the Chicago Classification 4.0 11, gives
thresholds for these metrics which define normal and achalasia swallows (Fig. 5, b-d,
dashed lines and grey fill). The measured mean metric values for swallows under the
typical 5mL water bolus 11 are shown in Fig. 5b–d. Both mean and standard deviations for
DL, DCI, and IRP in all four simulated states lie within the thresholds, showing successful
simulation. Omissions, such as achalasia type II with DCI (Fig. 5c), occur because that
metric is not applicable in its diagnosis 11.

The effect of bolus viscosity on these metrics was also investigated. Newtonian solutions
(50, 62.5, 75, 87.5, and 100 wt% glycerol in water) were used, alongside non-Newtonian
solutions made from a commercial dysphagia thickener mixed with water at levels
commonly used by patients (0.6, 1.35, 2.1, 2.85, and 3.6 wt%) 52 and equivalent to thin-to-
honey viscosities as per the National Dysphagia Diet 52,53.

Only DL was notably affected. In normal swallows (Fig. 5e), DL remained relatively stable
with increasing viscosity for both solutions, except for the non-Newtonian bolus at higher
viscosities. Here, DL drops from 6.6 ± 0.1s at 2.1wt% to 4.1 ± 0.0s and 4.4 ± 0.1s at
2.85wt% and 3.6wt% respectively. A similar downward trend is observed with the non-
Newtonian solution in the achalasia type III swallows (Fig. 5f), decreasing by 20.8% over
the entire range. The Newtonian solution sees little effect until 87.5wt% and 100wt%, when
DL drops from 2.8 ± 0.2s to 1.8 ± 0.9s and 0.6 ± 0.1s respectively. As IRP (Fig. 5, g-i) and
DCI do not change significantly, these trends suggest that in mild achalasia type III cases,
where the DL is close to normal (4.5s), increasing bolus viscosity could improve diagnosis
rates.

Yoghurt is a common dietary recommendation for patients with achalasia 54. Seeing the
lack of effect viscosity change had in diagnosing the more severe achalasia type II and I,
we evaluated how changing the viscosity of yoghurt through stirring would affect bolus
transit. The 9th segment was contracted pre-swallow to reduce travel distance. Unstirred
(US) and stirred (S) yoghurt was injected above this. Swallows were carried out in a worst-
case scenario with no LES relaxation (n=5). The weight of the bolus extruded was tracked
from swallow initiation over 5 minutes. Stirring significantly improved bolus passage for
both types of achalasia (P < 0.05) (Fig. 5j).

Methods
Study design
This study aimed to develop and demonstrate the versatility of an esophageal model that
more accurately describes esophageal motility through simulation of both circumferential
and longitudinal muscle groups. This model’s inner geometry was based on the average
diameter of the esophagus from literature 50 and shaped to mimic mucosal folding seen in
an esophagus 38,39. A dual-material model (softer EcoFlex 00-30 (circumferential) and
stiffer DragonSkin 20 (longitudinal) for independent actuation) was developed to achieve
independence between circumferential and longitudinal muscle movement, requiring a
bespoke lost-wax casting procedure to be molded. The model was used to carry out three
studies under various differing swallowing conditions. Each of these swallowing
conditions was defined by values given in the Chicago Classification 4.0 11 and validated
using high resolution manometry (Sierra Systems, ManoScan). Firstly, the model was used
to evaluate stent migration over 30 normal swallowing cycles with a 5mL bolus of water.
This was the only study in which the UES and LES were excluded. The displacement of 5
commercial stents in RoboGullet and RobotGullet+ was tracked using a washer (6.4g)
hanging from the stents by a suture being imaged post-swallow. Migration of one stent was
also evaluated with isolated circumferential and longitudinal muscle activation. Secondly,
RoboGullet+ was used to evaluate the effect of bolus viscosity on the diagnosis of
swallowing under achalasia conditions. Boluses used were water, glycerol mixed with
water (50%wt, 75%wt, and 100%wt) and ThickenUp mixed with water (0.6%wt, 2.1%wt,
and 3.6%wt). The swallows were recorded using HRM (ManoScan) and values for IRP,
DCI, and DL were obtained using ManoScan ESO Analysis. Lastly, RoboGullet+ was used
to investigate swallowing efficiency for non-Newtonian boluses (mixed/unmixed Greek
yoghurt) under severe achalasia types I and II. Swallowing efficiency was measured as the
weight of bolus passed through the model.

Model geometry and computational design.
The model dimensions, seen in Supplementary Fig. 1, were based on the average
esophageal dimensions (20mm ID, 32cm length) 50. The circumferential pneumatics were
based on that proposed by Bhattacharya et al. 23 and other foundational work by the Xu
group 80,81, and the longitudinal chambers that of a bellows 35. These internal dimensions
are also described in Supplementary Fig. 1. EcoFlex 00-30 was selected for circumferential
chamber use due to its similar properties to gastrointestinal tissues 43.

In order to select the material to be used for the longitudinal pneumatic chambers, a portion
of the model (5 segments) was modeled and simulated in Abaqus Explicit with the entire
model made of EcoFlex 00-30 and then with the longitudinal chambers made from
DragonSkin 20. The material properties for both materials were modeled using a
hyperelastic Ogden model 82 (Supplementary Table 2). The model was inflated to the
midpoint over 2 seconds, then the circumferential chambers inflated to maximum pressure,
while longitudinal chambers were held constant over 2 seconds, and then the longitudinal
chambers inflated to maximum pressure with the circumferential held constant. To achieve
a wave shape, the maximum pressure of each circumferential chamber was set to 12.5kPa,
16kPa, 20kPa, 16kPa, and 12.5kPa respectively for both studies. Longitudinal pressures
were tuned for equivalent axial displacement in the passive state (Supplementary Fig. 16).
The max pressure for each longitudinal chamber during the EcoFlex only study was
2.375kPa, 3kPa, 3.75kPa, 3kPa, and 2.375kPa respectively. The max pressure for each
longitudinal chamber during the DragonSkin study was 36.894kPa, 51.782kPa, 64.727kPa,
51.782kPa, and 36.894kPa respectively. The displacement of a node in the middle on the
surface of the model was tracked.

The number of folds in the human esophagus is between 3 and 6 38,39. To decide on the
optimum shape to replicate these folds, a single segment of the model was modeled and
simulated in Abaqus Explicit in five different shapes: a circle, a triangle, a square, a
pentagon, and a hexagon. A symmetry condition was applied to either side of the segment.
50 kPa of pressure was applied to the circumferential chamber over 2 seconds. Images were
taken at the midpoint of closure (1/2 the time to full closure). The resulting images were
compared to the shape of the inner lumen of an esophagus 38 by converting them to a
boundary and calculating the Dice-Sørensen similarity coefficient (DSC) using MATLAB.

The final model design was computationally validated by simulating a section of the model
(10 segments) undergoing a manometry procedure in Abaqus Explicit. A tube matching
the dimensions and sensor arrangement of a 4.2mm OD Sierra solid-state manometry
catheter 44 was placed within the model ‘tract’. Manometry catheter set as a stationary rigid
material due to relative difference in material stiffness (Young’s modulus (190 GPa) and
density of 1.07 × 10-9). Ogden model (Supplementary Table 2) used again for DragonSkin
20 and EcoFlex 00-30. A step of 1 second was used for the model to inflate to the midpoint,
and then the swallow step was carried out over 12.6. The Von Mises stress and equivalent
strain in two groups of central EcoFlex 00-30 elements were tracked up to the
midpoint/’passive state’ (Supplementary Fig. 17). This revealed negligible change in
instantaneous Young’s modulus (0.14%) from ‘relaxed’ to ‘passive’ states. ‘Simple’
swallowing wave (Supplementary Fig. 4c) then simulated and contact pressure was tracked
over pressure sensing catheter areas 44. A general contact with frictionless condition was
used over the entire model, and a surface-to-surface contact was used between the ‘tract’
wall of the esophageal model and the catheter with a friction coefficient of 0.213 83.
Pressure data extracted using a Python script (Supplementary Note 3), interpolated with
the same methods forming the basis of those used by manometry software 44,45, and plotted
in the form of manometry graphs 45. The resulting plots were then compared to
experimentally obtained HRM swallowing data. Velocity taken as time between peak
pressures at initial/final points over distance. Amplitude taken as baseline-peak average
across four evenly spaced points 46.

Model and pneumatic control board fabrication.
The molds for the beeswax cores, consisting of an outer shell and circumferential and
longitudinal chamber positives, were 3D printed using a Form 3 SLA printer (Formlabs).
Steel rods (3 mm OD, TOPPROS) inside silicone tubing (McMaster-Carr, 1/8” ID, ¼” OD)
held the positives in place within the mold. MoldStar 60 silicone (SmoothOn) was degassed
for 15 minutes, poured into the mold, and cured in a pressure pot at 4.48 bar. Once cured,
the positives, rods, and tubing were removed. To create the wax cores for the model, a base
and lid with a hole for pouring into the mold were laser cut (Geike Cloud 50W CO2 laser)
and screwed to the mold. Beeswax (Vanrener) melted in an oven at 80 °C, was poured in
and cooled to room temperature, thus creating wax cores.

To create the EcoFlex portion (inner circumferential chambers), 3D-printed inner segments
were stacked and secured with M3 threaded rods (316 Stainless Steel, McMaster-Carr) and
nuts (McMaster-Carr) to a laser-cut acrylic base. EcoFlex 00-30 parts A and B were mixed,
degassed for 30 minutes, poured into the mold. After 4 hours, the part was removed from
the mold. Using IPA as a lubricant, circumferential wax cores were inserted into position
on the EcoFlex part. Two of the 3D-printed inner segments were inserted at either end of
the EcoFlex part and held in place with 28cm M3 threaded rods and M3 nuts (McMaster-
Carr). This was mounted to the laser cut acrylic base of the DragonSkin 20 leaving a 15mm
gap between the base and EcoFlex part.

192 pieces of 4cm long high-purity silicone tubing (McMaster-Carr, 1/8” ID, ¼” OD) were
roughened with emery sheets (P80, RS Pro). The tubes, held upright on a 3D printed base
(Form 3, Formlabs), were cleaned with IPA and compressed air and placed into a plasma
chamber (Harrick Plasma PDC-32G-2). The tubes were treated with air (ECVP4300, Easy
Composites Ltd) for 3 minutes on high, the vacuum released and then reestablished for a
further 3 minutes.

The DragonSkin mold (outer longitudinal chambers) was assembled sequentially, with
each segment being stacked and secured with six 37cm 10-24 threaded rods (316 Stainless
Steel, McMaster-Carr) and nuts (McMaster-Carr). Three laser cut acrylic pieces of 5 mm
thickness (bottom endcap) were stacked first, followed by symmetrical 3D printed (Form
3, Formlabs) DragonSkin mold segments. The vacuum was released in the plasma
chamber, and the 6 pieces of silicone tube removed. A 3mm OD rod approximately 50mm
in length (Stainless Steel, TOPPROS) was inserted into each of these tubes, inserted into
the longitudinal wax cores onto the DragonSkin segments. Three of the tubes with rods
were then further pushed into the circumferential wax core. Silicone adhesive (MED3-
4013, NuSil) was brushed (Size 4 round, Winsor Newton) onto the tubing before repeating
the process another 15 times. Finally, 3 laser cut pieces of 5 mm acrylic were placed on top
(top endcap) and the mold tightened with 10-24 hex nuts (McMaster-Carr).

DragonSkin 20 (Smooth-On) parts A and B were mixed, degassed for 15 minutes, poured
into the mould, and cured under in a pressure pot (2 bars) for >4 hours. After demolding
and removal of the steel rods, the part was placed in an oven (LHT 6/30, Carbolite Gero)
at 90 °C to melt out the wax. After the wax had melted out, the part was submerged in
boiling water and then cleaned with compressed air.

Where indicated (RoboGullet+), porcine mucosal/submucosal tissue was added to the
model. Tissue was sourced from WetNab-Medmeat for migration studies and MIT for
motility studies. The tissue was flushed with physiological saline and its length measured.
The esophageal layers were separated following the process laid out Durcan et al. 58 and
the tissue frozen. When subsequently used, the tissue was defrosted in room temperature
physiological saline. This approach of using of frozen/thawed tissue over fresh tissue, the
procurement of which limits model utility, is one used by other biohybrid models 84–86.The
tissue was placed over a steel rod (12mm OD, 31.5cm length) and fed through the model.
Four equally spaced longitudinal cuts, about 2cm in length, were made at the distal end of
the tissue. These were stapled (26/6) to the bottom of the model. The tissue was then pulled
to its original length, and four equally spaced longitudinal cuts were made at the proximal
end. These were stapled to the top of the model and any excess tissue trimmed. The
submucosa, composed of loose connective tissue, separates the mucosa from the
muscularis and allows the mucosa to move freely 78,79. As the model simulates the
muscularis, we replicated this interlayer movement by excluding model-mucosal tissue
adhesion between stapled sections. Pre-tension in ‘passive’ state (1.54 ± 0.00 strain,
comparable with reported in-vivo axial strain is 1.35-1.5 39,87) was recorded across one of
each normal and achalasia I-III swallows. It was calculated by imaging (EOS R5, Canon)
the model in its ‘passive’ state with tracking markers and digitally measuring displacement
(ImageJ).

Friction testing was carried out between nitinol and the mucosal surface of esophageal
tissue that was frozen/thawed, both before and after use in stent migration testing, to
confirm no change in its mechanical performance (Supplementary Fig. 18). Experimental
setup (Supplementary Fig. 18a) was based on ISO 8295-1995 (200g weight and 20cm2
sample surface area) which has been used for soft-tissue friction testing 88. A rubber gasket
was used for tissue fixation. An average sliding speed equivalent to that of peristalsis (0.5
mm/s 89) was used, with resistive force measured via a tensile tester (10N load cell, SL1
Lloyds). Trip displacement was 22mm 61. The onset of sliding, used for static coefficient
measurement 90, was defined as when change in force over displacement dropped below
0.002 N/mm. Static coefficient of friction of tissue after use in stent migration studies (0.08
± 0.01) was not significantly different from before use (0.09 ± 0.02)p = 0.275). Friction
coefficients are similar to values of 0.1 reported in literature for fresh porcine esophageal
mucosa 56,61.

The soft-robotic model is pneumatically actuated using a general compressed air supply.
The compressed air supply is connected to a 2-port valve (VHK2, SMC Pneumatics) via
6mm OD tubing (PUN-6X1-BL, Festo). This is then in turn connected to a pressure
regulator (0.05 – 0.89 MPa, AP100, SMC), a flowmeter (10 L/min – 100 L/min, RS PRO),
a pressure relief valve (0.3bar to 7bar, NORGREN), and two manifolds (VV100-41-18-
M5 and VV100-S41-14-M5, SMC Pneumatics) via push-in fittings (M5 to 6mm, Festo)
connected in series, all elements being connected with the same type of PVC tubing. 32
solenoid valves (V114-5LZ, SMC Pneumatics) are mounted on these manifolds to control
the air flow. Each output from the manifold corresponds to an individual solenoid valve.
These are connected to the relevant chambers of the model via lines of tubing (ADF00011,
TYGON® ND 100-65, Saint-Gobain Medical), that separate into two via a wye connector
barb (4/16” ID, McMaster-Carr), and into its relevant chamber on the soft-robotic model
via a straight connector (3/16” ID, McMaster-Carr). Each chamber in the soft-robotic
model has an additional port from which a PVC tube runs via a straight connector (3/16”
ID, McMaster-Carr) to connect to 32 pressure sensors (0 – 700kPa, MPX5700GP, NXP
USA Inc.).

In terms of the electronics of the system (Supplementary Fig. 3), a microcontroller (MEGA
2560, Arduino) is used to control the pneumatic setup. It is connected to two PWM drivers
(16-Channel 12-bit PWM/Servo Driver, Adafruit). These are then connected to 8
MOSFETs (4 Channel IRIF-540, Gump’s Grocery) which connect to the solenoid valves.
The pressure sensors all run through two 16 channel multiplexers (BOB-09056, SparkFun
Electronics) to connect back to the Arduino. Arduino is also connected to a micro-SD card
reader (HiLetgAo). All electronic components run off either 24V or 5V. 24V power comes
from a wall mount DC power supply (L6R48-240, Tri-Mag LLC) and 5V power stepped
down from the 24V power via an adjustable DC-DC buck converter (LM2596S, Frienda).
The only component that is not powered through these channels is the Arduino, which is
powered via USB.

Evaluation of stent migration.
A study of stent design and its impact on migration was conducted using five different
commercially-available stents (expired stock samples available from eSutures) with
RoboGullet and RoboGullet+. Variations in brand, covering, and outer diameter (OD),
were considered. 30 sequential ‘simple’ swallows, or healthy swallows excluding UES or
LES, were simulated (Supplementary Fig. 4c). The soft-robotic model was placed on a box
with a panel on its side cut out for imaging the washer. A manometry catheter (ManoScan
ESO 3D, Given Imaging) was inserted into the model lumen and a PID regulated swallow
simulated, with PWM values recorded to be used for subsequent swallows. Imaging was
carried out with a camera (EOS Rebel T6 with EF-S 18-55mm f/3.5-5.6 IS II lens, Canon)
mounted on a tripod (Star 62, Hama) for a lumen view and phone camera (XQ-AS52g,
Sony) mounted to a lab tripod for laterally imaging the washer. The phone’s orientation
was aligned using a spirit (T3482, CK Tools). A suture line (W328H Mersilk, Ethicon)
was tied to each stent. Each stent was placed at the midpoint of the first onset segment via
tubing (10mm OD). A washer (6.4g) with a 3mm reflective marker (Revopoint) was
attached to the external portion of suture. For each swallow, 5mL of water was injected
upon relaxation wave initiation. The model was programmed to stay in its ‘passive’ state
for 20 seconds in between each swallow. During this ‘passive state’, where all model-
chambers are held at half-peak pressure, washer displacement was automatically imaged
through a timelapse set to image once per swallow (Velocity Lapse v1.1.0 PRO using Sony
XQ-AS52g phone camera) and luminal images captured manually (EOS Rebel T6, Canon).
As the model remains fixed in position during this 20s period, images taken of washer and
lumen are synchronized. Luminal images taken of the stents are included in Supplementary
Movie 2. After each set of swallows, if displaced, the stent was repositioned. Tests with
RoboGullet+ included 5mL injections of physiological saline after each swallow and a
20mL injection after stent removal.

Achalasia simulation and effect of bolus viscosity on swallow diagnosis and efficiency
Further versatility of the model was demonstrated by using RuboGullet+ to simulate
normal and achalasia swallows that incorporate the UES and LES. The Newtonian bolus
used was glycerol-water (50wt%, 62.5wt% , 75wt%, 87.5wt%, and 100wt%). The non-
Newtonian bolus used was a commercially available dysphagia thickening powder, which
uses xanthan gum as its main thickener and maltodextrin as an excipient 52. This was mixed
with water over a commonly used range of 0.6wt%, 1.35wt%, 2.1wt%, 2.85wt% and
3.6wt% 52, equivalent to thin-to-honey viscosities 52,53. A bolus of water was used for
validation of the swallows against clinical metrics and for comparison with viscous
boluses.

A manometry catheter (ManoScan ESO, Given Imaging) was placed inside the model
lumen and held with a lab tripod. Before each swallow the UES position was shifted to the
4th segment, contracted to closure for 40 seconds, and 5mL of bolus injected into the space
above (Supplementary Fig. 5-6). 5 seconds before swallow initiation the UES is switched
back to the 1st segment. The only exception to this was the achalasia II swallow, where
there was no gap before swallow initiation. Originally, a different waveform was used for
this swallow (Supplementary Fig. 6) with a slower contraction (𝜆 = 12s) and lower setpoint
pressure. However, this failed to produce pan-esophageal pressurization and was replaced
with a faster contraction (𝜆 = 3s) and higher setpoint pressure (1.7 times). HRM data was
recorded with ManoScan Acquisition (Medtronic). After each change of condition, the
manometry catheter was removed and wiped. Additionally, 20mL of physiological saline
was injected into the model lumen and a steel rod (12mm) inserted through the lumen to
flush out any remaining bolus. Data was analyzed using ManoScan Analysis ESO 3.0.
where DL and DCI, was calculated automatically. IRP (median of lowest pressures over 4
seconds 11) was taken to be the LES residual pressure (mean of lowest pressures over 3
second time period 91), as IRP was not given as an output in the program.

An additional study was examined the effect of non-Newtonian dietary recommendations
like Greek-style yoghurt 54 has on dysphagic swallowing efficiency. The Greek-style
yoghurt (Natural set, Onken) was refrigerated, then stirred before each swallow or left
unstirred. RoboGullet+ was placed on the top housing of a weighing scale (Pioneer
Precision, OHAUS) with a custom draft-shield with a 20mm OD hole aligned to the lumen.
Scale data was collected at 1s intervals starting at swallow initiation via a USB to 9-pin
RS232 Serial Port Adaptor (Aten Technology). Achalasia II and achalasia I swallows were
simulated without LES relaxation. Closure of the 9th segment was used for filling to reduce
transit time. Here 5mL of yoghurt was injected. After each swallow, 20mL of physiological
saline was injected to flush out remaining bolus.

Data Analysis
All plots were made using MATLAB and Excel. Correlation coefficients were calculated
using MATLAB. All of the HRM data was recorded with ManoScan Acquisition and
analyzed using ManoScan Analysis ESO 3.0. Computational predications were compared
to physical model predictions using a custom Python script for HRM plot generation
(Supplementary Note 3), combined with MATLAB for image analysis. For the stent
migration studies, Kinovea was used to get the displacement data from the timelapse
videos. Multiple linear regression model fit and analysis was carried out using MATLAB.
A two-tailed t-test was used for comparison of FC and PC Brand B stent migration. A two-
tailed t-test was used for comparison of tissue friction properties. A right sided t-test was
used to test significance of circumferential and longitudinal muscles in stent migration. For
the swallowing efficiency of yoghurt, a left -sided Mann-Witney test was used as the data
for one of the groups was not normalized. Significance threshold: P<0.05 (unless otherwise
noted). The scales were data logged using Serial Port Data Collection Software (SPDC
V2.04, OHAUS).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Major comments:\n1. For the stents, the authors mention Brand A and Stent II etc. What are the actual stents used? I presume the different\nstents have different features and sizes? Details should be provided to allow reproducibility.\n2. Can the authors comment on how valid are the stent migration tests? How might these studies be validated/compared in-\nvivo? How different is pig ex-vivo mucosa vs human in-vivo mucosa?\n3. Line 402. The authors say Bhattacharya proposed the circumferential pneumatic approach. From my understanding there\nwere a number of groups working in this area. For example the Xu group has published foundational work that lead up to\nthis article:\n• S. Dirven, F.J. Chen, W.L. Xu and L. Chen, Design and characterization of a peristaltic actuator inspired by esophageal\nswallowing, IEEE Transactions on Mechatronics, Vol.19, No.4, 2014. pp.1234-1242, 2013.\n• S. Din, W. Xu, L. Cheng and S. Dirven, A stretchable array of electronic receptors for esophageal swallowing robot for\nbiomimetic simulations of bolus transport, IEEE Sensors Journal, No. 13, Vol 18, pp. 5497-5506, 2017.\nDOI:10.1109/JSEN.2018.2836330.\n4. Line 414. Justify why these pressures were selected. Presumably these pressures were tuned to produce a given amount\nof contraction?\n5. Line 496. How valid is it to use frozen and then thawed tissue? How different are the properties compared to in-vivo tissue\nor recently excised tissue? I would presume the tissue properties are significantly different. I also note that the tissue is\nstapled at the top and bottom (and nothing in the middle. The level of pre-tensioning must be quite influential, and the lack of\nadhesion in the middle must cause some issues?\n6. In addition, where was the tissue obtained from? How long can a single fresh tissue stay in the simulator or how many\nsimulations can it undergo? How frequently were these tissues replaced? How many pigs were used to extract tissue?\n7. What does each ‘n’ represent? A single swallow or a series of swallows simulated for each bolus swallowed?\n8. Some data represented in Figure 5 is based on n=2. This is not a sufficient sample size to make meaningful conclusions.\nThe sample size seems variable across different studies and is not listed for every single study.\n9. The ‘Data Analysis’ section does not detail the statistical value of significance.\n10. Lines 214-222. It seems the increased and decreased displacements are a mixture of FC (fully covered) and PC\n(partially covered) stents and do not distinguish each type so how does this show the effect of PC stents?\n11. Lines 192-195, what do velocity and amplitude represent? Are they averaged over multiple swallows or simulations?\nWhat is the corresponding experimental value and what is the repeatability of this value?\n12. Figure 5c, is the threshold same for normal and achalasia III? Figure 5d, the threshold is same for all 3 types of\nachalasia? Then how would these be differentiated? This would not be a good metric.\nMinor comments:\n1. Line 375. It is probably unwise to say the model “fully describes esophageal motility”!\n2. Line 378. The journal style requests Results before Methods, but I would suggest that “dual material model” is explained\nfurther here in case people choose to read the methods independent of the Results.\n3. Line 540. A digital camera was used to image down the lumen, and a phone camera to image laterally. How were the\nvideos synchronized to the contractions of the esophagus? I would presume the luminal view was restructured to the exterior\nsurface of the lumen, and it is not possible to image within the esophagus. Please explain/discuss further.\n4. Line 392. Please explain the practical significance of 0.6% - 3.6% thickened mixtures. What type of thickness liquids do\nthese values these represent?\n5. Line 443. “Interpolate with the same methods used by the manometry software [ref 42]”. I would guess the interpolation\nmethods implemented by a commercial product are not fully disclosed. How do you know you used (exactly) the same\nmethods for interpolation\n6. Figure 4a, the color bar is too small and the text is not clear. Figure 4e-f, images of stent IV and V are not very clear.\n7. There are grammatical errors throughout the manuscript in the forms of incorrect English (e.g., lines 95, 214, 216, 442,\n493, 495, 515, 600) and typos such as missing/additional punctuation marks (lines 51, 97, 105, 207, 313, 364), spaces (lines\n74, 192, 333, 405, 438, 543,) and inconsistent indentation (e.g., Data Analysis section). Suggest the authors consider\nengaging with a proof-reading service.\n8. Some references should be reformatted (e.g., 6, 17, 21, 41, 43, 66, 69).", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "Summary: The authors proposed a biomimetic benchtop model of the esophagus by taking both the circular muscle and\nlongitudinal muscle layers into consideration. They built the model, validated it against the simulation, and tested it by two\nmajor applications in evaluating (1) commercial stent migration and (2) the effects of bolus properties on swallowing and\nachalasia diagnosis.\nGeneral comments: The paper was well-written, with results clearly presented and methods well described. I only have a\nfew minor comments specified below.\nSpecific comment\n1. Line 135-137. The sentence sounds ambiguous. Please consider being more specific.\n2. In physiological conditions, the circular muscle coordinates with the longitudinal muscle. However, the model design\nprefers independent actuation. Could the authors clarify why such independence between the two chambers could facilitate\ncoordination and better mimic physiological conditions?\n3. Figure 3b. The experimental characterization of the model from HRM shows a discrete pattern, compared with the\nsimulation. Could the authors clarify the potential causes? Is it because the sensors are relatively far away from each other?\n4. Figure 4h. It would be better to change the figure legend to be “RoboGullet” and “RoboGullet+”. This would be more\nconsistent with the main text. Also, the use of “tissue” is non-specific.\n5. When using segments to model UES and LES, are any special specifications required to model their different functions\ncompared with the rest of the esophageal tract?\n6. Could the authors offer some insights about how to adapt the model to simulate different individuals and facilitate\nprecision medicine?", "reviewer_4": "The soft robotic model presented in this study offers a valuable platform for mimicking esophageal peristaltic contractions\nunder both physiological and pathophysiological conditions. This model holds significant potential to support the design and\ndevelopment of medical devices.\nOverall, this is an engaging and well-executed study. The manuscript is clearly written and supported by compelling\nillustrations. I have only a few minor comments, primarily concerning the physiological aspects of the esophagus, which\nshould be taken into account during further model development.\n1. The model is composed of circumferential and longitudinal chambers, utilizing DragonSkin 20 for the longitudinal layer\nand EcoFlex 00-30 for the circumferential layer. Swallow-induced peristaltic contractions of the esophagus are simulated by\ncontrolling luminal pressure within each chamber. Although EcoFlex 00-30 exhibits passive mechanical properties similar to\nthose of gastrointestinal tissues, the structural design of the model does not closely replicate the actual anatomical\narrangement of longitudinal and circumferential muscle layers in the esophagus. I am concerned about whether the passive\nmechanical properties of the chambered model in the no-load state (i.e., when chambers are inflated to half of their peak\npressure), are comparable to those of the native esophageal muscle layer at the passive state.\n2. The term \"passive state\" requires clarification. I assume it refers to the no-load condition, but this should be explicitly\ndefined in the manuscript.\n3. In vivo, the esophagus exhibits a longitudinal stretch ratio of approximately 1.4. Since the model aims to replicate\nphysiological conditions, this stretch ratio should be considered in its application. The presence of longitudinal prestretch in\nthe model may influence both experimental measurements and simulation outcomes. This limitation should be\nacknowledged and discussed.\n4. The manuscript does not provide information regarding the wall thickness of the model. It is important to assess whether\nthe model’s wall thickness is comparable to that of the esophageal muscle layer. Additionally, the potential impact of wall\nthickness on both measurements and simulations should be addressed.\n5. There appears to be no reference listed beyond number 70. Several references cited in the supplementary material are\nnot included in the reference list. This should be corrected to ensure completeness and traceability."}, "author_response": "Point-by-point Response Nature Communications manuscript NCOMMS-25-43539\nWe thank the reviewers for their thoughtful and constructive feedback, which has significantly strengthened our\nmanuscript. We have addressed each comment point-by-point below, incorporating revisions to the\nmanuscript, supplementary materials, and figures where appropriate. Changes to the manuscript are\nhighlighted in tracked changes in the revised version. New experimental data has been included in the form of\nfriction tests on frozen/thawed porcine esophageal mucosa pre/post usage, and on the model’s ‘passive’ state\nmechanical properties. We have also supported our experimental data with additional literature references to\naddress concerns about tissue properties (e.g., fresh vs. frozen/thawed, porcine vs. human). These draw from\nrecent studies showing minimal impacts of freezing on mechanical properties in similar tissues and strong\nsimilarities between porcine and human esophageal mucosa. Concerns regarding model pre-strain, ‘passive’\nstate properties, and pressure selections have been addressed through additional analyses of our\ncomputational models and experimental data.\nWe have also proofread the manuscript thoroughly, corrected grammatical/typographical errors, reformatted\nreferences, and clarified ambiguities as noted.\nNewly added tables (Supplementary Table 1) and figures (Supplementary Fig. 15-17) have also been included\nat the end of this document.\nReviewer #1\nMajor Comment 1: For the stents, the authors mention Brand A and Stent II etc. What are the actual stents\nused? I presume the different stents have different features and sizes? Details should be provided to allow\nreproducibility.\nResponse: We appreciate the reviewer's emphasis on reproducibility. Brand A refers to Wallflex stents, and\nBrand B to Agile stents, both manufactured by Boston Scientific. We initially de-identified the brands to avoid\nimplying direct clinical performance claims, as the stents were expired stock samples sourced from eSutures\n(~$200-300 each, vs. >$2000 for new) and not manufacturer-supplied. This approach aligns with our prior work\non urinary catheters [Humphreys et al., J. Mech. Behav. Biomed. Mater. 2020]. However, we agree that\ntransparency is key. If the editor prefers explicit naming in the main text, we can add it; otherwise, we have\nnow included a new Supplementary Table 1 summarizing stent details (brand, construction, type, outer\ndiameter (OD), flare OD, length, and images) for clarity. This table is referenced in the Results section (line\n208: \"The type, outer diameter (OD), and length (L) for each stent are detailed in Supplementary Table 1.\").\nImages of each stent are already in Fig. 4b, and we have added cross-references. This provides sufficient\ndetail for reproduction while maintaining our rationale.\nMajor Comment 2: Can the authors comment on how valid are the stent migration tests? How might these\nstudies be validated/compared in-vivo? How different is pig ex-vivo mucosa vs human in-vivo mucosa?\nResponse: We believe this is the most representative ex-vivo simulation test for acute stent migration in the\nesophagus to date. Unlike endovascular stents, esophageal stents lack manufacturer-specified oversizing\nguidelines, relying on clinical experience. Deployment over restrictive lesions increases radial resistance,\nreducing migration, but our model simulates normal swallowing waves within established parameters (e.g.,\nwave velocity 4.16 ± 0.04 cm/s, amplitude 117.81 ± 12.32 mmHg) (ref. 11). We have clarified this in the\nResults (line 205: “These simulated swallow parameters, detailed in ‘Computational and experimental\ncharacterization of the soft-robotic model', fit within those of normal swallowing waves [11]”). Predicting\nPage 1\nnervous system adaptation to stents remains challenging, as noted in future work (line 371: \"integrating\nemerging neuromechanical esophageal models [77] with the model’s feedback system\").\nFor in-vivo validation/comparison: Traditional clinical studies evaluate migration as binary (migrated/non-\nmigrated (refs. 8; 61-63). Millimeter-scale tracking is difficult, but emerging smart sensor stents could enable\nthis (added to Discussion, line 382: \" However, obtaining quantitative clinical data upon the onset of stent\nmigration remains challenging, but could be achieved through emerging smart sensor technology [74]\").\nOn pig ex-vivo vs. human in-vivo mucosa: Literature indicates porcine and human esophageal mucosa behave\nsimilarly in morphological and functional properties (added to Discussion, line 309: \"Porcine mucosa is often\ncited as a valid surrogate for human tissue, with comparable morphology, permeability, and biomechanical\nresponses [ref. 58; new refs: Christie et al., Histochem. J. 1995; Yang et al., World J. Gastroenterol. 2007;\nYang et al., J. Biomech. Eng. 2005; Lin et al., Wear 2017]). Differences include slight variations in thickness\n(porcine often thicker) and stiffness, but functional friction and compliance are akin (e.g., no significant\npermeability changes post-freezing in porcine esophageal mucosa [new ref: Caon et al., AAPS PharmSciTech\n2011]). We supplemented with experimental data and literature on fresh vs. frozen (see Major 5) and added to\nDiscussion, line 311: “While ex-vivo porcine tissue may differ slightly from in-vivo, for example in levels of\nhydration, studies show minimal biomechanical alterations [new ref: O’Leary et al., J. Mech. Behav. Biomed.\nMater. 2014; Caon et al., AAPS PharmSciTech 2011]”. These points are now in Discussion (line 309; line 311;\nline 382) and Methods (line 543) for context.\nMajor Comment 3: Line 402. The authors say Bhattacharya proposed the circumferential pneumatic\napproach. From my understanding there were a number of groups working in this area. For example the\nXu group has published foundational work that lead up to this article: [citations provided].\nResponse: We thank the reviewer for highlighting these foundational papers, which we have now included as\nreferences (new refs. 78-79). The sentence (now line 427) has been updated: \"The circumferential pneumatics\nwere based on that proposed by Bhattacharya et al. [23] and other foundational work by the Xu group [new\nrefs: Dirven et al., IEEEASME Trans. Mechatron. 2014; Din et al., IEEE Sens. J. 2018]\".\nMajor Comment 4: Line 414. Justify why these pressures were selected. Presumably these pressures were\ntuned to produce a given amount of contraction?\nResponse: For circumferential chambers, pressures were selected to achieve a wave shape close to the\nmaximum pressure in the model's 'passive' state (mid-pressure inflation). The focus was longitudinal chamber\nindependence, so circumferential values were held constant. Longitudinal pressures were tuned for equivalent\naxial displacement in the passive state across models. Based on our FEA models, we have added a new\nSupplementary Fig. 15 showing this tuning process (referenced in Methods, line 441: “Longitudinal pressures\nwere tuned for equivalent axial displacement in the passive state (Supplementary Fig. 15)”).\nMajor Comment 5: Line 496. How valid is it to use frozen and then thawed tissue? How different are the\nproperties compared to in-vivo tissue or recently excised tissue? I would presume the tissue properties are\nsignificantly different. I also note that the tissue is stapled at the top and bottom (and nothing in the middle. The\nlevel of pre-tensioning must be quite influential, and the lack of adhesion in the middle must cause some\nissues?\nResponse: Frozen/thawed tissue bridges the gap between costly animal models and simple benchtop ones,\nenabling broader applicability (similar to other biohybrid models [new refs: May et al., Endoscopy 2005;\nNeumann et al., Endoscopy 2000; Vick et al., J. Surg. Educ 2007]; added to Methods, line 529). Fresh tissue\nprocurement limits utility. To address validity, compared to in-vivo/recently excised literature shows minimal\nPage 2\nchanges in mechanical properties post-freezing for thicker porcine soft-tissues like the esophagus (e.g., no\nsignificant elastic modulus shift in porcine aorta at -20°C [new ref: O’Leary et al., J. Mech. Behav. Biomed.\nMater. 2014]; negligible permeability alterations in esophageal mucosa [new ref: Caon et al., AAPS\nPharmSciTech 2011]; added to Discussion, line 311; for thinner tissues (large intestine) stiffness may increase\n[Hirst et al., J. Mech. Behav. Biomed. Mater. 2024]. Of primary importance here, however, are the frictional\nproperties – we have provided additional experimental data (Supplementary Fig. 17) showing similar friction\ncoefficients of frozen/thawed porcine mucosa in contact with nitinol (stent base material) before and after\nexperimental use compared to fresh tissue (0.09 ± 0.02 and 0.08 ± 0.01 found experimentally versus 0.1\nreported in literature [ref. 56; new ref. Lin et al., Wear 2017]). Added testing details to Methods, line 543.\nOn stapling/pre-tensioning: Esophageal mucosa attaches loosely to serosa via connective tissue, allowing\ninterlayer movement [new ref: Peirilinck et al., Biomech. Model. Mechanobiol. 2018], which our model\nreplicates without middle adhesion (added to Methods, line 536: “The esophageal mucosa attaches loosely to\nthe serosa via connective tissue, allowing interlayer movement [new ref: Peirilinck et al., Biomech. Model.\nMechanobiol. 2018]; the exclusion of model-tissue adhesion between stapled section replicates this”). In-vivo\naxial strain is 1.35-1.5 [ref. 39; new ref: Lu & Gregersen, J. Biomech. 2001]. Tissue was inserted at original\nlength (Methods, line 534), and pre-tensioned to 1.54 ± 0.00 in passive state. We have added details of this to\nMethods, line 539: “Pre-tension in ‘passive’ state (1.54 ± 0.00 strain, comparable with reported in-vivo axial\nstrain is 1.35-1.5 [ref. 39; new ref: Lu & Gregersen, J. Biomech. 2001]) was recorded across one of each\nnormal and achalasia I-III swallows. It was calculated by imaging (EOS R5, Canon) the model in its ‘passive’\nstate with tracking markers and digitally measuring displacement (ImageJ).”\nMajor Comment 6: In addition, where was the tissue obtained from? How long can a single fresh tissue stay in\nthe simulator or how many simulations can it undergo? How frequently were these tissues replaced? How\nmany pigs were used to extract tissue?\nResponse: Tissue sourced from healthy adult pigs: Wetlab-Medmeat for migration studies; MIT for motility\nstudies (added to Methods, line 525). Single-day testing is typical for fresh/frozen-thawed tissue models; we\nhydrated with saline as was done by Durcan et al. [Durcan et al., J. R. Soc. Interface 2024] (they noted no\nsignificant change in mechanical properties over days since dissection). Additional friction data of tissue post-\nuse in stent migration studies shows no significant change vs. frozen/thawed (new Supplementary Fig. 17,\nfriction coefficient of 0.08 ± 0.01 vs 0.09 ± 0.02; referenced in Methods line 552 and Data Analysis line 658).\nTissues replaced daily; one pig esophagus per model.\nMajor Comment 7: What does each ‘n’ represent? A single swallow or a series of swallows simulated for each\nbolus swallowed?\nResponse: For stent migration: n=5 independent experimental replicates over 30 swallow cycles (clarified in\nFig. 4 caption: “Measured displacements of stents over 30 swallow cycles (n = 5)”). For motility: n=3 or 5 per\nsingle swallow (now collated in Fig. 5 caption: \"Sample sizes: motility metrics (b-i) n = 3, yoghurt motility (j) n =\n5\").\nMajor Comment 8: Some data represented in Figure 5 is based on n=2. This is not a sufficient sample size to\nmake meaningful conclusions. The sample size seems variable across different studies and is not listed for\nevery single study.\nResponse: We apologize for confusion. n<3 data (e.g., 50wt% Newtonian, 2.1wt% non-Newtonian) was\nexcluded and not in figures (line 266 removed). Samples now clearly listed in captions (as above).\nVariability reflects study needs; n=3-5 is standard for repeatability in similar models.\nPage 3\nMajor Comment 9: The ‘Data Analysis’ section does not detail the statistical value of significance.\nResponse: Added to Data Analysis (line 662: \"Significance threshold: P<0.05 (unless otherwise noted)”.\nCorrected and added p-value listings to Fig. 4 and Fig. 5.\nMajor Comment 10: Lines 214-222. It seems the increased and decreased displacements are a mixture of FC\n(fully covered) and PC (partially covered) stents and do not distinguish each type so how does this show the\neffect of PC stents?\nResponse: Stents III and V (Brand B) are identical except covering, isolating its effect: tissue increases FC\ndisplacement (P<0.005) but decreases PC. Other stents vary multifactorially (length, flares, materials),\ncomplicating generalizations. This underscores model utility for complex interactions (added to Results, line\n223: “Stent migration is influenced by multiple factors; our model helps disentangle these factors to inform\nimproved stent designs”).\nMajor Comment 11: Lines 192-195, what do velocity and amplitude represent? Are they averaged over\nmultiple swallows or simulations? What is the corresponding experimental value and what is the repeatability of\nthis value?\nResponse: Velocity: time between peak pressures at initial/final points over distance. Amplitude: baseline-to-\npeak average across points [ref. 46] (added to Methods, line 475). Values averaged over 3 swallows (updated\nin Results line 197 to 117.81 ± 12.32 mmHg and 4.16 ± 0.04 cm/s for amplitude and velocity respectively; low\nvariability, SD ~10% & ~1%).\nMajor Comment 12: Figure 5c, is the threshold same for normal and achalasia III? Figure 5d, the threshold is\nsame for all 3 types of achalasia? Then how would these be differentiated? This would not be a good metric.\nResponse: Yes, thresholds per Chicago Classification 4.0 [ref. 11]. Achalasia III (mild) overlaps normal DCI\nbut differs in DL/IRP. Diagnosis uses all metrics + pan-pressurization (Fig. 5a). Model could refine guidelines\n(further discussed in Discussion. line 359: \"Clinically, the platform enables controlled transitions between\nmotility disease states…\").\nMinor Comment 1: Line 375. It is probably unwise to say the model “fully describes esophageal motility”!\nResponse: Changed to \"more accurately describes\" (line 399).\nMinor Comment 2: Line 378. The journal style requests Results before Methods, but I would suggest that\n“dual material model” is explained further here in case people choose to read the methods independent of the\nResults.\nResponse: Added explanation to Methods (line 402: \"softer EcoFlex 00-30 (circumferential) and stiffer\nDragonSkin 20 (longitudinal) for independent actuation\").\nMinor Comment 3: Line 540. A digital camera was used to image down the lumen, and a phone camera to\nimage laterally. How were the videos synchronized to the contractions of the esophagus? I would presume the\nluminal view was restructured to the exterior surface of the lumen, and it is not possible to image within the\nesophagus. Please explain/discuss further.\nPage 4\nResponse: Synchronization: 20 second interval of model ‘passive’ state set to occur between swallows.\nVelocity Lapse app used to automatically image with the phone camera the lateral view during this period;\nluminal image captured manually during this period with digital camera and tripod. Added to Methods, line 600.\nMinor Comment 4: Line 392. Please explain the practical significance of 0.6% - 3.6% thickened mixtures.\nWhat type of thickness liquids do these values these represent?\nResponse: These match patient-used levels [ref. 52]; viscosities akin to thin (0.6%) to honey (3.6%) per\nMowlavi et al. [ref. 52] and the National Dysphagia Diet (NDD) [new ref: McCullough et al., ASHA Lead. Arch.\n2003] (added to Results line 259: “and equivalent to thin-to-honey viscosities as per the National Dysphagia\nDiet [ref 52; new ref: McCullough et al., ASHA Lead. Arch. 2003]” and Methods, line 616: “equivalent to thin-to-\nhoney viscosities [ref. 52; new ref: McCullough et al., ASHA Lead. Arch. 2003]”).\nMinor Comment 5: Line 443. “Interpolate with the same methods used by the manometry software [ref 44]”. I\nwould guess the interpolation methods implemented by a commercial product are not fully disclosed. How do\nyou know you used (exactly) the same methods for interpolation\nResponse: Changed to \"methods forming the basis of those used\" (original ‘Clouse’ method of creating HRM\nplots used; adapted by Medtronic [ref. 44]).\nMinor Comment 6: Figure 4a, the color bar is too small and the text is not clear. Figure 4e-f, images of stent\nIV and V are not very clear.\nResponse: Enlarged color bar/text in Fig. 4a. Added Supplementary Table S1 with clearer side-on images.\nMinor Comment 7: There are grammatical errors throughout the manuscript...\nResponse: Corrected; manuscript proofread.\nMinor Comment 8: Some references should be reformatted...\nResponse: Reformatted. Changed Durcan et al., 2021 pre-print citation to accepted article [Durcan et al.,\nBiomech. Model. Mechanobiol. 2022].\nReviewer #3\nComment 1: Line 135-137. The sentence sounds ambiguous. Please consider being more specific.\nResponse: Revised: \"Bolus transport... has three stages: passive, pre-bolus, and post-bolus. In the pre-bolus\nstage longitudinal muscles contract, increasing circumferential muscle concentration.\"\nComment 2: ...clarify why such independence between the two chambers could facilitate coordination and\nbetter mimic physiological conditions?\nResponse: Independence enables simulation of both physiological (coordinated) and pathophysiological\n(asynchronous, e.g., achalasia) states (added to Results, line 123: “This independence allows\npathophysiological simulation where muscles are asynchronous [refs. 33-34; new refs: Jung et al.,\nGastroenterology 2005; Korsapati et al., Gut 2009]”).\nComment 3: Figure 3b. The experimental characterization... discrete pattern... potential causes?\nPage 5\nResponse: Due to computational simplifications (friction, sensor geometry); trends match (95.14% amplitude,\n89.70% velocity accuracy; added to Results, line 194: “Wave patterns differ slightly due to computational\nsimplifications but trends match”).\nComment 4: Figure 4h. ...change the figure legend to be “RoboGullet” and “RoboGullet+” ... “tissue” is non-\nspecific.\nResponse: Updated legends.\nComment 5: When using segments to model UES and LES... special specifications?\nResponse: No tissue compositional differences modeled; focus on functional performance\n(pressure/relaxation). Literature shows similar muscle composition (added to Results, line 242: “functional\nsimulation prioritized; there are no major histological differences between UES/LES and esophageal body [ref.\n50; new ref: Mittal et al., N. Engl. J. Med. 1997]”).\nComment 6: ...insights about how to adapt the model to simulate different individuals...\nResponse: Expanded Discussion – line 367: “with anatomy replicated from CT scans”, line 369: “Additionally,\nthe model’s ‘passive’ state could be adjusted for lumen/strictures.”, line 379: “This would allow clinicians to pre-\ntest treatments on patient-specific swallows.”\nReviewer #4\nComment 1: ...passive mechanical properties of the chambered model... comparable to those of the native\nesophageal muscle layer...\nResponse: EcoFlex 00-30 matches GI compliance [ref. 43] in ‘relaxed’ state and the dual design approximates\nlayered anatomy. Computational model shows negligible change of 0.14% in instantaneous Young’s modulus\nat central elements of model from ‘relaxed’ to ‘passive’ states (new Supplementary Fig. 16; referenced in\nMethods, line 464).\nComment 2: The term \"passive state\" requires clarification...\nResponse: Defined (Results, line 144: \" This no-load condition, where the chambers are at half-peak\npressures, is referred to as the model ‘passive’ state.\"). Updated to be consistent throughout (Results, line 163;\nResults, line 174).\nComment 3: ...esophagus exhibits a longitudinal stretch ratio of approximately 1.4... acknowledge and\ndiscuss.\nResponse: Addressed (see Reviewer 1 Major 5; added to Methods, line 539: “Pre-tension in ‘passive’ state\n(1.54 ± 0.00 strain, comparable with reported in-vivo axial strain is 1.35-1.5 [ref. 39; new ref: Lu & Gregersen,\nJ. Biomech. 2001]) was recorded across one of each normal and achalasia I-III swallows. It was calculated by\nimaging (EOS R5, Canon) the model in its ‘passive’ state with tracking markers and digitally measuring\ndisplacement (ImageJ).”).\nComment 4: ...wall thickness of the model... comparable... potential impact...\nResponse: Dimensions are included in Supplementary Fig. 1 (clarified in text, line 428: “These internal\ndimensions are also described in Supplementary Fig. 1.”). Wall thickness is 8mm which is considerably thicker\nPage 6\nthan esophageal muscle (1-3mm [ref. 34]). Impacts: Thicker walls may overestimate stiffness and thus stent\nmigration, a discussion of which has been added to limitations (line 384); however, as we highlight in\npreviously referenced additional data, the frictional properties (Supplementary Fig. 17) and instantaneous\nmodulus (Supplementary Fig. 16) are similar to that of native esophageal tissue.\nComment 5: ...no reference listed beyond number 70...\nResponse: Corrected; all supplementary refs now included.\nNew Tables\nFlare\nOD Length\nStent Image Brand Construction Type OD\n(mm) (cm)\n(mm)\nMultiple nitinol\nFully\nI A wire braided [ref. 23 28 12.5\nCovered\n48]\nMultiple nitinol\nPartially\nII A wire braided [ref. 18 23 15.3\nCovered\n48]\nDual-gauge nitinol Partially\nIII B 23 28 15\nwire [ref. 49] Covered\nDual-gauge nitinol Partially\nIV B 14 19 6.2\nwire [ref. 4] Covered\nDual-gauge nitinol Fully\nV B 23 28 15\nwire 2 Covered\nSupplementary Table 1. Commercially-Available Stent Information\nPage 7\nNew Figures\nSupplementary Fig. 15. Selection of longitudinal chamber pressures for use in computational design independence\nstudy\na-b, Axial displacement from FEA simulations under different longitudinal chamber pressure values, identified using\nlegend on right, for (a) EcoFlex 00-30 only model and (b) EcoFlex 00-30 and DragonSkin 20 model. Axial displacements\nat ‘passive’ state (t = 2s) marked with dashed lines. c, Longitudinal chamber pressures and corresponding axial\ndisplacements in model ‘passive’ state plotted with dots. Solid black line shows plotted least-squares regression formula\nfit to these points. Dashed line marks predicted longitudinal chamber pressure required in EcoFlex 00-30 and DragonSkin\n20 model (64.727 kPa) to achieve same target axial displacement in ‘passive state’ as EcoFlex 00-30 only model (3.043\nmm).\nPage 8\nSupplementary Fig. 16. Mechanical properties of EcoFlex 00-30 layer in model ‘passive’ state.\na-b, FEA simulations showing 10-segment model inflated to ‘passive state’ with (a) Von Mises stress contours and (b)\nlogarithmic maximum principal strains. Insets show elements where history outputs for Von Mises stresses and\nlogarithmic principal strains were recorded for subsequent calculations. c, Plot showing mean Von Mises stress versus\nmean Von Mises equivalent strain. Means taken over elements shown in inset). d, Change in instantaneous Young’s\nmodulus versus model relaxed to passive state completion percent. Instantaneous Young’s modulus calculated as the slope\nat a single datapoint on the Von Mises stress versus Von Mises equivalent strain plot.\nPage 9\nSupplementary Fig. 17. Coefficient of friction of esophageal mucosa/submucosa tissues before and after use.\na, Schematic of friction testing setup consisting of 200g weighted block with tissue attached being pulled along a Nitinol\nsheet through a string attached to a 10N load cell. b, Coefficient of friction over displacement for porcine esophageal\nmucosa/submucosa tissues. Circles denote static coefficient points. c, Mean static coefficients of friction (n = 5). Thoracic\nregion of esophagus used for all tests. Frozen/thawed refers to tissue that has been frozen (from fresh) and then thawed to\nroom temperature. Tissue that was used in model was utilized for stent migration study.\nPage 10\nPoint-by-point Response Nature Communications manuscript NCOMMS-\n25-43539\nWe thank the reviewers for their continued feedback, which further strengthens the\nmanuscript. We have made additional clarifications to the manuscript text in response to\nfurther reviewer queries on tissue attachment, image synchronization, and UES/LES\nsimulation.\nReviewer #1\nComment 1: R1 Major Comment/Response 1: For the stents types, I am happy with the\nauthors’ response, but suggest the editors double check this part.\nResponse: We defer this to editor’s preference on this.\nComment 2: R1 Major Comment/Response 5: I am not convinced having a “loose”\nesophagus (stapled at both ends) is quite the same having “esophageal mucosa attaches\nloosely to the serosa via connective tissue, allowing interlayer movement; the exclusion of\nmodel-tissue adhesion between stapled section replicates this.” I also think it is more\ncorrectly to state that there is a “loose connective tissue layer between the submucosa and\nthe circular muscle layer”. I suggest the author correct this text slightly and acknowledge this\nas a possible limitation.\nResponse: We have added a discussion of the model-mucosa tissue adhesion to the\nlimitations, line 384:\n“In our model, we approximate the loose connection of the submucosa, which allows\nmovement between mucosa and muscularis (ref. 78; new ref: Sommer et al., Acta\nBiomaterialia 2013), through fixed constraints of the mucosal tissue at either end of the\nmodel, frictional interaction along its length, and model pre-tensioning. This does not fully\ncapture the intricacies of connective tissue attachment, particularly in the radial direction, but\nsimilar to computational approaches (ref. 78), aims to replicate its functional role.”\nWe thank the reviewer for pointing out the correct anatomical definitions. We have made\nchanges to this in Methods, line 540:\n“The submucosa, composed of loose connective tissue, separates the mucosa from the\nmuscularis and allows the mucosa to move freely (ref. 78; new ref: Sommer et al., Acta\nBiomaterialia 2013). As the model simulates the muscularis, we replicated this interlayer\nmovement by excluding model-mucosal tissue adhesion between stapled sections.”\nand in the figure caption (line 989) of Extended Data Fig. 1.\nComment 3: R1 Major Comment/Response 7 & 8: I defer to the editors whether such\nreplicates and n-s are appropriate.\nResponse: As highlighted by the reviewers, when working with fresh tissue samples, it is\nimportant that tissue integrity is preserved and monitored. Therefore, n = 3 – 5 was the\nnumber of experimental replicates that we were comfortable testing with during a single-day\nPage 1\nwindow of testing. We are confident that these experimental replicants sufficiently\ndemonstrate the repeatability of our system and provide proof-of-principle demonstration on\nthe utility of our model.\nComment 4: R1 Minor comment/response 3: It remains unclear how the phone and the\ndigital camera data were synchronized. It also remains unclear how well the camera can\nreally image down the narrow lumen, especially when it contracts.\nResponse: On camera synchronization: We apologize for confusion. The phone camera\nused a timelapse app, programmed to take images during the model’s ‘passive’ state once\nafter every swallow. As defined in the manuscript (Results, lines 141, 143, 172), the model\n‘passive state’ is where all model chambers are held at half peak-pressure. This ‘passive’\nstate was programmed to last 20 seconds, during which a manual image down the model\nlumen was captured. As the model is in its ‘passive state’ during imaging, the lumen remains\nopen and the model fixed in position, rendering any images taken during this period\nidentical. We have clarified this in the Methods, line 607:\n“During this ‘passive state’, where all model-chambers are held at half-peak pressure,\nwasher displacement was automatically imaged through a timelapse set to image once per\nswallow (Velocity Lapse v1.1.0 PRO using Sony XQ-AS52g phone camera) and luminal\nimages captured manually (EOS Rebel T6, Canon). As the model remains fixed in position\nduring this 20s period, images taken of washer and lumen are synchronized.”\nOn lumen imaging clarity: The majority of luminal images taken with the camera were\nentirely clear, all of which can be viewed in Supplementary Movie 2. We have added this to\nMethods, line 612: “Luminal images taken of the stents are included in Supplementary Movie\n2.”\nAdditional camera setup details are given in same section – line 598: “Imaging was carried\nout with a camera (EOS Rebel T6 with EF-S 18-55mm f/3.5-5.6 IS II lens, Canon) mounted\non a tripod (Star 62, Hama) for a lumen view and phone camera (XQ-AS52g, Sony) mounted\nto a lab tripod for laterally imaging the washer…”.\nComment 5: R3 Comment/response 5: I think this question is not addressed. I think the\nauthors should explicitly say the UES and LES were not represented (if it has not already\nbeen done so).\nResponse: To clarify, the UES and LES are represented in the model through\npressurization of proximal and distal circumferential chambers and form an essential part of\nthe achalasia studies (see Fig. 5 and Supplementary Fig. 4-5), as referenced in Results line\n239: “These swallows are more complex than that used for stent migration studies, as these\ninclude the UES and LES”. It is anatomical differences that were not included in the model,\nas the UES/LES and esophageal body are made of the same chamber dimensions and\nmaterials. This was our reasoning for including, in response to the original reviewer question,\nin Results (originally line 242): “functional simulation prioritized; there are no major\nhistological differences between UES/LES and esophageal body [originally refs: 50-51]”. We\nhave now altered this to read (now line 241): “functional, not anatomical, simulation was\nprioritized as there are no major histological differences between UES/LES and esophageal\nbody [refs: 50-51]”.\nPage 2\nThe UES and LES are not represented in stent migration studies, which uses a ‘simple’\nswallowing wave (this is referenced in Results, line 148; Results, line 201; and defined in\nMethods, line 593), as this allowed simpler linear tracking of migration without the\ninterference of the UES/LES and demonstrates the versatility of our model. We have made\nthis more explicit in the stent migration results, adding to line 201: “A simple swallowing\nwave (Supplementary Fig. 3c), meaning one excluding UES/LES simulation to allow for\nlinear migration tracking without interference, was simulated…”\nComment 6: R4 Comment 4. I think this comment should be addressed in the main text (not\njust in supplementary material (if it not already done so).\nResponse: This was previously added to the main text in Discussion, line 381: “Model wall\nthickness is larger than esophagus muscle; this may overestimate stiffness and thus stent\nmigration. Despite this, thicker EcoFlex 00-30 remains an excellent analog for GI tissue (ref.\n43), and the comparison of stents under identical conditions means migration trends remain\nvalid.” Additional analysis of wall properties was also included in Supplementary Fig. 16.\nPage 3"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The requested changes have been addressed appropriately.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "All comments from the initial review have now been addressed, and there are no further comments on the manuscript.", "reviewer_4": "All my concerns raised in the original version are now satisfactorily addressed in the revision. I would like to recommend the\npublication of the article in its present form."}, "author_response": ""}]

Decision letter