While it was found that generating thin ice that is amenable to high-resolution imaging requires long blot times, the addition of glycerol did not result in increased beam-induced motion or an inability to pick particles. Overall, these findings indicate that glycerol should not be discounted as a cryoEM sample-buffer additive, particularly for large, fragile complexes that are prone to disassembly or aggregation upon its removal.Buffer-composition and sample-preparation guidelines for cryo-electron microscopy are geared towards maximizing imaging contrast and reducing electron-beam-induced motion. These pursuits often involve the minimization or the complete removal of additives that are commonly used to facilitate proper protein folding and minimize aggregation. Among these admonished additives is glycerol, a widely used osmolyte that aids protein stability. In this work, it is shown that the inclusion of glycerol does not preclude high-resolution structure determination by cryoEM, as demonstrated by an ∼2.3 Å resolution reconstruction of mouse apoferritin (∼500 kDa) and an ∼3.3 Å resolution reconstruction of rabbit muscle aldolase (∼160 kDa) in the presence of 20%( Often, substantial efforts are made in optimizing the recombinant expression or reconstitution of the targeted macromolecule and the subsequent isolation of the target into a solution that optimally preserves its structural composition and stability. Glycerol is a linear, three-carbon polyol that is widely used in buffer solutions to enhance macromolecular stability, since it prevents protein unfolding by stabilizing aggregation-prone intermediates and favoring more compact protein conformations  ; Bondos & Bicknell, 2003et al., 1991et al., 2019v/v)] and the concentration deemed practical for size-exclusion chromatography [<10%(v/v)], which is a ubiquitous step in protein purification. As expected, the increased viscosity of the buffer made optimal grid preparation more challenging and the associated increase in ice thickness complicated image analysis. We were nonetheless able to determine high-resolution structures of both model specimens. These findings demonstrate that the inclusion of glycerol in the final cryoEM buffer of a targeted sample does not abolish the ability to determine a high-resolution structure, thereby widening the range of biological targets that can be studied by cryoEM.These arguments against the use of glycerol in sample preparation for cryoEM structure determination resulted in the widespread avoidance of glycerol, even when macromolecular complexes require the biochemical stabilization that it provides. However, the limited literature on the subject describes experiments involving a glycerol concentration of 50%pLys cells from a pET-24a vector  supplemented with 1 mg ml−1 lysozyme and cOmplete Protease Inhibitor Cocktail (Roche). The cells were lysed by sonication and the resulting lysate was clarified by centrifugation at 20 000g for 30 min at 4°C. The clarified lysate was heated for 10 min at 70°C to precipitate endogenous E. coli proteins. Denatured proteins were pelleted by spinning at 20 000g for 15 min at 4°C. Ammonium sulfate was added to the clarified lysate to a concentration of 60%(w/v), followed by gentle stirring on ice for 10 min. The precipitate was centrifuged at 14 000g for 20 min. The resulting pellet was resuspended in 2 ml cold phosphate-buffered saline and dialyzed against Q1 buffer . After dialysis, the protein was further diluted by adding an equal volume of Q1 buffer and loaded onto a HiTrap Q HP anion-exchange chromatography column  equilibrated in Q1 buffer. The column was washed with four volumes of Q1 buffer and eluted with a 0–100% gradient of SEC buffer  over three column volumes. Apoferritin eluted between 150 and 200 mM NaCl, which was confirmed by SDS–PAGE. The relevant fractions were pooled and concentrated to 10–20 mg ml−1 for loading onto a Superdex 200 Increase 10/300  size-exclusion chromatography column equilibrated with 30 mM HEPES pH 7.5, 150 mM NaCl, 1 mM DTT. The peak fractions corresponding to apoferritin were pooled and concentrated to 15 mg ml−1. For long-term storage, glycerol was added to 5%(v/v).Mouse apoferritin (heavy chain) was prepared as described previously  column equilibrated in solubilization buffer for size-exclusion chromatography. The fractions containing native, pure rabbit muscle aldolase were pooled and concentrated to 14.5 mg ml−1.Rabbit muscle aldolase was prepared as described previously  or SEC buffer supplemented with glycerol (‘20% glycerol’ experiments) such that the final glycerol concentration was 20%(v/v). 4 µl of apoferritin sample was applied onto UltrAuFoil R1.2/1.3 300 mesh grids (Quantifoil Micro Tools GmbH) that had been freshly plasma-cleaned for 7 s at 15 W (75% nitrogen/25% oxygen atmosphere) using a Solarus plasma cleaner (Gatan). The grids were manually blotted using Whatman No. 1 filter paper and immediately plunge-frozen into liquid ethane cooled by liquid nitrogen using a custom-built manual plunger located in a cold room . For the samples with 1.7% glycerol we blotted for 6 s. Expecting high glycerol concentrations to pose issues with ice thickness, based on anecdotal evidence, we prepared grids of samples with 20% glycerol by blotting for 6, 8 and 10 s (one grid per blotting time). We chose manual blotting for two reasons. (i) By placing the blotting paper parallel to the grid, we tend to obtain squares of similar quality across the whole grid surface. Consequently, when optimal conditions are found, a greater surface of the grid is likely to display the optimal ice conditions, in contrast to the gradient obtained by blotting using a Vitrobot  with a defocus of −1.6 µm using the −1 using buffer supplemented with glycerol such that the final glycerol concentration was 20%(v/v). As for the apoferritin sample, 4 µl of aldolase sample was applied onto UltrAuFoil R1.2/1.3 300 mesh grids (Quantifoil Micro Tools GmbH) that had been freshly plasma-cleaned for 6 s at 15 W (75% nitrogen/25% oxygen atmosphere) using a Solarus plasma cleaner (Gatan). The grids were manually blotted using Whatman No. 1 filter paper and immediately plunge-frozen into liquid ethane cooled by liquid nitrogen using a custom-built manual plunger located in a cold room . In order to optimize the ice thickness in the presence of glycerol, we prepared grids by blotting for 6, 8, 10 and 12 s, with two grids prepared at each blotting time. As performed for the apoferritin samples, we screened these grids in search of thin ice and and to assess the particle concentrations in each hole on an F20 with a CCD. We verified that the ice thickness decreased as the blot time increased, except for the 12 s blotting, where surprisingly thick ice became apparent at the center of the squares. Based on these observations, we decided to collect data on grids blotted for 10 s. For the data-collection session in the Talos Arctica using a direct electron detector we prepared eight new grids, trying to accommodate for any variability in the manual blotting process: one grid at 7 s blotting, three grids at 8 s, one grid at 9 s and two grids at 10 s. Data were acquired on a grid that had been blotted for 10 s, which had the thinnest ice.Prior to application onto cryoEM grids, the aldolase stock was diluted to 1.6 mg mlFor single-particle data collection and ice measurement at 300 keV for apoferritin samples, new grids were prepared using the same grid-preparation parameters as for single data acquisition at 200 keV. Data for these samples were acquired on the first grid that presented acceptable ice. Four additional grids were prepared for this session at room temperature and ambient humidity (45%), but they did not present improvements over those prepared at 4°C and 95% humidity. For single-particle data collection and ice measurement at 300 keV for aldolase samples, four new grids were prepared: one at 7 s blotting, two at 8 s blotting and one at 9 s blotting. Consistent with the results from imaging at 200 keV, the 9 s blotted grid presented the best ice conditions.2.3.et al., 2017− Å−2, respectively  and a nominal defocus of −1.0 and −1.3 µm, respectively. Movies of apoferritin in the absence of glycerol were collected under the same imaging conditions as apoferritin in the presence of 20% glycerol, with the only difference being the collection of 113 frames of 100 ms.For the grids imaged at 200 keV, movies of frozen-hydrated aldolase and apoferritin in the presence of 20% glycerol were collected using a Talos Arctica TEM (Thermo Fisher Scientific) with a field emission gun operating at 200 keV equipped with a K2 Summit direct electron detector (Gatan). Alignments were performed as described previously to attain parallel illumination  and defocus ranges of between −0.5 and −1.5 µm for apoferritin and between −1.25 and −1.75 µm for aldolase. Tomography data were acquired on a small region of these same samples prior to data collection for single-particle analysis  with a field emission gun operating at 300 keV equipped with a K2 Summit direct electron detector (Gatan). Image shift was used to image holes in a 4 × 4 array for apoferritin and a center-of-five array for aldolase, using stage movement only to shift between subsequent arrays. Movies were collected in counting mode  at a dose of 50 eLeginon automated data-collection software . We aligned dose-weighted movie frames using MotionCor2 from within RELION 3.1 in order to enable subsequent Bayesian polishing, and estimated the initial CTF parameters using CTFFIND4 from within RELION 3.1. For particle picking, we began by manually picking ∼50 particles from 19 randomly selected micrographs. We extracted the picked particles in boxes of 192 pixels, without binning (1.15 Å per pixel), and classified them into five 2D classes. We selected the one class that most clearly resembled apoferritin as a template for template-based picking in the RELION 3.1 AutoPick node. This resulted in 876 503 picks from 1707 micrographs. 2D classification  of these picks produced three classes with clear secondary-structure features, which contained 875 694 particles. EMD entry EMD-21024 was low-pass filtered to 30 Å to serve as the initial model for auto-refinement (O symmetry imposed) of these particles, which yielded a reconstruction at 3.9 Å resolution using an FSC cutoff of 0.143. Particles were grouped by image shift followed by per-micrograph defocus and astigmatism refinement, combined with beam-tilt refinement, yielding a reconstruction at 2.4 Å resolution. Bayesian polishing, followed by 3D classification without alignment  and further CTF refinement, increased the resolution to 2.3 Å (FSC = 0.143).We used 2.4.2.RELION 3.1 for data analysis of apoferritin in the presence of 20% glycerol . We aligned dose-weighted movie frames using MotionCor2 from within RELION 3.1 in order to enable subsequent Bayesian polishing, and estimated the initial CTF parameters using CTFFIND4 from within RELION 3.1. We initially manually picked 2261 particles from 20 randomly selected micrographs. We extracted the picked particles in boxes of 192 pixels, without binning (1.15 Å per pixel), and classified them into five 2D classes. We used the four classes that most clearly resembled apoferritin as templates for template-based picking in the RELION 3.1 AutoPick node. This resulted in 830 042 picks from 2254 micrographs. 2D classification  of these picks produced two classes with clear secondary-structure features, which contained 829 923 particles. EMD entry EMD-21024 was low-pass filtered to 15 Å and used as an initial model to auto-refine (O symmetry imposed) these particles to yield a reconstruction at 4.2 Å resolution according to the FSC cutoff at 0.143. After grouping by image shift, CTF refinement  and 3D refinement (O symmetry imposed) the resolution improved to 2.9 Å at a FSC cutoff of 0.143. A round of 3D classification  and subsequent class selection by visual inspection yielded a smaller particle set (683 154 particles) which, after 3D refinement (O symmetry imposed), CTF refinement, Bayesian polishing and further CTF refinement, yielded the final reconstruction at 2.3 Å resolution (FSC = 0.143).We used 2.4.3.RELION 3.1 for data analysis of apoferritin in the presence of 20% glycerol . We aligned dose-weighted movie frames using MotionCor2 from within RELION 3.1, and estimated the initial CTF parameters using CTFFIND4 from within RELION 3.1. We manually picked 277 particles from five randomly selected micrographs, extracted the particles binned by 4  and ran 2D classification requesting a single class. This class was used as a template for template picking, resulting in ∼1.8 million picks that were extracted binned by 4  and subjected to 2D classification . The selection of 19 high-quality 2D classes in which secondary structure was visible yielded a particle stack of 778 762 particles from 1859 micrographs. An initial model generated in cryoSPARC version 2.14.2  of the particle stack. As 3D classification failed to generate distinguishable classes, the whole particle stack was re-centered and re-extracted at full resolution . Refinement (O symmetry imposed) of the unbinned stack resulted in a reconstruction at a nominal resolution of 2.9 Å at FSC = 0.143. The particles were grouped by image shift and iteratively CTF-refined with beam-tilt correction and per-particle defocus refinement. Subsequent auto-refinement (O symmetry imposed) improved the nominal resolution to 2.1 Å at FSC = 0.143.We used Phenix 1.19.2-4158 software suite . We aligned dose-weighted movie frames using MotionCor2 from within RELION 3.1 and then imported them to cryoSPARC for 2D analyses. Subsequent 3D classification and refinement was performed in RELION 3.1. We initially selected particles using DoG Picker 2  in a final box of 64 pixels. We performed four successive runs of 2D classification  at each iteration, discarding only class averages that clearly did not contain particles. We re-centered and extracted the remaining 322 648 particles binned by 2 (1.82 Å per pixel) in a 128-pixel box and performed one more round of 2D classification, where we only kept classes where secondary-structure elements were visible and radial streaks from noise were minimal. We re-centered and re-extracted  this 142 300-particle set, subjected it to 3D auto-refinement (D2 symmetry imposed) starting from EMD entry EMD-8743 low-pass filtered to 15 Å, and obtained a reconstruction of aldolase with a reported resolution of 4.8 according to FSC = 0.143. Grouping by image shift and subsequent runs of CTF refinement, firstly with refinement of beam tilt only and subsequently with refinement of defocus and astigmatism, followed by auto-refinement, yielded a reconstruction at 4.2 Å resolution (FSC = 0.143). We performed 3D classification without alignment  and selected particles from the highest resolution class. 3D auto-refinement using these 28 588 particles followed by CTF refinement  improved the resolution after auto-refinement (D2 symmetry imposed) to 4.0 Å. Bayesian polishing followed by CTF refinement  yielded a final reconstruction after auto-refinement (D2 symmetry imposed) at 3.3 Å resolution (FSC = 0.143).We used 2.4.5.RELION 3.1 and cryoSPARC version 3.1.0 . We aligned dose-weighted movie frames using MotionCor2 from within RELION 3.1 and then imported them to cryoSPARC for 2D classification; subsequent 3D classification and refinement were performed in RELION 3.1. We used the pyem library . We then performed template picking using low-pass filtered projections of EMD entry EMD-8743 as templates, yielding 1 255 598 particles that were extracted and input to cryoSPARC 2D classification. We performed two rounds of 2D classification. After the first run of 2D classification we selected all particles from classes that remotely resembled aldolase, even if the classes did not contain high-resolution features. The particles comprising these classes were used for a subsequent 2D classification run, after which only particles that were contained in classes bearing discernible secondary-structure elements were selected. The coordinates of these 143 234 particles were re-centered and re-extracted without binning with a 256-pixel box size using RELION 3.1. The initial refinement from these particles yielded a reconstruction at 6.5 Å resolution (FSC = 0.143). We then removed duplicates and re-centered and re-extracted the particles, after which 3D auto-refinement yielded a reconstruction at 6.0 Å resolution. We then performed 3D classification without alignment (six classes with Tau = 6), from which we isolated a single class where secondary-structure elements were discernible and radial streaking was minimal. This class contained 21 155 particles. Refinement using this particle set yielded a 5.6 Å resolution reconstruction. Grouping by image shift followed by beam-tilt refinement and 3D auto-refinement improved the resolution to 5.3 Å, and a final refinement round using ‘Mask individual particles with zeros = No’ yielded a reconstruction at 4.5 Å resolution (FSC = 0.143).We used 2.5.RELION 3.1 after running MotionCor2 from within the RELION 3.1 interface. Only micrographs containing particles from the final refinement set were used for this analysis . To obtain the accrued motion per frame, we calculated the distance between the location in the current frame and in the previous frame and summed it to the total previous traveled distance. The dose at each data point is calculated as the dose per frame multiplied by the frame number.To calculate the accrued beam-induced motion, we used the global shift data collected in the STAR files produced by 2.6.SerialEM , targeting holes randomly positioned within the chosen squares. For aldolase, we acquired tilt series on two sets of six holes each, arranged on two transects on neighboring grid squares where particles were visible under the imaging conditions used for single-particle data acquisition. For apoferritin, we acquired seven tilt series on a sample without added glycerol and 14 tilt series on a sample with glycerol in the buffer, on squares that were not used for single-particle data collection but were of similar quality. For both apoferritin and aldolase samples imaged at 300 keV, ice-thickness data were gathered on the same grids from which single-particle data were subsequently acquired. The squares selected for ice-thickness measurements were chosen to match the transparent ice area of those imaged for single-particle data.Tilt series for apoferritin and aldolase samples were collected using a Thermo Fisher Titan Krios TEM at 300 keV equipped with a Gatan K2 Summit direct electron detector. Tomography data were acquired on a small region of the grid which was later avoided for single-particle data collection. Data acquisition was performed using 2.7.newstack application from IMOD  roughly parallel to the field of view. These particle layers on the air–water interface were used as indicators of the ice boundary. Occasionally, ice contamination deposited on the ice surface further confirmed the location of the ice boundary. The 3dmodIMOD module was used to measure ice thickness at the hole edges and in the center, as delineated by the distance between the top and bottom particle layers.Image stacks from apoferritin and aldolase were binned by a factor of four prior to tomogram alignment using the Supplementary Fig. S9 were created using Dynamo .The grids were plasma-cleaned and protein samples were prepared as described above. Blotting was performed from the side using Whatman No. 1 filter paper, as normally performed for negative staining. Plunging was performed by hand, simply submerging the grids completely in liquid nitrogen. These frozen-hydrated samples were imaged at liquid-nitrogen temperature on a Tietz 4k × 4k CCD camera with a defocus of −2.5 µm using the 2.9.UCSF Chimera  and 20%(v/v) glycerol using a 200 keV Talos Arctica. The protein apoferritin assembles into an ∼500 kDa octahedron and is widely used for cryoEM studies due to its size and stability; it has been resolved to better than 2 Å resolution in previous studies . Moreover, the densities of both reconstructions show a tight local resolution distribution near 2.3 Å, not surpassing 2.4 Å , and the density quality and detail of local features are indistinguishable between the two reconstructions . However, the higher glycerol concentration indeed appeared to decrease the overall quality of the data, as more extensive processing was involved in attaining the final structure , a greater number of particles were required to achieve high resolution  and the estimated accuracy of angles was lower (0.24° in 1.7% glycerol versus 0.37° in 20% glycerol). These data indicate that while glycerol does not necessarily prevent the ability to resolve a high-resolution reconstruction of a large, symmetric and stable biological complex, it nonetheless negatively impacts the data quality.In order to determine the effect that glycerol has on the resolution of structures that can be obtained using single-particle analysis, we acquired data on mouse heavy-chain apoferritin in the presence of 1.7%. However, the images acquired in the presence of 20% glycerol showed a qualitatively lower contrast than the samples with no added glycerol. We suspected that the likely source of this lowered contrast was the apoferritin particles being embedded in a thicker layer of ice when glycerol is present. The increased viscosity of the 20% glycerol sample was evident during the grid-preparation process, as the blotting time required to obtain ice that was thin enough for data acquisition was increased nearly twofold for the 20% glycerol sample . A longer blotting time is generally associated with thinner ice, as the filter paper absorbs an increasing volume of sample solution. Despite this longer blotting time, we were unable to attain sufficiently thin ice to produce a monolayer of apoferritin across the holes in the presence of 20% glycerol. This is showed by the presence of overlapping apoferritin particles in the micrographs and 2D averages .These two data sets offered an opportunity to perform a quantitative characterization of the impact that glycerol has on cryoEM data collection and to identify the sources of data-quality degradation. Notably, there was no indication of increased radiation damage in the form of bubbling present in any of the micrographs containing 20% glycerol, nor did the accrued motion significantly differ between the samples with or without added glycerol Fig. 1e. Howevc). Ideally, for single-particle cryoEM analyses the ice thickness should only be slightly thicker than the diameter of the targeted particle in order to minimize the contribution of buffer molecules to the detected image. For this reason, images are routinely acquired at the center of the grid holes, where the ice is generally thinnest and particles are often arranged as a single monolayer. In the presence of 1.7% glycerol, particles are mostly located in a single layer between the two surfaces of the air–buffer interface. In contrast, we identified many fewer regions with comparably thin ice on the grid containing the 20% glycerol sample.We further characterized the difference in ice thickness between the samples of apoferritin with and without added glycerol using cryo-electron tomography. Newly prepared apoferritin samples containing 1.7% and 20% glycerol were loaded into a Titan Krios operating at 300 keV and tomograms of the grid holes were acquired (seven tomograms of the 1.7% glycerol sample and 14 tomograms of the 20% glycerol sample). Analysis of the tomograms revealed that the ice in the higher glycerol concentration samples was, on average, twice as thick. Notably, in the presence of 20% glycerol the ice thickness at the center of the hole is more variable in different holes, while at 1.7% glycerol the ice at the center of the holes was reproducibly thin Fig. 2c. Idealet al., 2018et al., 2018d and Supplementary Figs. S6 and S9). Micrographs for single-particle analysis were also acquired on the 20% glycerol sample that was used for tomographic data collection, and processing these data yielded a reconstruction with a reported resolution at the Nyquist frequency . An atomic model based on this density was practically identical to PDB entry 6v21 . Notably, the structural detail in our reconstruction was of sufficient quality to confidently assign side-chain identity in the core of the reconstruction . Analysis of the micrographs and frame-alignment statistics showed that, as for apoferritin, the added glycerol did not result in radiation-induced bubbling or any significant increases in beam-induced motion .Our apoferritin tests demonstrated that although glycerol increased the ice thickness, thereby decreasing the overall quality of a single-particle data set, we were nonetheless able to determine a high-resolution structure of a stable, symmetric, 500 kDa protein complex. We next set out to test whether the addition of glycerol imposes resolution boundaries on smaller specimens, which are much more sensitive to ice thickness due to the lower overall electron scattering contribution of the biological specimen. To test the impact of 20% glycerol on a sample smaller than 200 kDa, we selected rabbit muscle aldolase, a protein that assembles into a 160 kDa homotetramer with on Fig. 3c. Analyet al., 2018Supplementary Fig. S12). Single-particle data collected from this grid yielded a reconstruction at 4.6 Å resolution . It is unlikely that the difference in accelerating voltage was responsible for the difference in the attained resolution. Rather, the lower resolution of this reconstruction may stem from the lower dose used for imaging  and/or thicker ice than for the 200 keV aldolase data set, underscoring the challenge of consistently obtaining thin ice when glycerol is present at such high concentrations.We prepared additional aldolase grids with 20% glycerol to assess the ice thickness by tomography on the 300 keV Krios and confirmed that the ice was substantially thicker than that previously reported for aldolase prepared in the absence of glycerol (Noble 4.− Å−2) and buffer compositions used in this study, we do not observe significant changes in beam-induced motion or radiation-induced bubbling for glycerol-containing samples compared with those with little or no glycerol. We do, however, confirm that the introduction of glycerol into the buffer lowers the overall quality of the data. We found that the lower contrast of glycerol-containing samples originates from the difficulty of generating thin ice compared with samples lacking glycerol. Such issues with ice thickness can also arise from additives other than glycerol, such as DMSO, and ice-thickness optimization is a necessary aspect of cryoEM sample preparation. Sample volumes, ambient humidity, blotting times and forces could all be optimized to attain an appropriate ice thickness. Self-wicking grids may be used in combination with small-volume dispensing equipment to obtain consistently thin ice in samples with high glycerol concentrations. In addition to finely controlling the dispensed volume, the application-to-freezing time can be tuned to control sample absorption by the grid nanowires ] could potentially be used to attain thinner ice without substantially impacting macromolecular stability.Our results indicate that despite presenting challenges for optimal ice formation, the introduction of glycerol molecules into the buffer does not abolish the ability to determine high-resolution structures of biological specimens using single-particle cryoEM. At the electron doses  glycerol sample described by Karuppasamy et al.  than those used in past literature . As previously demonstrated by Taylor and Glaeser in the first studies of frozen-hydrated catalase crystals  and Electron Microscopy Public Image Archive (EMPIAR) and are accessible with the following IDs: apoferritin in 1.7% glycerol acquired at 200 keV, 6.supporting information for this article: Tan et al. (2017The following reference is cited in the  al. 2017.apoferritin in 1.7% glycerol acquired at 200 keV, EMD-24795EMDB reference: apoferritin in 20% glycerol acquired at 200 keV, EMD-24796EMDB reference: apoferritin in 20% glycerol acquired at 300 keV, EMD-24797EMDB reference: aldolase in 20% glycerol acquired at 200 keV, EMD-24798EMDB reference: aldolase in 20% glycerol acquired at 300 keV, EMD-24799EMDB reference: 10.1107/S2059798321012110/vo5007sup1.pdfSupplementary Figures and Tables. DOI:


An issue of modeling of twin-screw extrusion of polymeric materials is reviewed. The paper is written in honor of Prof. James L. White who was a pioneer in studying this issue. A global approach to process modeling is presented which includes solid polymer transport, polymer plasticating, and the flow of molten polymer. The methodology of CFD modeling of twin-screw extrusion is presented as well as the examples of this modeling which show the details of the process. Optimization and scaling of twin-screw extrusion are also covered. And finally, the future prospects of developments and research of twin screw extrusion is discussed. Extrusion is the most mass technology in the polymer processing industry. It is widely used for production of profiles, e.g., sheets, pipes, films, and for specialty operations, e.g., compounding, granulating, etc.Extruders can be classified as single screw, twin screw, and multi-screw machines. Special extruders can also be distinguished. Single screw machines may have smooth or grooved barrels and may be equipped with conventional or non-conventional screws with elements intensifying melting or mixing. Twin screw machines can be co-rotating or counter-rotating, with varying degrees of screw meshing. In the single screw extrusion mixing action is poor, and melting is slow. In the twin screw extrusion, mixing is much better and melting is faster. Extruders are usually fed with solid polymers (plasticating extruders), although they can also be fed with molten polymers (melt extruders). Extrusion can be performed with gravitational feeding (flood-fed extrusion) or with metering (starve-fed extrusion). Single screw extruders are usually flood fed, while twin screw extruders are starve fed.At present, designing the polymer processing is aided by process simulations which allow predicting the process, i.e., to compute the output process parameters using the input parameters .In the modeling of polymer processing, the models are generally deterministic, transport phenomena based, of distributed parameters or locally lumped parameters. For engineering calculations, the lumped parameter approach is generally sufficient. The main objective of engineering designs of extrusion is to predict the pressure and mean polymer temperature along the machine for a given screw/die geometry as a function of operating conditions. In these models, the screw channel is divided into short segments, where the input data come from the calculation in the previous segment, and the output data from the current segment are the input data for the next segment. In the segments, the local parameters are assumed to be constant. This locally lumped parameters concept is particularly useful when dealing with plasticating processes, such as extrusion, where in addition to the melt flow, solids transport and melting are modeled.Computer modeling allows us to answer practical questions of polymer extrusion. It allows to study the impact of the input parameters variation on the material/process outputs and to show the trends on the material reaction to hundreds of potential input data configurations. It allows to get quickly the process map for identifying the requested process parameters, according to the targeted materials and the selected screw profile, that is, distribution of the thermo-mechanical results  along the screw, detailed energy balance of the process, residence time analysis, etc. The scale-up modules of the software allow the transferring of processes to machines with larger or smaller diameters. Starting from the model process, the geometry of the target design and the associated process parameters are calculated on the basis of transfer rules.However, there are also some shortcomings in practical use. The important one is that the global models which describe the complete extrusion process including solid transport, polymer melting and melt flow are based on the relatively simple 1D or 2D models, and do not allow looking into details of the process.Many important books have been written on polymer extrusion, e.g., by Fischer , SchenkeThis paper is written in honor of Prof. James L. White who was a pioneer in studying and modeling twin screw extrusion, both co-rotating and counter-rotating. A comprehensive approach to process modeling is presented which includes the solid polymer transport, the polymer plasticating, and the flow of molten polymer. These are based on strong experimentation. The software used for global simulation of twin screw extrusion is also reviewed, as well as CFD modeling of the process is presented with the modeling examples which show the details of the process, which are not available when using the global software. Optimization and scaling twin screw extrusion are also covered. And finally, the future prospects of developments and research of twin screw extrusion are discussed.Twin screw extrusion is divided due to the direction of the screw’s rotation into: co-rotating extrusion, when the screws rotate in the same direction , and couTwin screw extruders are mostly used for processing of thermally sensitive polymers, e.g., PVC (polyvinyl chloride), and for specialty operations, e.g., compounding, polymer filling and reinforcing.The mechanism of polymer flow in twin screw extruders differs from that one in single screw extruders and to a large extent depends on the meshing degree. In single screw machines, the polymer flow is of drag type, while in twin screw machines there is also a positive displacement mechanism which is dependent on the degree of screws meshing. It occurs most fully in the intermeshing counter-rotating extruders. In the non-intermeshing screws, this mechanism does not occur. Generally, the closer the meshing is, the transport mechanism is more different. Non-intermeshing extruders resemble basically single screw extruders and therefore do not have such good transportability as the closely intermeshing screws.Another important difference is the polymer velocity distribution in the extruder. In twin screw machines, this distribution is complex and difficult to describe mathematically. However, this complicated nature of the flow causes that twin screw extruders have good mixing and devolatilizing abilities and are characterized by good heat exchange and fast polymer plasticating. The difficulty of describing such a flow, however, makes the twin screw extrusion theory less developed than the single screw process theory.Twin screw machines have significant disadvantages alongside their advantages. They are expensive, structurally complex and difficult to manufacture.Co-rotating twin screw machines are mainly applied for compounding, e.g., polymer mixing, devolatilization, filling, reinforcing or reactive extrusion. The schematic of the co-rotating process is depicted in In this process, the polymer flows from one screw to the other and moves in an open figure-eight pattern, along with the so-called twisted eight. There is a counter-rotating movement in the inter-screw gap, as a result of which high shear stresses are produced in this area. And therefore, this method is used in compounding.Co-rotating twin screw extrusion is usually performed with metered feeding (starve feeding), which makes the pressure profile in the extruder not continuous, and it contains sections where the pressure is reduced to zero. In these areas, i.e., in areas where pressure is not produced, the feeders of various additives may be placed, e.g., for polymer filling, polymer reinforcing or modifying. In these areas, it is also possible to place the devices for devolatilization or removal of chemical reaction products during reactive extrusion.In co-rotating machines, during each screw rotation, the polymer is transferred between the screws. The flow of polymer is dragged so that it resembles the flow in a single screw machine in this respect. However, when moving between the screws, the polymer travels a longer path and undergoes higher shearing.The flow of polymer in co-rotating machines is the result of the movement of the screws relative to the barrel, as well as the pressure gradient in the machine. It is the pressure-drag flow which takes place in open channels between screws  and the barrel.Counter-rotating twin screw machines are used mainly for extruding profiles from thermally sensitive materials, e.g., PVC (polyvinyl chloride). The schematic of the counter-rotating process is depicted in Counter-rotating extruders, in comparison with single screw extruders, provide better feeding the extruder with polymer , and provide a shorter and less varied residence time of the polymer particles in the extruder.In the counter-rotating extruders, the polymer essentially does not flow from one screw to the other, as in the co-rotating one. There is a co-rotating movement in the inter-screw gap so that high shear stresses are not produced in this area, as in the co-rotating extrusion.Counter-rotating extrusion, as with co-rotating extrusion, is usually performed with metered feeding (starve feeding), which results in the pressure profile in the extruder being non-continuous and having sections where the pressure is reduced to zero.The flow of material in the counter-rotating machine differs totally from that one in the single screw machine but also differs from the one in the co-rotating machine. The essence of this flow is the so-called positive displacement mechanism that does not occur in other extrusion variants. The positive displacement is dependent on the degree of screws meshing. It occurs most fully in the closely intermeshing counter-rotating screws. There is no drag flow here so the energy dissipation is insignificant.The flow in the counter-rotating machine is mainly performed in a closed space, in the C-shaped chamber, the so-called C-chamber, shown in c in the gap between the screw root and the screw flight, the flight flow Qf in the gap between the screw flight and the barrel, the back pressure inter-screw flow Qt (tetrahedron flow) in the tetrahedral gap between the screw flight flanks , and the side flow Qs in the side gap between the flight flanks of screws .The C-chamber is bounded by six surfaces, the screw root surface, the barrel surface, the screw flight side surface (twice), and the front surface of the screw flight (twice). There are leakage flows between these surfaces, i.e., the calendering flow QWhen modeling the twin screw extrusion process, the global approach for modeling is needed. It is necessary to describe the transport of solid polymer, the plasticating of polymer and the flow of molten polymer. The flows of molten polymers are rather well known. However, the transport of solids and polymer plasticating are poorly understood. The correct model of polymer plasticating is fundamental for global process modeling.A transport of the solid material in the single screw extruder was modeled first by Darnell and Mol  who descThe problem of modeling the transport of solid material in extruders may be solved by using the DEM method (discrete element method). The first studies using this method were performed by Pohl and Potente  who modeSolid transport in the twin screw extrusion was investigated mostly for co-rotating extruders, for example, by Carrot et al. , White aThe first model of solid transport in the intermeshing co-rotating extruder was developed by Carrot et al. . This moWhite and Bawiskar  observedPotente and Melish  presenteStudies on solid transport in counter-rotating machines were limited. Doboczky first  discusseThe first studies on plasticating in the single screw machine were carried out by Maddock and Street ,52. TheyTadmor et al. ,56,57 diMany other studies were performed by using the “screw pulling out” technique, and they confirmed the Tadmor plasticating model. This model was later extended by other researchers and new models were developed for non-conventional screws. These were recently discussed by Wilczyński et al. .The “screw pulling out technique” is a time-consuming method. Therefore, several other concepts were proposed for studying the plasticating of polymers in extrusion. Liu et al.  applied Many researchers extended the models of Tadmor and Klein , howeverIt is important to note that the plasticating models presented so far, that is the models based on the a priori assumed mechanism are not universal and are not valid for various polymers, various operating conditions, and various screw geometry. These models are useful only for predicting qualitatively the trends in plasticating of polymers in extruders.Plasticating of polymers in extruders can be simulated by solving the conservation equations of fluid mechanics with a constitutive equation for the polymer being melted.This concept was presented first by Viriyayuthakorn and Kassahun  who deveAltinkaynak et al.  carried Contrary to the plasticating in single screw extruders, the research on plasticating in twin screw extruders was limited. This research involved mostly self-wiping co-rotating extruders, e.g., by White and Bawiskar , Todd 7, Sakai , Wong 7, CarneirPlasticating in counter-rotating extruders is less known. Limited observations were presented by Janssen , and WhiRowell and Finlayson  were theLater studies included the non-Newtonian, one- and two-dimensional, isothermal flow of the power-law fluid in a channel of infinite width, which were discussed and extended by McKelvey  and TadmSince the basic models of Tadmor and Klein , many stThe studies on modeling the melt flow in twin screw extruders were limited. Erdmenger ,107 was Intermeshing counter-rotating extruders fundamentally differ from the single screw machines, and from the co-rotating machines. Kiesskalt , MonteliNowadays, 3D FEM simulations are available for single screw and twin-screw extrusion which allow the description of the velocity and temperature distributions and the pressure/flow rate relations, however, they require large computing resources and major calculation time. Therefore, these computations cannot be applied for global process modeling which requires hundreds of computing iterations. To avoid the time-consuming computations, the concept of screw pumping characteristics was developed which are defined by the dimensionless flow rate and the dimensionless pressure gradient . These cIn general, the global models which describe the complete extrusion process including solid transport, polymer melting and melt flow are the lumped parameter models, and are based on the relatively simple 1D or 2D models, and do not allow looking into details of the process. CFD modeling allows studying the process in detail calculating e.g., distributions of shear stress, shear rate, viscosity, residence time, etc.Three-dimensional FEM computations with the use of CFD  software are available for both, single screw and twin screw extruders. These allow the description of the velocity and temperature distributions and the pressure/flow rate relations, and many others. The CFD software is a powerful tool for modeling of polymer extrusion, however, its use is not easy. It requires a good knowledge of the process under study, a good understanding of the modeling procedures, and correct formulating of the boundary conditions, as well as proper interpretations of the modeling results. These issues were discussed in the recently published book of Wilczyński .ANSYS Polyflow software  can be uThe flow of polymer in co-rotating extruders is the result of the relative movement of the screws and the barrel and the pressure gradient in the machine. It is the pressure-drag flow which takes place in open channels between screws  and the barrel . In co-rThe flow of polymer in a counter-rotating extruder totally differs from that one in a single screw extruder, but also differs from that one in a co-rotating extruder. The essence of this flow is the so-called positive displacement mechanism that does not occur in other extrusion processes. The degree of positive displacement is dependent on the degree of screw meshing. It occurs most fully in the closely intermeshing counter-rotating extruders. There is no drag flow so that the energy dissipation is insignificant. The flow in a counter-rotating machine is mainly performed in a closed space, in the C-shaped chamber, shown in In twin screw extrusion, both co-rotating and counter-rotating, the flow is three-dimensional and unsteady since the screws rotate. To simplify the set-up of modeling of such a flow problem, the mesh superposition technique (MST) has been developed for the Polyflow software . A finitThe computation scheme is depicted in −in) = (Q) is imposed,at the inlet to the flow domain: boundary BC1—inflow, the flow rate  = , which means that the flow rate (Qout) = (Q) is imposed,at the outlet of the flow domain: boundary BC2—−Cartesian velocity, Cartesian velocities are imposed (vx and vy and vz) = (–N)—for co-rotating extrusion, which means that the screw is rotated at the screw speed (N) counterclockwise, or (vx and vy and vz) = (N)—for counter-rotating extrusion, which means that the screw is rotated clockwise,on the screw domain: boundary BC3—−Cartesian velocity, Cartesian velocities are imposed (vx and vy and vz) = (–N) for both co-rotating and counter-rotating extrusion, which means that the screw is rotated at the screw speed (N) counterclockwise,on the screw domain: boundary BC4—−zero wall velocity, vanishing normal and tangential velocities are imposed (vn and vs) = , which means that the velocity vanishes at the barrel wall.on the barrel wall: boundary BC5—The following flow boundary conditions were applied to define the process:in) = (Q), and BC2—outflow, i.e., normal forces and tangential velocities (fn and vs) = , imply that pressure may be developed over the screws. Since the pressure at the screw element end is not known, the pressure gradient is computed relative to the zero pressure at the element exit. Negative pressures which may result from simulations do not mean negative pressures in the extrusion process.The boundary conditions, BC1—inflow , zero gradient , and negative gradient , where Δp is the pressure difference between the pressure at the channel inlet and the pressure at the channel outlet.initial—outflow (Qout) = (Q), with other conditions unchanged, which implies that pressure is not generated along the screw, and the flow rate is computed which is equal to the drag flow Qd, that is, there is no pressure flow, and we have . Then, the simulations were performed with the basic boundary condition BC1—inflow (Qin) = (Q), for different values Q = 0, Q = Qd, Q = 2Qd, which corresponds to the different pressure gradients: positive gradient, zero gradient, and negative gradient.In the case of co-rotating extrusion, the simulations started with the initial boundary condition BC1initial—outflow (Qout) = (Q), with other conditions unchanged, which implies that pressure is not generated along the screw, and the flow rate is computed which is equal to the net flow Qnet, that is, there is no pressure flow, and we have . The net flow is composed of the chamber flow and the drag components of leakage flows. Then, the simulations were performed with the basic boundary condition BC1—inflow (Qin) = (Q), for different values Q = 0, Q = Qnet, Q = 2Qnet which corresponds to the different pressure gradients: positive gradient, zero gradient, and negative gradient.In the case of counter-rotating extrusion, the simulations started with the initial boundary condition BC1Pressure distributions are depicted in It is worth noting the characteristic pressure pulsation for all the screw configurations , the so-called pressure saw, which is the result of varying screw geometry in a given area of an analysis. At a certain point in the flow space, the screw flight and the screw channel alternate, which generates pressure pulsation. This phenomenon is an inherent and always occurring feature of screw flows. It is also the cause of pulsation in the extruder output. The frequency of this pulsation is equal to the frequency of screw rotation. Counter-rotating twin screw extrusion seems to be the most stable process, the pulsations are much lower than for single screw extrusion or co-rotating extrusion.Distributions of the velocity components for different pressure gradients are shown in x  in the tetrahedral gap between the screw flight flanks, in the radial direction,the back pressure inter-screw flow Q−s in the gap between the screw flight flanks, in the tangential direction.the side flow QThe following leakage flows can be distinguished:The leakage flows are pressure-drag flows, except the tetrahedral flow which is a pressure flow. The total leakage flow may be presented as a sum of drag flows and pressure flows. The leakage flows at the positive and negative pressure gradients are shown in It is interesting to note that in the co-rotating extrusion, there is a counter-rotating movement in the inter-screw gap a, as a rThe issue of modeling of polymer extrusion was discussed in books, by White and Potente , RauwendThe first computer program for simulating the extrusion process EXTRUD was developed by Tadmor and Klein  which waStudies on co-rotating extrusion were started by White group. Using the melt flow models ,118 and Studies on counter-rotating extrusion were also started by the white group. Using the melt flow models ,135 and At present, the most important software for twin screw extrusion is Ludovic  and SigmLudowic and Sigma allow for simulating extrusion of “pure” polymers, as well as extrusion of polyblends and filled polymers. Ludovic can also simulate extrusion of foodstuffs, pharmaceutics and cosmetics, explosives and building materials.Some materials, e.g., filled polymers and polymer suspensions, exhibit wall slip when processing. This phenomenon was studied first by Mooney , and latSome materials, e.g., filled polymers, exhibit a yield stress. This phenomenon was discussed first by Bingham , and wasThe essence of extrusion is the co-operation of the screw and the die. The global modeling means the modeling of this co-operation. When global modeling the process, the specific computation algorithm is necessary. For classical extrusion with flood feeding, the forward algorithm is suitable. For extrusion with starvation, the backward (inverse) algorithm is necessary. These issues were discussed in detail in .Modeling algorithms for flood fed single screw extrusion are known ,159,160.Modeling algorithms for starve fed extrusion are less known. The modeling requires an inverse approach. The modeling is proceeded from the die to the hopper. The flow rate is known here, and the die pressure is calculated first for some initially assumed temperature. Then, the process is simulated back along the screw using the screw pumping characteristics. When the pressure falls to zero, the starvation starts, and the screw filling is calculated.This inverse algorithm of computation was used by the authors to develop the global model of counter-rotating extrusion ,171,172.Inverse computations for single screw extrusion with starvation are more complex. The authors, using the polymer plasticating models ,185, buiIn extrusion, when modeling the polymer plasticating, the experiment is first performed to get to know the plasticating mechanism, then the physical model of this mechanism is proposed, and finally the mathematical model is developed. Thus, these models are not general in nature and are limited to the specific process and are dependent on the operating and geometrical parameters of the process.The concept of solving this problem using CFD computations seems to be promising. Instead of a priori assumed the specific solid/melt flow mechanism, the flow of the material in the extrusion can be described by solving the conservation equations (motion and energy).In the case of starve fed extrusion this concept may be difficult to implement because the flow area  is not defined. However, it seems to be reasonable to evaluate this area using the degree of filling the screw channel with material.The promising concept would be the coupled CFD/DEM modeling. Recently, such a coupling the DEM software EDEM and the open-sourced CFD software OpenFOAM has been developed.Optimization and scaling the polymer extrusion are the important tools of process designing. Optimization consists in searching the extreme values in the multidimensional space of the process output parameters. Scaling is about changing the scale of the process. It consists in minimizing the differences between the reference process and the novel process. This may be obtained with the use of optimization techniques leading to the minimization of these differences.Various optimization techniques have been used for optimization and scaling the polymer extrusion, among them the Genetic Algorithms  which haAn issue of modeling of twin screw extrusion of polymeric materials has been reviewed. Global modeling has been discussed which includes modeling of solid transport, polymer plasticating, and flow of molten polymer, as well as the screw/die co-operation. Methodology of CFD modeling of twin screw extrusion has been discussed as well as the examples of this modeling has been presented to show the details of this process. The future prospective of developments and research of twin screw extrusion have been presented.Limitations of the conventional approach to modeling the polymer plasticating based on the assumed polymer solid/melt flow mechanism were discussed, and the concept of modeling by solving the conservation equations (motion and energy) without any assumed mechanism of polymer flow has been presented. Attention has been paid to the promising progress in extrusion modeling with the use of the coupled DEM/CFD computing procedures.


The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. However, we demonstrate that these features vary substantially across tissue submitting sites in TCGA for over 3,000 patients with six cancer subtypes. Additionally, we show that histologic image differences between submitting sites can easily be identified with DL. Site detection remains possible despite commonly used color normalization and augmentation methods, and we quantify the image characteristics constituting this site-specific digital histology signature. We demonstrate that these site-specific signatures lead to biased accuracy for prediction of features including survival, genomic mutations, and tumor stage. Furthermore, ethnicity can also be inferred from site-specific signatures, which must be accounted for to ensure equitable application of DL. These site-specific signatures can lead to overoptimistic estimates of model performance, and we propose a quadratic programming method that abrogates this bias by ensuring models are not trained and validated on samples from the same site. Deep learning models have been trained on The Cancer Genome Atlas to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. Here, the authors demonstrate that site-specific histologic signatures can lead to biased estimates of accuracy for such models, and propose a method to minimize such bias. Quantification of more subtle pathologic features can further discriminate between good and poor prognosis tumors, such as the quantification of tumor-infiltrating lymphocytes in breast cancer, but such detailed analysis can be time-consuming and variable between pathologists2. The increasing availability of digital histology coupled with advances in artificial intelligence and image recognition has led to computational approaches to rigorously assess pathologic correlates associated with a variety of tumor-specific features. Deep learning is a subdomain of artificial intelligence, referring to the use of multilayer neural networks to identify increasingly higher-order image characteristics to allow for the accurate identification of features of interest. Deep learning on digital histology has exploded as a potential tool to identify standard histologic features such as grade4, mitosis6, and invasion8. Recently, deep-learning approaches have been applied to identify less apparent features of interest, including clinical biomarkers such as breast cancer receptor status9, microsatellite instability11, or the presence of pathogenic virus in cancer12. These approaches have been further extended to infer more complex features of tumor biology directly from histology, including gene expression15 and pathogenic mutations17. The predictive accuracy of many of these models has been validated in external datasets, but studies often rely on single-data sources for both training and validation.A standard component of the diagnosis of nearly all human cancers is the histologic examination of hematoxylin and eosin-stained tumor biopsy sections. Histologic characteristics identified by pathologists help characterize tumor subtypes, prognosis, and at times can predict response to treatment18. Due to the propensity of machine learning algorithms to overfit, performance is typically reported in a reserved testing set or evaluated with cross-validation, to avoid biased estimates of accuracy19. However, the overfitting of digital histology models to site-level characteristics has been incompletely characterized and is infrequently accounted for in the internal validation of deep learning models. The genomic batch effects in TCGA and other high-throughput sequencing endeavors have been well -characterized, and are the product of the hundreds of tissue source sites contributing samples and the multiple sites for genome sequencing and characterization22. Histologic imaging data similarly contain characteristic signatures unique to each tissue submitting site  has been critical for the development of deep-learning histology models, containing over 20,000 digital slide images from 24 tumor types, along with associated clinical, genomic, and radiomic data26. This includes methods designed to reduce color variation across images proposed by Reinhard et al.27, and methods designed specifically for histology by Macenko et al.28. Color augmentation , all demographic characteristics as well as estrogen receptor status (n = 969), progesterone receptor status (n = 966), HER2 expression (n = 847), PAM50 subtype (n = 914), TP53 mutational status (n = 1004), immune subtype (n = 1002), and 3-year progression-free survival (n = 458)34 varied significantly between cohorts, with false discovery rate correction and P < 0.05  and lung adenocarcinoma  and human papillomavirus (HPV) status in head and neck squamous cell carcinoma —all with P < 0.05 and significant after FDR correction  to 0.964 for TCGA-LUSC (n = 463). For comparison, AUROC for a neural network model trained to predict site from the clinical characteristics described in Supplementary Table To assess the ability of deep learning to predict tissue submitting sites, we trained a deep-learning convolutional neural network based on Xception architecture to predict siteite Fig.  and calcite Fig. . The slin = 115 ER-positive, n = 23 ER-negative). A single site was chosen as this would theoretically minimize any batch effect due to site-related differences in sample procurement, staining, or digitization. We assessed the ability of deep-learning models to identify 23 random slides from within a cohort of 69 patients while introducing both a biologic cofounder (ER status) and stain-related cofounder—representing two different contributors to a site-specific signature. ER status was chosen as the biologic cofounder as it is highly detectable from histology, and the University of Pittsburgh dataset has a reasonable number of positive and negative samples. We varied the ER negativity of the 23 target slides from 0 to 100%, whereas the remainder of the slides were maintained as ER-positive  are correlated with the outcome of interest, but only in specific sites. To evaluate the dependence of deep-learning model accuracy on site-specific digital histology signatures, we propose comparing models trained to assess features of interest using two different methods of cross-validation  had a decline in AUROC with preserved-site cross-validation, and 20 (35.7%) were no longer significantly detectable , TCGA-COADREAD , and TCGA-LUSC . Accuracy of age prediction in the TCGA-COADREAD cohort also declined with preserved-site validation , as did stage prediction in both lung cancer cohorts . As one might expect—these demographic features are often as strongly indicative of disease outcome as pure biologic factors—and outcome prediction demonstrated a significant impact of site-specific signatures in multiple disease cohorts. Performance declined significantly for prediction of 3-year PFS in the TCGA-LUSC  and TCGA-HNSC  cohorts.Of demographic features, the accuracy of genomic ancestryn = 578, AUROC 0.788 versus 0.712, P < 0.001) and with all forms of normalization/augmentation. Nonetheless, this decline was not dramatic and mucinous histology remained detectable with preserved-site cross-validation.The detection of standard histologic features was less perturbed by preserved-site cross-validation—with no difference in accuracy with the prediction of HER2 status in TCGA-BRCA and of grade in TCGA-HNSC. Other histologic features remained largely unaffected by preserved-site cross-validation—with minimal decreases in AUROC—including prediction of lobular versus ductal histology in TCGA-BRCA, prediction of estrogen, and progesterone receptor status in TCGA-BRCA and prediction of grade in TCGA-KIRC. Prediction of mucinous histology for TCGA-COADREAD, however, did decline with preserved-site cross-validation at baseline , RHOA in TCGA-HNSC , and RNF43 in TCGA-COADREAD . The detection of other genomic features was also dependent on site-specific signatures, including ALK fusion detection in lung cancer  and immune subtype34 detection in half of the cancers analyzed.There has been increasing interest in using deep learning to detect non-intuitive features directly from histology, including our previously described work on detection of genetic driver mutations directly from histologyTo further explore why some features exhibit a decline in accuracy, we produced a UMAP and mosaic map of two features in TCGA-BRCA: (1) ancestry, which correlates with site and declined substantially in accuracy  are significantly higher for standard cross-validation balanced by ancestry, as compared to preserved-site cross-validation  were also poorly stratified—in the case of MSI status, one validation fold contained over three-fourths of available patients because one organization contributed the majority of samples where MSI status was known. However, for MSI status, poor stratification did not significantly affect performance when tested with preserved-site cross-validation—consistent with the fact that MSI status has a well-proven histologic basis65. However, application to the aggregate of predictions using DeLong’s method fails to capture the variance in predictive accuracy seen when training with different subsets of data, and also is not extensible to multicategorical models such as those we used for submitting site and stage prediction. Bootstrapping as per Hanley and McNeil66 is also highly utilized, but in our preliminary analyses, we had planned to assess model performance without bootstrapping. As the number of features we planned on analyzing grew, we updated our analytic plan to include bootstrapping as described to allow for reasonable estimates of significance with false discovery correction, as well as to mirror the methods of our group’s prior work in genomic feature detection, allowing for better comparison to these results16.Multiple methods for assessment of statistical significance have been proposed for AUROC analysis, including seminal work by DeLong et al.25. We attempted to minimize the influence of resolution on our findings by sampling slides in our deep-learning models at a fixed pixel to μm ratio, but we did not directly assess the ability of deep-learning models to detect compression. Several of our findings support slide stain differences as a primary etiology of site-specific signatures in TCGA. First, a UMAP of final layer activations for site prediction as well as other highly affected features in TCGA-BRCA highlights that an azurophilic to eosinophilic gradient 69, lung squamous cell carcinoma (LUSC)70, lung adenocarcinoma (LUAD)71, renal clear cell (KIRC)72, and head and neck squamous cell carcinoma (HNSC)73. Slides and associated clinical data were accessed through the Genomic Data Commons Portal . Ancestry was determined using genomic ancestry calls provided by Carrot-Zhang and colleagues, with computation as described in their work42. Immune subtypes were used from the work published by Thorsson et al.34. Informed consent was obtained for all participants in TCGA, and ethics oversight is described at https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/history/policies.Patient data and whole-slide images were selected from six of the tumor types from TCGA with the highest number of slides available to better identify site-specific digital histology signatures. Tumor types included breast (BRCA)32. For analysis of first-order and second-order image characteristics, slides were downsampled to 5 microns per pixel or approximately ×2 magnification. For deep-learning applications, the tumor region of interest is tessellated into 299 × 299 pixel tiles for evaluation, each representing a 302 × 302 μm area of histology, effectively generating an optical magnification of ×10. A more in-depth description of our preprocessing methodology is publically available (10.5281/zenodo.3694994). An average of 1% of slides was excluded for quality issues as described in Supplemental Table 37 pre-trained on the ImageNet database74. The final fully connected layer of Xception is replaced by a single fully connected hidden layer with width 500, followed by a softmax layer for prediction. This architecture is analogous to what is used in other large pan-cancer studies of TCGA to allow comparison of our findings to such studies25. Models were trained over 3 epochs of data, using the Adam optimizer75, with a learning rate of 10−6, a batch size of 32, sparse categorical cross-entropy loss, and no L2 regularization or dropout. For the prediction of cancer sites using clinical tumor characteristics alone, a similarly structured neural network was used with a single hidden layer with a width of 500.Scanned whole-slide images of hematoxylin and eosin-stained tissue were acquired in SVS format from TCGA. Each slide was reviewed by a pathologist for manual annotation of the area of the tumor using QuPath version 0.12, to ensure ink or other non-cancer artifacts did not influence slide-level statistics28 using a publically available implementation76, grayscale refers to direct slide conversion to grayscale, and “grayscale normalized” refers to conversion to grayscale with histogram equalization77. Both light and heavy levels of hue saturation value (HSV) augmentation was applied, with light augmentation multiplying each of these three channels by a scalar from 0.9 to 1.1, and heavy augmentation multiplying the hue and saturation channels by a random scalar from 0.7 to 1.3. In addition, further augmentation through random tile rotation is performed, and further normalization ensures inputs have a mean of zero and a variance of one. Models are trained with threefold cross-validation, learning from two splits of the data and then evaluated on the third split  of 0.05 with the Benjamini–Hochberg method applied individually to each cancer subtype , averaged over the threefolds generated for cross-validation. Confidence intervals and statistical testing were computed using a ×10 bootstrapped experiment. For multicategorical models , the reported AUROC values are the one-versus rest AUROC, calculated using the Scikit-learn library, version 0.23.2.t-test with two degrees of freedom and an FDR of 0.05, and comparisons to assess if average AUROC was greater than random chance (AUROC 0.50) were performed with a one-sided t-test with an FDR of 0.05 and two degrees of freedom . Models were trained to detect 23 patients with a varied percentage of ER positivity (0–100%) and varied percentage of stain alteration (0–100%, as per Supplementary Fig. Deep-learning models were also trained in a series of artificial experiments to predict a simulated feature of interest within the University of Pittsburgh dataset (s:79. Our code used for fold generation for preserved-site cross-validation is available from https://github.com/fmhoward/PreservedSiteCV80.Accuracy for prediction of clinical variables is reported with standard cross-validation—stratifying by site, and with preserved-site cross-validation—where each site is isolated to a single fold, and secondarily stratifying by the site. In other words, for standard cross-validation, all sites are merged into a single dataset, and folds are created without respect for site, such that classes are balanced and three equal folds are produced. Conversely, for preserved-site cross-validation, the dataset is divided into several folds, such that patients from a single site are all contained within the same fold. The split of sites is also selected to ensure the distribution of patients in each fold with respect to the outcome of interest is reflective of the larger population. To calculate t-test with four degrees of freedom to compare standard and preserved-site cross-validated AUROCs. One-sided t-test was chosen given the distribution in Fig. We assess the impact of site-specific signatures on model accuracy across 58 features for the 6 aforementioned cancer types using both standard and preserved-site validation—the cross-validation folds and sample sizes used in this assessment are listed in Supplementary Table To ensure reproducibility, computer code used to support the main findings of this work was run in duplicate with equivalent results, including the subdivision of sites into groups for cross-validation using the provided software.Further information on research design is available in the Supplementary InformationReporting SummaryPeer Review File


Tick sialome is comprised of a rich cocktail of bioactive molecules that function as a tool to disarm host immunity, assist blood-feeding, and play a vibrant role in pathogen transmission. The adaptation of the tick’s blood-feeding behavior has lead to the evolution of bioactive molecules in its saliva to assist them to overwhelm hosts’ defense mechanisms. During a blood meal, a tick secretes different salivary molecules including vasodilators, platelet aggregation inhibitors, anticoagulants, anti-inflammatory proteins, and inhibitors of complement activation; the salivary repertoire changes to meet various needs such as tick attachment, feeding, and modulation or impairment of the local dynamic and vigorous host responses. For instance, the tick’s salivary immunomodulatory and cement proteins facilitate the tick’s attachment to the host to enhance prolonged blood-feeding and to modulate the host’s innate and adaptive immune responses. Recent advances implemented in the field of “omics” have substantially assisted our understanding of host immune modulation and immune inhibition against the molecular dynamics of tick salivary molecules in a crosstalk between the tick–host interface. A deep understanding of the tick salivary molecules, their substantial roles in multifactorial immunological cascades, variations in secretion, and host immune responses against these molecules is necessary to control these parasites. In this article, we reviewed updated knowledge about the molecular mechanisms underlying host responses to diverse elements in tick saliva throughout tick invasion, as well as host defense strategies. In conclusion, understanding the mechanisms involved in the complex interactions between the tick salivary components and host responses is essential to decipher the host defense mechanisms against the tick evasion strategies at tick-host interface which is promising in the development of effective anti-tick vaccines and drug therapeutics. The natural persistence and vectorial capabilities of ticks have been assisted by the evolution of sophisticated blood-feeding mechanisms and remarkable salivary molecules that come to play in the invasion of the vertebrate host immune responses . Ixodid During a blood meal, ticks constantly secrete salivary molecules into the blood-feeding cavity to modulate the rigorous actions of the recruited host immune cells to avoid rejection by the host . Tick saDermacentor or Rhipicephalus genera) with short mouthparts as compared to prostriate ticks (genus Ixodes) with larger mouthparts  . A partiOrnithodoros moubata, were the first protease inhibitors discovered to specifically inhibit fXa  and exosite I   with both exocites and active site of thrombin. However, AsKunitz inhibition of thrombin’s activity might be due to the interaction of 2 cysteine components in the carboxy terminal region with exosite I only  is comprised of 32 residues and a competitive inhibitor that interacts with the thrombin active site and exosite I  microplus-derived BmAP, microphilin, and boophilin  . In addie I only . The Ambxosite I . A. varixosite I . Rhipiceoophilin  can inhioophilin . A fractlatelets . Salivaractivity . Haemathinal end . A. sculgulation . The calin clots . It alsoin clots . The Haegulation . Haemaphgulation . A. sculthrombin . IxscS-1clotting . Other Igulation . It is cI. ricinus and I. scapularis promote the delay of wound healing and angiogenesis through binding the cascade fibrinogen and fibrin and are involved in tissue disruption by digesting structural components  and prevent thrombin activity by binding its active site and exosite I , and thrombin (RmS-15) . Both inhibitors have been found to be involved in the inhibition of thrombin and fXa  and coagulant activities involving the inhibition of serine protease elastase and trypsin (The  plasmin . Enolasetivities . R. micr(RmS-15) . I. scapregation . In vivoIIb/IIIa . Tick sathe host . In addithe host , and IRSthe host . DsCystativation . O. moubormation . D. varitor αvβ3 . O. savireceptor ; FXal bireceptor . Other sreceptor . BmTI-A llikrein . O. moubgicornis . TSGP2 aaffinity . Argas mregation . Iris ) to the C1 of the initially activated factor of C1qC1rC18 of the classical pathway, which in turn blocks the deposition of C4b  prevents the binding of mannose-binding lectin and ficolins to their ligands, thus inactivating the cascade of the lectin pathway  and O. savignyi (BSAP), resulting in the inactivation of the lectin complement pathway , Evasin-3 (CCL1 and CCL8), and Evasin-4 (CCL11 and CCL5)—are responsible for the inhibition of chemokines  and Japanin have been identified as possessing a high affinity toward histamine binding. Functionally, Ra-HBP1 is a weak histamine binder as compared to Ra-HBP2 and 3, which display potent affinity to histamine , that induce host immunomodulation . Among tinel MPs . Pro-infnguineus . Tick singuineus . Tick-seapularis , which capularis .in vivo model has shown that exosomes secreted in the saliva of A. maculatum and I. scapularis at the bite site modulate the production of cytokines or chemokines such as IL-8 and/or C-X-C motif chemokine ligand 12, which are responsible for controlling tissue injury, healing, and the wound-repair process for a successful blood-feeding mechanism   in early blood-feeding  , O. errarraticus , R. pulclchellus , R. zambeziensis , and H. omedarii . As a reomedarii  that havomedarii . Several-feeding . In contthe host . I. persorgement . A homolericanum , and O.  moubata  during bproteins  and O. mproteins . A largeproteins . This phechanism . This tiechanism . Moleculechanism . The rapI. scapularis nymphs and bind a Borrelia burgdorferi derived OspC, a spirochete surface protein  Grant Number JPMJTM20SV and Takeda Science Foundation.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background: Multi-site functional MRI (fMRI) databases are becoming increasingly prevalent in the study of neurodevelopmental and psychiatric disorders. However, multi-site databases are known to introduce site effects that may confound neurobiological and measures such as functional connectivity (FC). Although studies have been conducted to mitigate site effects, these methods often result in reduced effect size in FC comparisons between controls and patients.Methods: We present a site-wise de-meaning (SWD) strategy in multi-site FC analysis and compare its performance with two common site-effect mitigation methods, i.e., generalized linear model (GLM) and Combining Batches (ComBat) Harmonization. For SWD, after FC was calculated and Fisher z-transformed, the site-wise FC mean was removed from each subject before group-level statistical analysis. The above methods were tested on two multi-site psychiatric consortiums [Autism Brain Imaging Data Exchange (ABIDE) and Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP)]. Preservation of consistent FC alterations in patients were evaluated for each method through the effect sizes (Hedge’s g) of patients vs. controls.Results: For the B-SNIP dataset, SWD improved the effect size between schizophrenic and control subjects by 4.5–7.9%, while GLM and ComBat decreased the effect size by 22.5–42.6%. For the ABIDE dataset, SWD improved the effect size between autistic and control subjects by 2.9–5.3%, while GLM and ComBat decreased the effect size by up to 11.4%.Conclusion: Compared to the original data and commonly used methods, the SWD method demonstrated superior performance in preserving the effect size in FC features associated with disorders. Neuroimaging has become a powerful tool in studying psychiatric disorders . FunctioWhile there are many benefits to multi-site consortiums, there are significant challenges in combining the data for analysis. FMRI data from different sites may contain scanner and site variability, leading to conflicting results and inferior reliability . ScannerEfforts to reduce site variability have been made through homogenizing scanning protocols and/or through site-to-site quality assurance via standardized brain imaging phantoms . While tAttempts to reduce multi-site consortium variability in FC analysis of fMRI data after acquisition include generalized linear model (GLM) and Combining Batches (ComBat) harmonization . GLM modAlthough some multi-site consortiums may use phantoms or homogenous scanning parameters, there is always the possibility that sites will decide to aggregate FC data after image acquisition. There is a great need for a site-effect mitigation method that can be applied after acquisition, on heterogeneous scanning parameters, and that preserves functional networks associated with psychiatric disorders. Examples of such multi-site database are the Autism Brain Imaging Data Exchange (ABIDE) and the Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP) . ABIDE iHere, we describe a site-wise de-meaning (SWD) strategy for multi-site FC analysis of fMRI data and compare its performance with two common site-effect mitigation methods . We (1) establish the consistent FC differences between disease groups and control groups in literature, (2) apply site-effect mitigation methods  to multi-site FC data, and (3) compare the effect size of established FC findings of the three site-effect mitigation methods.n = 149) and the corresponding HCs (n = 168) were included in this study  SZ diagnosis (is study . Demogran = 355) and the corresponding HCs (n = 495) from the same sites were used in this study  pair, resulting in a 200 × 200 FC matrix for each subject. Each correlation coefficient was Fisher z-transformed, then linear regression was used to regress out age and sex covariates to ensure that these confounding variables did not affect results.The most common resting-state FC findings between disease and control groups was determined by performing a literature review in PubMed to identify relevant studies published within the last 15 years for FC differences between SZ and HC  and for FC differences between ASD and HC .We compared the effect size of FC differences between patients and controls for the following methods: (1) GLM, (2) ComBat , and (3)(1)GLMAfter Fisher z-transforming the FC data, multiple linear regression with terms for age, sex, and site was performed in MATLAB. The regression model can be written as:ijv represents the connectivity at every site (i), subject (j), and for every ROI pair (v), αv is the average connectivity value for a particular connectivity value (v), v is the vector of regression coefficients corresponding to Where y(2)ComBatFC values were Fisher z-transformed and a multivariate linear mixed effects regression with terms for biological variables and scanner were used to model FC . The Comijv represents the connectivity at every site (i), subject (j), and for every ROI pair (v), αv is the average connectivity value for a particular connectivity value (v), XTij is a design matrix for the covariates of interest  for every subject (j), βv is a vector of regression coefficients corresponding to XTij, γiv and δiv are the additive (or location parameter) and multiplicative , respectively, of site-effects of site i for connectivity value v SWDThe Fisher z-transformed data with age and sex regressed out is referred to in To evaluate how each method affects the underlying neurobiological measures, Hedge’s g was used to calculate the effect size of consistent FC alterations in group analysis (ASD vs. HC and SZ vs. HC) for (1) original data with sex and age regressed out, (2) GLM, (3) ComBat harmonized data, and (4) SWD. It is suggested that 0.2 is considered to be a small effect size, 0.5 represents a medium effect size and 0.8 represents a large effect size . The folHC is the mean connectivity of HCs for a particular connectivity value, MASD is the mean connectivity of ASDs for a particular connectivity value, and SD∗pooled is the weighted and pooled standard deviation.where Mhttp://fcon_1000.projects.nitrc.org/indi/abide/abide_I.html for ABIDE and http://b-snip.org/for B-SNIP , as well as between the MPFC and the anterior cingulate cortex (ACC) , was theThe hypoconnectivity hypothesis of ASD posits that behavioral features of ASD arise from reduced neural connections in the brain . The mosHedge’s g was used to calculate the effect size of SZ vs. HCs for the primary and secondary FCs depicted in Hedge’s g was used to calculate the effect size of ASD vs. HC for the primary and secondary FC features depicted in Previously introduced methods to reduce fMRI site-effects associated with multi-site disorders result in the loss of effect size associated with psychiatric or neurodevelopmental disorders. The SWD method reduced site-effects in large sample sizes in multi-site databases with heterogeneous scan parameters, while improving the effect size of FC features associated with ASD and SZ compared to previous site-effect mitigation methods. This simple method is computationally inexpensive, is applicable to multi-site consortiums post-acquisition, and can be applied to other multi-site fMRI databases.ComBat has been proposed to mitigate site-effects in small sizes, when using homogeneous scanning parameters, however it is unknown if it can accurately account for site-effects imposed by heterogeneous scan parameters and whether it can preserve the functional networks associated with psychiatric disorders . ComBat Hypoconnectivity in SZ has been widely reported and is associated with symptoms of SZ , while DThe superior performance of SWD compared to GLM may be due to better generalizability and removal of overall site-effects in site de-meaning. In sites with unequal cohort sizes, GLM may introduce a diagnostic group bias. In addition, while diagnostic group is a covariate used in ComBat, the FC values are shifted to an overall mean, which can result in the loss of important diagnostic group information.While there are advantages with SWD, there are several limitations as well. First, while there are many reports of hypoconnectivity in SZ and ASD, there is no conclusive ground truth fMRI neuromarker for either disorder. In addition, while we postulate that this method could be utilized on multiple multi-site databases with various other disorders, this has only been tested on two multi-site consortiums with two different disorders. Therefore, more extensive testing is needed.We introduce a site-size demeaning method for reducing site effects in multi-site studies and compared it with two existing methods. The SWD method improved the effect size across these features in two multi-site disorder databases as compared to the original data and previously used harmonization methods (ComBat and GLM).http://fcon_1000.projects.nitrc.org/indi/abide/) and Schizophrenia fMRI data is publicly available from B-SNIP (https://nda.nih.gov/edit_collection.html?id=2274).Publicly available datasets were analyzed in this study. This data can be found here: Autism data is publicly available from ABIDE (This study analyzed two public datasets whose participants were recruited under the ethical guidelines described in the original studies .AR: conceptualization, methodology, software, formal analysis, investigation, visualization, and writing—original draft. KL: methodology, conceptualization, supervision, and writing—review and editing. XH: conceptualization, supervision, and writing—review and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Selecting 2.05 m/s as a cut-off value yielded a sensitivity and specificity of 75.0% and 70.3%, respectively, in diagnosing malignant MMs . Conclusion: ARFI elastography may represent an additional non-invasive tool for differentiating benign from malignant MMs. However, to validate the results of this study, further prospective randomized studies are required.Purpose: To evaluate the diagnostic performance of ultrasound point shear wave elastography (pSWE) using acoustic radiation force impulse (ARFI) technology in different benign and malignant mesenteric masses (MMs). Methods: A total of 69 patients with MMs diagnosed from September 2018 to November 2021 were included retrospectively in the study. The inclusion criteria were (1) an MM over 1 cm; (2) valid ARFI measurements; and (3) confirmation of the diagnosis of an MM by histological examination and/or clinical and radiological follow-up. To examine the mean ARFI velocities (MAVs) for potential cut-off values between benign and malignant MMs, a receiver operating characteristics analysis was implemented. Results: In total, 37/69 of the MMs were benign (53.6%) and 32/69 malignant (46.4%). Benign MMs demonstrated significantly lower MAVs than mMMs (1.59 ± 0.93 vs. 2.76 ± 1.01 m/s;  The mesentery is a complex, contiguous, membranous peritoneal fold, which starts at the level of the aortic origin of the superior mesenteric artery (known as the root of the mesentery) and then extends in a fan-like fashion toward its insertion at the intestinal border, thus connecting the intestine  to the posterior abdominal wall . SeveralComputed tomography (CT) is the most widely utilized imaging modality in the diagnosis of mesenteric masses (MMs). In addition, magnetic resonance imaging (MRI), positron emission tomography–computed tomography (PET-CT), and B-mode ultrasound (US) are used for evaluating MMs ,3,9,10. The aim of this study was to evaluate the feasibility and diagnostic performance of US ARFI elastography as a quantitative, non-invasive method for characterizing MMs of various etiologies.This study included 80 consecutive patients with MMs detected on B-mode US between September 2018 and November 2021, who underwent an elastographic evaluation (ARFI) at our tertiary healthcare facility . This study was approved by the local ethics committee (protocol code: EK_MR_09_04_20_görg_2) and conducted in accordance with the amended Declaration of Helsinki. Informed consent for the US examination was obtained from each patient.The inclusion criteria were (1) an MM over 1 cm; (2) valid ARFI measurements; and (3) confirmation of the diagnosis of an MM by histological examination and/or clinical and radiological follow-up. In total, 11/80 patients (13.8%) with an MM were excluded due to the absence of diagnostic confirmation. Finally, data from 69 MMs were analyzed retrospectively. All US and ARFI elastographic examinations were performed using a Siemens Acuson S2000, Acuson S3000, and Acuson Sequoia  by a German Society for Ultrasound in Medicine (DEGUM) Level III-qualified examiner  with more than 35 years of US experience . With thThe ROI (dimensions = 10 × 5 mm) was positioned on the US screen completely within the MM. For each measurement, the patient was asked to hold their breath in mid-expiration for at least 6 s. The measurement was displayed as velocity (m/s) on the upper corner of the screen. If the lesion moved while being measured, this single reading “shot” was considered invalid and was repeated. In the event of air superimposition, the patient was positioned carefully to enable an adequate measurement to be made. A total of 11 valid measurements were obtained for each ARFI study ,30. Bothp-value of <0.05 was defined as significant. The statistical analyses were performed using Excel  and SPSS Version 26.0 statistics software .Demographic and biometric data were expressed as mean values ± standard deviations (SDs). Statistical evaluation was performed on the categorical variable using Fisher’s exact test and on the continuous data using Mann–Whitney tests. The diagnostic performance was assessed using receiver operating characteristics (ROC) curves. Cut-offs between groups were examined for accuracy using the area under the ROC curve (AUROC) and the 95% confidence interval (CI). A 2 (range 13.9–45.1). The final diagnosis was malignant mesenteric mass (mMM) in 32/69 cases (46.4%) and benign mesenteric mass (bMM) in 37/69 cases (53.6%). An overview of all diagnostic entities is shown in Of the 69 study patients, 51 (73.9%) were male and 18 (26.1%) female. The mean age was 62 ± 14 years (range 21–91). The mean body mass index (BMI) was 27.4 ± 5.5 kg/mIn 28/32 cases (87.5%), the diagnosis of an mMM was confirmed by histological examination of the MM. In the other 4/32 cases (12.5%), the diagnosis of an mMM was confirmed by radiological examinations of the mMM and a histological examination of a distant metastasis. Of the 37 bMMs, in a total of 17 cases (45.9%) the diagnosis of a bMM was confirmed by histological examination of the MM. In the remaining 20/37 cases (54.1%), the diagnosis of a benign lesion was confirmed based on clinical follow-up and/or a cross-sectional imaging (CT or MRI).p < 0.001, Fisher’s exact test). The mean size of all 69 MMs was 6.34 ± 3.93 cm (range 1.5–22.0 cm); the size did not differ significantly between bMMs (5.35 ± 2.40 cm) and mMMs . Malignant mesenteric masses were hypoechoic in 29/32 cases (90.6%) and isoechoic in 3/32 cases (9.4%), whereas bMMs were hypoechoic in 11/37 cases (29.7%) and isoechoic in 26/37 cases (70.3%). Hypoechoic MMs were significantly more frequently associated with malignancy compared to isoechoic MMs . The MAVs among benign and malignant subgroups of MMs are shown in Benign MMs demonstrated significantly lower mean MAVs than mMMs . Using the AUROC and in order to maximize sensitivity and specificity, we suggest a value of 2.05 m/s as a cut-off for mMMs . The calculated sensitivity, specificity, positive predictive value, and negative predictive value were 75.0%, 70.3%, 68.6%, and 76.5%, respectively. Shear wave elastography (SWE) uses the acoustic stimulation of tissue, which induces shear waves that propagate perpendicularly to the compressive (stimulating) signal; these waves are registered at different locations, allowing the estimation of their velocity, which in turn represents tissue elasticity . There aThese findings reveal that pSWE using ARFI may be an additional useful non-invasive tool for the evaluation of malignancy of MMs in addition to B-mode US and CEUS . Particup = 0.001). These results are important because sclerosing mesenteritis and mMMs may have similar features in different imaging modalities, such as CT or CEUS, and may not be differentiated from each other [p > 0.05), and a differentiation between hematologic and non-hematologic malignancies by ARFI elastography was not possible. A further notable finding of this study was the difference between the MAVs of sclerosing mesenteritis and those of mMMs. The MAVs of sclerosing mesenteritis  (1.53 ± 0.96 m/s) were significantly lower than those of mMMs  , further large prospective studies are needed to validate these results. (4) Validation of diagnosis: a histological examination was not performed in all the MMs. However, all the diagnoses in these patients were confirmed by a future cross-sectional imaging modality (CT or MRI) and clinical follow-up.There were some limitations to this study. (1) Technical difficulty and failure of measurement: due to the anatomical location of the mesentery and the superimposition of air-filled intestinal loops, ARFI examination of the mesentery may be difficult. In the event of air superimposition, the patient was positioned carefully to enable an adequate measurement to be made. (2) Reproducibility: due to the retrospective nature of the study, the evaluation of inter-rater reliability in the performance of the ARFI elastography investigation in the mesentery was not possible; however, the reproducibility of ARFI has been described in various studies in other organs ,37,43. (Our understanding of the mesenterial organ in terms of structure, composition, and role in health and disease is still evolving. In this study, we confirmed the feasibility of pSWE using ARFI in quantifying the degree of stiffness of different benign and malignant mesenteric pathologies. We also found significant differences in stiffness between bMMs and mMMs. Elastography, in particular pSWE, may represent a helpful diagnostic modality in the evaluation of mesenteric pathologies. However, to validate the results of this study, further prospective randomized studies are required.