Viscoelasticity and its alteration in time and space has turned out to act as a key element in fundamental biological processes in living systems, such as morphogenesis and motility. Based on experimental and theoretical findings it can be proposed that viscoelasticity of cells, spheroids and tissues seems to be a collective characteristic that demands macromolecular, intracellular component and intercellular interactions. A major challenge is to couple the alterations in the macroscopic structural or material characteristics of cells, spheroids and tissues, such as cell and tissue phase transitions, to the microscopic interferences of their elements. Therefore, the biophysical technologies need to be improved, advanced and connected to classical biological assays. In this review, the viscoelastic nature of cytoskeletal, extracellular and cellular networks is presented and discussed. Viscoelasticity is conceptualized as a major contributor to cell migration and invasion and it is discussed whether it can serve as a biomarker for the cells’ migratory capacity in several biological contexts. It can be hypothesized that the statistical mechanics of intra- and extracellular networks may be applied in the future as a powerful tool to explore quantitatively the biomechanical foundation of viscoelasticity over a broad range of time and length scales. Finally, the importance of the cellular viscoelasticity is illustrated in identifying and characterizing multiple disorders, such as cancer, tissue injuries, acute or chronic inflammations or fibrotic diseases. The phenomenon of viscoelasticity can be found in nature in several kinds of material, such as in the most prominent example, rubber, and can be employed in engineering of synthetic or biological materials. A material behaves elastically according to Hooke’s law if the applied stress is proportional to the strain generated in the material, which is true up to a certain level of stress. No material behaves exclusively in a purely elastic manner, but many of these materials can be modeled as one elastic material, which is especially the case when the strain is small. A key assumption in an exclusive elastic material is that the energy required for deformation is stored in the material and is released with its entire efficiency when the material returns to its initial state after deformation. Viscoelasticity is by definition a state of thickness or toughness based on the internal structure of the material. In viscous materials, which include liquids, this means that the energy required to deform it, is partly converted as heat, which is related to internal structural losses. In viscous materials, the force is proportional to the amount of change in deformation. It is generally accepted that some materials can be assumed to be purely elastic or purely viscous. Viscoelasticity can therefore be defined as a property of a material that has both an elastic and a viscous component.In biology, viscoelasticity refers to the property of living matter, including cells, spheroids, and tissues, that manifest both viscous and elastic properties when subjected to deformation. In the past, it was often assumed that cells behave in a purely elastic way, but this often turned out to be a too simplistic view of things. However, although cells are viscoelastic, it is not always straightforward to evaluate or simulate viscoelasticity, and adopting a simplified elastic behavior of cells for this purpose may be a reasonable alternative. The viscoelastic or material characteristics of cells, spheroids and tissues act as major regulators of cell and tissue growth, cell motility, and tissue homeostasis . BiologiCell, spheroids and tissues possess a multitude of inelastic characteristics, among them are viscoelasticity, plasticity and fracture. When mechanically deformed, it is usually impossible to avoid unfolding protein domains, unbinding cytoskeletal crosslinkers, dividing organelle assemblies, breaking weak sacrificial bonds, disrupting transient cell-matrix and cell-cell adhesions and inducing gene expression through translocation of transcription factors into the nucleus. All of which are regarded as inelastic. These structural transformations are mostly reversible and consequently not plastic in the strict meaning of the word, however they dissipate significant quantities of elastic energy through structural attenuation . The ineFollowing the introduction and explanation of the phenomenon of viscoelasticity, the review consists of presenting and discussing biophysical techniques for determining viscoelasticity. The review pursues a hierarchical structure that reflects the spatial scale, ranging from firstly molecular mechanosensing between cells and cell matrix, to secondly transcriptional regulation leading to thirdly changes in cell state, such as epithelial to mesenchymal transition (EMT), encompassing single cell migration, to fourthly multicellular processes, including collective migration, spheroid and tumeroid biology, and fifthly disease states such as cancer, fibrosis, and others.The majority of approaches to query the viscoelastic behavior of cells utilize forced induced deformations or probes  on relatAn overview of these selected biophysical techniques and their characteristics are provided in , which aThe measurement of the viscoelastic characteristics of active matter, such as cells, spheroids and tissues, has expanded in the recent years, since it has become a research focus to seek for a correlation of viscoelasticity and the state of disease, differentiation of cells, or the transformation of cells or collections of cells . Therefoeff, the complex geometry and characteristically very inhomogeneous nature of the cells are entirely discarded, and it is frequently challenging to reconcile the elastic response values obtained in this manner with those acquired in other kinds of measurements.Besides single biophysical technique approaches, a combined approach can be employed to further improve the mechanical characterization of cells. For instance, the pairing of an AFM with a confocal fluorescence lifetime imaging scanning microscope to examine the mechanical characteristics of individual adherent cells seems to be of high relevance in this context . As a reIn addition to analyzing the mechanical properties of adhesive cells, certain biophysical techniques such as the optical cell stretcher technique can also be used to analyze the mechanical properties of non-adherent cells. Two decades ago, the optical cell stretcher was invented, in which two counter-propagating divergent laser beams are pointed at a cell . When boAnother biophysical technique to probe the mechanical properties of adhesive cells, spheroids and tissues is magnetic tweezers. A totally different geometric attempt to investigate cells physically also leverages the interplay of magnetic particles with an external magnetic field. Perhaps the earliest record of the basic principle of magnetic tweezers brought to bear on biological systems was a 1950 experiment by Crick and Hughes, which it then referred to as the magnetic particle method . In specSubsequently, superparamagnetic beads coated with integrin ligands have been employed to mechanically explore the coupling strength between the extracellular matrix scaffold and the cytoskeleton . ViscoelNevertheless, it has been observed that these biophysical analyses may be heavily biased through the precise region of a cell being probed , changesTo determine stiffness, standard techniques  involve A QPI-based approach to accurately determine the viscoelastic properties of cells that is non-contact and non-invasive has been developed as a new technique referred to as quantitative phase rheology (QPR). Specifically, QPI  generallMoreover, previously QPR has been applied to determine the viscoelastic properties of the membrane of enucleated erythrocytes, that incorporated the design of an analytical model connecting the vibrational modes monitored with the viscoelastic characteristics by autocorrelation of the quantitative phase data . Since tSpecifically, the locomotion of the cell biomass, when quantified as the autocovariance of the quantitative phase image measurements, exhibits a harmonically oscillatory movement. The oscillation and decay of the autocovariance can be addressed by employing a two-parameter viscoelastic model. The fitting of this model to empirical data permits the estimation of separate quantities for the effective stiffness and viscosity of a cell. However, earlier methods exist for measuring stiffness  using QPTo differentiate among various cell types, states, and operating conditions using the QPR measurements, a uniform stiffness over the measurement interval of approximately 5 h has been assumed. However, this hypothesis is not valid in mitosis, when the stiffness of the cell undergoes a fundamental alteration . Hence, R2 = 0.81) can be attained when applying the Kelvin-Voigt model to QPR data sets relative to the fitting of the Maxwell model (R2 = 0.89). This implies that a Maxwell material model appears to be the more suitable two-parameter linear viscoelastic material model for the purpose of analyzing the QPR results. While this simple linear two-parameter model is a somewhat streamlined perspective on cell viscoelasticity, this model still accurately grasps the key attributes that have been delineated in the data.QPR and AFM measurements provide cellular viscoelasticity within a similar force and time regime. An interpretation of the experimental data can be provided by a model presented by Qian  that hasAlthough this physical explanation of the mathematical model contains an inertia expression, it still reflects the response of a fluid exhibiting a low Reynolds number. This phenomenological hypothesis permits fitting a two-parameter viscoelastic model and deducing the rheological features of the cells based on the QPI data, which correspond to the AFM results. With respect to a possible physical significance of this expression, it has been demonstrated that inertia-like oscillations can arise in actively propelled, viscoelastic fluids . Since t4, which indicates a QPR probe dimension of approximately 5 nm. In addition, the cell can be modeled as a strictly linear viscoelastic material, but in broad terms, the cell rheology is a function of the length scale, strain rate, and amount of imposed force, which may vary between these two different kinds of approaches. In this way, AFM determines the viscosity from viscous displacement, while QPR provides an effective coefficient of friction that a particle experiences due to the viscosity of a cell. From a technical point of view, these are two distinct characteristics that are intimately linked by viscosity. The QPR approach is closest to passive particle tracking in microrheology  = The relative degree to which a body yields to deformation by a force, typically sampled as time-dependent strain divided by constant stress.Deformation (of cells) = Cells possess the ability to change their shape in response to mechanical stress by remodelling their shape.Durotaxis = refers to the directional cell movement in which cells detect a gradient of varying stiffness (rigidity) in their microenvironment and preferentially migrate toward the stiffer extracellular matrix.Extracellular matrix scaffold = The extracellular matrix represents a non-cellular moiety that forms the material support backbone for cellular elements. Beyond its structural nature, it has an important, active function in morphogenesis, differentiation, and homeostasis.Elastic modulus = Otherwise referred to as the Young’s modulus, the E value provides a quantification of the strain reaction to a uniaxial stress in the direction of that stress in the linear range.Elasticity = The characteristic of a material to deform to a certain degree under the action of a force and to recover its initial shape after the force has been withdrawn. Elasticity in itself is not a measurement of stiffness; what is generally intended is the elastic modulus, the ratio of stress to strain for a completely elastic solid.Entanglement (of fibers) = Polymeric fibers can be wrapped around one another without being physically connected.Hysteresis = The term describes the dependence of the state of a system on its history.Jamming = Material viscosity becomes more divergent as the particle density rises.Linear elasticity = The Young’s modulus or shear modulus is constant over spectrum of strains; thus, the stress is proportional to the strain.Mechanotransduction = It is a process in which cells sense and react to mechanical cues by converting them into biochemical cues that drive specific cellular reactions.Non-linear elasticity = Young’s or shear modulus that alters due to strain.Phase transition = Macroscopic alteration of the characteristics of a system (order parameter) when a parameter exceeds a specific critical level (control parameter), also known as a critical point.Poroelasticity = Investigation of the interaction of a porous elastic array with an interpenetrating pore fluid in a poroelastic material.Strain = The value denoted by γ quantifies the deformation of a body. Specifically, it provides a quantification of the relative displacements of components of the body that are not accounted for by the movements of the rigid body. (unitless parameter that quantifies the amount of deformation after the exertion of stresses).Strain  = represents the fractional alteration in length or elongation: ε = δ/LStrain stiffening response = The nonlinear reaction of multiple biomaterials is the rise in stiffness with the augmentation of strain.Stress = Force is per unit area: σ = F/A, whereby the SI unit is N/m2.Tension = is the pulling force transmitted axially by the means of a string or similar object.Viscoelasticity = A combination of elastic and viscous responses to applied stress. Most biological materials are viscoelastic: when they are deformed, the degree of their resistance decays with time, usually to a stable baseline (viscoelastic solid) but sometimes to zero at long times (viscoelastic fluid).Viscosity = The quantity denoted by η quantifies the flow of the material at a specific velocity under load (Measure of the resistance of a liquid to deformation as a reaction to a shear stress. Viscosity is the relationship of stress to strain rate of a fluid).Young’s modulus = A constant that expresses the resistance of a material to deformation when stretched: E = σ/ε.Within the major elements of adherens junctions and desmosomes are molecules of the cadherin family . Among tfamilies . The latfamilies . The posfamilies . The earDue to the specific environment adherens junctions behave either highly dynamical or extremely stalled and these dynamical remodeling can be adapted at every single step of the assembly of adherens junctions. Adherens junctions assembly in three major steps. Firstly, cells attach to one another in an initial step where extracellular domains of cadherins are involved, with the type of cadherin being displayed by neighboring cells and facilitating the tethering strength of this particular step. A second step represents the lateral extension of a nascent adhesion that covers the additional engagement of cadherins to enlarge the interaction zone. A third step encompasses the stabilization of the adherens junctions, where the cytoskeletal activity of collectively migrating cells are tuned and guided . Whereas2) substrate, which has a shape, dimension, and discontinuity profile similar to the pore openings of a porous membrane, but with no pore edges. The 3 μm diameter pore size has been selected since it is a frequently employed pore size for membranes in the barrier modeling and it is sufficiently sized to accommodate leukocyte transmigration  branches impede cell adhesion to the coated substrate  of cells through an endothelial cell layer, a stiffer matrix, whatever the mechanism of stiffening, has been found to augment leukocyte-endothelial interactions in the inflammatory pathway. When endothelial cells are grown into a confluent monolayer on stiffer substrates, leukocytes can traverse the monolayer more efficiently than when the monolayer is built on a compliant medium . The ampHowever, the stiffness range is the crucial factor. Endothelial cell monolayers constituted more mature cell-cell junctions on soft substrates relative to glass, reflecting enhanced retention of vinculin and F-actin. Endothelial cell monolayers aided transendothelial migration on soft matrices . SpecifiWhen cells, such as cancer cells, transmigrate through the endothelial monolayer, they seem to exert forces on. It is know that force application to the endothelial adhesion molecule PECAM activates GEF-H1 and LARG , whereasThe formation and sustenance of the majority, perhaps all, of animal tissues and organs is governed to a certain degree through the action of mechanics . The focin vivo settings. 3D culture is this kind of technique. Within 3D culture setting, the storage and liberation of growth factors or cell surface receptor ligands or matrix-degrading enzymes and their regulatory molecules is possible compared to 2D culture systems. In addition, the diffusion of substances through a 3D matrix environment seems to be different and is likely to be hindered. The geometry and topology of the 3D matrix environment farther alters the cellular phenotype and consequently cell function. In agreement with this, the migration of cells can be impaired by a dense 3D matrix scaffold that cannot be broken down enzymatically by cellular substances or mechanically through bond rupture by cellular forces. Therefore, choosing the most adequate cell culture techniques in the field of cancer research may permit a better comprehension of cancer biology and thus optimize radio- and chemotherapy or discover innovative therapeutic options , in contrast to the majority of protein-protein interactions in which the lifetime reduces in response to the force (slip bond) . In addiMechanosensors do not function like ordinary on-off relays, instead their reaction involves different characteristics of the forces. Forces themselves can operate on distinct sections of the cell, and can have varying magnitudes, directions, and temporal characteristics, each resulting in a specific unique reaction and varying biological results. The intrinsic mechanisms of force transmission in an individual mechanosensor, and its organization inside the cell, dictate the capacity to discriminate between these various kinds of parameters. All of which is explained in more detail in the following sections.Caenorhabditis elegans spermatocytes by restricting extraneous protrusions ,” which characterizes reciprocal interactions between relatively static, ligand-bound integrins and centripetally flowing F-actin in the vicinity of cell borders . The linIn vivo, the fibronectin-based extracellular matrix of Xenopus, chick, or zebrafish embryos  overlap with other mechanical responses of tissues and extracellular matrices, involving nonlinear elasticity, viscoelasticity, and viscoplasticity , analogoNonlinear elasticity is also evident in cytoskeletal filament networks, such as actin, vimentin, and neurofilaments, however, the source of nonlinear elasticity in these structures may reflect a stronger input from entropic elasticity due to the semiflexible character of the filaments . In the Similar to adherens junctions, focal adhesions comprise a number of mechanosensor proteins employing various mechanotransduction mechanisms. The conversion of mechanical impulses into biological events and their subsequent reaction can be broadly classified into two distinct phenomena. The first phenomenon is the active or passive reaction of cells to externally exerted forces, such as when fluid shear stresses act on the endothelium as a result of blood flow across the vasculature, or when gravity acts on joints, bone, or adipose tissue. These types of excitements provide the foundation of Wolff’s law for bone remodeling, the connection between modified blood flow and the onset of atherosclerosis, or the stress of expanding cancers . The secThe transduction of biochemical cues frequently involves allosteric alterations in protein conformation or phosphorus regulation. Fluctuations in phosphorylation levels can directly affect the activity of a binding moiety or an active site within an enzyme, and they can cause conformational rearrangements in a manner akin to allosteric effects. Since mechanical stresses on a protein have also been found to modify its conformation, evolution efforts not surprisingly have yielded molecules that leverage force-dependent conformational modifications to convey and transduce mechanical inputs . The modCellular reactions to mechanical stimuli including flow, stiffness or viscoelasticity of the extracellular matrix, and tissue extension are related to the magnitude of the forces connected to these stimuli. The range of force magnitudes perceived by the cells and the susceptibility of the various mechanosensors in this range dictate how the cells react to the mechanical stimuli. Even though the molecular underlying mechanisms of susceptibility to force magnitudes have not been fully elucidated, multiple mechanisms whereby cells can derive this kind of information have been identified. A molecular concept for sensitivity to force magnitudes is that mechanosensors exhibit a critical threshold force for activation, such as the force necessary to uncover cryptic binding sites or the force regime where capture bonds are established. Moreover, this sensitivity can be finely tailored through the presence of stable intermediate modes for the force-induced conformations of specific mechanosensors. For example, single-molecule force spectroscopy of catch bonds identified three main modes  at a variety of force levels for fibronectin-integrin , vinculiRhoA has been determined to fulfill a function in mechanotransduction that depends on the tension exerted on fibronectin-coated beads attached to the surface of the cell membrane  and addiActivation of GEFs that occurs in reaction to mechanical force on fibronectin-coated beads is a downstream reaction to stress-induced activation of kinases, specifically members of the Src kinase family and/or FAK. The activation of FAK takes place after integrin entanglement and establishment of focal adhesions . InhibitFocal adhesions can be altered based on the turnover of focal adhesion proteins and the stretching of focal adhesion proteins that act as mechanosensors. There are several mechanisms through which focal adhesion proteins can be altered by a stretching force: the cryptic binding site, such as talin, and the cryptic phosphorylation motif, such as p130Cas. When talin connects integrins and actin, it transfers both cell-generated contractile forces and forces originating from externally applied loads across these constituents. Forces sampled through talin span from just a few to over 11 piconewtons . Talin rThe structure–function analysis of talin lead to a model for mechanotransduction. High affinity sites for vinculin had been charted on the talin rod, however succeeding structures of the respective domains exhibited that these sites remained masked . This leForce-independent cross-talk between talin and vinculin has also been noted , even thWhat role plays the interaction of DLC1 and talin? The tumor suppressor deleted in liver cancer 1 (DLC1) seems to be important due to its recruitment to focal adhesions . In focaThe cytoskeleton functions not merely as an integral regulator of molecular circuitry, but also as a mesoscale mechanosensor with its inherent level of regulation and dynamics. Specifically, the cytoskeleton has a critical involvement in the majority of suggested mechanisms for conveying the perception and transmission of mechanical signals from the microenvironment, and its constituents experience large alterations during cellular deformation in response to mechanical stress . The basThe mechanical performance and characteristics of these three filaments have been characterized in various systems. Microtubules emerged as the more rigid part of the cytoskeleton, with a less rigid meshwork of actin filaments and a more soft meshwork of intermediate filaments . MechaniWhat about the viscoelastic properties of cytoskeletal filaments? Actin networks cross-linked by filamin have been found to display dramatic macroscopic stress-hardening response. By imposing a prestress, the nonlinear stiffness is adjustable over multiple orders of magnitude, whereas the linear elasticity of the reticulation stays at a moderate level . In contContractile forces are extensively produced through the interaction of myosin II with actin filaments. GTP-bound RhoA upregulates myosin II activity through stimulation of Rho kinase (ROCK), that in turn increases phosphorylation of regulatory myosin light chain. This occurs in two ways, through direct phosphorylation of regulatory myosin light chain  and throCytoskeletal filament networks show viscoelastic behavior, since the disruption of the binding motif of filamin A that acts as an actin filament cross-linking protein. Additionally, the cryptic phosphorylation motif in When a solid tumor expands due to cell proliferation, it generally gets denser, and the cancer cells are subject to a harsher physical environment. In part this is attributable to the deposition of more extracellular matrix, as well as cell proliferation resulting in tighter packed cells within the disposable tissue volume, and enhanced contractility of stromal cells . Cancer Small molecules and genetic interferences are frequently used to affect regulatory pathways, for instance pathways regulating calcium content or Rho GTPases . The comA candidate for regulating RhoA activity is the DHPH domain of ARHGEF11 , which fAdditionally, it has been examined whether RhoA activation is accompanied with alterations in cell contractility after relocation of optoGEF-RhoA to the cell membrane or to the mitochondria. Hence, Traction Force Microscopy  has beenTo examine this hypothesis, the transcriptional regulator YAP has been selected, which has been reported to be translocated from the cytoplasm to the nucleus due to sustained elevated traction forces or increased substrate stiffness . Therefoin vivo, where cells have to modify their viscoelasticity as they migrate through restricted gaps.Intriguingly, not merely the cytoskeleton and its elements account for the viscoelasticity of cells . Apart fCell transitions, such as EMT, lead to cytoskeletal alterations of cells  that canThe nuclear rheology relies on the time and length scales of the forced deformations that are determined by a variety of techniques ranging from substrate stretching , indentaMicropipette aspiration can be used to determine the mechanical reaction of nuclei in intact cells across physiological length scales and loads. To assess the viscoelastic contributions of lamin A by itself, lamin-B1 by itself, lamin A and -B1 together, and lamin A phosphorylation levels, stable cultures of lamin knockout and lamin rescue mouse embryonic fibroblasts have been made. The mechanical function of chromatin has been analyzed with the aid of a compound pharmaceutical inhibitor of chromatin deacetylation, which reversibly causes chromatin decondensation. The integrated mechanical functions of lamins and chromatin have been dissected through RNA and protein profiling and transmission electron microscopy. A minimal linear viscoelastic model has been developed that examines the interrelated mechanical inputs of lamins and chromatin. A time scale is apparent of about 2 s that discriminates between two time-regimes that reveal distinct mechanical reactions of the nucleus to imposed stress. At short time periods, the nucleus expands elastically and effectively softens at long time periods. Effective nuclear stiffness is governed through lamin A and B1 and condensed chromatin. In cells expressing lamin A, decondensation of chromatin results in nuclear stiffening, possibly due to newly formed interactions with the nuclear envelope. In steady condition, the nucleus deforms viscously and is governed entirely by lamin A. The universality of the viscoelastic four-element model is not only shown for lamin expression patterns, phosphorylation levels, and chromatin condensation levels, but is also confirmed for nuclei of embryonic and pluripotent stem cells, whose lamin A and B1 levels and chromatin compaction are far reduced in comparison with fibroblastic cells. In the following the three-element models are presented and discussed. Creep test observations reveal complex viscoelastic reactions to applied loading that are a function the expression and phosphorylation of lamin, and the decondensation of chromatin. Cell nuclei across at each conditions display the following features. In the elastic reaction, the nucleus expands instantaneously at the moment the stress is imposed, similar to a spring. In viscoelastic stretching, the nucleus is viscoelastically sucked into the micropipette over a typical time scale. For viscous deformation, nuclear creep converges to a steady rate. This specific mechanical reaction has been seen at both low and high strain and is commonly used by mouse embryonic stem cells and induced pluripotent stem cells, although the organization of nuclear laminae and chromatin is quite dissimilar to that of mouse embryonic fibroblasts .All pertinent three-element viscoelastic models consistently could not accurately account for the principal characteristics of the nuclear reaction to imposed forces under all constraints within the measured time interval. The minimum linear viscoelastic model that correctly reproduced the nuclear deformations under all constraints has been the four-element Burger model. Specifically, the Maxwell and Kelvin illustrations of the Standard Linear Solid model are in fact the only two three-element models that account for instantaneous deformation. Therefore, their usefulness in modeling the aspiration-creep compliance curves of micropipettes is analyzed and compared with Burgers model. The Burgers model yielded the highest R-squared values for quality of fit, not just because it is composed of four items. It also accounts for the viscous deformation of the nuclei at stationary state, in contrast to the standard Linear Solid models. Actually, both standard linear solid models exhibited weaker fits for nuclei expressing lamin B while lacking lamin A, due to their long-term low viscosity deformation characterized by high steepness while maintaining stiffness.τ stays unchanged. The relationship between WT and TKO remained the same between low and high load conditions. Stiffness is defined as being governed by both lamin A and lamin B1, while viscosity is governed predominantly through lamin A  the below inputs: firstly, cell polarization, restructuring of the cytoskeleton, and generation and exertion of forces founded on the interaction between actin polymerization and contraction of actomyosin scaffold. Secondly, cell-cell adhesion and linkage through adherens-junction proteins that are connected the actin cytoskeleton. Thirdly, control through by chemical and physical cues. The fundamental functions realized by these various determinants endow cells with the capacity to generate forces, to adhere possible also in different ways to one another and to a substrate, and to be sensitive to mechanical and chemical cues. Nevertheless, it is still not fully understood how these fundamental characteristics are implemented during the migration of single cells. However, there exists an active self-regulation of the cytoskeleton. Assembly and disassembly of cytoskeletal scaffold and architectures are governed through multiple accessory proteins . These rStem cell differentiation is guided by microenvironmental stiffness and thereby cells adapt to the stiffness of the substrate on which they are cultured and adhered to , which rThere is strong compelling support for bidirectional mechanical signaling across the nucleus and the cytoskeleton. The mechanical characteristics of the nucleus have been the focus of mechanical analyses of cells for a while, especially when mutations in lamin A/C appeared to underlie a number of genetic diseases with a mechanochemical foundation . The conThere are two different strategies that can be implemented to separate the nucleus from the cytoskeleton: total excision of the nucleus (enucleation) and separation of the LINC complex through expression of dominant-negative KASH domains, dominant-negative SUN proteins, or exhaustion of LINC complex constituents . The exp−/−) showed that this lamin is especially relevant for these mechanical characteristics (citations...)  . In a siions...) . Expressions...) . Even th shorter . The Rho/− cells . RhoA acExpression of compounds of the dominant-negative LINC complex or their genetic removal are accurate tools to examine the contribution of the connections linking the nucleus to the cytoskeleton. A much coarser attempt is the total excision of the cell nucleus. This is a crude approach but it has the benefit of destroying all links to the nucleus, whereas cutting the LINC complex can still keep intact other types of interactions of the cytoskeleton to the nuclear envelope. Large-scale enucleation strategies for cells maintained in culture have been established in 1972 . EnucleaWhen techniques for enucleation first emerged, there has been just no examination in mechanotransduction. The question of whether the nucleus has any influence on the migratory behavior of a cell arose. It has been established that cytoplasts are able to migrate on glass, demonstrating that ownership of a nucleus is not a requirement for cell migration . This fiThe impact of enucleation on the migration velocity of intact cells has been imitated by blocking myosin II activity with the inhibitor blebbistatin . BlebbisWhat is the reason of reduced RhoA activity in cytoplasts? This needs to mirror either reduced GEF or enhanced GAP activity. This question has not yet been clarified. A possibility is that nuclear elimination depletes specific GEFs, such as ECT-2 and NET-1, that are localized in the nucleus. The extent to which these GEFs account for the total level of RhoA activity in cells is not clear. Another option is that a stiff nucleus connected to the actin cytoskeleton is a major driver of the total tension in the cell. As tension enhances the activities of multiple GEFs and conversely reduces GAP activity, reducing tension by excising the nucleus in this model is expected to lead to a decrease in RhoA activity. This is corroborated from the finding that cells missing lamin A have a soft nucleus, lower tension, and lower RhoA activity . In a siDrosophila, with exogenous compressive forces elevating the ectopic expression of twist , synonymously referred to as WWTR1 (together YAP/TAZ), are transcriptional regulators that react to various mechanical stimuli, encompassing extracellular matrix stiffness, cell form, and shear stress . Althougin vitro and in vivo is well settled, the involvement of mechanical interactions is comparatively less comprehended. Whether environmental viscoelasticity acts to modify EMT by influencing other facets of this intriguing event, such as apicobasal polarity, cadherin turnover, and MET, is just becoming unraveled.However, TGFβ interaction with SMAD proteins is critical for the activation of SNAIL, SLUG, and TWIST on a transcriptional level . The staCellular viscoelastic characteristics are likely to fluctuate according to the biological state of the cells, for example, during the EMT transition in cancer, and thus viscoelasticity has the potential to be a beneficial physical biomarker . EMT repin vitro settings and fails to consider the various tactics that cells and cell groups employ to migrate through divergent environments in vivo. The investigation of EMT has revealed that EMT may no more be handled as a linear, unidirectional event that switches the motility of single cells, but is instead now considered as a more dynamic event by which cells or sets of collectively migrating cells can accommodate themselves to the physical demands of their microenvironment. Several cues are known about the EMT. Firstly, EMT seems to be a reversible event and cells can also perform mesenchymal to epithelial transition (MET)  . Secondlon (MET) . Thirdlyon (MET) . Fourthlon (MET) . It can on (MET) . Moreoveon (MET) . Finallyon (MET) , or perfon (MET) . This tron (MET) , and on on (MET) . However assumed . Secondl assumed . To acco assumed . In line assumed  or cells assumed  or parti assumed  , boundary, rigidity of migrating substrates, shear flow of extracellular liquids, topology and density of the ambient tissue or extracellular matrix scaffold . This is consistent with a simple Maxwell viscoelastic fluid model in which an elastic intracellular constituent is connected in sequence with a viscous intercellular constituent representing cell redistributions . The ovein vivo through cell sorting and spatially constrained lineage commitment  yield distinct fluid-like characteristics delineated by the Maxwell model, whereas slower cell speeds and firmer cell-cell adhesion interactions (indicative of confluent multicellular regimes) assure solid-like characteristics delineated by the Zener model. The switch from convective to conductive mode is paralleled by a marked decrease in cell migration induced by the rise in normal inherent stress build-up. It is this phenomenon, referred to as the “plateau” mode in the vicinity of the system perturbation, such as cell jamming, that has been delineated through the Kelvin-Voigt model. A continued enhancement of cell packing tightness results in an anomalous character of energy dissipation throughout the collective cell migration as a feature of the damped-conducting mode regime. This regime incorporates two states of viscoelasticity, namely the cell transient condition and the jamming condition characterized through the fractional constitutive model. The process of viscoelasticity development is implemented by multiple current equilibrium conditions. Each equilibrium condition has been thermodynamically defined in terms of constant factors: on the one hand the Helmholtz free energy of cell restructuring and on the other hand, the effective temperature. The density-induced decrease in cell motility disrupts the equilibrium condition by lowering the effective temperature. The subsequent equilibrium condition accompanied by a new condition of viscoelasticity is determined for the new levels of effective temperature and Helmholtz free energy of restructuring events. Complementary experiments are needed to firstly relate the normal residual stress accretion to the variation of cell packing density, secondly reveal the cell number density and damping coefficient for the jamming condition of diverse cell types, thirdly identify the appropriate ingredients for the suggested constitutive models, fourthly outline the time evolution of the viscoelastic modification from the convective regime to the damped-conductive regime within several 2D and 3D experimental settings, and fifthly locate the required time span for the cells to pass through the unjamming transformation.There is agreement that there exist differences in viscoelastic characteristics of cells in the various diseases, such as cancer, fibrosis and inflammation. It seems to be likely that there are also pronounced differences of disease models, when the cells are cultured in 2D vs 3D. The viscoelastic value of cells is no static number, it is rather dependent on time and is hence dynamically regulated. It is rather suggested to correlate with the progression of the disease, including the malignant progression of cancer. Moreover, the viscoelastic response can also be implemented, when investigating the reaction toward specific drug treatments, since it has been shown that 2D cell culture-based drug screenings turned out to be ineffective in predicting whether a drug treatment helps to treat cancer and its malignant progression. Even the chemo- or radiotherapy approaches cannot be predicted due to altered tumor microenvironment and the broad heterogeneity of cancer types.The transition of cancer cells from a benign phenotype to an invasive or metastatic identity entails both biological features, such as upregulation, downregulation, or inhibition of the expression of specific genes and tumor markers , and phyBesides the investigation of single cells, cell spheroids seem to provide specific features that may help to understand what is happing in a solid tumor in more detail. For instance, spheroids can actually outgrow and produce their intrinsic extracellular matrix, and cell adhesion molecules, notably cadherins, such as E-cadherin, can arise to tether cells inside the spheroid. Several prior research efforts have centered on the specific impact of compressive stresses applied externally to the solid tumor , which hin vivo. The time-dependent characteristics of cartilage are based on paired mechanisms involving the solid matrix and fluid flow. The mechanisms have been typified as poroelasticity and intrinsic viscoelasticity, leading to effective and persistent broadspectrum dissipative characteristics , which is the simplest model, the viscoelastic Ogden (vOG) model, which is based on the strain-energy function, and the viscoelastic exponential (vEXP) model, which is the isotropic exponential Fung-type model based on the strain energy function. The latter two models are similarly appropriate, as the two models are better at accounting for the nonlinear tendencies .in vitro rebuilding of diseased and non-diseased states of the lung can be successfully performed employing hydrogels (The mechanical features of lung parenchymal tissue represent elastic and dissipative elements, as well as they exhibit a highly nonlinear response. The mechanical response of lung tissue is based on a macroscopic phenomenon that is itself evoked by interactions of the microscopic elements. The mechanics of the pulmonary system are governed through the individual contributions of and the intricate interference among its major compounds. On the one hand, the lung tissues display a quasi-static mechanical behavior. On the other hand, there exist computational models that indicate how smooth nonlinear stress-strain response can emerge by a percolation-like event, where the sequential homing of collagen fibers with elevating strain renders them to progressively involved in performing the load-bearing function from elastin. The viscoelastic nature of lung tissue has been measured . While tydrogels .Fibrosis and elevated stiffness are features of multiple solid cancers and encourage growth of the tumor. Cultivation of mammary epithelial cells on soft and rigid collagen matrices, respectively, reveals that with enhanced stiffness, epithelial tubulogenesis is reduced, contractility is augmented, and proliferation is amplified . It has Attenuation of p190RhoGAP has been implicated as an additional mechanism for enhanced RhoA activity in settings of mechanical tension, such as in fibroblasts derived from patients with idiopathic pulmonary fibrosis . The etiThe exploration of viscoelasticity is still a major issue and carries an enormous potential besides cell stiffness to serve as a reliable and possibly as a universal biomarker for cellular migration and invasion. It may also harbor the potential to be adaptable both the migration of individual cells and a collection of cells. In specific, the adaption of the viscoelastic characteristics of migratory cells through EMT-regulated adjustments of adherens junction seems to be critical for providing efficient collective migration through tissues that offer physically divergent structural and mechanical architectures. Moreover, it can be stated that viscoelasticity of the migratory microenvironment is crucial to induce cell migration, engineer a material that enables the efficient migration of cells, and/or to regulate migration through the process of durotaxis. Thereby, new advances strategies highlight possible mechanisms facilitating the transfer of mechanical cues into the cells, including the expression of traditional transcriptional controllers of EMT, and in turn their impact on the alteration of the viscoelastic phenotype of migrating cells and their local microenvironments. To understand these processes in a more precise manner the formation of hallmarks for the individual cell migration may be beneficial.In addition, it has to be accounted for that molecular signal transduction causes remodeling events at various cellular length scales, such as plasma membrane, cytoskeleton, organelles and nuclear components, These cellular remodeling events alter the tissue including its viscoelastic characteristics, and this new viscoelasticity of the tissue environment can now act on a long-range timescale to impact cellular, molecular, and viscoelastic characteristics of a neighbor tissue, such as a mechano-molecular feedback circuit timing the various processes, including the development of tumor microenvironments. Since tissue interferences are seen at a chemical scale through the secretion of molecules, it would be promising to investigate the interaction of viscoelasticity and secreted molecules in the regulation of single and collective cell movements. The involvement of these types of mechano-molecular feedback interferences seem to be highly crucial in the advancement for the forming and engineering of organ-analogous structures, such as organoids, in the field of mechanics-based cancer or inflammation research. Thereby, it is even crucial to combine the analysis of molecular elements, viscoelastic variables of cells and their local microenvironment .Consequently, viscoelasticity represents a general characteristic feature for the vast majority of biological constituents and the majority of cells and tissues that experience a single force or multiple mechanical forces. Finally, the requirement of multidisciplinary studies combining biophysical and biochemical variables seem to be critical to obtain a knowledge of growing intricate living biological systems.For future studies focusing on viscoelasticity and other cell mechanical characteristics, the biophysical probing techniques need to be comparable and hence an experimental study on the comparison of different biophysical techniques is highly needed to further explore the phenomenon of viscoelasticity and to develop a road-map on how to conduct viscoelasticity measurements and in what type of microenvironment. Ultimately, the future perspective is the investigation of viscoelasticity on various length scales ranging from single molecules to entire tissues or on different time scales in order to fully probe viscoelasticity.


The past years have shown a revolution in the way scientific workloads are being executed thanks to the wide adoption of software containers. These containers run largely isolated from the host system, ensuring that the development and execution environments are the same everywhere. This enables full reproducibility of the workloads and therefore also the associated scientific analyses performed. However, as the research software used becomes increasingly complex, the software images grow easily to sizes of multiple gigabytes. Downloading the full image onto every single compute node on which the containers are executed becomes unpractical. In this paper, we describe a novel way of distributing software images on the Kubernetes platform, with which the container can start before the entire image contents become available locally . Each file required for the execution is fetched individually and subsequently cached on-demand using the CernVM file system (CVMFS), enabling the execution of very large software images on potentially thousands of Kubernetes nodes with very little overhead. We present several performance benchmarks making use of typical high-energy physics analysis workloads. A software container is a packaged unit of software that contains all dependencies required to run the software independently of the environment in which the container is executed. Since the virtualization of the environment takes place at the operating-system (OS) level, the overall resource overhead is small, in particular compared to virtual machines that virtualize at the hardware level. Nevertheless, the containers run as isolated processes. Software containers are therefore widely used in industry but also in scientific high-throughput computing.In the context of reproducible scientific analysis workflows, containers play a very important role. In this study, we in particular focus on high-energy physics (HEP) applications. For example, the physics experiments at the Large Hadron Collider (LHC)  , unpacked.cern.ch repository in two different formats: as a flat file system, which can be used by the Singularity container platform, which is widely used on the WLCG, or as a set of layers, to be used by the containerd snapshotter or other OCI-compliant runtimes.The CVMFS containerd plugin relies on a defined structure of a particular CVMFS repository, which is called In the latter case, each layer is unpacked into a specific directory, referred to as the layer digest. This ensures that lower layers of a container image are not overwritten by higher (more recent) layers, and thus enables sharing of layers between images. This is particularly relevant when the same base images are used or changes to container images are only incremental. Moreover, this file system structure allows for an immediate lookup of the layer location given only its digest.The compute cluster used in this work consists of six virtual machines (VMs), each equipped with four-core Intel Xeon Skylake processors with 2.3 GHz clock, 7.1 GB RAM, and local 40 GB SSD storage. The VMs are deployed in the same networking zone within the CERN network to minimize possible effects caused by network latency . As described in section 2.2, five of the machines make up the Kubernetes cluster. These machines run Ubuntu Linux 20.04.2 LTS . Both services are containerized and deployed using podman v2.0.5  life-cycle details and transitions in the cluster. For each image and worker node combination, ten measurements are performed. For each run, we evaluate the image pull time, the time for the pod to be ready after pulling, and the overall run time. Furthermore, we extract the amount of data downloaded to the node.cvmfs_talk command, which is part of the CVMFS client installation. Furthermore, measurements without deleting the image cache are performed to evaluate possible overhead.Before starting each measurement, the node's image cache as well as the CVMFS cache for the snapshotter machines is wiped. For the legacy machines, the container image of interest therefore has to be downloaded from the local registry each time. Similarly, for the snapshotter machines, the container manifest needs to be downloaded each time and the node's CVMFS cache be populated accordingly. The measurement is started as soon as the pod is successfully scheduled on the respective node. We record the time again once the pod starts executing the actual workload and stop the time when the pod is succeeded, which means that execution of the workload has completed successfully. Failed runs are discarded. We found that the reason runs failed was that we hit the Docker Hub download rate limit when obtaining the image manifest, which means that in the future other/dedicated container registries should be used instead. The data downloaded is taken as the compressed image size for the legacy machines, and for the snapshotter machines we determine the data downloaded from the squid proxy using the Results are presented separately for container startup time and the measured data transfer.Figure 3). When using the legacy machines, the pull time scales linearly with the image size, as expected. The container creation time is negligible for both approaches. The workload execution time is a few seconds or less for the simple workloads (CentOS, Python, gcc, see The pull time when using the CVMFS snapshotter is found to be a few seconds for all images, increasing with image size. The largest image, the Higgs boson analysis image, takes 18 s to start (see The amount of data downloaded from the local container registry when using the legacy machines and the data downloaded from the proxy cache server when using the CVMFS snapshotter machines is shown for the container images analyzed in Our studies show that the snapshotter approach using lazy image-pulling in combination with aggressive caching using CVMFS is significantly faster than the legacy approach of completely downloading the container image before execution for all evaluated workloads. The time for the container to start is reduced by at least a factor two for the smallest CentOS image see  left, anFor images that use a large fraction of the image data, i.e., in our analysis the ROOT image see , an incrIf one optimized the container image by reducing the size overhead and thus increased the fraction of data used, the performance gains of the snapshotter compared to the legacy approach could decrease. However, this would make each image very use-case specific and would also require additional work when creating the image. An alternative approach could be to schedule jobs with the same base layers on the same execution nodes. However, due to the high variety and large number of workloads, this could be challenging to achieve and would need more work on the job scheduler side. Overall, the snapshotter approach presented in this work performs and scales significantly better than previous approaches, would not require any changes to current standard container image build processes, and integrates with modern compute platforms. At the time of writing, the CVMFS snapshotter is actively used for smaller Kubernetes installations at CERN. Furthermore, several WLCG sites are evaluating the use of Kubernetes on-premise as well as on public compute clouds for scalability, for which the CVMFS snapshotter will be an attractive tool.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.SM is the main developer of the CVMFS image snapshotter and contributed to the technical setup as well as the paper writing. CL set up the machines used for benchmarking, performed the measurements, and contributed to the paper writing. JB is the CernVM project lead and contributed to the overall discussion in this paper. 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.


Clofazimine (CFZ) is a weakly basic, small-molecule antibiotic used for the treatment of mycobacterial infections including leprosy and multidrug-resistant tuberculosis. Upon prolonged oral administration, CFZ precipitates and accumulates within macrophages throughout the host. To model the pharmacokinetics of CFZ, the volume of distribution (Vd) was considered as a varying parameter that increases with continuous drug loading. Fitting the time-dependent change in drug mass and concentration data obtained from CFZ-treated mice, we performed a quantitative analysis of the systemic disposition of the drug over a 20-week treatment period. The pharmacokinetics data were fitted using various classical compartmental models sampling serum and spleen concentration data into separate matrices. The models were constructed in NONMEM together with linear and nonlinear sigmoidal expansion functions to the spleen compartment to capture the phase transition in Vd. The different modeling approaches were compared by Akaike information criteria, observed and predicted concentration correlations, and graphically. Using the composite analysis of the modeling predictions, adaptive fractional CFZ sequestration, Vd and half-life were evaluated. When compared to standard compartmental models, an adaptive Vd model yielded a more accurate data fit of the drug concentrations in both the serum and spleen. Including a nonlinear sigmoidal equation into compartmental models captures the phase transition of drugs such as CFZ, greatly improving the prediction of population pharmacokinetics and yielding further insight into the mechanisms of drug disposition. Clofazimine (CFZ) is an antimycobacterial agent used to treat leprosy and multidrug-resistant tuberculosis alongside some non-tuberculosis mycobacterial infections. Recently, CFZ has also been found to inhibit SARS-CoV-2 infection in vitro and in animal models ,2, and iAlthough it has been used clinically for many decades ,11, CFZ Recently, the pharmacokinetics of clofazimine in tuberculosis patients was described as a three-compartment model . In ordeThe results reported herein are the first time that volume of distribution changes resulting from soluble-to-insoluble phase transitions of drug molecules are directly modeled and analyzed, using a population pharmacokinetics modeling approach that should be applicable to the study of CFZ pharmacokinetics in the human population. The significance of this analysis is not only relevant to improving our understanding of the mechanistic underpinning of CFZ pharmacokinetics, but it will help advance the development of macrophage-targeted drugs and the design of self-organizing drug depot systems.Individual mouse data were obtained from a previously published study in which CFZ concentrations were measured in the serum and spleen of healthy BALB/c mice given 25 mg/kg of CFZ by oral gavage once daily, Monday through Friday, over a 20-week period, together with measurements of the mass of the organ. Three mice were sacrificed then sampled at seven discrete intervals  after starting therapy and prior to administering the daily dose. CFZ concentrations were measured by liquid chromatography/mass spectrometry (LC/MS) using a previously validated method . Animal Four adaptive Vd models were used to capture different Vd expansion functions . The adequacy of each model was then tested and compared to the baseline, standard compartmental pharmacokinetics model with a constant Vd, to determine the best compartmental modeling approach and adaptive Vd function that fits the measured, temporal changes in the pharmacokinetics of CFZ over the 20-week treatment period. The baseline, standard compartmental model with a constant Vd is referred to as the “base model”. The predictive value of including a peripheral compartment to the base model was separately assessed to determine whether the phase transition can be captured by adding an additional compartment. Model superiority was determined by plotting the concentration predictions over time, comparing correlation of the residuals, coefficients of variation (CV%), and comparing Akaike information criterion (AIC) . NONMEM In pursuit of a robust estimation of the expansion of volume of distribution, a varying number of parameter estimates from the soluble phase model were used to fix the initial conditions of the full model with the expansion function. We will refer to the “soluble model” or “soluble phase model” as the two-compartment model without an expansion function, intended to capture the kinetics of clofazimine molecules in solution, prior to the phase transition leading to the formation of CLDI precipitates that result in drug accumulation in the spleen. Bootstrapping was then conducted on a subset of the models using 1000 runs in Wings for NONMEM (WFN). Parameter estimates, confidence intervals, standard deviations, and coefficients of variation were evaluated alongside histogram plots to determine the optimal expansion model . The choAt any given timepoint, the cumulative dose fraction of CFZ sequestered in the spleen was estimated by multiplying the spleen weight by the predicted spleen concentrations of the composite sigmoidal models, to give the resulting mass of CFZ sequestered. This mass was then divided by the total dose of CFZ administered up until that timepoint, The Akaike information criterion (AIC) reported in this article was calculated by multiplying the number of parameters by two and adding the reported objective function value (OFV) in NONMEM From 11 different models, we selected the simplest compartmental model that best captured CFZ accumulation in the spleen: a bidirectional two-compartment model with elimination from the serum compartment. This model had the lowest objective function value and most physiological relevance of the 11 total compartmental models tested in the first four weeks of treatment . The con2 values. The base model behaved as a standard two-compartment model with a constant Vd. The linear model increased Vd linearly with respect to time, and the three sigmoidal models  all nonlinearly increased Vd with respect to time. The three-compartment model is identical to the base model with an additional peripheral compartment connected to the serum . Incorporation of a sigmoidal function to capture the change in Vd over the treatment period improved all three models over the linear model  and the base model with the full dataset further reveal the inability of the base model to fully capture the range of concentrations over the course of dosing. The soluble model accurately captures the early spleen concentration values, but when extrapolated to the full course of dosing, vastly underpredicts the later spleen concentration values. The base model with the full dataset more closely captures the higher spleen concentrations at the later timepoints, but compensates by overpredicting the earlier spleen concentrations.We further analyzed the improved fits obtained with the models by incorporating a sigmoidal equation to capture the Vd expansion function. Quantitatively, the AIC of the Hill equation showed a significant quantitative improvement over the base model . The HilAdditionally, when comparing the fully optimized, unfixed models, the base model has a constant volume of distribution at 368.8 L/kg and a terminal half-life of 21 days, which is vastly different to the predicted volume and terminal half-life of the base model in the soluble phase . The exponentiated Hill function bridges the base model and soluble model pharmacokinetic parameters by allowing the soluble and insoluble phases to be incorporated simultaneously. The Vd of the Hill function increases from 62.7 L/kg to 147.9 L/kg, and half-life increases from 1.26 days to 343.0 days. This implies that the base compartmental model is unable to capture the entire dataset and requires an element of nonlinearity to predict the distribution of CFZ more accurately.serum was kept constant, and the increase in the volume of the spleen was captured by each of the three sigmoidal equations. The base pharmacokinetic parameters  for each of the models were fixed to the estimates from the fully optimized, unfixed exponential Hill function, allowing the adaptive parameters from the expansion function to distinguish amongst the sigmoidal models.Using all three sigmoidal equations to represent the adaptive pharmacokinetics of CFZ, two physiologically relevant pharmacokinetic parameters  were calculated to enable a consensus model that captures the drug concentrations and mass in the serum and spleen over the entire time course of the 20-week dosing regimen. For all models, the VThe sum of the volume of the serum compartment and the spleen compartment was used to calculate a total Vd at each timepoint To integrate the different models, the mean and standard deviation values of the pharmacokinetic parameters from the three sigmoidal functions were used to calculate composite values for total Vd and half-life B,C. The These results indicate that there is an increasing amount of drug being sequestered over time, with an associated nonlinear increase in the Vd. The changes in half-life are modeled to be secondary to the changes in the Vd. The results shown here are driven by an adaptive volume in compartment two induced by a soluble-to-insoluble phase transition. Each sigmoidal expansion function predicts adaptive pharmacokinetic parameters as a surrogate for CLDI growth at varying rates and extents based on the supplied dataset. The overall range of values for total Vd at week 1 extended from 62.7 L/kg to 62.8 L/kg , and a range for total Vd at week 20 from 143.9 L/kg to 155.1 L/kg . The overall discrepancy in total Vd between the sigmoidal functions over the course of dosing spanned a 0.16% difference at initiation to a 7.5% difference at the end of the dosing, with the largest difference in functions (44.2%) apparent at 76 days. Similarly, the overall discrepancy in half-life between expansion functions ranged from a 42% difference at week 1 to a 12% difference at week 20, with the largest difference in functions (100.6%) apparent at 76 days.Hill and f(t)Sqrt such that f(t)Hill = f(t)Sqrt for all timepoints t. Additionally, the rational square root and logistic growth curves are symmetric about their inflection point, while the exponential Hill curve is asymmetric in that its inflection point occurs before the function reaches half capacity.As shown in spleen at week 20 indicates that further increase in total volume is expected before reaching a loading capacity in the spleen. The resulting ETA values for the Hill coefficient and Emax parameters were very small (4.00 × 10−6 for both parameters) due to the homogenous mouse population and small sample size.The maximum estimated expansion of the spleen compartment is a 347-fold increase based on the Emax parameter of the exponentiated Hill function, and the maximum rate of volume expansion occurred at 81.8 days based on the inflection point of the expansion function. The time at 50% maximum volume expansion was calculated to be 98.1 days after initiating dosing based on the plotted exponential Hill equation. At a 25 mg/kg dosing regimen, this corresponds to a total load of 35.0 mg, or 1.75 g/kg of total CFZ load, to reach 50% of maximum volume expansion. These predicted values alongside the estimated 273-fold increase in VIn addition to CFZ serum and spleen concentration predictions, we determined the fraction of the total dose of CFZ that bioaccumulates in the spleen over the 20-week treatment period. At each time point, the mass of CFZ calculated in the spleen by each of the sigmoidal expansion functions was divided by the cumulative dose of drug administered to mice up to that time point A. The coWhen given a CFZ dose of 25 mg/kg per day, the mean cumulative fraction sequestered in the spleen among the consensus sigmoidal functions increased from 0.0017 on week one to 0.025 on week 20, equating to a 14.7-fold increase in drug sequestration over the 20-week period. The estimated fraction of CFZ sequestered per dose increased from 0.0017 on week one to 0.035 on week 20 with a peak of 0.036 on week 12. The cumulative dose fraction between sigmoidal models was consistent with a 165% difference at week 1 and a 10.5% difference at week 20. The most pronounced change in the fraction of CFZ sequestered occurs between weeks four and eight, coinciding with the time during which CLDI formation becomes microscopically visible in mouse organs (at around 3 weeks of treatment), after which the spleen dramatically increases in mass (between 4 and 8 weeks of treatment) . This obOur pharmacokinetic modeling of bioaccumulation shows the importance of considering phase transition kinetics in the evaluation of CFZ pharmacokinetic parameters. Of the six complete models evaluated in this study, the base two-compartment model performed the worst due to the inability to capture the insoluble precipitated phase. The three-compartment model improved upon predictions of the base two-compartment model as it could capture the additional distribution due to the CLDI phase as a static environment, but could not account for the variability of different amounts of precipitation at different total CFZ loads. The four expansion functions on the two-compartment model all outperformed the base two-compartment and three-compartment model since they were all able to adapt to the increasing sequestration of precipitated drug. The sigmoidal expansion functions performed comparably and superiorly to the linear growth model, implicating a nonlinear expansion of CFZ Vd during the initial 20 weeks of dosing.The limited sample size and long sampling interval introduces some inherent limitations to this study. While mice were administered the drug orally, absorption was not considered in the model due to infrequent sampling early after dosing. The exponential Hill function and rational square root functions evaluated in this study yielded low AIC, excellent visual predictive accuracy, and physiologically relevant parameters consistent with the measured pharmacokinetics and the biological mechanisms of CFZ precipitation and bioaccumulation in mice. Despite the apparent improvements by the sigmoidal expansion functions, the ability to distinguish between minute differences in models was hindered by the size of the dataset, limiting the inspection of interindividual variability and complicating investigation of which sigmoidal function best models the behavior of CFZ. However, with more data, more detailed mechanistic models could be evaluated with other expansion functions. Additionally, only the pharmacokinetics of healthy mice we evaluated in this study. Further analysis is needed to explore the effects of infectious disease on the pharmacokinetics of CFZ. As macrophages are implicated in CFZ accumulation, an infectious agent may alter the context-dependent pharmacokinetics of the drug.In mice, the accumulation and distribution of CFZ within the macrophages of different tissues is now a well-characterized phenomenon. CFZ undergoes phase transitions in vivo that are paralleled by changes in the pharmacokinetic parameters that govern the half-life and Vd of the drug. With increasing CFZ load, the fraction of drug sequestered as CLDIs increases, with an associated expansion in the Vd of the drug. This is a previously underappreciated phenomenon that demonstrates that, with prolonged dosing, a range of CFZ accumulation patterns over time could lead to very different drug efficacy and toxicity profiles over the course of dosing . Based oWhile this study presents an approach to modeling and analyzing the drug load-dependent pharmacokinetics profile of CFZ in mice, the parameter values obtained from mice may not directly correlate with parameter values in humans. Nevertheless, the phenomenon analyzed in this study bears relevance to the human situation, since it is known that CFZ precipitates and accumulates in macrophages of patients treated with CFZ ,16,17. DIn relation to the three-compartment approach for modeling CFZ pharmacokinetics in humans, our study suggests that adding more peripheral compartments may indeed improve predictive accuracy, but not beyond that of adaptive Vd models. This indicates that as CFZ precipitates with continued dosing, dynamic modeling approaches more closely approximate CFZ pharmacokinetics, relative to fixed volume, multicompartment models. Although there is variability in the estimation of volume of distribution and half-life using the different sigmoidal equations, the different functions converge towards consensus parameter estimates that are all within a 2-fold range of values. This indicates that the structure of the sigmoidal function is less important than the model flexibility in adapting to the expanding volume of distribution.CFZ loading capacity has been previously studied under a different dosing regimen . The totFrom these data, it is not clear whether CLDIs contribute to the pharmacological activity of the drug, or whether they may lead to toxicity, which may include changes in the immune system or metabolic changes that accompany CFZ accumulation . ClearlyThis work may also constitute a wider modeling framework for bioaccumulating drugs or molecules with context-dependent pharmacokinetic properties. For example, azithromycin has been shown to accumulate intracellularly in human skin fibroblasts, implicating a cell-dependent accumulation pathway . LikewisUsing mice as an experimental model, and based on a dosing scheme of 25 mg/kg of CFZ daily, the changing pharmacokinetics of CFZ over a 20-week time course of treatment were modeled using an adaptive Vd function to capture the bioaccumulation of the drug in the organism. By incorporating nonlinearity into our pharmacokinetic models to capture soluble-to-insoluble phase transitions, it was possible to more accurately predict serum and tissue concentrations. It is likely that such adaptive pharmacokinetics modeling framework would also be applicable to the analysis of human clinical data, which could better inform our dosing strategies and understanding of CFZ efficacy and side effects. By analyzing the accumulation of CFZ within macrophages that can be obtained in patient sputum samples, it is possible that human pharmacokinetics models of CFZ could be improved. While drug accumulation in mice tissues is analyzed after the animals are euthanized, one avenue to explore, analyze, and model the extent of CFZ accumulation in human PK datasets could involve noninvasive pharmacoimaging techniques, such as positron emission tomography (PET).


A 91-year-old woman was referred to our hospital with a chief complaint of unsatisfactory fit and pain associated with her complete dentures. She had moderate dementia with difficulty in communication . The closed impressions and jaw record were taken with the digitally fabricated copy dentures as follows. First, the tissue conditioner was used to correct the poor fit of the old dentures, following which minor occlusal alterations were made. Second, the copy dentures that copied the morphology of the corrected old dentures using three-dimensional (3D) scanner were fabricated with a 3D printer. The new dentures were then fabricated using conventional methods as follows. The impressions were cast and articulated, and the dentures were subsequently processed. This case report documented the following results. First, the acceptance of new dentures appeared to be easier since the new dentures copied the morphology of the familiar dentures digitally. Moreover, the 3D data of the dentures could be used for immediate denture fabrication in case of fracture or loss of the dentures. Second, only two visits were required for taking an impression and delivering the complete dentures. In addition, her old dentures were brought to our dental office by the patient's family after the patient's dinner; immediately after copying the dentures' morphology, the dentures were returned to the patient's family, thus avoiding any disturbance to the patient's eating routine. These reduced the burden on the patient and her family. Some elderly patients requiring complete denture fabrication are medically frail or are homebound with major limitations in mobility . While sIn contrast, digital dental technologies, with the aim of highly safe dental treatment and efficiency, have significantly improved in the last decade . For exaThe patient was 91 years of age and a female. As the chief complaint, the patient expressed unsatisfactory fit and pain associated with use of the complete dentures. The patient's family desired new dentures for her, as her dentures appeared to fit loosely, and she was unable to eat properly.For the degree of care, the patient exhibited difficulty in communicating . Her medical history is as follows: The patient developed symptoms of Alzheimer's-type dementia five years prior, following which she has been under the care of her family. In addition to taking an acetylcholinesterase inhibitor to treat the dementia, the patient had been prescribed antihypertensive and antihyperuricemic drugs.The patient has a height of 150.0 cm and weight of 47.0 kg.A home-visit dentist manufactured the complete dentures for the upper and lower jaws two years prior. The following year, she complained of pain in the left alveolar ridge of the mandible and requested a home visit for dental treatment; however, her symptoms and food intake did not improve. She was referred to our hospital for the fabrication of new dentures. First clinical stage: the minimum amount of tissue conditioner required  was used to correct the poor fit of the old dentures and to record dynamic impressions of the dentures, following which minor occlusal alterations were undertaken. One week later, a follow-up teleconsultation of the patient revealed disappearance of pain in the mucosa of the jaw ridge during meals and an increase in the food intake. We asked the patient's family (her son) to bring her upper and lower dentures to our hospital to record optical impressions for the upper and lower dentures using an optical scanner  Figures ; the denμm/layer. After washing the copy dentures with isopropyl alcohol, final polymerization was performed using a dental polymerization apparatus . The copy dentures were polished with a dental carbide cutting tool   data and exported to a three-dimensional (3D) printer interface software  , and the Figures . 3D scanSecond clinical stage: the copy dentures were slightly adjusted to ensure the correct occlusal contact. A closed mouth impression was recorded using the minimum required amount of a light-body wash impression  in the copy dentures for both the maxillary and mandibular bases inside the patient's mouth. While taking the impressions, the patient was asked to sit upright and close her mouth. The jaw record was obtained at the same timeSecond laboratory stage: the impressions were cast and articulated, the morphology of the polishing surface of the copy dentures were converted into wax rims utilizing plaster masks, and teeth were set on those rims. After deflasking, the dentures were checked using the articulator for processing errors and subsequently polished .Third clinical stage: we visited the patient's home and delivered the completed dentures following an intraoral check. The fit and occlusion of the dentures were goodFourth clinical stage: telephonic follow-up was performed twice every other week, and there was no complaint of pain during meals; an increase in food intake and a decrease in the duration of meals were noted. The procedures followed in this case are shown in Recording oral impressions in elderly individuals requiring long-term home care is often challenging. Although dentists have tried various measures, such as the use of impression materials with low fluidity and a short curing time, the risk of accidental ingestion and aspiration during impression taking has been reported in elderly patients . On the First, acceptance of new dentures in this case appeared relatively simple owing to the use of dentures that copied the morphology of a familiar denture that the patient had been using for a long time. In contrast, previous studies reported that older patients have difficulty accepting new dentures . DentistSecond, only two visits were required for taking the impression and delivering the complete dentures, reducing the quantity of impression material used and frequency of impressions taken. Many such patients who require long-term care, especially those with moderate or severe dementia, refuse long-duration or highly invasive treatment ; therefoFinally, the reduction in contact and time spent between patients and healthcare professionals owing to the use of digital dental technology could help reduce the risk of spread of the new coronavirus disease (COVID-19), which is currently present worldwide. According to public health authorities, significant exposure is defined as a “face to face contact within 2 m with a patient with symptomatic COVID-19” in the range of a few minutes up to 30 min . TherefoOur case of a patient with dementia with difficulty in communication, incorporating digital dental technology into the manufacturing process of the conventional method, enabled us to provide high-quality complete denture safely, due to the following reasons. First, acceptance of new dentures was enhanced by using dentures that copied the morphology of a familiar denture. Second, only two visits were required for recording the impression and delivering the complete dentures. Moreover, the patient's dentures were brought to the dental office by her son after her dinner and returned to her son immediately, thus avoiding any disturbance to her eating routines. These factors reduced the burden on the patient and her family.


The inner viscosity of a biological red blood cell is about five times larger than the viscosity of the blood plasma. In this work, we use dissipative particles to enable the proper viscosity contrast in a mesh-based red blood cell model. Each soft particle represents a coarse-grained virtual cluster of hemoglobin proteins contained in the cytosol of the red blood cell. The particle interactions are governed by conservative and dissipative forces. The conservative forces have purely repulsive character, whereas the dissipative forces depend on the relative velocity between the particles. We design two computational experiments that mimic the classical viscometers. With these experiments we study the effects of particle suspension parameters on the inner cell viscosity and provide parameter sets that result in the correct viscosity contrast. The results are validated with both static and dynamic biological experiment, showing an improvement in the accuracy of the original model without major increase in computational complexity. Blood is a multi-component suspension and consists mostly of plasma (~55%), red blood cells  and white blood cells and platelets ~1%). Due to their high content, RBCs play major role in flow dynamics and rheological properties of blood. A healthy RBC has a biconcave shape with a diameter 6–8 %. Due toThe blood flow in large vessels is mostly driven by a uniform shear flow that occurs near the vessel walls or by the fairly uniform flow in the central parts of the vessels. On the contrary, in vessel bifurcations and in small arterioles and microcapilaries with diameters comparable to the size of the cells, the parabolic velocity profile introduces non-uniformity and the flow patterns differ significantly from those in large vessels.The shape of the red blood cell in large vessels is mostly close to the relaxed biconcave shape or to prolonged ellipsoidal shapes, with dynamic patterns such as tumbling and tank-treading ,3,4,5. IBesides the flow conditions and the elasticity of the membrane, an important parameter influencing the cell dynamics is the viscosity contrast ear flow ,8,9,10. ear flow ,12,13,14ear flow , the autAn interesting result concerns the cell free layer (CFL). While in works ,13 the a average ,16.The viscosity contrast has proven to play an important role in microcirculation. In this work, we employ a novel approach to taking the viscosity contrast into account. We extend the existing red blood cell model based on lattice-Botzmann method (LBM) for governing the fluid and spring network for governing the elasticity of cell membrane ,18. We iMany of the recent simulation studies for simplicity assume the viscosity contrast Λ=1 ,21,22,23Λ=1 ,13.There are, however, models that assume non-unit viscosity contrast. The continuous model in  for examThe method in ,11 is baμm3) ,11. The The model in  consistssity. In , determiWe work with an RBC model that we previously developed and introduced in ,18. The The second component is a triangular mesh representing a closed surface of the cell membrane, see Unlike in the IBM described above, the fluid and the elastic objects are coupled via a dissipative force here. Forces induced by the cell deformation enter the lattice-Boltzmann equation. Analogously, fluid forces are also transferred to the object’s mesh points. This method has been known as Immersed Boundary via Dissipative Coupling (IB-DC).The two distinct meshes—the lattice mesh for the fluid and the triangular mesh for the cell—overlap, which results in uniform viscosity for the fluid inside and outside the cell. One solution for introducing the viscosity contrast in LBM was described above in combination with the IBM method . AnotherThe major component of RBC is water . Since modelling each individual hemoglobin molecule is computationally too expensive η=η01The Formula  was lateFor even higher volume fractions there is an analytical expression  that tak spheres .While the formulas for rigid spheres are not easily applied to soft-sphere suspensions that exhibit much richer rheological properties, they can serve as an upper bound.For colloidal suspensions ,39, the In Ref. , the autWhen modeling the colloids, the particles in the model typically represent individual physical objects. In our case, the molecules of hemoglobin are too small to be treated individually and we coarse-grain thousands of them into one particle.Looking at the biological viscosity values, listed in To measure the viscosity of a particle suspension, we design two computational experiments that resemble the laboratory experiments for measuring the viscosity. The classical viscometers employ either rotational shear or falling of a sphere principle. In both approaches, the fluid exerts drag force either on a rotating plate or on a sinking ball. Simulation parameters can be found in the h is the height, is constant.To mimic the shearing viscometer, we model a simple shear flow between two plates. In Couette (shear) flow, the velocity varies linearly from zero at the bottom to The relationship between the applied force per unit area and the (dynamic) viscosity is as follows:While this relationship between shear stress and viscosity is linear and can be used as a first approximation, it is known that blood is a shear-thinning fluid, meaning that its viscosity decreases as the shear stress increases, due to blood cells aligning and sliding over each other.A, measure the shear rate In this computational experiment, we apply the given force x-direction. In order to validate this viscosity measurement, we tested an empty channel and recovered the same viscosity as was set as the fluid parameter.In order to apply the force per unit area on the moving upper plate, we modify the experiment, To measure the viscosity of the simulated particle suspension, we also fix a layer of stationary particles at the bottom and top walls to avoid slip issues at the boundaries.The shear rates that we want to examine range from 0 to 200 seriments ,48.x-axis to have a sphere moving in a stationary fluid (suspension of soft DPs), see To make a computational analogue to the falling-sphere experiment, we apply force R is the radius of the sphere and v is the  flow velocity.The viscosity is then measured, using the Stokes Law, asR = 3 L to measure the boundary effects, This relation holds in an ideal infinite domain. For practical computational purposes, we cannot use periodic boundary conditions since this leads to ever-increasing velocity of the sphere. On the other hand, by imposing solid boundaries, i.e., zero velocity on the boundary, we introduce an artificial boundary effect and the terminal velocity is lower than theoretical. In order to compensate for this, we first evaluate the motion of the sphere in free fluid without particles. In this case, the fluid viscosity is known and thus we can compute the terminal velocity from . Given FThe boundary effect is calculated as relative deviation of computed terminal velocity from the theoretically predicted valueL of the channel but rather to the ratio Testing various sizes of the sphere revealed that the effect of the boundary is not fixed to the size We also tested a range of drag forces 0.05–0.4 nN and two values of fluid viscosity 0.7, 1.2 mPas. These variations had only insignificant impact on the terminal velocity.x-direction.Due to the shear thinning properties of particulate suspensions, we need to compare results corresponding to the same shear rates. While in the experiment with two parallel moving plates, the shear rate is explicitly given by the velocity of the moving plates, in the drag force experiment, the shear rate is computed from the fluid velocity gradient near the top and bottom pole of the sphere, assuming horizontal movement of the sphere in the positive The computational complexity of large dense simulations restricts the number of dissipative particles for this computational experiment and implies a lower bound on the radius of the particles. In order to maximize the domain size which reduces the correcting factor depicted in Our aim is to reach the viscosity ratio of at least 4. In relation  leverages the LBM fixed lattice that is naturally suitable for parallelization and decreases computational time. The increase in inner viscosity is thus an emerging property without the need to solve equations to track the inner points at individual timesteps or detect them anew using ray-casting or similar methods.The newly developed model proves to be more accurate while maintaining the computational complexity.


Due to upcoming higher integration levels of microprocessors, the market of inertial sensors has changed in the last few years. Smart inertial sensors are becoming more and more important. This type of sensor offers the benefit of implementing sensor-processing tasks directly on the sensor hardware. The software development on such sensors is quite challenging. In this article, we propose an approach for using prerecorded sensor data during the development process to test and evaluate the functionality and timing of the sensor firmware in a repeatable and reproducible way on the actual hardware. Our proposed Sensor-in-the-Loop architecture enables the developer to inject sensor data during the debugging process directly into the sensor hardware in real time. As the timing becomes more critical in future smart sensor applications, we investigate the timing behavior of our approach with respect to timing and jitter. The implemented approach can inject data of three 3-DOF sensors at 1.6 kHz. Furthermore, the jitter shown in our proposed sampling method is at least three times lower than using real sensor data. To prove the statistical significance of our experiments, we use a Gage R&R analysis, extended by the assessment of confidence intervals of our data. In recent years, inertial sensors have become more common for a wide range of applications implemented on a multitude of different devices. The applications range from gesture recognition  to step Modern inertial sensors are often so-called smart sensors. This type of sensor consists of the actual sensor device and an additional processing unit. Using this unit, smart sensors are capable of preprocessing captured data directly on the sensor before they are used and processed by the system application processor.In a big variety of applications, especially in safety critical systems, it is crucial that the whole system, including the inertial sensors, can be evaluated for all possible aspects. Evaluating systems regarding their physical aspects can be quite challenging , dependiBoth methods of sensor system evaluation, have their advantages, but also their drawbacks. To overcome the drawbacks, we proposed a novel approach in  which caBesides the correct functionality, the timing behavior of such systems becomes more and more critical. In particular, the jitter in the time series of the sensor data has a significant influence on the quality of the functional properties . Since iIn the following sections, we will describe our approach for debugging smart inertial sensors in a repeatable and reproducible way. This approach enables the sensor firmware developer to inject prerecorded or artificially created sensor data and stimulate the smart sensor firmware running on the target system. As a result, the developer can gain higher confidence in the functionality and time behavior of the firmware. Hence, our proposed method closes a gap between simulation-based and rapid hardware prototype debugging approaches. To increase the applicability, the injection process must support multiple sensors simultaneously and provide direct feedback from the on-sensor computations. In our experiments, we systematically study these aspects and show the viability of our approach.The work at hand is structured as follows:At first, we will discuss the related work in Sensor Simulation PC. It allows for a deterministic simulation of the physical models of a sensor in real time  shows that the method, i.e., the scenario, has, by far, the largest influence on the total variation. The remaining two parameters only contribute marginally to the overall result. The influence of the part or the different sensors used is close to zero. The repetition of the experiment also has close to zero influence on the overall result.The results of the statistical analysis for the test cases without compiler optimization, can be seen in As said before, we also did the same experiments with a compiler optimized firmware version. The results for the optimized firmware are depicted in The Gage Repeatability analysis shows that the results for the Cycle-to-Cycle-Jitter values are statistically significant. The results of the analysis and the small values in for the variance of the data shows that the experiments are valid to determine the timing behavior and jitter of the Sensor-in-the-Loop implementation. Furthermore, because of the little influence of the repeatability on the overall result, we conclude that the timing and the jitters are not data-dependent.To have a second gage for the statistical significance of the timing and jitter results, we calculated the confidence intervals for the Jcc results for all measured scenarios. These confidence intervals are shown in Due to the higher integration levels of microprocessors in recent years, the market of sensors changed from using simple dedicated sensor, to using high integrated smart sensors. This change enables sensor developer to implement sensor tasks such as processing sensor data or more complex algorithms such as gesture recognition directly on the sensor. Firmware development on smart sensors can be quite challenging, especially when it is necessary to test software parts in different test runs. With state-of-the-art methods is not possible to do repeatable or even reproducible test, directly on the sensor hardware.In this article, we presented our approach for a Sensor-in-the-Loop architecture. This approach can significantly improve the development process of firmware for smart inertial sensors. It allows the developer to test the firmware in a repeatable and reproducible way. This is possible by injecting previously recorded or artificially generated sensor data into the running sensor firmware in real time.We identified the criteria that must be met to implement such a SiL architecture, so that it will work in real time. Following these criteria, we designed an example implementation of our approach using the BMF055, a state-of-the-art smart inertial sensor by Bosch Sensortec. The evaluation of this implementation was done using different sensor configurations. These configurations used one or multiple hardware sensors at a time. Even configurations with multiple sensors and different sampling rates were tested.In comparison to simulation-based approaches described in  or 12],,12], we The evaluation experiments we did on our example implementation are dived into two different main experiments.:At first, we investigated the overall performance of our implementation, this was done in The second experiments focused on the jitter in the sampling process. We measured and compared different kinds of jitter for the sampling of the actual hardware sensors and using our Sensor-in-the-Loop architecture. Furthermore, we measured the jitter for different hardware configurations, e.g., timer-based sampling or interrupt-based sampling. The results show that the sampling jitter is different for sampling actual hardware sensors and sampling using our SiL approach. That means that our implementation cannot reproduce the timing behavior perfectly. Considering this, the jitter for the SiL implantation has the same order but remains smaller than the jitter for the actual sensor devices, we conclude that this implementation reproduces the timing behavior close enough to use it for most use cases. According to  the measTo prove the statistical significance of our experiments, we used two different statistical methods. We used the Gage Repeatability analysis to investigate the influence of the different parameters on our results. Using this, we showed that the results are data independent and are not influenced using different sensors of the same kind. The Gage R&R test showed that the actual measured scenario has more than 99% influence on the result. The small variance in the analysis results proof the statistical significance of our test data. To substantiate the Gage R&R analysis, we also calculated the confidence interval of our test data. The small confidence interval in comparison to the calculated mean value and the small overlap of the separate intervals also speaks for statistically significant data. We performed the statistically tests only for Cycle-to-Cycle-Jitter data as all other jitter and timing calculations are statistically connected.In this article, we presented our Sensor-in-the-Loop approach, which can be a great aid for developer of smart sensor firmware. It can be used to inject prerecorded or artificial sensor data directly into the smart sensor in real time. This enables the developer to do repeatable and even reproducible test on the real sensor hardware during the development process.We showed that our approach can handle sensor data of three 3-DOF Sensors with a sampling rate of 1.6 kHz in real time. Furthermore, we investigated the influence of our implementation on the timing behavior and jitter. The influence on the timing and jitter is negligible, this enables our approach to be used on a wide range of use cases.The timing analyses were confirmed by two statistical significance analysis methods. The conducted Gage Repeatability analysis as well as the confidence intervals support the correctness of our test results.In future studies we are planing to use our approach for investigation on extra functional properties in real world smart sensor application.