Data generated in this study can be used as physiologically relevant input for biorelevant in vitro tests and in silico models tailored to the pediatric population, thereby contributing to the efficient development of successful oral drug products for children.The volume and distribution of fluids available in the gastrointestinal (GI) tract may substantially affect oral drug absorption. Magnetic resonance imaging (MRI) has been used in the past to quantify these fluid volumes in adults and its use is now being extended to the pediatric population. The present research pursued a retrospective, explorative analysis of existing clinical MRI data generated for pediatric patients. Images of 140 children from all pediatric subpopulations were analyzed for their resting GI fluid volumes in fasting conditions. In general, an increase in fluid volume as a function of age was observed for the stomach, duodenum, jejunum, and small intestine (SI) as a whole. No specific pattern was observed for the ileum and colon. Body mass index (BMI), body weight, body height, and SI length were evaluated as easy-to-measure clinical estimators of the gastric and SI fluid volumes. Although weight and height were identified as the best estimators, none performed ideally based on the coefficient of determination (R Although one of the oldest forms of drug delivery, oral administration continues to be the preferred route for drug therapy. In the past few decades, extensive research has been performed to map and characterize the complex processes that determine drug absorption, including drug release, dissolution/precipitation, solubilization, and permeation ,2. Such Even though several absorption-related physiological factors have been investigated, research has mainly focused on the adult population, largely neglecting specific populations such as children . This laFor the pediatric population, regulatory authorities acknowledged this gap and reacted with Regulation No 1901/2006, the Pediatric Research Equity Act (PREA), and the Best Pharmaceuticals for Children Act (BPCA) ,9,10. AsA physiological variable with a potentially substantial impact on drug absorption is the volume of GI fluids. It is quite evident that a larger volume available for dissolution allows more drug to dissolve and become available for absorption. For bioavailability and bioequivalence studies in adults, regulatory and drug development guidelines advise administration of drugs with 150–240 mL of water ,17. In pWhile MRI has been extensively used in the past to determine GI fluid volumes in adults, its use is now being extended to the pediatric population, with two recent small-scale studies analyzing the stomach and small intestine (SI)  and the colon  ,14. The This study was a retrospective observational study held at the University Hospitals Leuven and approved by the Ethics Committee Research UZ/KU Leuven .All pediatric abdominal MRI images obtained in children aged 0–16 years at the University Hospitals Leuven between 1 January 2010 and 1 August 2020 were evaluated for inclusion. All data prior to this date were excluded to reduce the effect of variability in the MRI acquisition protocol. Children older than 16 years are handled as adults when imaging at the University Hospitals Leuven and were therefore not considered. Only images obtained in fasted state conditions were included, excluding all postmortem imaging and imaging that requires a fluid-fed state. As described by Papadatou-Soulou et al. , fluid-fAlthough the hospital MRI protocol requires a fasting period of 6 h, it could not be excluded that some patients were not fully fasted. Especially younger children tend to get irritated when being fasted and are often allowed some food . AdditioNext, images with insufficient quality were excluded. This selection involved exclusion based on image contrast, image resolution, motion degradation, and incomplete visibility of the GI tract. Lastly, patients were excluded based on the presence of pathologies that may affect the fluid in the GI tract . The excluded number of patients per criterium is summarized in All MRI images included in the analysis were obtained using a clinical magnet of 1.5 Tesla (T)  or 3T . The images were acquired following the routine clinical protocol in the axial and coronal planes using either T2-weighted ultrafast spin echo sequences  or T2-weighted single-shot turbo spin echo sequences (SS-TSE)) or T2-weighted steady-state balanced gradient echo sequences . Over the 10-year period considered for data selection, only minimal adjustments were made to the mentioned sequences to improve image quality after system upgrades; therefore, all generated data were considered comparable.TM , a free and open-source code software (FOSS) medical image viewer. Integration of free fluid was performed in accordance with the protocol published by de Waal et al. [Images were analyzed to calculate fluid volumes in the GI tract using Horosl et al. . In briel et al. ,22.For all included patients, fluid volumes were quantified over the whole GI tract. Fluid pockets in the stomach, duodenum, jejunum, ileum, and colon were segmented separately. Using the integrated area of the fluid pockets and the slice thickness with the interslice gap, the respective volumes in the GI segment were calculated.To ensure consistency, different GI segments were identified based on the following identification criteria for each segment. Identifications of the different GI segments are described from the patient’s perspective. An example can be found in Y-axis to the top right quadrant. Due to the presence of often larger and localized fluid volumes, it is relatively easy to identify the stomach and integrate the fluid volume. The pylorus was identified as the end of the stomach and served as a marker for the start of the duodenum.When dividing the body using an X, Y coronal plane, the stomach is located in the top left quadrant and passes over the Y-axis of the coronal plane and continues up again until the ligament of Treitz. The ligament of Treitz, and thus the end of the duodenum, was identified as being roughly the same height as the duodenal bulb but on the left side of the spine [The duodenum starts at the duodenal bulb immediately after the pylorus and then typically follows a path down, after which the duodenum crosses the he spine .valvulae conniventes. Due to the closely spaced circular folds and the consequently thicker intestinal wall, the jejunum can be identified by a typical striped pattern in the GI fluid [As commonly accepted, the jejunum starts at the ligament of Treitz . The endGI fluid  , after which it comes back down (descending colon) to end as the sigmoid and rectum.The colon is the last part of the GI tract and has a larger diameter as compared to the jejunum and ileum. Additionally, the position of the colon is more fixed , therefoPBPK models for hypothetical compounds were developed in Gastroplus . The default values of input data for a newly created drug record in Gastroplus were taken except for the solubility at pH 7 and the effective permeability. Based on the requirements for the different biopharmaceutical classification system (BCS) classes and the Gastroplus generated dose number and absorption number, 9 hypothetical neutral drugs with a 100 mg dose were generated, representing the 4 different BCS classes complemented by 5 boundary BCS class drugs. The dose number is a non-dimensional number calculated by dividing the dose by the amount that will dissolve in the volume of co-administered fluid  at the lowest solubility between pH 1 and 8 . A dose Next, a manual sensitivity analysis of the simulated fraction absorbed (Fa%) as a function of SI fluid volume was performed for each of the compounds. GI fluid volumes in Gastroplus can be set by specifying the % of the total segmental volume filled with fluid.To extend the knowledge on GI fluid volumes in the different pediatric subpopulations, the present study opted for a retrospective analysis of lower abdomen MRI data from pediatric patients obtained for diagnostic purposes in clinical practice. Even though such a study design does not allow full control over the experimental conditions, it provides an opportunity to obtain relevant physiological data in vulnerable populations for which prospective studies are challenging due to ethical concerns.2; 52.14% of them were female. The majority of the patients (58.57%) were preschool children aged 2–5 years, but the other pediatric subpopulations were also represented. Most of the patients (51.43%) had an oncology-related diagnosis or history, with the majority being patients with neuroblastomas in regression. Oncology was followed by urogenital (21.43%) and hepatic (20.71%) pathologies. Lastly, 6.43% of the patients received other diagnoses, such as lymphatic and cardiovascular issues. The pathologies of the included patients, extracted from their medical history records, were assumed not to affect the fluid volumes in the GI tract. As per the MRI protocol of the University Hospitals Leuven, MRI was performed under anesthesia for patients under the age of 6 years.To ensure the relevance of the obtained data, a careful selection of available patients and images was performed. As described in the Methodology section, patients with pathologies expected to affect the GI environment and patients in a non-fasted state were excluded. After applying the exclusion criteria, MRI data from 140 patients were evaluated for GI fluid volumes. Demographical data from the included patients can be found in The median GI fluid volumes for each pediatric subpopulation are summarized in In the stomach, the resting fluid volume was present as a bright homogeneous and continuous fluid pocket, which is in contrast to the rest of the GI tract. As described by Mudie et al. , intestiIn line with the physiological function of the colon to reabsorb water , most coThe obtained data from 140 children allowed evaluation of the correlations between the GI fluid volumes and easy-to-measure body characteristics. Such correlations may provide the basis for simple estimations of the GI fluid volume by identifying a normalization factor or clinical estimator. These normalized fluid volumes can then be used for more representative and tailored dissolution studies . The eva2, calculated as the square of the correlation coefficients) indicated a reliable prediction. The strongest correlations were found between GI fluid volumes and body height or weight, for which R2-values ranged between 0.17 and 0.31, which is still poor  and school-age children (23.52 ± 10.40 mL) are in line with the results available for adults .As this is, to the best of our knowledge, the first time that jejunal and ileal fluid volumes are determined separately using MRI, comparison with literature data is difficult. One exception to the integration of fluid pockets in individual SI segments is de Waal et al. , who meaA comparison of the total SI fluid volume shows that the volumes measured in the current study are larger than previously reported volumes in children by Papadatou-Soulou et al. . SimilarWhen comparing the adolescent population assessed in the present study to data available for the adult population, the average adolescent SI fluid volume of 94.01 ± 70.39 mL is comparable to published data by Schiller et al.  Figure . HoweverThe average fluid volumes measured in the colon of children are clearly lower than in a previous MRI study by Goelen et al. . As addrAs no specific age-related pattern was found for the colonic fluid volumes in pediatrics, a direct comparison with the adult population is made. When comparing average fluid volumes, pediatric colonic fluid volumes seem to be in line with previously reported volumes in adults, as few to no fluid pockets are typically found in studies using a similar technique ,33,34 F.Although data on absolute pediatric gastric fluid volumes are scarce in the literature, multiple papers reported weight-corrected gastric fluid volumes (WCGFV) in children generated in the context of anesthesia, where gastric fluid is often aspirated or measured using ultrasound or sometimes using MRI . These WAndersson et al.  showed tAdditionally, some observed differences might originate from the technique used. When using an aspiration technique where gastric fluid is extracted from the body, it is possible that some fluid remains in the stomach. MRI considers all free fluid present though its accuracy is, as previously mentioned, dependent on the technique used. As no fluid integration protocol was available for the MRI studies, their technique could not be compared to the one used in this study.The importance of GI fluid volumes is obvious during in vitro dissolution testing. As assessed by Kostewicz et al. , the curAdditionally, a major use of the data presented in this publication is as input for PBPK modeling of drug absorption. Gastroplus and Simcyp, currently the most used PBPK modeling platforms, implement different approaches to calculate GI fluid volumes. In Simcyp, the resting gastric fluid volume is calculated based on the gastric fluid secretion and gastric emptying rate. These parameters are age dependent but fixed by the software based on Simcyp in-house data and literature research . In GastIn the framework of the present study, PBPK modeling was used to explore the potential impact of physiologically relevant GI fluid volumes on drug absorption. To this end, models for hypothetical drugs representing the different BCS classes and their boundaries were developed . Next, tThe sensitivity of the fraction absorbed to the available SI fluid volume strongly depends on the BCS class of the hypothetical drugs . The medThese sensitivity simulations indicate that the use of biorelevant GI fluid volumes is important when evaluating poorly soluble drugs since it may substantially affect their absorption. In clinical practice, such simulations should obviously take into account other factors influencing drug exposure for poorly soluble drugs, including the use of advanced formulations to reduce solubility issues and possible dose reductions in younger children. This retrospective study successfully quantified GI fluid volumes in the pediatric population resulting in the largest and most detailed dataset currently available. We analyzed pediatric abdominal MRI images gathered by the University Hospitals Leuven for GI fluid volumes using a method that has already been successfully used in adults. In addition, the existing method was used to differentiate between fluid pockets in the duodenum, jejunum, and ileum instead of considering these parts of the SI as a whole.In general, the median fluid volumes increased with age in all GI segments except for the ileum and colon. Additionally, an increase in fluid volumes was observed in more distal SI segments except for the adolescent subpopulation, where the median fluid volume was lower in the ileum than in the jejunum. Most of the colonic fluid pockets were located in the first part of the colon (ascending colon).PBPK simulations using hypothetical compounds suggested that the range of pediatric fluid volumes observed in this study can have a substantial impact on the absorption of poorly soluble drugs. This highlights the importance of using physiologically representative fluid volumes in in vitro and in silico simulation tools to predict drug absorption in children.Lastly, the observed GI fluid volumes appeared to correlate only poorly with easy-to-measure body characteristics . This observation suggests the need for caution when scaling GI fluid volumes using these clinical estimators.


An incredible eradication of thermal indulgence is required to enhance the flow and heat transfer enhancement in micro/nanofluidic devices. In addition, the rapid transport and instantaneous mixing of colloidal suspensions of metallic particles at nanoscale are exceptionally crucial at ascendency of inertial and surface forces. To address these challenges, the present work is intended to investigate the role of trimetallic nanofluid comprising of three kinds of nano-sized granules  with pure blood through a heated micropump in the presence of inclined magnetic field and axially implemented electric field. To ensure rapid mixing in unidirectional flow, the pump internal surface is lined-up with mimetic motile cilia with slip boundary. The embedded cilia whip in pattern due to dynein molecular motion controlled by time and produce a set of metachronal waves along the pump wall. The shooting technique is executed to compute the numerical solution. In a comparative glance it is revealed that the trimetallic nanofluid exhibits 10% higher heat transfer efficiency as compared to bi-hybrid and mono nanofluids. Moreover, the involvement of electroosmosis results in almost 17% decrease in the heat transfer rate if it values jumps from 1 to 5. The fluid temperature in case of trimetallic nanofluid is higher and thus keeps the heat transfer entropy and the total entropy lower. Furthermore, involvement of thermal radiated and momentum slip significantly contribute in reducing heat losses. Burgreen and Nakache6 and Wang et al.7 debated the rational implications of electrokinetically driven pumping transport through microchannel. Mekheimer et al.8 have explored the impact of electric force on blood motion through peristaltic symmetric channel and informed that the submission of electric field in the flow direction strengthens blood flow. Concurrent effects of electric and magnetic field on ciliary transport of bio-fluid were examined by Munawar9. Prakash et al.10 have investigated electroosmosis regulated peristaltic motion of the Newtonian fluid enclosure with slip and radiation effects.Microfluidic devices have tremendous uses in physiology and biochemical engineering, for instance, drug delivery micropumps, dialysis pumps, protein and DNA analysis and lab-on-chip devices etc. However, at small scale dimensions, the primary concerns are flow manipulation at low Reynolds number, precise pumping, active mixing. However, in order to deal with the mentioned issues, electroosmosis delivers a favorable approach to address these tasks. The fluid transport endorsed by electric field across the conduit is referred as electroosmosis. The submission of externally applied electric field nearby a solid–liquid edge may induce liquid flow owing to neighboring charged surface. This process generates EDL (electric double layer) at the solid–fluid edge by forming an ionic ambiance all around the charged species with assistance of boundary charges and its counter ions. Some noteworthy attempts high-lightening the significance of electroosmosis in microflows are reported in Refs.12. But unfortunately, the traditional blood keeps deficient thermal conductivity. This inadequacy of conventional liquids has been conquered by introducing the concept of nanofluids. Nanofluid is prepared by the amalgamation of one or more types of metallic nanogranules with elevated thermal conductivity in conventional base fluid. The very first attempt introducing the idea of nanofluids was presented by Choi13. The ensuing fluid is identified as nanofluid that keeps higher heat transfer properties in contrast to conventional fluids. Due to its prospective application and ample uses, it has achieved immense attention of many researchers16. Over the course of time, researchers considered the hybridization of two distinct kind of metallic nanoparticles with base fluid to obtain bi-hybrid nanofluid with enhanced thermal characteristics than mono nanofluid. Since last decade, the investigation of hybrid nanofluid flow has become a mounting field of research. Some interesting applications of hybrid nature nanofluid are mentioned in threptic process17, drug supply18, micro heat exchangers19, lubricants20, cooling agents21 and thermal pumps22. The concept of ternary fluids that is the incorporation of trimetallic nanoparticles with base fluid has been recently established. Experimental attempts25 are given to report the significance and remarkable features of ternary nano fluids. Whereas a very few theoretical attempts have been presented to emphasize the promising and discerning role of trimetallic nanofluid in heat transfer augmentation. In this regard, a mathematical ternary fluid model elaborating enhanced heat transfer characteristics was given by Manjunatha et al.26. Elnaqeeb et al.27 explored the effects of stretching and suction on aqueous based trihybrid nanofluid. Recently, an entropic study of Williamson ternary nanofluid has been conducted by Munawar and Saleem28 and informed that radiated ternary fluid acquired enhanced heat transfer properties than bi/mono nanofluids.The intended thermoosmotic transport deals with the blood flow through micropump. On account of its rheology, the nature of blood running inside the electroosmotic pump is deemed as the Carreau fluid model34.At low Reynolds number, motile cilia play a considerable part in fluid manipulation and mixing. Cilia move in groups by performing backwards and forwards strikes in elliptical course to stimulate recurring waves named as metachronal waves. Some significant applications of fabricated cilia in the perspective of microfluidic handling, unidirectional flows, immediate pumping, and mixing are given40. A pioneering attempt regarding the entropy production in thermal system was contributed by Bejan41. Misra et al.42 and Munawar et al.43 presented optimum design of micropump and corrugated triangular cavity to reduce entropy formation. Basha et al.44 conducted entropic analysis of tangent hyperbolic nanofluid over a circular cylinder. Recently, entropic analysis of biological flows through ciliated channel was aided by Saleem and Munawar45 and Munawar et al.46.One of the important challenges in thermo-mechanical flows through microfluidic pump is thermal management. Recent trends in the development of bioengineered microelectronic devices require elevated thermal efficiency and effective cooling activity. The existence of substantial velocity and temperature differences in microchannel streams yield a huge heat indulges. Accumulation of unnecessary heat losses adversely influences device performance. Thus, to achieve an optimal thermo-mechanical design with proper thermal management, entropy formation requires to be diminished at most. Therefore, it is essential to find the ways that contribute to entropy minimization and flow optimization. Some interesting studies describing the entropic analysis in micro conduit are givenThe above literature review reveals that the mono and hybrid nanofluids flow in biological environments have been considered significantly. Nevertheless, the case of trihybrid nanofluids in physiological flows is still needed to be further explored, especially, in recent trends towards modern microfluidic appliances. To fill-up this gap, the current work aims to investigate the role of thermal radiation and applied elevated magnetic field in a forced convection motion of ternary nanofluid through an electroosmotic flexible pump. The ternary nanofluid is comprised of three distinct types of metallic nano-sized granules of titanium oxide, Silica and Aluminum dioxide blended with pure blood characterized by Carreau fluid model. The internal surface of pump is fabricated with uniform cilia layer with momentum slip boundary. The numerical solutions of the intended flow model are computed by shooting method and are validated by a comparison with a previously published study. A comparison between the thermal features of mono/hybrid/ternary nanofluids and traditional blood is presented. To the best of our knowledge such flow situation has never been considered earlier. The proposed study may provide a profound understanding in bioengineering models and drug supply.χ from horizontal axis 2), silicon dioxide or silica (SiO2) and Alumina (Al2O3) with 1% volume proportion of each with pure blood (Carreau fluid model). The velocity partial slip at the surface of elastic walls of the pump is implemented with a slip length λ1. Moreover, the walls of pump are kept at a relatively higher temperature T1 and the pump also radiates the heat with flux 47:Consider a two-dimensional pumping motion of Carreau ternary fluid through an electroosmotic micropump in the presence of magnetic field applied at an angle mentclass2pt{minim47):a stands for mean width of the ciliated channel, ε indicates the cilia length, λ denote the wavelength of metachronal wave, α represents measure of eccentricity of elliptic course, X0 is the location of liquid particle and The cilia tips embrace in elliptical course and are horizontally positioned at , where A1 is the first Rivlin Erickson tensor. HenceFor the Carreau trimetallic nanofluid, the extra stress tensor is communicated asThe extra stress components are determined from Eq.  as7\docun = 1 or Γ = 0.The Carreau model in Eq.  exhibitsE =  and uniform magnetic field B =  are noted as48Under the contemplation of above scenario, in laboratory frame, the conservation laws for flux, motion and heat in the presence of axially applied electric field Subject to the boundary conditions19:K* and σ* correspond to the absorption coefficient and the Stefan-Boltzmann constant. The Taylor series expansion of 4 about the temperature difference (∆T) in the presence of trivial temperature gradient in the micropump is noted as: 4 ≅ 4 3 − 3(∆T)4. Therefore, Eq.  is expressed as8:e, no, kb, z and Tave respectively stand for the protonic charge, average concentration of positive and negative charges, the Boltzmann constant, the charge valence, and absolute temperature for medium. The supposition of homogeneous concentration of solid nanoparticles appears to be justifiable when there are insignificant concentration-gradients present in the ternary fluid and the appropriately small Peclet number.By using the Poisson equation, the representation of electric potential distribution in fixed frame inside the micropump is conveyed as49 turns toIt is further considered that the zeta-potential at pump surface (≤ 25 mV) is significantly small and thus the Debye-Hückel linearizationOn employing Eqs.  and 19)19) into 2), silicon dioxide (SiO2) and Aluminium oxide (Al2O3) assorted thoroughly in pure blood. Titanium dioxide possesses various applications in medicines, chemical and textile engineering due to its coolant and photo catalytic properties. Whereas, silicon dioxide nanogranules are commonly used in drug delivery owing to its nontoxic nature and stability. Aluminium oxide is ideal for pharmaceuticals and materials manufacturing industries owing to its corrosion and thermal resistance properties. The numerical values of thermophysical properties for the considered nanoparticles and base-fluid are reported in Table 27:ϕ denotes the volume fraction of metallic nanogranules, ρ denotes the density, ρCp stands for heat capacity, σ the electrical conductivity, k is the thermal conductivity and µ holds for viscosity . The numeric subscripts 1, 2 and 3 correspond to the characteristics associated with solid nanogranules of TiO2, SiO2, and Al2O3. The alphabetical index thnf, hnf, nf and f respectively denote the characteristics related with ternary, binary, mono nanofluid and blood.It is considered that the blood based ternary fluid is diluted colloidal suspension of trimetallic nanogranules of Titanium dioxide  in the formβ ≪ 1) and the small Reynolds number (Re ≈ 0) approximations, the dimensionless forms of the governing equations are reported as:After using Eqs. –28) int int28) iCross-differentiating Eqs.  and 30)30) givesCorrespondingly, the boundary conditions are stated asUHS), the Knudsen number (L), the thermal radiation number (Rn), the Joule heating parameter (Sp) and the electroosmosis number (K) are expressed asThe non-dimensional quantities arising in the governing equations such as the Hartmann number (Ha), the Weissenberg number (We), the Prandtl number (Pr), the Eckert number (Ec), the Helmholtz–Smoluchowski velocity (Q) frame and the wave (F) frame is stated by the following equation:The transformation between the volume flow rates in the laboratory (The Nusselt number (Nu) is defined as:Equation  along wiHowever, Eqs.  and 33)33) along48. For this purpose, we assume ϕ → 0, χ → 0, Ha → 0, Sp → 0 and L → 0 and solved the limiting problem numerically. A comparison of this numerical solution with the perturbation series solution reported by Ref.48 is provided in Fig. To validate our code and the numerical solution, we compare our result at limiting case with an analytic series solution provided in Ref.43:SG0 provides the total entropy number NS asIn the ciliated electroosmotic pump, the primary factors to yield entropy are heat transfer effects due to thermal radiation and convection, fluid friction, the Joule heating and electric potential. Therefore, the entropy generation equation for the intended flow in the regime of second law of thermodynamics is expressed as of Eqs.  and 27)43:39\docτ (= ∆T/T0) is kept equal to one.In Eq.  the veryThe Bejan number is named after Adrian Bejan and is specified asIn the regime of thermodynamics, the Bejan number belongs to  and distinguishes the heat transfer irreversibility effect and the system’s total irreversibilities owing to other causes. The values of Be greater than 0.5 describes the consequential role of heat transfer irreversibility over the total irreversibility of the system. Also, the values of Be lower than 0.5 declare ascendancy of the system’s combined irreversibility attributable to the other causes like fluid friction and magnetic field and electric potential.u), the streamline pattern (Ψ), the temperature profile (θ), the entropy formation number (NS) and the Bejan number (Be) for the ternary nanofluid. Several graphs for sundry values of the pertinent parameters of concern are delivered by Figs. ϕ (= ϕ1 + ϕ2 + ϕ3) dispersed in the conventional blood is assumed as 3%. Thus, the blood based ternary nanofluid is supposed to be prepared by mixing 1% concentration of every type of solid nanogranules like titanium dioxide, silicon dioxide and Alumina in the Carreau fluid. Since the present parametric analysis is conducted for applicable parameters of interest, therefore some of the parameters are kept fixed such as the values of the Eckert number, the cilia length parameter and the pawer law index are select as 0.2, the Prandtl number is chosen as 1, The Weissenberg number is 0.01, inclination angle for the magnetic field is π/3, wave number and the value for the measure of eccentricity parameter are selected as 0.2.This section is focused for the interpretation of numerical simulation of the computational results for the velocity distribution (UHS) and the momentum slip parameter (L), Fig. UHS). The positive values of UHS represent the application of the electric field in the opposite direction of the ternary fluid stream. The counter positive values of UHS lead to the submission of the electric field in the flow direction. Furthermore, the value of UHS equal to zero implies the absence of electric field. Thus, Fig. L) causes an acceleration in fluid flow around the pump surface region. But near the pump core section, this conduct of ternary fluid motion is entirely reverse.To study the alternations in the velocity profile of the ternary nanofluid for altered values of the magnetic number (Ha), the electroosmotic parameter (K), the Helmholtz–Smoluchowski velocity (Rn) and the Joule heating parameter (Sp), respectively. Figure Rn) induces a substantial deterioration in the ternary fluid temperature as seen through Fig. Sp) owing to the internal heat creation by electric current.Figure Rn) and the Joule heating parameter (Sp), Fig. L = 0), and it considerably reduces when taking large values of L. Figure Rn) are considered. This may compel that the thermally radiated ternary nanofluid can optimize the thermal efficiency of the flow system. To examine the influence of the ohmic heating number (Sp) on the total entropy generation number of ternary fluid, Fig. Sp.Measure of randomness and chaos in the system is identified as entropy of the system. In order to attain optimized efficiency of the flow system, heat indulges during an irreversible process should be lessened. In order to conduct second law analysis of the trimetallic nanofluid for various values of magnetic number (Ha), the electroosmotic parameter (K), momentum slip parameter (L), the thermal radiation parameter (L) and radiation number (Rn) aid to the overall system’s irreversibility over the heat transfer irreversibility. Figure Sp). Near the pump core portion, the Bejan number shifts to the origin that communicates to the nil heat transfer irreversibility but in the proximity of the pump surface, the heat transfer results are perceptible.The Bejan number conveys a profound perception about the discrimination in various kinds of irreversibilities. In the regime of thermodynamics, the Bejan number is the ratio of heat transfer irreversibility (convection and radiation) to the system’s overall irreversibility owing to the other causes like fluid friction, conduction, and the joule heating etc. The values of the Bejan number alter from zero to one inclusive. Its value lower than 0.5 demonstrates the leading effect of fluid friction and other irreversibilities over the heat transfer irreversibility. While its value greater than 0.5 represents the ascendancy of heat transfer irreversibility over the system’s total irreversibility due to other causes. Figure 2/blood), hybrid nanofluid (TiO2–SiO2/blood), ternary fluid (TiO2–SiO2–Al2O3/blood) and traditional base fluid (Carreau fluid model), Fig. 2) act as an effective catalyst to assist conduction, Titanium Dioxide (TiO2) consists of covalent bonds and therefore endorses convection and Alumina (Al2O3) keeps chemical inertness, thermal stability and high corrosion resistance.In energy transformation processes, one of the primary challenges is to optimize the efficiency of the thermal system. Such thermal performance is deeply influenced by the thermophysical properties of the working fluids running in considered thermal system. Thus, to investigate of the thermal characteristics of mono nanofluid TiO/blood, hThe available work capability of energy to perform useful work is known as exergy. It means it is the measure of energy transformations to the other forms. During this conversion process, the unusable energy or heat losses are termed as entropy. Figure ϕ on the temperature profile, heat transfer rate, total entropy number and the Bejan number, respectively. Figure ϕ increases which is obvious result due to increase in thermal conductance of nanofluid. Figure ϕ augments. Such result is also expected due to the increase in the temperature difference at the surface of pump caused by higher fluid temperature. The total entropy number also increases as the volume fraction ϕ increases . From Fig. UHS) on streamlines pattern of the ternary fluid. The higher values of UHS place a significant increment in fluid stream rate of the ternary fluid. This results in expansion of bolus size and generation of new bolus.An appealing feature of fluid stream endorsed by the peristaltic pumping or ciliary motion is termed as trapping. The trapping occurs at elevated amplitude ratio and persistent fluid stream rate. Under certain constraints, a set of closed streamlines divide to restrain a fluid mass. This confined fluid mass named bolus is carried by the streamlines and travels with the same velocity of peristaltic wave or metachronal wave. Whereas as the rest of the fluid travels ahead with less speed than the confined bolus. Figures The ternary fluid showed effective role in enhancing the heat transfer rate as compared to the pure blood, mono/hybrid nanofluid. At a fixed value of electroosmosis parameter, the heat transfer rate is elevated up to 10.86% when compared with the conventional blood model.The ternary fluid helps in raising fluid temperature and reduces the heat transfer rate. Thus, it lowers down the total entropy production in the pump.The role of thermal radiation is to lessen the fluid temperature which helps in reducing the heat transfer irreversibility and thus the total entropy of the pumps minimizes.Momentum slip condition performs an essential part in cooling process by substantially falling the fluid temperature down.Around the pump peripheral zone, the elevated values of the Hartmann number and the momentum slip parameter contribute to reinforce the ternary fluid stream.The temperature of the ternary nanofluid cools down by considering weak magnetic field and strong EDL effects.Heat losses in ternary nanofluid stream can be lessened by integrating strong EDL, thermal radiation and momentum slip effects.The present work is initiated to investigates the role of ternary nanofluid along with the contribution of momentum slip and inclined magnetic field on thermally radiated electroosmotic pump with flexible walls. The ternary nanofluid is comprised of three distinct types of metallic nano-sized granules of titanium oxide, Silica and Aluminium dioxide blended with pure blood characterized by Carreau fluid model. The internal flexible wall of the channel is lined-up with fabricated cilia. The Carreau nanofluid flow with such settings has never been considered before. The problem is modelled in wave frame of reference and simplified under the lubrication approximation, the Debyeh–Hückel linearization and the Rosseland approximation. The analysis comprises of the comparative investigation of the heat transfer rate and the second-law for simple base-fluid, mono, hybrid and ternary nanofluids. In the light of the above analysis, the following concluding remarks are drawn:In future such work may be extended to investigate the rheological effects in electroosmotic pump with trimetallic nanofluids through porous or composite channels. Such studies are highly emerging in drug delivery and various medical applications.


Ultrasonic fluid bubble detection is important in industrial controls, aerospace systems and clinical medicine because it can prevent fatal mechanical failures and threats to life. However, current ultrasonic technologies for bubble detection are based on conventional bulk PZT-based transducers, which suffer from large size, high power consumption and poor integration with ICs and thus are unable to implement real-time and long-term monitoring in tight physical spaces, such as in extracorporeal membrane oxygenation (ECMO) systems and dialysis machines or hydraulic systems in aircraft. This work highlights the prospect of capacitive micromachined ultrasonic transducers (CMUTs) in the aforementioned application situations based on the mechanism of received voltage variation caused by bubble-induced acoustic energy attenuation. The corresponding theories are established and well validated using finite element simulations. The fluid bubbles inside a pipe with a diameter as small as 8 mm are successfully measured using our fabricated CMUT chips with a resonant frequency of 1.1 MHz. The received voltage variation increases significantly with increasing bubble radii in the range of 0.5–2.5 mm. Further studies show that other factors, such as bubble positions, flow velocities, fluid medium types, pipe thicknesses and diameters, have negligible effects on fluid bubble measurement, demonstrating the feasibility and robustness of the CMUT-based ultrasonic bubble detection technique. For instance, in clinical medicine, the existence of air bubbles in extracorporeal blood circulation (ECBC) and extracorporeal membrane oxygenation (ECMO) systems can lead to air embolisms (blocking blood from passing through) when bubbles infiltrate a vein or artery, resulting in serious injuries such as strokes and heart attacks or even death5. In aerospace applications, air bubbles in propellant can cause a rapid increase in air pressure in spacecraft tanks and thus an explosion7. To date, a variety of technologies have been developed for fluid bubble detection, including image analysis8, electroresistivity9, optics10, capacitance wire-mesh sensors11, X-rays12 and ultrasound14. Compared with other methods, the ultrasonic detection approach is noninvasive, nondestructive, simple in installation and not susceptible to electromagnetic interference in complex industrial environments15. These unique advantages position ultrasound technology as a promising alternative for fluid bubble detection in pipes.The ability to measure fluid bubbles in pipes is in high demand in industrial controls, pharmaceuticals, chemical production and clinical medicine because it can be leveraged to monitor the presence of air bubbles, fluid flow stability and chemical reaction processes and in some cases prevent fatal mechanical failure and life-threatening conditions17, which are not suitable for applications in small pipes 19, either for in situ, real-time or long-term monitoring, because of their shortcomings of large volume, high power consumption and difficulty in integration with ICs22. In situ, real-time and long-term fluid bubble monitoring in small pipes is extremely important in engineering situations that have tight physical spaces and require the precise and continuous monitoring of air bubbles, such as ECBC, ECMO and dialysis machines in clinical medicine or hydraulic systems in aircraft7.However, most current ultrasonic detection techniques are based on conventional bulk PZT ultrasonic transducers24. Currently, MUTs can be categorized into two types: capacitive and piezoelectric micromachined ultrasonic transducers (CMUTs and PMUTs)25. Compared to PMUTs, CMUTs take advantage of a high electromechanical coupling coefficient , wide bandwidth (~150%) and high receiving sensitivity31 (16.6 mV/Pa) and have been widely used in 3D ultrasonic imaging, ultrasonic figure printing and range finding33. Most recently, CMUTs demonstrated successful use in blood flow velocity measurement by ultrasonic Doppler techniques because of their superior performance in comparison with their conventional counterparts34. However, CMUT-based ultrasonic fluid bubble detection has rarely been reported thus far.Micromachined ultrasonic transducers (MUTs) provide a promising solution for the aforementioned applications because of their miniature size , low power consumption, low fabrication cost, and easy integration with ICsHerein, we successfully demonstrate a CMUT-based ultrasonic measurement technique for the noninvasive monitoring of fluid bubbles in small pipes. A pair of CMUTs are employed and directly clamped on two opposite sides of a pipe for ultrasound wave transmission and reception due to their miniaturized volumes. The acoustic energy attenuation and thus acoustic pressure changes induced by air bubbles are exploited to detect bubble existence and size. Closed-form expressions for the relationship between the corresponding received voltages of CMUTs and bubble sizes are established and validated using finite element simulations, which provide the theories for CMUT-based ultrasonic bubble detection. The relative received voltage variation is first proposed as the sensing response. Subsequently, bubble detection experiments in pipes with diameters less than 10 mm are implemented using our fabricated CMUT chips, and the effects of pipe diameters and thicknesses, bubble positions, flow velocities and fluid media on bubble measurement are also investigated.V1. As shown in Fig. V2. The received voltage variation can be utilized to realize bubble detection.The sensing schematic of ultrasonic bubble detection in pipes is shown in Fig. s to simplify the analysis of cell vibrations35. The theory of sound was used to calculate the acoustic pressure emitted by each cell, in which the acoustic waves were assumed to be continuous, and the heat loss of acoustic energy was neglected36. When there is no bubble in the propagation path, the acoustic pressure at point A  can be calculated as37j is the imaginary unit, ρ is the density of the fluid, ω is the angular frequency of acoustic waves, k is the wavenumber,  is the index of a CMUT cell located in row number n and column number m, vnm is the vibration speed of a CMUT cell, and dnm and ∂nm represent the length of PA and the angle between PA and Z-axis, respectively, which can be given ash is the distance between the transmitter and receiver. D (∂nm) is the directivity function of the CMUT cell at point P , which can be written as37a is the radius of the cells and J1 (kasin∂nm) is the Bessel function of the first kind of the first order.To quantitatively analyze the relationship between these variations, we derived theoretical expressions for the received voltage variation under different bubble sizes based on the theory of sound. Figure tp1 and tp2 in (1) are sound transmission coefficients when acoustic waves pass through the front and rear walls of pipes, respectively, which can be respectively expressed as36p1 and p2 are the incident and outgoing acoustic pressure of the front pipe wall, respectively; p3 and p4 are the incident and outgoing acoustic pressure of the rear pipe wall, respectively; Z1 and Z5 are the matching layer impedance, Z2 and Z4 are the pipe wall impedance, Z3 is the fluid impedance, h2 and h4 are the pipe wall thickness, and k2 and k4 are the wavenumbers of acoustic waves in the pipe wall.As shown in Fig. Sc, of CMUTs asl1 and l2 are the side lengths of the receiver.Furthermore, the received voltage without bubbles in pipes can be given by multiplying the received acoustic pressure and the receiving sensitivity, 38. The acoustic energy passing through the bubble can be neglected because almost all the acoustic energy incident is reflected20. As shown in Fig. A  in the presence of air bubbles, we first derive the projection area based on the geometry given in Fig. x0, ax,y, bx,y, θx,y can be given by (B.1)–(B.17) in Appendix B.When air bubbles exist in the propagation path, the incident acoustic waves are reflected at the air bubble and fluid interface due to their significant acoustic impedance differenceA  with the existence of air bubbles can be obtained by summing the acoustic pressure generated by CMUT cells outside the projection area, which can be given asTherefore, the acoustic pressure at point Furthermore, the received voltage in the presence of air bubbles can be written asTo avoid the influence of other factors, such as pipe diameters, pipe thicknesses, and fluid media, on the results of fluid bubble measurement, the relative voltage variation is proposed as the sensing response of the CMUTs, which is written asThe advantage of the sensing response definition for air bubble detection given in (13) will be further validated by experimental testings.To prove the feasibility of the aforementioned bubble measurement principle, we established a finite element model (FEM) to simulate acoustic pressure and received voltage variations with bubble sizes using commercially available COMSOL Multiphysics software. Figure α△V, is presented in Fig. α△V, increased with bubble radii, demonstrating the correctness of our proposed theories for ultrasonic fluid bubble measurement. The relationship between them could be well fitted by a quartic polynomial function, in which the slope of the fitting curve, i.e., the relative voltage variation rate, decreased with increasing bubble radii. This is attributed to most of the acoustic energy emitted by a transducer being concentrated in the region near its axis direction. When the bubble size is larger than the central energy area, the imposed energy attenuation amplitude decreases. Furthermore, we explored the effect of frequencies on the received voltage variation, α△V, by varying it from 0.7 MHz to 1.3 MHz and keeping other parameters fixed. As shown in Fig. α△V under different frequencies almost overlapped with each other, indicating that α△V could hardly be affected by the working frequency of the transducers. This is because the response of ultrasonic receivers is defined by the relative voltage changes, as given in (13). The frequency of ultrasound waves does affect the acoustic energy attenuation and the amplitude of the received voltage; however, it does not affect the relative voltage changes.Based on the FEM model, we first investigated the variation in acoustic pressure with bubble size. Figure To demonstrate the feasibility of ultrasonic fluid bubble measurement in small pipes using CMUTs, we implemented experimental testing under different bubble sizes with the fluid velocity and bubble position in pipes fixed. Air bubbles of five different sizes were generated using vent holes with different radii, and their positions were kept in the center of the pipes. The radii of the generated air bubbles varied from 0.5 mm to 2.5 mm with a step of 0.5 mm, and their digital photos are shown in Fig. α△V, increased with increasing bubble size, and the experimental data of α△V were well fitted with a quartic polynomial function. Since the size of the CMUT chip was 4.7 × 4.7 mm, the sectional area of bubbles with a radius of 2.5 mm was almost the same as the area of the chip. Therefore, the acoustic wave emitted by the transmitter was almost completely reflected by the bubble with a radius of 2.5 mm, resulting in a received voltage V2 close to zero and α△V close to 1. These experimental results agreed well with those from the FEM simulation and theoretical analysis given in Fig. α△V, and thus decrease the sensing sensitivity.The experimental results of fluid bubble testing are shown in Fig. In the experimental testing above, the position of air bubbles was controlled in the center section of the pipes. In a more realistic situation, the position of air bubbles in pipes is random. To investigate the effect of position on bubble detection, we conducted fluid bubble testing under different positions. The position of the air bubble can be controlled by adjusting the position of the vent hole, and a high-speed camera was used to confirm the position of the bubble. As shown in Fig. Generally, air bubbles flow with the fluid inside pipes. Therefore, the effect of the flow velocity on ultrasonic bubble measurement was further investigated. For this, the fluid flow velocity was set to 0.2 m/s, 0.5 m/s and 0.8 m/s. The received voltages under these flow velocities with bubble radii varying from 0.5 mm to 2.5 mm were measured, the results of which are shown in Fig. α△V, was almost the same for the three fluid medium types. This could be ascribed to the use of the relative voltage variation-based response definition given in (13), which largely removed the influence of fluid media. The different types of fluid media could affect the acoustic energy attenuation and the amplitude of the received voltage; however, their effect on the relative voltage changes, α△V, was negligible. The slight difference in α△V under larger bubble sizes in Fig. α△V calculation, where the size of air bubbles approximated the CMUT chips, leading to a significant decrease in the received signal amplitude and a remarkable increase in the noise signal. These results demonstrated the robustness and advantages of our proposed CMUT-based air bubble detection method in different fluid media. Apart from the aforementioned homogeneous fluids, the proposed sensing method could also be applicable to heterogeneous fluids, such as for air bubble detection in blood in ECMO systems. This is because although heterogeneous blood induces scattering effects, resulting in increasing ultrasonic energy attenuation and reduction in the absolute received voltages, the sensing principle, which only depends on the received voltage difference between with and without the presence of air bubbles, can effectively eliminate the influence from scattering effects for a certain fluid medium.The fluid media in pipelines are different in various practical applications. To investigate the dependence of bubble sensing on fluid media, we conducted bubble testing under different fluid media, such as water, silicone oil and hydraulic oil. Figure Furthermore, we investigated the effect of pipe thicknesses and diameters on ultrasonic fluid bubble detection. The experiment was conducted by varying one parameter while keeping the other parameters fixed. The results of the measured voltage variation under different pipe diameters  are shown in Fig. 2 insulation layer, silicon substrate and Al bottom electrode  was as low as 148 μm to maximize the filling density and improve CMUT performance, and the size of CMUTs was designed as 4.7 × 4.7 mm to meet the application of bubble detection in pipes with an inner diameter less than 10 mm. The aforementioned CMUT-compatible CMOS was successfully fabricated using the low-temperature direct wafer-bonding technique at 350 °C40, as shown in Fig. 2 insulation layer was generated on the low-resistivity silicon substrate by the thermal oxidation technique (step b). The SiO2 insulation layer was then etched to form the cavity through dry etching (step c). A silicon layer of the SOI wafer was used to form the membrane of the CMUT (step d). After cleaning, the silicon substrate and SOI wafer were bonded by the low-temperature direct wafer bonding process at 350 °C (step e). Then, mechanical polishing and dry etching technology were used to reduce the thickness of the SOI wafer to 2 μm to form the membrane (step f). A SiO2 layer was then deposited on the silicon membrane by the plasma-enhanced tetraethyl orthosilicate process (step g). After that, Al was sputtered and etched on the SiO2 insulation layer to form the top electrode (step h). Finally, hydrofluoric acid was used to remove the SiO2 layer at the bottom of the silicon substrate, and Al was then sputtered onto it to fabricate the bottom electrode (steps i and j).To implement fluid bubble measurements, the basic performances of the used CMUT chips were first characterized, the structural schematic of which is shown in Fig. ode Fig. . To meet37. The acoustic pressure increased with increasing AC voltage when keeping the total voltage amplitude of the DC and AC voltages at 25 V. This is because the membrane vibration caused by the AC voltage includes the kinetic energy and potential energy of the membrane, but the DC voltage only provides the potential energy. A maximum transmitting sensitivity of 0.6 kPa/V was obtained under a 20 V DC bias and 5 V AC voltage, which was used as the excitation voltage in the following bubble testing experiments.Figure 41.Figure 42.The experimental setup used for fluid bubble measurement is shown in Fig. In summary, we successfully demonstrated a noninvasive ultrasonic fluid bubble measurement technique for small pipes using CMUTs. A pair of CMUTs was used and directly clamped on two opposite sides of a pipe for ultrasound wave transmitting and receiving. The acoustic energy attenuation and thus received voltage changes caused by air bubbles were harnessed for bubble existence and size evaluation. The theoretical expressions for the received voltage and air bubble sizes were established and well validated using FEM simulations. The relative variation in the received voltages of CMUTs was proposed as the sensing response for air bubble detection. The fluid bubbles inside pipes with a diameter as small as 8 mm were measured successfully. The relative voltage variation decreased with increasing air bubble diameter. A variety of possible factors affecting fluid bubble detection, such as bubble positions, flow velocities, fluid media, pipe diameters and pipe wall thicknesses, were experimentally investigated. The results showed that all other factors had negligible effects on the sensing performance because of the sensing response definition based on relative voltage variations. This study provides a real-time, continuous and robust noninvasive fluid bubble detection technology for small pipes, which is promising in clinical medicine, industrial controls and aerospace systems.


Improvements to patient care through the development of automated image analysis in pathology are restricted by the small image patch size that can be processed by convolutional neural networks (CNNs), when compared to the whole-slide image (WSI). Tile-by-tile processing across the entire WSI is slow and inefficient. While this may improve with future computing power, the technique remains vulnerable to noise from uninformative image areas.tumour. Subsequent uniform sampling across the enclosing region of interest (ROI) is used to mitigate sampling bias. Benchmarking tests informed the adoption of VGG19 as the main CNN architecture, with 79% classification accuracy. A further CNN was trained to separate false-positive normal epithelium from tumour epithelium, in a novel adaptation of a two-stage model used in brain imaging.We propose a novel attention-inspired algorithm that selects image patches from informative parts of the WSI, first using a sparse randomised grid pattern, then iteratively re-sampling at higher density in regions where a CNN classifies patches as These subsystems were combined in a processing pipeline to generate spatial distributions of classified patches from unseen WSIs. The ROI was predicted with a mean F1 (Dice) score of 86.6% over 100 evaluation WSIs. Several algorithms for evaluating tumour–stroma ratio (TSR) within the ROI were compared, giving a lowest root mean square (RMS) error of 11.3% relative to pathologists’ annotations, against 13.5% for an equivalent tile-by-tile pipeline. Our pipeline processed WSIs between 3.3x and 6.3x faster than tile-by-tile processing.We propose our attention-based sampling pipeline as a useful tool for pathology researchers, with the further potential for incorporating additional diagnostic calculations. The gigapixel resolution of whole-slide images (WSIs) is a source of valuable diagnostic information to pathologists. However, there is a worldwide shortage of pathologists,A key problem in applying AI to digital pathology lies in reconciling this capability with the comparatively vast scale of a WSI. Results obtained in this work for tile-by-tile classification across the entire WSI area showed this approach to be prohibitively slow, and noise from less informative regions led to false-positive ‘tumour’ classifications outside the ROI. This suggests that a viable AI solution must reduce the volume of information from the WSI that is irrelevant to making a contextual diagnosis, but nonetheless reliably detect diagnostically important features such as regions of tumour.We propose a solution based on attention, a cognitive process in which features of interest are selected from a complex, high-resolution input. In human vision, the small central fovea of the eye gathers information from a small, high-resolution sample of the scene. The brain directs multiple fixations and assembles these into an internal representation of the scene, requiring less bandwidth than processing the whole scene at full resolution.Attention-like processes have been used to reduce the rate of false positives (FP) in diagnostic imaging, in a manner akin to searching for objects in one’s peripheral vision, before directing high-resolution glimpses to examine the targets more closely. One trial,et alIn digital pathology, attention-like processes have been used to guide patch-scale AI algorithms towards informative areas of the WSI.Given an algorithm that can predict the ROI, further diagnostic metrics can be derived from the image patches within this region. In colorectal and other cancers, the proportion of cancer cells in relation to background supportive tissue (stroma)—the so-called tumour–stroma ratio (TSR)—is a significant predictor of survivalThe patch size used in WSI analysis influences classification accuracy. There is a trade-off between isolating only the ground-truth sampling point, and incorporating enough surrounding tissue to provide structural context.Our WSI-processing pipeline represents a novel extension of Cruz-Roa’s algorithm, predicting the ROI whilst enclosing a uniform sampling distribution required for accurate TSR calculation.Our WSI processing pipeline was trained and evaluated using data from the QUASAR study,Pathologists in the QUASAR study annotated each of the resulting 2211 WSIs with the tumour outline (ROI), and tissue classifications for approximately 50 locations in each WSI. The 50 classification locations were allocated within a 3 mm square box, 1 per WSI, placed for maximum density of tumour cells and factors such as proximity to the luminal aspect . Within the 3 mm box, the sampling locations were determined using RandomSpot software by Wright,A subset of 689 WSIs, with mean size 175.3 MB, satisfied QC criterianon-informative, tumour, stroma or fibrosis, necrosis, vessels, inflammation, lumen, mucin, and muscle. The non-informative class represents tissue that was unclassifiable as other types, whether this is due to blank background and a lack of surrounding context that would identify the gap as lumen or vessel, or due to a mix of cell types from which a dominant tissue type could not be inferred at that location.Image patches were extracted from the QC-passed WSIs at locations corresponding to the original RandomSpot sampling points, then saved to sub-directories corresponding to the pathologist’s classification. A patch size of 224×224 pixels was chosen for compatibility with established convolutional neural network (CNN) architectures. This corresponds to a square of side 0.11 mm at the 0.49 μm/pixel QUASAR image resolution. This was found to enclose sufficient structural context around the sample point to enhance classification accuracy, for example when distinguishing lumen from blood vessels or background in images where the central region of the patch is empty.,CNNs were trained to classify the QUASAR-derived patches into our 9 tissue classes. Architectures were shortlisted for their performance on general challenge image sets such as ImageNet,The randomly initialised CNNs were trained for 100 epochs to ensure full convergence. ImageNet-pre-trained versions were trained for 25 epochs. The CNNs were implemented in Python v3.7 with PyTorch v1.2.0 and CUDA v10.0.130. We used Stochastic Gradient Descent (SGD) with a learning rate of 0.001 and momentum of 0.9. Training, test, and evaluation patch sets were created by randomly grouping the 689 parent WSIs in the ratio 489:100:100. The training process was executed on NVIDIA V100 Graphics Processing Units (GPUs) on the Advanced Research Computing 4 (ARC4) system at the University of Leeds.The best-performing CNN architectures from these experiments were then selected for use in the image processing pipeline. Here, the resulting percentage of tumour-classified points within the original ground-truth ROI was logged as a further indicator of CNN performance.tumour epithelial cells. Initial experiments showed that patches of normal epithelium, from outside the ROI, were being classified as tumour. Sporadic patches of false-positive tumour had previously led to inaccurate predictions of the tumour ROI outline, with the attendant risk of misdiagnosis.In the QUASAR annotated data, all patches were sampled from within the annotated ROI. We believe this may increase the risk of bias in a classifier trained on this data alone, particularly when classifying patches from other regions of the WSI. QUASAR patches annotated as ‘tumour’ actually represent A two-way VGG16 CNN classifier was trained to distinguish between normal and tumour epithelium. The training data consisted of two balanced directories, each with 28,589 patch images labelled as tumour and normal epithelium respectively. These were identified by applying the original CNN classifier to patches sampled from throughout the WSI. Patches classified as ‘tumour’ were assumed to contain epithelial tissue. The expert-annotated ROIs allowed us to sort these patches into ‘tumour epithelium’ (inside ROI) and ‘normal epithelium’ (outside ROI), for use in training the two-way classifier.The complete pipeline is shown in Patches predicted to be of class ‘tumour’ were then passed to the second CNN, to further categorise these patches as ‘true positive’ tumour epithelium, or ‘false positive’ normal epithelial cells. This process was inspired by Hong’s two-stage FP-correctionA second iteration of sampling patch generation was then performed. Wherever a tumour patch was reported, a further 4 sampling patches would be placed randomly within the tumour patch’s parent grid box. Where the new patch overlapped the existing patch, a new random location would be tried until a clear space was found. Additional image patch data would be extracted from the WSI at the new locations. These new patches were also classified and added to the output class map.The FP-corrected map of tumour patches and their locations was then used to predict the tumour ROI. Density-Based Spatial Clustering of Applications with Noise (DBSCAN)A final stage of patch generation and classification was then performed, to obtain a uniformly higher sampling density across the entire ROI. The convex hull of the ROI polygon was evaluated, then new patches were added within this region, up to a limit of 5 patches per grid box within the ROI. New patches would be sampled from the WSI and classified as before, to provide an output of patch locations and classes at a uniform higher density within tumour regions. Non-tumour regions, outside the predicted ROI, would retain the lower original density of one patch per grid box.To avoid overfitting, the pipeline was assessed using the previously unseen 100 WSIs in the evaluation set, from the data split that provided the test and training patches for CNN training.To assess the comparative performance of our sampling pipeline, we developed an additional pipeline to perform patch classification and generate TSR and ROI estimates for WSIs divided into contiguous, tiled image patches. The pipeline was similar to that in tumour (T) and stroma or fibrosis (S), using the formula:Now possessing a collection of output patches with tissue class and position data, we explored several algorithms for calculating tumour–stroma ratio. For each WSI in the evaluation set, we estimated the TSR from the proportions of patches classified as 1)100 patches uniformly distributed within predicted ROI.2)120 patches within 3mm square box (6122x6122px at 0.49 μm/px), using RandomSpot-based triangular grid centred on point of highest tumour patch density, to simulate sampling approach used in QUASAR study.3)Mean of TSRs based on all sampled patches, within sliding 3 mm box, across predicted ROI.4)Ground-truth (GT) sampling locations based on original QUASAR data points.5)4-layer, 9-channel CNN trained to estimate TSR from patch class totals per parent grid box.The following sampling algorithms were used in the pipeline, to compare the effects on TSR of different patch sampling regimes:The GT sampling locations were included to assess the maximum theoretical accuracy of our TSR prediction, that could be achieved if it were possible to predict the exact sampling location that a pathologist would choose for TSR calculations. It is acknowledged that this approach would not be available for unseen WSIs.TSRGT was derived from the totals of RandomSpot samples that the original pathologist had classified as tumour (T) and stroma or fibrosis (S) for each WSI. The accuracy of the TSR predicted by our pipeline was calculated for each algorithm under test, using root mean square error (RMSE), calculated across the N WSIs in the evaluation set according to:The ground-truth The mean error (ME) across all evaluation WSIs was also recorded for each method of calculation. The ME is the mean difference between ground truth and estimated values:The pipeline was executed for grid boxes of 640, 768, and 1024px on each side, and with both one and two iterations of re-sampling before the final whole-ROI sampling step. ROI estimates were compared with the ground-truth ROI using F1 (Dice) score. TSR mean and RMS errors were logged for each of the sampling methods under test.The VGG19 gave the joint-best accuracy, and the highest percentage of predicted tumour points inside the ground-truth ROI. This model was therefore selected as the main classifier CNN1 in  for subsThe pre-trained VGG16 model, trained to distinguish false-positive normal epithelium from true-positive tumour epithelium, recorded an overall accuracy of 92.7% against the test set. Measuring before the FP-correction stage, 91.6% of predicted tumour was within the annotated ROI. This value increased to 97.1% when measured after false positive correction.42020.svs after each processing step, with the ground-truth ROI outline for reference. The first iteration shows the initial uniform sampling pattern (with background areas excluded). After resampling around predicted tumour locations, the denser sampling pattern begins to emerge in this region. The final iteration reveals a uniform, random distribution of sample patches across the estimated region of interest.The output polygons for the estimated ROI were compared with the pathologist’s original ROI annotations in the QUASAR dataset, by calculating the mean Dice Similarity Coefficient  for all WSIs in the evaluation set.A breakdown of the comparative performance of all TSR algorithms is shown in Of the CNN models tested, the ImageNet-pretrained VGG19 gave the joint-best classification accuracy (79%) alongside the non-pretrained GoogLeNet. Examination of the confusion matrices revealed that the VGG19 gave the highest rate of correct classifications of tumour patches, explaining this model’s higher proportion of predicted tumour inside the ground-truth ROI. However, the estimated inference time was relatively slow (65.9 ms/patch), and a faster model such as DenseNet (35.6 ms/patch) is therefore recommended for pipeline configurations involving larger patch counts.Use of the CNN trained to distinguish tumour epithelium from normal epithelium resulted in a 2.8-fold reduction in predicted tumour patches outside the ground-truth ROI, enabling a more accurate estimation of this region.The F1-scores in The DBSCAN clustering for ROI estimation excludes single tumour points outside the tumour region, by design. These points are rejected as noise caused by occasional classification errors. Tumour patches on an irregular ROI border would nonetheless be included in the ROI outline by DBSCAN, due to the proximity of similar neighbouring patches in the tumour body.Generally, increasing the number of sampled patches resulted in more accurate ROI prediction, at the expense of a longer processing time. The highest F1 score was seen in tile-by-tile sampling, while the attention-based pipeline in its various configurations was between 3.3 and 6.3 times faster per WSI.As expected, the lowest RMS error (6.4%) in estimated TSR was observed when sampling at the same ground-truth points as the original pathologist. This error is assumed to be due to CNN classification errors, but is lower than the mean classification error for a single image patch. We suggest that averaging across multiple patches reduces the effect of individual misclassifications in the CNN.For unseen WSIs, the lowest RMS error (11.3%) in estimated TSR was measured when using the smallest grid size, giving a higher sampling density. However, the benefit of increasing density was not as dramatic as that seen with the ROI F1 score, with the RMSE rising again to 13.5% in the extreme case of tile-by-tile processing.Sampling algorithms that attended to region of densest tumour yielded slightly more accurate TSR values than algorithms that averaged across the whole predicted ROI. In the original QUASAR dataset, the ground-truth classifications were usually evaluated around the maximum perceived tumour density. Emulating this approach gave an optimal TSR prediction as we anticipated.The TSR-predicting CNN was developed to avoid hard-coding the assumed behaviour of a pathologist, when choosing patch-sampling locations. It was intended that the CNN would learn the optimum spatial weightings to apply to the input patch distribution, when predicting an overall ratio for each WSI. Although we have not yet reached ‘CNN supremacy’ over other methods for calculating TSR, there is much scope for exploring more sophisticated CNN architectures in pursuit of this goal.Our pipeline has been optimised for mapping H&E stained WSIs. It has not been tested on immunohistochemistry (IHC) staining, which would require re-training on annotated IHC images to process the different staining colours. It is possible that for more general segmentation/characterisation, we would observe higher accuracy with IHC than H&E, because of the greater colour contrast between blue and brown image features, but further work is needed to explore this.The QUASAR dataset was chosen for its detailed level of annotation for ROI and tissue classifications, representing many hours of pathologists’ effort. The low rate of WSIs passing QC reflects a non-routine research workflow, where some older slides with inconsistent or faded staining were retrospectively scanned for the trial. In a clinical workflow, with more consistent staining and faster transfer from staining to scanning, we anticipate a higher rate of QC-passed WSIs.The WSIs were captured using Aperio XT scanners as the clinical trial was performed some time ago. While scanning was not replicated on newer instruments, the general principles would be the same and we expect that the improvements in image quality of newer WSIs could further improve the performance of our pipeline.The original ground-truth class annotations referred to single-pixel points in the WSIs. We extracted 224×224px patches around each point, to allow the CNN to analyse the structural context of, for example, blank background within hollow structures such as vessels. We acknowledge that the enclosing patch area may contain cell classes other than the ground-truth label. We hypothesise that our CNN embeds a spatial bias towards the centre of the image patch, mitigating the misleading effects of surrounding tissue types. Ongoing work is exploring this effect, together with the use of feedback attention to improve classification accuracy for mixed-class patches.We present our pipeline as a proof of concept, acknowledging that clinical adoption would depend on further development and validation, to meet FDA (US) or CE-mark (Europe) standards. This would include training with new data from a range of state-of-the-art scanners, and testing CNNs and pipeline against further unseen images. Subject to approval, our pipeline might have application in rapid triage of cancer patients according to their TSR and inferred risk of more aggressive disease.Overall, we believe that our WSI-processing pipeline represents a useful analytic tool, with the potential to automate labour-intensive assessments of histopathological images. We have showed that this can be achieved using an attention-based pipeline to deliver processing times 3.3 to 6.3x faster than tile-by-tile analysis, with minimal cost in ROI accuracy and improvements in TSR estimation.The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Andrew Broad reports financial support was provided by Roche Tissue Diagnostics. Andrew Broad reports financial support was provided by UK Research and Innovation.


Biomolecules participate in various physiological and pathological processes through intermolecular interactions generally driven by non-covalent forces. In the present review, the force-induced remnant magnetization spectroscopy (FIRMS) is described and illustrated as a novel method to measure non-covalent forces. During the FIRMS measurement, the molecular magnetic probes are magnetized to produce an overall magnetization signal. The dissociation under the interference of external force yields a decrease in the magnetic signal, which is recorded and collected by atomic magnetometer in a spectrum to study the biological interactions. Furthermore, the recent FIRMS development with various external mechanical forces and magnetic probes is summarized. The binding force generated by non-covalent interactions in organisms plays an important role in regulating and controlling the cellular and molecular behavior ,2,3, invTo date, the single-molecule force spectroscopy techniques have proven to be powerful tools for probing the non-covalent interaction of biomolecules; major examples are atomic force microscopy ,9,10, opF) generator. The principle is illustrated in Recently, the force-induced remnant magnetization spectroscopy (FIRMS) is developed as an available technique to address the issues above, where a large number of events can be detected at once, with the highest resolution up to 0.5 pN . The FIRThis review focuses on the advances in the biological application of FIRMS, harnessing various external forces and magnetic probes, with the intention of assisting in the selection of a suitable mechanical force and probe to detect non-covalent interactions.In this section, we present various external forces that may be applied in FIRMS, including shaking force, centrifugal force, ultrasound radiation force, and fluid shear force. The implementation of these external mechanical forces plays different roles, such as the parameter in molecular recognition, reference for determining the force that cannot be measured directly, and characterization of cell adhesion.The shaking force is obtained by a vortex mixer, which can coarsely distinguish the difference between physical adsorption and non-covalent specific binding. Yao et al. characterized these two types of interactions via human CD3+ T cells binding to magnetic probes by the CD3 antibody . The phymρ is the density of the magnetic probe, bρ is the density of the buffer solution, Vm is the volume of magnetic probe, r is the distance of the sample to the rotating axis, and ω is the angular speed, which can be calculated by the following equation involving the revolution speed n , ω = 2nπ/60.In comparison to the shaking force, the force created via a centrifuge can finely reach the needed values by adjusting revolution speed. For molecular systems, since the mass of magnetic probe is much heavier than molecule, the mass of the molecule can be neglected when calculating the centrifugal force, and the magnetic probe can be regarded as the sole carrier of centrifugal force. The centrifugal force applied on the magnetic probe is calculated as2+ and daunomycin . S. S34]. Sη is the viscosity of solution, r is the radius of the magnetic probes, and v is the fluid velocity. The fluid shear could have merit over SURFS in controlling the force more precisely. Feng and co-workers used a parallel-plate flow chamber to apply fluid shear force and analyzed the through-bond effects on the bivalent binding of the thrombin–aptamer complex (The fluid shear force generated through a parallel-plate flow chamber is tiny and can be integrated into the atomic magnetometer, realizing auto-injection detection. The fluid shear force can be calculated by the following equation: complex d 39]. T. TF = 6πIn addition to the external mechanical forces, another important factor is magnetic probes. In a bid to meet the requirements of the remanent magnetic signal and FIRMS spectrum precision, molecular and cellular system usually use two different types of magnetic probes, microbeads M280 and nanoparticles.® M-280 Streptavidin. They are superparamagnetic materials with a monodisperse diameter of 2.8 μm and size distribution CV < 3%. The monodispersed size ensures a uniform force during external force application. Additionally, the feature of M280 include a hydrophobic bead surface, low charge, and albumin from bovine serum as a blocking protein, which greatly reduce nonspecific adsorption. The surface with covalently coupled streptavidin allows the beads to rapidly bind to the desired target. The molecular interactions detected via magnetic probe M280 include antigen/antibody, DNA/RNA, and aptamer/protein.M280 with iron content is a commercial product. Take, for example, Invitrogen company’s DynabeadsSince MNPs possess significant magnetism, the biocompatible and monodisperse MNPs are optional FIRMS probes for the cell system. The thermal decomposition is one of the most commonly used methods to synthesize monodisperse MNPs due to the advantages of simple operation and chemical inertness. In this route, the non-magnetic organometallic precursor is heated in a container with organic solvents and surfactants . GeneralYao’s group labeled cells with MNPs in two ways. First, they added MNPs into the bacterial suspension and mixed to disperse them uniformly, helping the magnetic nanoprobes absorb on the surface of the bacteria . AlternaIn this review, we presented the main features and applications of FIRMS offered by various external mechanical forces  and magn


Although varicella-zoster virus (VZV) is known to affect the central nervous system in a protean manner, hemorrhagic VZV meningitis has not been well documented in the literature. Here, we correlate the clinical, cytologic, and radiologic findings in an immunocompromised patient presenting with subarachnoid hemorrhage associated with VZV meningitis. Clinical findings included multidermatomal zoster, myelitis, and neurapraxia. Magnetic resonance imaging findings included superficial siderosis and diffused linear and nodular leptomeningeal enhancement. Cerebrospinal fluid cytology revealed hemorrhage and lymphocytic pleocytosis. This case report adds hemorrhagic meningitis to the spectrum of complications associated with disseminated VZV infection. Varicella-zoster virus (VZV) is known to affect the central and peripheral nervous system in a protean manner ,2. In imA 53-year-old man presented to our emergency department with 10 days of painful right leg rash and intermittent fevers. Pertinent background history included dermatomyositis complicated by polyarthritis and interstitial lung disease, treated with tacrolimus and mycophenolate and 25 mg prednisolone. He had recently ceased intravenous cyclophosphamide treatment. He was a non-smoker, with no history of hypertension, excess alcohol consumption, diabetes, or bleeding diatheses, and was not on antiplatelet or anticoagulant therapy. Human immunodeficiency virus status was negative. There was no documented history of VZV vaccination. Examination revealed a unilateral vesicular rash with confluent lesions and crusting along the L4 and L5 dermatomes of the medial right leg  revealed focal hyperintensity and enhancement at the right dorsal horn of the lower thoracic cord Figure . DiffuseSubsequent lumbar puncture revealed turbid red cerebrospinal fluid (CSF) in all three samples. Cytology revealed an increased number of leukocytes, including mature lymphocytes and monocytes, on a bloodstained background. A proportion of the lymphocytes demonstrated atypical morphology, characterized by increased amounts of cytoplasm, enlarged nuclei with irregular nuclear membranes, and coarse chromatin Figure , Panel BThe CSF protein was elevated, and CSF glucose was at the upper limits of normal, with an elevated white cell count and markedly elevated red cell count Table . CSF PCRThe unifying diagnosis was of disseminated zoster with hemorrhagic meningitis, myelitis, and neurapraxia. Intravenous acyclovir was continued for a total duration of three weeks, and pregabalin was added for the treatment of neuropathic pain. The zoster lesions crusted over and resolved. The patient’s leg weakness improved after several months of rehabilitation to his baseline of symmetric weakness due to dermatomyositis. However, his post-herpetic neuralgia persisted at the one-year follow-up.VZV is a prevalent infection responsible for varicella (chickenpox) and herpes zoster (shingles)  and a spThe pathologic hallmarks of VZV include ganglionic hemorrhage, necrosis, and inflammation; demyelination can be seen with mononuclear infiltrates and microglial proliferation . DetectiMeningitis and other CNS complications arise in a retrograde manner from infected dorsal root ganglia . CSF cytVZV myelitis may occur via direct infection, vasculopathy, or as a post-infectious phenomenon, and may be fatal in immunocompromised patients . MRI feaSubarachnoid hemorrhage is a rarely described complication of VZV infection. A recent literature review identified ischemic stroke, intracerebral hemorrhage, and venous sinus thrombosis as the most common cerebrovascular complications following VZV infection . IntrapaThe pathogenesis of subarachnoid hemorrhage in our case could be due to a direct neurotropic effect as hemorrhagic necrosis is a hallmark of zoster . AlternaPrompt treatment with steroids and intravenous acyclovir remains the first-line therapy for CNS complications of VZV . The minOur case report adds hemorrhagic meningitis to the spectrum of complications of VZV reactivation, providing clinical, cytologic, and radiologic correlation. The disseminated pattern of disease in our case also included multidermatomal zoster, myelitis, neurapraxia, and post-herpetic neuralgia. In immunocompromised patients presenting with zoster, early empiric antiviral therapy and expedited investigation for disseminated infection are essential. Although gadolinium-enhanced MRI of the entire neuraxis is a useful tool to assess for disseminated disease, CSF analysis remains the gold standard for diagnosis of CNS involvement of VZV.


Kip1 regulates cell proliferation. Phosphorylation of tyrosine residue 88 (Y88) converts the inhibitor into an assembly factor and activator of CDKs, since Y88-phosphorylation restores activity to cyclin E,A/CDK2 and enables assembly of active cyclin D/CDK4,6. To investigate the physiological significance of p27 tyrosine phosphorylation, we have generated a knock-in mouse model where Y88 was replaced by phenylalanine (p27-Y88F). Young p27-Y88F mice developed a moderately reduced body weight, indicative for robust CDK inhibition by p27-Y88F. When transformed with v-ABL or BCR::ABL1p190, primary p27-Y88F cells are refractory to initial transformation as evidenced by a diminished outgrowth of progenitor B-cell colonies. This indicates that p27-Y88 phosphorylation contributes to v-ABL and BCR::ABL1p190 induced transformation. Surprisingly, p27-Y88F mice succumbed to premature v-ABL induced leukemia/lymphoma compared to p27 wild type animals. This was accompanied by a robust reduction of p27-Y88F levels in v-ABL transformed cells. Reduced p27-Y88F levels seem to be required for efficient cell proliferation and may subsequently support accelerated leukemia progression. The potent downregulation p27-Y88F levels in all leukemia-derived cells could uncover a novel mechanism in human oncogenesis, where reduced p27 levels are frequently observed.The cyclin-dependent kinase (CDK) inhibitor p27 Kip1 (hereafter p27) is a member of the Cip/Kip family of cyclin-dependent kinase (CDK) inhibitors. In its canonical function, it controls CDK activity and cell proliferation by binding to both subunits of a cyclin/CDK complex [p27 complex –6. CDK i complex –6.p27 is phosphorylated by tyrosine kinases including Abelson tyrosine kinase (ABL1), Janus kinase 2 (JAK2), FMS-like tyrosine kinase 3 (FLT3), Breast Tumor Kinase (BRK) and SRC family kinases . Tyrosin10-helix is ejected from the ATP-binding pocket of the catalytic cleft of CDK2 [SKP2 E3 ubiquitin ligase-dependent proteasomal degradation of p27 [p27 controls the G0/G1-phase to S-phase transition. In early G1 phase, p27 is required for the assembly of cyclin D/CDK4,6 holoenzymes –14. Alth of CDK2 . This co of CDK2 , 16, 17.n of p27 , 17. Actn of p27 . Elevaten of p27 . Studiesn of p27 .p190 fusion protein gives rise to B-cell acute lymphoblastic leukemia [SKP2 dependent proteasomal degradation [The oncogenic tyrosine kinases BCR::ABL1, JAK2-V617F and FLT3-ITD can cause hematological malignancies . All of leukemia , which rradation , 22.Although p27 tyrosine phosphorylation and its molecular and structural consequences have been intensively studied, the physiological relevance in vivo remains unexplored. We generated a mouse model where tyrosine-88 of p27 was mutated to phenylalanine (Y88F), resulting in a p27 protein lacking only the hydroxyl group of Y88. Young p27-Y88F animals were smaller than wild type (WT) littermates. Compared to WT, p27-Y88F protein levels were reduced in multiple tissues from knock-in mice. In v-ABL/BCR::ABL1 transformed cells, initial transformation was impaired in p27-Y88F cells, as revealed by colony outgrowth. This is consistent with the hypothesis that Y88F might protect from transformation induced by oncogenic tyrosine kinases. Surprisingly however, v-ABL induced leukemia/lymphoma progression was strongly accelerated. Immortalized leukemia cells from p27-Y88F mice revealed strongly reduced p27 levels compared to WT cells, suggesting that the robust CDK inhibition by p27-Y88F must be compensated by decreased p27 expression.Experiments were approved by the Austrian Federal Ministry of Science and Research (license GZ BMWFW-66.011/0033-WF/V/3b/2016). p27-Y88F knock-in mice were generated according to Besson et al.  and Malep190 pMSCV-IRES-GFP and v-ABL pMSCV-IRES-GFP plasmids as well as ecotropic replication-deficient A-MuLV produced by A010 cells used for injection of new born mice were a generous gift of Veronika Sexl . Colony formation was assessed in duplicates in semisolid methylcellulose . A detailed method description is provided in the Supplementary Methods.BCR::ABL1- [Tyrosine 88 of p27 was mutated to phenylalanine in pDONR207 WT p27 or in the cyclin/CDK-binding deficient variant p27-CK-  by Quickg for 20 min. Cells from cell culture were washed with ice cold PBS and suspended in 2x Lämmli buffer  and sonicated. Protein concentration was determined using the DC Protein Assay . The signals were detected using the Clarity detection reagent (Biorad), the ECL Prime or Select detection reagents  and the LAS4000 imaging apparatus (Cytiva). The mouse anti-p27Kip1-HRP conjugated antibody  was used for the detection of p27, except for HeLa Flp-In cell extracts where the rabbit anti-p27Kip1 antibody  was employed. A detailed list of antibodies and the description of flow cytometry analyses are provided in the Supplementary Material and Methods.Immunoblot analysis was performed as described . TissuesTotal RNA from cells or snap frozen grounded mouse tissues was extracted using the RNeasy Mini Kit . Reverse transcription was performed with the RevertAid Reverse transcriptase (Fisher scientific) following the manufacturer’s instructions. All qPCRs were performed in triplicates with the SensiFAST SYBR No-ROX Kit  on a Rotor Gene Q cycler (Qiagen). mRNA expression was normalized and expressed relative to the endogenous reference gene TATA box binding protein (TBP): fold induction = 2(−ΔCt), where ΔCt = Ct(p27)–Ct(TBP). Primer sequences and cycling conditions are listed in the Supplementary Material and Methods.t test was carried out when comparing two groups unless otherwise specified. Normal distribution was tested by the Kolmogorov–Smirnov Test and similarity of variances by the F-test. Survival was represented with a Kaplan–Meier curve and statistical significance was determined with the log-rank test. Data were analyzed by GraphPad Prism 8 software  or SPSS software  and are represented as mean ± standard deviation unless otherwise specified. Statistical significance  and sample size are indicated in each figure legend.Two-tailed unpaired Student’s Phosphorylation of tyrosine residue 88  in thymus and 17% (p = 0.012) in spleen  and by 33% in spleen (p = 0.023) , which assimilated after 6–8 weeks to the weight of their WT littermates Fig. . Organ wTo investigate if hematopoiesis is affected in p27-Y88F animals, we determined the hematopoietic populations. Macroscopic appearance and histological analyses of spleen and bone marrow did not reveal any significant differences . A comparable number of colonies grew out from WT und p27-Y88F bone marrow cells isolated from females and males  in HeLa Flp-In cells. Following induction, p27-Y88F levels were decreased and remained importantly reduced throughout doxycycline treatment compared to p27-Y88F-CK- levels at the respective time points . p21Cip1 was expressed in v-ABL+ cell lines and in thymocytes, but protein levels were not altered between WT and p27-Y88F (Fig. All human and mouse Cip/Kip proteins share an inhibitory 3orylated . One migThe mechanism uncovered here for the posttranscriptional downregulation of p27-Y88F levels in knock-in cells is independent of SKP2 deregulation and involves cyclin/CDK binding. This mechanism may promote tumor growth during oncogene transformation when p27 does not become phosphorylated on Y88 as part of the oncogenic process.As many human tumors are characterized by low p27 expression , and smaSupplemental Material