Periprosthetic joint infections are some of the leading causes of revision prosthetic surgery, accounting for 25% of failed total knee replacements and 15% of failed total hip replacements. The search for a biomarker that, together with clinical and radiological findings, could improve the management of such patients is currently a significant challenge for orthopaedic surgeons. Synovial fluid is a viscous and mucinous substance produced by the synovium, a specialized connective tissue that lines diarthrodial joints. Synovial fluid is an ultrafiltrate of plasma but also contains proteins secreted from the surrounding tissues, including the articular cartilage and synovium. Therefore, synovial fluid represents a source of disease-related proteins that could be used as potential biomarkers in several articular diseases. Based on these findings, the study of synovial fluid has been gaining increasing importance in recent years. This review aims to assess the accuracy and the limitations of the most promising synovial fluid biomarkers—i.e., Alpha-Defensin, Leukocyte Esterase, C-Reactive Protein, Interleukin-6, Calprotectin, Presepsin and Neopterin—in the diagnosis of PJI. Special attention will be given to emerging synovial biomarkers, which could soon be important in diagnosing PJIs. Periprosthetic joint infections (PJIs) are some of the leading causes of revision prosthetic surgery, accounting for 25% of failed total knee replacements (TKR) and 15% of failed total hip replacements (THR) .The search for a biomarker that, together with clinical and radiological findings, could improve the management of such patients is currently a significant challenge for orthopaedic surgeons.In this context, the study of synovial fluid biomarkers might play a central role in future research settings and daily clinical practice.Compared with plasma, synovial fluid (SF) has the advantage of being near the joint tissues, which are primarily altered during these articular diseases. It can be collected with arthrocentesis, i.e., a minimally invasive articular procedure . Thus, iThis review aims to assess the accuracy and the limitations of the most promising synovial fluid biomarkers—i.e., Alpha-defensin (Alpha-D), Leukocyte Esterase (LE), Interleukin-6 (IL-6), Calprotectin, Presepsin and Neopterin—in the diagnosis of PJI.Special attention will be given to emerging synovial biomarkers that could play an important role in the diagnosis of PJIs shortlySynovial fluid (SF) is a viscous and mucinous substance produced by the synovium, a specialized connective tissue that lines diarthrodial joints. Although the main function of SF is to lubricate joints, thus reducing friction between the articular surfaces, it also allows nutrients and catabolites to circulate between the avascular articular cartilage and the vascularized synovial membrane .SF is an ultrafiltrate of plasma, containing the same plasmatic levels of glucose and uric acid. The SF protein concentration, however, is about one-third of the plasmatic one . Plasma Therefore, SF might represent a potential source of disease-related proteins that could be used as biomarkers in several articular diseases. Consequently, in recent years, the study of SF has gained an increasing interest in orthopaedics and rheumatology to better investigate the pathogenesis and improve the management of several joint diseases, including osteoarthritis (OA), rheumatoid arthritis (RA) and other autoimmune non-rheumatoid-arthritides, osteochondrosis, articular infection and periprosthetic joint infection (PJI) ,10,11,12Hip and knee PJI are currently diagnosed according to the 2018 Philadelphia ICM criteria  13]. Tw. Tw13]. In absence of major criteria, minor criteria are considered to diagnose a PJI; in this case, a minimum score of 6 (out of 12) is needed to diagnose a PJI . Minor cIntraoperative findings include favourable histology (3 points), purulence (3 points) and single positive culture (2 points). The sum of preoperative and intraoperative scores was finally assessed: a combined score ≥ 6 defines a PJI, a score between 4 and 5 is considered inconclusive and a score ≤ 3 depicts the lack of infection.Synovial fluid biomarkers and blood cell composition acquired a relevant role, among minor criteria, in the diagnosis of hip and knee PJI according to the ICM 2018 of Philadelphia.The present paragraph will discuss the main features of the synovial fluid biomarkers recognized by ICM Philadelphia 2018 and emerging synovial fluid biomarkers. As recently stated by Ahmad et al. , no synoAlpha-defensin (AD) is an antimicrobial protein released by neutrophils in response to pathogens . AD thenAccording to ICM Philadelphia 2018, an elevated synovial fluid AD, defined as signal-to-cut-off ratio >1, is a minor criterium to define PJI diagnosis; hence, without major criteria, AD should be routinely assessed to confirm or rule out a PJI .However, it is important to remark that AD can be detected both by an Enzyme-Linked Immunosorbent Assay (ELISA) laboratory test or by an AD test kit . Ahmad eKuo et al. , in a reLeukocyte Esterase (LE) is an esterase expressed in white blood cells; thus, its presence in a sample indicates leucocyte expression. An elevated synovial fluid LE is a minor criterium used to define PJIs, according to 2018 ICM Philadelphia; it is a diagnostic tool with a 2+ cut-off, and a rapid and inexpensive test [Ahmad et al. , in a reInterestingly, however, Logoluso et al. , in a reFurthermore, Chisari et al. , in a reC-Reactive Protein (CRP), i.e., a pentameric protein member of the pentraxin family of proteins, is an acute-phase protein synthesized by the liver.Although the diagnostic utility of synovial fluid CRP concentration has been debated in recent years, according to ICM Philadelphia 2018, a synovial CRP concentration > 6.9 mg/L is a minor criterion for PJI diagnosis.Baker et al. , in a reIn this session emerging synovial fluid biomarkers, not included in the 2018 ICM Philadelphia criteria will be discussed . The folIL-6 is a pro-inflammatory cytokine that induces the expression of a variety of proteins responsible for acute inflammation .Yu et al. , in a prNonetheless, Quin et al. , in a prCalprotectin is cytoplasmatic calcium- and zinc-binding protein expressed mainly in neutrophils; it is released in the extracellular environment following neutrophil activation and exhibits anti-microbial activity .Faecal CPT has been used for years in the diagnosis of inflammatory bowel disease (IBD) , but in It is a cheap and easy-to-use test compared to other biomarkers.Wouthuyzen-Bakker et al. , in a piTherefore, these authors conclude that synovial CPT may be a valuable biomarker and useful, especially in excluding a PJI .Salari et al. , in a prThe same research group , in a moMoreover, Lazic et al.  recentlyFinally, Hantouly et al. , in a rePresepsin, i.e., the N-terminal fragment of the soluble cluster of differentiation 14-SubType (sCD14-ST), is released into circulation after the activation of defence mechanisms, mainly bacterial phagocytosis ,30,31.This biomarker, originally studied and validated in the diagnosis and prognosis stratification of sepsis , has recImagama et al.  recentlyThese authors observed that synovial fluid, blood presepsin and blood PCT were significantly higher in the SA group than in the OA group. Synovial fluid presepsin exhibited both 100% sensitivity and 100% specificity in the SA group, which were at higher rates than those for blood presepsin and PCT. Thus, Imaga et al. concluded that synovial fluid presepsin could be a new biomarker of septic arthritis .Nonetheless, recent studies ,35 have Neopterin (NPT), a member of the pteridine family, is a catabolic product of guanosine triphosphate via guanosine triphosphate cyclohydrolase and is released from macrophages following T-cell-dependent interactions involving interferon-γ (IFN-γ). Therefore, it indicates a pro-inflammatory status and serves as a marker of cellular immune system activation ,36.Busch et al. , in a prBased on these findings, synovial fluid NPT was found to be a reliable diagnostic marker for the detection of PJI, while synovial fluid TNF-α and PS were too inconsistent to exclude or diagnose PJI.The diagnosis of PJI is currently based on ICM Philadelphia 2018 guidelines. Nonetheless, in daily clinical practice, decision-making in patients with a suspected PJI could be challenging . Hence, In 2021, the European Bone and Joint Infection Society (EBJIS) published a novel three-level approach to the diagnosis PJIs, which was revealed to be useful in reducing the number of uncertain diagnoses, allowing easier clinical decision-making ,40. AlthThe ICM 2018 and EBJIS criteria retain a central role in the diagnosis of PJIs in daily clinical practice; however, they might be further improved by a more detailed synovial fluid analysis .Some synovial biomarkers—i.e., AP, LE and CRP—are included in the ICM criteria, and therefore should be assessed when major criteria for PJI diagnosis are not present. More recent papers have confirmed P and LE accuracy while contrasting data have been recently reported regarding CRP.Interestingly, a recent meta-analysis by Ahmad et al.  demonstrLE synovial strip test recently proved to be a reliable tool in diagnosing the persistence of PJI since it outperformed the serum CRP and ESR assays . TherefoConcerning synovial IL-6, recent papers ,21,22 haSome emerging synovial fluid biomarkers have been studied in recent years to better identify and treat PJIs. Among these biomarkers, calprotectin and neopterin seem to be the two most useful tools that could improve PJI diagnosis ,27,28,29In a recent meta-analysis, calprotectin has shown a high sensitivity and specificity in the diagnosis of PJI . MoreoveNeopterin is another emerging biomarker that has shown promising results in the diagnosis of PJIs; however, further studies with a bigger sample size are needed to better define the role of this biomarker in daily clinical practice.All the findings about emerging synovial fluid biomarkers described in the present paper are still limited to the research field and further studies and consensus are needed to apply these data to daily clinical practice. Remarkably, the routine use of SF analysis is limited by costs and lab-scientific competencies; thus, the development and validation of rapid test kits are essential to bringing the most promising innovations produced by preclinical and clinical research to the bedside.We strongly recommend the use of the ICM 2018 and EBJIS criteria in daily clinical practice and, in the research field, the study of validated and emerging synovial fluid biomarkers to improve accuracy in the diagnosis of PJIs in daily clinical practice.The routine use of the discussed synovial fluid biomarkers might improve the diagnosis of PJIs shortly. In the present review, we summarized the state-of-the-art synovial fluid biomarkers, highlighting the limits and potentials of their uses both in research and in daily clinical practice.Further studies and consensus statements will define the roles of these emerging biomarkers in the diagnostic work of PJIs.


We then used the model to compare different intratumoural implantation protocols and gemcitabine-release rates. In our model, the primary driver of pancreatic tumour growth was the rate of tumour cell division. We were able to demonstrate that intratumoural placement of gemcitabine loaded fibres was more effective than peritumoural placement. Additionally, we quantified the efficacy of different release profiles from the implanted fibres that have not yet been tested experimentally. Altogether, the model developed here is a tool that can be used to investigate other drug delivery devices to improve the arsenal of treatments available for PDAC and other difficult-to-treat cancers in the future.The prognosis for pancreatic ductal adenocarcinoma (PDAC) patients has not significantly improved in the past 3 decades, highlighting the need for more effective treatment approaches. Poor patient outcomes and lack of response to therapy can be attributed, in part, to a lack of uptake of perfusion of systemically administered chemotherapeutic drugs into the tumour. Wet-spun alginate fibres loaded with the chemotherapeutic agent gemcitabine have been developed as a potential tool for overcoming the barriers in delivery of systemically administrated drugs to the PDAC tumour microenvironment by delivering high concentrations of drug to the tumour directly over an extended period. While exciting, the practicality, safety, and effectiveness of these devices in a clinical setting requires further investigation. Furthermore, an in-depth assessment of the drug-release rate from these devices needs to be undertaken to determine whether an optimal release profile exists. Using a hybrid computational model , we developed a simulation of pancreatic tumour growth and response to treatment with gemcitabine loaded alginate fibres. The model was calibrated using  in silico. We modelled the individual cancer cells and their growth and death from gemcitabine loaded into the sustained delivery devices. Our platform allows future investigations for these devices to be run in silico so that we may better understand the forms of the drug release-profile that are necessary for optimal treatment.Pancreatic cancer has a dismal prognosis with a median survival of 3–5 months for untreated disease. The treatment of pancreatic cancer is challenging due to the dense nature of pancreatic tumours which impedes retention of drug at the tumour site. As such, systemic administration of chemotherapies, such as gemcitabine, has a limited efficacy. To overcome this, sustained-release devices have been proposed. These devices are injected locally and release drug over time, providing a concentrated local, sustained drug concentration. To investigate the possible efficacy of these devices, we developed a mathematical model that would allow us to probe treatment perturbations  Inoperable pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis, with a median survival of 3−5 months for untreated disease . TreatmeFig 1). For example, drug-eluting polymeric implants are designed to deliver high concentrations of chemotherapeutic drugs directly at the tumour site over some period of time, overcoming transport and tissue barriers as well as limiting off-target toxicities [in vivo half-life [The majority of chemotherapeutics, gemcitabine included, are administered systemically via bolus or infusion intravenous administration. This often results in significant systemic toxicity, with only a fraction of the injected dose reaching the tumour. As such, there has been a growing interest in the development of localised targeted delivery systems which can modify the bio-distribution of drugs and achieve local drug accumulation in the tumour tissue –12   to modelAll animal experiments were conducted in accordance with the NHMRC Australian Code for the Care and Use of Animals for Scientific Purposes, which requires 3R compliance  at all stages of animal care and use, and the approval of the Animal Ethics Committee of the University of Wollongong  under protocol AE18/13.et al. [in silico. Fibre diameter also varied depending on the materials used [Full details for the fabrication and characterisation of alginate fibres loaded with or without gemcitabine are described in Wade et al. ,14. Brieet al. [Full details for the experiments measuring gemcitabine release can be found in Wade et al.  with briet al. [Gemcitabine loaded fibres were tested for their cytotoxicity against human pancreatic cancer cells (Mia-PaCa-2) cells over 72h. Cells were incubated with 0.5 cm lengths of gemcitabine loaded or non-drug loaded fibre formulation before an endpoint MTS cell viability assay was performed. Results are displayed as a percentage of an untreated control. Experiments were performed in triplicate. Full details for the toxicity experiments can be found in Wade et al. .6 Mia-PaCa-2 cells in PBS. Tumour volume measurements began when tumours reached a volume of 200 mm3 usingFig H in S1 Text). Tumour volume was measured daily for a duration of approximately 33 days. Full details for this experiment can be found in Wade et al. [Animals were subcutaneously inoculated with 100μL suspension of 1×10e et al. . All aniin vivo tumour growth measurements were taken as only width and height measurements and we assumed any third-dimension effects were analogous to what happens in 2 dimensions. All parameters introduced for the model are summarised in Tables A-E in S1 Text and a schematic for the model is in Fig 2.The model developed for the release of gemcitabine from alginate fibres and the impact on a growing PDAC tumour is formulated in two parts below. The first describes the PDE describing the concentration of gemcitabine in the tumour microenvironment (TME) and surrounding tissue over time. The second describes the VCBM  that capB . Inside this domain, is implanted a gemcitabine drug-loaded fibre which is represented by a vertical line source . Gemcitabine diffuses from the line source at some time-dependent rate that decreases as the polymeric fibre degradation slows. The gemcitabine concentration in the domain outside the fibre is diffusing and decaying. PDAC cells in the domain are also taking up gemcitabine, removing it from the concentration in the domain. Inside the fibre, we model the diffusion of drug as radially symmetric .To capture the concentration of gemcitabine in the tumour microenvironment, we first considered a 2D rectangular domain with boundary x,y) by C and model this concentration byD is the diffusion coefficient in the TME, and λ is the decay rate of the drug. To model cancer cells taking up gemcitabine, we used δ(x) which is the Dirac delta function in one-dimension, where  is the ith cancer cell’s Voronoi centre position in the domain , and Wi is the cell’s volume. Pancreatic cancer cells take up drug in the domain at a rate νc. Cell uptake was modelled by point sinks analogous to that in PhysiCell and BioFVM [x = xF, y0≤y≤y0+L to model the release of gemcitabine from the polymeric fibre, where y0 is the location of the bottom of the fibre and L is the fibre length . This line source was represented by a Dirac delta function in one-dimension and the drug diffuses from the line source with flux J.We denote the concentration of drug in the TME at position  by F . We model the diffusion and movement of drug inside the fibre assuming radial symmetry. We assumed that diffusion in the radial direction is significantly faster than along the fibre since the radius of the fibre rtotal is significantly less than the length of the fibre L . This givesDF(t) is the time-dependent diffusion of drug inside the fibre. We imposed the continuity conditionxF, y) and local exterior concentration. The flux out of the line source J in Eq  can then be approximated from the release of drug across the boundary of the fibre:To derive the flux of drug from the line source, we first assumed that the release of drug from the fibre would be time dependent. As such, we chose to explicitly model a concentration of drug diffusing inside the fibre. We denote the concentration of gemcitabine at radial position Eq  and converting to a concentration per surface area where h is the depth of the rectangular region . Both Eq  and Eq  are necessary boundary conditions for Eq  and Eq . In this way, we assume the concentration is continuous and the flux of the fibre is equal to the flux into the TME, equivalent to a conservation of mass.This term is derived by converting the flux out of the radial fibre into the flux represented by the line source in DF(t), is modeled by the functionk controls the gradient of the drug release rate(i.e. how quickly the fibre swells), Dconst is the constant decay rate from the fibre and ϵ is a tuning constant to provide a finite initial diffusion coefficient, i.e. DF(0) = k/ϵ+Dconst. We expect DF(0) to be initially large (>1) since the polymeric fibre is hydrophilic and drug would immediately diffuse out of the fibre. In addition, some drug is never properly loaded into the fibre and can be released instantaneously. The formalism in Eq  was broadly chosen to capture the rapid decline in release as the polymeric fibre degrades. It is possible to model the breakdown of the drug release mechanisms to include device swelling and degradation and for examples of this see [The diffusivity of the drug, this see ,59–61.B, the exterior of the TME, are imposed:B . In the case of a fibre implantation, all drug in the domain is initially situated in the fibre:C0 is the amount of drug in μg, the denominator is the volume of the fibre and there is no drug initially in the domain B. We assume the location of the fibre is fixed in space over the course of the simulation and is not affected by cells around it. For more details on the derivation of the model see No-flux boundary conditions on Eqs –4)4) numerically using a Finite Volume approximation. In particular, the diffusion of drug within the fibre, Eq , was solved through discretising the cross section of a fibre into annuli . The model is solved using a finite volume method (FVM) discretisation, for examples of this form of discretisation in cancer growth and treatment see [Eq  by the corresponding profile being examined and imposed drug conservation on the total drug released. More details on this can be found in .We solved ment see –70. As px,y) points in the domain B as representatives of cell centres and then overlay this with a Voronoi tessellation to define individual cell boundaries. A Voronoi tessellation defines the region of space where the Euclidean distance to a point is less than the distance to any other point e in the lattice. Voronoi tessellations have been used to model tissue and cancer cell dynamics for some time [Fig C in ). To model pancreatic tumour formation, we assumed the primary functions of pancreatic tumour cells were movement and proliferation. Below are details of the cell types, the model for cell movement and proliferation, a description of the dynamics of tumour mesenchymal cells, the model for cell death and details of how the domain changes as the tumour grows.Agent-based models (ABMs) are primarily used to simulate heterogeneity that arises through stochasticity in cellular interactions. We present an ABM to capture the 2D formation of a pancreatic tumour in the pancreas. Our model extends a Voronoi cell-based model (VCBM) for tumour growth already published in . The modome time –75. UsinPDAC cells can acquire mesenchymal-like phenotype properties through a process known as epithelial-mesenchymal transition (EMT) –79. In tFig 2C. Each cell has an initial location in domain B such that for cell i its cell centre would be at Fig A in S1 Text). These pancreatic tumour cells could proliferate, die from gemcitabine, or form MCCs. Once formed, these MCCs then move and proliferate until they die. Healthy cells are assumed to be able to move or be removed from the domain by being replaced by MCCs .We considered four main cell types in the model: healthy pancreatic cells, PDAC cells, MCCs and dead cells (cancer cells that have experienced drug-induced death); see terature ,81. To ix,y) position is updated by calculating the effective displacement of the cell’s lattice point by the sum of the forces exerted on that cell, where force is modelled as a network of damped springs connecting a cell to its nearest neighbours (defined by a Delaunay triangulation). To determine the movement of an individual cell, we use the vector representation for that cell’s  position from the origin i, the displacement of this point in time Δtcells is given byith cell’s centre position in the lattice at time t, λm is a damping and mobility constant, i and j, si,j is the spring rest length (equilibrium distance) between cell i and j. Cells j are the cells connected to cell i in a Delaunay triangulation, i.e. in a neighbourhood of cell i. The introduction of new cells in the lattice through proliferation introduces new spring connections and shortens or extends others, promoting the movement of cells in the environment .Cell movement is governed by pressure-driven motility, modelled using Hooke’s law . Each cedneut, to the nutrient source . The maximum radial distance for nutrient-dependent cell proliferation is dmax. Cells that are a further distance from the nutrients than dmax enter a quiescent (non-proliferative state), forming what is commonly known as a necrotic core. The probability of a cell dividing pd in time step Δtcells is given byp0 is a proliferation constant derived based on the maximum rate of cell proliferation ϕ . To simulate the enlargement and repositioning of the daughter cells, the resting spring length of the connection between i and j linearly increases over time from s/page to the mature resting spring length s as was formulated in our previous work [Fig C in S1 Text. Once a cell has proliferated, it takes gage time steps before the daughter cell will try to proliferate again, accounting for G1 phase of the cell cycle where the cell transitions from mitosis M to DNA synthesis S [Mechanical feedback between cells has been shown to be a regulatory mechanism for growth control of tissue cells  in vivo . A cance in vivo ,86. To mous work ; see Figthesis S . It is wpMCC. These cells are created from tumour cell differentiation. In this way, a single tumour cell can either proliferate into two new tumour cells or undergo differentiation into a tumour cell and an MCC. We model the invasive property of MCCs by placing the newly differentiated MCC daughter cell at the position of a neighbouring healthy cell, removing that healthy cell from the domain; see Fig B in S1 Text. As such, through their creation, these MCCs contribute to the degradation of the healthy tissue surrounding the tumour.MCCs are created at the boundary of the tumour with probability δm is the maximum death rate due to the drug, Ci,j is the concentration of drug at the grid position  in the FVM discretisation closest to the cell’s centre , and IC50 is the concentration at which half the effect of the drug is attained. From this, the probability of an individual cell dying can be determined by assuming Prob(cell death) = dage hours to disintegrate. To simulate disintegration, at each time increment the spring rest lengths of a dead cell to each of its neighbours, si,j, decreases by si,j/dage. Once the dead cell has disintegrated, it is removed from the simulation and the space left behind becomes “empty space” which is plotted in white. Cells are free to move into this space over time following Hooke’s law.As in –90, we a develop ,4, we beFig D in S1 Text). A summary of the overall model rules is provided in a decision tree diagram in Fig E in S1 Text.As the tumour grows, the model domain expands. To reduce computational cost, new healthy cells are added to the domain only when a tumour cell’s radial distance from empty space is <10 model units  can be found on github (https://github.com/AdrianneJennerQUT/hybrid-VCBM-of-gemcitabine-and-pancreatic-cancer). Full details on all aspects of the code can be found in .The VCBM-PDE model was written in C++ and simulations called through Matlab 2021b by creating a definition file for the C++ library using σ:width is the longest distance of a cell on the periphery from the centre and length is the distance of the farthest cell from the centre on the radial axis perpendicular to the radial axis of the longest distance  where σ unit length of the model is equivalent to 1 mm. This calculation choice was made to closely resemble the tumour volume calculation with calipers done in vivo. As the size of the computational domain was smaller than the size of the real tumour, the length unit was scaled by σ, which scaled the unit length in the VCBM domain to a comparable mm unit measurement that reduced the computational cost. We chose σ = 0.1728.An approximation for tumour volume was then determined from the 2D simulations using the same formula as the calibre measurements, multiplied by a scalar Fig F in S1 Text). We found that from 500 simulations onward, the average tumour volume at day 33 had converged to some mean value. As such, we picked n = 500 to be the number of simulations of the model we would run at each investigation.We performed a convergence test for the VCBM to determine a threshold for the minimum number of simulations of the model to have average convergence at day 33 .To fit the parameters in the model, we first consider only the drug release and diffusion compartment of the model and using lsqnonlin in Matlab 2021b using pdepe and ode45 to simulate the model. Parameters in the model were fit using experimental data or estimated from the literature. To fit the parameters relating to drug release from the fibre we used the in vitro drug release experiments. We simplified the model to consider only one cross section, i.e. Fm,j = Fm, since the outside concentration of drug was independent of location in the absence of cells in the in vitro experiment.All fitting was undertaken using in vivo control tumour growth measurements. To do this, we set an initial seed and obtained 1000 samples of the parameter space and simulated the model 30 times for each sample each time calculating the residual between the data and the simulation. The parameter sample with the lowest average was then fixed for the remainder of the model simulations. It should be noted that this method does not provide us with a fitted parameter value but was a numerical way of obtaining a reliable estimate for these parameters. Other parameters were either fixed to previous values in the literature or estimated based on previous work. See Tables A-E in S1 Text for a full summary of all parameter values and relevant references.To estimate parameters for the pancreatic cell growth kinetics, we did a large Latin Hypercube sample of the parameter space and determined parameters that resulted in a minimal least squares distance to the Fig 3A). To obtain a model describing the release rate of the drug from the fibre, we fitted parameters from Eqs –4)4) to these in vitro measurements for the release of gemcitabine from 3% alginate 15% PCL fibres [k, dconst, C0 and Aout gave the fit in Fig 3B and parameter values in Table A in S1 Text. Overall, the model was able to obtain the fit to the data and followed the trend which showed a rapid initial release of gemcitabine followed by a steady-state threshold. We validated the model’s predictive capability by also fitting gemcitabine release from 1% and 2% alginate fibres .Gemcitabine-loaded fibres were placed in a solution bath and the resulting cumulative concentration of gemcitabine measured (L fibres . FittingPL(t), dead cancer cells PD(t) and a concentration of drug C(t):ϕ is the exponential proliferation rate of cancer cells in vitro, δm is the death rate of cancer cells by gemcitabine, IC50 is the drug’s half effect concentration, and λ is the decay rate of the drug . To first determine the proliferation rate of pancreatic cancer cells in vitro, an exponential growth curve was fit to cell count measurements for Mia-PaCa-2 cells [Fig 3D, parameter values Table B in S1 Text) using simple exponential growth (i.e. setting C(0) = 0 in Eq ). Fixing this growth rate and the estimate for the decay rate of drug, we then determined the antitumour efficacy of gemcitabine-loaded fibres in the cell viability experiments. Cells were treated with aliquots of simulated body fluid from gemcitabine-loaded fibres that had been incubating for 24, 48 or 72 h . To simulate these experiments, the model is solved piecewise such that μ(t) = δC, where taliquot are the times of the drug administrations. An approximation for the concentration of drug at each time point, C, can be determined using the calibrated PDE model for drug release from the fibres. Fitting the drug-induced death rate and IC50 gave a good approximation to the data . The resulting parameter values from the fit of the model can be found in Table B in S1 Text.To assess the efficacy of the drug on inducing death in PDAC cells, cell viability studies were performed using Mia-PaCa-2 cell lines. To model these experiments, we considered a simplified deterministic and spatially independent version of our model with only live cancer cells -2 cells  . To calibrate tumour growth parameters in the model, we used an exhaustive numerical search of the parameter space using a Latin Hypercube Sampling for gage, dmax, p0 and pMCC, where we were minimising the least squares of the simulation with the in vivo tumour volume of Mia-PaCa-2 cells over 33 days . To obtain an understanding of the stochasticity in our model, we fixed the parameter values obtained and we simulated the model 500 times and plotted the tumour volume over 33 days. From Fig 4B, while the growth is varied at points, there are no distinct outliers or unusual tumour growth rates, and the standard deviation throughout the entire period of observation remains small. In addition, the simulations sit within the in vivo tumour growth measurements from Wade et al. [Fig 4C) shows only a small number of MCCs are created over the 33 days of growth, which is realistic when considering the ratio between a single cell agent in the model and a real cell in a biological tumour and matches findings that MCCs will compose only a small subset of the tumour [The VCBM simulation of pancreatic tumour growth in the absence of treatment depicts invasive and disorganised movement of cancer cells into surrounding healthy tissue . This sensitivity analysis can identify combinatorial influences of multiple parameters and elucidate systemic features of the model. The average tumour volume predicted by the model at day 33 for 500 simulations was recorded for each parameter set. Pairs of parameters were varied, with each cell of Fig 4D depicting the weighting applied to each parameter in p from 0.25, 0.75, 1.25, 1.75, and 2.25. This allowed for all combinations of alterations for two parameter values to be tested.To analyse the drivers of pancreatic tumour growth dynamics in our model, we conducted a detailed sensitivity analysis. A systematic multi-parameter sensitivity analysis was performed for s et al.  gage, has the greatest impact on final tumour volume . Increasing gage decreases tumour volume and conversely a decrease in gage increases the final volume. As a result, the model predicts that if cells take longer to move through the cell cycle and undergo mitosis this will result in a smaller tumour volume. Reducing the maximum distance, dmax, from the periphery at which a cell can still proliferate decreases the final tumour volume. This is to be expected, as reducing the proliferating cell rim (through decreasing the distance from the periphery for which cells can proliferate) will reduce the number of cells available to proliferate and subsequently reduce the tumour volume. Decreasing the value of dmax only appears to have a significant impact on the final tumour volume when the weighting applied is ≤50%. In comparison with dmax and gage, the tumour volume is insensitive to changes in both the probability of a cell proliferating if it has reached mitosis, p0, and the probability of a new pancreatic cancer stem cell being created, pMCC. The time taken for a cell to reach adult size (when it can proliferate), gage, similarly has a negligible impact on the tumour volume.The time taken for a cell to prepare for mitosis, et al [Fig 1) of gemcitabine on the tumour volume. Simulating single point free-drug injections with the VCBM-PDE is a simplification of the full model presented in Eqs –4)4) where F = 0. More details on this can be found in . We considered free-drug injections of gemcitabine as administered along a radial axis of the tumour in either a single dose or four free-drug injections which are rotationally symmetric . In the case of the four injections, the total dosage is spread across the injections so that the total amount of drug administered is conserved.As previously shown by Wade et al –16, pancFigs 5B and J in S1 Text. The sensitivity of parameter values governing tumour volume were again probed, now under a single administration of gemcitabine at the centre of the tumour . The same trends with gage and dmax were observed; however, an additional parameter, which represents the concentration the drug required to have an impact on the tumour volume, IC50, was found to influence the volume under further perturbations of the parameter value . As expected, a lower value of IC50, which indicates that a smaller concentration of the drug is required for it to influence cancerous cells, leads to a lower tumour volume, while an increased value of IC50 leads to a higher tumour volume when compared to original estimate for IC50.Simulations of the model under the different injection protocols can be found in C0, and the second was four injections into the tumour, each of concentration C0/4 placed at equal rotations around the tumour. For each of these protocols we considered five potential placements of each injection at a distance dm from the centre: a central injection (dm = 0), and injections dm = 0.9 mm from the centre, dm = 1.7 mm from the centre, dm = 2.5 mm from the centre and dm = 3.5 mm from the centre . For example, for the second protocol where four injections are being placed, we could choose a distance dm = 2.5mm from the centre of the tumour for the placement of the injections and the four injections were then placed at equal rotations around the tumour centre. For each of these placements, 500 simulations were run over 33 days and both the number of tumour cells and the tumour volume over time were measured . For a single injection, distance did not impact the effectiveness of the injection and the tumour volume is qualitatively similar. The tumour volume was more significantly affected by distance in the case of four injections , with free-drug injections further away from the centre of the tumour performing worse than those intratumoural injections. Primarily, single free-drug injections implanted peritumourally may encourage branching of external tumour structures in the model, and hence increase the calculated volume as it is based on the maximum distance from the centre of the tumour to the edge. Tumours with invasive edges or branching formations have been shown in some cases to be more challenging to treat in the long term [in vivo models (i.e. mm3 and mm respectively), more work needs to be done to validate that the efficacy of treatment predicted by the model would map to the human scale.To determine the effect of injection placement on tumour volume over time, we considered two protocols. The first was a single injection into the tumour of concentration ong term ,96. FromFig 6A). We introduced three classifications for the tumour growth dynamics: tumour eradication (i.e. a tumour volume <1mm3) tumour stabilisation, i.e. a tumour volume at day 33 less than the initial tumour size (≈ 100mm3), and tumour growth, i.e. a tumour volume on day 33 greater than the initial tumour volume. Large concentrations of gemcitabine loaded into the fibre positioned at dm = 3.5 mm or dm = 4.3 mm from the tumour centre were unable to stabilise or eradicate the tumour, also known as tumour arrest . Once the fibre was positioned closer to the tumour centre (≤1.7 mm) lower concentrations of drug were sufficient to result in stabilisation of the tumour growth . It was only with high drug concentration and centered fibres that we saw complete tumour eradication . There are large variations in the response of tumour growth to the different protocols, suggesting that tumour stabilisation or arrest might be achievable for some tumours whereas others might experience tumour growth even in the presence of drug-loaded fibre.Using the VCBM-PDE, we analysed the impact of varying the position of the fibre and the initial drug concentration on the tumour growth dynamics (Section TS3 in ). Each of these release profiles were parameterised by a release rate γ and for the Emax and Imax curves a half-effect term η. The different release profiles were tested with the fibre placed either centrally   or on the periphery of the tumour  . The four different release profiles  were tested with 8 different release rates. For each parameter value, 500 simulations were run over 33 days, with an initial amount of 500 μg of gemcitabine.To then analyse the effects of changes to the drug release profile on the tumour growth, we investigated four different release profiles: constant release, exponential release, sigmoidal Emax/Imax release profiles –99 , it is possible to eradicate the tumour with all release profiles considered given a small enough value of γ. In comparison, none of the drug release profiles resulted in tumour eradication when positioned peripherally . Although, of note, the exponential release profile performed best out of the four release cases in both the centered and peripheral fibre configurations. Overall, the results suggest that it is possible to reduce tumour size with peripheral fibre injections irrespective of the shape of the release curve.For fibres positioned in the centre . Polymeric fibres loaded with gemcitabine have shown increased therapeutic efficacy over conventional treatment delivery. To further analyse the potential of these novel therapeutic implants, we have designed a hybrid Voronoi cell-based model (VCBM)-partial differential equation (PDE) model to describe pancreatic tumour formation in healthy pancreatic tissue and the resulting effect of gemcitabine on the tumour tissue when delivered locally. With this model, we considered both the impact of a single fibre implanted with varying drug release profiles and hypothesised alternative and more effective treatment protocols.PDAC is a difficult-to-treat cancer with a poor prognosis. Novel therapeutic interventions are desperately needed to improve patient survival. While chemotherapy drugs, such as gemcitabine, have shown durable efficacy for pancreatic cancer, there has been little to no improvement in patient survival in the last 30 years . PDACs ain vitro and in vivo data. A limitation of this calibration was the lack of spatial data available to calibrate the parameters in the model. To try and quantify the impact of this limitation we performed a range of sensitivity analyses at different stages of the model investigation. The parameter sensitivity analysis then revealed that the fundamental driver of tumour growth in our model was the rate of cell mitosis. The idea that the cell cycling time is a fundamental part of tumour progression has been found in other mathematical models [The model was calibrated to l models , suggestl models . Given aDepending on the cancer type, administering an intratumoural injection of a drug can be extremely difficult and administering treatments on the periphery can be an easier course of action. Simulating the model, we found that intratumoural administration of gemcitabine-loaded fibres significantly outperforms peritumoural administration. However, there is a threshold distance from the tumour to achieve an effective treatment, beyond which placing fibres further into the tumour bulk sees no added benefit. There is a clear benefit to increasing the dosage multiplicity and spreading the administered drug out amongst the tumour compared to a single high dose. Tumour volume was most significantly decreased when four free-drug point injections were administered compared to a single free-drug point injection. This proposes the existence of a potential threshold above which increasing the multiplicity of dosages or dosage size has a negligible effect over spreading out the dosages.The location of the fibre and the total drug concentration in the fibre was a major driver of tumour eradication. For fibres located within the centre of the tumour with a significantly high drug concentration, it was possible to completely eradicate the tumour. Moving the fibre farther away from the centre, we found that there was no concentration of drug that would inhibit growth. This suggests that a large amount of drug from the implants is lost to the surrounding tissue, and this has detrimental effects on the efficacy of these devices. Fortunately, simulations show there is a minimal concentration of drug necessary for stabilisation, allowing these predictions to be used a way to guide dosage so that toxicity is minimised, and efficacy is maximised.γ between 10−3 and γ−1. This suggests that there is some margin of error for the creation of these devices and provides some promise for their optimisation.Interestingly, we found that changing the release profile of the drug from the device had a similar effect for fast enough release curves, i.e. et al. [More recently, research has been focused on combining gemcitabine with other drugs to improve its efficacy in the sustained-release devices. For example, nanoparticle albumin-bound paclitaxel (nab-paclitaxel) administered in combination with gemcitabine  is one oet al.  and usin6 cells. An improvement for this model, could be to parallelise the agent update step to increase the speed of the simulation. In addition, we consider only a 2-dimensional cross section of the tumour, which is a simplification given tumour’s grow in 3-dimensional environments. While this is a simplification, since the measurements obtained by Wade et al. [Due to wanting to reduce the computational complexity of the VCBM, we made some simplifying assumptions that have introduced limitations into our model. To avoid simulating excessively large numbers of cells, we have chosen to scale the spatial unit appropriately so that we simulate on the order of ~10e et al.  only meaet al. [There are considerable avenues for future extensions of this work, and we feel the platform we have built is easily extendable by other computational oncologists. In particular, future modelling could extend the model to account for the dense fibrotic nature of PDAC ,102 and et al. ,4. A simet al. ,108.Treatment for cancers with a poor prognosis, such as PDAC, are in vital need of novel therapeutic approaches that provide sustained, heightened, localised drug concentrations. The computational platform developed in this work can recapitulate spatially heterogeneous tumour growth and treatment with the chemotherapy drug gemcitabine. Investigating the efficacy of gemcitabine released from a degradable polymeric fibre implant, we can suggest that a minimum dosage for maximum efficacy exists based on the location of the device within the tumour. Furthermore, certain release profiles are more effective than others, suggesting that the way in which drug is released from these devices is crucial to improving patient treatment. Moving forward, a study of this form could be used to help inform experimental design and be integrated into future device development.S1 TextSupplementary Tables and Figures that support results in the main text. Includes Tables A-E and Figs A-O.(DOCX)Click here for additional data file.S1 Technical Supplementary InformationIncludes Figs A-F.(DOCX)Click here for additional data file.S1 Code DocumentationCorresponds to the code on Github.(DOCX)Click here for additional data file.


Biomarker-guided therapy in an experimental setting has been suggested to improve patient outcomes. However, trial-specific pre-screening tests are time and tissue consuming and complicate the personalised treatment of patients eligible for early-phase clinical trials. In this study the feasibility of whole-genome sequencing (WGS) as a one-test-for-all for guided inclusion in early-phase trials was investigated.Phase I Molecular Tumor Board (MTB) at the Erasmus MC Cancer Institute reviewed patients with advanced cancer without standard-of-care treatment (SOC) options for a ‘fresh-frozen’ (FF) tumour biopsy for WGS based on clinical-pathological features. Clinical grade WGS was performed by Hartwig Medical Foundation. MTB matched the patient with a trial, if available.From September 2019–March 2021, 31 patients with highly diverse tumour types underwent a tumour biopsy for WGS. The median turnaround time (TAT) was 15 days [10–42 days]. At least one actionable event was found in 84% of the patients (26/31). One-third of the patients (11/31) received matched experimental treatment.WGS on fresh FF biopsies is a feasible tool for the selection of personalised experimental therapy in patients with advanced cancer without SOC options. WGS is now possible in an acceptable TAT and thus could fulfil the role of a universal genomic pre-screening test.  ALK and NTRK fusions . Based on initial shallow sequencing (~10–15× coverage) four specimens were deemed not evaluable due to a very low abundance of genomic aberrations. Because the corresponding FFPE tissue of these specimens was assessed to contain a sufficient tumour cell percentage, full sequencing (~90–100×) was performed on all samples. Eventually, WGS did not yield evaluable results in two out of 31 samples (7%) due to low tumour purity (Supplementary Data The majority of the samples 26/31) contained a molecular targetable alteration of ESCAT IV or lower , the tumour revealed a dominant mutational signature , high CD8-positive cell count (≥ 95 cells/mm2) and high CD68-positive cell count (≥133 cells/mm2). The median for CD68/PD-L1 double-positive cells was 0 cells/mm2, therefore samples were classified as CD68/PD-L1 double-positive or CD68/PD-L1 double-negative samples. Eleven tumour samples were CD68/PD-L1 double-positive.For comparing the groups, we arbitrarily determined the cut-off for high cell count at the 50th percentile of the respective populations of high CD8/PD-L1 double-positive cell count (≥ 3 cells/mmp = 1.000) . Nine tumour samples contained both a high CD68/PD-L1 double-positive cell count and a high CD8/PD-L1 double-positive cell count. There was an association between CD68/PD-L1 double-positive and negative samples and high and low CD8/PD-L1 double-positive cell count (p = 0.008) and the median cell count of CD8/PD-L1 double-positive cells was significantly higher in the CD68/PD-L1 double-positive tumours than in the negative tumours (p = 0.000) . No association could be found between high CD68/PD-L1 double-positive cell count and high CD8-positive cell count (p = 0.128).There was no association between TMB and CD8-positive cell count (00) Fig. . A signi00) Fig. . In addin = 2) and CDK4 amplification (n = 1), experimental ATR inhibitor—ARID1A mutation (n = 1), trametinib—NRAS mutation (n = 1), gemcitabine—ATR mutation (n = 1). Patient 20 harboured a CDKN2A homozygous deletion but did not receive a CDK4/6 inhibitor due to the presence of additional driver mutations (TP53 mutation and MAP2K7 mutation). Patient 19, anaplastic thyroid carcinoma with an NRAS mutation, had spontaneous shrinkage of the tumour. Due to this atypical clinical course for anaplastic thyroid carcinoma, no therapy was started. Patient 25 had rapid clinical progression and was deemed not eligible for targeted treatment for an ADAM9 exon 19 - BRAF exon 8 fusion.For the majority of the patients with an  actionable event 18/26, 69%), a matched experimental treatment was available at our institute . An MTB with dedicated experts in medical oncology, molecular biology and pathology discussed and integrated the WGS results with clinicopathological features and available current evidence to ensure adequate interpretation and therapeutic decision making.The rate of treatment allocation based on the WGS results in early-phase clinical trials was relatively high (35%) Table . Still, In this study, five patients were included in clinical trials with ICI treatment based on TMB or MSI. However, patients with low TMB but with inflammatory tumours can also benefit from ICI treatment . TME feaDespite the obvious advantages of WGS, its implementation comes with some challenges, mainly due to the requirement of FF biopsy material but also because the biopsies need to contain at least 20% tumour cells. In our study we did not use micro-dissected tumour enriched regions for DNA isolation, which could improve the tumour cell percentage and increase the success rate. However, prior micro-dissection would hamper the standardisation of the methodology and is more challenging for FF material. Interpretation of tumour-specific DNA alterations and their variant allele frequency and bi-aIlelic status (to identify loss-of-heterozygosity) makes use of tumour purity assessment using the sequencing data . Since tAn addition to WGS is whole-transcriptome sequencing (WTS), which can provide us with more information about the overexpression of biomarkers (e.g. fusion genes), epigenetic changes, e.g. silencing of genes, and immunophenotypical gene expression profiles. But to get the complete tumour profile, both tests would be required, because important genomic information regarding mutational signature (e.g. COSMIC) is (largely) missed using WTS, as it is typically based on non-coding regions. This information is important for WGS-based tumour typing, such as in the case of a patient with carcinoma of unknown primary origin. Also, WTS misses structural variations that can provide information on possible inactivation mechanisms of tumour suppressor genes. Currently, we are exploring the clinical feasibility of combining WGS and WTS in this patient population.We realise that currently WGS has some limitations regarding practicality and costs in comparison to smaller gene panels. However, with the continuing surge of molecular targets and targeted drugs we believe that in the near future extensive genomic testing, especially for less common tumour types, will play an important role in personalised treatment selection. WGS also has major scientific value as it reveals unbiased etiological information, which may answer both questions for patients as larger pathogenic questions, and even challenge the clinicians and researchers to ask new questions.Supplemental Data 1Supplemental Data 2Supplemental Data 3Supplemental Data 4Supplemental Data 5Supplemental Data 5 legendeSupplemental Data 6Reproducibility checklist


In an overlapping generations economy with incomplete insurance markets, the introduction of an employment fund—akin to the one introduced in Austria in 2003, also known as ‘Austrian backpack’—can enhance production efficiency and social welfare. It complements the two classical systems of public insurance: pay-as-you-go (PAYG) pensions and unemployment insurance (UI). We show this in a calibrated dynamic general equilibrium model with heterogeneous agents of the Spanish economy in 2018. A ‘backpack’  (BP) employment fund is an individual (across jobs) transferable fund, which earns a market interest rate as a return and is financed with a payroll tax (a BP tax). The worker can use the fund while unemployed or retired. Upon retirement, backpack savings can be converted into an  retirement pension. To complement the existing PAYG pension and UI systems with a welfare maximizing 6% BP tax would raise welfare by 0.96% of average consumption at the new steady state, if we model Spain as an open economy. As a closed economy, there are important general equilibrium effects, and as a result, the social value of introducing the backpack is substantially greater: 16.14%, with a BP tax of 18%. In both economies, the annuity retirement option is an important component of the welfare gains. The reform of twentieth-century public insurance systems is an ongoing issue which the twenty-first-century crises—financial, euro-debt and COVID-19—has only exacerbated. It has been the focus of extensive research in economics and other social sciences, most of it analyzing the possible reform of separate specific systems: either the pay-as-you-go pension system or the unemployment insurance system (UI). However, there is an interesting reform that, in our opinion, deserves further analysis: this is the introduction of an employment fund, also known as the ‘Austrian backpack’  as a social protection policy.The basic features of a ‘backpack’  (BP) employment fund are: it is a fund owned by the employee which accumulates, with a basic payroll tax, while working; it is transferable across jobs and can be used while unemployed and as a pension fund; it earns a market interest rate , but there may be restrictions in its use . While different forms of private employment funds are not a novelty, in most advanced countries such funds are not part of the public social protection policy. Similarly, some countries have tax exemptions for limited amounts of savings placed in private pension funds.The ‘Austrian backpack’  was introduced in 2003 as part of a broader labor reform in Austria. In particular, it was the socially agreed exchange for the gradual phase out of severance payments. The ‘backpack tax’  of 1.5% was set according to this trade-off. Kettemann et al.  have shoOur paper is related to the literature on unemployment insurance and retirement pension systems. On the UI literature, it is related to the studies on unemployment insurance savings accounts  do not change under an open economy assumption. Nevertheless, there are significant variations on households’ decisions: substitution of private for backpack savings with an overall increase of asset holdings; consumption is smoother during unemployment spells, and when BP savings are annuitized, smooth and substantially higher upon retirement. Labor market participation is higher, with a substantial reduction of inactive people, and retirement is slightly delayed. In sum, there is a small increase on employment, hours worked, productivity and, therefore, output. We assess the welfare gains by the lifetime consumption equivalent variation of the average household entering the economy at 20. The introduction of the backpack represents a 0.96% increase in welfare. There is a small increase in income inequality and larger one on wealth inequality since, at the individual level, more productive agents accumulate a larger fraction of assets.We also calibrate and analyze Spain as a closed economy, as an alternative benchmark, to understand how a non-fully open economy will react to the introduction of the backpack. Aggregate general equilibrium effects are significant, with a substantial reduction of the economy’s interest rate and increase in wages as the Backpack system is introduced. As a result, the effect on households’ decisions is exacerbated, which translates into substantial aggregate effects and welfare gains. The introduction of the backpack in the closed economy enhances efficiency more than in the open economy , and it also exacerbates a distortion on consumption plans that the backpack generates: a jump on consumption at retirement, when the backpack assets become fully available for consumption.The comparison of the open and closed economies is also relevant when we assess the role of the different aspects of the backpack system . We find that annuity payments are an important feature of the backpack contract. As we show in our simulations, without the option to annuitize BP savings there are no welfare gains from introducing the backpack system in an open economy, while there are still substantial gains in the closed economy.We also show (in Appendix) that a reform consisting on replacing the existing unemployment system for the backpack is not welfare improving, because younger generations do not accumulate backpack assets fast enough as to provide enough insurance during unemployment spells at the beginning of the career. In other words, the backpack tax would be too high in early ages, decreasing after-tax labor income, and the gains in insurance provision at older ages, during retirement, are not enough to compensate this distortion.The next section presents our model economy, Sect. This section presents the model economy. We study an overlapping generations economy with heterogeneous households, a representative firm, and a government. We use the same model in related work in Brogueira de Sousa et al. . Althougt, but we omit this dependence since we focus on steady-state equilibria. In a steady state, as defined below, all aggregate variables and individual policy functions are constant with respect to calendar time. We begin with a description of household heterogeneity.Time is discrete and runs forever, and each time period represents one calendar year. All model quantities depend on calendar time J, H, S, A, and B are all finite sets and we use j, h, z, s, a, b). They also differ in their claims to different social insurance systems: unemployment benefits UB, retirement PAYG pensions P, and government transfers TR. We think of a household in our model as a single individual, even though we use the two terms interchangeably. To calibrate the model, we use individual data of persons older than 20.Households in our economy are heterogeneous and differ in their age, j to age j, but not on education or other factors.Individuals enter the economy at age 20, the duration of their lifetimes is random, and they exit the economy at age Households can either be high school dropouts with z. The deterministic component depends on the household’s age and education, and we use it to characterize the life cycle profiles of earnings. The stochastic component is independently and identically distributed across households, and we use it to generate earnings and income dispersion in the economy. This component does not depend on the age or the education of the households, and we assume that it follows a first order, finite state, Markov chain with conditional transition probabilities given by z. Total labor productivity is then given by l hours of labor has gross labor earnings y given by:Individuals receive an endowment of efficiency labor units every period. This endowment has two components: a deterministic component, denoted In the model, an agent is either employed, unemployed, non-active, or retired. Among the unemployed, there are individuals who are eligible to receive unemployment benefits and access their backpack savings (workers who have recently been laid off), and others who are not eligible . Worker decide when to retire (with a minimum retirement age), leaving the labor force permanently once they do. Upon entering the economy, individuals randomly draw a job opportunity and then decide to work or not during the first period. Similarly, in subsequent years the labor market status evolves according to both optimal work and job search decisions (described below), and exogenous job separation and job finding probabilities.An individual with a job in the beginning of the period, and who decides to work, is employed in that period and his labor market status is denoted by unemployed eligible, with unemployed non-eligible, An agent may not have a job opportunity at the beginning of a period, because he lost his job last period, because he quit his job, or because he was unemployed last period and did not find (or did not accept) a new job offer. Without a job, households may actively search for a job offer next period. If they do actively search we label them as unemployed. Unemployed agents who have lost a job are eligible for unemployment benefits and to use accumulated backpack savings . They take this decision after observing their current labor productivity. If they decide to retire, Households in our model economy endogenously differ in their asset holdings, which are constrained to being nonnegative. The absence of insurance markets give the households a precautionary motive to save. They do so by accumulating real assets which take the form of productive capital, denoted Workers accumulate backpack savings while they work. These savings result from a mandatory contribution out of workers’ salaries, and are invested in productive capital and earn the real rate of return in the international capital market. When workers lose a job, they can access their accumulated savings and decide how much to keep in their individual accounts or how much to use, while out of work, to finance consumption. A formal description of the decision problem is given below. At retirement, backpack assets are converted into retirement pension payments .u satisfies standard assumptions, Households derive utility from consumption, and disutility from labor and the search effort. Labor is decided both at the extensive and intensive margins, while search is a discrete choice. The period utility is described by a utility flow from consumption and the utility cost of time allocated to market work and to job search. Non-active and retired agents dedicate all the time endowment to leisure consumption. Accordingly, lifetime utility is given byz, households’ stochastic productivity component, is realized.At the beginning of each period, The firm. In our model economy, there is a representative firm. Aggregate output depends on aggregate capital, K, and on the aggregate labor input, L, through a constant returns to scale, Cobb–Douglas, aggregate production function of the formr and hires workers in the domestic market at a wage rate Insurance markets. An important feature of the model is that there are no insurance markets for the stochastic component of the endowment shock, for unemployment risk, or survival risk.r. If R and allocate b amount of BP savings to the purchase of the annuity contract, he receives in return:The BP economy features a fully funded employment fund, financed by individual worker contributions. Workers may choose to use all or a fraction of the BP savings during periods of involuntary unemployment. Every individual enters the economy without backpack claims. For every period of employment, a worker sees a fraction h are:h during the last j and with education h. We assume that there are no early retirement penalties, nor minimum or maximum pensions.The PAYG system is an unfunded defined contribution pension system, where pension payments mostly depend on individual workers’ history of salaries, among other factors. In the model, pension payments depend on average earnings during the Here we describe the government programs other than retirement pensions, discussed above, and the government and social security budgets that we model separately.Unemployment benefits. The government taxes workers and provides unemployment benefits to the unemployed. Eligibility for unemployment benefits—denoted j and with education h. Unemployment benefits are financed with payroll taxes, described below.Other transfers Households below an income level TR. Eligibility for transfers is conditional on income only and denoted by We model the government budget restriction with two separate identities. Unemployment benefits and PAYG pensions are financed with payroll taxes and form the social security budget. Other government expenditures and revenues form the government budget.6The government taxes capital income, household income and consumption, and it confiscates (part of the) unintentional bequests. It uses its revenues to finance an exogenous flow of public consumption and debt, and to make transfers to low-income households. In addition, the government provides unemployment benefits and runs a PAYG pension system.t and The government budget constraint is then:Capital income taxes Capital income taxes are given by a denotes households’ capital holdings and r the economy rate of return on capital.Payroll taxes Payroll taxes are proportional to before-tax labor earnings: Backpack taxes Similarly, contributions to accumulate assets in the individual Backpack account are given by: Consumption taxes Similarly, consumption taxes are simply c is consumption.Income taxes We assume a simplified income tax formula according to which the income tax is proportional to the income level: BP economy, i.e., a steady-state economy with a Backpack system and a PAYG pension system. The households’ problem is described recursively. To simplify the notation, we omit the dependence of the value functions on the state variables age, education, private savings, backpack savings, and unemployment spell duration.We describe the problem in the c, save We first state the decision problem of a worker at the beginning of the period after the job acceptance decision was taken. Given the value functions, we define below the job acceptance and retirement decisions. An individual who is currently employed decides how much to consume J that depends on next period’s private and backpack assets, respectively U. This value depends on the number of periods after an involuntary job separation (relevant to determine eligibility for unemployment benefits), d. In the first period after a layoff, Equation  reads inmentclass2pt{minima, backpack savings b, stochastic productivity z, and layoff duration d. Given the current state, the agent chooses consumption, future asset holdings and the search effort z. Higher search effort  and government transfers for low-income households, TR(y). The laid-off worker may be entitled to unemployment benefits: d evolves deterministically according to Equation  is the bnstraint : The laigiven by .Finally, an individual can start the period without a job because he has decided not to work or not to search in previous periods, not having found a new job yet. In this scenario, he solves the following problem:milar to . But in Retired individuals are not in the labor market and have no endowment of efficiency units of labor. They finance consumption with past private savings, backpack annuity payments, and PAYG pension payments. The problem is a standard consumption-savings decision, with survival risk and a certain maximum attainable age, assumed to be To close the description of the household’s problem, we define the job acceptance and retirement decisions. These jointly pin down the value of having a job offer at the beginning of a period. For a household older or at the minimum retirement age (as defined by the PAYG pension system rules), The formal definition of a stationary equilibrium in the open economy, as well as in the closed economy, is postponed to Appendix G.In order to calibrate the model parameters using Spanish data, we need to modify the environment described in Sect. Baseline economy. The status quo economy, calibrated to the Spanish data in 2018, which includes a pay-as-you-go retirement pension system . In the model, aggregate accidental bequests as a fraction of output are significantly higher (2.63). In the results shown below, we assume that the portion of the accidental bequests that is not taxed by the government is wasted (thrown to the sea).The model is able to capture the main output ratios in the calibrated year, as shown in Table The tax rates implied by the calibration are shown in Table The model also does a good job at replicating the aggregate labor market stocks , and the age distribution of workers, unemployed, inactive, and retirees—which is not part of the calibration targets.Our model features labor market frictions and a detailed description of government tax and transfer systems and is able to capture the inequality in after-tax earnings, income and net wealth in the Spanish economy, as summarized in Table In this section, we show the effect of introducing the backpack in Spain while maintaining the existing unemployment insurance (UI) and pension systems. We first compare different steady states indexed by the contribution rate, and calculate the difference in average social welfare relative to the baseline economy (without backpack system). entclass1pt{minimaG at the baseline level, and let The introduction of the backpack with its corresponding contribution rate, while fully funded, does not imply that the government budget is satisfied in the new steady state since, for example, revenue from income taxation may be lower . The assumption we follow is to fix government consumption In general, individual decisions and aggregate variables are affected by the introduction of the backpack. At the individual level, it affects decisions on savings, since accumulating BP assets is a form of forced savings, subject to dis-investment restrictions, with two advantages: It reduces the need for precautionary private savings and, in contrast to private savings, is not subject to household income taxation. It also affects consumption, since, on the one hand, it helps to smooth consumption after a job loss, when jobless individuals can withdraw from their backpack, while on the other hand the retirement annuity implies an increase in lifetime consumption. Additionally, it affects labor supply, since consumption and saving plans are affected, direct labor income taxes are likely to be lower, while effective labor taxes may be higher, and importantly, there is an incentive to postpone retirement and further accumulate BP assets.We now quantify the effects of introducing the BP in the (open) Spanish economy. Table Table In Table A closer look at average age profiles of consumption, hours of work and asset holdings, in Fig. In comparing status quo and reformed economies, welfare and inequality measures are most relevant and we close the section with these results. As already mentioned, our welfare measures are in terms of lifetime consumption improvement, expected by a new entrant in the economy (age 20). Therefore, the back-loaded increase of consumption at retirement age in the BP economy is properly discounted by the young workers. Taking into account the distribution by types , the overall social welfare gains are significant: 0.95%. The breakdown of the welfare gains across different groups is reported in Tables Regarding inequality, moving from the baseline to the BP economy increases wealth inequality since low earners (those who receive the lowest stochastic productivity shock) reduce their holdings of liquid assets proportionately more than those medium and high earners and the latter—in particular, the top 5In this section, we consider Spain as a closed economy. The general equilibrium effects exacerbate the differences between the BP and the baseline economy. We first need a new calibration for the baseline as a closed economy (see Appendix C for details). We proceed as in Sect. Table The aggregate changes due to the introduction of the backpack have an important impact on tax revenues. As Table As Table Figure 11The comparison of the welfare gains in the closed economy of Tables Our backpack contract adds to the Austrian backpack the option to convert the backpack savings, upon retirement, into an actuarially fair annuity, providing insurance akin to the retirement insurance of a PAYG pension. Hence, the paper’s motivation of studying this policy and contrasting it to existing UI and PAYG pension systems. In this section we show that this feature is an important component of the welfare gains described in Sects. Figure These results raise an obvious issue: should the annuity option be available for all liquid assets? It could, and should, since it is a dominant contract when agents want to insure their longevity risk. Nevertheless, private annuity contracts exists but this is a very thin market, raising concerns regarding its ‘actuarially fairness,’ which is easier to achieve in a thick—possibly regulated—insurance market. The introduction of the BP system is an occasion to establish a competitive and actuarially fair annuity market. The role of supervision and competition policy is better anchored when it is a complement to a public policy generating a broad base for this market. It is for this reason that we do not include this option in our analysis and emphasize the social value of developing the retirement annuity market.We have studied the effect of the introduction of the Austrian backpack contract (BP) as complement to other existing social policies—in particular, unemployment insurance and a PAYG pension system—using the Spanish economy in 2018 as a benchmark for our calibration and experiments. By studying the BP in itself, and not as a part of a broader reform, we have been able to isolate its more salient features and effects. To this end, we have used a detailed overlapping generations model with heterogeneous agents making decisions on employment, savings and consumption, subject to idiosyncratic risk. We compare stationary equilibria without and with the BP. As a baseline, we consider Spain as an open economy. The basic element that defines the size of the BP is the payroll contribution tax. We choose the one that maximizes a Benthamite social welfare function of the generation entering into the labor market for the first time (20 years old). In our exercise, the optimal contribution rate is a 6% tax, resulting in small, but significant, aggregate effects. Nevertheless, behind them, there are multiple effects on individual decisions. All groups of agents, but one, benefit from the introduction of a 6% BP. With the BP, they can reduce their precautionary savings and substitute liquid private assets by income tax-free BP assets; smooth better their consumption during unemployment spells and during their retirement.Even if the Spanish economy is part of the European Union, the European Financial Union is not as developed as to consider Spain a fully open economy. For this reason, we also analyze it as a closed economy. There are important general equilibrium effect of introducing the Austrian backpack: the economy capitalizes, with wages increasing and the interest rate decreasing. As a result, the welfare maximizing backpack rate is larger, 18%, as well as the corresponding welfare gains.This comparison between the open and closed economy is also relevant when we analyze the role of the link between lifetime BP savings and annuity payments after retirement. It accounts for the average welfare gains of introducing the backpack system in the open economy, while it plays a complementary welfare improving role in the closed economy, for which, without the annuity option, the welfare maximizing backpack rate would be 15%. Therefore, we conclude that the improved Austrian backpack design is the appropriate one, since it also helps to sustain an annuity insurance market.minimum retirement age, together with the inability to borrow against future income (PAYG pension plus BP annuity), introduce a distortion into agents’ intertemporal consumption plans: upon retirement there is a jump in consumption when backpack assets become liquidWe unveil a special feature of how the BP contract interacts with PAYG pensions: for good and for bad, the BP contract backloads a substantial part of its benefits into the retirement age. For good, avoiding the drop of consumption in old age that characterizes the ‘only PAYG’  benchmark economy. For bad, because the Finally, we also show (in Appendix) that, in fact, the BP is not a good substitute for the existing unemployment insurance system; the problem being the insufficient unemployment insurance coverage that the BP provides at the early stages of workers’ careers. In contrast, in a follow-up paper, Brogueira de Sousa et al. , we show


Stereotactic body radiotherapy (SBRT) is an emerging cancer treatment due to its logistical and potential therapeutic benefits as compared to conventional radiotherapy. However, its mechanism of action is yet to be fully understood, likely involving the ablation of tumour microvasculature by higher doses per fraction used in SBRT. In this study, we hypothesized that longitudinal imaging and quantification of the vascular architecture may elucidate the relationship between the microvasculature and tumour response kinetics. Pancreatic human tumour xenografts were thus irradiated with single doses of  SBRT has shown promise for improving local tumour control for many of the most lethal cancer types such as pancreatic ductal adenocarcinoma6. It remains an open question, however, whether its apparent enhanced efficacy is due only to its higher biologically effective doses, or whether an alternate mechanism of action is at play, over and above the standard linear quadratic model of tumour cell kill through the DNA damage mechanisms10.Recent technological advances in image-guided radiotherapy have opened the door to the delivery of hypofractionated ablative radiotherapy (RT), most notably stereotactic body radiotherapy (SBRT), by ensuring that doses to organs-at-risk remain acceptable13 which in turn leads to an improved tumour response by depriving it of nutrients necessary to regrow post-SBRT15. The most important such nutrient is oxygen, due to its role in the fixation of RT-induced DNA damage16 and subsequent tumour metabolism and re-growth17. Indeed, the change in oxygen levels throughout the course of fractionated radiotherapy—a process known as “reoxygenation”—is one of the major predictors of tumour response18. The role of microvasculature may prove even more important in SBRT, with its higher-doses-per-fraction regimen appearing to invoke additional effects beyond the cellular DNA damage mechanisms13. Thus, the temporal kinetics of the microvascular response to SBRT are likely a major determinant of its efficacy and need to be studied in detail.It has been suggested that high doses per fraction  more sensitive to microvascular heterogeneity and (2) capable of quantifying changes in the efficiency of transport of nutrients/drugs/oxygen from the vasculature to the tissue.Towards this goal, we have developed an optical coherence tomography angiography (OCA) platform to measure and quantify the temporal kinetics of the vascular volume density (VVD), defined as the total proportion of the tumour volume occupied by the vasculature, in response to high single-dose irradiation in a pre-clinical in-vivo study26. The feasibility of using svOCT to measure these metrics longitudinally in time, in response to RT of different doses, is part of our larger research effort towards “shedding light on radiotherapy”.The temporal kinetics of two such potential metrics for describing the vascular geometry captured via an OCA technique called speckle variance optical coherence tomography (svOCT), following high single-dose irradiation, are investigated here: the diffusion limited fraction  fraction DLAF, was proposed by Janssen and colleagues for resected tumour tissue slices, by analyzing the spatial distribution of histological blood vessel markers25. However, because DLAF ignores the 3D volumetric nature of the microvascular network and must be quantified ex-vivo , histopathological analysis may not be an optimal tool to study the longitudinal evolution of this quantity, further motivating the significance of our svOCT-based in-vivo 3D approach.The former biomarker is defined as the fractional volume of tissue beyond a given distance . to quantify the shape of the distance-to-nearest-vessel histogram at “short” distances . Proper measures were taken to minimize animal discomfort through administration of anesthesia and analgesia as necessary. Results of this study are reported in compliance with the ARRIVE guidelinesnull IL2rgnull (NRG) mice were selected as the animal model for this longitudinal study due to their radioresistance comparable to humans and high immunodeficiency suitable for tumour generation31. All animal procedures and data acquisition protocols have been described previously15. In brief, 2.5 × 105 human BxPC-3 cells, labeled with Discosoma sp. Red Orange Mushroom (DsRed) derived red fluorescent proteins  and suspended in 30. Tumours were treated using an X-RAD 225Cx small animal irradiator  through an 32. The system was calibrated following the American Association of Physicists in Medicine TG-61 protocol33.NOD-Rag115).Tumour macroscopic and microvascular monitoring was performed every 3 (2 field-of-view (FOV) up to 15. This allowed for contrast enhancement of blood vessels with respect to surrounding static tissue and hence microangiography, via speckle-variance (sv) processing:22 for more details.Volumetric microstructural imaging of the tumours was performed at 22. To account for signal depth attenuation described by Beer-Lambert law, as well as to remove “shadowing” artifacts due to Mie scattering and absorption from red blood cells, a depth-decaying thresholding and a step-down exponential filter steps were applied22.The obtained “raw” svOCT 3D microvascular maps were then segmented to prepare for metric extraction using a lab-designed pipeline implemented through MATLAB R2021a software  as represented in Fig. en-face projection of the subsequently acquired “raw” svOCT microvasculature images, benefitting from the enhanced normal tissue to tumour contrast afforded by these modalities. Due to accumulation of optically translucid exudate, arising from elevated cytokine activity in the DSWC over the period of weeks following surgery34 (seen in some of the mice), accurate manual contouring of the tissue interface along the axial (depth) direction via structural OCT was not feasible at all timepoints. Instead, the VOIs were contoured axially from the inner surface of the DSWC glass down to a fixed depth of Tumour volume of interest (VOI) contours were delineated at each timepoint, using multi-modal acquisitions—brightfield microscopy, structural OCT, speckle-variance OCT, Ds-Red fluorescence—to reduce subjectivity and improve inter-timepoint VOI accuracy. Specifically, in the transverse  plane, a 2D tumour mask was created based on the fluorescence or brightfield images co-registered to a maximum intensity 36. Finally, the generated 3D microvasculature map was binarized and visually compared to the “raw” svOCT volume to guide in removal of small artifacts. The binarized 3D vascular maps were rescaled to have isotropic voxel size of (3 in order to compute the distance-to-nearest-vessel (DNV) histogram 26.All vessels within the delineated tumour VOI were then segmented with a Frangi-vesselness filter based on Hessian eigenvalue decompositionThe diffusion-limited distance 26:The convexity index 26 fit the DNV histogram using Eq.  decreases post-irradiation in a dose-dependent manner using the same tumour model as explored here28. Hypoxic cancer cells are known to be non-proliferative, potentially more metastasizing, and resistant to radiation37. Consequently, an evolving 38. Second, this same fraction of tissue may become non-proliferative, enhancing the impact of radiation on tumour volume growth delay29.The diffusion-limited fraction 39, inter- and intra-subject heterogeneity in perfusion and metabolism spatio-temporally23 challenge the identification of a universal relationship between hypoxia and 25.The relationship between 15. However, the precise relationship between randomly arranged array of parallel cylinders of uniform radii Given that large doses of radiation ablate blood vessels, it is arguably not surprising that Deviations from this relationship thus describe the inhomogeneous structure in the vascular architecture, beyond simple randomness. In Fig. 26. A positive value of 40.The convexity index 40. This may be a useful finding for locally advanced pancreatic cancers, where chemotherapy is the backbone of treatment to mitigate metastatic dissemination and questions surrounding the best way to combine radiotherapy with chemotherapy exist41. It remains to investigate whether this effect persists for more clinically relevant, fractionated radiation schedules.The fact that thy Fig. . It impl42 and especially vessel segmentation43, the accuracy and inter-timepoint consistency of the OCA post-processing should be significantly increased. Applying these improvements along with a larger sample size to mitigate the influence of biological variability and possible post-processing related uncertainties, the observed temporal fluctuations in the 46. Future studies should elucidate this behaviour.The temporal fluctuations in 48.While fluctuations in preceded that of the macroscopic tumour tissue response as seen in see Fig. 14. Consequently, there may be a narrow window of time in which vessel architectural changes can have an impact on RT response.It is interesting to note as well that despite the modest sample size of this study, both metrics showed trends which in-vivo. Therefore, the temporal trends in the vascular distribution convexity index (This study presents microvasculature assessment of pancreatic human tumour xenografts irradiated with


Specific diagnosis of adnexal tumours at fine needle aspiration cytology (FNAC) is challenging. In a recurrent scalp tumour, a startling array of lesions ranging from benign to malignant tumours fall into differential diagnosis. Accurate preoperative diagnosis is essential for planning management and to improve clinical outcome. Herein, we report a rare case of recurrent cylindroma of the scalp in a 62-year-old male patient. Cytology smears showed tight clusters of round to oval basaloid cells, few clusters with peripheral palisading. Basement membrane material was seen surrounding the cell clusters. FNAC diagnosis of a benign adnexal tumour, possibly cylindroma, was made. The diagnosis was confirmed at histopathology. We emphasize the decisive role of FNAC in arriving at an accurate diagnosis of a recurrent scalp tumour. Cylindromas are rare benign adnexal tumours, accounting for 0.7% of all the adnexal tumours . Most cyRecurrence in a cylindroma is rare, mainly attributed to incomplete excision. When a recurrent tumour is encountered in the scalp region, a startling array of lesions ranging from benign to malignant tumours fall into differential diagnosis. Accurate preoperative diagnosis is essential for planning management and in improving clinical outcome.Despite known benefits of accurate preoperative diagnosis, scanty literature exists describing specific cytomorphological findings of individual adnexal tumours, especially cylindroma -8.Hence we report the cytomorphological findings in a rare case of recurrent cylindroma of the scalp and discuss the differential diagnosis in detail.A 62-year-old male presented with swelling over the scalp for 10 years duration. The swelling was slow growing, not associated with pain or discharge. Patient did not complain of any other swelling anywhere else on the body. There was no history of weight loss or loss of appetite. The swelling was excised one year after the immediate appearance of the swelling. As per the patient's narration, swelling was removed in toto initially. Currently, he did not have any details of the radiological or histopathological examination performed during the initial excision. The swelling recurred after a duration of three years from the initial excision. The recurrent swelling was also slow growing in nature. A provisional clinical diagnosis of sebaceous cyst or fibroma was made.On examination, swelling was noted in the scalp in the right frontal region. Swelling was nodular, pink, with bosselated appearance, measuring 2.5 x 2 x 2 cm, mobile and non-tender Figure .It was firm in consistency. The skin overlying the swelling was unremarkable with no evidence of ulceration. On careful examination, no other swelling was identified anywhere else in the body. There was no evidence of regional lymphadenopathy. The patient was well built and well-nourished. Radiological investigations were not performed.Fine needle aspiration cytology (FNAC) was performed by standard technique, using a 22 gauge needle attached to a Comeco syringe holder. Slides were fixed in 95% ethyl alcohol and stained with haematoxylin and eosin stain. The air-dried slides were stained with Giemsa stain. Smears studied showed moderate cellularity comprising tumour cells in tight sheets, small clusters, vague acini and in singles. In places, some clusters showed peripheral palisading. Individual tumour cells were uniform, small, basaloid, round to oval with dark nuclei and inconspicuous nucleoli. Plenty of thick basement membrane material is seen surrounding the tumour cells and some in the background Figures .There was no evidence of atypia, mitosis or necrosis in the smears studied. Hence a diagnosis of the benign adnexal tumour with a possibility of cylindroma was made.The swelling was excised and the specimen was sent for histopathology. On gross examination, the swelling was 2.5 x 2 x 2 cm. The external surface was bosselated. There was no ulceration. The cut surface showed an encapsulated tumour with a solid, uniform, grey-white appearance. There was no evidence of necrosis, haemorrhage or cystic change.At histopathology, a well-circumscribed, encapsulated tumour was noted. Tumour cells were uniform, basaloid, arranged in jigsaw puzzle pattern. Each cluster exhibited peripheral palisading and was surrounded by thick basement membrane material. Individual tumour cells were round to oval with hyperchromatic nuclei, scanty cytoplasm and inconspicuous nucleoli. There was no evidence of atypia, mitosis, necrosis or haemorrhage. A final diagnosis of benign cylindroma was confirmed at histopathology. There was no evidence of recurrence or any other complaint since eight months of follow-up after the surgical excision of the tumour.Cyclindromas defy categorization as to eccrine or apocrine origin because of differing evidence of the line of differentiation. Though ultrastructural and immunohistochemical examination point towards the eccrine differentiation, its occurrence in hair follicle-rich regions like the scalp and paucity in eccrine gland-rich areas like the palmoplantar region disputes the eccrine origin theory. The possibility of a hair follicle derivation via epithelial-mesenchymal transition was proposed by Massoumi et al. -11.Cylindromas occur most frequently in the head and neck region with 80% occurring in the scalp area. Sometimes, multiple cylindromas cover a large area on the scalp, giving an appearance of a “turban”, hence the name turban tumour. Other regions where cylindroma has been reported include extremities and trunk with rare cases reported in the orbit, ear, abdomen and breast . These tThere are few studies describing the role of FNAC in skin adnexal tumours, many restricted to case series. Additionally, one could agree that the subcategorization of benign adnexal tumours at FNAC is largely academic because it rarely makes a difference in clinical management. However, the close relationship of cylindroma to the genetically determined syndrome makes them susceptible to internal malignancy or occurrence of other tumours or tumour-like lesions at other sites, making subcategorization of benign adnexal tumours critically significant. Nevertheless, determining the subcategory will help exclude other malignant primary skin tumours mimicking cylindroma as well as metastatic deposits of look-alikes at FNAC. Finally understanding the differentiation pathway may lead to new inventions with a significant impact on other areas of medicine.When a tumour that morphologically resembles a cylindroma recurs, differentiating a malignant cylindroma from a benign cylindroma becomes crucial. Similarly, when the true nature of the initial tumour is not known, as was in our case, differentiating it from other skin adnexal tumours like eccrine spiradenoma, trichoepithelioma and other primary malignant or metastatic tumours like adenoid cystic carcinoma and basal cell carcinoma becomes important ,5,8.Cylindromas at FNAC show moderate to abundant cellularity with two major constituents. The first is the predominant basaloid cells and the second is the extensive deposits of basement membrane material ,5. OtherIt is important to pay careful attention to the overall smear pattern and the details while attempting to distinguish ADCC from cylindroma. The characteristic patterns seen in ADCC, like the cup-shaped structures, three-dimensional cell clusters and many bare nuclei are sparingly seen in cylindroma . IndividThe salient distinguishing features between cylindroma and other entities like eccrine spiradenoma, trichoepithelioma, adenoid cystic carcinoma and basal cell carcinoma are shown in Table Malignant transformation of cylindroma is rare and may arise in a setting of multiple cylindromatosis. Malignant cylindromas are aggressive tumours with a propensity for local destruction and distant metastasis . In a scWe report a rare case of recurrent cylindroma of scalp diagnosed at cytology. Cylindroma should be considered in the differential diagnosis of basaloid cell rich cytology aspirates from a scalp tumour. The present case underscores the importance of considering cylindroma in differential diagnosis of basaloid cell rich cytology aspirates from a scalp tumour. Careful attention to the pattern and cellular details helps in differentiating it from the look likes like adenoid cystic carcinoma, eccrine spiradenoma and basal cell carcinoma.