The autophagic response after infection of SOC with a recombinant VZV genome containing the herpes simplex virus ICP34.5 neurovirulence gene was similar to wild-type VZV (p = 0.3). These results suggested that the VZV-infected SOC system resembled biopsy data from herpes zoster infection of skin. An enhanced autophagy response has now been reported after infection with two additional alpha herpesviruses besides VZV, namely, pseudorabies virus and duck enteritis herpes virus; both lack the ICP34.5 protein.The goal of this project was to document the autophagy response in human neonatal skin organ culture (SOC) after infection with varicella-zoster virus (VZV). The VZV-infected SOC model has attributes of herpes zoster, in that an injection of virus into the skin is analogous to exit of virus from the sensory nerve termini into skin during herpes zoster. Cultures were maintained for 28 days and periodically examined for an autophagy response by quantitation of autophagosomes with Imaris software. Expression of the STAT3 protein was plentiful in the VZV-infected SOC. Abundant autophagy was observed in VZV-infected SOC between 14 and 28 days after infection, while autophagy in mock-infected SOC was minimal ( Herpesviruses are ancient viruses that have retained autophagy as a modulator of innate immune mechanisms . varicelIn earlier studies, we have demonstrated that VZV infection induces both an unfolded protein response (UPR) and an autophagy response . We haveBecause of previously documented differences in VZV pathogenesis between cultured cells and skin xenografts in the mouse model, we first examined transcription of antiviral innate immune transcripts in mock-infected SOC and infected SOC. We found unexpectedly that both interleukin-6 (IL-6) transcription as well as protein expression were upregulated following VZV infection . Recent VZV-32 is a low passage laboratory strain; its genome has been completely sequenced (GenBank DQ479961.1) . rVZV/34oC in a humidified incubator with 5% CO2. We have carried out 9 independent explant procedures.De-identified foreskins from circumcision procedures were collected at the University of Iowa Hospital under an exempt Institutional Review Board protocol. As described , we prepAfter 24–48 h in culture, pieces of explanted human skin were inoculated with cell associated VZV. A VZV infected monolayer (25 sq. cm.) at 70–80% cytopathology was trypsin-dispersed, sedimented by low speed centrifugation and resuspended into 1 ml of MEM. Each skin piece was pierced to the depth of 1 mm in multiple sites with a hypodermic needle and then an aliquot  of VZV-infected cells was injected onto the surface of the epidermis and allowed to penetrate into the holes created by piercing the skin surface. Infected skin explants were then incubated for as long as 28 days. Mock infections were handled similarly except for inoculation with uninfected cells. Preparation of the skin samples for cryosectioning and subsequent immunohistochemistry (IHC) protocols have been described in detail .oC; the secondary antibody (1:1250) and Hoechst 33342 DNA stain (1:1000) were added for 2 h. All samples were viewed on a Zeiss 710 confocal fluorescent microscope. Murine monoclonal antibody (MAb) 3B3 against VZV gE, MAb 233 against gC and MAb 251D9 against the capsid protein were produced in this laboratory   Figure . As expe) Figure . Finally) Figure , much hiAfter our analyses in SOC described previously as well as the above new data , we carrp = 0.3). We have previously shown that autophagy and LC3 production were not diminished in cells infected with vaccine virus (Thereafter, we examined the infected SOC culture at 28 dpi Figures –H. Time ne virus . This fuA goal of the current study was to investigate autophagy in the human SOC model for herpes zoster. A prior investigation in the SOC model documented a dramatic increase in IL-6 transcription in VZV-infected skin explants . IL-6 haThe above VZV IL-6 data are relevant because a recent paper describes a previously unrecognized pathway between elevated IL-6 and autophagy. Hypoxia within a brain tumor stimulated IL-6, which acted upstream as an inducer of autophagy in glioblastoma tumor cells via the IL-6-STAT3-MIR155-3p-CREBRF-CREB3-ATG5 pathway . In addiFurther, our autophagy quantification after infection of SOC with the rVZV/34.5 virus may support HSV1 data that the primary anti-autophagy effects of the ICP34.5 protein are recognized mainly in HSV1-infected neuronal tissues . A complWhen these VZV data about IL-6/STAT3/ATG5 are considered together with the results from the glioblastoma/autophagy study, we conclude that the elevated levels of IL-6 may also facilitate elevated levels of autophagy in VZV-infected skin explants. In support of this hypothesis, we point out the robust autophagosome formation seen in the 3D images of our SOC model of VZV skin infection Figure . There wFinally, we would like to point out papers that describe enhanced autophagy following infection with two other alpha herpes viruses: PRV and duck enteritis herpes virus . The datCG provided overall supervision for the project. EB, JG, and WJ carried out experiments shown in Figures The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The delivery of blood-borne therapeutic agents to solid tumours depends on a broad range of biophysical factors. We present a novel multiscale, multiphysics, in-silico modelling framework that encompasses dynamic tumour growth, angiogenesis and drug delivery, and use this model to simulate the intravenous delivery of cytotoxic drugs. The model accounts for chemo-, hapto- and mechanotactic vessel sprouting, extracellular matrix remodelling, mechano-sensitive vascular remodelling and collapse, intra- and extravascular drug transport, and tumour regression as an effect of a cytotoxic cancer drug. The modelling framework is flexible, allowing the drug properties to be specified, which provides realistic predictions of in-vivo vascular development and structure at different tumour stages. The model also enables the effects of neoadjuvant vascular normalisation to be implicitly tested by decreasing vessel wall pore size. We use the model to test the interplay between time of treatment, drug affinity rate and the size of the vessels’ endothelium pores on the delivery and subsequent tumour regression and vessel remodelling. Model predictions confirm that small-molecule drug delivery is dominated by diffusive transport and further predict that the time of treatment is important for low affinity but not high affinity cytotoxic drugs, the size of the vessel wall pores plays an important role in the effect of low affinity but not high affinity drugs, that high affinity cytotoxic drugs remodel the tumour vasculature providing a large window for the normalisation of the vascular architecture, and that the combination of large pores and high affinity enhances cytotoxic drug delivery efficiency. These results have implications for treatment planning and methods to enhance drug delivery, and highlight the importance of in-silico modelling in investigating the optimisation of cancer therapy on a personalised setting. One of the main challenges in optimising cancer therapy is understanding the in-vivo cancer environment and how it changes over time. The efficacy of chemotherapeutic drugs is known to be strongly dependent on blood vessel wall properties and the architecture of the developing tumour vasculature, which in turn are dependent on biochemical and mechanical interactions between cancer cells and their microenvironment. Here we present a novel in-silico modelling framework of dynamic tumour growth, angiogenesis and drug delivery, and we use it to explore biophysical factors governing the efficient delivery of cytotoxic drugs to solid tumours. We find that the time of treatment and vessel permeability are important factors for the efficacy of chemical agents with low binding affinity, that high affinity drugs can impact the tumour vasculature remodelling and bring vascular structure back to a more normalised state, and that the combination of large-sized vessel wall pores and high affinity enhances cytotoxic drug delivery and efficacy. These results have implications for treatment planning and optimisation, and show how in-silico models can be used to help understand and optimise cancer therapy. Both model parameters can be modified before the in-silico analysis to effectively control the magnitude and time-pattern of the bolus injection.The above differential cf, the bound-drug, cb, and the internalised drug, ci, concentration respectively. Balance of all three state variables is described through a set of coupled differential equations—where the free-drug governing equation incorporates the advection of the drug at the interstitial space—which readskon, koff and kint are the association (binding), disassociation and internalisation rate coefficients respectively, while δi is the decay rate of the drug—owing to the depletion of the cancer cells after the drug has found its target and the drug natural decay. Since we study here the interaction of the drug with the cancer cells, the above rate parameters take non-zero values in the tumour region, ΩT, and zero in the host tissue region, ΩH  of the cytotoxic drug. Note that in X) ⋅ vint = 0, where the interstitial fluid velocity can be evaluated using Darcy’s law: vint = −Kint ∂pint/∂X.Following the mechanistic compartmental drug delivery model of Thurber and Wittrup , we iden, ΩH see . The frePvsc,d is the diffusive permeability of the blood vessels with respect to the solvent (drug molecule), and σf,vsc the solvent drag reflection coefficient at the blood vessel wall. Both latter quantities can be estimated numerically as a function of the drug size to the size of the pores of the vessel wall [By employing Starling’s equation to relate the rate of solute transport per unit volume through the microvascular endothelial wall into the interstitium , one obtsel wall .Svsc is the surface area per unit volume of tissue at the interstitium (otherwise referred as blood vessels’ density).Also, Klmp the hydraulic permeability of the lymphatic wall, and Slmp the lymphatic vessels’ density. The solvent drag reflection coefficient at the lymphatic vessel wall, σf,lmp, can be also estimated numerically using Deen’s formulas [plmp = 0 in the entire lymphatic network.Similarly, the lymphatic (drainage) contribution in formulas . For simcf = cb = ci = 0), while zero flux boundary condition is applied on the external boundaries of the domain of analysis (i.e. X ∈ Γ). Also, we denote here the total drug concentration that has ‘targeted’ the cancer mass with ch, which is equal to the sum of the bound-drug concentration in the tumour, cb, and the internalised (within the tumour cells) drug concentration, ci. The drug delivery model parameters of Eqs The above coupled reaction-diffusion-convection Eqs ϵ—are modelled via a first-order ordinary differential equation:δϵ is the degradation rate of the ECM structural components (given in days-1) owing to the matrix degrading enzymes (MDEs), secreted by the tumour and the tip-endothelial cells of the tumour vasculature, and the effect of the cytotoxic drugs in depleting the tumour respectively. Variable μ denotes the concentration of the MDEs in the matrix, where detailed description of the balance PDE for μ can be found in [-1) describing the remodelling of the ECM, for both the host and tumour region, is expressed as: ϵ is an ECM remodelling rate parameter. The last right-hand-side term in ΩT. This is due to the effect of the cytotoxic agent that has already found its target (cancer cells) in regressing the tumour. As such, the matrix reduction-rate function is expressed by the following polynomial expressionδd is the ECM decay rate (in days-1) and ΩT—while ad are scalar model parameters controlling the reduction-rate function, C. The parameter values are provided in part in Following Vavourakis et al. , structufound in   part, Fg, to the deformation gradient tensor: F = Fe ⋅ Fg. As in [Fg = λgI, where I the identity tensor. The volumetric stretch ratio λg is related to the Green-Lagrange volume strain through ξ, that promotes cancer cell proliferation, as well as with respect to the tissue structural integrity, ϵ, within the tumour (i.e. ∀X ∈ ΩT). Therefore, for the cancer mass growth, the following modified Gompertz function is proposedαg, βg, γg, δg are dimensionless parameters of the growth constitutive function  that modulates the tissue softening/stiffening with respect to the ECM density, while model parameters μ and κ are material constants representing the stiffness of the ECM .Working in a similar modelling approach to , where t(a) the model describing the extension of the tip blood vessels (by following a snail-trail modelling approach), the sprouting of blood vessels and the formation of vascular anastomoses, and (b) a model that describes the capillary endothelial-wall remodelling and structural integrity with respect to tumour growth-induced solid and fluid mechanical forces. Regarding point (a), vessel-tip and sprout elongation is described via a combination of the chemotactic contribution due to gradients of the angiogenic factors promoting vasculogenesis, the haptotactic contribution due to insoluble gradients of the ECM, and the mechanotactic contribution due to the mechanical forces elevation while the tumour develops in the host. Also, regarding point (b), blood vessel lumen and wall is remodelled with respect to the capillaries haemodynamics. As such, wall shear stress works as a stimulus for vessel remodelling (see Eqs (18)—(20) from [ϑg, owing to the tumour development, as well as the tissue macroscopic elastic parameters of the stored-energy function, m in Detailed description of the dynamic angiogenesis model can be found in the recent paper of Vavourakis et al. . In brie20) from ), while 20) from ). HoweveFinally, one modelling aspect the present in-silico framework does not account for is lymph-angiogenesis or the mechanics of lymphatic vessels in response to external mechanical stimuli. However, we leave this as a future modelling development to the present in-silico framework.The governing equations describing the biochemical model of the in-silico framework, i.e. the balance of the oxygen/nutrients, the tumour-angiogenic factors, and the matrix degrading enzymes are defined in detail in our recent paper (see Eqs (12)—(14) in ). The adVascular Network Module, the Biochemical Solver Module, the Solid Solver Module, the Fluid Solver Module and the Drug Delivery Solver Module. The corresponding modules and building blocks of the proposed in-silico framework are illustrated in The present multiscale, multiphysics, in-silico modelling framework consists of five interconnected core compartments that encompass different aspects of the tumour–host micro-environment mechano-biology. The compartments of the framework—called here modules—are the The numerical procedure of the coupled in-silico tumour-growth, hypoxia-induced angiogenesis and drug delivery solvers, in Biochemical Solver Module (see the time (integration) step of the reaction-diffusion equations of the Solid Solver Module (see Eq (2) from [the time increment between successive solutions of the linear momentum equation for the (tissue mechanics) (2) from ), which Vascular Network Module, which is of the order of hours;the time increment used to update the tree of the Fluid Solver Module, which is also of the order of hours  step used to solve the drug transport equations of the  see Eqs , which iBiochemical and Solid Solver Module and the extravascular model of the Drug Delivery Solver Module were discretised using three-dimensional FEs, while for the intravascular model of the Drug Delivery Solver Module and the Fluid Solver and the Vascular Network Module a one-dimensional FE discretisation has been adopted  method, while time-stepping for the five modules has been implemented in a staggered manner. As illustrated in ils read ).Biochemical Solver Module PDEs are solved together, the solution of which is projected into the Solid Solver Module and equilibrium of solid forces is sought numerically (owing to the tumour development). Subsequently, the solution of both above-mentioned modules is transferred into the Vascular Network Module and the tree gets updated . Then, the Fluid Solver Module is invoked to compute the interstitial and intra-/trans-vascular flow. The Fluid Solver Module operates at the end of every successful Solid Solver and Vascular Network Module, and before the Drug Delivery Solver Module, due to both blood/plasma and interstitial fluid flow being assumed viscous-dominated (at extremely low Reynolds numbers) and quasi-steady. This is necessary to account: (a) for the localised effects of solid stresses elevation or reduction, which is owed respectively to the natural tumour growth or the regression of the tumour in response to the cytotoxic drug, and (b) the dynamic changes of the vascular network growth  which may directly affect blood flow and, thus, implicitly interstitial fluid flow. With regard to (a), as elaborated in [Vascular Network Module. The solution of the Fluid Solver Module together with the solution of the Biochemical Solver Module is transferred to the Drug Delivery Solver Module to compute numerically the drug distribution in the vascular and extravascular spaces. Finally, the state of the vascular segments inside or proximal to the tumour region is re-assessed, and the list of collapsed blood vessels is revised while the microvascular pressure and interstitial fluid pressure distribution is updated by re-invoking the Fluid Solver Module. In summary, the coupled solver  iterates until the simulation reaches the desired time point (e.g. here is set to 40 days), where the frequency with which each module is invoked depending on the time-step/increment chosen.Numerical solution of the balance equations has been accomplished sequentially and according to the following order: First, the rated in , during C++ code of the in-silico cancer modelling platform can be accessed online via Bitbucket from: FEB3)Finite Element Bioengineering in 3D , and thus mimic desmoplastic tumours . All model/material parameters are presented in the supplementary tables: 3 cubic domain of the tumour–host tissue. The domain is represented by a three-dimensional finite element mesh, constructed using Gmsh (http://gmsh.info/) and consists of 3,320 hexahedra and 3,963 nodes. Embedded to the three-dimensional (tissue) mesh is a one-dimensional, non-conforming mesh to represent the vascular network, consisting of 2,880 linear line elements (baseline mesh at day 0). Simulations were performed for a spherical cancer mass with 1 mm initial diameter inside a ≈ 1.7 cmδmax and λ to describe the three-dimensional architecture of the vascular network [δmax variable we consider its dimensionless form, i.e. the ratio of the variable at a specific time point with respect to the baseline value (control) at day 0, which designates the transition from avascular to vascularised tumour growth. We will also use henceforth the term ‘vascular architecture normalisation’ to refer to the process of remodelling δmax and λ to their normal physiological values [δmax in the value range between 1.0 and 1.4, while for the second parameter when λ > 0.As explained in the Introduction, we use the variables  network ; that isl values , while tl values . Hence, rp (referred hereafter as ‘poresize’ for brevity), ranging from 10 nm to rp = 150 nm (see rp = 150 nm) versus mature / less permeable vessels (rp = 10 nm). Also, to simplify the presentation of the in-silico results and aid the discussion, we choose henceforth the two extreme values of rp, which were termed low and high poresize, respectively. As such, low poresize reflects the case where vascular normalisation has been performed in accordance to previous studies [To test the effect of neoadjuvant vascular normalisation by a vascular normalising agent, we conducted simulations with three different initial values of the size of the vascular wall pores, 0 nm see . However studies .kon, spanning from 0.005 s-1 to 5 s-1  is reserved for future work.To test the effect of cytotoxic drug binding properties we also conducted simulations with different rates of affinity,  s-1 see , with thFinally, we define three tumour stages: early- (10 days from baseline), mid- (20 days from baseline) and late-stage (30 days from baseline). This allows us to test the effect of initial vascular architecture and tumour size on drug efficacy.sc = 1 nm approximately and the specific interstitium properties . For a  Dc, see  is of thBox 1 in ). TherefBox 1 in , 40, 41,Kvsc definition in Transvascular flow model). Also, in view of Comparing quantitatively the results between In summary, these predictions reflect the complex relationship between tumour growth and vessel poresize at low affinity, shown in kon, and vessel poresize, rp, on tumour regression, To investigate the relative effects of drug affinity (binding rate), The control case shows that after 10 days the tumour grows approximately with constant rate  that diffusion is the dominant mode of transport for chemotherapeutic agents, and (ii) that for high-binding drugs the penetration length is small; while for high-binding drugs pertaining very fast diffusion rates, the drug clears out of the tumour quickly. Therefore, the limiting factor can be implicitly regarded as the size of the tumour, since it defines the limit on the mass of internalised drug. Interestingly, at low affinity a dependency on poresize is predicted: in cb such that the tumour size—and hence the mass of drug that can be internalised—becomes the limiting factor. This effect can be predicted in the concentrations of bound/associated and internalised drug, cb and ci respectively, over time (see cb has little effect on ci level.At high affinity  the resptime see : there i3 day-1) for the control versus the treated simulations  and for the four combinations of poresize and drug affinity values. In accordance with the results shown in 3 day-1 rate for late-stage injections of a high affinity drug; whereas for early-stage injections of the same drug can fairly regress a “premature” tumour at a rate 0.2—0.7 mm3 day-1 initially, but as depicted in sc, is comparably smaller to the poresize, rp, in both cases) the average tumour development rate—within a 5-day time frame post injection at day 10—is 3.6 and 2.8 mm3 day-1 respectively, while the corresponding tumour growth rates of the control—at the same time frame—are 5.3 and 5.1 mm3 day-1 respectively. In summary, the above findings elucidate the implications for the outcome of low/high affinity cytotoxic drugs and the importance of the time of injection with respect to the tumour stage.Another interesting prediction from (a) the concentration of the drug that has “hit” the cancer mass is approximately fifteen times higher for when the affinity is increased by four orders of magnitude  Also, it is interesting to observe the direct effect of the tumour vascular tree non-hierarchical structure and pattern to the distribution of the drug in the cancer mass. For high affinity drug and early-stage drug administration, the concentration of the cytotoxic is enhanced at sparse locations of tumours—especially those where perfused newly-formed vessels are located adjacent to the tumour. On the contrary, for late-stage injection , the concentration of the cytotoxic is rather evenly distributed at the tumour periphery.In support to the above observations, see also . (b) AlsThe effects of binding affinity and vessel poresize on the tumour’s physical environment were investigated by plotting the tissue hydrostatic pressure (THP) of the solid stresses and the interstitial fluid pressure (IFP)—both evaluated at the tumour and the peri-tumoural host tissue—as a function of time for the four combinations of low and high affinity and poresize . As befoBoth THP and IFP show similar trends as the tumour volume , reflectIn the control case, the IFP increases logarithmically for high poresize, which produces a sharper initial gradient and maximum value than for low poresize due to the increased extravasation flux. After injection, the maximum value of the IFP decreases with increasing affinity, and at high affinity converges to approximately the same value, independently of poresize or injection time . At low (a) THP and IFP are implicitly reduced by cytotoxic drug delivery, and (b) as a result of the reduced IFP, drugs that are dependent on convective transport—such as liposomes or nanoparticles—should not be administered after treatment by cytotoxic drugs. This has implications for therapies that aim to alleviate solid stresses in order to decompress collapsed vasculature and, hence, enhance drug delivery and for staged treatments that aim to maximise delivery of nanoparticles [Taken together, these results propose two main points: articles , 43.δmax while To investigate the effect of cytotoxic drug delivery on tumour vessel architecture, δmax increases approximately linearly and in phase with the relative tumour volume increase, as can be seen by comparison of Figs δmax increases with tumour volume until treatment, when it either continues to increase but to a smaller maximum (injection at day 10), or decreases after treatment (injections at day 20 or day 30). This is due to the development of compressive solid stress that results in larger avascular regions within the tumour. As a result, δmax has a similar dependency on affinity and poresize: high affinity produces a more normalised vascular structure than low affinity, for all injection times  cytotoxic drugs can implicitly normalise the tumour-associated vasculature, and (b) this normalisation is only dependent on the size of the pores of the tumour vessels for low affinity drugs.The value of λ decreases smoothly from a positive to a negative value in the control case, which reflects the pathological change in distribution of the vasculature from a regular/uniform to an irregular/non-uniform pattern e.g. . After iδmax, and (plates E—H) λ as a function of injection time and time from baseline for the four combinations of low and high affinity and poresize. The contours were calculated by cubic interpolation of the injection data  presented in SciPy’s interpolation module (scipy.interpolate).The optimal time period (‘window’) for vascular architecture normalisation is explored in δmax to 1 and increase λ to positive values). This is evident in δmax → 1; whereas in the corresponding As in the previous section, δmax across time post the time of injection, while λ can be normalised only at a broad range of injection—especially in early and intermediate times (with respect to the tumour volume) of injection. Interestingly, as indicated by the regions bounded by the dashed lines, in δmax approaching the physiological range, 1—1.4—orients the optimal window where the tumour vessels become relatively even spaced; while in On the contrary, by comparison of columns 1 and 2 in δmax versus λ with respect to the variable ch, which denotes the total  cancer drug concentration, for the four combinations of low and high affinity and poresize are illustrated in SciPy library.To explicitly link vascular architecture with cytotoxic delivery efficiency and follow-up drug treatment potential, contour plots of δmax and λ, i.e. the area of the contour, depends on poresize only at low affinity . In summary, these results, and in conjunction with the extensive series of simulation results shown in The range of (i) dynamic remodelling of the tumour-associated vascular network as a result of growth-induced solid stresses, (ii) biophysical vessel sprouting that explicitly accounts for chemo-, hapto- and mechanotaxis, and (iii) solid stress-dependent vascular remodelling and compression/collapse. Here we extended the model to include extra- and intravascular drug delivery, and specified the model to simulate the transport of cytotoxic drugs to murine mammary carcinomas. The complete model has been implemented in our in-house, open-source numerical platform FEB3 (https://bitbucket.org/vasvav/feb3-finite-element-bioengineering-in-3d/wiki/Home). The proposed in-silico framework allowed us to study the dynamics of tumour growth and cytotoxic drug delivery, and hence comment on the following key topics identified by [(a) the relative roles of convective and diffusive transport for cytotoxic drugs, (b) the effects of tumour microvascular structure and function, and (c) methods to enhance delivery by modification of IFP and reduction of tissue stress by induction of tumour cell apoptosis.We have presented a novel in-silico multiscale modelling framework of coupled tumour growth, angiogenesis and drug delivery. The model builds on our previous work , allowinified by : (a) theFurthermore, our model is flexible: drugs of varying size and affinity can be simulated, while the poresize of the tumour vessels can be specified. We can thus implicitly simulate the effect of neoadjuvant blood vessel normalisation by setting a small initial vessel wall poresize. This allowed us to also comment on, firstly, the relative importance of affinity and poresize in targeting tumours, and subsequent normalisation of vasculature structure. Secondly, the in-silico model permitted us to investigate the potential implications for staged delivery .(i) confirm that chemotherapeutic agents’ delivery is dominated by diffusive transport; (ii) the time of treatment is important for low affinity but not high affinity drugs; (iii) vessel poresize plays an important role in the effect of low affinity but not high affinity cytotoxic drugs; (iv) high affinity cytotoxic drugs provide a large window for vascular architecture normalisation; and (v) the combination of large poresize and high affinity enhances cytotoxic drug delivery efficiency. The model provides valuable insight into the complex system of biophysical factors that generate these hypotheses. In particular, it suggests that the combination of diffusive transport and pathological tumour-associated angiogenesis allows cytotoxic drugs to target the tumour across a broad range of vasculature architectures. Furthermore, our modelling framework allows for these hypotheses to be tested by comparing directly to experimental data from mouse models treated with vascular normalisation agents and drugs of varying affinity, when it becomes available.The main hypotheses proposed by our model are: We note that it is difficult to directly validate our drug delivery model predictions, given the sparsity of literature available that is both suitable in terms of the application and that can completely specify the model. Here we have made every effort to specify the model where the data were available; this is evidenced in our previous work , where wWhile we have strived to make the model representative of in-vivo conditions, it has limitations. The high complexity of the model requires a large number of model parameters to be specified and, hence, the results are dependent upon the choice of these parameters. Where possible we chose experimental data from the literature to best represent the growth of murine mammary carcinomas, detailed in In order to reduce the dependency of the model on material parameters, and in particular those where the literature is sparse, some simplifications were made. The tissue hydraulic conductivity was assumed isotropic and independent of solid deformations, which would affect fluid flow as the tumour grows , 45. HerS1 File(PDF)Click here for additional data file.S2 File(PDF)Click here for additional data file.S1 Fig(A) Clipped mesh, showing the internal structure of the grid. (B) The extracted tumour region, shown here as a spheroid, with the complete vascular tree rendered as red segments and the black points denoting the vascular nodes.(TIF)Click here for additional data file.S2 Figrp = 10 nm, 50 nm or 150 nm, and kon = 0.005 s-1, 0.05 s-1, 0.5 s-1 or 5 s-1, respectively. All sub-figures illustrate the predictions for the control and the treated case (drug injected at day 10 (D10), day 20 (D20) or day 30 (D30)).Line plots of the averaged IFV magnitude as a function of time. The 3×4 matrix of plots depicts the in-silico results (both the control and the treated cases) for three poresizes and four affinities: (TIF)Click here for additional data file.S3 Figcb, and (E—H) internalised drug concentration, ci, expressed in dimensionless form  as a function of time. Each 2×2 matrix of plots depicts the in-silico results (treated case) for two poresizes: rp = 10 nm or 150 nm, and two affinities: kon = 0.005 s-1 or 5 s-1.Line plots of the (A—D) bound/associated drug concentration, (TIF)Click here for additional data file.S4 FigV = Vol.(t)/Vol.(t = 0)−1) as a function of time. The 3×4 matrix of plots depicts the in-silico results (both the control and the treated cases) for three poresizes and four affinities: rp = 10 nm, 50 nm or 150 nm, and kon = 0.005 s-1, 0.05 s-1, 0.5 s-1 or 5 s-1, respectively. All sub-figures illustrate the predictions for the control and the treated case (drug injected at day 10 (D10), day 20 (D20) or day 30 (D30)).Line plots of the relative tumour volume ((TIF)Click here for additional data file.S5 Figrp = 10 nm, 50 nm or 150 nm, and kon = 0.005 s-1, 0.05 s-1, 0.5 s-1 or 5 s-1, respectively. All sub-figures illustrate the predictions for the control and the treated case (drug injected at day 10 (D10), day 20 (D20) or day 30 (D30)).Line plots of THP as a function of time. The 3×4 matrix of plots depicts the in-silico results (both the control and the treated cases) for three poresizes and four affinities: (TIF)Click here for additional data file.S6 Figrp = 10 nm, 50 nm or 150 nm, and kon = 0.005 s-1, 0.05 s-1, 0.5 s-1 or 5 s-1, respectively. All sub-figures illustrate the predictions for the control and the treated case (drug injected at day 10 (D10), day 20 (D20) or day 30 (D30)).Line plots of IFP as a function of time. The 3×4 matrix of plots depicts the in-silico results (both the control and the treated cases) for three poresizes and four affinities: (TIF)Click here for additional data file.S7 Figδmax as a function of time. The 3×4 matrix of plots depicts the in-silico results (both the control and the treated cases) for three poresizes and four affinities: rp = 10 nm, 50 nm or 150 nm, and kon = 0.005 s-1, 0.05 s-1, 0.5 s-1 or 5 s-1, respectively. All sub-figures illustrate the predictions for the control and the treated case (drug injected at day 10 (D10), day 20 (D20) or day 30 (D30)).Line plots of normalised (TIF)Click here for additional data file.S8 Figrp = 10 nm, 50 nm or 150 nm, and kon = 0.005 s-1, 0.05 s-1, 0.5 s-1 or 5 s-1, respectively. All sub-figures illustrate the predictions for the control and the treated case (drug injected at day 10 (D10), day 20 (D20) or day 30 (D30)).Line plots of λ as a function of time. The 3×4 matrix of plots depicts the in-silico results (both the control and the treated cases) for three poresizes and four affinities: (TIF)Click here for additional data file.S9 Figδmax as a function of time and injection time. The 3×4 matrix of plots depicts the in-silico results for three poresizes and four affinities: rp = 10 nm, 50 nm or 150 nm, and kon = 0.005 s-1, 0.05 s-1, 0.5 s-1 or 5 s-1, respectively.Contour plots of (TIF)Click here for additional data file.S10 Figrp = 10 nm, 50 nm or 150 nm, and kon = 0.005 s-1, 0.05 s-1, 0.5 s-1 or 5 s-1, respectively.Contour plots of λ as a function of time and injection time. The 3×4 matrix of plots depicts the in-silico results for three poresizes and four affinities: (TIF)Click here for additional data file.S11 Figch as a function of tumour development time and injection time. The 3×4 matrix of plots depicts the in-silico results for three poresizes and four affinities: rp = 10 nm, 50 nm or 150 nm, and kon = 0.005 s-1, 0.05 s-1, 0.5 s-1 or 5 s-1, respectively.Contour plots of (TIF)Click here for additional data file.S12 Fig-1) to the length of all functional vessels (i.e. that have not collapsed). The 3×4 matrix of plots depicts the in-silico results (both the control and the treated cases) for three poresizes and four affinities: rp = 10 nm, 50 nm or 150 nm, and kon = 0.005 s-1, 0.05 s-1, 0.5 s-1 or 5 s-1, respectively. All sub-figures illustrate the predictions for the control and the treated case (drug injected at day 10 (D10), day 20 (D20) or day 30 (D30)).Line plots of the fraction of perfused vessels (FPV) as a function of time. FPV is described as the ratio of the length of all functional vessels that are sufficiently perfused (blood flow velocity is >0.1 mm s(TIF)Click here for additional data file.S1 Videorp = 10 nm poresize, molecule (drug) size about sc = 0.5 nm, and drug affinity rate kon = 0.005 s-1. The wireframe on the left-hand-side depicts the tumour boundary and the coloured cloud on the right-hand-side corresponds to the distribution of the (dimensionless) drug concentration inside the tumour. The vascular network is shown as grey tubes on both sides of the animation, while the 3D tissue domain is made transparent for illustration purposes.Side-by-side comparison of a control and a treated murine mammary carcinoma after a bolus injection (at day 10) of a cancer cytotoxic agent. The simulation features a tumour vasculature of (MP4)Click here for additional data file.S2 Videorp = 10 nm poresize, molecule (drug) size about sc = 0.5 nm, and drug affinity rate kon = 0.005 s-1. Refer to the description of Side-by-side comparison of a control and a treated murine mammary carcinoma after a bolus injection (at day 20) of a cancer cytotoxic agent. The simulation features a tumour vasculature of (MP4)Click here for additional data file.S3 Videorp = 10 nm poresize, molecule (drug) size about sc = 0.5 nm, and drug affinity rate kon = 0.005 s-1. Refer to the description of Side-by-side comparison of a control and a treated murine mammary carcinoma after a bolus injection (at day 30) of a cancer cytotoxic agent. The simulation features a tumour vasculature of (MP4)Click here for additional data file.S4 Videorp = 10 nm poresize, molecule (drug) size about sc = 0.5 nm, and drug affinity rate kon = 5 s-1. Refer to the description of Side-by-side comparison of a control and a treated murine mammary carcinoma after a bolus injection (at day 10) of a cancer cytotoxic agent. The simulation features a tumour vasculature of (MP4)Click here for additional data file.S5 Videorp = 10 nm poresize, molecule (drug) size about sc = 0.5 nm, and drug affinity rate kon = 5 s-1. Refer to the description of Side-by-side comparison of a control and a treated murine mammary carcinoma after a bolus injection (at day 20) of a cancer cytotoxic agent. The simulation features a tumour vasculature of (MP4)Click here for additional data file.S6 Videorp = 10 nm poresize, molecule (drug) size about sc = 0.5 nm, and drug affinity rate kon = 5 s-1. Refer to the description of Side-by-side comparison of a control and a treated murine mammary carcinoma after a bolus injection (at day 30) of a cancer cytotoxic agent. The simulation features a tumour vasculature of (MP4)Click here for additional data file.S1 TableFluid Solver Module (see List of model parameters associated with the dule see . Cells m(PDF)Click here for additional data file.S2 TableVascular Network Module (see List of model parameters associated with the dule see . Paramet(PDF)Click here for additional data file.S3 TableBiochemical Solver Module and the Drug Delivery Solver Module (see List of model parameters associated with the dule see . Cells m(PDF)Click here for additional data file.S4 TableSolid Solver Module (see List of model parameters associated with the dule see . Cells m(PDF)Click here for additional data file.S5 Table3 day-1) of the control versus the treated cases (for the three injection instants). Each sheet lists the results for a different pair of poresize and affinity values .The tabulated data compare the tumour volume rates (in mm(XLSX)Click here for additional data file.


These systems showed a very high binding preference for K+ over Na+ ions. The use of confocal fluorescence microscopy indicated successful anchoring of the cholesterol-bearing fluorescent probes to the living cell membrane. These structurally simple cholesterol-based fluorescent probes have good potential for opening up new and exciting opportunities in the field of biosensors; e.g., in vivo detection of K+ ions.The purpose of the present work was to design, synthesize and spectrally characterize cholesterol-anchored fluorescent oligonucleotide probes (Ch(F-TBA-T), Ch(py-TBA-py)), based on G-quadruplexes, which were able to incorporate into a lipid structure . The probes, based on the thrombin-binding aptamer (TBA) sequence, were labeled with fluorescent dyes which enabled simultaneous monitoring of the formation of G-quadruplex structures and visualization of probe incorporation into the cellular membrane. The combinations of fluorophores used included fluorescence resonance energy transfer (FRET) and excimer emission approaches. The structural changes of the probes upon binding with K The structure and function of biological membranes are largely controlled by their lipid composition, and particularly by cholesterol, which represents up to 40% of the lipids in plasma membranes ,2. SinceOn the other hand, functionally designed lipid-anchored DNA oligonucleotides have significantly promoted many important developments in fields such as nanobiotechnology ,8,9, cel+ (1.3 Å) and Na+ (1.16 Å); hence, physiological buffer conditions favor their formation . T. T48]. T+/Na+ selectivity.Two cholesterol-bearing fluorescent potassium probes (Ch(F-TBA-T), Ch(py-TBA-py)) based on G-quadruplexes, for spontaneous anchoring into the hydrophobic interior of lipid structures, were designed. The oligonucleotide probes preserved their abilities to fold into a G-quadruplex structure and to interact with metal cations with binding affinity and selectivity comparable to unmodified oligonucleotides. All probes exhibited higher potassium ion binding affinity and advantageous KWe also reported the G-quadruplex folding ability of cholesterol-anchored fluorescent oligonucleotide probes at the Langmuir monolayer interface, which mimicked the biomembrane surface. The monolayer (cell surface) interaction with the probe is dictated by the physicochemical properties of the probe and the monolayer (cell) interface. In our study, there were two major driving forces involved in the monolayer/probe interaction: (i) the hydrophobic insertion of cholesterol moieties into the membrane; and (ii), the electrostatic interaction between the positively charged DODAB monolayer and the negatively charged DNA. The fluorescent probes without cholesteryl-TEG anchors  appeared to bind membranes with positive charge , which is expected for the negatively charged polyelectrolytes like DNA. On the other hand, the cholesterol-anchored probes (Ch(F-TBA-T), Ch(py-TBA-py)) penetrated the hydrophobic domain of the lipid monolayer. Based on confocal fluorescence microscopy, the cholesterol-containing fluorescent oligonucleotide probes (Ch(F-TBA-T), Ch(py-TBA-py)) were shown to be located on the cell membrane of HeLa cells.


Three transketolase genes have been identified in the human genome to date: transketolase (TKT), transketolase-like 1 (TKTL1) and transketolase-like 2 (TKTL2). Altered TKT functionality is strongly implicated in the development of diabetes and various cancers, thus offering possible therapeutic utility. It will be of great value to know whether TKTL1 and TKTL2 are, similarly, potential therapeutic targets. However, it remains unclear whether TKTL1 and TKTL2 are functional transketolases.Homology modelling of TKTL1 and TKTL2 using TKT as template, revealed that both TKTL1 and TKTL2 could assume a folded structure like TKT. TKTL1/2 presented a cleft of suitable dimensions between the homodimer surfaces that could accommodate the co-factor-substrate. An appropriate cavity and a hydrophobic nodule were also present in TKTL1/2, into which the diphosphate group fitted, and that was implicated in aminopyrimidine and thiazole ring binding in TKT, respectively. The presence of several identical residues at structurally equivalent positions in TKTL1/2 and TKT identified a network of interactions between the protein and co-factor-substrate, suggesting the functional fidelity of TKTL1/2 as transketolases.Our data support the hypothesis that TKTL1 and TKTL2 are functional transketolases and represent novel therapeutic targets for diabetes and cancer.The online version of this article (10.1186/s12900-018-0099-y) contains supplementary material, which is available to authorized users. Three transketolase genes have been identified in the human genome to date. Transketolase EC 2.2.1.1; TKT) is a crucial enzyme that links the pentose phosphate pathway (PPP), a non-oxidative glucose pathway (NOGP), to glycolysis. The TKT gene is located on chromosome 3 at position 3p21.1 and is responsible for generating sugar phosphates for intracellular nucleotide metabolism as well as for the production of nicotinamide adenine dinucleotide phosphate (NADPH), a reducing agent and anti-oxidant [.1; TKT iAn altered function of transketolase is linked to various pathophysiologic complications, suggesting TKT as a potential therapeutic target. TKT is the best studied transketolase, and previous studies reported an altered activity in patients suffering from diabetes and various cancers –5. For eWhat about TKTL1 and TKTL2? There is limited information regarding the role of TKTL2; more is known about TKTL1. TKTL1, previously postulated to be a pseudogene, encodes a transketolase like-protein that is also linked to cancer . For exaHuman TKT and TKTL1 differ in both primary structure and in amino acid composition , and sevIn light of conflicting findings and uncertainties, the current study investigated the possible function of TKTL1 and TKTL2 as putative transketolases. We employed homology modelling and structural analysis of TKTL1 and TKTL2 using the 0.97 Å crystal structure of TKT in complex with a TDP co-factor-substrate as the template .Escherichia coli TKT, that appear to have been lost in the mammalian group enzymes during evolution, map to unstructured regions between α5-β2 and α9-β6 and within the α9 and α12 helices , including H77, that is involved in stabilizing TDP in the active site of TKT. However, as we show below, this deletion does not affect the overall structure of the modeled TKTL1 enzyme and the absence of an H77 homolog may be compensated for by alternative stabilizing interactions to TDP. TKTL2 does not contain this deletion and is 64% identical and 92% conserved compared to TKT in this region  as an appropriate template for modelling , was aligned to chain A of the TKTL1 or TKTL2 model using the Matchmaker tool in Chimera , 23. ThiThe binding of the TDP substrate and substrate adduct in the active site of TKT has been pursued by a number of previous studies , 16, 17.2+ cation  or that assayed endogenous TKTL1, reported transketolase activity , 32. HerIn light of these two studies demonstrating TKT activity for recombinant  and endoTogether these results support our hypothesis that TKTL1 and TKTL2 are functional transketolases and thus opens up the possibility that altered activity/function may be implicated in diseases such as diabetes and cancer. Additional physiological and biochemical studies should therefore be pursued to assess how modulation of such enzymes may be exploited as putative therapeutic interventions for diabetes and cancer.Additional file 1:TKTL1_model: TKTL1 in covalent complex. X-ray diffraction data. (PDF 2100 kb)Additional file 2:TKTL2_model: TKTL2 in covalent complex. X-ray diffraction data. (PDF 2185 kb)


Dan Cao's name appeared incorrectly throughout the article as Cao Dan.In the article, “Comparative effectiveness of hyperthermic intraperitoneal chemotherapy for gastric cancer: A systematic review and network meta-analysis protocol”,The corresponding author information should be “Correspondence: Bo Zhang, Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China (e-mail: hxwcwk@126.com); Dan Cao, Department of Medical Oncology, Cancer Center of West China Hospital, Sichuan University, Chengdu 610041, China (e-mail: caodan316@163.com).”


Glioma is the commonest primary intracranial tumour and it has been the most predominant tumour in many studies. It accounts for 24.7% of all primary brain tumour and 74.6% of malignant brain tumour. Intraoperative diagnosis plays a crucial role in determining the patient management. Frozen section has been the established technique in providing rapid and accurate intraoperative diagnosis. However due to some disadvantages like ice crystal artefact, high expenditure and requirement of skilled technician, there is increase usage of cytology smear either replacing or supplementing frozen section technique. The aim of this review is to determine the diagnostic accuracy of cytology smear and frozen section in glioma and to see whether there is significant difference between those techniques. The overall diagnostic accuracy for frozen section in glioma ranging from 78.4% to 95% while for cytology smear, the diagnostic accuracy ranging from 50% to 100%. Based on certain literatures, no statistically difference was observed in diagnostic accuracy of cytology smear and frozen section. Thus, cytology smear provides an alternative method in establishing intraoperative diagnosis. Both cytology smear and frozen section are complimentary to each other. It is recommended to use both techniques to improve the diagnostic accuracy in addition with adequate knowledge, clinical history, neuroimaging and intraoperative findings. Glioma is a central nervous system tumour arises in glial tissue and it can be astrocytoma, oligodendroglioma, ependymoma or mixture of these tumours.Worldwide, glioma is the most common primary intracranial tumour. It accounts 24.7% of all primary brain tumour and 74.6% of malignant brain tumour . It also has been the predominant neoplastic lesion in most of the studies . Intraoperative histopathological diagnosis plays a crucial role in evaluating the sample adequacy, determining the diagnosis and tumour margins, optimizing the surgical procedure, post-surgical follow up and treatment. In certain situation, it can detect an unexpected lesion that cannot be determined by clinical or radiological imaging. This will reduce the surgical related morbidity and avoid the need for unnecessary second invasive procedure. Although advances of neuroimaging have been established, histopathological examination remains the gold standard in diagnosing glioma . Frozen section has been the established intraoperative histopathological evaluation worldwide since it was first introduced in 1891. Subsequently, cytology smear was introduced later and there was an increased usage of this technique for supplementing or replacing the use of frozen section technique due to advent of stereotactic procedures and the need for rapid intraoperative consultation . Cytology smear technique includes crush preparation and touch imprint. In crush preparation, the fresh tissue is gently crushed on a glass slide by a second slide held at right angle. Meanwhile, the fresh tissue is gently touched on a glass slide using a forcep in touch imprint.Both air dried smears and 95% ethyl alcohol fixed imprints will be stained using haematoxylin and eosin stains. Both cytology and frozen are important diagnostic tools in providing rapid and accurate intraoperative diagnosis. However in some hospital centres, frozen section facilities are limited and only cytology smear is available. The latter technique can only be applied in diagnosing intraoperative glioma replacing frozen section if there is no difference in diagnostic accuracy. Thus, the rationale of this article is to determine the diagnostic accuracy of cytology smear and frozen section in glioma and to see whether there is significant difference between those techniques.From the According to , they concluded that there was no statistically difference in diagnostic accuracy of crush preparation and frozen section in intraoperative diagnosis. The p value was > 0.05. Thus, cytology smear can be used as a replacement or supplementing technique to frozen section. This is basically important when frozen section facilities and skills are unavailable, unequivocal result from frozen section technique or tissue sample is limited. In some studies, the accuracy of intraoperative diagnosis increases when both techniques are used . It is said that both techniques complemented each other in getting the accurate diagnosis.Frozen section or cytology smearA rapid and accurate intraoperative diagnosis is mandatory in determining the patient management. Frozen section has been the established technique as it provides good architectural detail of the lesion and display better histologic pattern and cytomorphology . For example, palisading necrosis and microvascular proliferation are better appreciated in frozen section in a case of high grade glioma . This technique is said to be useful in diagnosing firm and rubbery central nervous system tumour such as meningioma, ependymoma and metastatic tumour . In glioma, frozen section showed poor quality slide preparation due to its soft nature .  The diagnostic accuracy of frozen section for glioma is limited. This is due to ice crystal artefact causing distortion of architectural details which can lead to over grading of glioma . Furthermore, trained staff and expensive equipment are also needed to prepare the slide where in some countries these facilities are lack.Meanwhile, the cytology smear is simple, rapid, technically easier and reliable. It requires only a small piece of tissue for each slide especially when the tissue is limited and further paraffin section is needed . This technique is significantly useful in soft and friable tumour such as in glioma as it can show good cellularity on smear . In addition, the nuclear and cytoplasmic details are better observed by cytology smear . Based on literatures, squash smear is superior to frozen section in demonstrating the characteristic features of glioma such as thick and coarse glial fibril background, rosettes, pseudo-rosettes and Rosenthal fibres . The main disadvantages of squash smear are crush or overstretch artefact and difficulty in interpreting the slide if the smears are too thick .Meanwhile, touch imprint is best for cellular lesion and demonstrate better morphological details without crushing artefact that are encounter in squash smear preparation . The major disadvantage of this touch imprint technique is poor cellular yield which reduce the diagnostic accuracy of this technique . Careful handling is also needed in the preparation of touch imprint slide as the architecture might be distorted due to soft nature of the specimen .CytomorphologyBased on literatures, it is unwise and careful caution should be practise upon categorizing and grading glioma in rapid intraoperative diagnosis. This is mainly due to heterogeneity of the tumour making it difficult to categorize and grade glioma . It is reported that glioma had attributed the highest discrepancy of cases . One of the common reasons causing discordant of result is sampling error . It includes inadequate sample or sampling at the wrong site.Due to this reason, making an intraoperative diagnosis can be quite difficult and there are higher chances of making an error. This especially important in mixed gliomas where difficulties are encounter due to predominance of one histological subtype. The other component may be missed especially if they are lesser in quantity and bland with dense fibrillary background of the astrocytic components . Other than that, high cellularity and nuclear pleomorphism might be wrongly interpreted especially in thick smear . This eventually causing overgrading of the tumour. Interference like necrosis and inflammation might also obscure the true morphology of the tumour . Due to this reason, the surgeon will try to avoid sampling from necrotic area. Unfortunately, this action will sometimes attributed to tumour undergrading . In a case of oligodendroglioma, freezing tissue often alter the morphology of the nucleus causing nuclear irregularity. This will make the tumour cells look similar to astrocytoma . Other than that, a prompt fixation is needed to visualize perinuclear halo in oligodendroglioma.Development of intraoperative molecular genotyping test in gliomaChallenges might happen in the situation of low grade glioma (grade II and III glioma), low cellularity yield and small stereotactic biopsy specimen. Fortunately, recent studies have identified somatic mutation in the isocitrate dehydrogenase 1 and 2 (IDH 1 and 2) as well as Telomerase Reverse Transcriptase (TERT) promoter genes which present over 80% of glioma . This has led to development of molecular genotyping test to detect these types of gene mutation using polymerase chain reaction (PCR) method.It required about 60-65 minutes to obtain the result . Despite longer period of time required in comparison with frozen and cytology smear, it has good sensitivity and specificity for WHO grade II and III glioma with 96% sensitivity and 100% specificity . Other than that, it may help in differentiating low grade glioma with other tumour or reactive gliosis . As a result, maximum tumour removal is achieved and repeated operative procedure can be avoided. Other test such as mass spectrometry and immunohistochemistry test are also available in detection of IDH1 mutation .In conclusion, frozen section technique is rapid and able to demonstrate the architectural characteristic however the major drawback of using this technique is freezing artefact. Besides that, this technique requires trained staff and expensive equipment. Based on many literatures, the diagnostic accuracy of cytological smear is almost equal to that of frozen section technique. Furthermore, cytology smear technique (squash smears and touch imprints) is rapid, less expensive, and technically much easier and requires only a small piece of tissue for each slide. It provides an alternative method when facilities for frozen section and skills are unavailable. Many studies have been proved that cytology smear and frozen section are complimentary to each other. It is recommended to use both techniques to improve the diagnostic accuracy if facilities and skills are available. Other than that, a trained neuropathology in addition with good clinical history, neuroimaging and intraoperative findings are crucial in yielding higher diagnostic accuracy. Study concepts/ review-analysis/ manuscript drafting and approved the final version: all authorsNone.


The objective of this study was to investigate, by means of a D-optimal mixture design, the combined effects of hydroxypropyl methyl cellulose (HPMC), xanthan (XG), and guar (GG) gums on physicochemical, rheological, and textural properties of gluten-free batter and bread. For each of the quality properties measured, a two-factor interaction model was fitted, and the significance of its terms was assessed by analysis of variance. Sticky batters were produced with a combination of high dose of GG (0.60%), high-intermediate dose of HPMC (3.36%), and low dose of XG (0.04%). Combinations of high XG dose (0.60%) and intermediate doses of HPMC (3.08%) and GG (0.32%) rendered GF breads of greater specific volume, while lower bread crust luminosity was obtained with combinations of high GG dose (0.60%), low XG dose (0.04%), and high-intermediate HPMC dose (3.36%). Combinations of high-intermediate HPMC dose (3.36%), high GG dose (0.60%), and low XG dose (0.04%) produced both softer crumbs and bread slices of more open visual texture. By using a desirability function that maximized specific volume while minimizing crust luminosity, crumb hardness, and mean cell density, the optimization of hydrocolloids mixture rendered a value of 0.54, for a combination of 0.24% XG, 0.60% GG, and 3.16% HPMC. Hydrocolloids have been widely used in food products to modify texture, improve moisture retention, control water mobility, and maintain overall product quality during storage . They arXanthomonas campestris bacterium, is also used in GF breadmaking, since it contributes to obtaining stable foams based on protein–gum interaction, with rheological properties typical of viscoelastic solids [Cyamopsis tetragonoloba beans, is used to improve mixing and batter tolerance; to extend the shelf life of products through moisture retention; and to improve loaf volume, bread crumb/crust, and color [Xanthan gum (XG), a polysaccharide produced by the c solids . In bakend color ,9,10. Hynd color ,12. SincUnderstanding the effect of combinations of ingredients in food products can be achieved through a special case of the response surface statistical approach: the mixture design . In mixtSaccharomyces cerevisiae lyophilized, Instaferm Lallemand, Setubal, Portugal) and HPMC (E464), XG (E415), and GG (E412) were provided by TecPan . All batches were produced using an own recipe with base formulation of rice flour (50%), maize flour (30%) and quinoa flour (20%) to which water (100% flour weight), sunflower oil (6% flour weight), white sugar (3% flour weight), refined salt (1.5% flour weight), instant yeast (3% flour weight) and hydrocolloid mixtures (set at 4% flour weight) were added following a standardized procedure. Demineralized water (pH = 6.8) kept at 5 °C overnight was used. After mixing dry ingredients for 2 min at speed 1, liquid ingredients were added and mixed for an additional 5 min at speed 4, in a professional food processor  equipped with a batter blade.GF breads were elaborated with rice flour , maize flour , white quinoa flour  obtained from quinoa grains milled in a laboratory disc mill , and then sieved through a rotating sifter. Sunflower oil, white sugar, and refined salt were purchased from a local supermarket . Instant yeast . Afterwards, all molds from the same batch were placed in a pre-heated convection oven  for 60 min at 190 °C. Bread loaves were un-molded after 2 h. All analyses were performed after 24 h.Batter and crumb rheology parameters were obtained using a texture analyzer TA-XT Plus implemented with the Exponent software version 6.1.11.0 . For calibration, a 5-kg load cell for stickiness analysis, and a 30-kg load cell for back-extrusion and TPA analyses were usedFor analysis of the batter rheological properties of GF batter, 175 g obtained after the mixing process was used. For the batter stickiness analysis, 5 g batter was weighed in a SMS/Chen-Hoseney stickiness cell (A/DSC) screwed to a SMS/Chen-Hoseney stickiness rig; and then examined by the texture analyzer using a 25-mm perspex cylinder probe (P/25P). Ten repetitions were done for each GF batter formulation. The parameter analyzed was the batter stickiness  .The back-extrusion analysis was performed using a 35 mm-diameter perspex flat probe (model A/BE), a standard size back-extrusion cylindrical container , and a backward extrusion rig (model A/BE). The container was filled with 85 g of batter (75% full) using a spatula for each analysis to minimize the presence of air bags when filling the container. Four repetitions were tested from each batch of GF batter. The back-extrusion parameter analyzed was the batter firmness . The settings of the stickiness and back-extrusion analyses were assessed according to Encina-Zelada et al. .The GF bread loaf volume (mL) was determined using a modified standard rapeseed displacement method 10-05 , but usiw) of the bread crumb was measured at 20 °C using an AquaLab , measurements were taken from four central slices from each loaf. Crust color was measured on five different zones of the top of the entire loaf, while crumb color was measured on the center of five slices per each loaf using the L* parameter (luminosity) according to color space CIELab system, with a colorimeter CR-400  which was calibrated using a white ceramic plate reference  before each measurement.Water activity . The GF loaves were sliced using an electric slicer  to obtain 20-mm thickness. The two extreme slices of each loaf were discarded, and cylindrical crumb samples of 30 mm-diameter were cut off from the center of each slice. Four sub-samples were analyzed per loaf. For the TPA, a 36 mm-diameter aluminum probe (model P/36R) was used. The settings of the TPA was assessed according to Encina-Zelada et al. , with a 2); mean cell density , and void fraction , calculated as the proportion of the two-dimensional space occupied by the cells, were computed using the binary segmentation procedure based on the k-means clustering algorithm, proposed in Gonzales-Barron and Butler [Slices of bread were scanned  using the IJ Scan Utility software  in grey level at −10% brightness, +15% contrast and 350 dpi resolution. Using the ImageJ software , the center of the bread crumb image was cropped into a 3.9 × 3.9 cm field-of-view ; and saved without any image compression in TIF format for posterior analysis. For each formulation, 24 images (3 loaves × 4 slices × 2 sides of the crumb) were acquired. The grain crumb features of mean cell area p > 0.05) of the terms was appraised. At a second stage, the model was re-fitted without the non-significant terms, in order to improve the precision of the estimates a non-significant linear term remained in the model only if it was significant in interaction with another independent variable. Model lack-of-fit test values were examined by the Fisher’s F-test p-value at a level of significance α = 0.05. Normal probability plots of residuals were built to evaluate data normality for all the models. External studentized residuals less than −4 or greater than +4 were considered outliers and were not included in the regression analysis. R2 and adjusted R2 were computed to evaluate the fitting quality of potential models; while predicted R2, adequate precision (AdPrec) and predicted residuals sum of squares (PRESS) were used to evaluate the prediction quality [For each quality property, a systematic procedure for model fitting was followed: first, the full model (Equation (1)) was adjusted and the significance  is converted into an individual desirability function (dn) that varies from 0 to 1; that is, in order to find the factor levels that take to a maximum response variable value, it is necessary to set dn = 1 for high values and dn = 0 for low values of the response variable. Oppositely, for a minimum response variable value, it is necessary to set dn = 0 for high values and dn = 1 for low values [1), variable 2 (d2), variable “n” (dn), and “n” is the number of response variables to optimize. The algorithm should search for response variable values where D tends to 1 [The doses of XG, GG, and HPMC hydrocolloids were optimized based on four quality attributes of bread: specific volume (maximization), crust luminosity (minimization), TPA hardness (minimization), and mean cell density (minimization). In this process, each response variable  was highly significant for describing the effects of the gums on batter stickiness. Likewise, the p-values of the STIba model were all highly significant for the linear mixture terms and the interactions XG × GG, XG × HPMC, and GG × HPMC. The XG × HPMC interaction was a non-significant term which was eliminated from the FIRba model , R2 adjusted (0.8185), and R2 predicted (0.8067) values. On the contrary, low R2 (0.6456), R2 adjusted (0.6092), and R2 predicted values (0.5575) were obtained for FIRba. In relation to the lack-of-fit test for GF batter properties, nearly all (except SCOba model) regression models yielded a significant lack-of-fit (p < 0.05), which may indicate that a higher-order model is needed so as to approximate the true response surface [R2 values.The model for STIba showed high  surface . Howeverp < 0.001, in p < 0.05) for STIba; while for FIRba, ‘XG × HPMC’ was the only non-significant interaction term .As regards coefficient of variation (CV), both batter properties analyzed (STIba and FIRba) showed CV values below 10%, having stickiness as the lowest variability (CV = 2.51%). The values of AdPrec, were all higher than 4, indicating that the polynomial models of all GF batter properties could adequately discriminate among treatments. STIba had lower PRESS value than FIRba. For each of the GF batter properties, the model’s F-test was highly significant  and three-dimensional plots are given in The response surfaces suggest that batters of lower stickiness were obtained with a combination of a high XG dose (0.60%) and intermediate doses of HPMC (3.00%) and GG (0.40%), while stickier batters were obtained with a combination of a high GG dose (0.60%), a low XG dose (0.04%) and a high-intermediate HPMC dose 3.36%; . For theAlthough not directly comparable with this study, using a higher level of water (158%) in a mixture of rice/maize/soy (40:40:20), Sciarini et al.  obtainedw, crust L* and crumb L* are presented in w; although GG in interaction with HPMC appears stronger than with XG for SVO (equation Y3). GG in interaction with HPMC appears slightly stronger than XG × GG interaction for aw (equation Y5). In agreement with equation Y4, GG interacts with XG and with HPMC to cause a decreasing effect on BLO. GG in interaction with XG boosts BLO more strongly than GG × HPMC interaction. GG in interaction with XG and with HPMC causes a synergetic effect on crust L* (equation Y6), and a decreasing effect on crumb L* (equation Y7).Differences in height and crust appearance of the GF bread treatments can be appreciated in p-value <0.0001) was highly significant for describing the effects of the gums. Likewise, the p-values of the SVO model were significant for the linear mixture terms and the interactions XG × GG and GG × HPMC. Since XG × HPMC was non-significant, it was eliminated from the models for SVO, BLO, aw and crust L*; while the non-significant XG × GG term was removed as a predictor of crumb L*. The GG × HPMC interaction was non-significant for crumb L* , R2 adjusted (0.9666), and R2 predicted (0.9654) values. On the contrary, low R2 (0.2161), R2 adjusted (0.2015), and R2 predicted (0.1789) values were obtained for crust L*. Among the GF bread physicochemical properties, SVO presented the highest R2 value (0.9677) compared to BLO (0.7834) and aw (0.5987); while among the colorimetric parameters, crust L* had a moderate R2 value (0.4944) in comparison with crumb L* (0.2161). In relation to the lack-of-fit test for the GF bread physicochemical properties, nearly all (except BLO model) regression models yielded a highly significant lack-of-fit (p < 0.001).The model for SVO showed very high w (0.07%), whereas the highest CV value was for baking loss (3.73%); moreover, all GF bread physicochemical properties showed CV values below 10%. Results inferior to 10% might be considered accurate, while values up to 20% are considered acceptable; however, for applications in agriculture, biotechnological processes, microbiology, and clinical protocols, the coefficients of variation are naturally high because of a wide dispersion in data [w, SVO and BLO had lower PRESS values than parameters of crust L* and crumb L*.In relation to CV, the lowest CV value was for a in data . The valp < 0.001, in p < 0.05) for all physicochemical properties. The interaction term ‘xanthan × guar’ was significant (p < 0.05) for SVO, BLO, aw and crust L*; interaction term ‘guar × HPMC’ was significant (p < 0.05) for SVO, BLO, aw and crust L*; while interaction term ‘xanthan × HPMC’ was highly significant (p < 0.001) for crumb L*. For each of the GF bread physicochemical properties, the model’s F-test was highly significant  and high doses of XG (0.60%) and GG (0.60%). Regarding the colorimetric properties of GF bread crust, lower crust L* values  were produced with a combination of a low XG dose (0.04%), a high GG dose (0.60%) and a high-intermediate HPMC dose (3.36%). In relation to the crumb color, higher crumb L* values  were obtained with a combination of a high XG dose (0.60%), and intermediate doses of GG (0.26%) and HPMC (3.14%).The response surfaces modeled suggest that bread loaves of high specific volume can be obtained with a combination of a high XG dose (0.60%) and intermediate doses of HPMC (3.08%) and GG 0.32%; . Lower mw (0.89–0.97 g) than those found in the present study; and found values of crumb L* (65.8–80.2) that were comparable to those measured in this study. Pongjaruvat et al. [Evaluating GF breads from different specialized supermarket sources, Matos and Rosell  measuredt et al.  formulatt et al.  studied t et al.  studied w and lighter crusts. Gum blends of low HPMC dose (2.80%) and high doses of XG (0.60%) and GG (0.60%) would appear as adequate since they lead to GF breads of higher SVO (2.85 mL/g) and lower values of BLO (12.6%), aw (0.982) and crust L* (68.7).In this specific topic, considering that from a consumer, producer, and food safety points of view, larger-sized breads with lower baking losses, lower water activity crumbs, and darker crusts are preferred to smaller breads with higher a8 and Y9, GG in interaction with both XG and with HPMC causes a synergetic effect on HARbr and ADHbr, respectively; however, GG × XG has a stronger effect on HARbr than GGxHPMC, and the opposite is true for ADHbr. According to equation Y10, GG in interaction with XG and with HPMC, and XG in interaction with HPMC cause a decreasing effect on COHbr; although XG in interaction with HPMC increases COHbr to a greater extent than XG × GG and GG × HPMC interactions, respectively. GG in interaction with XG has a stronger effect than in interaction with HPMC (equation Y10). In relation to equation Y11, GG in interaction with XG and with HPMC, and XG in interaction with HPMC causes a decreasing effect on RESbr; although XG in interaction with GG improves RESbr more strongly than both XG × HPMC and GG × HPMC interactions.According to p-value <0.0001) was highly significant for describing the individual effect (linear mixture terms) and the combined effects of interactions: XG × GG and GG × HPMC. In the prediction of HARbr, ADHbr, and RESbr, the XG × HPMC interaction was not significant. Likewise, the GG × HPMC interaction was eliminated from the RESbr model (R2 (0.7741), R2 adjusted (0.7670) and R2 predicted (0.7575). On the contrary, the lowest R2 (0.2076), R2 adjusted (0.189), and R2 predicted (0.1513) values were obtained for RESbr. Among the GF bread crumb textural properties, ADHbr presented the highest R2 value (0.7741) compared to HARbr (0.6602), COHbr (0.2849), and RESbr (0.2076). In relation to the lack-of-fit test for GF bread crumb textural properties, all regression models yielded a highly significant lack-of-fit (p < 0.001), most likely due to data variability and the non-use of higher order terms, as explained before.The ANOVA F-tests of textural properties of GF bread crumb models are shown in br model . The modp < 0.0001, in p < 0.0001) only for COHbr; and the interaction term ‘GG × HPMC’ was significant (p < 0.05) for HARbr, ADHbr, and COHbr. As regards CV, COHbr presented the lowest value (3.29%), while the highest one was for ADHbr (42.1%). Furthermore, COHbr and RESbr (6.49%) showed CV values below 10%, whereas HARbr (16.8%) and ADHbr presented CV values higher than 10%. The values of AdPrec, were all higher than 4, indicating that the polynomial models of all GF bread crumb textural properties could adequately discriminate among treatments. Crumb textural properties of COHbr and RESbr had lower PRESS values than HARbr and ADHbr. For each of the GF bread crumb textural properties, the model’s F-test and the linear mixture terms were highly significant (The response surfaces suggest that softer crumbs were obtained with a combination of a high-intermediate HPMC dose (3.36%), a high GG dose (0.60%) and a low XG dose 0.04%; . MixtureAccording to Cornejo and Rosell , high reIn this specific topic, since from a consumer standpoint, softer and more cohesive and elastic crumbs are preferred than harder and stiffer crumbs, combinations of a high HPMC dose (3.60%), a low XG dose (0.04%), and an intermediate GG dose (0.36%) would be appropriate to obtain GF bread crumbs of higher values of COHbr (0.433) and RESbr (0.167) and lower values of HARbr (2362 g) and ADHbr (−163 g·s).12, GG in interaction with XG causes a decreasing effect on MCA. GG in interaction with XG and with HPMC causes a synergetic effect on CDE (equation Y13), while XG in interaction with HPMC causes an opposite effect on CDE. GG interacts with XG to affect CDE to a greater extent than GG × HPMC and XG×HPMC do. According to equation Y14, GG in interaction with XG and with HPMC causes a decreasing effect on VFR; although GG in interaction with XG has a stronger effect than in interaction with HPMC.Differences in crumb grain features of the GF bread treatments can be appreciated in p-value <0.0001) was highly significant for describing the individual effect (linear mixture terms) and the combined effects of interactions of XG × GG, XG × HPMC and GG × HPMC. Since XG × HPMC interaction was non-significant, it was eliminated from the VFR model (R2 (0.6919), R2 adjusted (0.6871), and R2 predicted (0.6812). On the contrary, the lowest R2 (0.6513), R2 adjusted (0.6445), and R2 predicted (0.6337) values were obtained for CDE. Among the GF bread crumb grain features, VFR presented the highest R2 value (0.6919) in comparison with MCA (0.6522) and CDE (0.6513). In relation to the lack-of-fit test for the GF bread crumb grain features, all regression models yielded a highly significant lack-of-fit (p < 0.0001).The ANOVA F-tests of the GF bread crumb grain features’ models are shown in FR model . The modp < 0.0001, in p < 0.05). The interaction term XG × HPMC was significant (p < 0.05) for CDE, UNI, COM, and ARA; while the interaction term GG × HPMC was significant (p < 0.05) for CDE, UNI, and VFR.In relation to CV, the lowest CV value was for VFR (8.92%), while the highest one was for MCA (31.2%). Moreover, only VFR (8.92%) showed a CV value below 10%, whereas CDE (21.1%) and MCA had values higher than 10% of CV. Crumb grain features of CDE, VFR, COM, and ARA had lower PRESS values than MCA and UNI. For each of the GF bread crumb grain features, the model’s F-test and the linear mixture terms were highly significant  can be obtained with a combination of a low XG dose (0.04%), a high GG dose (0.60%), and a high-intermediate HPMC dose 3.36%; . Higher 2). Sciarini et al. [2), and higher values for CDE (0.89–1.67 cells/mm2), in a matrix of rice flour (45%), cassava starch (45%), soy flour (10%), WC (75%), and hydrocolloid (0.5%). As found in this study, they also concluded that GF breads often produce a dense crumb structure with thick cell walls; hence, the presence of larger cells producing a spongier crumb is not easily obtained in GF breads.Bourekoua et al.  obtainedi et al. , formula2), and higher values of MCA (4.17 mm2) and VFR (0.45).In this specific topic, considering that from a consumer viewpoint, bread loaves of more open crumbs and greater cell size (2). The bread quality features to optimize were assigned the same relative importance. The optimal formulation of 3.16% HPMC, 0.60% GG, and 0.24% XG produced a desirability of 0.54. This optimized mixture of gums would produce in the final bread product, the following estimated values for specific volume: 2.57 mL/g; crust L*: 67.0; crumb hardness: 2082 g; and mean cell density: 0.142 cells/mm2. The maximum prediction point indicated in The quality requirements for bread production can be set from the producer and/or the consumer perspective. One of the most important parameters in GF breadmaking, known to strongly influence consumer’s choice, is the loaf specific volume, as larger loaves are perceived as more appealing. According to Dapčević-Hadnađev et al. , the quaw, crust lightness, crumb hardness, adhesiveness, mean cell density, and cell size uniformity; while decreasing batter stickiness, crumb cohesiveness, resilience, mean cell area, and void fraction. XG interacted with HPMC to increase batter firmness, bread crumb hardness, and adhesiveness; and to reduce batter stickiness, cohesiveness, resilience, and mean cell density, cell size uniformity, and void fraction. Furthermore, GG also interacted with HPMC to increase batter firmness, bread specific volume, crumb aw, crust and crumb lightness, crumb hardness, adhesiveness, mean cell density, and cell size uniformity; and to decrease batter stickiness, bread baking loss, cohesiveness, and resilience. The three-way interaction XG × GG × HPMC was not assessed in this study.In relation to the hydrocolloids’ interaction effect on the GF bread properties, it was found that XG × GG interacted to increase batter firmness, bread specific volume, baking loss, crumb a2). Further studies can be conducted to validate the optimal formulation.It can be concluded that the quality of the final gluten-free bread is affected by the interactions that take place between the three hydrocolloids. Xanthan in interaction with guar, and guar in interaction with HPMC improved the bread specific volume, the softness and the mean cell density, associated with more open crumb grain structure. On the other hand, xanthan in interaction with HPMC produced batter of decreased stickiness, crumb cohesiveness and resilience, and mean cell density, associated with denser crumb appearance. Sticky batters were produced with a combination of high dose of GG (0.60%), high-intermediate dose of HPMC (3.36%), and low dose of XG (0.04%). GF breads of greater specific volume were obtained with a combination of high XG dose (0.60%) and intermediate doses of HPMC (3.08%) and GG (0.32%). Darker bread crust was obtained with a mixture low XG dose (0.04%), high-intermediate HPMC dose (3.36%) and high GG dose (0.60%); while a mixture of high-intermediate HPMC dose (3.36%), high GG dose (0.60%), and low XG dose (0.04%) rendered desirable softer crumbs and bread slices of more open visual texture. From a desirability approach, the optimum hydrocolloid mixture was obtained with a blend of 0.24% XG, 0.60% GG, and 3.16% HPMC, which would yield loaves of good quality in terms of high specific volume (2.57 mL/g), and low values of crust luminosity (L* = 67.0), crumb hardness (2082 g), and mean cell density (0.14 cells/mm


Approximately 33.5% of the recipes contained a specific temperature for doneness, with 73% of those cooked to ≥165 °F/74 °C, as recommended by USDA. Ninety-four percent of recipes used cooking time and about half of the recipes used visual measurements, such as color or juices running clear, to determine doneness. This study showed that most recipes do not contain appropriate information to assure safe cooking of poultry by consumers. Modifying recipes by adding food safety information, such as thermometer use and proper temperatures, could increase the use of proper food preparation behaviors by consumers.Research has shown that consumers use unsafe food handling practices when preparing poultry, which can increase the risk of foodborne illness such as salmonellosis or campylobacteriosis. Recipes from cookbooks, magazines, and the internet commonly are used as sources for consumers to prepare food in homes and the expectation is that food will be safe when prepared. According to the United States Department of Agriculture (USDA), using a thermometer properly is the only way to accurately check for doneness of poultry. The objective of this study was to assess poultry recipes, including recipes for whole birds and poultry parts, to determine if food safety information concerning thermometer use was included within the recipe. Poultry recipes ( Foodborne illness, including that from poultry products, continues to be a major public health burden, resulting in illness and mortality at national and global levels . In the Many foodborne infections in the United States occur from improper food handling practices in the home . AccordiResearch has shown the need to improve food-handling practices among consumers who cook poultry . ConsumeResearch concerning food safety information of recipes for cooking poultry, including grilling, is lacking. Thus, the objective of this study was to evaluate poultry recipes based on the inclusion of temperature information, determination of doneness methods, and other food safety information.Recipes that called for cooking poultry, either chicken or turkey, whole or in parts, were analyzed. Recipes from multiple sources, both online (internet recipe sites and blogs) and in print (cookbooks and magazines) were selected for analysis. Sources in print were found at the university library, local public library, or cookbooks provided by friends and family. Online sources were found by searching “poultry (or chicken or turkey) recipes” in the Google search engine. No source was older than the year 2000 in order to ensure reasonably current information. To qualify, the recipe had to be obtained from either a primary ‘cookbook’ (books about dieting or lifestyle that included a few recipes were not selected), magazines where recipes are a typical section or category, or other sources that publish recipes as a primary part of their presence (any of those categories could be in-print or online). Because of the number of sources available, in most cases only one recipe and in no cases were more than three recipes selected from any single source. Only in the case of large online resources or extensive cookbooks focused on poultry was more than one-recipe selected. An attempt was made to select many different types of sources and many types of recipes among those available, e.g., baking, frying, grilling, stewing, etc. For print sources, if a large number of poultry recipes were given in the source, the number of poultry recipes were counted from the index. A random number generator (random.org) was used to generate up to three numbers from one to the number of recipes that were in the source. The numbers generated were used to select the recipes in the order of which they were presented in the source. For example, if there were fifteen poultry recipes in a cookbook, the number generator would produce one number from one to fifteen. If the number generated was nine, then the ninth recipe was selected for analysis. For online recipes or if an index was not available, the first one to three poultry recipes that were viewed within the source were selected. A checklist  was creaIn total, 474 recipes were analyzed from 311 sources. There were 410 chicken recipes and 64 turkey recipes. Recipes were collected from 217 cookbooks, 28 magazines, 59 websites, and 7 blogs. Of the 474 recipes analyzed, only 33.7% of the recipes recommended use of a thermometer to determine doneness . Most reRecipes that specified using a thermometer provided specific endpoint temperature within the recipe. Almost 74% of those that specified using a thermometer provided a correct temperature of 165 °F/74 °C . Approxin = 49) of the recipes that specified a temperature provided information on the correct location to insert the thermometer. Of those recipes that did provide thermometer placement information, approximately half gave either incorrect or partly incorrect information for location. If the thermometer is inserted into the wrong location, the temperature readings will be inaccurate [Even though some recipes provided temperature information and many of those provided a correct temperature, only 30.1%  as well as popular bloggers could help to increase awareness of safe cooking of poultry by providing appropriate food safety and sanitation steps as well as providing doneness directions in recipes though use of thermometers, accurate placement of thermometers, and correct end-point temperatures.