The X-ray structure of human calbindin-D28K, a calcium-buffering protein that is highly expressed in the central nervous system, is reported. 2g9b), despite 98% sequence identity. Small-angle X-ray scattering (SAXS) indicates that the crystal structure better predicts the properties of calbindin-D28K in solution compared with the NMR structure. Here, the first direct visualization of the calcium-binding properties of calbindin-D28K is presented. Four of the six EF-hands that make up the secondary structure of the protein contain a calcium-binding site. Two distinct conformations of the N-terminal EF-hand calcium-binding site were identified using long-wavelength calcium single-wavelength anomalous dispersion (SAD). This flexible region has previously been recognized as a protein–protein interaction interface. SAXS data collected in both the presence and absence of calcium indicate that there are no large structural differences in the globular structure of calbindin-D28K between the calcium-loaded and unloaded proteins.Calbindin-D28K is a widely expressed calcium-buffering cytoplasmic protein that is involved in many physiological processes. It has been shown to interact with other proteins, suggesting a role as a calcium sensor. Many of the targets of calbindin-D28K are of therapeutic interest: for example, inositol monophosphatase, the putative target of lithium therapy in bipolar disorder. Presented here is the first crystal structure of human calbindin-D28K. There are significant deviations in the tertiary structure when compared with the NMR structure of rat calbindin-D28K (PDB entry  A polyhistidine tag followed by a rhinovirus (HRV) 3C protease cleavage sequence was included in the synthetic construct directly 5′ to the calbindin-D28K start codon. The recombinant plasmid was then transformed into E. coli Rosetta 2 (DE3) cells (Novagen) and plated on LB agar containing 50 µg ml−1 ampicillin. A 100 ml preculture was grown in LB broth (50 µg ml−1 ampicillin) overnight at 310 K with shaking at 200 rev min−1. 10 ml of the preculture was used to inoculate the main 1 l cultures, which were grown at 310 K with shaking at 200 rev min−1 to an OD600 of ∼0.6. The cultures were incubated on ice for 2 h before IPTG induction (0.4 mM) and incubation overnight at 298 K with shaking at 180 rev min−1.The 2.2.g for 20 min, and the pellet was resuspended in sonication buffer . Cell lysis was achieved by sonication for 5 min with 5 s intervals and DNA was removed by the addition of a nonspecific endonuclease  and incubation on ice for 10 min. The lysate was clarified at ∼30 000g for 20 min and the supernatant was retained. His-tagged recombinant protein was purified by cobalt-affinity chromatography ; the clarified lysate supernatant was incubated with 10 ml binding buffer-equilibrated resin for 1 h at 277 K . The flowthough was removed and the resin was sequentially washed with seven column volumes of binding buffer. The bound proteins were then eluted with binding buffer containing 250 mM imidazole. The His tag was removed by incubation with HRV 3C protease overnight at 277 K. The cleaved protein was further purified by size-exclusion chromatography on a Superdex G75 column in 5 mM Tris pH 8.0, 1 mM CaCl2, yielding ∼25–30 mg calbindin-D28K per litre of culture.The cells were harvested and spun at ∼95002.3.−1 protein in 1 mM CaCl2, 5 mM Tris pH 8.0 added in a 1:1 ratio to 0.5 M ammonium acetate, 0.1 M bis-Tris pH 6.5, 24% PEG 3350; Zhang et al., 2008et al., 2005M potassium thiocyanate, 30%(w/v) PEG 2000 MME]. Recombinant calbindin-D28K was concentrated to ∼40 mg ml−1 in 1 mM CaCl2, 5 mM Tris pH 8.0. Crystallization was performed in MRC 2-well crystallization plates (Swissci) with a reservoir volume of 50 µl and a drop volume of 0.2 µl consisting of a 1:1 ratio of precipitant solution and protein solution. The crystallization plates were incubated at 293 K and calbindin-D28K crystals grew within a week.The calbindin-D28K crystals that initially yielded the calcium SAD phases were replicated from previously reported crystallization conditions . A total of 8000 images from two 400° sweeps were collected with a 0.1 s exposure time using the inverse-beam method. The data were processed with the DIALS and AIMLESS packages  prior to experiments. A 45 µl sample of 11.5 mg ml−1 calcium-loaded calbindin-D28K or 12.2 mg ml−1 unloaded calbindin-D28k was injected onto a Shodex KW402 size-exclusion column pre-equilibrated with dialysis buffer and run at a flow rate of 0.16 ml min−1. Intensity [I(q)] data were collected as the eluate passed through the X-ray beam and were plotted against q = 4πsinθ/λ. The system operated with an exposure time of 3 s at 12.4 keV (1 Å) using a PILATUS 2M detector located at a distance of 4 m. Data were analysed using ATSAS and SCÅTTER  determined to a resolution of 1.51 Å are presented in Table 1et al. . Pairwise alignment with FATCAT . The distance between pairs of Cα atoms in EF1 and EF6 of the calbindin models indicates that the NMR model is more compact compared with the crystal structure. For instance, the distance between Lys34 Cα and Lys224 Cα is 35.59 and 27.79 Å for PDB entries 6fie and 2g9b (model 2), respectively .Calculated scattering curves for PDB entry ls Fig. 3a. Pairwly Fig. 3c.4.et al., 1989et al., 1969et al., 2013et al., 2001et al., 2011et al., 2014et al., 2000et al., 2005et al., 2002et al., 2003et al., 2006M in the crystallization buffer, a concentration that is significantly higher than the usually nanomolar physiological (cytosolic) concentration. This would imply that the structure presented here represents a calcium-saturated form of the protein with respect to physiological conditions . We surmise that the high-resolution X-ray structure of calbindin-D28K presented here should be used as the model of choice for future experimentation and in silico modelling.We present here the first X-ray structure of calbindin-D28K at near-atomic resolution. Elucidation of the calcium coordination geometry in the EF-hand loops is consistent with previous reports that the protein has four calcium-binding sites. Calcium SAD at long wavelength demonstrated that the N-terminal EF-hands are particularly flexible and possesses two calcium-binding conformations. Residues that have previously been shown to be involved in protein–protein interactions are demonstrated to also coordinate calcium, potentially bestowing a calcium-sensor function on calbindin-D28K. Calbindin-D28K maintains the same globular shape in both the calcium-loaded and unloaded forms. There is also no significant increase in the flexibility of the calbindin-D28K protein in the unloaded form. Intriguingly, PDB entry 6.The following reference is cited in the Supporting information for this article: Thorn & Sheldrick 2011.human calbindin, 6fiePDB reference: 10.1107/S2059798318011610/jc5014sup1.pdfSupplementary Figures.. DOI:


In order to improve the oil displacement effect of alkali/surfactant/polymer (ASP) solution in low-permeability oil layers, Daqing Oilfield has proposed a separate injection technology. The objective of separate injection technology is to reduce the viscosity of ASP solution through a different medium injection tool and increase the injection amount of ASP solution in low permeability oil layer, thus improving the oil displacement effect. In order to study the effect of the different medium injection tool on ASP solution, SEM is used to observe the changes in molecular micromorphology before and after the ASP solution flows through the tool. Then, the influence of the tool on viscosity and the first normal stress difference of the solution are studied through static shear experiments. Finally, the storage and loss modulus of the solution are measured through dynamic mechanical experiments and the relaxation time and zero shear viscosity of the solution are verified and compared. The results show that molecular chains are obviously broken and the grid structure is destroyed after the ASP solution is acted on by the different medium injection tool. The viscosity and elasticity of ASP solution decrease, and the influence degree of the different medium injection tool on viscosity is greater than elasticity. The results of the steady shear experiment and dynamic mechanics experiment are consistent. Therefore, the different medium injection tool can achieve the purpose of use, which is conducive to the injection of displacement fluid into low-permeability oil layers and enhance the recovery ratio. During the continuous exploitation of crude oil in oil fields, alkali/surfactant/polymer (ASP) flooding is a method of greatly improving the oil recovery rate. Under the synergistic effect of alkali, surfactant, and polymer, the use of ASP solution for oil displacement can reduce the oil-water interfacial tension and fluidity ratio to improve solution viscosity and sweep efficiency and give full play to the oil displacement effect ,3,4,5,6.7- and 0.7-mm microporous membrane match well. Zhao et al. [Huang et al.  conducteo et al.  studied o et al.  studied o et al. . TherefoFor the Daqing oil fields with serious formation heterogeneity, the general injection method will result in low sweep degree of medium and low permeability oil layers, which will seriously affect the development effect of the oil fields. Daqing oil fields proposed separate injection technology can control the injection amount of displacement fluid in high permeability oil layers, increase the injection amount of medium and low permeability oil layers, and improve the overall displacement effect. Researchers have carried out the following related studies on separate injection technology. Chen and Duan , throughBased on the technique of separate injection and the theory of solution shear degradation ,23,24,25The above-mentioned studies have little research on improving the oil displacement effect of low-permeability reservoirs. The implementation of separate injection technology by using the different medium injection tool to improve the contradiction between reservoirs is only at the stage of theoretical research and numerical simulation and cannot actually simulate the field situation. In addition, most of the experiments and simulations take polymer solution as the research object and there is less research on ASP solution. And most of the experimental research methods for polymer and other displacement agents are mainly aimed at analyzing the characteristic structure of molecular micromorphology ,35,36,37In this paper, ASP solution with molecular weights of 16 million, 19 million, and 25 million and concentrations of 1000 mg/L and 2000 mg/L is taken as the research object. Scanning electron microscopy (SEM) is used to study the changes in molecular morphology and molecular structure of ASP solution with different molecular weights and concentrations before and after the action of the different medium injection tool. The changes in viscosity and the first normal stress difference are studied through rheological steady shear experiments. Finally, the changes in storage modulus and loss modulus are studied through dynamic mechanical experiments, the degree of influence of viscosity and elasticity is analyzed, and the results obtained from the steady shear experiment are verified.The different medium tool used in the experiment is made of 316 L stainless steel . The surface of the tool is smooth, and the surface of the channel must be polished. The polymer used to prepare ASP solution in this experiment is partially hydrolyzed polyacrylamide (HPAM), with relative molecular weights of 16 million, 19 million, and 25 million and a hydrolysis degree of about 26% . The surfactant is ORS-41  with an effective content of 50% . The alkali is NaOH with a concentration of 30% .Under the actual production conditions of an oil field, because the formation water is not pure water, in order to simulate the oil displacement effect of ASP solution in the formation more realistically, brine is selected as a diluent to prepare the solution . The composition of the brine is shown in A YP-B2003 electronic balance  is used to measure the dry powder of polymers with different molecular weights and experimental water (brine) in proportion. The mixture is put into a glass beaker and dissolved and stirred with a EURO-ST D S25 electronic stirrer  at 250 r/min for 2.5 h to form a transparent aqueous solution, which is then put into an HW-III thermostat  for constant temperature treatment. The weighed surfactant and alkali are added to the polymer solution, and the mixture is continuously stirred in an electronic stirrer at a rotating speed of 400 r/min for 30 min. Then, a certain amount of experimental water (brine) is added to dilute the ASP solution, and ASP solution with molecular weights of 16 million, 19 million, and 25 million and concentrations of 1000 mg/L and 2000 mg/L are obtained according to the solution concentration experimental scheme . The prepared solution is allowed to stand for 6 h before use to ensure full dispersion of molecules and uniformity.3/d, the solution flows through the different medium tool  to sublimate the water in the sample and obtain the final dry sample. The prepared dry sample is placed in a high-voltage electric field with a certain vacuum degree to ionize the air, and then a layer of conductive metal (gold) film is plated on the surface of the dry sample, and gold spraying is carried out twice for 30 s each time. The sample is quickly transferred to the observation table in the sample room of a Quanta 450 FEG scanning electron microscope  for observation, photos of typical areas of the sample are taken, and relevant sample structure analysis is carried out.−1. The rheometer is started to rotate the rotor, and the viscosity and the first normal stress difference are detected in a stable measurement mode. Then dynamic measurement is adopted, setting the frequency at 0.01–10 Hz (in linear viscoelasticity), scanning the frequency at 0.1 Pa, and measuring the storage and loss modulus in the stress scanning range of 0.01–50 Pa. When the indication value is basically stable, start recording, and then record every 5 min. If the deviation between the three calculated values of the four continuously recorded values and the first one is not more than 5%, the system is considered to have reached the dynamic equilibrium value.The viscoelastic experimental study of ASP solution before and after the action of the different medium tool is conducted by starting the constant temperature circulation system of an RS-150 rheometer  and heating it to the measured temperature 45 °C for 15 min. The ASP solution is put into the preheated measuring outer cylinder and keep the temperature 45 °C constant for 20 min, so that each point of the sample can reach the testing temperature uniformly. The constant shear rate CR mode is selected, and the shear rate tested is set to be 5–1500 sASP solutions of each molecular weight and concentration were measured three times, and the measured results were averaged.The ASP solution is a non-Newtonian fluid. At low shear rate, it generally shows the characteristics of Newtonian fluid. At this time, the viscosity is a constant. With increased shear rate, the viscosity of the fluid decreases, showing the characteristics of shear thinning. When the shear rate increases to a certain value, the viscosity tends to reach a second stable value . The comIn the region of high shear rate and low shear rate, the Carreau model is suitable for all shear regions:In the process of ASP solution flow, the total stress on the internal volume element of the solution is expressed by tensors as follows:In the macromolecule system, the chain molecules are approximately a spherical envelope volume in the static state, and become ellipsoid in the flow field. Restoring force will be generated in these deformed microstructures. As these structures are anisotropic, the restoring force generated is also anisotropic, and the main axis of the ellipsoid after deformation tends toward the flow direction. Therefore, the restoring force in this direction is greater than that in the other two vertical directions, and these restoring forces generate normal stress components.The first normal stress difference is the main characteristic of viscoelastic fluid , which cIn the dynamic mechanics experiment, if a harmonic stress (or strain) is applied to the solution, the stress (or strain) of the solution also changes with time in a harmonic law. The stress (or strain) is a function of time, and its modulus is usually expressed by a complex modulus. The strain change of viscoelastic solution lags behind the stress change by a phase angle. For the latter, the stress change leads the strain by a phase angle, then the change of strain and stress of ASP solution with time can be expressed as follows:According to the definition of modulus, two different moduli can be obtained. Equation (5) can be changed toThe above equation is converted into a complex form, called complex modulus In the formula, According to the constitutive equation of Maxwell’s model:Stress and strain are expressed as circular vibration functions:According to the Equation (9), it can be obtained:Therefore, the storage modulus and loss modulus of Maxwell model are:Scanning electron microscopy is used to observe the molecular micromorphology of ASP solution with molecular weights of 16 million, 19 million, and 25 million and concentrations of 1000 mg/L and 2000 mg/L before flowing through the different medium tool, as shown in As shown in During the action of the different medium tool, the ASP system passes through the flow channel and enters the deep part of the oil layer through the bottom hole. The flow area of the tool is small, and after ASP solution flows through the tool, the speed of the solution flowing through the contraction section changes sharply, elongating the polymer molecules. When the molecular chain is pulled to a certain length and exceeds its strength, it breaks or is degraded. The microstructure of molecules and the size of molecular coils change to some extent, which will reduce the molecular weight of the polymer and increase the viscosity loss of the solution ,30. The As can be seen from Comparing the molecular morphology of ASP solution with different molecular weights and concentrations before and after flowing through the different medium tool, it can be seen that the tool has an obvious effect on the solution, in that it can effectively change the molecular structure, reduce the molecular weight, and increase the viscosity loss of the solution, and it meets the design purpose.The viscoelasticity of ASP solution is involved in preparation, injection, pipeline transportation, wellbore flow, and flow in porous media under reservoir conditions. Viscoelasticity is an important characteristic in the performance of solutions. It usually refers to the obvious viscous and elastic characteristics when multiple macromolecular motion units respond to forces ,64,65,66A steady-state shear experiment generally refers to the study of the change behavior of viscosity and elasticity of solution within a wide range of shear rates, usually including the stages when the solution structure will not be destroyed under low shear rate and will be destroyed under high shear rate. In simple steady shear flow, viscosity can be used to characterize the solution, and the first normal stress difference can be used to characterize the elasticity. In order to study the overall change trend of viscosity ignoring the unchanged viscosity under low and high shear rates, As shown in The viscosity curve in The higher the molecular weight and concentration of ASP solution, the greater the relaxation time value and zero shear viscosity. After the action of the different medium tool, the viscosity decreases, and power law index n after the action of the tool increases, and power law index n can represent the degree of flow characteristics deviating from Newtonian fluid, the more obvious the solution deviates from the flow characteristics of Newtonian fluid.Bird et al.  describeW is the system constant, c is the concentration of the solution, For ASP solution, the intrinsic viscosity is:It can be seen from the Equations (16)–(18) that the molecular weight and concentration of ASP solution are positively correlated with the viscosity and relaxation time of the solution, so in After ASP solution flows through the different medium tool, the change trend of viscosity is similar to that before the tool, but after the tool, the viscosity decreases, because the action mechanism of the tool is to reduce the viscosity of ASP solution through shearing action. When the solution flows through the contraction section of the tool, its speed change is the most intense due to the reduced cross-sectional area forming high-strength shearing action, and the damage to the intermolecular network structure is the most serious , causingAccording to the shear test, the different medium tool can effectively reduce the viscosity of the injected ASP solution, meet the design requirements, and achieve the purpose of injecting ASP solution into low-permeability oil layers for oil displacement.The analysis of first normal stress difference test data shows that the solution is unstable at low shear rates, which is due to the limitation of the measuring range of the instrument. In order to ensure analytical accuracy, the data of the stable section are selected for curve fitting and data analysis.As shown in When ASP solution flows through the different medium tool, the first normal stress difference of the solution and the elasticity decrease. This is because before the molecular conformation is unfolded, the molecular chains are wrapped into clusters. This is the unwrapping phase, and the elasticity at this time is unwrapping elasticity. When ASP solution flows through the different medium tool, the shearing action on the molecules is strengthened, and the molecular conformation is completely expanded. At this time, the molecular chains are stretched and the elasticity is obvious, and it is called stretch elasticity. Before flowing through the different medium tool, the molecular chains of the polymer solution are long and short, with obvious size distribution and tensile elasticity of the solution. After passing through the tool, the shearing degree of the long-chain molecules is greater than that of the short-chain molecules, which makes the length distribution of the chains more uniform. Therefore, the tensile elasticity of long-chain molecules decreases significantly.Since the first normal stress difference of ASP solution in the stable section increases linearly with the shear rate, and the higher the concentration and molecular weight of the solution, the higher the elasticity and the greater the slope of the first normal stress difference and the shear rate straight line, the slope of the first normal stress difference and the shear rate straight line (expressed by SN) can quantitatively represent the elasticity of the solution, which is convenient for comparing different solutions. The elasticity SN1 and SN2 of ASP solution with different molecular weights and concentrations before and after flowing through the different medium tool are shown in In this study, the linear slope of the first normal stress difference with the change of shear rate is used to quantitatively express the elasticity of the solution, which can result in a more accurate comparison. As can be seen from the table, the higher the molecular weight and concentration of ASP solution, the greater the elasticity and the greater the degree of decreased elasticity after passing through the different medium tool. At the same time, the degree of influence of molecular weight on elasticity is greater than that of concentration.According to the results of the steady shear test, the molecular weight or concentration of the solution can be increased in actual production to improve the viscoelasticity and the oil displacement effect.Dynamic viscoelasticity is the study of linear viscoelastic behavior of a solution, which represents the viscoelasticity of the solution without destroying its structural characteristics. In the dynamic mechanical test, the storage modulus can reflect the elasticity and the loss modulus can reflect the viscosity of viscoelastic fluid. As can be seen from The intersection point between the storage and loss moduli is usually associated with the largest relaxation time of the solution, according to the Equations (14) and (15), the relaxation time calculated by dynamic test is shown in The storage modulus and loss modulus of ASP solution decrease after the solution flows through the different medium tool, but the overall change trend is consistent with that before flowing through the tool, which indicates that the viscosity and elasticity of ASP solution decrease after shearing by the tool, which is consistent with the effect of the tool on viscosity and first normal stress difference in the steady shear experiment. After the action of the different medium tool, the intersection point of the storage modulus and loss modulus curve moves to the high-frequency direction, and the viscosity property is greater than the elastic property, so the effect of the tool on viscosity is greater than the effect of elasticity, and the flow will mainly be viscous flow. With increased molecular weight and concentration of ASP solution, the intersection point of storage modulus and loss modulus curves after the action of the different medium tool moves to the low-frequency direction, then the effect of the tool on viscosity is higher than that of elasticity. This is because, at a lower shear rate, molecular force plays a major role. The higher the concentration, the more molecules per unit volume, and the stronger the attraction between molecules, so the greater the viscosity. And the larger the relative molecular weight, the greater the attraction between molecules, and the more stable the conformation of the molecular chain, so its viscosity is also larger. Viscoelasticity of ASP solution is because its molecular conformation can be changed under the action of external force (shear force under the action of the different medium tool), that is, the curled polymer chain can be stretched, and when the stretching force is removed, it can return to its natural curled shape. However, the chain segment adjusts its conformation slowly under the action of external force, so the molecular deformation lags behind the stress and shows viscoelasticity. The viscoelasticity of the solution depends on the flexibility of the molecular chain. The greater the flexibility of the chain, the more obvious the viscoelasticity.Based on the analysis results of steady shear experiment and dynamic mechanics experiment, the effect of the different medium tool meets the design requirements. Compared with ASP solution with low molecular weight and low concentration, ASP solution with high molecular weight and high concentration has better viscoelasticity and higher oil displacement effect under the effect of the different medium tool.The molecular chain of ASP solution is broken and the spatial grid structure is destroyed under the action of the different medium tool. The viscosity and elasticity of the solution decreased, but the effect on the viscosity was greater.The elastic effect of ASP solution is stronger than the viscous effect after the action of the different medium tool.The relaxation time, zero shear viscosity and other physical parameters obtained in the steady shear experiment are basically consistent with the results of the dynamic mechanical experiment, and the experimental results of the effect of the different medium tool on ASP solution are true and effective.The shearing effect of the different medium tool on ASP solution with high concentration and high molecular weight  is the best, and the oil displacement effect in low permeability oil layer is the best.In this paper, scanning electron microscopy, steady shear testing, and dynamic mechanical testing were used to study the molecular micromorphology of ASP solution with different molecular weights and concentrations before and after flowing through the different medium tool, and the rheological property and viscoelasticity were tested. According to the experimental results, the following conclusions were obtained:


Evaluating the mechanical ability of nanofibrous membranes during processing and end uses in tissue engineering is important. We propose a geometric model to predict the uniaxial behavior of randomly oriented nanofibrous membrane based on the structural characteristics and tensile properties of single nanofibers. Five types of silk fibroin (SF)/poly(ε-caprolactone) (PCL) nanofibers were prepared with different mixture ratios via an electrospinning process. Stress–strain responses of single nanofibers and nanofibrous membranes were tested. We confirmed that PCL improves the flexibility and ductility of SF/PCL composite membranes. The applicability of the analytical model was verified by comparison between modeling prediction and experimental data. Experimental stress was a little lower than the modeling results because the membranes were not ideally uniform, the nanofibers were not ideally straight, and some nanofibers in the membranes were not effectively loaded. Electrospinning is an inexpensive and simple method that is broadly applicable for the controllable production of ultrafine continuous fibers with a high surface-to-volume ratio and high porosity. The tuning and controlling of these properties are often crucial for advanced biomedical applications like wound dressings ,2, scaffAmong the numerous materials suitable for tissue engineering, silk fibroin (SF) obtained from Bombyx mori silkworms is one of the most widely implemented as a scaffold material for tissue engineering due to its excellent biocompatibility and low immune reaction ,10. HoweUnderstanding the mechanics of nanofibrous membranes is important when evaluating their mechanical properties at various structural levels both during processing and use for final applications. Many researchers have tested the mechanical properties of polymer nanofibers and membranes. Ko et al.  obtainedSeveral analytical ,38, semiIn this paper, a geometric modeling analysis is proposed to predict the uniaxial behaviors of randomly oriented nanofibrous membranes. For this purpose, we used the tensile and structural characteristics of single fibers and nanofibrous membrane, respectively. Five types of SF/PCL nanofibers with different mixture ratios were prepared via an electrospinning process. The stress–strain responses of single nanofibers from these nanofibrous membranes were tested for further use in the model. The applicability of the analytical model was examined by a comparison between the modeling prediction result and the experimental data.−1). Neither the strain rate effect nor the stress relaxation were considered.Firstly, the nanofibers in electrospun membrane were simplified as continuous and straight filaments. The membrane consisted of layers of randomly oriented nanofibers in the in-plane direction, as shown in θ) of inclination. If a stretch along the y axis is applied, the circle becomes an ellipse after tensile stretching. Then, the angle of inclination (θ) becomes The membrane consists of layers of randomly oriented nanofibers. Many intersections exist where fibers meet. As shown in Therefore, the strain of the membrane is described as follows:Due to the large deformation during stretch progress, the instant etterson , since fd is the diameter of the fiber and The force in the single fiber along the y axis by the applied loading is described as follows:a, b, and c are fitting parameters, and D is the harmonic average of the diameters of the nanofibers. The fibers in the membrane are randomly oriented. Therefore, the integral algorithm was used to obtain the average force Then the harmonic stress n is the amount of fibers in unit volume V and p is the porosity of the electrospun membrane.Finally, the total force −1, Guanghua Weiye Co., Ltd., Shenzhen, China). Other materials  were obtained from Hangzhou Gaojing Fine Chemicals Co., Ltd. . Formic acid (FA. 98.0% pure) was purchased from Shanghai Lingfeng Chemical Reagent Co., Ltd. . All materials were used as received without any additional treatment.SF/PLC spinning dope solution was prepared using regenerated SF sponge and PCL  at 75 °C for 5 min. After the silk threads dissolved, they were dialyzed using a cellulose 12–14 kDa tubing. Distilled water was used as counter-solution. Dialysis lasted for 3 days. The resulting aqueous SF solution was removed from the dialysis tubing, filtered and freeze dried. The resulting materials were regenerated SF sponges, which were stored in a desiccator prior to their use.Silk cocoons were boiled in 0.05 vol% NaMixtures containing regenerated SF sponges and PCL at 100/0, 75/25, 50/50, 25/75, and 0/100 weight ratios were dissolved in 18 wt % FA solution and maintained at room temperature for 2 h. After that, the mixtures were stirred for 3 h.−1 feed rate and 15 kV voltage applied between the needle tip and an aluminum sheet collector located 10 cm away from the needle and mounted on the vertical metal mesh surface. Relative humidity was maintained below 50% during electrospinning.A syringe with a stainless-steel needle (22 G) containing 10 mL of spinning dope solution was placed into a special pump. Electrospinning conditions were 0.6 mL·hNanofiber morphologies were characterized by a Carl Zeiss  field emission scanning electron microscopy (FE-SEM) instrument operated at 3 kV accelerated voltage. A thin gold layer was sputtered on the fibers to improve their conductivity. The average diameter was calculated based on 100 measurements of different fibers, which were recorded using Image-Pro Plus 6.2 software  using FE-SEM images. Micrographs and diameters of nanofibers from five different kinds of membranes are presented in Stress along the axial of a single nanofiber was tested using a nano-mechanical stretching system , which provides excellent mechanical characterization at the nano-scale due to its unique actuating transducer that is capable of generating tensile force load on individual fibers by electromagnetic actuation combined with a precise capacitive gauge .The uniaxial stress–strain curve of the nanofibrous membranes were tested using the KES-G1 tensile system  as shown in By comparing the modeling prediction and experimental data, we found that the analytical model is somewhat applicable. The proposed modeling prediction demonstrated similarity to the testing results, showing that the mechanical behaviors of membranes could be predicted based on their structural characteristics and the properties of single nanofibers. However, the experimentally recorded stress values were a little lower than the modeling values. The morphology of the membranes could explain this difference. Firstly, the nanofibrous membranes were not ideally uniform, which could lead to the existence of some weak regions. Secondly, nanofibers were not ideally straight, which could lead to a slow initial increase in stress due to the adjustment of the nanofibers themselves. Thirdly, some nanofibers in membranes were potentially not loaded during tensile process.SF/PCL nanofibrous membranes were produced using an electrospinning technique using formic acid. The presence of PCL helped to improve the flexibility and ductility of the membrane without compromising the strength.In this study, we proposed a geometric modeling analysis based on the tensile properties of single fibers and the structural characteristics of nanofibrous membranes. The applicability of the analytical model was verified by a comparison between the model prediction and experimental data. The experimentally recorded stress level was a little lower than the modeling results for three reasons: (1) the nanofibrous membranes were not ideally uniform, (2) the nanofibers were not ideally straight, and (3) some nanofibers in the membranes were not effectively loaded.


Recent landmark publications from our research group outline a transformative approach to defining, studying and treating amyotrophic lateral sclerosis (ALS). Rather than approaching ALS as a single entity, we advocate targeting therapies to distinct “clusters” of patients based on their specific genomic and molecular features. Our findings point to the existence of a molecular taxonomy for ALS, bringing us a step closer to the establishment of a precision medicine approach in neurology practice. Their visions and perspectives marked a turning point in the history of medical diagnosis and therapy, paving the way for a new era of healthcare, the era of “personalized medicine.”Traditionally, diagnostic practice relied upon assessing physical signs and symptoms or on physiological and biochemical alterations that, despite providing valuable information about clinical course, are often not sufficient to fully characterize the complexity and heterogeneity underlying molecular mechanisms in different individuals. As a result, drugs continue to be ineffective in a large proportion of patients, while they cause side effects, or may result in overall treatment effects that are clinically insignificant, leading to the conclusion of minimal effectiveness.the right therapeutic strategy, for the right person, at the right time , and diagnostic techniques throughout the past decades. Construction of a standardized model for the integrated analysis and interpretation of such multiple heterogeneous data types in the context of complex molecular pathways and networks is an imperative requirement to improve diagnostic accuracy and to optimize therapeutic strategies based on specific molecular profiles of diseases Figure . In thisOncology represents the first and the most well-characterized example of the application of this molecular-based taxonomy paradigm to daily practice, with a multitude of successful individualized or customized therapeutic strategies and immunotherapies available for a wide range of cancers. Key examples include glioblastoma, breast, and colon carcinoma, for which have been developed commercially available assays  able to subdivide patients into selective clinically relevant sub-types characterized by different genetic alterations, genomic profiles and prognostic biomarkers.Despite considerable progress in cancer and other therapeutic areas, including cardiovascular, rheumatic, and neuromuscular diseases, precision medicine remains an aspirational objective when dealing with multifaceted heterogeneous clinical states such as neurodegenerative disorders, which may reflect combined effects of various genes and their interaction with environmental factors. Because of their heterogeneous and multifactorial etiology and the limited accessibility to post-mortem tissues, knowledge about molecular underpinnings leading to the majority of neurological diseases is still incomplete, and translation of current data into tailored clinical applications remains a challenging task.SOD1 mutations in familial ALS cases (fALS) in 1993, ALS research has in fact entered a molecular genetics era, revealing ALS as a complex disease characterized by a high degree of genetic heterogeneity in which a constellation of causative genes and risk factors have been identified , a severe clinical condition characterized by upper and lower motor neuron degeneration for which there is no truly effective treatment. Since the discovery of In the context of a personalized medicine initiative, there is considerable interest in developing accurate biomarkers to detect pathophysiologic processes while still latent, providing the greatest opportunity for effective therapeutic intervention. To this regard, the combination of clinical and genomic data with structural/functional/metabolic neuroimaging techniques as well as biofluid  analysis, offers a valuable resource to increase overall power and accessibility to detect previously inaccessible disease-related biomarkers and mechanisms prior to symptom onset. A recent example is represented by the strong association existing between higher CSF and blood levels of phosphorylated neurofilament heavy chain (pNFH) and shorter survival in ALS patients, in particular those carrying C9orf72 expansion, supporting a potential value of this marker for identifying more homogenous patient subgroups and guiding individual treatment decision-making (Gendron et al., In summary, although still in its early stages, the potential of molecular taxonomy in neurology is clear and the hurdles for the employment of this powerful new tool are rapidly being overcome. Looking into the future, we envision that prodigious advances in genomics, proteomics and biomarker development for ALS and other complex disorders may combine with the standard classification system, enriching books of neurology with the wholly new vocabulary of genomic and personalized medicine.GM and AS wrote the manuscript. FC participated in revising the manuscripts. SC conceived, directed, and supervised the project. All authors have read and approved the final version of this manuscript and agreed to be accountable for all aspects of the work.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.


After the publication of this work , an erro


Antimicrobial resistance (AMR) is a serious threat to global public health, but obtaining representative data on AMR for healthy human populations is difficult. Here, we use metagenomic analysis of untreated sewage to characterize the bacterial resistome from 79 sites in 60 countries. We find systematic differences in abundance and diversity of AMR genes between Europe/North-America/Oceania and Africa/Asia/South-America. Antimicrobial use data and bacterial taxonomy only explains a minor part of the AMR variation that we observe. We find no evidence for cross-selection between antimicrobial classes, or for effect of air travel between sites. However, AMR gene abundance strongly correlates with socio-economic, health and environmental factors, which we use to predict AMR gene abundances in all countries in the world. Our findings suggest that global AMR gene diversity and abundance vary by region, and that improving sanitation and health could potentially limit the global burden of AMR. We propose metagenomic analysis of sewage as an ethically acceptable and economically feasible approach for continuous global surveillance and prediction of AMR. Obtaining data on antimicrobial resistance (AMR) from healthy human populations is difficult. Here, Hendriksen et al. use metagenomic analysis to obtain AMR data from untreated sewage from 79 sites in 60 countries, finding correlations with socio-economic, health and environmental factors. AMR is a complex problem with multiple and interconnected drivers, which may include changing dynamics in travel, trade, climate change, and populations. Reliable information that accurately describes and characterizes the global occurrence and transmission of AMR is essential to address this challenge and to support national and global priority setting, public health actions, and treatment decisions.Antimicrobial resistance (AMR) is a cross-cutting and increasing threat to global health5. This procedure leads to significant time delays, often incomparable data, and a narrow pathogen spectrum not capturing all relevant AMR genes, where the major part might be present in the commensal bacterial flora of healthy individuals. However, obtaining fecal samples from healthy humans is logistically difficult.Current surveillance of AMR is often focusing on a few pathogens only and mainly based on passive reporting of phenotypic laboratory results for specific pathogens isolated from human clinical infections6 and an increasing proportion is connected to a sewer system8. In addition, analyzing sewage samples does not require informed consent, thus limiting ethical concerns and has limited practical and logistical barriers for sampling. Most microbiological studies on sewage have focused on the risk of discharge of insufficiently treated sewage or problems related to heavy rainfall overflow, but some evaluations on the surveillance of pathogens have also been performed10. Additionally, sewage has proven useful for surveillance in the global polio eradication program12.From a surveillance point of view, urban sewage is attractive because it provides sampling material from a large and mostly healthy population, which otherwise would not be feasible to monitor. Globally, a rapidly increased proportion of the human population live in urban areas13 and has also been utilized for the surveillance of global AMR gene dissemination through international flights14. Additionally, an extensively shared resistome was observed across urban sewage samples within China15, as well as between individuals and environmental samples in Lima, Peru16. Interestingly, Pehrsson et al.16 showed that even though changes in the bacterial composition were observed between feces and sewage, this was not the case for AMR genes.Metagenomic techniques, using short-read next-generation sequencing data, benefit from the ability to quantify thousands of especially transmissible resistance genes in a single sample. Moreover, it can provide additional information about the presence of bacterial species, pathogens, and virulence genes and the data can be reanalyzed if novel genes of interest are identified. It should, however, also be acknowledged that short-read metagenomics might provide limited information regarding the host of the genes or the genetic environment. Metagenomics has been found to be superior to conventional methods for AMR surveillance in pig herdsHere we use metagenomic analysis of untreated sewage to characterize the bacterial resistome from 79 sites in 60 countries. We find systematic differences in abundance and diversity of AMR genes between Europe/North-America/Oceania and Africa/Asia/South-America. Antimicrobial use data and bacterial taxonomy only explain a minor part of the AMR variation that we observe. However, AMR gene abundance strongly correlates with socio-economic, health, and environmental factors, which we use to predict AMR gene abundances in all countries in the world. Our findings suggest that global AMR gene diversity and abundance vary by region and that improving sanitation and health could potentially limit the global burden of AMR.17. The average number of reads per sample was 120 million reads (range: 8 million –398 million). An average of 0.03% of the reads were assigned to AMR genes, while on average 29%, 1%, 0.4%, and 0.2% were assigned to bacterial, protozoa, plants, and human genomic material, respectively  variance across sites within countries than across sites between different countries , but a limited number of bacterial genera dominated 1, suggest a high prevalence of AMR in many developing countries, even though several of the national studies reported by the WHO give contradictory results1.The total AMR gene abundances varied across sites and continents Fig. . The higmcr, and optrA  and heat maps Fig. . With reSeveral alpha diversity indices for each sample resistome were calculated .Analyses of the alpha diversity of the bacterial taxonomic composition showed less separation according to geographical regions compared with the AMR genes  selects for AMRwww.ecdc.dk] and IQVIA, formerly Quintiles IMS Holdings, Inc. (see Methods). In the regression model, we accounted for the potential effects of cross-resistance by fitting fixed effects of both usage of the antimicrobial class that a resistance gene primarily confers resistance to (direct selection for resistance) and the total AMU (indirect selection via cross-resistance). While our model showed a significant increase in the abundance of AMR genes belonging to a specific antimicrobial class with increasing usage of that antimicrobial class, we found no significant effect of total usage of all antimicrobials on abundance of the different classes  and that the number of passengers arriving in a country via flights has no effect on the abundance of AMR genes .In this study, the association between AMU and the occurrence of AMR in the sewage samples was estimated using a generalized linear mixed-effects model, with the counts of genes in the different antimicrobial classes as an outcome (Poisson) adjusting for sequencing depth and gene length. As AMU data, we included 2015 data from Europe , WHO Advisory Group on Integrated Surveillance of Antimicrobial Resistance (AGISAR) [http://www.who.int/foodsafety/areas_work/antimicrobial-resistance/agisar/en/], European Union Reference Laboratory for AMR network [www.eurl-ar.eu/], and to the European Food- and Waterborne Diseases and Zoonoses Network (FWD-Net) [https://ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/fwd-net].The National Food Institute, Technical University of Denmark (DTU Food) launched an open invitation 25 September 2015 seeking potential collaborators to participate in the pilot study of the Global Sewage Surveillance Project (GSSP). The invitation was sent by electronic mail to the following networks and individual research collaborators for further dissemination: WHO Global Foodborne Infections Network (GFN)  with a quality threshold at 20 and minimum length of 50 bp. Trimmed reads were used as input to the reference-based mapping and taxonomy-assignment tool MGmapper17, which is based on BWA-MEM33 version 0.7.12 and SAMtools34 version 1.6. Reads were aligned against reference sequence databases for the best hit . An acquired AMR gene database (ResFinder)35 was used to annotate properly paired reads (MGmapper option fullmode) where each read pair had sequence coverage of at least 80% compared with the length of the trimmed reads. This was the only filter that was applied to discard a read pair. The AMR genes were of bacterial origin and could therefore align to both bacteria databases and the Resfinder database. To enable multiple database hits, AMR genes were mapped using the Fullmode option in MGmapper for the most optimal abundance calculation. Genomic annotation was performed by identifying the best hit (MGmapper option bestmode) for a pair of reads when aligned against a range of reference sequence databases. Databases were primarily downloaded via NCBI genbank clade specific assembly_summary.txt files unless another ftp site is provided below. The list of databases used by MGmapper includes: Human (GRCh38.p3), bacteria (closed genomes), MetaHitAssembly (PRJEB674—PRJEB1046), HumanMicrobiome (genomes assemblies), bacteria_draft, plasmid, archaea, virus, fungi, protozoa, vertebrates_mammals, vertebrates_other, invertebrates, plant, and nt. For the bacteria and bacteria_draft databases, sequences were selected from the assembly_summary.txt file, when annotated with the tags version_status=‘latest’ and genome_rep=‘Full’. Furthermore, assembly_level= ‘Complete genome’ or ‘Chromosome’ were required for entries in the bacteria database and refseq_category=‘representative genome’ for entries in the bacteria_draft database. The plasmid database was constructed as the subset of bacteria and bacteria_draft sequences having the word ‘plasmid’ in the fasta entry header line.The reads were trimmed, including adaptor removal, using BBduk , GEMS cluster (identifies countries with similar dietary intake)38, the HDI39, and GBD 2015 super-regions40.The relative abundances (FPKM) of AMR genes were log-transformed and visualized in a heat map  to obtain the first two principal coordinates as well as the variance explained by each, ignoring negative eigenvectors. This was done using the cmdscale command in R.The BC dissimilarity matrices used for PCoA were also used for permutational multivariate analysis of variance (adonis2 function in vegan). The geographical group assigned to each sample was used as a predictor for dissimilarity.Diversity and richness were calculated on rarified versions of the resistance and bacterial count matrices. For resistance genes, the two samples with <1000 read pairs were excluded. Subsequently, count matrices were subsampled to the lowest samples’ depth, using the Vegan rarefy function. The Simpson diversity index (1-D), Pielou’s evenness, and the Chao1 richness estimates were calculated using the diversity function in the vegan package.The vegan package was used for comparing the resistome dissimilarities with the bacteriome dissimilarities. The protest function was used to scale and rotate the principal coordinates of the bacterial PCoA onto the principal coordinates of the resistome PCoA and testing the strength of the association.All plots and statistical analyses were carried out in Microsoft R Open 3.3.2.A multilevel Poisson model was developed to investigate the sources of variance for the abundance of AMR genes and the relationship between AMU and abundance of AMR genes found in the samples. The counts of the individual AMR genes in each of the samples (see “Collection of urban sewage samples” and “Whole-community sequencing“ sections) aggregated at the antimicrobial class level was used as the dependent variable.41. Because several samples were sequenced more than once on separate sequencing runs, we were able to estimate and correct for the noise in mean AMR gene abundance owing to the sampling process. Therefore, a categorical variable indicating which sample was used was included as a random effect (sample). A categorical variable identifying each location to allow for the estimation of variation in abundance of resistance genes between the different sampling locations (location) was included. Furthermore, a variable identifying the resistance class a resistance gene is a member of was included to estimate the variance due to differences in abundance between antimicrobial classes (class).An observation-level random effect was used to model the over dispersion inherent to count datar = 0.97, p < 0.01, Pearson’s product moment correlation), we could infer the (approximate) corresponding ECDC value from the IMS value for that particular country. The antimicrobial usage data were then log-transformed. Because it has been argued that many antimicrobial classes show cross-resistance, where resistance to one antimicrobial class also confers resistance to another class, we accounted for the potential effects of cross-resistance by fitting effects of both usage of the antimicrobial class that a resistance gene primarily confers resistance to (direct selection for resistance) and total AMU (indirect selection via cross-resistance). We also included the total number of passengers arriving in a country in 2015 as a fixed effect in the model. Data on the number of passengers were extracted from the ICAO international flight database  and log-transformed. Lastly, we included the United Nations HDI as a fixed effect. HDI data from 2015 were extracted for the United Nations Development Programme website39 and log-transformed and scaled before including them in the final model. The same model set-up was used to investigate the association between drug residue levels and AMR gene abundance. In this model, data on the drug residues in the samples was included as a fixed effect instead of the AMU data. All other effects (random and fixed) were kept the same. Others models including temperature at the collection site at the day of sampling and Gross Domestic Product showed no significant association with AMR genes abundance .For the fixed effects, we included a measure of AMU at the country level. Data from the ECDC database  estimates and from the law of large numbers. This followed from earlier proposals on the use of OOB estimates as a key part of estimation of generalization error47. In the study of error estimates for bagged classifiers, Breiman49 provided empirical evidence demonstrating same accuracy from using the OOB estimate as using a test set of the same size as the training set and further indicated that the use of the OOB error estimate removes the need for a set aside test set. OOB is the mean prediction error on each training sample xi, using only the trees that did not have xi in their bootstrap sample50. During random forest training, approximately one third of the instances are left out from each bootstrap training set. This means that the OOB estimates are computed from only about one third as many classifiers as in the ongoing main combination. However, the error rate decreases as the number of combinations increases, which means the OOB estimates will tend to overestimate the current error rate. To get unbiased OOB estimates, random forests are run past the point where the test set error converges. This has an added advantage that, unlike cross-validation, where bias is present but its extent unknown, the OOB estimates are unbiased51.Overfitting remains a major hurdle when applying predictive models especially involving many predictors. Breiman45.Random forests also aggregate many decision trees to limit overfitting as well as error due to bias because of the law of large numbers. Random forests limit overfitting without substantially increasing error due to bias due to their ability to mitigate the problems of high variance and high biasHowever, a ten-fold cross-validation was included during our model building by randomly partitioning model input samples into ten sets of roughly equal size followed by estimation of accuracy based on held-out samples. This held-out sample was each time returned to the training set and the procedure was repeated with the second subset held out and so forth. Cross-validation and checking for valid accuracy were also performed with an implication that accuracy scores reduce if there is overfitting and only “valid accuracy” is finally used.QGIS 2.18.11 using the cartogram package was used to create colored and distorted maps.52, with adaptations as summarized in the following text. For pretreatment, two 10-mL portions of each sample of sewage supernatant were weighed into separate 50-mL tubes, after which internal standards were added. To one of these portions, antimicrobials were added at a level of 25 ng/L for the sulfonamides and 100 ng/L for the tetracyclines, quinolones, and macrolides. Four mL of EDTA-McIlvain buffer  were added, after which the samples were shaken for 5 min head-over-head. The residue was taken up in 100 μL MeOH, after which 400 μL of water was added. For liquid chromatography tandem mass spectrometry (LC-MS/MS), the following gradient was applied: 0–0.5 min 1% B; 0.5–2.5 min linear increase to 25% B; 2.5–5.4 min linear increase to 70% B; 5.4–5.5 min linear increase to 100% B, with a final hold of 1.0 min. The injection volume was 5 μL. Detection was carried out by an AB Sciex  Q-Trap 5500 or a Q-Trap 6500 mass spectrometer in positive electrospray ionization (ESI). The parameters used for the Q-Trap 5500 and the Q-Trap 6500 were: capillary voltage, −4.0 kV; declustering potential, 10 V; source temperature, 450 °C; GAS 1 and 2, 50 (arbitrary units).The analysis of tetracyclines, sulfonamides, macrolides, and quinolones in the sewage samples were performed as in Beredsen et al.53, with adaptations as summarized in the following. For sample pretreatment, two 10-mL portions of each sample of sewage were weighed into separate 50-mL tubes, after which internal standards were added. To one of these portions, aminoglycosides were added at a level of 50 μg/L. Twenty mL of extraction liquid (10 mM KH2PO4 with 0.4 mM EDTA and 2% TCA) were added, and samples were mixed by means of a vortex and shaken head-over-head for 10 min. The extract was then brought to pH 7.6–7.9 and centrifuged . The complete extract was transferred to a conditioned CBX cartridge, followed by washing with 4 mL of water and drying. The aminoglycosides were eluted with 3 mL of acetic acid (10% in MeOH). The eluate was dried at 60 °C, evaporated under N2 and taken up in 400 μL of HFBA (0.065%). For LC-MS/MS, the following gradient was applied: 0–0.5 min, 0% B; 0.5–5 min, linear increase to 45% B; 5–8 min, linear increase to 60% B; 8–10 min, linear increase to 100% B. The injection volume was 40 μL. Detection was carried out by a Waters  Quattro Ultima mass spectrometer in positive ESI mode. The parameters used were: capillary voltage, 2.7 kV; desolvation temperature, 500 °C; source temperature, 120 °C; cone gas, 150 L/h; and desolvation gas 550 L/h.The analysis of aminoglycosides in the sewage samples was performed as in Bello54, with adaptations as summarized in the following. For sample pretreatment, two 10-mL portions of each sample of sewage were weighed into separate 50-mL tubes, after which internal standards were added. To one of these portions, β-lactams were added at a level of 50 μg/L for the penicillins and 500 μg/L for the cephalosporins and carbapenems. Detection was carried out by a Waters model Xevo TQS or an AB Sciex  Q-Trap 6500 mass spectrometer in positive ESI mode. The parameters used for the QTrap 6500 were: capillary voltage, 2.0 kV; cone voltage, 25 V; source offset, 20 V; source temperature, 150 °C; desolvation temperature, 550 °C; cone gas flow, 150 L/h; and desolvation gas, 600 L/h.The analysis of β-lactams in the sewage samples was performed as in Beredsen et al.After consumption, antimicrobials undergo 1) metabolization in the body, (2) are eliminated from the body with urine and/or feces, and might (3) further degrade in the sewer. A proper calculation of DDD/person/day from concentrations in sewage therefore requires information on elimination rates and a precise estimate of the number of persons connected to a sewer and the total water flow at this specific location (or the average water consumption per person as surrogate). Here information on elimination rates and water consumption were not included. In addition, we can only calculate DDD for human drugs used for systematic infections. As seen from Supplementary Data  metaboliwww.whocc.no/atc_ddd_index]. Some residues were excluded as their predominant usage was not systemic. This was the case for Dapson (main treatment indication: Lepra treatment), sulfacetamide (main indication: acne), sulfadoxine , sulfathiazole , enrofloxacin , tylosin , and dihydrostreptomycin . The DDD were summed over the respective antimicrobial classes. Calculation of the sum of DDD across classes was not deemed meaningful, because excretion rates differ largely between antimicrobial classes and also within antimicrobial classes.The concentrations of antimicrobial residues were transformed into DDDs of antimicrobials for systemic use using the WHO DDD database [Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8


In broiler chickens, the total AMU in milligrams/population correction unit (mg/PCUBr) decreased by 6%, the number (n) of defined daily doses for animals using Canadian standards (nDDDvetCA) per 1,000 broiler chicken-days decreased by 12%, and nDDDvetCA/PCU decreased by 6%. In turkeys, the mg/PCUTk decreased by 1%, whereas the nDDDvetCA/1,000 turkey-days and the nDDDvetCA/PCU increased by 1 and 5%, respectively. The types of antimicrobial classes used in both species were similar. Using the frequency of flocks reporting use  as a measurement, the use of certain antimicrobials changed over time . The trends in resistance to specific antimicrobials paralleled the frequency and quantity of use  in some situations, but not others (decreased fluoroquinolone use—increased ciprofloxacin resistance). AMR data were summarized using the AMR indicator index (AMR Ix). The most notable AMR Ix trend was the decrease in ceftriaxone AMR Ix among Escherichia coli (0.19 to 0.07); indicative of the success of the poultry industry action to eliminate the preventive use of third generation cephalosporins. Other trends observed were the increase in ciprofloxacin AMR Ix among Campylobacter from 0.23 to 0.41 and gentamicin AMR Ix among E. coli from 0.11 to 0.22, suggestive of the persistence/emergence of resistance related to previous and current AMU not captured in our surveillance timeframe. These data highlight the necessity of multiple AMU and AMR indicators for monitoring the impact of stewardship activities and interventions.Using data from the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS), we aimed to describe trends in antimicrobial use (AMU) in broiler chickens and turkeys, to compare AMU across species, to compare with trends in antimicrobial resistance (AMR), and to assess the effects of various AMU/AMR units of measurement (metrics and indicators) on data integration. Data on AMU and AMR in enteric bacteria, collected from 2013 to 2017 from broiler chickens ( Strengthening current surveillance capacities and expertise in antimicrobial use (AMU) and antimicrobial resistance (AMR) is one of the strategic objectives identified in “Tackling Antimicrobial Resistance and Antimicrobial Use: A Pan-Canadian Framework for Action” . This efMany countries have established surveillance systems for AMR in food animals –9. SimilOnce national action plans (NAP) have been developed and implemented , it is eFor AMU surveillance, metrics  and indicators  have been developed. Milligrams weighted by population and weight (mg/PCU) is used for reporting national sales and distribution data across countries in the European Union . AnotherIn Canada, CIPARS  collects, analyses, and communicates trends in AMU and AMR for select bacteria from humans, and food animals along the production continuum . The broPoultry data used in the analysis were collected through CIPARS from BC between 2013 and 2017. From this point forward, poultry refers to combined data from commercial broiler chickens and turkeys, unless indicated otherwise.n = 4) represented 100% of the poultry veterinary practices in BC. A flock, assigned with a unique code , is defined as a group of broiler or turkey birds, hatched and placed in the designated production unit  approximately the same day. A farm is a registered establishment that may have one or more barns in the premise. For farm selection, veterinarians were instructed to follow certain inclusion and exclusion criteria. The farms must be a commercial quota-holding operation  and compliant to on-farm food safety program  . The final criteria ensured that farms selected were demographically reflective of the veterinary practice and varied in terms of flock and farm capacity, animal health programs, biosecurity measures, management practices, and production efficiency parameters . As previously described  , 33. Varescribed , 31, theEscherichia coli, Salmonella, and Campylobacter. Antimicrobial susceptibility testing was conducted using Minimum inhibitory concentrations (MIC) using an automated broth microdilution and the Clinical and Laboratory Standard Institute (CLSI) M7-A8 standards and breakpoints when available  and susceptibility of Campylobacter isolates was tested using the NARMS CAMPY plates   designed by the National Antimicrobial Resistance Monitoring System (NARMS) of the United States . The characteristics of the data collected, and detailed data collection methods are described elsewhere , 23. ForBacterial isolation and AMR information from flock samples were extracted from the Public Health Agency of Canada's data repository .AMU metrics utilized in this document were count-based , and the weight- or dose-based AMU indicators described in Equations 1–4. For the current paper, the Category IV antimicrobials  and antimicrobials with no classification at the time of writing of this report  were excluded.Frequency of use : This count-based AMU metric was created for each poultry species and stratified by antimicrobial and route of administration.Weight-Based Indicator. The mg/PCU was used to compare trends in AMU quantity between broiler chickens (mg/PCUBr) and turkeys (mg/PCUTk) and the total poultry AMU (mg/PCUpoultry) in BC.mg/PCU (by species): This was derived by dividing the total milligrams (mg) of antimicrobial active ingredient (AAI) administered by the biomass or PCU as per the ESVAC methodology for calculating national sales and distribution data (Br) and turkeys (mg/PCUTk). This measure was also estimated per antimicrobial class, and for specific antimicrobials, such as TIO, GEN, and LINC-SPEC.ion data . As per ion data , 31, theion data , was useEquation 1. milligrams/population correction unit by species mg/PCUpoultry: sum of the amount of AAI (mg) administered to broiler chickens and turkeys divided by the total poultry biomass.Equation 2. mg/PCUPoultryDose-Based Indicators. Two dose-based indicators, nDDDvetCA/1,000 animal-days at risk and nDDDvetCA/PCU, were calculated to assess trends over time and comparability of the AMU data in broiler chickens and turkeys.nDDDvetCA/1,000 animal-days at risk : This dose-based indicator was calculated by dividing the DDDvetCA (mg/kg/day) by the biomass and time-animal unit . As previously described The average broiler chicken-days at risk used in the above calculations were 33–34 days depending on the year (as reported in the surveillance data). Average turkey-days at risk used in the above calculations were 84–89 days depending on the year (as reported in the surveillance data).nDDDvetCA/PCU: This dose-based indicator was derived from the amount of AAI used (mg) divided by the DDDvetCA standard and the animal biomass. This was calculated for each species.Equation 4. nDDDvetCA/PCU by species (nDDDvetCA/PCUBr and nDDDvetCA/PCUTk)Frequency of resistance: As per routine CIPARS AMR analysis  or resistant, using Clinical Laboratory Standards Institute (CLSI) breakpoints. If no CLSI interpretative criteria were available for a specific antimicrobial/bacterial combination, breakpoints were based on the distribution of MIC and harmonized with those of the United States' National Antimicrobial Resistance Monitoring System  was determined for each bacterial species as per routine CIPARS analysis : This is a novel AMR indicator, calculated as the percentage of resistance (or susceptibility) to a certain antimicrobial/s, adjusted by PCU (poultry) combines CIPARS AMR data from the broiler chickens and turkeys sampled in BC using the formula for food-producing animals described in the literature ; calculated for all sampled flocks, per year from 2013 to 2017.RTkY-% resistance or fully susceptible in turkeys (Table 2); calculated per year from 2013 to 2017.RBrY-PCU for broiler chickens; calculated per year from 2013 to 2017.PCUTkY-PCU for turkeys; calculated per year from 2013 to 2017.PCUPoultryY-total PCU for all poultry species; calculated per year from 2013 to 2017.PCUYear-specific biomass for each species is summarized in Primary AMR Indicator Index: AMR IxE. coliSusceptible  was calculated as the proportion of E. coli isolates fully susceptible to the antimicrobials tested for by CIPARS adjusted by the PCU; this is consistent with ECDC/EFSA/EMA's primary AMR index (MR index .Secondary AMR Indicator Index: Four secondary AMR Ix were determined: (1) AMR Ix E.coliCRO−R , was calculated as the proportion of E. coli isolates resistant to ceftriaxone (CRO-R) adjusted by the PCU; this AMR Ix was used instead of the ECDC/EFSA/EMA's Extended-Spectrum Beta-Lactamases (ESBL) and AmpC- producing E coli since these are not yet routinely tested at CIPARS  adjusted by the PCU; the organism-antimicrobial combination is closely monitored by CIPARS due to the emerging resistance observed  adjusted by the PCU. This indicator was selected as CIPARS has detected an emerging increasing trend in gentamicin use and corresponding resistance in broiler chicken isolates  due to more robust CIP-R Campylobacter data.t CIPARS ; (2) AMRU on AMR ; (3) AMRobserved , 37, andisolates . It is iE. coli resistance to, CRO, GEN, and LINC-SPEC and (2) AMU in mg/PCU and AMR Ix for the following: (a) total AMU for broilers and turkeys across all antimicrobials (mg/PCU) and AMR Ix E. coliSusceptible  and AMR Ix≥2E. coliMulticlass−R , (b) TIO mg/PCU and AMR Ix E.coliCRO−R , and (c) GEN and LINC-SPEC mg/PCUpoultry and AMR Ix E.coli.GEN−R AMU and AMR indicators were combined into a figure to descriptively assess potential similarities in trends over time: (1) AMU frequency and AMR (% R), by species, for use of, and Analysis of the data was conducted in Microsoft Excel (Office 14), Stata SE Version 15  and SASv12.1 .AMU. Temporal changes were determined following routine CIPARS analysis protocols . For the AMU indicators, data by antimicrobial class and the total of all classes per year were described. Changes in AMU indicators, between surveillance years were expressed as percent change .rotocols , 23. In AMR. Resistance prevalence estimates were adjusted for clustering at the flock level using generalized estimating equations (GEE) with a binary outcome, logit-link function, and exchangeable correlation structure. Null binomial response models were run for each antimicrobial and from each null model, the intercept (β0) and 95% confidence intervals were used to calculate population-averaged prevalence estimates using the formula [1 + exp(-β0)]−1. For the temporal analysis, models were developed similar to those described above for the analysis of AMU data with year as a categorical independent variable and P ≤ 0.05 for significance. Temporal changes in multiclass resistance prevalence are expressed as percent change in multiclass resistance .AMR Ix. Change in AMR Ix, by indicators, between surveillance years are expressed as percent change .Farm-level characteristics during the study period are summarized in P ≤ 0.05). Avilamycin (AVI) was used beginning in 2014 and the frequency of use increased from 7 to 23%. The frequency of flocks not reporting any AMU in feed increased significantly, from 13% (2013) to 37% (2017) (P ≤ 0.05). In turkeys, the top 3 ranking antimicrobials had similar frequency to broilers: BAC (51%), VIR (40%), and PEN (5%).Antimicrobials administered via water were infrequently used both in broiler chickens  and turkeys (1–4%) . Two broP ≤ 0.05). In turkeys, there was only one flock (3%) with reported use of TIO in 2013, with no use reported from 2014 to 2017. Gentamicin was consistently used in turkey poults (73–90%). Turkey flocks not using any antimicrobials at the hatchery were generally stable over time (17–27%).As for the injectable antimicrobials, the reported frequency of ceftiofur (TIO) use in broiler chickens decreased from 58% (2013) to 7% in 2014, with none reported from 2015 to 2017. The use of GEN was consistently reported during the study timeframe; the frequency peaked in 2015 (40%), decreased to 6% in 2016, and then increased to 17% in 2017. Lincomycin-spectinomycin was reportedly used for the first time in broilers in 2015 (20%), then in 2016 use dropped to 3%, and in 2017 it increased to 7%. Flocks not using antimicrobials at the hatchery level varied depending on the year (dropped to 40% in 2015) but between 2013 and 2017, the number of flocks significantly increased from 35 to 77% (Weight-based (mg/PCU): In broilers, the mg/PCUBr decreased by 6% between 2013 and 2017 (104–98 mg/PCUBr) and during the last 2 years, it increased marginally by 2% (96–98 mg/PCUBr). In turkeys, the mg/PCUTk decreased by 13% between 2013 and 2017 (90–78 mg/PCUTk), but during the last 2 years, 2016–2017, it increased significantly by 61% (49–78 mg/PCU). There was a decrease in mg/PCUTk between 2014 and 2015 due to the decrease in BAC use (49–17%) reported in the sampled turkey flocks. The magnitude of change between the antimicrobial classes varied depending on the year or antimicrobial class.Dose-based : In broilers, nDDDvetCA/1,000 broiler-chicken-days at risk decreased by 11% between 2013 and 2017 , with a 13% (493–431) decrease from 2016 to 2017. As with the mg/PCUBr, AVI (orthosomycins) gradually increased between 2014 and 2016 but decreased in 2017. In turkeys, this AMU indicator fluctuated over time due to the shifts in the use of 3 antimicrobial classes, BAC, VIR (streptogramins), and PEN. Unlike in broiler chickens, overall, it decreased by 11% between 2013 and 2017, but remarkably increased between 2016 and 2017 by 40% . The increase during the last 2 years was primarily due to the increase in the use of BAC and VIR and TMPS (trimethoprim-sulfonamide) (reported for the first time in BC in 2017). Compared to broiler chickens, the yearly total values for turkeys were lower.Dose-based (number of DDDvetCA/PCU): Between 2016 and 2017, the nDDDvetCA/PCUBr decreased by 9% while the nDDDvetCA/PCUTk increased by 46%, but during the study timeframe (2013–2017), this indicator showed a marginal change in the total nDDvetCA/PCU .Br, nDDDvetCA/1,000 broiler chicken-days at risk, and the nDDDvet/PCUBr decreased between 2013 and 2017. However, during the later 2 years of the study timeframe, the mg/PCUBr increased while the 2 dose-based indicators decreased. This was due to the shifts in the use of certain antimicrobial classes such as increased penicillins and bacitracins  and decFrequency of resistance (%R): The frequency of resistance to select antimicrobials in E. coli, Salmonella, and Campylobacter from the broiler chickens and turkeys are presented in E. coli and Salmonella isolates and CIP-R increased in Campylobacter from broiler chickens and turkeys.Broiler chickens–E. coli: From 2013 to 2017, resistance to ceftriaxone (CRO-R) decreased from 63 to 21%, while resistance to gentamicin (GEN-R) increased from 8 to 21%. Resistance to nalidixic acid (NAL-R) was detected in 9–19% of the isolates. Streptomycin resistance and TET-R increased significantly between 2013 and 2017.Broiler chickens–Salmonella: Resistance to CRO was 18% in 2013, it decreased from 32% in 2015 (highest observed) to 10% in 2017. Nalidixic acid resistance was detected in 2013 (5%) and 2015 (30%) but no NAL-R Salmonella isolates were recovered in 2016 or 2017. Gentamicin resistance emerged between 2014 and 2017 (1 to 4%).Broiler chickens–Campylobacter: Ciprofloxacin resistance significantly (P ≤ 0.05) increased between 2013 (30%) and 2017 (36%).Turkeys–E. coli: Ceftriaxone resistance was generally lower than in broilers (9% peak in 2014) and remained stable (1–2%) between 2015 and 2017. Gentamicin resistance significantly (P ≤ 0.05) increased from 12% (2013) to 27% (2017).Turkeys–Salmonella: Ceftriaxone resistance peaked at 42% in 2014 then decreased to 6% in 2015 and no resistant isolates were detected in 2016 and 2017. Gentamicin resistance fluctuated over time, but markedly increased between 2015 and 2017 (6–35%).Turkeys–Campylobacter: Ciprofloxacin resistance significantly increased (P ≤ 0.05) from 20% in 2013 to 53% in 2017. Tetracycline resistance increased significantly (P ≤ 0.05) from 36% in 2013 to 54% in 2017; compared to 2016, TET-R increased significantly (P ≤ 0.05) from 19 to 54%.E. coli, Salmonella, and Campylobacter isolated from the 2 poultry species. Overall there were 4 E. coli isolates (3 from broiler chickens and 1 from turkeys) that were resistant to 6–7 antimicrobial classes detected during the study timeframe. None of the Campylobacter isolates were resistant to 4 or more classes of antimicrobials. The distribution of susceptible and multiclass resistant isolates varied over time across the 2 poultry species , in contrast to the decreasing trend observed amongst the turkey isolates (35–21%). As for Salmonella, fully susceptible isolates decreased in both broiler chickens  and turkeys . Amongst Campylobacter, an increase in fully susceptible isolates was noted in broiler chickens (41–52%) while a decrease was noted in turkeys (62–29%).Results are summarized in Primary AMR indicator: The AMR Ix E.coliSusceptible  decreased by 34% from 0.29 in 2013 to 0.19 in 2017.Secondary AMR indicator: The AMR Ix E. coliCRO−R  decreased by 61% from 0.19 in 2013 to 0.07 in 2017. During the same timeframe, the AMR Ix Multiclass−R E. coli≥2 increased by 12% from 0.53 in 2013 to 0.59 in 2017; AMR Ix CIP−RCampylobacter increased by 79% from 0.23 to 0.41, AMR Ix E.coliGEN−R  markedly increased by 103% from 0.11 in 2013 to 0.22% in 2017.E. coli (E. coli isolates is depicted in The integration of the percentage (with low and high confidence limits) of CRO-R E. coli , 4 and tE. coli  is depicCampylobacter trends for both broiler chickens and turkeys generally increased but no corresponding increase in reported ENR use was noted during the study timeframe as the use of this antimicrobial, a veterinary fluoroquinolone not approved for use in any poultry species in Canada, was relatively low . However, AMR Ix Multiclass−R E. coli≥2paralleled the trends in total mg/PCU between 2013 and 2016 (increased) but dropped by 10% between 2016 (0.61) and 2017 (0.59). poultry and the AMR Ix E.coli.CRO−R  The TIO mg/PCUpoultry decreased from 0.08 in 2013 to 0.01 in 2014, and no TIO use was reported from 2015 to 2017. A corresponding decrease in AMR Ix E.coliCRO−R  was noted over time as previously described (E.coliCRO−R  persisted until 2017. Also in poultry) to 2017 (0.07 mg/PCUpoultry) (E.coliGEN−R  from 0.24 in 2015 (peak of use) to 0.22 in 2016 and 2017.The findings are summarized in escribed  however,poultry)  but therThis paper described temporal trends in AMU and AMR in poultry flocks, compared AMU and AMR between the poultry species, and evaluated the effects of various AMU and AMR units of measurement for reporting flock-level data on AMU and AMR. The farm-level data provided a comprehensive overview of AMU in the broiler chicken and turkey sectors within the province in Canada. Our data showed that the direction and magnitude of either trends or discrepancies between populations could change based on the metric or indicator chosen, but when the indicators were applied simultaneously to the same dataset, multiple study objective/s described above could be achieved. Hence, we believe there is much value to a comprehensive reporting of AMU data using different indicators.Salmonella Heidelberg and Escherichia coli (The count-based measurement (frequency of flocks medicated), when used by itself in a surveillance program, can detect changes over time; indicate how extensively on antimicrobial is used; detect shifts in AMU, particularly the shifts in the use of antimicrobials with higher importance to human medicine to antimicrobials with lower importance to human medicine or vice versa; and highlight the proportion of farms not using antimicrobials, which can be an indication of the changing production profile . In Canada, frequency data have been used in studies and risk assessments exploring the link between AMU and AMR, for example, the use of TIO in poultry and AMR among hia coli .The weight-based indicator, mg/PCU, is becoming one of the most frequently used AMU indicators internationally . In the The dose-based indicator, nDDDvetCA adjusted by animal time , or biomass (PCU) is currently used by 8/16 countries participating in the AACTING network. It was also cited for animal AMU reporting in the European Union/European Economic Area . The DDDAll three AMU indicators used; mg/PCU, nDDDvetCA/1,000 animal-days at risk, and nDDDvetCA/PCU detected temporal AMU changes . The reduction in total AMU (mg/PCU) could indicate early industry actions in anticipation of the forthcoming changes in industry policies , 25. TheTk AMU was generally lower compared to mg/PCUBr. The practice of feeding unmedicated rations during the late growing to finishing stages of the production period in turkeys is potentially driving the lower AMU. When nDDDvetCA/1,000 animal-days at risk was used, the magnitude of the difference between turkeys and broiler chickens was greater than the mg/PCU. This may be due to the relatively larger number of days required to achieve marketing weight for some categories, such as heavy tom turkeys. The turkey and broiler chicken sectors of the Canadian poultry industry collectively agreed to eliminate the preventive use of VDD Category II antimicrobials by the end of 2018 and VDD Category III antimicrobials by the end of 2019  to a specific antimicrobial is a standardized unit of measurement used internationally for reporting resistance prevalence from animals and humans. This complements trends in AMU data to assess the impact of an AMU reduction strategy , 43.E. coli were based on their relevance to overall AMU selection pressure and their importance to human medicine . The AMR Ix is also a good complement to mg/PCU in monitoring the overall impact of changes in antimicrobial usage patterns as demonstrated in the integrated figures, such as the parallel trends noted between mg/PCUpoultry and the AMR IxR E.coli≥2Multiclass− or the disparate trends observed between mg/PCUpoultry and AMR Ix E. coli.Susceptible  The latter observation is consistent with the literature   . FurtherCampylobacter remained moderately high and the reason for the persistence in Canadian flocks is unclear and ongoing monitoring and research is needed to determine the main drivers of CIP-R in poultry. The literature indicates that mutation in the Thr-86-Ile mutation in gyrA is directly assocaited with the enhanced fitness of fluoroquinolone-resistant Campylobacter  and farm-level risk factors such as insufficient downtime/rest period, cleaning, and disinfection have been hypothesized as potential on-farm sources (Surveillance data indicated that 8% of broiler flocks in 2013 and 4% of turkey flocks in 2017 reported fluoroquinolone use, with no use from 2014 to 2016. In BC in 2014, ~38 kg of fluoroquinolones were distributed by BC livestock and poultry veterinarians , howeverlobacter . This maironment , 48. ChaAs described in our previous analysis , AMU datNo single AMU or AMR indicator can meet all possible surveillance objectives. Different indicators, in isolation or when integrated with others, highlight different aspects of the complex AMU/AMR issue. Choosing appropriate indicators and then applying them appropriately requires careful consideration of both the data available and the desired objectives. Our findings highlight the utility of AMR Ix in monitoring changes in AMR of organisms of interest to the animal and public health sectors. For integration to be meaningful, data collection, sampling, laboratory techniques, and data management across all sectors must be harmonized. As in any other surveillance program , multi-sAA, SG, DFL, and CC conceived the study. RI and RR-S supervised all surveillance components. AA, SG, DFL, and AD validated, analyzed, and interpreted the data. AB, DL, DFL, and CC conceptualized and developed the DDDvetCA standards. AA prepared the initial draft, figures, tables, and appendices. All authors contributed to the writing and editing of the manuscript.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.