Fluid administration is the initial step of treatment of unstable pediatric patients. Evaluation of fluid responsiveness is crucial in mechanically ventilated children to avoid fluid overload, which increases mortality. We aim to review and compare the diagnostic performance of dynamically hemodynamic parameters for predicting fluid responsiveness in mechanically ventilated children.A systematic review was performed using four electronic databases, including PubMed, EMBASE, Scopus, and Central, for published articles from 1 January 2010 to 31 December 2020. Studies were included if they described diagnostic performance of dynamic parameters after fluid challenge was performed in mechanically ventilated children.Pediatric intensive and cardiac intensive care unit, and operative room.Children aged 1 month to 18 years old who were under mechanical ventilation and required an intravenous fluid challenge.Twenty-seven studies were included in the systematic review, which included 1,005 participants and 1,138 fluid challenges. Respiratory variation in aortic peak velocity was reliable among dynamic parameters for predicting fluid responsiveness in mechanically ventilated children. All studies of respiratory variation in aortic peak velocity showed that the area under the receiver operating characteristic curve ranged from 0.71 to 1.00, and the cutoff value for determining fluid responsiveness ranged from 7% to 20%. Dynamic parameters based on arterial blood pressure (pulse pressure variation and stroke volume variation) were also used in children undergoing congenital heart surgery. The plethysmography variability index was used in children undergoing neurological and general surgery, including the pediatric intensive care patients.The respiratory variation in aortic peak velocity exhibited a promising diagnostic performance across all populations in predicting fluid responsiveness in mechanically ventilated children. High sensitivity is advantageous in non-cardiac surgical patients and the pediatric intensive care unit because early fluid resuscitation improves survival in these patients. Furthermore, high specificity is beneficial in congenital heart surgery because fluid overload is particularly detrimental in this group of patients.https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=206400 Fluid administration is the first line of treatment for critically ill children who are admitted to the pediatric intensive care unit (PICU) with unstable hemodynamics. However, only 40% to 69% of these children show a response to fluid administration . Fluid rMany hemodynamic parameters have been used to predict fluid responsiveness in critically ill children. These parameters can be divided into static and dynamic parameters . Static Dynamic parameters can be measured in an invasive or non-invasive manner. Ultrasonic cardiac output monitoring and electrical cardiometry are non-invasive methods that are commonly used to assess dynamic parameters in the intensive care unit (ICU) setting.Previous studies of dynamic parameters were conducted in different circumstances and populations –36. To dThis study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) reporting guideline . The proThe primary outcome was to study the diagnostic performance of dynamic hemodynamic parameters, including sensitivity, specificity, and the area under ROC curve, for the prediction of fluid responsiveness in mechanically ventilated children. The secondary outcome was to identify the reliable dynamic parameters among mechanically ventilated children in different clinical circumstances.fluid, volume, response, challenge, bolus, and guided. These words were combined with the medical subject heading (MeSH) terms hemodynamics, hemodynamic monitoring, fluid therapy, cardiac output, infant, child, adolescent, and pediatrics. An additional search for potentially eligible articles was carried out using references of selected retrieved articles.A systematic review was performed using four electronic databases, including PubMed, EMBASE, Scopus, and Central, for published articles from 1 January 2010 to 31 December 2020. The last search was conducted on 15 January 2021. The search terms were Two authors (P.Y. and W.K.) independently reviewed abstracts of the retrieved articles for their eligibility. Articles that clearly did not fulfill the inclusion criteria were excluded at this stage. The remaining articles underwent a full-text review for final determination of their eligibility Any disagreements were resolved by conference with a third author (R.L.). The risk of bias was assessed using the Quality Assessment of Studies of Diagnostic Accuracy tool , 39, whiTwo authors (P.Y. and R.L.) independently extracted data from the included articles using a standardized data extraction form derived from the Cochrane Public Health Group Data Extraction and Assessment Template. We contacted the corresponding author of the included articles for missing data. However, only 2 of 10 corresponding authors replied. Those missing data were labeled as not reported.The following data were collected for systematic review: sample size, age, specific circumstance of participants, definition and percentage of fluid responsiveness, cutoff value, and diagnostic performance of dynamic parameters.The identification and selection of studies are shown in The respiratory variation in aortic peak velocity (ΔVpeak) was the most common dynamic parameter examined (12/27 studies). Moreover, ΔVpeak provided a reliable diagnostic performance. All studies of ΔVpeak showed that the area under the ROC curve ranged from 0.71 to 1.00, and the cutoff value of ΔVpeak for determining fluid responsiveness ranged from 7% to 20%.Because patients with congenital heart surgery were included in approximately half of all studies, we allocated participants to 2 new subgroups as follows: the congenital heart surgery subgroup –23 and tThe risk of bias assessment of all included studies is shown in In 2013, Gan et al.  have been extensively studied in the adult population for predicting fluid responsiveness . HoweverThe ΔVpeak exhibited a promising diagnostic performance in predicting fluid responsiveness in mechanically ventilated children. The sensitivity of ΔVpeak is advantageous in non-cardiac surgical patients and the PICU setting because early fluid resuscitation improves survival in these patients. Furthermore, the specificity of ΔVpeak is beneficial in congenital heart surgery because fluid overload is particularly detrimental in this group of patients.


This method involves injecting a magnetic nanofluid into the tumour and applying an external AC magnetic field to induce heat in the magnetic nanoparticles (MNPs) and raise the tumour temperature to ablation temperature ranges. Because of the complexity of considering and coupling all different physics involves in this phenomenon and also due to the intricacy of a thorough FEM numerical study, few FEM-based studies address the entire MFH process as similar to reality as possible. The current study investigates a FEM-based three-dimensional numerical simulation of MFH of breast tumours as a multi-physics problem. An anatomically realistic breast phantom (ARBP) is considered, some magnetic nanofluid is injected inside the tumour, and the diffusion phenomenon is simulated. Then, the amount of heat generated in the MNP-saturated tumour area due to an external AC magnetic field is simulated. In the end, the fraction of tumour tissue necrotized by this temperature rise is evaluated. The study’s results demonstrate that by injecting nanofluid and utilizing seven circular copper windings with each coil carrying 400 A current with a frequency of 400 kHz for generating the external AC magnetic field, the temperature in tumour tissue can be raised to a maximum of about 51.4°C, which leads to necrosis of entire tumour tissue after 30 minutes of electromagnetic field (EMF) exposure. This numerical platform can depict all four various physics involved in the MFH of breast tumours by numerically solving all different equation sets coupled together with high precision. Thus, the proposed model can be utilized by clinicians as a reliable tool for predicting and identifying the approximate amount of temperature rise and the necrotic fraction of breast tumour, which can be very useful to opt for the best MFH therapeutic procedure and conditions based on various patients. In future works, this numerical platform’s results should be compared with experimental  Breast cancer is one of the most common cancers nowadays, and many people from both genders die because of this type of cancer every year. According to the American cancer society’s statistics, the estimated new breast cancer cases and deaths in 2021 are 284200 and 44130 people in the USA [Hyperthermia is a heating procedure in which the tumour’s temperature rises above the normal body temperature (37°C) and is held for several minutes to damage and ablate cancerous tissues [ tissues . Heating tissues . Unlike  tissues . There a tissues .Magnetic fluid hyperthermia (MFH) is a treatment that involves injecting magnetic fluid into the tumour and then applying an external electromagnetic field. It can raise the temperature of the tumour to 43°C or more, causing apoptosis. In recent decades, researchers have considered the application of magnetic fluids in hyperthermia treatments. Because, unlike laser, microwave, and ultrasound hyperthermia, this minimally invasive treatment avoids overheating healthy tissues. In this method, the magnetic field is absorbed solely by magnetic nanoparticles (ingredients of magnetic fluids). Also, unlike other noninvasive treatment techniques, this method can be used to treat deep-seated tumours , 7. Manyin-vitro experimental research on MFH in soft tissue by considering artificial blood perfusion. The focus was to investigate the thermal behaviour of dead kidney tissue during MFH. Recently, Suleman et al. [3O4) nanoparticles. A 3D FEM (finite element method) based model investigated different physics incorporated in the MFH process. By adopting a realistic breast model, Miaskowski and Subramanian [in-silico study on tumor-size-dependent thermal profiles inside an anthropomorphic female breast phantom. They exposed the breast tissues to an EMF generated by an eight-element dipole antenna matrix surrounding it and used a finite-difference time-domain (FDTD) engine for solving the Maxwell equations coupled with the modified Pennes’ bioheat equation. Raouf et al. [In 2010, Miaskowski et al.  proposedramanian  performeramanian  carried f et al.  carried f et al.  introducf et al.  producedf et al.  carried f et al.  investigf et al.  investigf et al.  carried f et al.  establisBased on the literature mentioned above, only a few studies have addressed challenges involving 3D modeling of MFH processes that can consider the entire governing equations of this phenomenon. Because of the complexity of a thorough FEM study, almost none of them considers an anatomically realistic breast phantom for this purpose. Therefore, to bridge this gap, a FEM-based simulation is carried out in the current study, which can address the entire MFH process problem and demonstrate the real physics involved in this phenomenon in great detail. Unlike previous studies, all different physics involved in the MFH procedure is considered and numerically simulated. Furthermore, by utilizing an anatomically realistic breast phantom (ARBP), the goal was to get outcomes that were as similar to reality as feasible. This ARBP was derived using one of the models in a collection of T1-weighted magnetic resonance imaging (MRIs) of patients in the prone position as explained in the next section .The whole MFH process and the steps of the simulations used to predict reality using an ARBP are described here. First, diffusion of MNPs inside tumour tissue is simulated, and the concentration of these MNPs is obtained in different parts of the tumour and breast. After that, the AC magnetic field generation is simulated, and by calculating the amount of magnetic energy absorbed by the MNPs, the amount of heat-induced and temperature increase in the tumour area is calculated. By knowing the amount of temperature increase, the fraction of necrotic tissue is calculated. This thorough numerical platform can be utilized by clinicians as a reliable predicting tool for opting for the best therapeutic plan based on various patients.The remainder of this paper is organized as follows: In section 2, the materials and methods are presented. In section 3, numerical results of the MFH processes are presented. Section 4 provides a conclusion for the study.This section begins with a brief overview of the MFH procedure. Then there is a description of governing equations and mathematical models. The remaining subsections provide essential information about various stages of the numerical modelling process.The MFH aims to induce local heating by utilizing some radio-frequency electromagnetic waves. For this purpose, first, MNPs should be injected inside the tumour. Due to the diffusion process, these MNPs will propagate inside the tumour and its surroundings. After that, generating an external AC magnetic field in the breast area would induce heat in these MNPs. Through the magnetic coupling between the magnetic component of the generated magnetic field and the magnetic moment of MNPs, MNPs can be employed as nano-heaters triggered by an external AC magnetic field. The energy absorbed by these MNPs is dissipated as heat by this coupling mechanism .Dynamic hysteresis losses caused by the relaxation of the magnetic moments generate this heat induction in MNPS. The relaxation process consists of two simultaneous mechanisms. One of them is called Brownian relaxation and is correlated to the physical rotation of the MNPs on the surrounding medium, which depends on the medium viscosity and hydrodynamic volume of MNPs. The other one is the Neel relaxation, which relates to the rotation of the atomic magnetic moments within the crystal lattice of the MNPs. Both mechanisms coincide for some frequency range; however, they are independent. The faster mechanism is the dominant one for heat induction .This heat induction leads to a temperature increase in the surrounding medium (tumor). If the temperature remains high (over 41°C) for some time, tumour tissues will start to be damaged and die, which is called Thermal ablation. So, different steps of MFH can be outlined as follows:Injection of MNPS and diffusion of them inside a tumour and its surrounding.Generating an external magnetic field and inducing heat in the MNPs.Temperature increment in the tumour leads to necrosis of the tissues.For a better understanding, a schematic of the MFH process on a breast tumour is illustrated in The governing equations that should be solved for a thorough simulation of the MFH process include four parts. Diffusion of MNPs in the tumour, generation of the magnetic field by a current-carrying coil, heat generation and heat transfer in biological tissue, and prediction of the fraction of the tumour damage and necrosis. The governing equations for each of these steps are mentioned in the following.c is the concentration of the MNPs, and D is the diffusion coefficient formulated by the Stokes-Einstein equation and considered to be constant for the given fluid. Also, kB, T, η, and rp are Boltzmann constant (1.38×10−23 J K-1), absolute temperature, nanofluids viscosity, and radius of the MNPs, respectively.In the above equations, The governing equations for a magnetic field generated by a current-carrying coil in the frequency-transient domain can be expressed by the Maxwell-Ampere’s and Faraday’s laws as follows , 26:∇×0 is the permittivity of free space (8.85×10−12 F m-1), ϵr is relative permittivity, μ0 is the permeability of free space (4π×10−7 H m-1), μr is relative permeability, and σ is electrical conductivity. It should be mentioned that the current density inside the homogenized multi-turn source coil can be expressed as follows [In the above equations,  follows :Jcoil=N, Icoil, and Acoil are the number of turns, coil current, and total cross-section of the coil domain, respectively.In this equation, In the presence of this external alternating magnetic field (AMF), the amount of power losses dissipated by MNPs can be modeled by the Rosensweig formulation as follows , 28:Pd0 is the permeability of free space equal to 4π×10−7 H m-1, χ0 is ferrofluid susceptibility, H0 is the amplitude of magnetic field strength, f is the cyclic frequency equal to ω/2π, ω is the angular frequency, τ is relaxation time, Ms is the magnetic fluid saturation magnetization, VM is the magnetic volume of MNP equal to 4πrp3/3 for a nanoparticle with a radius equal to rp, and T is the absolute temperature. It should be mentioned that in this formula, ferrofluid susceptibility (χ0) is assumed to be constant and not dependent on the magnetic field [τ can be expressed as follows:In the above equations, μic field . A compric field . The effτB and τN are Brownian and Neel relaxation times. As mentioned before there are two physical processes responsible for the dissipation of power and heat induction, that are Brownian and Neel relaxations, and can be expressed as follows [In this formula,  follows , 28:τNe above equations, K is the magnetic anisotropy constant, η is the viscosity of the magnetic fluid medium, VH is the hydrodynamic volume of an MNP, and δ is the surfactant layer thickness.In thTemperature distribution in biological tissues can be expressed by the Penne’s bio-heat transfer equation as follows :ρcp∂T∂ρ is the tissue density, cp is the specific heat in constant pressure of the tissue, k is the thermal conductivity of the tissue, ωb is local blood perfusion rate, ρb is the density of blood, cp,b is the specific heat of blood, Tb is local arterial blood temperature, Qmet is local metabolic heat generation rate, and QMNPs is the heat dissipation rate by MNPs equal to Pdis mentioned in the previous section.In the above equation, The degree of tissue injury due to the hyperthermia process can be evaluated by the Arrhenius kinetic model as follows , 30–32:α is the degree of tissue injury, n is the polynomial order of the equation (in this study n is considered to be 1), R is the universal gas constant (8.314 J K−1 mol−1), and T is the tissue temperature. Parameters A and ΔE are frequency factor and activation energy and are dependent on the type of tissue and have been characterized for different tissue types. These parameters for the breast tissue are calculated to be A = 1.18×1044 s-1 and ΔE = 3.02×105 J mol-1 [θd) is expressed by [In this equation,  J mol-1 . After cessed by :θd=min2. To implement the magnetic field around the breast, all of these parts are located inside a hypothetical cube filled with air. This whole considered geometry is shown in The FEM-based commercial software COMSOL Multiphysics  is used The following are descriptions of the steps involved in importing and applying 3D grid-based anatomically realistic numerical breast phantoms in the COMSOL software. The numerical breast phantom used in our simulations was chosen from the numerical breast phantom repository created by UWCEM (University of Wisconsin cross-disciplinary electromagnetics laboratory) , which iA cloud of points is created in 3D space using these mentioned data and a MATLAB  functionx = −70 mm, y = −10 mm, and z = 10 mm. This tumour size is categorized as T1c [The tumour in our simulations is considered as a sphere with a diameter equal to 16 mm and its center located at the point: d as T1c  and is aIn this section, the numerical modelling of MFH will be introduced step by step. This section is divided into several subsections and each of these steps is explained accordingly in these subsections. The needed information, material properties, and initial and boundary conditions for each physics will be explained. First, the simulation of diffusion of MNPs inside the tumour and breast tissues should be discussed.The nanofluid considered in the current study is a diluted fluid consisting of magnetite MNPs with the base fluid water. The volume fraction of MNPs in the fluid shown in  is consiVnp and Vnf are volume fraction of MNPs in the nanofluid, total volume of nanoparticles, and volume of nanofluid. According to previous clinical and non-clinical studies, different dosages of nanofluid injection can be considered based on the tumor size and the time considered for each MFH therapy session. In the current study, the total volume of nanofluid injection inside the tumor is considered to be equal to 0.1 mL, which is less than half of the amount considered by Tang et al. [Vnf, Vnp will be calculated as 0.02 mL. By having magnetite density and molar mass based on −4. By considering that all of the injected nanofluid will be accumulated inside of a sphere with an 8 mm diameter at the beginning, the initial concentration of MNPs in this sphere will be calculated as 1669 mol m−3. Transport of diluted species physic is considered in COMSOL software and diffusion of these MNPs is simulated inside of the tumor and breast porous media. A time-dependent study is considered and the diffusion process is considered to be equal to 24 hours. It means that after injection of nanofluid, one day will be given to the nanofluid to diffuse completely inside the tumor area. So, after 1 day of injection, the patient will come for the MFH therapy. By considering the diameter of MNPS equal to 19 nm and based on −11 m2 s−1. The concentration distribution of the MNPs in the tissue will be simulated in this step.In the above equation, VF, g et al.  , respectively.In the above equations, -2 K-1 for free heat convection of breast skin with the ambient air [All different material properties considered and utilized in the current study are as follows. the heat convection coefficient is considered to be 5 W mient air , 46. Theient air . All of ient air  and mentρ, Cp, k, ϵr, σ, Qmet, and ωb are density, specific heat in constant pressure, heat conductivity, relative permittivity, electrical conductivity, metabolic heat generation rate, and blood perfusion rate respectively.In the above table, It should be mentioned that the discretization methods used in our simulations are linear for the Transport of diluted species physic, quadratic for the magnetic fields physic, and quadratic Lagrange for the bioheat transfer physic. All simulations are performed by a system equipped with an Intel Core i5-8600 Processor at 3.10 GHz and 32 GB DDR4 RAM, and the computation time for each simulation is approximately 27 hours.x-y, x-z, and y-z surfaces intersect the tumour’s centre. Three sliced surfaces are evaluated to assess the simulation results, where the A few samples of the computational mesh are constructed, and the amount of temperature increase in the centre of the tumour is measured after 30 minutes of therapy under the magnetic field to study grid independency. A suitable number of grids is determined by balancing the expense of computational capabilities with the precision of simulation results. When the finer computational meshes do not affect the simulation results, the most refined grid is obtained to provide a reasonable grid independence level. As shown in In order to validate our method of simulation, first, the diffusion phenomenon (transport of diluted species physic) is validated with two different studies. Next, the heat induction phenomenon (magnetic field coupled with bioheat transfer physic) is validated by two different studies as well. These two validation steps are as follows:For validating the diffusion of MNPs inside the tumor and breast, the diffusion of a spherical source that the diffusion substance is initially distributed uniformly through a sphere (like our simulations) is simulated in COMSOL and compared with the analytical results of Crank’s study  and alsoc at radius r and time t for a spherical source of radius a with a uniformly initial concentration of c0 can be formalized as follows [Based on Crank’s analytical study, the concentration  follows :c=12c0c, c0, a, r, D, and t are concentration, the initial concentration of the source sphere, radius of the source sphere, radius, diffusion coefficient, and time respectively. The plot of concentration distribution for a spherical source and at (Dt/a2)1/2 = 1/4 from Crank’s study along with the same plots resulting from simulation by Suleman et al. and by us are shown in In the above equation, As can be seen, there are small gaps between our simulation, Suleman et al. simulation, and Crank’s analytical study results. These little variations can be regarded as tolerable inaccuracies.To validate the simulation of heat induction inside tumour and breast tissues, we selected the Miaskowski et al. simulation results. Suleman et al. validate their simulation with their results, and we validate ours with both Miaskowski et al.’s simulation results and the results of Suleman et al.’s validation section.Miaskowski et al. considered five concentric planar coils with a current equal to 400 A and frequency equal to 150 kHz to generate a magnetic field at the breast model area. They also considered that the MNPs only exist inside the tumour, and their concentration is uniform in every part of the tumour. An identical simulation model to Miaskowski et al. with the same frequency, coil arrangement, and coil winding number is considered for validation. So, a different model with a planar coil with a frequency of 150 kHz same as Miaskowski et al. is created for the validation. The point with the maximum temperature obtained in the whole breast model is considered and the plot of temperature difference of this point during 20 minutes of exposure is compared with Miaskowski et al. and Suleman et al.’s plots. It should be mentioned that in our simulation, the point with the maximum temperature obtained is the centre of the tumor. The plot of this comparison is shown in As can be seen, the plot in the present study agrees well with the plots of Miaskowski et al. and Suleman et al. study. A relative error analysis is performed for a more quantitative comparison and is shown in This section will illustrate results acquired from different simulated physics in 4 different subsections. First, MNPs concentration distribution in tumours and breast tissue due to the diffusion phenomenon will be shown. Then, the magnetic field generated and the induced temperature distribution in the breast model will be shown. In the end, the distribution of necrotized tissue fraction will be presented, and the amount of tumour and breast tissues that dies due to the temperature rise will be evaluated.As mentioned before, after injection of the magnetic fluid in the centre of the tumour, total amount of magnetic fluid accumulates in a sphere with a radius equal to 4 mm. A duration of 1 day is given to the MNPs to diffuse completely inside the tumour and breast tissues. Thus, the concentration distribution of these MNPS inside the breast after one day is shown in -3 in the sphere with a radius equal to 4 mm. After diffusion, the maximum concentration reached around 301.5 mol m-3 in the center of the tumor and gradually decreased by moving away from the center of the tumor. For a better illustration of the results relevant to the diffusion phenomenon, the plot of the MNPs concentration versus time at different specified points and also the plot of the concentration of MNPs versus distance from the tumour’s center at different times are shown in Because of the existed concentration gradient, a net flow of MNPs will be created from a region of high concentration to a region of low concentration. This diffusion process results from the random motion of molecules. As can be seen in this figure, MNPs diffuse inside tumour tissue and, after that, a little inside the fatty breast tissue. The initial concentration of MNPs was considered to be 1669 mol mAs shown in x-y, z-y, and x-z surfaces) that pass through the middle of geometry .Using seven circular copper coils mentioned that each coil carries a 400 A alternative current with a frequency equal to 400 kHz, generating a magnetic field in the breast area. The contour of the module of the magnetic flux density and the magnetic streamlines are shown in The alternative current passing through the coils will create a changing electric field. This changing electric field will create an AC magnetic field at the location of the coils. H×f = 4.85e+08 A m−1 s−1 as the safe exposure limit, where H and f are magnetic field intensity and frequency, respectively. Their test was based on the patient withstanding the treatment for more than one hour without any major discomfort. In our simulation, the maximum amount of magnetic field magnitude obtained in the tumor area is equal to 13.3 mT (10583.8 A m-1). So, the highest amount of H×f in the tumor area is equal to 4.23e+09 A m−1 s−1. This amount is higher than the Atkinson-Brezovich limit but still is lower than Hergt-Dutz [−1 s−1, 8.3e+09 A m−1 s−1, and 18.7e+09 A m−1 s−1, respectively. Considering that our exposure time is 30 minutes and not 1 hour, this generated magnetic field can be considered safe. Different magnetic field intensities and frequencies can be opted by the clinician to reduce the feeling of discomfort for the patient.There are several magnetic field exposure limits expressed by different researchers. For instance, Atkinson and Brezovich  proposed3.3 mT 103.8 A m-1rgt-Dutz , and Mamrgt-Dutz  limits eThe conversion of heat from magnetic nanoparticles via magnetic energy loss will lead to a temperature increase at the location of MNPs. This heat generated will be transferred to other parts of the tumor and if the temperature rise is high enough it would kill the tissues. The produced temperature distribution after 30 minutes on different mentioned cut surfaces along with the 3D view is shown in As can be seen in this figure, the temperature has risen in the tumor area and the maximum temperature achieved is about 51.4°C. This temperature increase will lead to necrosis and the death of tumor cells. For a better elaboration of the results, the plot of the temperature generated versus time at different specified points and also the plot of the temperature versus distance from the tumor’s center at different times are shown in In Also, it can be seen that the amount of temperature increase is higher at points near the tumour centre and will decrease by going farther from the tumour centre, which is rational due to the higher concentration of MNPs at the centre of the tumour. However, the interesting thing is that in point 3 (the point at 4 mm distance from the tumour’s centre), the temperature rise is higher than in point 1 (the point at the centre of the tumour). This phenomenon lies in the sinusoidal nature of the magnetic field. As shown in The temperature increase in the tumor area will lead to the ablation of tumor cells. When tissue temperature rises above a certain threshold, an irreversible cellular injury will occur, with interruption of metabolic processes. The distribution of the fraction of necrotic tissue after 30 minutes is shown in As shown in As can be seen in this figure, the fraction of necrotized tissue will increase through the exposure time. This is rational because, through exposure time, tumour tissues will remain longer at high temperatures, leading to a higher proportion of cell damage. It can be seen that after 30 minutes of exposure, 100% of tumour tissues (points 1 to 5) are ablated, and some of the normal tissues adjacent to the tumour are ablated as well (points 6 and 7). Also, as said before, the fraction of necrotic tissue directly relates to the temperature. Thus, the fraction of necrotic tissue in areas with higher temperatures will be higher. This is the reason why a higher fraction of necrotic tissue will be obtained sooner in point 3, for example in comparison with point 1.It should be mentioned that the overheating of adjacent tissues is unavoidable but can be reduced by changing different parameters such as the amount of nanofluid injected, frequency and amplitude of the magnetic field, and exposure time. This depends on the different MFH therapeutic procedures chosen by the clinician.MFH can be considered one of the novel options available for ablation of deep-seated tumours like breast tumours, consisting of injection of magnetic nanofluid in the tumour area and exposure of the breast to an external magnetic field. This magnetic field will induce heat at the location of MNPs and lead to necrosis of tumour tissues. In the present investigation, the performance of a 3D mathematical modelling that can consistently solve the entire MFH phenomenon problem is examined. First, some of the outcomes of the present work are compared to other research outcomes cited in the article (to verify the simulation procedure). Afterward, an ARBP is utilized, and governing equations for all involving physics are solved numerically. MNPs concentration distribution, generated magnetic flux density, induced temperature distribution, and distribution of the necrotic tissue fraction are obtained in different steps for depicting the MFH phenomenon.Results indicate that by injecting 0.1 mL diluted nanofluid consisting of magnetite MNPs with the base fluid water and using seven circular copper coils that each winding carries a 400 A alternative current with a frequency equal to 400 kHz, the temperature in tumour tissue can be raised to a maximum of about 51.4°C that leads to necrosis of entire tumour tissue after 30 minutes of EMF exposure.The current simulation method can depict all the different physics involved in the MFH of the breast tumour in detail. This numerical platform can be used as an effective and reliable technique for predicting the outcome of any MFH process with different therapeutic conditions. various conditions may be applied based on each patient with specific tumour shapes and sizes (different levels of cancer development). These conditions are a higher or lower amount of magnetic nanofluid to be injected, a stronger or weaker magnetic field  to induce higher or lower temperature in the tumour, and longer or shorter exposure time. This numerical platform can be utilized as an initial clinical tool for predicting and identifying the approximate results  for helping clinicians to choose the best therapeutic procedure based on different patients and different treatments that may be required. Also, this numerical platform can be used as a prediction tool to improve MFH experimental setups. By utilizing this platform, without any excessive expenses, it can be concluded that by changing each parameter in an MFH experimental setup how outcomes would be altered.in-vivo experiments should be performed and their results should be compared with the current numerical method’s results. The current numerical method should be modified accordingly. Therefore, as a future research direction, for the next step, several in-vivo and clinical experiments should be carried out. This numerical platform should be modified and improved based on the obtained results in order to be ready for utilization in clinical applications.For achieving the current results, several physical factors have been simplified to solve the governing equations. This is one of the limitations of the discussed approach. Thus, this approach needs further modifications based on experimental results. For modifying this numerical simulation platform in order to obtain more accurate results, several S1 Data(XLSX)Click here for additional data file.


Melanoma is known as one of the most immunogenic tumours and is often characterised by high mutation burden, neoantigen load and immune infiltrate. The application of immunotherapies has led to impressive improvements in the clinical outcomes of advanced stage melanoma patients. The standard of care immunotherapies leverage the host immunological influence on tumour cells, which entail complex interactions among the tumour, stroma, and immune cells at the tumour microenvironmental level. However, not all cancer patients can achieve a long-term durable response to immunotherapy, and a significant proportion of patients develops resistance and still die from their disease. Owing to the multi-faceted problems of tumour and microenvironmental heterogeneity, identifying the key factors underlying tumour progression and immunotherapy resistance poses a great challenge. In this review, we outline the main challenges to current cancer immunotherapy research posed by tumour heterogeneity and microenvironment complexities including genomic and transcriptomic variability, selective outgrowth of tumour subpopulations, spatial and temporal tumour heterogeneity and the dynamic state of host immunity and microenvironment orchestration. We also highlight the opportunities to dissect tumour heterogeneity using single-cell sequencing and spatial platforms. Integrative analyses of large-scale datasets will enable in-depth exploration of biological questions, which facilitates the clinical application of translational research. In recent years, the emergence of single-cell sequencing and spatially resolved multi-omics platforms have rapidly advanced the field of immuno-oncology research. These technologies brought higher resolution to characterise the cellular composition and transcriptional programs within the tumour microenvironment (TME), offering new opportunities to uncover rare cell types, infer cellular interactions and identify molecular targets, hence offering better understandings of the mechanisms of immunotherapy response and resistance. In this section, we provide a brief overview of single-cell platforms that are widely used in cancer immunotherapy research.Single-cell sequencing platforms are subdivided into cell-dissociated  and spatially resolved methods (map the location of the transcripts in an intact tissue section). Technologies that sequence the dissociated cells are broadly classified by their respective cell isolation methods. Droplet encapsulation methods, including 10x Genomics Chromium and Dolomite-Bio Nadia, are popular platforms for the sequencing of tumour dissociate samples due to the high number of sorted cells, allowing for high-throughput and relatively higher cost-effectiveness . MicroweThe TME is a heterogenous tissue architecture containing not only the tumour cells, but also a variety of non-immune and immune cells within the microenvironment. Each cell is constantly communicating with other cells and this in turn influences the function and fate of the cell. Furthermore, intracellular organisations of DNA and RNA can provide critical clues to the gene expression and post-transcriptional regulation underlying drug resistance. Thus, the spatial organisation of cells is critical for the accurate interpretation of tissue functions and cellular interactions. Single-cell spatial omics technologies have been developed to preserve the tissue context which retain the cell location information that helps to provide further insights into the interplay between tumour and the microenvironmental cells.Two classes of methods have been popularised for imaging-based single-cell genomics and transcriptomics, based on multiplexed fluorescence in situ hybridisation (FISH) or in situ sequencing. FISH has been well-established for the visualisation and quantification of RNA at the single-molecule level ,7, and mImaging-based in situ sequencing approaches for single cell transcriptomic analysis are developed in parallel, and can be performed in either a targeted or untargeted manner for hypothesis-testing or empirical studies in cancer research. For targeted in situ sequencing, Ke et al. first described a method based on padlock probing, rolling-circle amplification and sequencing-by-ligation chemistry that can be applied to fixed cells and tissues . More reSpatial transcriptomic analysis can also be achieved by spatially resolved RNA capture followed by sequencing. This was first demonstrated by Ståhl et al., where spatially barcoded and oligo(dT) probes are printed as microarrays of spots on the surface of glass slides . After pIntegrated analyses of single-cell sequencing and spatial multi-omics are revealing TME details of cellular and subcellular organisation at an unprecedented level. Applications of these technologies have demonstrated exciting results, such as the generation of highly granular and functionally annotated spatial cell atlases , identifTumour heterogeneity is a major driver of cancer progression, treatment resistance, and recurrences ,29. The Inter-tumoural heterogeneity  refers to the variations of different tumour genotypes among patients, even within the same histological subtypes ,33. UponIntra-tumoural heterogeneity  refers to the presence of distinct subpopulations of cancer cells with unique genetic traits in the single tumour of a patient, which can exhibit across different spatial locations of a tumour or evolve over time ,38. By iHeterogeneity in the tumour is not only limited to cancer cells, but also influences peritumoural stroma cells, tumour-infiltrating lymphocytes, and other stimulatory/inhibitory elements ,40. ThisSpatial heterogeneity is often defined as the differences in features such as genomics and cell morphology within an individual primary or metastatic tumour , and between different tumours within the same individual (intra-patient). In a retrospective cohort study, Jerby-Arnon et al. revealed a resistance programme associated with T cell exclusion and cyclin-dependent kinases (CDK4 and CDK6) using single-cell RNA sequencing from 33 melanoma patients, and further validated on 90 samples from 26 patients with multiple biopsies per patient . The intTemporal heterogeneity refers to the initial (primary) tumour cells that have evolved with different mutational processes and selective pressures, resulting in variations of molecular composition of the tumours. The tumour cells also affect the intravascular cells, immune cells, stroma cells, and other cell types that constitute the complex TME ,45. The In recent years, a plethora of evidence has demonstrated that the genetic and immunological heterogeneity in the TME influences the likelihood of malignant cells surviving immunotherapies in patients with solid tumours, including melanoma ,40,43,46DCT, which encodes for dopachrome tautomerase [The patterns of treatment response heterogeneity are becoming more relevant from a clinical point of view, especially for patients who relapse after treatment and develop disease progression or recurrence ,54. Drugtomerase . Ho et atomerase . Anothertomerase ,69. Seletomerase . OverallPD-L1 expression level is identified in either tumour or immune cells and is often a selection criterion for predicting clinical response to PD-1 or PD-L1 inhibitors across various solid tumour types, including melanoma ,72. AlthThe rapid advancements in single-cell sequencing and spatial technologies have shed light on modern cancer research, while the improved understanding of patient and tumour microenvironmental heterogeneity has brought new challenges to optimising therapeutic strategies. The field is moving towards translational applications of biological discoveries and has great potential in guiding clinical decision making.As reviewed in the earlier sections, inter- and intra-tumoural heterogeneity encompasses a multitude of factors that define the unique biology of a tumour or tumour clone. These unique factors are driven by a combination of intrinsic and extrinsic changes which can be detected as variability at a multi-omics level. These changes can be leveraged using next-generation sequencing to reveal therapeutic vulnerabilities of cancer patients. This was demonstrated by the development of highly selective BRAF inhibitors (vemurafenib and dabrafenib) for melanoma patients after the identification of the target mutation ,82. AlthIn relation to discovering the features of TME heterogeneity and mechanisms of resistance, combinatory and personalised checkpoint immunotherapy strategies for melanoma are being actively researched. The CheckMate 067 trial demonstrated the superior efficacy of combination nivolumab plus ipilimumab over nivolumab or ipilimumab alone, though with increases in the rates of immune-related adverse events . ClinicaThe complex and dynamic signalling network within the TME can also provide insight into the degree of interactive heterogeneity seen in tumours. Inhibition of the RAS/RAF/MAPK pathway not only results in the upregulation of compensatory receptor tyrosine kinase pathways , but hasThe genotypic and phenotypic variability posed by inter- and intra-tumoral heterogeneity has shown promising potential in biological research, but several challenges remain in its integration into personalised drug design and clinical decision making. Firstly, a single tumour biopsy is a static and partial representation of the overall heterogeneity in a patient. Temporal analyses of multiple biopsies throughout the course of disease and treatment are required for the accurate depiction of spatial and temporal heterogeneity. Till date, repeated biopsies during and after treatment for disease progression have been incorporated into clinical trials ,98. FurtHeterogeneity of the tumour and microenvironment complexity are the main contributors to different treatment responses and clinical outcomes. The single-cell approach has contributed to the study of tumour heterogeneity and drug resistance arising from the TME. The advancement in single cell and spatial multi-omics platforms, as well as the integrated analysis of these large-scale datasets, are offering unprecedented powers to identify the intrinsic, extrinsic and interactive processes driving the development of tumour heterogeneity. The identification of the key and rare subpopulations of cells and molecular processes will aid in improving diagnosis and novel drug development for cancer patients.


The large load loss of piezoelectricity pumps leads to fluid energy in the fluid chamber during fluid transportation. In this paper, the output performance of a piezoelectricity pump is improved by changing the structure parameters of the fluid chamber to reduce the fluid load. The mechanism of fluid flow energy loss in the body cavity of hydraulic pumps is simulated and analyzed, and the influence of the dimensions of the inlet and outlet valves and the height of the cavity on fluid energy loss is obtained. The flow rate and pressure of inlet and outlet valves with different cavity heights and different driving frequencies are obtained. The results show that the flow rate and output pressure of the hydraulic pump are optimized when the cavity height is 3 mm, and the inlet and outlet valve diameters are 2.5 mm. Piezoelectric pumps have found wide application in the field of fluid delivery thanks to their simple structure, small volume, low manufacturing cost, and accurate output ,2,3. TheTo improve the output pressure and flow rate of the piezoelectric pump, Moradi-Dastjerdi et al.  used a pTo improve the oil supply pressure and flow of piezoelectric pumps, Wu  and otheTo reduce the energy and load losses of piezoelectric pumps and improve their comprehensive performance, a piezoelectric pump with a new structure of compressible cavity is designed in this paper. A deformable elastic cavity is set inside each of the inlet and outlet valve pipelines of the piezoelectric pump to limit the flow of liquid outside the valve into the compressible cavity close to the valve to avoid load loss due to excessive liquid mass during the working process of the piezoelectric pump. The vibration damping of the piezoelectric pump system is also greatly reduced, and the energy conversion efficiency of the piezoelectric vibrator is improved at the same time.As fluid flows through a piezoelectric pump, flow resistance is generated between the various flow layers. During the working process, part of the mechanical energy of the piezoelectric vibrator is irreversibly converted into thermal energy generated by the flow resistance. The main source of energy loss within the piezoelectric pump is the fluid cavity, and the pipeline material, length, effective diameter, height, fluid viscosity, and fluid flow rate are the key factors affecting its performance.The flow loss is given by :(1)hf=μlThe performance of the piezoelectric pump is affected both by the energy loss caused by the friction between the fluid layers and by losses caused by the sharp change of the local boundary. The geometric loss is given by:It can be seen from the above analysis that the performance of the piezoelectric pump is closely related to the shape, length, and internal structure of the fluid cavity. Reducing the energy loss of the piezoelectric pump can directly improve the energy conversion efficiency of the piezoelectric vibrator and effectively improve the output performance of the pump.During the working process of the piezoelectric pump, the fluid inside and outside the pump cavity becomes connected when the valve plate is opened, such that the drive of the piezoelectric pump is not limited to the fluid in the pump cavity, but also includes the fluid outside the valve. If the cavity structure is complex, the contact between the fluid and the cavity wall is high, and the cavity is narrow, the energy required by the piezoelectric vibrator to drive the fluid of the entire system is very large. As xh, of sudden expansion pipe flow in the energy loss of piezoelectric pump is:If we let the flow velocity in the inlet be By the continuity equation:Substituting Equation (5) into Equation (3) gives:It can be seen from the above theoretical analysis that, as the height of the fluid cavity increases, the energy loss of the fluid flowing in the cavity also increases, resulting in a decrease in the output performance of the piezoelectric pump.To simulate the fluid flow at different heights, we will define the upper part of the compressible cavity model as the inlet, with a pressure of 15 KPa, and define the side of the model as the inlet and outlet, with a pressure of 0. In addition, the elastic diaphragm material is defined as silicone rubber, and the fluid is defined as incompressible pure water. Fluid chamber heights of 2 mm, 3 mm, 4 mm, and 5 mm were simulated  when changing the height of the fluid chamber with constant inlet and outlet pressure, the flow at the outlet of the 2 mm compressible chamber and the incompressible chamber decreases with the increase of the height of the fluid chamber, (2) the flow output of the piezoelectric pump with a compressible cavity is significantly higher than that of a piezoelectric pump without a compressible cavity; and (3) the lower the height of the compressible cavity, the greater the flow output of the piezoelectric pump.The layout of the prototype pump and the as-constructed device are shown in The material chosen for the compressible cavity diaphragms was silicone, as silicone membranes have strong corrosion resistance, and, as no plasticizer is added during the preparation process, they can maintain their elasticity for a long time, giving them a long service life. Diaphragms of thicknesses of 0.1 mm, 0.4 mm, 0.7 mm, and 1 mm were used. The dimensions of the pump are inlet and outlet valves We measured the flow rate at different frequencies without the compressible cavity and with compressible cavities with heights of 2 mm, 3 mm, 4 mm, and 5 mm. The resulting flow rate curves  show thaWe also measured the output pressure at different frequencies without the compressible cavity and with compressible cavities with heights of 2 mm, 3 mm, 4 mm, and 5 mm. The resulting curves  show thaA new approach to mitigating the energy loss of fluid motion in a piezoelectric pump pipeline based on the use of a compressible cavity has been developed. Using a fluid-structure interaction simulation combined with the theory of fluid flow characteristics and head loss in the piezoelectric pump, it is verified that the height of the compressible cavity affects the fluid flow in the compressible cavity. Compared with the traditional piezoelectric pump without a compressible cavity structure, the output flow of the piezoelectric pump with a compressible cavity is significantly improved under the conditions of the same volume and power consumption by up to 30%.


This cross-sectional study examines changes from 1992 to 2020 in the use of promotional language in National Institutes of Health (NIH) funding opportunity announcements in comparison with trends reported in NIH grant applications. JAMA Network Open1 found that in abstracts of successful National Institutes of Health (NIH) grant applications the use of promotional language (hype) increased substantially from 1985 to 2020. As part of the discussion stimulated by the article, the question was raised as to what was motivating this change in language. In particular, some commentators questioned whether some of the responsibility for the proliferation of hype might not, in part, reside with the NIH itself.3 This study therefore assesses the use of hype in NIH funding opportunity announcements (FOA) from 1992 to 2020 and compares trends with those reported in grant applications.An article in STROBE) reporting guideline for cross-sectional studies. Because it did not involve human participants, approval was waived by the University of Tsukuba institutional review board. A text corpus of all FOAs within the NIH archive4 was compiled, loaded into the CQPweb corpus analysis system,5 and searched for the same 139 hype adjectives previously identified in successful grant applications.1 For each available year (1992-2020), 2 quantitative measures were calculated: (1) the normalized frequency of the hype term (words per million [wpm]); and (2) the percentage of FOAs containing the term (dispersion). To compare longitudinal trends in FOAs with those found in grant applications, superimposed plots of the 2 time series were inspected and cross-correlation analysis was conducted using dispersion of terms in FOAs and normalized frequency in grant applications with a lag of 0 years. Correlations with a coefficient greater than 0.5 were reported with statistical significance set at P < .001 using a 2-tailed test. Statistical analyses were performed using R version 4.1 .6This study was designed to comply with relevant items of the , senior , scientific , successful  and unique . Adjectives showing the largest increase in dispersion  were accessible (+70%), meaningful (+69%), robust (+67%), accurate (+64%) and qualified (+63%). A total of 37 adjectives were absent in 1992, of which the most frequent in 2020 were sustainable (+22 wpm), scalable (+19 wpm), transformative (+16 wpm), transdisciplinary (+9 wpm) and user-friendly (+6 wpm). Statistically significant cross-correlations between use in FOAs and grant applications were observed for 55 of the 138 hype adjectives  (r > 0.9) .A total of 16 495 FOAs were analyzed (160 151 827 words). All but 1 of the 139 hype adjectives occurred at least once in the FOAs  with the prevalence of all hype adjectives increasing by 41% from 7737 wpm in 1992 to 10 914 wpm in 2020. Adjectives showing the largest absolute increase in frequency were  < .001) . The Fig1 This study shows that almost all the same terms (138 out of 139) are also used by the NIH in funding opportunity announcements. In FOAs, from 1992 to 2020, the overall use of these hype terms increased, and, for many individual terms (55 out of 138), the patterns of change are correlated with those in grant applications. Among the strongest correlations are those adjectives that increased rapidly and suddenly in popularity , and have been described elsewhere as buzzwords.1 These results suggest that increase in the use of hype language in grant applications may, in part, be a response to instructions from the NIH. One limitation is that in the context of FOAs some adjectives might not represent hype . Furthermore, the analysis compared overall trends without assessing the relationship between individual FOAs and corresponding applications.Previous research shows that from 1985 to 2020, the use of hype terms in NIH grant applications increased substantially.


In “The Facilitation of Clinical and Therapeutic Discoveries in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Related Diseases: Protocol for the You + ME Registry Research Platform” :e36798) the authors noted an error in the final paragraph of the Acknowledgments section.In the originally published article, the final paragraph of the Acknowledgments section was published with an incorrect grant number as follows:“The development of the Registry database and mobile app was supported in part by the National Institutes of Health (NIH) grant U24-NS-105525 as part of the ME/CFS Collaborative Research Network.”The final paragraph of the Acknowledgments section has been updated with the correct grant number (U24-NS-105535), to read:“The development of the Registry database and mobile app was supported in part by the National Institutes of Health (NIH) grant U24-NS-105535 as part of the ME/CFS Collaborative Research Network.”The correction will appear in the online version of the paper on the JMIR Publications website on October 13th, 2022, together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories.


HepB-CpG  is a licensed hepatitis B vaccine with a novel adjuvant that requires only 2 doses  compared to a 3-dose  HepB-alum vaccine . Monitoring of safety outcomes following receipt of vaccines with novel adjuvants is important. Hence, as part of an FDA postmarketing commitment, we compared the incidence of new-onset immune-mediated diseases, herpes zoster (HZ), and anaphylaxis among recipients of HepB-CpG versus HepB-alum at Kaiser Permanente Southern California (KPSC).This cohort study included adults not on dialysis who received ≥1 dose of a hepatitis B vaccine from 8/7/2018 to 10/31/2019, during which HepB-CpG was routinely administered in 7 of 15 KPSC medical centers while HepB-alum was administered at the other 8 medical centers. Recipients of HepB-CpG or HepB-alum were followed through electronic health records for 13 months after receipt of the first dose during the vaccine accrual period for occurrence of pre-specified new-onset immune-mediated diseases, HZ, and anaphylaxis identified using diagnosis codes. Incidence rates were compared using Poisson regression with inverse probability of treatment weighting when there was 80% power to detect a relative risk (RR) of 5 for anaphylaxis and a RR of 3 for all other outcomesThere were 31,183 HepB-CpG and 38,442 HepB-alum recipients . Among immune-mediated events that occurred frequently enough for formal comparison, rates among HepB-CpG versus Hep-B-alum recipients were similar except for rheumatoid arthritis (RA) . (Table 1) After adjudication of new-onset RA, the adjusted RR was 0.93 . (Table 2) The adjusted RR for HZ was 1.06 . Anaphylaxis occurred in 0 HepB-CpG and 2 HepB-alum recipients.These data suggest no safety concerns for HepB-CpG compared to HepB-alum for select immune-mediated diseases, HZ, or anaphylaxis in this observational study of over 69,000 recipients of hepatitis B vaccines.Bradley Ackerson, MD, Dynavax: Grant/Research Support|Glaxosmithkline: Grant/Research Support|Moderna: Grant/Research Support|Pfizer: Grant/Research Support|Seqirus: Grant/Research Support Lina S. Sy, MPH, Dynavax: Grant/Research Support|Glaxosmithkline: Grant/Research Support|Moderna: Grant/Research Support|Seqirus: Grant/Research Support Jeff Slezak, MS, ALK, Inc.: Grant/Research Support|Dynavax: Grant/Research Support|Novavax, Inc.: Grant/Research Support|Pfizer, Inc.: Grant/Research Support Lei Qian, PhD, Dynavax: Grant/Research Support|Glaxosmithkline: Grant/Research Support|Moderna: Grant/Research Support Kristi Reynolds, PhD, Amgen: Grant/Research Support|Dynavax: Grant/Research Support|Merck: Grant/Research Support|Novartis: Grant/Research Support Runxin Huang, MS, Dynavax: Grant/Research Support Zendi Solano, BS, Dynavax: Grant/Research Support|Gilead: Grant/Research Support|GlaxoSmithKline: Grant/Research Support William Towner, MD, Dynavax: Grant/Research Support|Gilead: Grant/Research Support|Merck: Grant/Research Support|Moderna: Grant/Research Support|Pfizer: Grant/Research Support|ViiV: Grant/Research Support Sijia Qiu, MS, Dynavax: Grant/Research Support|Moderna: Grant/Research Support Sarah Simmons, MPH, Dynavax: Grant/Research Support|Glaxo-Smith Kline: Grant/Research Support|Pfizer: Grant/Research Support Steven Jacobsen, MD, Dynavax: Grant/Research Support Katia J. Bruxvoort, PhD, MPH, Dynavax: Grant/Research Support|Gilead: Grant/Research Support|Glaxosmithkline: Grant/Research Support|Moderna: Grant/Research Support|Pfizer: Grant/Research Support|Seqirus: Grant/Research Support.


Fluid therapy in patients with suspected infection is controversial, and it is not known whether fluid treatment administered in the prehospital setting is beneficial. In the absence of evidence-based guidelines for prehospital fluid therapy for patients with suspected infection, Emergency Medical Services (EMS) personnel are challenged on when and how to initiate such therapy.This study aimed to assess EMS personnel’s decision-making in prehospital fluid therapy, including triggers for initiating fluid and fluid volumes, as well as the need for education and evidence-based guidelines on prehospital fluid therapy in patients with suspected infection.An online survey concerning fluid administration in prehospital patients with suspected infection was distributed to all EMS personnel in the Central Denmark Region, including ambulance clinicians and prehospital critical care anaesthesiologists (PCCA). The survey consisted of sections concerning academic knowledge, statements about fluid administration, triggers to evaluate patient needs for intravenous fluid, and clinical scenarios.In total, 468/807 (58%) ambulance clinicians and 106/151 (70%) PCCA responded to the survey. Of the respondents, 73% (n = 341) of the ambulance clinicians and 100% (n = 106) of the PCCA felt confident about administering fluids to prehospital patients with infections. However, both groups primarily based their fluid-related decisions on “clinical intuition”. Ambulance clinicians named the most frequently faced challenges in fluid therapy as “Unsure whether the patient needs fluid” and “Unsure about the volume of fluid the patient needs”. The five most frequently used triggers for evaluating fluid needs were blood pressure, history taking, skin turgor, capillary refill time, and shock index, the last of which only applied to ambulance clinicians. In the scenarios, the majority administered 500 ml to a normotensive woman with suspected sepsis and 1000 ml to a woman with suspected sepsis-related hypotension. Moreover, 97% (n = 250) of the ambulance clinicians strongly agreed or agreed that they were interested in more education about fluid therapy in patients with suspected infection.The majority of ambulance clinicians and PCCA based their fluid administration on “clinical intuition”. They faced challenges deciding on fluid volumes and individual fluid needs. Thus, they were eager to learn more and requested research and evidence-based guidelines.The online version contains supplementary material available at 10.1186/s13049-022-01025-1. Sepsis is one of the most common presentations to Emergency Departments (ED) globally and is associated with significant morbidity and mortality –3. Most Although the definition of sepsis has continually evolved, the emphasis has remained on assessing patients for early diagnosis and treatment, mainly fluid and antibiotics. EMS personnel, as frontline health workers, are important in the early diagnosis and treatment of sepsis , 8. ApprThis study aimed to investigate fluid treatment in patients with suspected infection by EMS personnel, namely ambulance clinicians and prehospital critical care anaesthesiologists (PCCA). In particular, the study focuses on triggers to initiate fluid therapy and the fluid volumes administered. Furthermore, we aimed to investigate whether EMS personnel feel confident and have the required skills to handle the fluid therapy of patients with suspected infection, sepsis and suspected infection with hypotension, and whether they want to request better evidence and guidelines.The Danish EMS is a two-tiered ambulance system consisting of ambulances as first-line response and an advanced response from PCCAs in rapid-response vehicles or helicopters –16.Healthcare in Denmark is a tax-supported service. Each of the five regions has its own EMS and a varying number of regional general hospitals capable of treating most common medical and surgical conditions. The Central Denmark Region has 69 ambulances staffed by ambulance clinicians  students, ambulance assistants, EMTs, or paramedics) and ten rapid-response vehicles staffed by a PCCA and an EMT or a paramedic.We identified all EMS personnel in Central Denmark Region from mailing lists provided by the Prehospital EMS, Central Denmark Region and Falck Denmark A/S. The survey was distributed to 810 ambulance clinicians and 152 PCCAs.The present guideline for prehospital fluid administration in the Central Denmark Region recommends 1000–2000 ml NaCl 0.9% as a fast infusion for septic shock; however, no recommendations for suspected infection or sepsis without hypotension are given.To examine the fluid treatment administered by ambulance clinicians and PCCAs in patients with suspected infection, an online survey was developed.The survey instrument included both 5-point Likert-scale questions rated from ‘strongly disagree’ to ‘strongly agree’ and multiple-choice options for different parameters. More attitude-based questions included a “Don’t know” option, to ensure, that respondents would complete the survery in stead of abandoning the survey or answering randomly. The survey consisted of three sections: (1) baseline information, (2) perception of, education and knowledge about fluid administration and (3) scenarios about fluid administration . The survey was in Danish. A translated version is provided in the Additional file.The online survey was developed and refined through a thorough, stepwise process. First, the survey was developed with a group of paramedics and physicians with special knowledge of and interest in fluid therapy, inspired by a similar in-hospital survey which was modified to reflect prehospital challenges: i.e. about fluid administration to patients with suspected infection as patients are in the prehospital setting in stead of a firm diagnosis of sepsis and to cover only the period of transportation to the hospital. Then, the survey was pretested by six EMS employees who volunteered to do so, followed by a 30–60-min semi-structured interview. The interview first covered the volunteers’ perception of the entire field of prehospital fluid administration in general, followed by going though all questions ensuring, they perceived the questions as intended. The interviews resulted in a more exact and improved survey in terms of understanding.Baseline information covered age, educational level, years of experience and gender. In the section ‘Perception of, education and knowledge about fluid administration’, the respondents were asked how much they agreed to statements about fluid therapy, as well as whether they were aware of and used the current guideline. Afterwards, they had to indicate if they faced challenges and if yes, then which kinds of challenges they faced from a predefined list, with the option to add further challenges as comments. Finally, they were asked to select the five most commonly used triggers, from a list of 22 predefined triggers, for fluid initiation. We created two almost identical clinical scenarios: A 55-year-old, previously healthy woman (70 kg), complaining of fever and dyspnoea was assessed by the ambulance clinicians. For the last 14 days she had been coughing and spitting. She was slightly confused  15), blood pressure 120/75, pulse rate 120, respiration frequency 28, temperature: 39.1 Celsius and saturation 92% . The expected transportation time was 30 min.In the second, she was hypotensive with a blood pressure of 88/60 mmHg. For each scenario, respondents were asked (1) how much fluid they would administer to the patient during 30 min of transportation, (2) how fast they would infuse and (3) how they made their decision.The survey was distributed through emails with an embedded survey link. Emails were sent on June 9th, 2021, with up to six reminders. The survey was closed on July 19th, 2021. Responses were anonymous. Participation was voluntary, and informed consent was obtained at the beginning of the survey. No incentives were offered. Partly completed surveys were excluded from the analysis. All responses were collected through REDCap, version 10.6.16, hosted at Aarhus University .All data were presented as numbers and proportions (%) for dichotomous and categorical variables. Data were analysed using Stata Version 16 .Of the 962 individuals contacted, 58% (n = 468) ambulance clinicians and 70% n = 106) PCCAs completed the survey. The distribution of personnel among ambulance clinician respondents were EMT: 49% (n = 279), ambulance assistants: 11% (n = 61), paramedics: 16% (n = 92) and EMT students: 6% (n = 36). The majority in both groups were male. PCCAs were in general older, but had less prehospital experience than ambulance clinicians. The characteristics of the respondents are shown in Table 06 PCCAs In total, 100% PCCA (n = 106) and 69% ambulance clinicians n = 321) ‘strongly agreed’ or ‘agreed’ that ‘they feel confident and have the required skills to handle the fluid therapy of patients with sepsis’. The same pattern was found for septic shock  being ‘Unsure whether the patient needs fluid’ and 13% (n = 21) citing ‘lack of evidence in this field’  of the ambulance clinicians, in contrast to 9% (n = 9) in the PCCA group, ‘agreed’ or ‘strongly agreed’ with the statement ‘I have the feeling of at least doing something when I give the patient intravenous fluid—It is better to do something than nothing’. One in four administered intravenous fluids to ensure the placement of the peripheral venous catheter in both groups ), history taking (12% (n = 320)), skin turgor (11% (n = 271)), shock index (8% (n = 198)) and capillary refill time (7% (n = 208)), while for PCCA they were pulse rate (13% (n = 67)), blood pressure (10% (n = 55)), history taking (10% (n = 55), capillary refill time (10% (n = 55)) and ultrasound of heart, lungs or vena cava inferior (10% (n = 51)). The top ten triggers for ambulance clinicians and PCCA are shown in Fig. The first clinical scenario described a patient with suspected infection. The majority in both groups chose to administer 500 mL intravenous fluid (ambulance clinicians: 28% (n = 130); PCCA: 40% (n = 41)). The prefered infusion method for ambulance clinicians was slow infusion (> 30 min) (70% (n = 223)); however, fast infusion was often used by PCCAs (n = 35 (45%)).The patient in second scenario had suspected sepsis-related hypotension. In the second scenario, 47% (n = 222) of the ambulance clinicians and 42% (n = 44) of the PCCA administered 1000 mL intravenous fluid. Ambulance clinicians’ most frequently used infusion method was ‘fast infusion over 15–30 min’ (53% (n = 241)) in this scenario; however, the PCCAs more often chose ‘as fast as possible’ (n = 53 (52%))  of the ambulance clinicians and 51% (n = 55) of PCCAs ‘strongly agreed’ or ‘agreed’ that there is a lack of research into and evidence about prehospital fluid therapy, and 97% vs. 67%, respectively, ‘strongly agreed’ or ‘agreed’ that they were interested in more education about fluid therapy in patients with suspected infection Table .Table 3SOur survey found that the majority of both ambulance clinicians and PCCAs felt confident about administering fluids to prehospital patients with suspected infection; however, both groups faced challenges in deciding on fluid volumes and individual fluid needs, basing their decisions on clinical intuition. Seventy-two percent of ambulance clinicians and 88% of PCCAs considered fluids to be medication; however, 35% of the ambulance clinicians felt they administered fluids to ‘just do something’ and one in four administered fluids to ensure placement of the peripheral venous catheter. Blood pressure, pulse rate and history taking were the overall most common triggers for administering fluids. Most frequently, 500 mL were administered to a normotensive patient with suspected sepsis and 1000 mL to a hypotensive sepsis patient within 30 min. of transportation, with a more rapid infusion for hypotension. Overall, 67% of the ambulance clinicians strongly agreed or agreed that there is a lack of research and evidence within the field of prehospital fluid therapy of patients with infections.We found that a higher proportion of respondents felt confident in administering fluid to septic shock than to sepsis patients. This may be due to the fact that the Surviving Sepsis Campaign gives recommendations for fluid administration in sepsis-related hypotension (30 ml/kg) but not for sepsis . FurtherIn this study, blood pressure was the most frequently used trigger for initiating fluid therapy in septic patients across both respondent groups and > 60% of respondents in both groups agreed that intravenous fluids raises blood pressure in patients with sepsis. In line with these findings, a study showed that systolic blood pressure ≤ 100 mmHg was strongly associated with the diagnosis of sepsis . Blood pOur study found that a larger proportion of ambulance clinicians than PCCAs disagreed that intravenous fluid was medication (13% vs 6%). In line with this finding, 5% of ambulance clinicians disagreed that intravenous fluids could have side effects vs only 1% of PCCAs. Although the numbers in general are small, the differences are noteworthy. The ambulance clinicians are the first EMS personnel to handle and treat the patients; however, follow-up on their patients is not possible after patient handover in the ED. Some side effects from fluid therapy often present later in the course of hospital treatment; thus, the PCCAs, who also work in-hospital, are more prone to experience the side effects than the ambulance clinicians.Several studies point towards an insufficient level of knowledge and awareness regarding sepsis care in the EMS , 25. In The findings of this survey provide important insights and opportunities to improve the quality of care of patients with suspected infection in the prehospital setting. These findings also provide further support for the development of an educational program for ambulance clinicians, in particular, with an in-depth focus on sepsis and fluid therapy, as requested by the respondents, however again stressing the problem with wanting evidence-based guidelines and education and at the same time lacking substantial scientific evidence—pre- as well as in-hospital.This study has several strengths and limitations. Our survey was developed to reflect prehospital challenges and piloted for face validity before distribution. The research instrument was validated internally in the prehospital setting in the Central Denmark Region; however, we expect that results could be both used and reproduced in other settings. We rigorously identified all EMS personnel, used reminder emails to improve the survey response rate and achieved an acceptable 63% response rate. These steps suggest the internal validity of our data is high and representative of the Central Denmark Region and Danish practice in the prehospital setting. However, the respondents may represent the fraction of prehospital personnel who are interested in this field and topic and, thus, introduce bias. Furthermore, the hypothetical scenarios were limited by the clinical detail that could be included and the responses may not represent actual daily practice i.e. it is not from this survey possible to determine what actually happens in the prehospital setting in terms of fluid administration to patients with suspected infections.The majority of ambulance clinicians and PCCAs based their fluid administration on 'clinical intuition'. They faced challenges in deciding on fluid volumes and individual fluid needs. In general, respondents were eager to learn more and requested research and evidence-based guidelines within the field.Additional file 1. Prehospital fluid therapy in patients with suspected infection: a survey of ambulance.


Spontaneous bacterial peritonitis is a known complication of patients with decompensated cirrhosis and ascites. It is differentiated from secondary bacterial peritonitis by the absence of an intraabdominal source of infection. We present a 56-year-old man with alcoholic cirrhosis who underwent multiple paracenteses that yielded fluid with progressively increasing neutrophil counts and several different organisms, recurring despite numerous treatments for SBP. Eventually, a computed tomography (CT) of the abdomen and the pelvis revealed a large intraabdominal abscess (22 × 13 cm) treated with an ultrasound-guided drain and IV antibiotics. Recurrent episodes of SBP despite appropriate antibiotics should raise suspicion for secondary bacterial peritonitis. It is crucial to differentiate SBP from secondary bacterial peritonitis as the mortality of the latter is much higher without prompt treatment. Appropriate antibiotic regimens, prompt surgical treatment, and postoperative care are crucial to improving clinical outcomes in these patients. SBP is an ascitic fluid infection without an intraabdominal surgically treatable source [Spontaneous bacterial peritonitis (SBP) is diagnosed by positive ascitic fluid bacterial culture or an absolute polymorphonuclear leukocyte (PMN) count ≥250 cells/mme source . It resu3, and cultures showed no growth. Shortly after that, he underwent a screening colonoscopy, during which a 4 cm cecal tubulovillous adenoma was removed. His ascites initially responded to diuretics and reduced alcohol consumption, but abdominal distention recurred four months after the first tap. A repeat paracentesis removed 6 liters (L) of cloudy amber fluid with 2648 PMNs/mm3; cultures grew no organisms, and no antibiotics were prescribed. Eleven days later, the ultrasound showed complex loculated ascites. Subsequent paracentesis yielded 1.5 L of yellowish-milky fluid with 28,292 PMNs/mm3, and the cultures grew Klebsiella oxytoca and Streptococcus salivarius. The patient was diagnosed with SBP, admitted, and treated with IV cefotaxime for four days. A paracentesis three days after admission removed 1.4 L of tan-cloudy fluid, with >3 billion nucleated cells/mm3, 90% PMNs, and the cultures grew the same two organisms. A comparison of the ascites fluid analysis is presented in 3; cultures grew Streptococcus anginosus.A 56-year-old man with alcoholic cirrhosis and diabetes mellitus presented approximately six months earlier with abdominal distention at an outside hospital. An abdominal computed tomography (CT) showed ascites and a cirrhotic-appearing liver with no other abnormalities. The ascitic fluid contained 36 polymorphonuclear leukocytes (PMNs)/mm3/mm3) with normal neutrophil count and mildly elevated bands (582/mm3), hemoglobin was 8.6 g/dL, bilirubin and liver enzymes were normal. A CT abdomen/pelvis with contrast revealed a large (22 × 13 cm) intraabdominal abscess , the paracentesis showed a high PMN count with negative cultures. The patient was not treated with antibiotics, and a week and a half later, the fluid was grossly purulent, with a 10-fold increase in PMNs and grew two organisms. Given that the PMN count was already elevated on the preceding paracentesis, it seems unlikely that the subsequent culture-positive infection was caused by bowel injury during the earlier tap. It is also doubtful that the abscess was itself a manifestation of SBP since the presence of multiple organisms is not typical of SBP; furthermore, we found no instances in the literature of abdominal abscess arising from SBP. The possibility that the abscess resulted from either a microperforation caused by the cecal polypectomy or bowel puncture during the initial paracentesis seems remote since those procedures were done months before the onset of symptoms; however, given his indolent course and lack of systemic toxicity even when presented with a large abscess, this cannot be excluded entirely.This case illustrates the importance of recognizing that not all cases of neutrophilic ascites in cirrhosis represent SBP. The lack of recognition of ascitic fluid findings that differentiate SBP from secondary bacterial peritonitis led to a delay in diagnosing a huge intraabdominal abscess. Fortunately, the patient eventually received definitive treatment and recovered well.