Objective: Conventional methods of fluid assessment in critically ill children are difficult and/or inaccurate. Impedance cardiography has capability of measuring thoracic fluid content (TFC). There is an insufficient literature reporting correlation between TFC and conventional methods of fluid balance and whether TFC predicts outcomes in critically ill children. We hypothesized that TFC correlates with indices of fluid balance [FIMO (Fluid Intake Minus Output) and AFIMO (Adjusted Fluid Intake Minus Output)] and is a predictor of outcomes in critically ill children.Design: Retrospective chart review.Setting: Pediatric intensive care unit of a tertiary care teaching hospital.Patients: Children <21 years, admitted to our Pediatric Intensive Care Unit (PICU) between July- November 2018 with acute respiratory failure and/or shock and who were monitored for fluid status using ICON® monitor.Interventions: None.Measurements and Main Results: We collected demographic information, data on daily and cumulative fluid balance (CFB), ventilator, PICU and hospital days, occurrence of multi-organ dysfunction syndrome (MODS), and mortality. We calculated AFIMO using insensible fluid loss. We analyzed data using correlation coefficient, chi-square test and multiple linear regression analysis. We analyzed a total 327 recordings of TFC, FIMO and AFIMO as daily records of fluid balance in 61 critically ill children during the study period. The initial TFC, FIMO, and AFIMO in ml [median (IQR)] were 30, 300, and 21.05, respectively. The peak TFC, FIMO, and AFIMO in ml were 36, 322, and 108.8 respectively. The initial CFB was 1134.2. TFC did not correlate well with FIMO or AFIMO , but a significant proportion of patients with high TFC exhibited pulmonary plethora on x-ray chest  (p = 0.015). The multiple linear regression analysis revealed that initial and peak TFC and peak and mean FIMO and AFIMO predicted outcomes  in critically ill children (p < 0.05).Conclusions: In our cohort of critically ill children with respiratory failure and/or shock, TFC did not correlate with conventional measures of fluid balance (FIMO/AFIMO), but a significant proportion of patients with high TFC had pulmonary plethora on chest x-ray. Both initial and peak TFC predicted outcomes in critically ill children. Fluid overload (FO) is associated with poor outcomes in critically ill patients –7. IncreMonitoring fluid balance (FB) is therefore important in a pediatric intensive care unit (PICU) . HoweverMeasurement of thoracic fluid content (TFC) using Bioimpedence is based on the theory that the thoracic cavity is an inhomogeneous electrical conductor . TFC is Similarly, measurement of TFC using a non-invasive ICON® device, has shown that TFC is a good indicator of fluid status in adults undergoing hemodialysis . RecentlWe conducted a retrospective chart review of critically ill children admitted to our PICU between July-November, 2018. The institutional review board of Baylor College of Medicine and feasibility committee of Voelcker Clinical Research Center of The Children's Hospital of San Antonio approved the study and waived the need for informed consent.All the patients admitted to our PICU between July to November 2018, with respiratory failure and/or shock, in whom the ICON® monitor data on fluid and hemodynamic status was available were included in the study. Exclusion criteria: Children in whom daily measures of indices of fluid balance using ICON were not feasible and/or available.The primary outcome was mortality. The secondary outcomes were ventilator days, length of PICU stay (days) and length of hospital stay (days), and occurrence of multi-organ dysfunction syndrome (MODS).Our unit acquired ICON® monitor in May 2018 and we started using the monitor on patients with respiratory failure and/or shock for non-invasive assessment of fluid and hemodynamic status. Four ICON® monitor sensors were applied to the left side of the neck and thorax of the patient . The twoStroke Volume Variation (SVV), is the percentage of change between the maximal and minimal stroke volumes divided by the average of the minimum and maximum over a floating period of 30 s. A greater SVV value represents intravenous volume depletion . Correct2/day and for patients on invasive and non-invasive mechanical ventilation, insensible fluid loss was calculated as 400 mL/m2/day in case of invasive or non-invasive ventilation , which was calculated as the difference between total input  and total output . Based on the prior study by Bontant et al., for patients not on any invasive or non-invasive mechanical ventilation, insensible fluid loss was calculated as 800 mL/mtilation  and addeIn order to correlate the findings of chest radiograph with corresponding TFC as measured by ICON®, an independent reviewer (USB) who was blinded to TFC values, evaluated chest radiographs for presence of pulmonary plethora as defined by increased bronchovascular markings and/or presence of pleural effusion. Data was also collected regarding the ventilator days, length of PICU and hospital stay (days). MODS was defined as presence of 2 or more organ system dysfunction . Organ fp-values. Therefore, Bonferroni correction was not applicable in our methodology. Patients with FO were compared with those without FO using chi—square test. P-value ≤ 0.05 was considered significant.Correlation between TFC, FIMO, and AFIMO was studied using correlation coefficients. Multiple linear regression analysis was used to determine if indices of fluid balance  predicted outcomes . While we conducted the multiple linear regression analysis, we analyzed each variable for the outcome individually/separately and we created the table with all the variables and outcomes with corresponding A total 327 recordings of TFC, SVV, FTC, FIMO, and AFIMO were available as daily records of FB in 61 critically ill children between July and November 2018. All the eligible patients had data related to fluid balance and TFC during study period, which we were able to retrieve retrospectively. p = 0.015) (p < 0.05) (p > 0.05).The median (IQR) daily intake and output were 793.54  and 694  ml, respectively. The median (IQR) daily urine output was 2.24  ml/kg/h. The initial and peak TFC, FIMO (ml), AFIMO (ml), and CFB (ml) [median (IQR)] were 30 , 36 , 300 , 322 , 21.05 , 108.8 , and 1134.2  respectively . TFC did= 0.015) . TFC, FI= 0.015) . The ini < 0.05) . We alsoThis retrospective, single-center chart audit suggests that initial and peak TFC as measured by non-invasive, electrical bio-impedance technology and conventional measures of FB predict morbidity in critically ill children with respiratory failure and/or shock. To our knowledge, this is the first study, which has assessed predictive value of TFC for outcomes in critically ill children. In our cohort, TFC did not correlate with either FIMO or AFIMO. We believe that this finding is possibly related to inherent inaccuracies of conventional measures of FB as eluded by Perren et al. . ApproprDifferent fluid metrics and weight metrics have been used for assessing fluid status and outcomes. But, prior studies, which have used weight metrics, have used different weights such as PICU, hospital admission weights, outpatient weights, or unspecified weights with varying results . In a stAlso, in our retrospective inquiry, we found that daily weights were not recorded in all the patients. We therefore decided to use fluid intake –output in our study instead of weight metrics. In pediatric critical care setting, fluid intake-output is monitored at a regular interval each day. This information is utilized to make decisions regarding titration of diuretics, fluid management, and renal replacement therapy. Also, evidence of pulmonary plethora and/or pulmonary edema on chest x-ray is utilized to decide fluid management in critically ill patients. In our study TFC did not correlate well with conventional measures of FB. In our study, TFC correlated with findings of pulmonary plethora on chest x-ray. Fluid retention in lungs could be related to local cardiopulmonary and lymphatic abnormalities, such as pneumonia, lymphatic malformation, and pulmonary vein stenosis or other systemic abnormalities. Because TFC is calculated by measuring the electrical conductance in the chest , 39, it FO has been shown to determine outcomes in critically ill children. FO portends worsened respiratory status with need of prolonged ventilatory support. It also portends multi-organ dysfunction needing greater intensification of support. Management of FO therefore becomes an important target for intervention in ICU. The concept of active de-resuscitation after initial fluid resuscitation has been introduced to minimize fluid accumulation and negative consequences of FO. The active de-resuscitation after initial stabilization can be achieved through pharmacologic and non-pharmacologic measures. Study in critically ill adults with Acute Kidney Injury (AKI) has shown improved survival with diuretic-induced negative FB . A randoThe trial demonstrated that prophylactic PD was better than furosemide in minimizing the occurrence of FO, ventilator days, and need for inotropic support after cardiac surgery in children . A systeOur study had several limitations—a retrospective chart review from a single center, a small sample size, and lack of any information on daily weights of the patients.Unfortunately, in our PICU, we do not routinely assess severity of illness scores such as PRISM or PIM 2 at admission and therefore in our study we did not have information on measure of severity of illness at admission. We understand that this is one of the limitations of our study. This being a retrospective study there was limitation with respect to the timing of documentation of FIMO and occurrence of Xray chest. Therefore, we could not examine the relationship between conventional fluid status and pulmonary plethora. In our study, it was interesting that though TFC and FIMO/AFIMO did not correlate with each other, both were still able to predict outcomes. The possible reason that TFC, FIMO, and AFIMO did not correlate with each other could be related to frequency and timing of acquiring TFC, FIMO, and AFIMO data. This being a retrospective study, it is a potential limitation of the study. Also, it is likely that the sample size was small for true reflection of correlation between TFC, FIMO, and AFIMO. It is very clear in the literature that fluid overload is associated with poor outcomes in critically ill children and therefore, peak and mean FIMO and AFIMO predicted outcomes in our cohort of critically ill children. In addition, our study, for the first time demonstrated that initial and peak TFC predicted outcomes in critically ill children with respiratory failure and/or shock.FO is associated with poor outcomes in critically ill children. Conventional methods of fluid assessment are often not reliable and/or difficult to perform in critically ill patients. In our cohort of critically ill children, conventional indices of FB did not correlate with newer indices of FB such as TFC. TFC as measured by impedance cardiography and electrical cardiometry is a useful, non-invasive index of fluid status in critically ill children. Both initial and peak TFC predict morbidity in critically ill children.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Baylor College of Medicine IRB. Written informed consent from the participants' legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.All authors contributed to study design, data gathering and analysis, manuscript writing, and editing before the final submission 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.


Fault-zone fluids control effective normal stress and fault strength. While most earthquake models assume a fixed pore fluid pressure distribution, geologists have documented fault valving behavior, that is, cyclic changes in pressure and unsteady fluid migration along faults. Here we quantify fault valving through 2-D antiplane shear simulations of earthquake sequences on a strike-slip fault with rate-and-state friction, upward Darcy flow along a permeable fault zone, and permeability evolution. Fluid overpressure develops during the interseismic period, when healing/sealing reduces fault permeability, and is released after earthquakes enhance permeability. Coupling between fluid flow, permeability and pressure evolution, and slip produces fluid-driven aseismic slip near the base of the seismogenic zone and earthquake swarms within the seismogenic zone, as ascending fluids pressurize and weaken the fault. This model might explain observations of late interseismic fault unlocking, slow slip and creep transients, swarm seismicity, and rapid pressure/stress transmission in induced seismicity sequences. Coupling between fault zone fluid flow, permeability evolution, and elastic stress transfer produces fault valving and fluid-driven aseismic slip and pore pressure pulses. This model might explain late interseismic fault unlocking, slow slip, and rapid pressure transmission in induced seismicity. Much attention in the earthquake modeling community has been placed on friction over the past decades, with specific focus on rate- and state-dependent effects that control the stability of sliding, as well as additional dynamic weakening processes that are likely relevant at coseismic slip velocities. With some exceptions9, less attention has been placed on pore pressure dynamics, and most earthquake simulations use pore pressure  as a tuning parameter chosen to produce reasonable stress drops and slip per event11.Fault shear strength 16. The fluid transport properties of fault zones are highly variable, as a consequence of differences in structure, lithology and composition, stress state, and deformation history18. For most mature faults in crystalline rocks, fault permeability is anisotropic and varies with distance normal to the fault core, with the low permeability core acting as a barrier to across-fault flow and the high permeability damage zone facilitating upward flow along the fault20. Pressure gradients that exceed the hydrostatic gradient induce flow along faults, and fluid overpressure is one of the classic explanations for the weakness of the San Andreas and other plate boundary faults21. Fluids are even more important in subduction zones, owing to dehydration reactions at depth as well as overpressure from burial of sediments in the uppermost portion of the seismogenic zone22. Fluids and pore pressure influence fault strength and can trigger seismicity, as evidenced in both energy production activities23 as well as naturally occurring swarm seismicity28—which might also involve fluid-driven aseismic slip29.Continental strike-slip faults like the San Andreas (CA) and Alpine  faults can act as conduits or at least guides for mantle-derived fluid, fluids released during metamorphic dehydration reactions, and meteoric fluid that circulates in the upper crust31. The intermittency of fluid pressurization and release, a concept known as fault valving31, is a consequence of feedback between fault slip and deformation, which typically elevate permeability34, and healing and sealing processes, like pressure solution transfer, that reduce permeability39. These feedback effects are amplified by nonlinear dependence of permeability on effective normal stress due to mechanical compression of pores and microfractures40.Fluid flow and pore pressure are likely to be dynamic quantities, particularly near the base of the seismogenic zone, over earthquake cycle time scales. Geologists document mineral-filled veins that provide evidence for episodic fluid pressurization events in which pore pressure locally exceeds the least principal compressive total stressIn this work, we test the viability of fault valving and explore phenomena that arise from the coupling between fluids and faulting processes. This is done in an earthquake sequence model that accounts for the coupling between fault zone fluid flow, pore pressure and permeability evolution, and elastic stress transfer from fault slip. In certain parts of parameter space, this model produces fault valving cycles of overpressure build-up and release. The model also predicts the spontaneous generation of fluid-driven aseismic slip and overpressure pulses near the base of the seismogenic zone and earthquake swarms within the seismogenic zone, as ascending fluids pressurize and weaken the fault. These phenomena might explain many related observations, such as late interseismic fault unlocking, slow slip, swarm seismicity, and rapid pressure transmission along faults in induced seismicity sequences.41 two-dimensional antiplane shear model of a vertical strike-slip fault in a uniform elastic half-space  give rise to a one-dimensional (1D) diffusion equation for pore pressure:n is the pore volume fraction, β is the sum of fluid and pore compressibility3, k is permeability, η is fluid viscosity, ρ is fluid density, g is gravity, and z is the distance from the Earth’s surface (positive down). Our 1D fluid transport model approximately captures pressure evolution and flow over time scales that are longer than the hydraulic diffusion time across the damage zone, which we estimate to be of order of days to weeks for representative damage zone properties. This 1D treatment neglects pressure gradients in the fault normal direction that arise over coseismic time scales from thermal pressurization3 and poroelastic effects45 from localized shearing or slip within the fault core. Inelastic changes in pore volume fraction (and storage nβ), which can arise from shear-induced dilatancy1, mineral precipitation in pores and microfractures37, and viscous flow of the matrix47, have been neglected for simplicity, and because we anticipate that changes in permeability will be more significant. The vertical (positive upward) fluid flux  isFluids migrate vertically along a tabular, porous fault zone, as in Rice’s modelp = 0 at z = 0  and q = q0 (constant) at the bottom of the simulation domain placed well below the seismogenic zone. The latter is a crude approximation for a fluid source at depth and avoids more sophisticated descriptions of fluid-producing dehydration reactions, meteoric water input from the crust surrounding the fault, and other sources. We set q0 = 3 × 10−9 m s−1, which is within the range of fluxes inferred for continental plate boundary faults16.Equation  requiresq = 0 for the hydrostatic condition p = ρgz, whereas fluid overpressure leads to upward flow (q > 0): p = (ρg + ηq/k)z for constant q and k. However, permeability k is unlikely to be constant. Many experiments show that permeability decreases as effective normal stress increases, due to mechanical closure of fractures and pores42. We capture this effect asσ* = 30 MPa is a stress-sensitivity parameter determined by experiments40 , 2 is a minimum bound on permeability, and k* is a reference permeability that is discussed below. Typically Note that 49. Here we introduce an idealization that captures two fundamental processes: permeability increase with slip and permeability reduction from healing and sealing processes over longer time scales. The simplest linear evolution equation capturing these processes isV is slip velocity. The first term on the right side describes permeability increase toward maximum permeability 2 (based on laboratory and in situ measurements50) over slip distance L = 1 m, and the second term describes permeability decrease toward minimum permeability 2 over time scale T; see Fig. k* instead of k in Eq. (k on σ − p is captured by utilizing the evolving reference permeability k* from Eq. (49), as well as the pressurization that comes from inelastic compaction of pores47, all of which should be added in future studies. Additionally neglected is the temperature (and hence depth) dependence of the time scale T from an Arrhenius thermal activation rate factor for chemical sealing processes like pressure solution37.Permeability also evolves due to a range of mechanical and chemical processesfrom Eq. . Our modT is poorly constrained, owing to the complexity of processes controlling healing and sealing. Predictions from pressure solution and crack sealing kinetics suggest time scales ranging from days to thousands of years37, and in situ permeability estimates following the 2008 Wenchuan earthquake show healing of the shallow fault within a year49. Likewise, high-temperature laboratory experiments demonstrate that hydrothermal reactions can dramatically reduce permeability, with estimated time scales at mid-seismogenic zone temperatures of a few to a few tens of years36. We set T = 3.17 years in our featured model but also explore models with alternative choices of T varying over several orders of magnitude.The time scale L, as our simplified evolution equation is an attempt to parameterize complex processes like cracking and yielding within the damage zone from stress concentrations at the rupture tip, dilatancy during shearing of the fault core and slip surface, and unclogging of pores and disruption of grain contacts. We select L so that the steady-state permeability curve . Hence, the effective stress distribution becomes independent of depth over most of the seismogenic zone.First consider steady sliding at plate rate Equation  yields ainto Eq.  and solventclass1pt{minimap and k evolve in time following the equations presented above. Results are shown in Figs. 2–We use the steady-state distribution of effective stress in Fig. 8. When this overpressure and aseismic slip front penetrates some distance into the VW seismogenic zone, it nucleates a small earthquake that ruptures 13–18 km depth. In phase 3, overpressure and aseismic slip continue to advance upward, stalling at 8 km after about a decade. Simultaneously, a second aseismic slip and overpressure pulse develops at about 20 km and migrates upward, nucleating a larger earthquake at 12 km depth that ruptures between 8 and 19 km depth. Phase 4 marks the transition to swarm-like seismicity, featuring many relatively small earthquakes that migrate upward following the fluid overpressure pulse as it ascends through the seismogenic zone  show reduced or even negligible fault valving behavior, as the fault remains a high permeability pathway throughout the earthquake cycle. Models with T much shorter than the recurrence interval  also have reduced overpressure cycling in the seismogenic zone but do exhibit quasi-periodic slow slip events that are spontaneously generated at the base of the seismogenic zone. These slow slip events are the fluid-driven aseismic slip fronts identified in Figs. T = 3.17 years, but the shorter T increases the rate at which they are generated so that many occur between each earthquake. Furthermore, decreasing T increases the propagation rate of the aseismic slip fronts . The depressurization rate of the seismogenic zone follows from integrating Eq. , with outflux equated to −1. The actual depressurization rate is smaller than this upper bound, due to somewhat lower pressure gradient, permeability, and outflux. The depressurization duration is controlled by T, leading to an overall pressure drop of Returning to the featured model in Figs. mentclass2pt{minim51.Our modeling predicts two phenomena that can be compared to observations: fluid-driven aseismic slip and swarm-like seismicity, both arising as overpressure pulses migrate upward along the fault. Fluid-driven aseismic slip might be observable in geodetic data as a progressive decrease of plate coupling or an ascending locking depth, though trade-offs in geodetic inversions might make this hard to resolve. Furthermore, if the deep part of the fault has heterogeneous frictional properties, microseismicity might accompany aseismic slip, as shown by Jiang and Lapusta52. The data are consistent with a model in which deep aseismic slip migrates up-dip at a rate of 30–120 m year−1, not too dissimilar to our example fault valving simulation in Figs. T = 3.17 years, has a migration rate of 380 m year−1, and we find that increasing T decreases the migration rate  in the Cascadia subduction zone provides evidence for a gradual unlocking of the transition zone between the locked seismogenic zone and the deeper region of episodic tremor and slip57 and at the base of the seismogenic zone in the Parkfield section of the San Andreas fault58. These tectonic settings are associated with high pore pressures, arising in part from metamorphic reactions like serpentinite dehydration that liberate fluids at near-lithostatic pressures. In these various settings, slow slip can migrate both along-strike and up- and down-dip. The fluid-driven aseismic slip phenomenon that we identified could equally well occur in the horizontal direction, if there exist lateral variations in frictional properties, fluid production rate, slip velocity, or simply nonlinear dynamics that give rise to spatial variations in pore pressure and associated horizontal pressure gradients. However, the migration rates in our simulations are much slower than observed slow slip propagation rates, and additional simulations exploring higher fluid fluxes q0, lower effective stresses, and other parameter variations are required to test the viability of this hypothesized explanation for slow slip events. That said, our model with T = 0.317 years . Further studies exploring a broader range of parameters, particularly fluid fluxes q0 and dependence on frictional parameters like a − b and dc (see “Methods”), are required to match seismicity migration rates observed in specific sequences.The second phenomenon in our simulations, swarm seismicity, is commonly associated with regions of active fluid transport, such as volcanic fields and geothermal sitesOverall, we have demonstrated the viability of fault valving in an earthquake sequence model that accounts for permeability evolution and fault zone fluid transport. Predicted changes in fault strength from cyclic variations in pore pressure are substantial (~10–20 MPa) and perhaps even larger than those from changes in friction coefficient. We have also shown how fluids facilitate the propagation of aseismic slip fronts and transmission of pore pressure changes at relatively fast rates. The modeling framework we have introduced here can be applied to a wide range of problems, including tectonic earthquake sequences, slow slip and creep transients, earthquake swarms, and induced seismicity.68 using fourth-order summation-by-parts finite differences for spatial discretization and adaptive Runge–Kutta time stepping. The frictional strength of the fault is determined by rate-and-state friction with an aging law:ψ is the state variable, V is the slip velocity, a is the direct effect parameter, V0 is the reference velocity, b is the state evolution effect parameter, dc is the state evolution distance, and f0 is the reference friction coefficient for steady sliding at V0.The numerical method for the friction and elasticity problem is identical to that in several previous publicationsu (in the x direction) is governed by the static equilibrium equation and Hooke’s law:μ is the shear modulus. Symmetry conditions across the fault (y = 0) are used to solve the problem on one side of the fault only, in the domain 0 ≤ y ≤ Ly, 0 ≤ z ≤ Lz.The antiplane displacement ηrad is the radiation damping coefficient41, f is the friction coefficient, σ is the total normal stress on the fault, and p is the pore pressure. Slip δ is defined via ∂δ/∂t = V. In solving the elasticity problem for u, slip is prescribed on the fault, tectonic loading displacement is prescribed on the side boundary, and traction-free conditions are prescribed on the top and bottom boundaries:Fault frictional strength is equated to the shear stress on the fault, which is the sum of the quasi-static shear stress and a radiation damping term:The pore pressure diffusion equation is discretized using fourth-order summation-by-parts finite differences, like the elasticity equation.δ and ψ with variable time steps Δt, as in previous work68. The difference here is that we must simultaneously solve the pore pressure diffusion and permeability evolution equations to update p, k*, and k. This is done using operator splitting at the Runge–Kutta stage level, with backward Euler used for the pore pressure diffusion equation. More details are provided below, with the algorithm explained using forward Euler instead of the explicit Runge–Kutta method for simplicity.Here we explain the time-stepping method. An adaptive Runge–Kutta method is used to update δ, ψ, p, k*, k) are known at time t. Then we update from time t to t + Δt following the procedure below:u(t) and calculate Solve the equilibrium Eq.  for u(t)V(t) using p(t) when evaluating fault strength.Solve Eq.  for veloψ(t + Δt), δ(t + Δt), and k*(t + Δt) explicitly, e.g.,Update p(t + Δt) and k(t + Δt):Implicitly update All dependent variables (The nonlinear system is solved using fixed-point iteration:Convergence is declared when the difference of successive updates to a and a − b is similar to Allison and Dunham68 and other previous modeling studies41 and is based on laboratory experiments69 and an assumed geotherm. The state evolution distance dc, which is proportional to the earthquake nucleation length, is chosen to be as small as possible while balancing computational cost.The parameters used in this study are shown in Table Supplementary InformationDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Peer Review File


Nevertheless, most critically ill conditions demand lower Vt. We sought to evaluate the operative performance of several predictors of fluid responsiveness at Vt ≤ 8 mL kg−1 by using meta-regression and subgroup analyses.Dynamic predictors of fluid responsiveness have shown good performance in mechanically ventilated patients at tidal volumes (Vt) > 8 mL kg−1, without respiratory effort and arrhythmias, published between 1999 and 2020. Operative performance was assessed using hierarchical and bivariate analyses, while subgroup analysis was used to evaluate variations in their operative performance and sources of heterogeneity. A sensitivity analysis based on the methodological quality of the studies included (QUADAS-2) was also performed.A sensitive search was conducted in the Embase and MEDLINE databases. We searched for studies prospectively assessing the operative performance of pulse pressure variation (PPV), stroke volume variation (SVV), end-expiratory occlusion test (EEOT), passive leg raising (PLR), inferior vena cava respiratory variability (Δ-IVC), mini-fluid challenge (m-FC), and tidal volume challenge (VtC), to predict fluid responsiveness in adult patients mechanically ventilated at Vt ≤ 8 ml kgp < 0.05). Operative performance of PPV was also significantly affected by the compliance of the respiratory system (p = 0.05), while type of patient (p < 0.01) and thresholds used to determine responsiveness significantly affected the predictability of SVV (p = 0.05). Similarly, volume of fluids infused to determine variation in cardiac output, significantly affected the performance of SVV (p = 0.01) and PLR (p < 0.01). Sensitivity analysis showed no variations in operative performance of PPV (p = 0.39), SVV (p = 0.23) and EEOT (p = 0.15).A total of 33 studies involving 1,352 patients were included for analysis. Areas under the curve (AUC) values for predictors of fluid responsiveness were: for PPV = 0.82, Δ-IVC = 0.86, SVV = 0.90, m-FC = 0.84, PLR = 0.84, EEOT = 0.92, and VtC = 0.92. According to subgroup analyses, variations in methods to measure cardiac output and in turn, to classify patients as responders or non-responders significantly influence the performance of PPV and SVV (−1. Nevertheless, technical and clinical variables might clearly influence on their operative performanceMost predictors of fluid responsiveness reliably predict the response of cardiac output to volume expansion in adult patients mechanically ventilated at tidal volumes ≤ 8 ml kg Fluid administration is one of the first-line therapy interventions used to reverse tissue hypoperfusion during acute circulatory failure. Nevertheless, fluid administration is not free of adverse effects, especially when fluids are excessively administered. Dynamic assessment of preload responsiveness appraising heart–lung interactions is commonly used during the resuscitation of mechanically ventilated patients with acute circulatory failure. In this scenario, assessment of fluid responsiveness might limit fluid administration, potentially reducing the risk of fluid overload, avoiding complications derived from tissue oedema and increasing mechanical ventilation-free days, among others .Several predictors of fluid responsiveness have been described in the medical literature . Dynamic−1 without arrhythmias and increased respiratory effort. Additionally, we aim to identify clinical variables or methods affecting the operative performance of dynamic predictors of fluid responsiveness under such particular conditions.Several meta-analyses evaluating the operative performance of fluid responsiveness predictors in different clinical settings have led to variable results –20. Theshttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019138147)) on August 12, 2020.This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines  and was Study selection and inclusion criteria1 and without respiratory effort and arrhythmias were selected for full-text reading. In addition, studies including subgroups of patients fulfilling our inclusion criteria were also selected and included for the analysis. No language restriction was applied. Only studies recording data about the operative performance of any fluid responsiveness test and including an explicit definition of fluid responsiveness after fluid loading were finally incorporated for the analysis. Studies conducted in the operating room, case reports, and studies including patients < 18 years old, pregnant women were excluded.Studies prospectively evaluating the operative performance of PPV, SVV, VtC PLR, EEOT, m-FC, and Δ-IVC as predictors of fluid responsiveness in critically ill ventilated patients at Vt ≤ 8 ml kgSearch strategy, data extraction and quality appraisalA comprehensive search was conducted in the MEDLINE and Embase databases, between January 1999 and May December 2019. Moreover, reference lists of each initially selected manuscript were manually reviewed searching for potential studies not retrieved by the original search. The complete search strategy and the terms used are available in the protocol recorded at PROSPERO. Two reviewers (J.I.A.S. and J.D.C.R.) independently assessed search results for inclusion and undertook data extraction and quality appraisal.Data extracted from each clinical trial included: authors, year of publication, number of patients enrolled, type of critically ill patient, age, height; norepinephrine dose, dobutamine, epinephrine, and vasopressin doses; main diagnosis; APACHE  II score; SOFA  score; method used to evaluate fluid responsiveness; amount and type of fluids used during the fluid challenge; diagnostic test or fluid responsiveness predictor assessed; definition of fluid responsiveness used; % of response ; cut-off point or threshold used to determine fluid responsiveness; tidal volume (Vt); respiratory system compliance; positive end-expiratory pressure (PEEP) level; airway driving pressure; presence of acute respiratory distress syndrome (ARDS); and finally, the sensitivity and specificity, and the area under the ROC curve (AUC) of the diagnostic test used.Two authors (JIAS and JDCR) independently assessed the quality of each study by using the QUADAS-2 tool  . DisagreData regarding sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated by using a contingency table. In some trials, prediction of fluid responsiveness was assessed by using different ventilation parameters or different thresholds, which resulted in multiple data about operative performances; in such cases, all data regarding operative performances were included for analysis.2). An I2 > 50% was considered significant [Fitted sensitivity, specificity, and AUC data were assessed through bivariate and hierarchical analyses. The summary of receiver operating characteristic (ROC) curves was assessed by using the method of Rutter and Gatsonis . Operatinificant .Asymmetry was assessed by the Thompson and Sharp test. Nevertheless, this was not applicable for PLR, Δ-IVC, VtC, and m-FC because the low number of studies addressing these predictors impedes the application of such test. Publication bias was fitted using the trim-and-fill method.Subgroup and meta-regression analyses were performed for all the clinical and physiological variables potentially influencing the operative performance of fluid responsiveness predictors: tidal volume, PEEP, driving pressure, compliance of the respiratory system, type of patient, method used to calculate the index, threshold used to predict fluid response, volume of fluid finally administered. This analysis was also used to determine the source of heterogeneity among studies.p < 0.05 was considered statistically significant.A sensitivity analysis was carried out by performing a meta-regression based on the methodological quality of included studies (QUADAS-2). The threshold effect was assessed using Spearman´s rank correlation coefficient and the Moses–Shapiro–Littenberg method. Data analysis was performed using R software, version 3.4.3, together with the mada and meta packages. Data are expressed as a value ), and A total of 644 studies were retrieved, including 612 from the MEDLINE and Embase databases, and 32 obtained from the reference lists of the studies retrieved from the original search. Finally, 33 studies fulfilling all the inclusion criteria were included for the quantitative analysis , SVV (p = 0.04), and EEOT (p < 0.03), and it was caused by publication bias , and the DOR obtained using the random effects model was decreased . On the other hand, when the asymmetry of studies that assessed SVV was fitted, DOR by random effects also decreased , but there were no changes in the heterogeneity . Finally, when asymmetry among studies that assessed EEOT was fitted, both DOR by random effects  and heterogeneity decreased .Asymmetry among studies on PPV was fitted by using the trim-and-fill method, improving heterogeneity (p = 0.02) and by the compliance of the respiratory system (p = 0.05) .Subgroup and meta-regression analyses attaining statistical significance are shown in Table m p = 0.0 p = 0.01), the threshold selected to define positive fluid responsiveness (p = 0.05), the type of critically ill patient (p < 0.001), and the volume of fluid finally used during the fluid challenge (p = 0.01). These subgroups were a source of heterogeneity since they disappeared among studies .Operative performance of SVV was affected by the method to calculate cardiac output (p < 0.01), and it was a source of heterogeneity since it disappeared among studies (p = 0.93). Subgroup and meta-regression analyses of the remaining predictors did not show any change in their operative performance or heterogeneity , SVV (p = 0.23) and EEOT (p = 0.15) .According to the sensitivity analysis based on the methodological quality of the included studies (QUADAS-2), there were no changes in the operative performance of PPV  to predict fluid responsiveness, which suggests that effects of respiratory pressure and tidal volume mainly rely on the degree to which these variables are transmitted to the pulmonary circulation and not on their absolute values [Prediction of fluid responsiveness of some indices rely on tidal volume and intrathoracic pressure variations , 5. Intee values .p = 0.05).Methods used to classify patients as fluid responders or not responders after the final fluid loading significantly affecting the reliability of PPV and SVV to predict fluid responsiveness. In this regard, operative performance was lower when transpulmonary thermodilution was used (through a PiCCO monitoring system) than when using the conventional thermodilution (through a pulmonary artery catheter) (see Table p < 0.01): better performance was found in post-cardiovascular surgery patients and in those with septic shock , than in post high-risk surgery patients . We hypothesized that this finding represents a higher proportion of abdominal hypertension cases in the last group of patients since this might be a common complication in the postoperative period [As expected, lower thresholds might increase operative performances in some cases  was not used to determine or assess the meta-analysis’s quality since it was not established in our protocol. Conversely, we performed a sensitivity analysis based on the methodological quality of the included studies (QUADAS-2).This meta-analysis had several limitations. First, only adult critically ill ventilated patients with a Vt ≤ 8 ml kgFourth, moderate heterogeneity was found for some predictors, so these findings should be interpreted with caution. Nevertheless, other sources conversely decreased heterogeneity, which would allow extrapolation of our findings to clinical practice. Finally, operative performance of fluid responsiveness test was classified according to ROC curve analysis, which does not consider the DOR, a variable that summarizes the relation between sensitivity and specificity; however, in our opinion, DOR should always be considered for measuring operative performance when choosing a predictor of fluid responsiveness.In conclusion, VtC, EEOT, and SVV have excellent operative performance, while ∆-IVC, PLR, m-FC, and PPV had good operative performance as predictors of fluid responsiveness in our setting. Method to calculate the cardiac output, threshold used to determine fluid responsiveness, volume administered during the fluid loading, and type of patient in which the test has been applied should have in account at moment to use it in clinical practice.Additional file 1: Table S1. Additional. Risk of bias of the trials as assessed by QUADAS-2 criteria.Additional file 2: Figure S1. Diagnostic odds ratios of pulse pressure variation in adult critically ill ventilated patients with a Vt 8 ml kg−1 and without arrhythmia or respiratory effort.Additional file 3: Figure S2. Diagnostic odds ratios of stroke volume variations in adult critically ill ventilated patients with a Vt 8 ml kg−1 and without arrhythmia or respiratory effort.Additional file 4: Figure S3. Diagnostic odds ratios of passive leg raising in adult critically ill ventilated patients with a Vt 8 ml kg−1 and without arrhythmia or respiratory effort.Additional file 5: Figure S4. Diagnostic odds ratios of End-expiratory occlusion test in adult critically ill ventilated patients with a Vt 8 ml kg−1 and without arrhythmia or respiratory effort.Additional file 6: Figure S5. Diagnostic odds ratios of mini-fluid challenge in adult critically ill ventilated patients with a Vt 8 ml kg−1 and without arrhythmia or respiratory effort.Additional file 7: Figure S6. Diagnostic odds ratios of tidal volume challenge in adult critically ill ventilated patients with a Vt 8 ml kg−1 and without arrhythmia or respiratory effort.Additional file 8: Figure S7. Diagnostic odds ratios of inferior vena cava respiratory variability in adult critically ill ventilated patients with a Vt 8 ml kg−1 and without arrhythmia or respiratory effort.Additional file 9: Figure S8. Contour enhanced funnel plot for a meta-analysis of pulse pressure variation for prediction of fluid responsiveness in patients with tidal volume 8 mL kg−1. Filled circles show an estimated treatment effect (Log diagnostic odds ratio) and its precision (standard error). In addition to individual study results, the fixed-effect estimates  with 95% confidence interval limits  and the random-effects estimate  are shown in the figure. The number of the point is the reference number of each study.Additional file 10: Figure S9. Contour enhanced funnel plot for a meta-analysis of stroke volume variation for prediction of fluid responsiveness in patients with tidal volume 8 mL kg−1. Filled circles show an estimated treatment effect (Log diagnostic odds ratio) and its precision (standard error). In addition to individual study results, the fixed-effect estimates  with 95% confidence interval limits  and the random-effects estimate  are shown in the figure. The number of the point is the reference number of each study.Additional file 11: Figure S10. Contour enhanced funnel plot for a meta-analysis of end-expiratory occlusion test for prediction of fluid responsiveness in patients with tidal volume 8 mL kg−1. Filled circles show an estimated treatment effect (Log diagnostic odds ratio) and its precision (standard error). In addition to individual study results, the fixed-effect estimates  with 95% confidence interval limits  and the random-effects estimate  are shown in the figure. The number of the point is the reference number of each study.Additional file 12: Table S2. Other findings of meta-regression and subgroup analysis.


I examined the use of Twitter during the COVID-19 pandemic to find out how many Twitter users started to follow relevant Canadian political and health authorities, and I investigated their activity and interests. To this end, I analyzed 398,037 Twitter accounts. The results reveal that the Twitter accounts of relevant authorities gained a significant number of new Twitter followers during the pandemic. The Twitter users who joined during the pandemic were rather passive; they tweeted and liked fewer tweets than Twitter users who registered in the months prior to the pandemic. They also chose to follow Twitter accounts predominantly related to news, politics and governmental agencies. These findings suggest that during the pandemic, numerous information-seeking citizens joined Twitter for the purpose of obtaining information about public health matters, which in turn suggests that authorities should incorporate Twitter into their information dissemination tools, especially during emergencies, to meet the public demand for information. The 2019 novel coronavirus disease (COVID-19) has significantly affected the lives of people across the world, and Canada has been no exception. Government officials charged with taking measures to prevent the spread of COVID-19 have had several options for informing the public about decisions being made and steps being implemented. They could use traditional media, such as television, radio or print, but they also could use newer forms, such as social media. Social media can be an ideal tool to offer information about the state of the country in times of crisis, when it is crucial to swiftly provide correct information to citizens. One of the social networking services currently quite popular in Canada is Twitter. At the beginning of 2020, almost 7 million Canadians were registered on Twitter  to generate word clouds and basic information about his followers. Next, I gathered detailed information about the Twitter followers of Health Canada and the Public Health Agency of Canada (PHAC). Health Canada and the PHAC have a joint Twitter account: @GovCanHealth. Trudeau, as a leading Canadian politician, is charged with informing the public in general terms, while Health Canada and the PHAC, as a governmental department and agency responsible for crafting federal public health policy, are responsible for publishing updates and timely information about the COVID-19 pandemic for the public. I then analyzed two other Twitter accounts related to the government and public health that were significantly active during the COVID-19 pandemic: the Twitter account of Patty Hajdu, the Minister of Health (@PattyHajdu), and the Twitter account of Theresa Tam (@CPHO_Canada), the Chief Public Health Officer and head of the PHAC Canada. I collected data through the Twitter application programming interface (API) from June 15 to June 20, 2020. More details are given below with each figure, and the summary of data is in Table 1 in the online Appendix. However, for more detailed analysis, I also collected data on the Canadian Space Agency (@csa_asc) and Environment and Climate Change Canada (@environmentca).COVID was the most frequently used within English tweets during the examined period, and it was the second most frequently used word in the French tweets . Other frequently used words found in Trudeau's tweets were those connected with morale-boosting related to the pandemic. In English, those words included Canada, Canadians, Canadian or country, underscoring the importance of uniting as a nation; other words such as help, support, keep, together and protect emphasize the need for cooperation and encourage citizens to consider others. For comparison, two word clouds based on tweets that preceded the COVID-19 pandemic are presented in the top half of Canada and Canadians, were used more frequently before the pandemic but that other words, such as support, help and need, were used more often during the pandemic.I excluded stop words from each tweet by using R package stopwords  from all four analyzed accounts. For this analysis, I included only original tweets, since these accounts often retweet to each other. Information-seeking citizens would be expected to follow Twitter accounts providing information about the COVID-19 pandemic if they were interested in relevant updates. From each graph, it is possible to compare the pre-pandemic period to a period during the COVID-19 pandemic. As mentioned above, on March 5, 2020 (green line in figures), the first case of COVID-19 community transmission in Canada was confirmed . To answer this question, People who joined Twitter during the COVID-19 pandemic could be expected to have different behaviour on Twitter, since they were primarily information-seeking users and less interested in social interaction; this assumption is based on the fact that they were not sufficiently interested in Twitter before the COVID-19 pandemic to create an account. The analysis of user activity for this part of the study was based on the followers of @GovCanHealth, as it is the Twitter account with the largest number of followers that is strictly related to the Canadian public health system. Twitter users who created accounts a few days before and after COVID-19 was declared a pandemic by the World Health Organization (WHO), which happened on March 11, 2020, have significantly fewer tweets per month. For @GovCanHealth followers who created accounts during the week of March 9, 2020, and during several of the weeks following, the median number of tweets is around 0.25. This suggests that users who created a Twitter account during the pandemic period used Twitter mainly as a source of information rather than as a platform for sending tweets. Twitter users can “like” tweets, and it is one type of account interaction; I applied the same process described above for liked tweets and tweets marked as “favourite.” To further examine the accounts of people who joined Twitter during the early months of the COVID-19 pandemic, I collected data on the followers of @GovCanHealth and examined what other accounts they followed to look for common interests. I analyzed only the accounts that were created in March and April 2020, and it was only possible to analyze accounts that were not set to private. The total number of examined accounts was 10,698. If, as hypothesized, these new Twitter users are primarily information-seeking citizens interested in information about the COVID-19 situation, then they should follow accounts that are likely to provide them with this information. I classified all Twitter accounts that were followed by at least 10 per cent of followers of @GovCanHealth into four categories: celebrities, governmental, news and politics. Governmental accounts are not necessarily connected with the Canadian government since I included, for example, @WHO and @CDCgov as governmental accounts. A total of 54 Twitter accounts were followed by more than 10 per cent of followers of @GovCanHealth The hypothesis was that newly joined Twitter users would be most interested in news, politics and governmental Twitter accounts rather than celebrities, since celebrities offer different kinds of information than relevant authorities do—although celebrities can, of course, also tweet about COVID-19. However, it must be emphasized that these findings do not imply that the interests of people who joined Twitter during the COVID-19 pandemic are significantly different from other Twitter users. In comparison to all Canadian Twitter users, it can be expected that @GovCanHealth followers are always skewed toward news-oriented Twitter accounts, regardless of whether they joined Twitter during or before the COVID-19 pandemic. Therefore, these findings cannot help us with the answer to what users who joined Twitter during the COVID-19 pandemic are interested in, compared to other followers of @GovCanHealth, and whether their preferences are systematically different across the board.The findings in this research suggest that Twitter has served as an information source for many during the COVID-19 pandemic. Several observations support this claim. First, there was a significant increase in people following the Twitter accounts of relevant authorities  during the COVID-19 pandemic. Second, for all four Twitter accounts, the number of followers who created accounts at the beginning of the COVID-19 pandemic and in the following weeks is significantly overrepresented when compared to the pre-pandemic months. This means that during that period, new Twitter accounts represented a significant portion of new followers. Although many people were using Twitter before the COVID-19 pandemic, a significant number decided to join at this time. Third, the followers of @GovCanHealth and those who created an account during the COVID-19 pandemic had significantly different behaviour from users who had joined Twitter before the COVID-19 pandemic; these new users posted fewer tweets and liked fewer tweets. This suggests that these new users are more passive and more interested in obtaining information than in interaction. Fourth, I analyzed what other accounts followers of @GovCanHealth followed and determined that they were mainly drawn to Twitter accounts related to news, politics and governmental agencies and, to a much lesser degree, celebrities. This suggests that these new users turned to Twitter primarily as information-seekers rather than entertainment-seekers. All findings suggest that even in countries such as Canada where Twitter is highly popular, there is still room for growth. When people who do not normally use Twitter join it to seek information during a crisis, politicians and state agencies should recognize this behaviour and use social media as one of the platforms for spreading news and information. Also, this research note provides a counterpoint to the literature that emphasizes the role that social media have played in spreading misinformation during the COVID-19 pandemic. Relevant authorities can provide legitimate information to citizens via social media, and the findings show that many citizens are indeed interested in this news.It is important to note that Twitter is used by a minority of people in Canada and that it is not the dominant source of news information. Only 16 per cent of the Canadian public uses Twitter as a source for political news. The dominant mode of news consumption in Canada is still television (Owen et al., One of the limitations of the study is that it was not possible to determine whether newly gained followers are Canadians and legitimate accounts; however, the same limitation applies to the pre-pandemic months. At the moment, there is no reason to assume that the number of fake accounts would be significantly greater during the COVID-19 pandemic than before, and the more detailed analysis of @GovCanHealth followers did not indicate such an increase. As the pandemic continues, further studies could continue the analysis of Twitter accounts of federal and provincial political actors and authorities, taking the research in different directions.


Blood Cancer JournalCorrection to: 10.1038/bcj.2017.29 published online 7 April 2017Following the publication of this article the authors noted that Grant numbers were omitted in the Acknowledgements section. This work was supported by grants from the Cancer Research Society of Canada (grants no. 16255 to M.G.) and from the Fond de Recherche Santé Québec (grant no 20219 to J.R.) and in part by the Foundation de l’Hôpital Maisonneuve-Rosemont, Montreal, QC, CAN. The authors wish to apologize for any inconvenience caused.


Protamine is a natural cationic peptide mixture used as a drug for the neutralization of heparin and in formulations of slow-release insulin. In addition, Protamine can be used for the stabilization and delivery of nucleic acids , immunostimulatory nucleic acids, plasmid DNA, or messenger RNA) and is therefore included in several compositions that are in clinical development. Notably, when mixed with RNA, protamine spontaneously generates particles in the size range of 20–1000 nm depending on the formulation conditions . These particles are being used for vaccination and immuno-stimulation. Several grades of protamine are available, and we compared them in the context of complex formation with messenger RNA (mRNA). We found that the different available protamine preparations largely vary in their composition and capacity to transfect mRNA. Our data point to the source of protamine as an important parameter for the production of therapeutic protamine-based complexes. Furthevo Amos, . Because2.2.1.in vitro transcription at the ‘ivt mRNA production and formulation platform’ in Zürich (http://www.cancer.uzh.ch/en/Research/mRNA-Platform.html). The 5′ end consisted of a CleanCapTM (Trilink) followed by an eIF4G aptamer as the 5′ untranslated region  that is a solution, and seven Sigma-Aldrich products in powders: protamine grade II–V, protamine grade X, protamine meeting USP standards  and protamine for biochemistry. The protamine sources and formulations are shown in All protamines were diluted in pure water to a concentration of 1 mg/ml and stored at 4 °C.2.3.SDS-PAGE was carried out using 5–20% polyacrylamide gels. Five micrograms of each Protamine type solution was mixed with 2x NuPAGE LDS sample buffer  and 1 M dithiothreitol (DTT) solution at a ratio of 5:4:1 (sample in water:LDS:DTT). Samples were loaded on the gel and subjected to electrophoresis. After being separated, the proteins were visualized by staining with GelCode™ blue safe protein stain .2.4.w/w) and left for 10 min at room temperature. One milliliter of 5% glucose solution in pure water was added, and the size distribution of the protamine-mRNA complexes was characterized by dynamic light scattering with a ZetaSizer 3000HSA . Data were analyzed with the built-in DTS software .Preparation of the proticles was performed as described previously  cells were maintained in Roswell Park Memorial Institute (RPMI) medium (Thermo Fisher Scientific) containing 10% fetal calf serum (FCS), 200 mM L-glutamine  and 0.2% antimicrobial reagent Normocin . For Luciferase experiments, the transfection of tumor cells was performed with 100,000 tumor cells per well in 100μl of RPMI medium supplemented with 10% FCS, 200 mM L-glutamine (Gibco) and 0.2% antimicrobial reagent Normocin (InvivoGen) by adding the mixture of RNA and protamine to obtain 1 µg of mRNA per well. The luciferase activity was recorded one day after transfection by adding 25 μl of Bright-Glo luciferase assay solution  and measuring the activity using GloMax luminometer equipment (Promega).2.6.Human PBMCs were isolated from the blood of healthy donors by using Ficoll-Paque™ Plus . A total of 100,000 cells per well were plated on 96-well plates and incubated with protamine-mRNA nanoparticles overnight. After that time, supernatants were taken, and interferon alpha (IFN-α) concentration was measured via enzyme-linked immunosorbent assay (ELISA) by following the manufacturer’s protocol . The absorbance was measured on an ELISA reader .2.7.w/w). The transfection efficiency was measured in HEK cells by luciferase assay, as described above.Clinical protamine (2.5 ml at 50 mg/ml) was fractionated with a PD-10 desalting column  using a gravity protocol and pure water for elution. Twenty-four fractions were collected and visualized with SDS-PAGE. Subsequently, the protein concentration of all samples was measured with a BCA assay (Thermo Scientific™ Pierce™ BCA™ Protein Assay). The protein concentration in all fractions was adjusted to 90 µg/ml, and the fractions were mixed with mRNA at a ratio of 3:1 . In line with this observation, Benayahu and Aronson  were checked for their peptide composition using electrophoresis on SDS-PAGE gels, as shown in  Aronson  reportedSupplementary Figure 2). Thus, the protamine-to-RNA mass ratio that we used in the past to generate nanoparticles was not 1:1 but 3:1.First, their capacity to generate nanoparticles when mixed with mRNA in conditions suitable to generate less than 200 nm complexes was evaluated. At this stage, by comparing the concentrations of clinical protamine to those of Sigma protamines, we found that the concentration of clinical protamine is three-fold greater than previously reported - 7 and 8) . However, most protamines were relatively inefficient at all ratios (grade IV giving barely any detectable luciferase activity), while clinical chloride protamine 5000 remained the most efficacious one. This feature might be attributed to several functional characteristics of clinical chloride protamine 5000 such as the capacity to protect mRNA from degradation, to facilitate cellular uptake, to induce endosomal escape, or to enable mRNA dissociation from proticles when within the cytosol. Further mechanistic studies will be required to analyze those aspects in details. Meanwhile, to further characterize the type of protamines that allow transfection, we fractionated clinical chloride protamine 5000 on Sephadex P25 columns  (Hoerr et al.,


Glioblastomas (GBs) are malignant brain tumours with poor prognosis even after aggressive therapy. Programmed cell death-1 (PD-1) immune checkpoint blockade is a promising strategy in many types of cancer, but its therapeutic effects in GB remain low and associated with immune infiltration. Previous work suggests that oscillations of magnetic resonance spectroscopic imaging (MRSI)-based response pattern with chemotherapy could act as a biomarker of efficient immune system attack onto GBs. The presence of such oscillations with other monotherapies such as anti-PD-1 would reinforce its monitoring potential. Here, we confirm that the oscillatory behaviour of the response biomarker is also detected in mice treated with anti PD-1 immunotherapy both in combination with temozolomide and as monotherapy. This indicates that the spectral pattern changes observed during therapy response are shared by different therapeutic strategies, provided the host immune system is elicited and able to productively attack tumour cells. Moreover, the participation of the immune system in response is also supported by the rate of cured animals observed with different therapeutic strategies (in the range of 50–100% depending on the treatment), which also held long-term immune memory against tumour cells re-challenge. Taken together, our findings open the way for a translational use of the MRSI-based biomarker in patient-tailored GB therapy, including immunotherapy, for which reliable non-invasive biomarkers are still missing. Glioblastoma (GB) is the most common primary malignant tumour of the central nervous system (CNS) in adults. Clinically, the 5-year survival rate of patients with GB is less than 9%, and the median survival time of them is only around 15 months despite the availability of multimodal therapies ,3.In recent years, an increasing number of studies indicated that immune escape has played an important role in tumour progression ,5,6. In With the rapid development of immunotherapy, several anti PD-1 antibodies are being extensively used in clinics. These were approved in the treatment of melanoma, non-small cell lung cancer, renal cell cancer, Hodgkin’s lymphoma, and bladder cancer ,10,11,12Magnetic resonance imaging (MRI) plays a crucial role in GB detection, treatment planning, and therapy response assessment, evaluating tumour presence, size and characteristics in a non-invasive way . MRI proIn the present clinical landscape, patients with GB always relapse even after the best accepted therapeutic protocols are applied, including surgical resection. Accordingly, both response improvement and the generation of tumour-specific immunological memory would be crucial to improve prognosis. Thus, we tested how the different treatment modalities had an impact on tumour recurrence after curing mice and whether MRSI-based nosological images previously developed to monitor the response to TMZ treatment could also reflect tumour response to immunotherapeutic strategies, acting as a generic surrogate biomarker of therapy response.Results reflect first the methodologic steps taken in order to achieve the best protocol for immunotherapy. This means first to produce proper response to therapy, and from tumours large enough to acquire data for non-invasive MRSI-based biomarker therapy response determination. Secondly, results also comprise the evaluation of the MRSI-based nosological images along treatment time, in order to check whether we were able to non-invasively assess response with immunotherapeutic approaches, either alone or in combination with TMZ.n = 4), anti-PD-1 , TMZ  and a combination of anti-PD-1 and TMZ . Mice tumour volume and body weight were inspected twice a week (Four treatments in IMS (every 6 days) were conducted as follows: isotype immunoglobulin G (IgG) control  when comparing the control group with the TMZ monotherapy group and combined anti-PD-1/TMZ group. Moreover, Kaplan–Meier curves were elaborated (Tumours on anti-PD-1 monotherapy (100 μg/day) group and on isotype murine IgG control (100 μg/day) presented similar results with no apparent response to therapy. Tumours in the TMZ monotherapy group showed transient shrinkage followed by relapse, except in one case (C1382) in the TMZ group which was cured after six doses of TMZ treatment. Notably, all mice in the anti-PD-1/TMZ combined therapy group were declared cured, i.e., all tumours were reduced to a stable tissue scar for one month and the mice were re-challenged see . Tumour aborated  to compan = 6) [p < 0.001). The best results were obtained with the combined anti-PD-1 and TMZ treatment, with a full survival rate of 100 ± 0 days (n = 5), which is significantly different (p = 0.02) from IMS–TMZ alone. Importantly, at the time of comparing the different treated groups, significant differences were found regarding mice body weight changes, tumour volume evolution and survival average, the best overall outcome always being obtained with the anti-PD-1/TMZ combined treatment in IMS, which proved clearly better than either monotherapy alone.For the anti-PD-1 treatment (100 µg/day), a survival rate of 23 ± 2.9 days was found, which was not significantly different neither from the isotype IgG control group (21.8 ± 3.3 days) nor from the untreated (control) mice previously studied in our group, 22.5 ± 3.0 days (n = 6) . During p < 0.005) were found between the cases assigned to the large initial tumour group  and the small initial tumour group . The “normal initial tumour volume” was defined based on average GL261 GB tumour volumes studied by our group in preceding years, for example 5.4 ± 2.6 mm3 (n = 19) described in [Since there was no consensus in the literature (or even lack of information) regarding the initial tumour volume or dosing schedule when administering anti-PD-1 treatment to mice, we designed two series of experiments to assess these variables. The first series of experiments was designed to explore the impact of initial tumour size on treatment effect. Mice were divided into two groups according to the tumour size registered at day 11 post-implantation. As shown in ribed in . Thereforibed in . Thus, a3 in the group with a dosing schedule of every 3 days (E3D) and 0.37 ± 0.06 mm3 in the standard IMS (every 6 days) group. p > 0.05). In E3D group, anti-PD-1 was administered from day 6 p.i. (500 μg/dose) followed by repeated injections (250 μg/dose) every 3 days. In the IMS group, anti-PD-1 was administered from day 6 p.i. (500 μg/dose) followed by repeated injections (250 μg/dose) with IMS (every 6 days). The experimental schedule for each group is shown in The second series of experiments aimed to investigate the effect of changes in the dosing schedule while using similar tumour volumes. Mice bearing similar sized tumours were divided randomly into two groups on day 6 post-implantation, being 0.39 ± 0.19 mmp < 0.001) according to the tumour starting volumes. Survival was remarkably higher in the small initial tumour volume group  than in the large initial tumour group  at the same dosing schedule, Under the higher dosage (500/250 μg) chosen for anti-PD-1 monotherapy at the E3D schedule, the mice survival rate was significantly different (p > 0.05 Regarding the series of mice bearing similar size tumour volumes on the therapy starting day treated with anti-PD-1 (500/250 μg) but using a different dosing schedule , no significant differences were found between the E3D group (91.3 ± 53.1 days) and IMS group (85.8 ± 49.6 days) p > 0.05 A. The tuResults show that, regarding the two parameters investigated, the tumour volume at the therapy starting point is definitely a determinant for therapy outcome. On the other hand, and using the chosen dosage of 500/250 µg, no significant differences were observed when treating mice every 3 days or every 6 days, with a survival rate and percentage of cure being similar in both cases. In this sense, there would be no improvement with the increase in the anti-PD-1 frequency of administration and a 6-day schedule would be more suitable, since it produces the same results, consumes less therapeutic agent and produces less stress to animals. Accordingly, the IMS schedule and small initial volumes on the therapy starting day were chosen for further non-invasive biomarker assessment studies.n = 6 GL261 tumour bearing mice: three were treated with IMS-anti-PD-1/TMZ and three with IMS-anti-PD-1. Mice were studied every two days until endpoint, and the start of MRSI explorations was conditioned by the measured tumour volume. Results are summarized below.A longitudinal study was performed with The results of the multi-slice MRSI-based volumetric analysis of GL261 GB treated with IMS-anti-PD-1/TMZ combination therapy of a representative, responding case C1446 is summarized below. Results for additional responding cases are described in the C1446 was analysed by MRI and multi-slice 3D MRSI from day 15 until day 23 post-implantation. Therapy was administered at days 11, 17 and 23 p.i. right after MRSI analysis, and continued to be given in IMS until the animal was considered cured (day 41 p.i.). The relationship between tumour responding index (TRI) and tumour volume, as well as the corresponding nosological images, are shown in In case C1446, TRI increased from 0% at day 15 p.i. to 74.2% at day 19 p.i. and at day 23 TRI decayed to 0% again. After this period, the tumour entered a “below threshold detection period” (BTDP) period, thus only one TRI peak could be observed. It is noteworthy that the tumour volume of C1446 reached the maximum size on day 17 p.i., when responding tumour pixels started to be observed. Along with the presence of the TRI peak, the tumour volume dropped by 78.9% from its maximum between day 17 p.i. and day 23 p.i., probably due to the combined action of TMZ and immune system attack on the tumour during this shrinkage. The outstanding effectiveness of the treatment prevented observing further TRI cycles, but the appearance of a TRI maximum at day 8 is within the range of values observed for IMS–TMZ oscillation periods.The results of the multi-slice MRSI-based volumetric analysis of GL261 GB treated with anti-PD-1 monotherapy of a representative, responding case C1480 is summarized below. Additional cases with transient or no response are described in 3 scar, entering the BTDP. Therapy was administered on days 6, 12, 18, 24, 30 and 36 p.i., and was maintained in IMS until the animal was considered cured. The relationship between tumour volume and TRI evolution accompanied by the corresponding nosological images is shown in Case C1480 was analysed by MRI and multi-slice 3D MRSI from day 16 until day 26 p.i. MRSI acquisitions were forced to be stopped from day 26 p.i. since the C1480 tumour had shrunk down to a 4.12 mmIn this case, the TRI increased from 6.3% at day 16 p.i. to 71.7% at day 18 p.i. and a further increase to 93.1% was measured at day 22 p.i. After this period, the tumour entered a BTDP period so only one clear TRI peak was observed. It is noteworthy that it is unclear whether the inflection point seen at day 20 p.i. has some biological meaning , which would place the maximum TRI at 6 days from the administration point. However, the criteria set to decide whether a TRI change was a cycle or not does not agree with this possibility, and the maximum TRI was considered to be reached at day 22 p.i., 10 days after the second anti-PD-1 round.The tumour volume of mouse C1480 grew until day 18 p.i., when abundant responding tumour pixels started to be observed. Then, along with the presence of a TRI peak, the tumour volume dropped 87.5% between day 18 p.i. and day 26 p.i., probably due to an immune system sustained attack onto the tumour leading to shrinkage. One clear TRI cycle was observed in this case, 10 days in length, which seemed slightly longer than the average observed with other therapy response TRI cycles. We cannot discard that a slower immune system build-up effect takes place from the effect at day 12 p.i. therapy administration time.Seven TRI oscillations were observed in the IMS-anti-PD-1/TMZ and IMS-anti-PD-1 treated mice and these were generally coincident with stable disease (SDi) and partial response (Pre) tumour stages , accordiThe MR spectra acquired were of overall good quality and examples of chosen MRSI spectra for cases C1480 (IMS-anti-PD-1 treated) and C1446 (IMS-anti-PD-1/TMZ treated) are shown in n = 6 in the IMS-TMZ/PD-1 combination group, and n = 8 in the anti-PD-1 monotherapy group, see From the aforementioned work, fourteen mice had tumours which disappeared after TMZ and/or anti-PD-1 treatment  were acquired twice a week to check for tumour development and 3 wt C57BL/6 mice were implanted in parallel as controls. Mice in the wt control group had a 0% tumour rejection rate and died by day 21 post-implantation. No significant differences were found when compared to the control mice from our group, 19.7 ± 2.7 days. In addition, control tumours grew normally and no significant differences were found in comparison with a standard GL261 tumour doubling time of 2.4 ± 0.3 days .Regarding the cured mice from IMS-Anti-PD-1/TMZ combined therapy group, three  out of six tumours (50%) re-implanted in the cured mice grew after 6 days. Anti-PD-1/TMZ combined therapy was administered as usual in IMS immediately after regrowth detection. One mouse (C1402) showed a transient response with tumour shrinkage but eventually died on day 38 post re-challenge, while the remaining (C1431 and C1433) were cured again after 2 doses of anti-PD-1/TMZ therapy. Therefore, 3/6 of the combined treatment showed “suboptimal memory”, and the initial tumour rejection rate of IMS-anti-PD-1/TMZ cured mice was 50% (3/6).Cured mice that had received anti-PD-1 monotherapy exhibited the best tumour rejection rate , and all of them displayed optimal immune memory, with no tumour growth detectable in any of the mice after the re-challenge experiment.As summarised in As a natural anti-tumour defence system, the immune system plays an important role in the response to anti-tumour therapy, and the relevance of harnessing the immune system to control cancer has been highlighted in recent years ,29.Anti-tumour immune responses can be strongly stimulated by multimodal therapies targeting different aspects of cell killing. In our case, we aimed, on the one hand, to induce immunogenic tumour cell damage while sparing replicating immune system cells (with IMS chemotherapy). On the other hand, we wanted to actively counteract the immune suppression microenvironment within the tumour (PD-1/PD-L1 pathway blockade). We studied relapsing GL261 tumours treated with IMS–TMZ, and found that the upregulated PD-L1 content in tumour tissue is a contributor to TMZ resistance . In thisAnother contributing factor to the synergism may come from TMZ contribution. The therapeutic effects of anti-PD-1 in GB have been reported to be associated with immune infiltration . The mos3, which finally led to the curing of 75% of the animals, allowing ample time for the evaluation of biomarker performance , which was a disappointing result. Other authors reported that anti-PD-1 at a higher dose and different schedule (500/250 μg) could significantly improve the long-term survival rate (50% mice survived over 100 days) . Therefoformance .A second relevant question was related to timing in immunotherapy administration. We wondered whether the “every 3 days (E3D)” administration schedule described in  was moreThe classifier of MRSI-based pattern oscillatory changes (TRI oscillation) acting as a potentially useful biomarker for therapy response was initially developed with TMZ-treated cases . One of n = 7) were observed in both groups, except by mouse C1479 which did not respond to therapy. After therapy administration, TRI increased from near-zero to high values  and decayed two days after the TRI peak. In this respect, it should be noticed that the TRI peak maxima is also followed by a reduction in tumour volume in four out of five cases, an effect also observed in IMS–TMZ treated cases [TRI oscillations . We named this period the “below threshold detection period” (BTDP) since the semi-supervised source analysis software was not able to properly segment the tumour. However, since small tumours have a trend for better survival or either cure, the metabolomics signature would not be as relevant as in tumours with larger volumes, in which this information is relevant to assess the efficacy of a therapeutic strategy.Results from previous work suggest that this oscillatory TRI behaviour could serve as a potential immune system efficiency biomarker during the therapy response of GB. Our studies with immunotherapy have highlighted the importance of this biomarker, since the therapeutic effects of anti-PD-1 have been reported to be associated with changes in immune infiltration . It has It is worth mentioning that in the final stages of tumour evolution in PD cases, high TRI values can be also seen, although without an oscillatory pattern . The single fact that TRI value increases in these cases may indicate that the ‘responding source’ is not only related to a pattern of therapy response but also to local changes due to a rapid tumour expansion. For example, the presence of necrotic, non or low oxygenated (hypoxic) areas that may cause cell death or the activation of the mouse immune system ,40. ThusFinally, with anti-PD-1 monotherapy, the observed pattern changes should be essentially due to the action of the host immune system alone, without the confounding effect of chemotherapy. The oscillatory mode detected reinforces the translational potential of the MRSI-based biomarker for patient-tailored GB therapy, including immunotherapy.In the present clinical landscape, patients with GB always relapse even after the best accepted therapeutic protocols are applied, including surgical resection. Accordingly, both response improvement and the generation of tumour-specific immunological memory would be crucial to improve prognosis. Thus, we tested how the different treatment modalities had an impact on tumour recurrence after curing mice. A strong immune-memory effect (ca. 100% tumour rejection rate) is observed after GL261 tumour ablation with anti-PD-1 monotherapy, whereas a weaker anti-tumour immune memory capability (50% tumour rejection rate) was found in mice cured by anti-PD-1/TMZ combined therapy.One exciting feature of immunotherapy is its ability to conduct a dual-phase therapeutic benefit, which initially involves the effective treatment of existing tumours, followed by a successful activation of tumour-specific immune response to fight a possible tumour recurrence. In a clinical study, Ribas and colleagues  analysedInterestingly, weaker anti-tumour immune memory capability  was found in mice cured by anti-PD-1/TMZ combined therapy. Three of six re-implanted tumours in these cured mice grew after 6 days, although two of them disappeared immediately after repeated IMS-type therapy application. The kinetics of tumour growth after the re-challenge experiment was slower in these tumour-recurrence mice compared to the control mice , suggesting that combination therapy had been able to generate at least partial immune memory. Combination therapies are reported in the literature with different outcomes. Authors in  reportedOpdivo (nivolumab) to the current standard of care b (TMZ and radiation therapy) vs. the standard of care alone did not meet one of its primary endpoints, PFS, in patients with newly diagnosed GB that is O6-methylguanine-DNA methyltransferase (MGMT)-methylated. It is tantalizing to speculate that the radiochemotherapy standard of care treatment (daily TMZ and radiotherapy administration) does not respect the needed time gap to allow for unencumbered lymphocyte amplification, which our IMS protocol does. Accordingly, further clinical trials targeting maximal beneficial effect for patients may want to consider incorporating “immune respectful” time gaps when combining chemotherapy and CKI agents.It may be relevant to return here to the apparent discrepancy between promising preclinical results after single or combination anti-PD-1 based GB treatment (this work), which would agree with the earlier neoadjuvant use of anti PD-1 ,14 produIt is wise to recognize that our preclinical study is limited to only one immunocompetent GB model. In this respect, we cannot ignore that GL261 is a moderately immunogenic cell line . Thus, aOverall, our results support that (a) the participation of the immune system in response to therapy in preclinical GL261 GB is clear either in monotherapy or combined therapy and (b) our non-invasive MRSI-based therapy response biomarker is clearly related to immune system local action rather than linked to single therapeutic agent local effects.The mouse glioma cell line GL261 was obtained from the Tumour Bank Repository at the National Cancer Institute  and was grown as previously described .Comissió d’Ètica en l’Experimentació Animal i Humana, https://www.uab.cat/etica-recerca/) according to regional and state legislations (protocol CEEAH-3665 approved on 5th February 2018).All animal studies were conducted according to the protocol approved by the local ethics committee  mice were used in this study, among them, 35 C57BL/6 GL261 tumour bearing mice were generated for this work. They were obtained from Charles River Laboratories  and housed in the animal facility of the ed by us . Tumour For combination therapy experiments, mice in the anti-PD-1/Temozolomide combined group were given TMZ  60 mg/kg by intragastric administration and anti-PD-1  100 μg/dose via intra-peritoneal injection on the same day, both therapies administered in IMS (every 6 days) from day 11 post-implantation until tumour escape from therapy. Mice in the monotherapy groups received equivalent doses of drugs and control mice received equivalent doses of isotype IgG  according to the same dosing schedule.For high dosage (500/250 μg) anti-PD-1 monotherapy experiment, see specific result sections for the detailed anti-PD-1 administration scheme. After treatment, animals meeting endpoint criteria were euthanized by cervical dislocation according to animal welfare protocol advice for ethical reasons. In all the therapy strategies, cured mice were followed up and a re-challenge experiment was carried out  Unit 25 of the ICTS NANOBIOSIS (https://www.nanbiosis.es) with a 7T horizontal magnet  equipped with actively shielded gradients (B-GA12 gradient coil inserted into a B-GA20 gradient system) and a quadrature receive surface coil, actively decoupled from a volume resonator with 72 mm inner diameter. Mice were anesthetized by inhalation of a 0.5–2.0% isoflurane in O2 and placed on the scanner bed prior to the MR study, keeping the respiratory frequency at 60–80 breaths/min. Mice body temperature was controlled using a recirculating water system incorporated to the scanner bed. Animal breathing was monitored constantly with a breathing sensor .MR studies were carried out at the joint NMR facility of the Universitat Autònoma de Barcelona and CIBER-BBN (GL261 tumour bearing mice were screened using the Rapid Acquisition with Relaxation Enhancement (RARE) sequence to acquire high-resolution axial T2w images (TR/TEeff = 4200/36 ms) to detect brain tumour presence and track its evolution process. The acquisition parameters for MRI studies can be found in the Where: TV is the tumour volume, surface area (AS) is the number of pixels contained in the region of interest in each slice of the MRI sequence, ST is the slice thickness, IT the inter-slice thickness and 0.0752 the individual pixel surface area in mm2. The tumour area was calculated from pixels in each slice with ParaVision 5.1 software. The inter-slice volume was estimated adding the inter-slice thickness to the corresponding slice thickness.The manual segmentation of abnormal brain mass in T2w images was performed and tumour volumes were calculated from T2w high resolution horizontal images using the Equation (1):The MRSI acquisitions were performed every two days with longitudinally studied mice treated with IMS-anti-PD-1/TMZ combined therapy or IMS-anti-PD-1 monotherapy, starting at day 15–16 post-implantation. MRSI grids were spatially located such that the volume of interest (VOI) included most of the tumour mass as well as normal/peritumoural brain parenchyma  semi-supervised methodology ,50,51 waIn order to measure the extent of response to treatment using the obtained nosological images, a numerical parameter named TRI was calculated (Equation (2)) :(2)TRI=TTRI is stated as the percentage of green, responding tumour pixels of all grids over the total tumour pixels of all recorded grids.An adapted set of RECIST criteria (described in 3) and no tumour relapse over a 30 day observation period, mice were transiently declared "cured" as in [n = 6; anti-PD-1 monotherapy, n = 8) were re-challenged with 100,000 GL261 cells injected into the contralateral hemisphere, symmetric to the initial injection site. A group of three C57BL/6 female wt mice were also implanted as controls, to check for consistency and growth rate in the contralateral side. All mice were monitored (weight + welfare parameters) twice a week and volumetric T2w MRI was acquired twice a week.When the tumour was reduced to a stable scar . Comparisons of survival rates were performed with the Log-Rank test. The significance level for all tests was p < 0.05.Variance homogeneity was assessed with the Levene’s test. Sample distribution was assessed with the Kolmogorov–Smirnov test. A two-tailed Student’s Suitable combination of oncology and immunology knowledge will pave the way for future GB treatment and improve patient outcome. We explored in an immune-competent, aggressive GB preclinical model how using the direct mitigation of the tumour-based immune suppression microenvironment (PD-1/PD-L1 pathway blockade) either as monotherapy or in combination with TMZ, produced outstanding results, not achieved before with comparable treatments. We also proved, for the first time, that the metabolomic spectral patterns spotted by our MRSI-based surrogate biomarker of therapy response were also observed using anti-PD-1 monotherapy, in agreement with these changes being, at least partially, contributed by the local immune system action against tumour cells. This finding confirms that the MRSI-derived biomarker is not linked to a given therapeutic agent or protocol and should be investigated in any therapeutic approach for GB involving host immune system activation. The possible translation of these protocols would allow to assess, in an early and confident way, therapy efficacy and help in patient management. Further studies may be needed in order to fully characterize the molecular/cellular changes related to the spectral pattern changes detected. Last, but not least, such an imaging biomarker could be useful in the follow-up of other brain pathologies in which the immune system can play a role such as a stroke or neurodegenerative diseases.