The small arginine-rich protein protamine condenses complete genomic DNA into the sperm head. Here, we applied its high RNA binding capacity for spontaneous electrostatic assembly of therapeutic nanoparticles decorated with tumour-cell-specific antibodies for efficiently targeting siRNA. Fluorescence microscopy and DLS measurements of these nanocarriers revealed the formation of a vesicular architecture that requires presence of antibody-protamine, defined excess of free SMCC-protamine, and anionic siRNA to form. Only these complex nanoparticles were efficient in the treatment of non-small-cell lung cancer (NSCLC) xenograft models, when the oncogene KRAS was targeted via EGFR-mediated delivery. To show general applicability, we used the modular platform for IGF1R-positive Ewing sarcomas. Anti-IGR1R-antibodies were integrated into an antibody-protamine nanoparticle with an siRNA specifically against the oncogenic translocation product EWS/FLI1. Using these nanoparticles, EWS/FLI1 knockdown blocked in vitro and in vivo growth of Ewing sarcoma cells. We conclude that these antibody-protamine-siRNA nanocarriers provide a novel platform technology to specifically target different cell types and yet undruggable targets in cancer therapy by RNAi. In the last years, the directed transfer of nucleic acids for therapeutic purposes has attracted much attention. For gene therapy, by far the majority of approaches rely on viral transfer by lentiviruses or adenoviruses to the cells of interest. The transfer of RNA molecules is more complex.High expectations were raised concerning the use of small interfering RNAs (siRNA) against gain-of-function gene products such as oncogenes in malignant neoplasia. However, therapeutic use of siRNAs was always compromised by their instability and missing cell-specific carrier systems. Thus, the development of an efficient siRNA nanocarrier is a major goal to make use of RNAi as a molecular therapeutic modality. To achieve this, we developed a technique for antibody-mediated siRNA therapy comprised of electrostatic nanocarriers consisting of antibody-protamine, protamine and electrostatically bound siRNA (α-P/siRNA/P).Protamine is a small arginine-rich protein that displaces histones from chromatin during spermatogenesis leading to a specific DNA density that approaches that of a crystalline state. Clinically used as a heparin antidote , 2, protHere, we employed the high RNA binding capacity of protamine to form a therapeutic, systemically applicable, targeted nanoparticle functioning as nanocarrier of tumour-cell-specific siRNA for delivery into tumour cells. In previous publications, we showed that the siRNA in this complex is stabilised by the tight interaction with protamine, internalises into EGFR-positive cells and exerts therapeutic anti-cancer activity in vitro and in vivo –8.During the process of further characterisation of our antibody-protamine-siRNA complexes, we unexpectedly detected that unbound bifunctional crosslinker sulfo-SMCC (SMCC)-protamine is an indispensable component of our targeting complex. Further analysis revealed that our conjugates do not consist of a linear molecule antibody > SMCC-protamine > siRNA, as suggested by others before , but ratTo apply this strategy to target two different tumour entities, non-small cell lung cancer (NSCLC) as well as Ewing sarcoma, a mesenchymal paediatric bone cancer, we have used our nanocarrier system with protamine-bound siRNA linked to the cancer cell-specific anti-EGFR-antibody cetuximab , 8 and tAn siRNA-carrier has to fulfil two essential requirements to serve as effective therapeutic agent: first, it has to bind siRNA cargo efficiently and prevent siRNA degradation, second it must bind to a cell determining and internalising moiety to deliver this complex to the intended tumour cells and internalise the therapeutic cargo. In order to optimise our antibody-protamine-siRNA-carrier system , 8, we tNext, we determined the functional impact of the different conjugation ratios on their ability to inhibit the driving oncogene KRAS. To this purpose, we treated NSCLC A549 cells with different αEGFR-antibody-protamine-complexes with control  vs. anti-KRAS siRNA, respectively, and cultured equal numbers of cells in soft agar for anchorage-independent colony growth indicative for tumourigenicity. Of note, only the 1:32 Fig. , the 1:3To this end, we conclude that the conjugation reaction between the αEGFR-mAB coupled to SMCC-protamine and a 32x excess of SMCC-protamine which is combined with 10 mol siRNA per mol of IgG-conjugate yields an optimal formulation to bind, transport, internalise and consequently liberate functionally effective siRNA to EGFR-expressing tumour cells.To translate siRNA-carrier production into clinical application according to good manufacturing practice (GMP), we intended to remove all excess free SMCC-protamine from the reaction mixture by preparative size exclusion chromatography or by protein G interaction chromatography Fig. .Fig. 2AnZ-axis in confocal laser microscopic (CLS) analysis. On the other hand, SMCC-protamine alone with no attached targeting antibody cannot serve as an unspecific transfection reagent  spectroscopy, a size-motion correlation that is based on Brownian molecular motion , the sinαEGFR-protamine/P-siRNA nanocarriers were next subjected to electron microscopy, revealing spheroid particles also of 100–200 nm size after 2 h of auto-assembly Fig. . In ordeNext, we further analysed the properties of the different conjugation products according to their capacity to form vesicular structures without cells when these conjugation products were incubated with Alexa488-control-siRNA Fig. . When thTo further elucidate the function of SMCC-protamine coupled to the antibody and as free molecule within the complex, we re-added and titrated the amount of free SMCC-protamine to the antibody-protamine-conjugates that were previously depleted from free SMCC-protamine by HPLC as described in Fig. Finally, we asked if siRNA is needed to form vesicles. We incubated a constant amount of αEGFR-mAB-P with constant 32x free SMCC-protamine with different amounts of Alexa488-control-siRNA Fig. .Thus, the three components (1) antibody-SMCC-protamine, (2) siRNA and (3) unbound SMCC-protamine form a stable nanostructure necessary for functional activity of the nanocarrier.As a first example for the efficiency of our modular nanoparticle therapy platform, we applied αEGFR-mAB-P/siRNA/P nanoparticles to highly EGFR-positive NSCLC cells. These nanoparticles internalised into five EGFR-positive NSCLC cell lines with high efficiency  route as depicted schematically in Fig. After 26 days of treatment the A549 tumours were dissected and weighed Fig. . The A45Tumours treated with αEGFR-mAB-P/KRAS-siRNA/P nanostructures had reduced proliferation rates compared to PBS or control-treated tumours as determined by detection of the proliferation marker Ki67 by immunofluorescence  and teprotumumab (here referred to as “Tepro”), of which the latter recently has been approved for thyroid eye disease . Both an7 human SK-N-MC cells subcutaneously (s.c.) into the flank of CD1-nude mice and treated cohorts of at least 7 mice with either PBS or αIGF1R-mAB-P/P in complex with scrambled control-siRNA, or in complex with the above mentioned EWS-FLI1-siRNA i.p. .Protamine has been intensively studied as a carrier- and coordination molecule for substances other than DNA, for instance solid lipid nanoparticles , gold naFor expression of anti-IGF1R-antibodies, published sequences of the heavy chain and light of cixutumumab (“A12”) and tepTM, Merck-Serono, Darmstadt, Germany) or the anti-(α)IGF1R-antibodies A12 and Tepro produced in our laboratory under GMP-like conditions as described above in a 32:1 molar ratio or as indicated in Fig. The conjugation methodology has been described by us before . In thisAfter chemical conjugation, αEGFR-mAB-protamine containing unbound SMCC-protamine in molecular excess is applied to protein G-sepharose equilibrated with PBS, washed with 10 CV of PBS and then eluted with a steep gradient of 100 mM glycine-HCL pH 2.5. Fractions were collected and checked for presence of unbound SMCC-protamine by SDS-PAGE and Coomassie stain. Fractions depleted of unbound SMCC-protamine were subjected to further analysis.For the estimation of siRNA coupling, stability and internalisation efficiency, antibody-protamine conjugates were coupled to scrambled siRNA (see below), Allstars negative control-siRNA-Alexa488  or Allstars negative control-siRNA-Alexa 555 . Treatment experiments were done using siRNA duplexes against KRAS  and as control a scrambled siRNA . Band-shift assays were performed as described previously [The EGFR-positive NSCLC cell lines A427, A549, HTB56, SK-LU1 and H1975 were treated with antibody-siRNA-complexes as described previously . The cel4 cells/cm2, cultivated on sterile cover slips o/n and treated with 60 nM antibody-protamine or PBS incubated with Alexa Fluor 488-labelled Allstars negative control-siRNA (Qiagen 1027284) and Allstars negative control-siRNA-Alexa 555 (Qiagen 1027286) at 1:10 molar ratio each o/n at 37 °C and 5% CO2. Subsequently, cells were washed with PBS, fixed with ice-cold 4% paraformaldehyde (PFA), stained with Hoechst 33342, mounted with Dako fluorescence mounting medium and photographed on a Zeiss Axioskop. To detect endosomal escape, cells that internalised cetuximab-Alexa488-siRNA overnight were incubated with 60 nM LysoTrackerTM red DND-99  for 30 min, then washed, fixed, stained with Hoechst and mounted with Dako fluorescence mounting medium and photographed as described above.Cell lines were seeded at 2 × 10For cell-free nanoparticle-self-assembly studies, the antibody-P Alexa488-siRNA conjugates were applied to cell-culture treated chamber slides overnight in order to settle the nanoparticles by gravity, washed with PBS, fixed with 4% PFA, stained with Alexa647-anti-hIgG  and mounted in DAKO mounting medium for microscopical analysis.Freshly prepared nanoparticles were sedimented on an formvar-coated, carbon-sputtered copper grid. After negative staining with 1% phosphor-tungstic acid, pH 7, the samples were analysed at 80 kV on a Tecnai 12 electron microscope . Images of selected areas were documented with Veleta 4k CCD camera .For flow cytometrical analysis, extracellular fluorescence was quenched using trypan blue to a final concentration of 0.2%, which does not enter intact cells . The intFor confocal laser scan (CLS) microscopy, A549 cells were grown on 10 mm glass coverslips and treated with the coupled αEGFR-mAB-P-siRNA/P nanostructures to a final concentration of 600 nM (siRNA). The cells were washed three times with PBS, fixed using 4% (w/v) PFA in PBS (20 min), quenched with 0.2% (w/v) glycine in PBS (20 min) and permeabilized using 0.2% (w/v) triton X-100 in PBS for 10 min). Actin and DNA were stained with phalloidin-tetramethyl rhodamine isothiocyanate  and with Draq5 , respectively. After further washing, the preparations were embedded in fluorescence mounting medium  and analysed with a Zeiss LSM 510 Meta CLS microscope equipped with a Plan-Apochromat 63/1.4-numeric-aperture oil immersion objective .Particle size detection by means of DLS was performed on a zeta-counter , which correlates light diffusion caused by particles in a solution to their size and the zeta-potential. Measurements were performed in at least three technical replicates.5 cells of each cell line were seeded and cultivated overnight, treated with cetuximab-protamine (60 nM) coupled to the indicated siRNAs at 1:10 molar ratio once a day for 3 days, harvested, lysed in RIPA buffer and cleared by ultrasonification and centrifugation. Xenograft tumours were homogenised as 10% w/v in RIPA buffer using an ultraturrax, cleared by ultrasonification and centrifugation. Western blot analysis was performed using standard protocols with the following antibodies: anti-KRAS , and anti β-Actin antibody .In total, 5 × 10In brief, 4000 trypsinised cells in 120 µl per sample were incubated with antibody-protamine coupled in a 1:10 molar ratio to the indicated siRNAs with free SMCCC-protamine upon ~2 h of spontaneous nanobody assembly at 60 nM end concentration for 1 h at RT, resuspended in 168 µl of 3% agar (Difco Agar Noble) and 432 µl adequate cell-culture medium and cultivated in triplicate for colony formation in 96-well format (180 µl/well). After one week, the assays were stained with 20 µl 4 mg/ml Iodonitrotetrazolium chloride solution and incubated over night at 37 °C. The next day, the assays were counted for colony numbers.2 inhalation followed by cervical dislocation. For each experiment, the single animal was an experimental unit.All animal experiments in this study were carried out in accordance with the recommendations of the Institutional Animal Care and Use Committee “Landesamt fuer Natur, Umwelt und Verbraucherschutz NRW” (LANUV). This study was performed with permission of the Institutional Animal Care and Use Committee and of the local veterinary administration of Muenster (Permit no. 84-02.04.2015.A158 and 81-02.04.2020.A001). Mice were kept in individually ventriculated Typ II cages  in groups of 5 mice, in a 12-h light/dark cycle, with RT at 22 ± 2 °C and a relative air humidity of 45–65%. All mice were allowed free access to water and a maintenance sterile diet. All reasonable efforts were made to ameliorate suffering, including isolation of affected mice. Mice were monitored daily for signs of pain or distress, and repeatedly for body weight. Moribund mice were humanely sacrificed as described below. Study design and biometric planning of each experiment was performed in accordance with a biostatistician. Mice were sacrificed for sample preparation by deep anaesthesia via CO7 A549 cells or SK-LU1 cells mixed in matrigel or SK-N-MC cell without matrigel were injected subcutaneously in the flanks of 8–12 weeks old female CD1-nude mice. When the tumours reached a size of 50–150 mm³, systemic treatment with mAB-siRNA-complexes at 4 mg/kg were applied intraperitoneally (i.p.) three times per week in NSCLC and two times per week for Ewing sarcoma. Xenograft tumour size determined the antitumour effectivity. Tumour growth was followed with caliper measurements and tolerance for the animal was assessed by measuring body weight and scoring overall appearance. Tumour volumes were calculated by the formula length × width2 × 0.52. At the end of the experiment, animals were euthanized, tumours were isolated, and tumour weight was determined.For xenograft transplantations, 1 × 10For immunostaining, the A549 tumours were embedded in cryomatrix and SK-LU1 tumours in paraffin using standard methods. Apoptotic cells were detected using TUNEL Assay Kit—HRP-DAB  according to the manufacturer’s recommendations.For Ki67 immunofluorescence, dehydrated paraffin sections were boiled for 60 min in 10 mM citric acid, 0.01% Tween 20, pH 6.0, washed twice in TBS and blocked in blocking solution . Cryosections were washed three times in TBS/0.1% Tween 20. All sections were incubated with Ki67 antibody  diluted 1:300 in blocking solution overnight at 4 °C, washed three times in TBS/0.1% Tween 20 for 5 min, incubated with secondary antibodies (goat anti-rabbit-Alexa594 diluted in blocking solution for 1–2 h at RT, counterstained with Hoechst 33342 for 5 min in TBS, washed three times with TBS/0.1% Tween 20 and mounted using Dako mounting medium.t test. All p values are representing two-tailed analysis.All data are presented as means ± standard deviation or standard error, if not indicated otherwise. The mean values of two groups were compared by Student’s Baeumer Supplementary material


As the number of survivors of childhood brain tumor grows, fatigue is being increasingly recorded as a long-term consequence of both the cancer itself and the treatment received. Survivors of childhood brain tumour report more significant fatigue than children with other cancers, often impacting all aspects of life, including academic attainment, self-concept and social relationships with peers, leading to reduced health-related quality of life.This study aimed to systematically evaluate the evidence for fatigue in paediatric brain tumour survivors.A systematic search using EMBASE, MEDLINE and PsycINFO identified 20 papers meeting the inclusion criteria. Scientific rigor was used throughout by following Scottish Intercollegiate Guidelines Network (2015) guidance for systematic reviews. Quality Assessment of Evidence Rating tool - Fatigue (QAERT) was developed with substantial inter-rater agreement found.19 of the 20 studies reviewed showed conclusive evidence of fatigue in survivors of paediatric brain tumour. One study offered adequate evidence that there was no difference in levels of fatigue in paediatric cancer survivors, including survivors of paediatric brain tumour, when compared to healthy controls. Three studies found that fatigue was worse in survivors of paediatric brain tumour when compared to survivors of other paediatric cancersThis review provides evidence for the presence of fatigue in survivors of paediatric brain tumour. However, the construct of fatigue was poorly defined throughout, with fatigue associated with physical effects of treatment and fatigue associated with long-term cognitive impairment not distinguished. This poor construct validity, coupled with a lack of comparison groups in 12 of the 20 studies, reduces the generalizability of the data and its usefulness for developing effective psychological interventions. Further research is needed, built on a clear fatigue construct definition, and including well defined exclusion criteria, to provide a sound basis for improving the quality of life of these children.


Induction of anesthesia causes a drop in arterial pressure that might change the kinetics of infused crystalloid fluid. The aim of this report is to provide a mathematical view of how fluid distributes in this setting.Data were retrieved from three studies where 76 patients  had received approximately 1.1 L of Ringer’s solution over 60 min by intravenous infusion before and during induction of spinal, epidural, or general anesthesia. A population kinetic model was used to analyze the fluid distribution and its relationship to individual-specific factors. Frequent measurements of blood hemoglobin and the urinary excretion served as dependent variables.Before anesthesia induction, distribution to the extravascular space was threefold faster than elimination by urinary excretion. Both distribution and elimination of infused fluid were retarded in an exponential fashion due to the anesthesia-induced decrease in the mean arterial pressure (MAP). A decrease in MAP from 110 to 60 mmHg reduced the rate of distribution by 75% and the rate of elimination by 90%. These adaptations cause most of the infused fluid to remain in the bloodstream. Age, gender, type of anesthesia, and the use of ephedrine had no statistically significant effect on plasma volume expansion, apart from their possible influence on MAP.The decrease in MAP that accompanies anesthesia induction depresses the blood hemoglobin concentration by inhibiting both the distribution and elimination of infused crystalloid fluid. The report provides mathematical information about the degree of these changes.The online version contains supplementary material available at 10.1186/s13741-021-00204-5. Crystalloid fluid is often administered intravenously (i.v.) during induction of anesthesia , but other factors, such as gender and the choice of fluid, were also evaluated as having potential importance regarding this aspect of the adaptation to the anesthetized state.This study is a retrospective population kinetic analysis that uses data from three previously published studies of fluid distribution during induction of anesthesia  was injected as required to achieve surgical anesthesia.Anesthesia was usually induced when 1/3 of the infusions had been administered. The types of anesthesia used in the three studies are shown in Table Epidural anesthesia was applied through a 17-gauge Tuohy needle with the patient in the same body position. Increments of 3–5 mL 0.5% ropivacaine  were injected every 5 min until a surgical block was achieved.General anesthesia was induced with propofol (1.5 mg/kg), midazolam (0.05 mg/kg), sufentanil (0.6 μg/kg), and rocuronium bromide (0.6 mg/kg), and anesthesia was then maintained with propofol (3 mg/kg/h) and rocuronium bromide (4 mg/h).The plasma volume was expanded by infusing the Ringer solution i.v. using infusion pumps. During and after these infusions, venous blood (3–4 mL) was withdrawn to measure the Hb concentration and the hematocrit (Hct) on the apparatus used for routine measurements in the hospital’s Clinical Chemistry Laboratory. The samples were withdrawn every 3–5 min in a standardized manner to ensure a coefficient of variation (CV) of about 1%. The baseline sample was drawn in duplicate, and the mean of the two concentrations was used in subsequent calculations.The excreted urine was collected via an indwelling catheter, which had been inserted into the bladder under topical anesthesia before the studies started.MAP was measured non-invasively with an automatic device  . No other vasopressor was used.Population (mixed effects) kinetics is an industry-standard tool for evaluating and recommending dosing regimens for drugs with regard to individual-specific factors, such as age, gender, and body weight .In the finally used base model, fluid is infused at rate k12, and the flow from the central to the peripheral space at any time is given by the product of k12 and (vc–Vc). Similarly, the elimination is the product of the volume expansion of Vc at any time and an elimination rate constant, k10  was used to identify covariate candidates. A trend in an eta plot could suggest the existence of a significant covariate. The most promising candidate parameters were then tested, one by one, by adding them to the base model in a diagonal design, which assumes no correlation between random effects. The threshold for accepting one curve-fit as significantly better than another was guided by a reduction of > 3.8 points in the goodness-of-fit (−2 LL = log likelihood) for the model; where > 6.6 points represents Gender, age, body weight, general or regional anesthesia , type of Ringer’s (lactated or acetated), the use of ephedrine, and the rate of infusion per kilo body weight, as well as various time factors, were sequentially tested as covariates to the three fixed parameters in the model. The mean arterial pressure (MAP) and the heart rate were evaluated as time-varying covariates, which mean that a new value was considered for each point of measurement. Both the crude value of MAP and the change in MAP from baseline were tested.The fixed parameters in the model and the statistically significant covariates, if any, were estimated simultaneously using the Phoenix software for nonlinear mixed effects (NLME), version 1.3  with the First-Order Conditional Estimation Extended Least Squares as a search routine and an additive model for the random-error variability  and bootstrap analysis , using the built-in features of the Phoenix software.Demographic data were reported as the mean (standard deviation), and the kinetic data were reported as the mean .Table k21 was removed due to lack of statistical significance. Several add-ons were tried that have occasionally been included in previous work. These include a second elimination function and an absorption function, but none of them improved the model. Hence, the variant described in the “Vc), one distribution (k12), and one elimination (k10) function, was found to be optimal. The search strategy used to find the final population kinetic model is shown in Table The search for an optimal base model resulted in a number of modifications of the conventional two-volume model Fig. A. The moVc and that the absolute (crude) value of MAP significantly influenced both k12 and k10. The model parameters in the final analysis are shown in Table The covariate search showed that the body weight (BW) affected where 66 is the mean patient body weight and 94.4 is the mean MAP for all 1198 observations. The MAP at baseline was 107.3  mmHg.n = 13); use of regional/general anesthesia; and the time period before versus after the induction. Acetated Ringer’s seemed to distribute more rapidly than lactated Ringer’s, but this difference lost significance in the final model.Several potential covariates were tested that did not reach statistical significance. These included age; gender; ratio and change of MAP from baseline; the use of ephedrine (k12) was hypothesized to be turned off for a period of time after the induction; therefore, “turn-off” time periods of 3, 5–6, 9–10, 15, 20, and 25 min were tested. The amount of fluid infused up to those timepoints was evaluated, but none of those variables was strong enough for inclusion in the model.In accordance with a previous study /(k10 + k12) was 15 min at a MAP of 110 mmHg while being 80 min at a MAP of 60 mmHg. The corresponding values for the elimination T1/2, obtained as ln 2/k10, were 77 min and 737 min, respectively.The rate constants can be used to calculate the distribution and elimination half-lives for different MAP. For example, the intravascular half-life Tk12) from the central fluid space  to the extravascular space. Fluid was distributed more slowly.This report provides a mathematical description of what happens to the fluid distribution when anesthesia is induced in the middle of a continuous crystalloid infusion. The most apparent effect consists of a marked reduction in the rate constant that governs the distribution of infused fluid  also decreased, and to an even greater extent than was observed for the distribution. Both reductions were proportional in a non-linear fashion to the anesthesia-induced decrease in MAP, as shown in Fig. The elimination rate constant that describes urinary excretion  had been administered . The volume of the infused fluid residing in the two body fluid spaces is obtained directly in the micro-constant model, while their dilution must be multiplied by the volume of distribution to obtain volume expansion in the clearance model.The current model also uses micro-constants instead of clearances, which makes it independent of plasma volume and body fluid volumes. The micro-constant model detects a “wall” between a central space, where fluid equilibrates very rapidly with the site of infusion, and a more remote peripheral space. The space with this fast equilibration is very likely to represent the plasma volume contained in blood vessels that are allowed to expand. The exchange of infused fluid between these two body fluid spaces is determined by rate constants (The limitations of the present study include that the data were obtained from three previously published works, although all three used a similar protocol and sampling had been performed in the same way.k12 and k10 before the induction may reflect preoperative anxiety, which has been observed in adults and children alike (Li et al. The fairly low values of The strength of this study is that a modern and robust kinetic approach was used that allows simultaneous analysis of all studied patients, as well as comparisons of several different covariances that could potentially influence the fluid kinetics.The induction of regional and general anesthesia was followed by a MAP-dependent decrease in both the distribution and elimination of an infused crystalloid fluid. Both changes markedly increased the hemodilution, which might become large enough to affect oxygen delivery and distort schemes for deliberate hemodilution and estimates of allowable blood loss.Additional file 1.


Fluid overload (FO) is characterized by hypervolemia, edema, or both. In clinical practice it is usually suspected when a patient shows evidence of pulmonary edema, peripheral edema, or body cavity effusion. FO may be a consequence of spontaneous disease, or may be a complication of intravenous fluid therapy. Most clinical studies of the association of FO with fluid therapy and risk of harm define it in terms of an increase in body weight of at least 5–10%, or a positive fluid balance of the same magnitude when fluid intake and urine output are measured. Numerous observational clinical studies in humans have demonstrated an association between FO, adverse events, and mortality, as have two retrospective observational studies in dogs and cats. The risk of FO may be minimized by limiting resuscitation fluid to the smallest amount needed to optimize cardiac output and then limiting maintenance fluid to the amount needed to replace ongoing normal and pathological losses of water and sodium. The mammalian stress response to injury, hypovolemia, or critical illness includes retention of sodium and water and, at least early on, increased thirst –5. TheseThe concept of FO as a clinical entity to be avoided appeared in earnest within the medical literature during the 1970's, with 54 PubMed citations from that decade using that phrase. Although for many years FO has been recognized as a potential complication of anuria in chronic hemodialysis patients, in this century the phrase has more often been used to describe a complication of fluid therapy in any patient at risk for hypervolemia or edema. The importance of FO has been underlined by a growing number of reports of observational studies that associate the condition with higher morbidity and mortality in hospitalized patients, an association that held true in recent meta-analyses of 44 studies in children  and 31 iClinically, FO is usually defined by some combination of edema, excessive weight gain, or excessively positive fluid balance in a patient that has received intravenous fluid therapy. In fact, FO is almost universally defined in such terms in clinical studies of humans , 8. HoweIf the MCFP becomes sufficiently elevated by circulatory failure or fluid overload, the elevated venule pressure requires higher capillary pressures to maintain blood flow, and increased capillary pressure promotes fluid movement to the interstitial space. A high rate of fluid administration alone is not enough to produce FO ; development and maintenance of FO requires impaired excretion of water or abnormal function of the interstitial compartment, or both. For example, administration of 90 mL/kg of lactated Ringer's solution in 1 h to mildly dehydrated dogs produces none of the features of FO, and some features of FO seen during administration of 360 mL/kg in 1 h largely resolve within 30 min, accompanied by voiding large quantities of urine .Hypervolemia secondary to impaired excretion of excess fluid may be seen in animals with heart, kidney, or liver disease, and some animals with these chronic conditions present for care precisely because they have clinical signs of FO. Organ dysfunction as a component of acute illness—for example, impaired heart and kidney function observed in some dogs with septic shock—may contribute to FO in the face of overzealous replacement fluid administration. Another factor contributing to impaired excretion of excess water is dissociation of arginine vasopressin (AVP) release from osmoregulation, a situation brought about by disorders and drugs that result in water retention, hyponatremia, and edema. As reviewed by Moritz and Ayus, the list of conditions associated with hospital-acquired hyponatremia due to excessive secretion of AVP and water retention is quite long and varied . Water rAs anyone who has examined a patient with a soft tissue infection will recognize, inflammation also promotes edema in ways that are independent of volume status and AVP release. As described by Bhave and Neilson inflammatory states yield a reduction in interstitial fluid pressure by disrupting the tension of the collagen fibrils responsible for maintaining a tight interstitial matrix, increasing the compliance of the interstitial compartment . Under pRegardless of whether edema was initially caused by an increased MCFP or reduced interstitial pressure, once interstitial fluid volume has grown enough to increase tissue weight by more than 10–20% the compartment becomes highly compliant . This inIn the case of edema caused by intravenous fluids administered to animals with systemic inflammation, there are also contributions from reduced plasma albumin concentration and disruption of the endothelial barrier to albumin. The dilutional effect of fluid therapy and the acute phase response to reduce plasma albumin concentration decreases plasma oncotic pressure and favors fluid filtration. The capillary glycocalyx barrier to albumin may be compromised by inflammation , releaseFO causes harm due to the effects of edema fluid in the interstitial space. In the lung, the presence of excess extravascular water impairs gas exchange, reduces pulmonary compliance, and increases the work of breathing, complications that reduce the oxygen content of blood and increase the amount of oxygen consumed by the muscles of ventilation. In the systemic circulation, FO may impair diffusion of oxygen and energy substrates, obstruct capillary blood flow and lymphatic drainage, distort tissue architecture, and impair cell-to-cell interactions. Every major organ system may manifest complications of the syndrome , but theAlthough FO is often first diagnosed based on recognition of edema or effusion, a better approach is to identify it earlier by monitoring changes in body weight or cumulative fluid balance. Measuring change in body weight has been considered the gold standard clinical measurement approach to monitor fluid balance in hospitalized humans since the 1970's and has been used to monitor critically ill companion animals , 30. HowThe advent of more widespread use of intensive care beds with built-in scales may make routine use of weight change more practical for human patients. Veterinary application of weight change assessment in critically ill patients is impeded by several factors, including the expense of purchasing and maintaining the calibration of accurate scales, the need to lift patients onto scales, and the weight effects of monitoring devices, bandages, and bladder size in patients that often weigh less than human infants. Commonly used formulas for % weight gain and % fluid overload include these:The data used to populate these formulas may reflect the entire duration of hospitalization or may be used to monitor daily changes. In patients judged to be dehydrated at baseline, the formula may be modified to subtract the % dehydration  from the numerator . Althougdistribution of excess water. In critical illness there may be a variable relationship between total body water content and the compartmental distribution of water between extravascular and intravascular, and within the vascular compartment the distribution between the unstressed volume and the stressed volume that creates cardiac preload. An excess of extracellular water is more likely to produce clinical complications of FO than an excess of intracellular water. Supporting this is the observation of Bihari et al. that sodium balance—a key determinate of extracellular fluid volume—correlates better with respiratory dysfunction than does fluid balance  that were monitored with a closed urine collection system, and compared outcomes between 34 dogs with critical illness and 15 hemodynamically stable dogs with neuro-orthopedic disease that had closed urine collection systems in place to assist with nursing care  fluid administration with fluid restriction on %FO and outcomes are needed to address this.Most reports of studies of FO have focused on the effect of fluid administration in the early hours to 1–2 days of treatment of life-threatening illness. Because of the growing evidence that FO is associated with worse outcomes in the critically ill, there has been considerable interest in validating non-invasive techniques to identify patients who respond to intravenous resuscitation fluid with an increase in cardiac output, and avoid (or at least limit) administration of fluids to those who do not. Those techniques are reviewed in detail by Boysen and Gommeren elsewhere in this issue; however, it is worth mentioning here that there are unresolved questions about the benefit of an increase in cardiac output immediately following a fluid bolus when that increase may be transient and yet result in longer-lasting edema. For example, Roger et al. demonstrated that of the septic patients who responded to a fluid bolus with an increase in cardiac stroke volume, half lost that benefit within 20 min . Most stFluid therapy does not end with initial resuscitation and in fact often continues for days in the critically ill, in stages that have been characterized as rescue, optimization, stabilization, and de-escalation  42). Du. Du42). The optimization and stabilization phases are times when development of FO due to inappropriate administration of excessive quantities of both water and sodium in “maintenance” fluids and fluid vehicles for drug administration is probably common. Routine practice in charting human fluid balance ignores the volume of fluid administered as a vehicle for drugs, and this “fluid creep” can create a large volume of unaccounted fluid administration that contributes to FO. In fact, maintenance fluid and fluid creep may account for the majority of parenteral water and sodium administration in human ICU patients . The comThe clearest indication for intervention in animals with FO is the presence of hypervolemia, which should be managed with sodium and water restriction, diuretics, and in selected life-threatening situations, hemofiltration when the potential for a diuretic response is impaired. The most obvious cases of hypervolemia include edematous animals with kidney injury causing oliguria or anuria, and animals with pulmonary edema secondary to heart failure. Physical examination alone is likely to correctly identify these and inform treatment. Diuretic treatment for left sided congestive heart failure usually consists of furosemide 1–2 mg/kg by intravenous or intramuscular injection, followed by an intravenous constant infusion (0.66 mg/kg/hour) for 6 h treatment blocks when venous catheterization can be obtained without compromising the patient . AnimalsAnimals with other causes of FO and edema may be hyper-, normo-, or hypovolemic, and classification and treatment of these may be more difficult. For example, an animal with sepsis or pancreatitis may become edematous at a low MCFP because of changes in the microcirculation and interstitial matrix, and aggressive treatment with diuretics for imagined hypervolemia will compromise circulation and cause harm. These animals may develop relatively severe FO after even tiny increases in MCFP. If there is clinical evidence of impaired circulation that may respond to fluid therapy, benefit from resuscitation fluids should be predicted and monitored with adjunct assessment methods such as ultrasound measurement of dynamic variables, central or mixed venous oxygen saturation, and central venous or arterial blood pressure responses to incremental doses of fluids or drugs. Replacement fluids should be used only during the resuscitation and optimization phases, with an eye toward discontinuing them as soon as a transition to the stabilization phase is possible. Albumin solutions or plasma may be administered to animals with hypoalbuminemia that is sufficiently severe to contribute to edema formation. Although administration of albumin solutions instead of crystalloids to critically ill humans provides little improvement in most outcome measures, those patients generally require less total fluid for resuscitation/optimization and may have less tendency for FO –48. We hAnimals in septic shock are rarely resuscitated with fluid therapy alone. Removal of the underlying cause, for example infection source control, is a critical step to reversing pathology of the microcirculation and interstitial matrix to correct FO. Mechanical ventilation to reduce the work of breathing and decrease pulmonary shunt may be required to address an increase in extravascular lung water. Pharmacological management of the circulation, for example the administration of pressors to maintain MCFP, arterial pressure, and cardiac output, is routinely essential to correct hypotension restore adequate oxygen delivery and allow earlier transition to a stabilization phase with less fluid administration.Once an animal with FO edema has reached the stabilization phase it may be much more tolerant of fluid restriction and graded diuretic therapy. In this stage, a low test dose (<1 mg/kg) of intravenous furosemide may be administered and the patient response evaluated. If urine output increases the patient is monitored for evidence of hypovolemia, and in its absence diuretic therapy may be continued with a goal of gradually reducing FO to <5% over 1–2 days. Fluid therapy at this stage is often provided partly or wholly via enteral nutrition, but if intravenous fluid is continued it should be restricted to water and electrolytes calibrated to meet metabolic need . By the Fluid overload is likely to be a detrimental and sometimes life-threating complication of disease that is much more likely to occur when fluid therapy is not carefully calibrated to maintain an adequate circulation without causing edema. It is clearly much better to avoid it in the first place rather than treat it after it appears. This is best accomplished by administering replacement fluids only to those patients capable of responding with an increase in oxygen delivery during the rescue and optimization phases of fluid therapy, and by using a protocol for maintenance fluid that is guided by careful consideration of the actual metabolic needs of the animal.The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The motivation for this study was to develop a microdevice for the precise rheological characterization of biofluids, especially blood. The method presented was based on the principles of rheometry and fluid mechanics at the microscale. Traditional rheometers require a considerable amount of space, are expensive, and require a large volume of sample. A mathematical model was developed that, combined with a proper experimental model, allowed us to characterize the viscosity of Newtonian and non-Newtonian fluids at different shear rates. The technology presented here is the basis of a point-of-care device capable of describing the nonlinear rheology of biofluids by the fluid/air interface front velocity characterization through a microchannel. The proposed microrheometer uses a small amount of sample to deliver fast and accurate results, without needing a large laboratory space. Blood samples from healthy donors at distinct hematocrit percentages were the non-Newtonian fluid selected for the study. Water and plasma were employed as testing Newtonian fluids for validation of the system. The viscosity results obtained for the Newtonian and non-Newtonian fluids were consistent with pertinent studies cited in this paper. In addition, the results achieved using the proposed method allowed distinguishing between blood samples with different characteristics. The study of the viscosity of biofluids, such as blood, has been used to understand their physiological mechanisms and biological functions . Due to As illustrated in The gold standard for viscosity measurements comprises a wide range of types of rheometers . The specific kind of rheometer that can only measure the steady shear viscosity function of a material is called a viscometer . RheometIn general, macroscopic rheometers provide less accuracy than their microfluidic counterparts. In contrast to classical methods, the study of rheology through microfluidic techniques is a widely used method to analyze the viscosity of complex fluids. Several microfluidic platforms have been developed to reach high confinement levels ,10,11  ,20. Micr) [The scale reduction allows for an increase in sensitivity and accuracy. Other factors such as hematocrit level, plasma, and coagulation could affect blood viscosity. On that matter, point-of-care devices such as thromboelastograph (TEG)  and the ROTEM thromboelastometer  ,24 have ) .The technology presented here consists of a front microrheometer mainly composed of three parts: a microfluidic consumable that comprises a microchannel with an inlet and an outlet; the electronic setup, including pump, electronic detection, and data acquisition; and a mathematical model to properly convert obtained data into viscosity measurements. The electronic detection is performed with a sensor array  located along the microchannel. This technology allows us to characterize the rheological properties of the fluid as a function of the fluid-front advancement, using a small amount of sample, reducing space required, and finally minimizing time and material consumption. The tested samples come into contact only with the walls of the microfluidic channel; therefore, there is neither a need for cleaning protocols, nor for the use of cleaning substances. Unlike other rheometers that require extensive cleaning of their components, the microfluidic chip is intended to be used as a consumable. The consumable is disposed of after each experiment. Thanks to the number of pairs of electrodes (24), situated at each side of the microchannel, it is possible to obtain a high number of data points, thus enhancing the accuracy of measurements. Finally, the integration of electronic detection into microfluidic devices improves the reliability of measurements.The general goal of this study was to develop the method and model for a microrheometer capable of characterizing the viscosity of biofluids by analyzing the fluid-front advancement, and reducing the required sample’s amount, space, and time consumption. First, we characterized the viscosity of healthy blood samples at distinct hematocrit levels. The viscosity of blood obtained at  authors ,27,28. T authors . Second,The development of mathematical models describing the Newtonian and non-Newtonian characteristics of blood as a complex fluid ,31 are vThe linear displacement of a fluid layer with respect to another in the time interval An easy method to study fluid flow consists of the study of the fluid-front (fluid–air interface) advancement. In a microfluidic channel, the front velocity, By studying the fluid/air interface, the shear rate (1) see .(2)γ˙=hm corresponds to the value of viscosity at n indicates the nature of the viscosity of the studied fluid. Depending on the value of n, the behavior of a fluid is characterized as Newtonian or non-Newtonian. Values of n = 1.The viscosity of non-Newtonian fluids can be described using different models according to the characteristics of each fluid. The most commonly used are the Carreau–Yasuda and power-law models . The powUsing the Stokes equation, it is possible to calculate the flow through a known geometry as a function of the average velocity. In most cases, microfluidic systems are composed of pressure sources and microfluidic channels connected by cylindrical tubing, creating coupled fluidic systems. The flows passing through two coupled geometries are equivalent due to the mass conservation principle. The mathematical deductions concerning these calculations are detailed in In a closed system composed of a pressure source, a fluid reservoir, and the coupling of tubing with a microchannel of rectangular cross-section, the pressure inside the rectangular microchannel, ΔP, is the summation of all the pressures in the system, as shown in m and n) and the geometrical parameters of the system shown in Equation (4):As properly deduced in According to the mass conservation principle, in our system, = π r2vt . This isWe used a coupled system in which a tube was connected to a microchannel structure. The effective pressure, stem see  caused fn can be obtained from Equation (6). By equating experimental data in Equation (5) with the mathematical model in Equations (3) and (6), we could calculate the value of m as follows:Terms A and n and m were obtained, the viscosity values of a fluid were calculated using the power-law model in Equation (2).Once A Newtonian fluid has a constant viscosity. In opposition to non-Newtonian fluids, it does not depend on the applied pressure. Analogously, as it has been done in the general case, the mathematical model for Newtonian fluids Pressure for the entire system is described in Equation (8), which has a linear dependence on the mean front velocity.For the Newtonian case, Along the same lines, the experimental data of the shear rate values obtained from applying a known pressure to a Newtonian fluid have the linear relation shown in Equation (10):Using the experimental results alongside the mathematical model, m can be calculated as:n = 1, the power-law equation for viscosity in Equation (2) can be re-written as:In Newtonian fluids for which The experiments were performed using microfluidic chips made of polydimethylsiloxane (PDMS) attached to glass substrates s 8.5 mm b. The chThe experimental setup used to carry out the experiments was composed of a pressure pump (Dolomite Fluika) connected to a closed fluid reservoir. From the reservoir, Tygon tubing with an internal radius Ten samples of 500 μL from each different test fluid were used during this study: , plasma, and blood from healthy donors). We selected MGVS60, a viscosity standard tested in strict accordance with ASTM D2162, the primary method for viscosity standards calibration that is typically used for the calibration and verification of viscosity-measuring equipment. Its viscosity is 01 mPa·s . Blood sTo obtain the plasma samples, we centrifuged whole blood samples at 2500 rpm for 5 min. The plasma that rested at the top was collected with a pipette. Samples were prepared at three different hematocrit concentrations, Ht (%) of 50%, 42%, and 35%. These were prepared by taking plasma previously separated from whole blood and then adding to it the desired volume of RBCs corresponding to each hematocrit percentage. The hematocrit selected for further comparison to a benchtop device was 42% Ht, consistent with the levels found in healthy adults. All the tests were performed at room temperature (25 °C) and, for the case of blood, within two days of extraction, to avoid cell damage from aging . At the For a correct rheological characterization of the fluid, it was necessary to properly determine the pressures inside the system . The preng blood .When studying blood-front behavior, dependence between the applied pressure and the dynamic contact angle measured was observed. Hence, To validate our setup and the use of electronic detection as a proper method to determine viscosity for Newtonian fluids, two different strategies were followed: first, a Newtonian standard fluid and DI water with reported viscosity value were evaluated. Second, the obtained results with our technology were compared to the results obtained with a commercial macrorheometer, the Malvern Kinexus Pro+ .From the data obtained (applied pressure and time), knowing the device geometry, resistances, and the pressures added to the system, it was possible to plot the effective pressure as a function of shear rate for each sample.A linear fit of the data obtained when plotting the applied pressure with an external pressure pump n was very close to 1, which indicated that the fluid was Newtonian, compatible with the behavior expected for both.As shown in From these data, as previously explained, the viscosity could be obtained by using Equation (13). These results were compared to those obtained with a commercial macrorheometer see .Water has been found to exhibit a constant viscosity of η ≈ 1.002 mPa·s . The samThese results validated that the accuracy of the new electronic sensing method was adequate  regarding the measurement of viscosity in an automatic, fast, and effective way.Once the measurement system was validated for Newtonian fluids with known characteristics, the experiment was repeated with plasma and blood at different % Ht values. The results are shown in eff applied to the fluid variance is shown in m and n were obtained. The values for each are shown in The behavior of the shear rate as PWith the effective pressure, and after obtaining m and n , viscosiAs depicted in 10 mPa·s . DispersRegarding the rheological properties of RBCs, blood showed higher resistance to flow at low eter see .Residuals, R, for the micro- and macrorheometer were calculated as the difference between experimentally obtained viscosity, η, and predicted viscosity, The theoretically predicted viscosity of blood, A good correlation was observed when plotting experimentally obtained values for viscosity and theoretically predicted values using the power-law model shown in The technology presented here was based on the detection of the advancement of the fluid front using electrodes printed underneath a microfluidic channel. The accuracy of the method was tested by measuring the viscosities of plasma, DI water, and a commercial viscosity standard. Experiments to measure human blood viscosity also were performed. Unlike other microfluidic capillary viscometers, the microrheometer presented here was based on the detection of the fluid/air interface as it advanced through a microchannel. Through the experimental method described, it was possible to produce a series of shear rates that constituted a proper range to observe how the viscosity of non-Newtonian fluid changed as the applied pressure increased. Effective pressure, the pressure acting on the fluid is composed of all the interactions between the pressures acting in the system.The characterization of the non-Newtonian behavior was obtained by applying the power-law model in Equation 2). The experimentally obtained results included a shear-rate window range of less than terature ,39. Alte. The expThroughout this study, it has been demonstrated that the microrheometer using electronic detection of the fluid front can deliver viscosity results comparable to those obtained using traditional equipment, while saving time and reducing the sample size and space required. At the same time, this technique permits a better control at low shear rates for shear-thinning fluids, such as blood. The development of this experimental and theoretical method for blood will be useful in the near future to determine the presence of RBC abnormalities through the rheological characterization of blood. It could be useful for medical diagnosis in the study of diseases associated with changes in blood viscosity, operating in a wide range of shear rates .


Image segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature, segmentation methods are empowered by open-access magnetic resonance imaging datasets, such as the brain tumour segmentation dataset. Moreover, with the increased use of artificial intelligence methods in medical imaging, access to larger data repositories has become vital in method development.To determine what automated brain tumour segmentation techniques can medical imaging specialists and clinicians use to identify tumour components, compared to manual segmentation.We conducted a systematic review of 572 brain tumour segmentation studies during 2015–2020. We reviewed segmentation techniques using T1-weighted, T2-weighted, gadolinium-enhanced T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and perfusion-weighted magnetic resonance imaging sequences. Moreover, we assessed physics or mathematics-based methods, deep learning methods, and software-based or semi-automatic methods, as applied to magnetic resonance imaging techniques. Particularly, we synthesised each method as per the utilised magnetic resonance imaging sequences, study population, technical approach (such as deep learning) and performance score measures (such as Dice score).We compared median Dice score in segmenting the whole tumour, tumour core and enhanced tumour.We found that T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging are used the most in various segmentation algorithms. However, there is limited use of perfusion-weighted and diffusion-weighted magnetic resonance imaging. Moreover, we found that the U-Net deep learning technology is cited the most, and has high accuracy (Dice score 0.9) for magnetic resonance imaging-based brain tumour segmentation.U-Net is a promising deep learning technology for magnetic resonance imaging-based brain tumour segmentation. The community should be encouraged to contribute open-access datasets so training, testing and validation of deep learning algorithms can be improved, particularly for diffusion- and perfusion-weighted magnetic resonance imaging, where there are limited datasets available. Components such as tumour core, boundary of tumour core, peritumoral oedema, cellular proliferation (an increase of the number of cells), cellular infiltration (migration of cells or excessive growth) and vascular proliferation (leaky blood vessels) are of great clinical interest.3 Current practice involves various magnetic resonance imaging (MRI) approaches to visualise these tumour components. Particularly, the tumour core can be visible in T2-weighted MRI, and T1-weighted MRI, the enhanced boundary of tumour core can be seen in gadolinium-based T1-weighted MRI (T1-Gd), peritumoral oedema can be visible in fluid-attenuated inversion recovery (FLAIR) MRI, while cellular proliferation, cellular infiltration and vascular proliferation can be visualised in diffusion-weighted MRI and perfusion-weighted MRI.Brain tumours are malignancies of brain tissues. Characterising such tissues and identifying related genes can help to estimate tumour spread, and further help to identify tumour grades for the treatment planning. These methods can distinguish between healthy and tumour tissues. Moreover, they can distinguish among different tumour components within the tumour. Such methods are commonly known as segmentation methods, which can be manual, semi-automatic or automatic. With the increase of segmentation methods, the medical image analysis community has reviewed them using publicly available benchmark datasets (e.g. the brain tumour segmentation – BraTS dataset) to assess their performance.6 Additionally, individual reviews addressing only deep learning methods, only automated methods, classical reviews, and practical implications are reported. However, a systematic review to identify promising, and widely adapted brain tumor segmentation methods is not reported in the literature. Moreover, in the literature, individual methods, and their validation show technical advancements in most MR image-based approaches, but they are not fully explored in all MRI sequences in the imaging of brain tumours. For example, diffusion-weighted, and perfusion-weighted MRI are often overlooked or limited in brain tumor segmentation method development, and validation. In addition, a recent trend of deep learning methods has extensively contributed to the development of automatic segmentation methods, in order to avoid the subjective, and time-consuming nature of manual techniques. However, the performance of these deep learning methods relies on the data types used, single or multi-centre data collection, the number of available data samples for training, validation and testing of the approach. Therefore, it is crucial to explore the various studies in deep learning methods. Such a study can help understanding and justifying the need for further large open-access data repositories and alternative artificial intelligence (AI)-based techniques, such as transfer learning.In the literature, MRI-based image processing methods addressed the outlining of tumour components.In this systematic review, we addressed the following PICO  format research question:What automated brain tumour segmentation techniques can medical imaging specialists, and clinicians use to identify tumour components, compared to manual segmentation?To answer the above question, we reviewed brain tumour segmentation methods which are based on physics or mathematics models, deep learning models, and software or semi-automatic methods. The methods, which use at least four types of MRI sequences , are included in the synthesis as they are common in clinical practice. Articles based on images from other MRI sequences are discussed, individually. The accuracy measures and study population of the various segmentation methods are also reviewed. Moreover, deep learning architectures are reviewed for their underlying network architecture  with a list of articles that adapted such architectures in their studies. Technical specifications of such architectures are listed in the discussion.A protocol for this study is prepared internally but not registered elsewhere. However, the PROSPERO database is checked to ensure the originality of the study. software. After removing the duplicates, study inclusion/exclusion criteria were applied.For source articles’ systematic identification, we have searched the following online databases: PubMed, Embase Ovid and Engineering Village. The different combinations of the keywords ‘glioma’, ‘medulloblastoma’, ‘brain tumour’ and ‘segmentation’ were used. After discussion among the authors, the combination, ‘glioma’, and ‘segmentation’, and ‘brain tumour’, was used for the article search. The search duration was defined to include articles published in the period from 2015 until 2020. In PubMed, the specific search filters were full-text, humans, English and segmentation keyword must be in the article title or abstract. In Engineering Village, the specific search filters were that the segmentation keyword must be in the abstract, and glioma, and brain tumour keywords must be in the subject or title or abstract. All the articles were stored using ZoteroArticles are screened at two levels for inclusion/exclusion. First, the articles are screened at the abstract, and title level. At this level, the studies, which are not segmentation studies, are excluded. The excluded studies are grouped into the following categories: clinical analysis studies, case studies, image pre-processing studies, general overall surveys, tumour classification studies, tumour detection and identification studies, information learning for model training studies, surgical planning studies, datasets and not accessible studies. diagram of Second, the segmentation studies are screened by reading the full text. At this level, the articles, which are eligible for synthesis, are identified by reviewing the imaging modalities, and associated data types used in their segmentation method. The studies, which are not MRI-based, are excluded. As a result, among the MRI-based studies, articles that utilised T1-weighted, T2-weighted, T1-Gd and FLAIR MRI are included for the final synthesis. All the inclusion/exclusion criteria are mentioned in the PRISMAS1 Appendix). For each article, data is extracted according to the following parameters: publication id, publication type, publication year, author, article title, publisher, DOI, issue number, volume number, type of study, type of technical methodology, type of imaging modality, type of MRI, type of tumour, single or multi-centre data collection, the number of data samples and performance score.After removing duplicates, all the articles are organised, as a table, in Microsoft Excel . We synthesised the performance of each method in segmenting whole tumour (WT), non-enhancing tumour core (TC) and enhanced tumour (ET). The values are stored as a tuple of ‘whole tumour, tumour core, enhanced tumour’. Each missing value in a performance score tuple is recorded as null. As shown in the PRISMA diagram of Among the aforementioned parameters, the ‘data samples’ parameter is sub-categorised into study population, training data, validation data and test data. The types of technical methods in image-based tumour segmentation are organised into three categories: physics or mathematics-based methods, deep learning-based methods and software or semi-automatic methods. The performance score of each segmentation method is retrieved from the respective article and included in our table. In articles, authors have used various performance score measures, which include match, accuracy, Jaccard similarity coefficient, Tanimoto similarity, Hausdorff distance, dice score, positive predictive value, specificity, sensitivity, negative predictive value, precision, recall, misclassification error, intersection over union, Lin's concordance correlation coefficient, quality measure, balanced error rate, kappa, correlation, mean square error, false-positive rate per patient, the extent of resection, mutual information, residual tumour volume, root mean square error, the ratio of overlap, coefficient of variation, agreement index, interoperator variance, F1 score, volume difference, peak signal-to-noise ratio, qualitative evaluation, difference ratio of pixels, similarity, overlap index, absolute error, percent error, the difference with the gold standard, paired All reviewers assessed studies or validated data extraction matrix independently. JMB assessed each study and populated data extraction matrix records. SLCK validated the data extraction matrix. TNA verified, and confirmed the data extraction matrix. The whole process was performed manually, and without using any automation tools.Studies are synthesised according to the publication year, technical methods used in method development (type of study), MRI sequences used in the segmentation method development, deep learning methods , performance score to evaluate accuracy in segmenting three tumour components – whole tumour, and tumour core, and enhanced tumour, study population, and specific studies with additional MRI sequences. The studies having at least T1-weighted, T2-weighted, T1-Gd and FLAIR MRI sequences used are included for the synthesis. The synthesis of a number of articles is visualised as a bar plot, and pie charts showing the total number of articles in each year, and category. The synthesis of the study population in terms of data samples used, and performance score measure in terms of median Dice score are visualised as results. The outcome is depicted as Matlab box plots as commonly found in the synthesised articles. Deep learning studies are tabulated to identify the most widely adapted deep learning technology.Benchmark review articles and particular imaging sequences studies are mentioned in the specific imaging studies’ section. Studies of segmentation methods using diffusion-weighted, and perfusion-weighted MRI, are reported individually.null values.During the synthesis, the unavailable values of performance score measures and data samples are considered as In this systematic review, 572 articles are identified from online publication repositories. Out of these, 14 duplicates are removed and the remaining 558 articles are screened for the eligibility criteria. After screening titles and abstracts, 246 articles are excluded, and the remaining 312 articles are screened by reading the full text. After the full-text screening, 89 articles are excluded with reasons, and 223 articles are included for synthesis. The summary of exclusion reasons and the number of excluded articles are shown in the PRISMA diagram .As shown in the PRISMA diagram, after screening at the abstract and title level, we selected 312 articles. Following that we applied the eligibility criteria, which provided us with 223 articles for the synthesis. These articles are categorised over the publication year, as shown in 1374 156 are deep learning-based and six are software-based or semi-automatic7580 methods articles.After the screening at the title and abstract level, 312 studies are included. Among them, 102 are physics or mathematics-based, 189 are deep learning-based and 21 are software-based or semi-automatic methods articles. After the full-text screening, 223 studies are included for synthesis. Among them, 61 are physics or mathematics-based,A total of 223 studies have used at least four MRI sequences  which are included in the final synthesis.8183 visual geometry group (VGG) network,8486 DeepMedic,8792 U-Net,93114 autoencoder,115117 generative adversarial network (GAN),118120 W-net, a cascade of W-Net E-Net, and T-Net, squeeze, and excitation network (SENet), multiresolution neural network, holistically-nested edge detection (HED) network, multi-level upsampling network, V-net, residual network (ResNet),126 hourglass network, multi-view network (MvNet), DeepSCAN,130 densely connected, inception,99 ensemble net, PixelNet,133 ContextNet, dense neural network, MC-Net, OM-Net, ConvNet, wide residual network, and pyramid pool network (WRN-PPNet), deep convolutional network,140 neuromorphic neural network, DeepLabv3 +, recurrent neural network and German cancer research centre (DFKZ) network. Moreover, we mentioned the extensions of these architectures in the discussion section. Deep architectures, and their extensions, with associated publications, are summarised in We synthesised 156 deep learning articles to identify the commonly reported deep learning architecture in automatic brain tumour segmentation. We reported deep learning architectures such as convolutional neural network (CNN),From our data extraction matrix, it was apparent that the most common accuracy measure used is the Dice score. Therefore, in The study population is summarised as the total number of data samples used in the study. The median data sample used in segmentation studies is 351 (median ± stdev = 351 ± 232.67). Moreover, deep learning segmentation methods are noted to have data samples divided into three categories: training data, validation data and test data. As shown in  which encouraged a tumour segmentation method development using T1-weighted, T2-weighted, T1-Gd and FLAIR MRI sequences. Among the 223 synthesised articles, five studies have used additional imaging sequences. Three studies236 have used diffusion imaging sequences, and one study has used perfusion imaging and multiple CT imaging sequences. These three studies are noted to have deep learning, or physics or mathematics-based methods. Moreover, we noted that a few studies have used perfusion imaging sequences, and diffusion imaging sequences but did not use the aforementioned all four MRI sequences. These studies are perfusion imaging studies238241 and diffusion imaging studies.239247 Also, two studies are noted to have used ultrasound imaging.249A rigorous review, using an open-access dataset, is published as the gliomas segmentation benchmark article,In this systematic review, we addressed a PICO style question to put the brain tumour segmentation methods in the context of clinical utility. Our population (P) is brain tumour patients, intervention (I) is segmentation methods, comparison (C) with manual segmentation evaluated as Dice score and outcome (O) is automated segmentation methods. Accordingly, we systematically found a promising technology, U-Net, which performs automated brain tumour segmentation using multiple MRI sequences. Its validation with manually segmented ground truth has achieved a Dice score of 0.9. Moreover, U-Net is majorly cited compared to other automated methods. Such merits show that U-Net has the potential to be considered for the clinical efficacy studies of automated brain tumour segmentation.As shown in 250 Specific details, such as cellular proliferation and cellular infiltration, can be visualised with diffusion-weighted imaging.252 Moreover, vascular proliferation can be visualised with perfusion-weighted imaging. The inclusion of tumour components, such as cellular proliferation, infiltration and vascular proliferation, are addressed in very few articles. The potential reason could be the limited availability of open-access diffusion and perfusion-weighted imaging datasets.From a clinical point of view, the articles, which have reported segmentation of all three regions , are synthesised. The whole tumour, including peritumoral oedema, is visible in FLAIR MRI, non-enhancing solid tumour core is visible in T2-weighted MRI, and necrotic/cystic core and enhanced core are visible in T1-weighted, and T1-Gd MRI. As the whole tumour covers a larger area, the value of WT is higher compared to TC or ET in each category.Segmentation methods are commonly validated in terms of Dice scores ranging from 0 to 1.0. 1.0 shows the best matching between segmentation ground truth and method output. As shown in  Modifications of CNN are fully CNN,8183 hyper-dense CNN, cascaded CNN,163 cascaded fully CNN, multi-path CNN,165 ensembled CNN, holistic CNN, full-resolution residual CNN, two-phase patch-based CNN and cascaded anisotropic CNN. U-Net is modified as cascaded U-Net,163 residual U-Net,171 domain adapted U-Net and efficient spatial pyramid (ESP) network. A modification of W-net is reported as the cascade of W-net, E-net and T-net, and a modification of V-net is reported as the cascaded V-net. Modifications of ResNet are dilated ResNet and fully convolutional residual neural network. An ensemble of multiple deep architectures is also reported as ensembles of multiple models, and architecture (EMMA). A modification of GAN is reported as conditional GAN. Modifications of ConvNet are reported as classification ConvNet and detection ConvNet.As shown in the PRISMA diagram As summarised in In this study, we have synthesised the study population and performance measures from the articles. Among the 223 synthesised articles, 217 studies have used multi-centre datasets, five studies have used single-centre datasets, and one study has used only a synthetic dataset. Note that, data variety could be limiting in the generalisation of Dice score performance comparison reported in our study. A potential solution is to develop an open-access data repository and review the studies with the same data samples. Moreover, some of the studies have missing values, which could limit our synthesis results. A benchmark framework to report the evaluation measures could be useful to mitigate such limitations in the future. In this study, we identified brain tumour segmentation techniques and synthesised results as found from their respective articles, which could be a limitation. In order to apply these methods at clinics, a separate efficacy study should be performed by clinical staff members utilising data at respective clinics.In conclusion, we systematically addressed a review question that can help medical imaging specialists, and clinicians to identify automatic brain tumour segmentation techniques, compared to manual segmentation. Our specific inclusion criteria emphasised having multiple MRI sequences in the method development. We noted that four MR-based sequences, i.e. T1-weighted, T2-weighted, T1-Gd and FLAIR MRI, are used the most. Diffusion weighted, and perfusion-weighted MRIs are rarely used. Among the segmentation methods, deep learning methods have contributed the most compared to other methods during the 2015–2020 period. Within the deep learning methods, U-Net-based methods are adapted the most and have an accuracy of approximately 0.9 Dice score in segmenting a brain tumour. We also noticed that the benchmark BraTS dataset does not have perfusion-weighted and diffusion-weighted MRI data, motivating the development of an open-access data repository with such MRI sequences.6 which contains the data samples of T1-weighted, T2-weighted, T1-Gd and FLAIR MRI of gliomas patients. Creating such an open-access dataset can help to include cellular proliferation, infiltration and vascular proliferation in brain tumour segmentation techniques . Moreover, medulloblastoma cases are rare .254 Therefore, a comprehensive adult dataset may be useful to address childhood tumours with transfer learning methods.In the future, a novel dataset can be developed with additional imaging data such as diffusion-weighted, and perfusion-weighted MRI, similar to the frequently reported open-access datasetClick here for additional data file.Supplemental material, sj-xlsx-1-dhj-10.1177_20552076221074122 for Magnetic resonance image-based brain tumour segmentation methods: A systematic review by Jayendra M Bhalodiya, Sarah N Lim Choi Keung and Theodoros N Arvanitis in Digital Health


Meningioma is the most common primary intracranial tumour. However, the histopathological diagnosis remains simplistic and to some extent insufficient compared to other brain tumours. Surgery is the primary treatment, and radiation therapy is secondary treatment for tumours that recur. Traditional chemotherapy has so far been ineffective, as these tumours are resistant, and research on meningioma biology is lacking compared to other tumour types. The tumoral microenvironment (TME) plays a key role in understanding various cancers. In meningiomas, however, the TME is poorly understood. It is unknown how the brain immune cells contribute to meningioma behaviour and aggressiveness, and the relationship between meningioma cells and TME involved in treatment resistance also needs to be investigated. It is therefore necessary to explore if the literature holds any evidence regarding meningioma–brain crosstalk in order to identify the gaps of knowledge. The sparse amount of available literature on the subject necessitates a scoping review approach.Background: In recent years, it has become evident that the tumoral microenvironment (TME) plays a key role in the pathogenesis of various cancers. In meningiomas, however, the TME is poorly understood, and it is unknown if glia cells contribute to meningioma growth and behaviour. Objective: This scoping review investigates if the literature describes and substantiates tumour–brain crosstalk in meningiomas and summarises the current evidence regarding the role of the brain parenchyma in the pathogenesis of meningiomas. Methods: We identified studies through the electronic database PubMed. Articles describing glia cells and cytokines/chemokines in meningiomas were selected and reviewed. Results: Monocytes were detected as the most abundant infiltrating immune cells in meningiomas. Only brain-invasive meningiomas elicited a monocytic response at the tumour–brain interface. The expression of cytokines/chemokines in meningiomas has been studied to some extent, and some of them form autocrine loops in the tumour cells. Paracrine interactions between tumour cells and glia cells have not been explored. Conclusion: It is unknown to what extent meningiomas elicit an immune response in the brain parenchyma. We speculate that tumour–brain crosstalk might only be relevant in cases of invasive meningiomas that disrupt the pial–glial basement membrane. AccordiWe analyse these qualities in order to explore if the literature holds any evidence regarding meningioma–brain crosstalk with emphasis on interactions mediated by soluble messengers. In addition, we identify gaps of knowledge, necessary to elaborate the role of parenchymal glial cells in meningioma pathogenesis. The complex and diverse nature of tumour–brain crosstalk coupled with the sparsity of the literature necessitate a scoping review approach.A broad systematic search strategy was used to maximise the capture of relevant information on the topic. Search terms included the cells of interest  and molecules that mediate reciprocal tumour–brain crosstalk in other primary brain tumours (cytokines and chemokines) . On the (meningioma[MeSH Terms]) AND (astrocytes[MeSH Terms]) OR (meningioma[MeSH Terms]) AND (microglia[MeSH Terms]) OR (meningioma[MeSH Terms]) AND  OR (meningioma[MeSH Terms]) AND (“chemokines”[MeSH Terms]) OR (meningioma[MeSH Terms]) AND (cytokines[MeSH Terms])Additional relevant references from the obtained literature were reviewed. With exception of case reports, clinical trials and letters to the editor, we included all English-written studies based on original data for title and abstract screening, regardless of year of publication. As no articles directly assessed the topic of our review, our inclusion and exclusion criteria were dynamic, as new ideas emerged. Articles related to peritumoral oedema, tumour angiogenesis, systemic inflammatory markers and tumour genetics were excluded. A PRISMA flowchart was made including reasons for exclusion of the literature .Several studies reported the infiltration of tumour-associated microglia/macrophages (TAMs) in meningiomas ,12,14,15TAMs differentiate into a classical  or alternative  phenotype. However, polarisation more likely represents an overlapping continuum of phenotypes rather than separate categories ,25,26. TIn meningiomas carrying the monosomy 22/del(22q), TAMs are polarised towards the pro-inflammatory M1 phenotype , and theAccording to the small available number of studies, there were variable degrees of gliosis in the brain parenchyma adjacent to meningiomas ,35,36,37A substantial number of studies have investigated the expression of cytokines in meningiomas with different methods, including rt-PCR, immunohistochemistry, Western blot and flow cytometry. From these studies, it is evident that meningiomas express a wide range of cytokines, chemokines and growth factors with pro- and anti-inflammatory properties. The roles of these substances in the course of meningioma progression remain largely unexplored. Cytokines can stimulate or inhibit meningioma proliferation in vitro ,59,60,61The majority of the studies on cytokines in the meningioma TME focused on the effect on tumour cells upon receptor binding, and, thus, little is known regarding other potential cellular sources and targets of cytokines in the TME. Some cytokines/chemokines, e.g., IL-6, CXCL12 and TGF-ß, show a formation of autocrine loops in meningioma cells. Such loops have been demonstrated as a signalling mechanism that impact tumour behaviour  43,45,5,61. SomeThis study provides an overview of the existing evidence of reciprocal crosstalk between tumour cells and the brain parenchyma in meningiomas. Meningiomas express several factors, which are known to have chemotactic properties on microglia in glioblastoma multiforme  ,26,56,69The pia limitans  is found in the perivascular space, composed of astrocytic end feet connected with inducible tight junctions and constitute the last frontier before the brain parenchyma ,82. JoosIn summary, the few available studies indicate that the pial–glial basement membrane hinders a microglial response in the brain parenchyma, but it may simultaneously support and ensure survival of the subpial astrocytes.The TAMs were predominantly of the pro-tumoral M2 type, and the M1/M2 ratio decreased with increasing WHO grade ,84. ThisMicroglia/astrocyte–tumour interactions in primary brain tumours are mediated by several factors, including cytokines and chemokines and neurotrophic, morphogenic and metabolic factors . These fIn this work, we defined two preconditions necessary for reciprocal tumour–brain crosstalk: (1) attraction and activation of glial cells from the parenchyma to the tumour site and (2) the reciprocal exchange of substances between glial cells and tumour cells that impact meningioma growth and behaviour.Next, we used this framework to investigate if the literature described such elements of crosstalk in meningioma biology. Based on our search, it is still uncertain to what extent these preconditions are present. Microglia play a central role in tumour–brain crosstalk in other primary brain tumours ,21. To eOne of the main barriers to understanding meningioma biology is the lack of in vitro models that faithfully mimic in vivo conditions . LimitatMeningioma–brain crosstalk has not been directly assessed before, which, of course, imposes possible limitations. Choosing search terms that successfully cover our topic poses a challenge. We chose search terms to cover tumour cells, glia cells and cytokines, as they are key mediators of crosstalk in other brain tumours . There aThe role of the brain parenchyma in the pathogenesis of meningiomas is largely unexplored. In this scoping review, we summarised and disseminated research findings that might elucidate whether tumour crosstalk with the brain parenchyma plays a role in meningioma growth and behaviour. It is still questionable to what extent meningiomas elicit an immune response in the brain. Furthermore, the extent and type of immune response seem to vary amongst tumours. Phagocytes are frequently detected as the most abundant infiltrating immune cells in meningiomas, but it seems reasonable that these are blood-derived macrophages rather than resident microglia. As the pial–glial basement membrane might protect the brain parenchyma adjacent to meningiomas from inflammation, it is possible that tumour–brain crosstalk is of the highest significance in meningiomas with disruption of this membrane. We suggest that future research should focus on the inflammatory response at the tumour–brain interface of invasive meningiomas, as these cells are more likely to represent activated glia cells from the brain parenchyma.