We found that the luminal fluid volume in the jejunum and ileum, but not the colon, gradually decreased and reached a steady state. In contrast, [3H]water almost completely disappeared in all intestinal regions. Kinetic analysis revealed the following rank order for the rate constant of fluid secretion: jejunum > ileum > colon, whereas a negligible regional difference was observed in the rate constant of fluid absorption. Fluid secretion under an isosmotic condition (300 mOsm/kg) was higher than that at 0 mOsm/kg in all intestinal regions, though no significant changes in fluid absorption were observed. Thus, the fluid secretion process appears to be the major determinant of the regional differences in GI fluid dynamics. Our findings indicate that the luminal fluid volume is altered as a result of water ingestion, absorption, and secretion, and finally reaches an apparent steady state, which is regulated mainly by the process of fluid secretion.The drug absorption profile is dependent on the luminal drug concentration, which in turn is influenced by the gastrointestinal (GI) fluid dynamics. In the present study, therefore, we aimed to examine the luminal fluid dynamics by kinetically analyzing fluid absorption and secretion along the GI tract in rats using the in situ closed-loop technique with non-absorbable fluorescein isothiocyanate-dextran 4000 (FD-4) and tritium water labeling ([ Furthermore, we and other research groups demonstrated that water secretion and drug resultant dilution of drug concentration in the GI tract may contribute to beverage-drug interactions that result in decreased drug absorption7. Thus, it has been noted that GI fluid dynamics concepts to date are not sufficient for the development of oral drug delivery and prediction of oral drug absorption8.Gastrointestinal (GI) absorption of drugs in solution occurs via both passive diffusion and active transport via drug transporters, and, in addition, may be influenced by metabolic enzymes. Generally, GI absorption of a drug through these mechanisms depends upon the concentration gradient across the membrane and/or the solubility of the drug. Based on the Biopharmaceutical Classification System (BCS), when a drug has low solubility but high permeability (BCS class II), dissolution becomes the rate-limiting step in drug absorption, while when a drug has high solubility but low permeability (BCS Class III), permeation becomes the rate-limiting step. To understand the GI absorption of drugs, including these concepts of dissolution and permeation, it is important to know the drug concentrations in various parts of the GI tract and the relevant variables. Fluid volume is a major factor directly determining GI drug concentration and the amount of dissolution of poorly soluble drugs in the GI tract. We recently reported that changes in the GI fluid volume may indirectly influence the drug absorption profile by altering the drug concentration and absorption kinetics11. However, GI fluid dynamics is complicated by multiple factors such as fluid flux across the intestinal membrane, GI transit, gastric emptying rate, and physiological environment 12. Therefore, GI fluid dynamics observed by MRI and PET may include the effects of these multiple factors, implying apparent fluid dynamics in the GI tract. On the other hand, real fluid dynamics  remains unclear. This may also have regional differences in fluid absorption and secretion . Thus, in order to clarify the real fluid absorption and secretion, it is necessary to separate the apparent fluid dynamics from the real fluid dynamics.GI fluid volume in humans (including children and adults) and rats has been investigated by means of magnetic resonance imaging (MRI) and positron emission tomography (PET)14. Murray et al. reported colonic fluid volume after water administration in humans14. They also reported colonic fluid volume after administration of hyperosmotic solutions such as carbohydrate solutions and fruit juices15. However, it is not easy to compare such fluid dynamics in the colon with those in the small intestine in terms of fluid flux across intestinal membranes. It is well-known that the function of the colon is to absorb water, keep the fluid volume low, and form feces, and fluid movement in the colon is of great pathophysiological importance in relation to diarrhea and constipation. On the other hand, since the major site of drug absorption is not the colon, but the small intestine, it seems that fluid dynamics in the small intestine is more important than that in the colon in determining the absorption and pharmacokinetics of oral drugs. Nevertheless, the colon can be an important site of drug absorption from some controlled-release formulations  and thus must sometimes be considered during the development of drug delivery systems16. Therefore, an accurate assessment of fluid dynamics throughout the GI tract is necessary to predict GI drug concentrations and absorption kinetics.The fluid volumes in the stomach and small intestine have been measured, but data on the fluid volume and its variation in the colon are still scarce19. However, because fluid flux across the membrane remains unclear, these models and related studies employ apparent fluid dynamics, and do not reflect real fluid absorption and secretion behavior. Also, reports on GI fluid dynamics include both apparent and real fluid dynamics due to differences in experimental methods. In the present study, we aimed to clearly separate and analyze fluid absorption and secretion along the GI tract in rats.Several physiologically based pharmacokinetic (PBPK) models for predicting oral drug absorption  and the advanced dissolution, absorption, and metabolism (ADAM) models, which are also included in commercially available PBPK modeling software) have already incorporated fluid dynamicskabs,app) of fluid were calculated from the data up to 30 min , 17.2 ± 4.1 × 10–3 min-1 (n = 21), and 42.3 ± 5.8 × 10–3 min-1 (n = 19), respectively. The kabs,app of fluid was significantly higher in the colon than in the jejunum or ileum, whereas it was similar in the jejunum and ileum.The regional differences of GI fluid absorption were evaluated by monitoring the time courses of the remaining fraction of fluid in the rat jejunum, ileum, and colon after administration of fluorescein isothiocyanate-dextran 4000 (FD-4) using an in situ closed-loop method. As shown in Fig. min Fig. B. The va3H]water. Interestingly, rapid water absorption was observed in all intestinal regions  of [3H]water, that is the real fluid absorption rate constant, was estimated from the data up to 30 min. As shown in Fig. kabs in the jejunum, ileum, and colon were 0.136 ± 0.009 min-1 (n = 31), 0.131 ± 0.010 min-1 (n = 28), and 0.127 ± 0.008 min-1 (n = 23), respectively, and there was no significant difference, in contrast to the case of kabs.app , the time courses of the remaining fraction of fluid  were investigated using in situ closed-loop method. Apparent fluid absorption and real fluid absorption after administration of mannitol solution (300 mOsm/kg) were evaluated using FD-4 and [kabs) of [3H]water in mannitol solution (300 mOsm/kg) were estimated to be 0.128 ± 0.011 min-1 (jejunum), 0.121 ± 0.018 min-1 (ileum), and 0.0900 ± 0.00703 min-1 (colon)  were 14.8 × 10–3 min-1 (jejunum), 13.0 × 10–3 min-1 (ileum), and 9.45 × 10–3 min-1 (colon), respectively  Fig. A. The va11. Furthermore, our recent studies have shown that the solution osmolality influences the GI fluid volume5. We also demonstrated that osmolality-dependent variations in GI fluid volume can alter drug concentration in the GI tract, and consequently change drug absorption characteristics2. This could be one of the factors underlying beverage-drug interactions8. However, when ingested substances (including food and beverage components) are absorbed, the effect of osmolality on fluid dynamics will be complicated. Therefore, it is important to understand the basics of the fluid environment involved in pharmacokinetics, and quantitative analysis of GI fluid dynamics provides valuable information not only for pharmacokinetics but also for GI physiology and pharmacometrics. In the present study, we aimed to quantitatively examine the luminal water dynamics by kinetically analyzing fluid absorption and secretion along the GI tract in rats.Gastrointestinal fluid volume has an important influence on drug absorption profiles defined in terms of drug concentration, and in recent years, GI fluid dynamics has attracted increasing attention. Indeed, dynamic variations in fluid volumes along the GI tract have been observed by means of fluid-sensitive MRI and PET analyses in healthy volunteers and/or experimental animalskabs,app value of fluid is higher in the colon than in the small intestine (jejunum and ileum). Interestingly, luminal fluid volume reached a steady state within 30–60 min in a site-dependent manner. A similar tendency has been observed after the oral administration of water in humans14. However, the luminal fluid volume is regulated not only by the fluid absorption process but also by the secretion of biological fluid. Thus, the results  in Fig. 3H]water  showed no significant difference among regions, unlike the kabs,app values  25. Since it was reported that Qvilli in humans represents about 20% of the portal blood flow  and Qportal in rats is 32.9 mL/min/kg, Qvilli is estimated to be 6.58 mL/min/kg (= 0.2 × 32.9 mL/min/kg), assuming no species difference between human and rat27. Then, the Qvilli in each intestinal region used in this experiment can be calculated to be 0.116 mL/min on the assumption that Qvilli is proportional to the length of each region . In this case, calculated CLabs is very similar to Qvilli, further supporting the idea that the rate-limiting step for fluid absorption is blood flow. On the other hand, in the in situ study, anesthesia could alter blood flow, which might affect fluid dynamics in the GI tract. The similarity between CLabs (obtained under anesthesia) and Qvilli (obtained under non-anesthesia) suggests that anesthesia may not have a significant effect on fluid dynamics . Further analysis of anesthetic effects on blood flow and fluid dynamics is warranted.A previous study with D3H]water, rapid absorption was observed, regardless of isosmotic condition, suggesting that osmotic pressure has no influence on real fluid absorption  may not be rate-limited by blood flow. Predictions of GI fluid movement and its effect on oral drug absorption after ingestion of hyperosmotic solution, beverages, and food may need to take account of these possibilities.Figure 32. In the present study, we analyzed GI fluid dynamics quantitatively from both perspectives  using FD-4 and [3H]water. Our results indicate that real fluid absorption is independent of GI regions and osmolality, but is rate-limited by blood flow . Furthermore, the fluid secretion process is the major determinant of fluid flux across the membrane and its regional differences in GI tract. The use of apparent fluid dynamics may be sufficient to predict drug absorption under normal conditions using PBPK models, including the ACAT and ADAM models. However, precise analysis of fluid dynamics should make it possible to predict GI fluid movement under the influence of food and beverages, disease states, and even GI toxicity (diarrhea or constipation) of drugs, over a wide range of environments and conditions.In recent years, some studies dealing with GI fluid dynamics have not adequately distinguished real fluid movement from apparent fluid movement. The effect of gastric emptying of ingested water and gastric fluid on drug absorption was reported by Grimm et al., while many aspects of intestinal fluid dynamics remain unclearMale Wistar rats weighing 230 g ± 10% (7 weeks old) were purchased from Tokyo Laboratory Animals Science Co., Ltd. , and were maintained in a climate-controlled breeding facility under 12-h automatic ambient light cycle control at the Tokyo University of Pharmacy and Life Sciences. Treatments of experimental animals and relevant protocols complied with the Regulations on Performing Animal Experiments established by the Committee of Animal Care and Welfare of Tokyo University of Pharmacy and Life Sciences  and the ARRIVE guidelines. All animal experiments  were performed in accordance with the relevant guidelines and regulations. Additionally, we carried out animal anesthesia and euthanasia in accordance with American Veterinary Medical Association (AVMA) Guidelines. Specifically, rats were anesthetized with a triple anesthetic combination  as intraperitoneal anesthesia and euthanized with an overdose of isoflurane as inhalation anesthesia. Propofol, midazolam, and alfentanil were purchased from Meiji Seika Pharma Co., Ltd. , FUJIFILM Wako Pure Chemical Corporation , and Nippon Zenyaku Kogyo Co., Ltd. , respectively. Isoflurane was purchased from Pfizer Inc. .etal7. Before the experiments, rats were fasted for at least 12 h with free access to water. Anesthesia was induced with a triple anesthetic combination. The intestine was exposed through a midline abdominal incision and an intestinal loop  was made by cannulation into both ends of the jejunum, ileum, or colon with silicone tubing . Each intestinal segment was cleaned by flushing with saline (37 °C). One mL of FD-4  dissolved in purified water or mannitol solution  or 1 mL of [3H]water (1 μCi/mL) dissolved in purified water or mannitol solution was introduced into the intestinal loop, and both ends of the loop were ligated to determine the impact of applied fluid volume on the remaining fraction of fluid in the rats. FD-4 was purchased from Sigma-Aldrich Company . [3H]Water was purchased from PerkinElmer Life Sciences . The osmolality was measured with a cryoscopic osmometer, OSMOMAT 030-D .The fluid volume and drug concentration in the rat GI tract were determined using the in situ closed-loop methods described by Funai Cout,FD-4, and then made up to 10 mL with buffer solution to measure the amount of FD-4, Xout,FD-4. Assuming that FD-4 is homogeneously distributed in the lumen after intestinal administration, the fluid volume in the intestine (mL) was calculated using the following equation:Vwater,app (mL) is the calculated fluid volume in the intestine. Xout,FD-4 is the amount of FD-4 (μmol), and Cout,FD-4 is the luminal concentration of FD-4 (μM). The Vapp,water was normalized to the surface area (11.2 cm2) calculated from the radius and length of the intestine reported by Komiya etal33. To verify the validity of the calculated Vwater,app values, the volume of the withdrawn samples was routinely confirmed by directly weighing the samples. In addition, if the samples contained blood or the length of the intestinal loop used was not adequate, those rats were excluded from the analysis. To assure sampling accuracy, it has been confirmed that the recovery of FD-4 is more than 80% in all experiments. The concentration of FD-4 was measured using a microplate fluorescence reader  at excitation and emission wavelengths of 492 and 515 nm, respectively.In experiments using FD-4 solution, the test solution in the loop was collected after 5, 10, 20, 30, or 60 min by flushing with air to measure the luminal concentration of FD-4, 3H]water, the test solution in the loop was collected at the specified time points by flushing, and then made up to 10 mL with saline to measure the radioactivity of remaining [3H]water. Assuming that 3H-labeled and non-labeled water behave similarly in the lumen after intestinal administration, the concentration of [3H]water in purified water is always constant , and the remaining administered water volume in the intestine (mL) was calculated using the following equation:Vwater (mL) is the calculated remaining fluid volume of administered water in the intestine. Xout,[3H]water is the amount of radioactivity of [3H]water (μCi), and Cout,[3H]water is the concentration of radioactivity of [3H]water (1 μCi/mL). The Vwater was normalized to the surface area. Radioactivity of [3H]water was measured with a liquid scintillation counter .In experiments using water. In addition, the population pharmacokinetics and Bayesian estimation were used to calculate the mean and SEM of the kabs of fluid. The real fluid secretion rate constant (ksec) was determined using the simple rat GI kinetic model shown in Fig. VGI is the time course of the remaining fraction of fluid calculated using FD-4 Vbody is the total body fluid volume 34, and S and Ssum are the intestinal surface area under the conditions of the in situ closed-loop experiment and the intestinal total surface area, respectively. The kabs values of jejunum, ileum, and colon were those determined above . The value of ksec was optimized in the fitting step using this model.The rate constant of apparent fluid absorption (Peff) of fluid was calculated by the following equation:CLabs is the absorption clearance of fluid. Vlumen is the volume of the lumen (volume of distribution: 1 mL) under the conditions of the in situ closed-loop experiment.Permeability . All data are expressed as the mean of values obtained in at least three experiments with the standard error of the mean (SEM). Statistical analysis was conducted using GraphPad Prism 9 software. Multiple comparisons were done using one-way analysis of variance (ANOVA) followed by the Tukey–Kramer test, respectively.


Geologic subsurface energy storage, such as porous-media compressed-air energy storage (PM-CAES) and underground hydrogen storage (UHS), involves the multi-phase fluid transport in structurally disordered or heterogeneous porous media . Furthermore, such multi-phase fluid transport is likely to repeatedly occur due to successive fluid injections and extractions, thus, resulting in cyclic drainage–imbibition processes. To complement our preceding study, we conducted a follow-up study with microfluidic pore-network devices with a square solid shape (Type II) to further advance our understanding on the effect of the pore shape , pore-space heterogeneity , and flow rates (Q = 0.01 and 0.1 mL/min) on the repetitive two-phase fluid flow in general porous media. The influence of pore shape and pore-space heterogeneity were observed to be more prominent when the flow rate was low  on the examined outcomes, including the drainage and imbibition patterns, the similarity of those patterns between repeated steps, the sweep efficiency and residual saturation of the nonwetting fluid, and fluid pressure. On the other hand, a higher flow rate  appeared to outweigh those factors for the Type II structure, owing to the low aspect ratio (~1). It was also suggested that the flow morphology, sweep efficiency, residual saturation, and required pressure gradient may not severely fluctuate during the repeated drainage-–imbibition processes; instead, becoming stabilized after 4–5 cycles, regardless of the aspect ratio, COV, and Q. Implications of the study results for PM-CAES and UHS are discussed as a complementary analysis at the end of this manuscript. Energy storage refers to the process of capturing energy produced at one time and storing it for later use. Energy storage is important because it enables better balancing of the supply of and the demand for energy, which is essential for a reliable and efficient energy system. That is, energy storage allows the excess energy produced during periods of low demand to be stored for use during periods of high demand, thereby reducing the need for new-power-generation capacity and helping to stabilize the grid. Energy storage is also important for enabling the integration of renewable energy sources, such as wind and solar power, into the currently existing energy system. These sources of energy can be variable, with energy production affected by weather conditions and the time of day. Energy storage can help to smooth out fluctuations in energy production, providing a more stable and predictable source of energy for consumers.There are various types of energy storage technologies, including batteries, pumped hydro storage, compressed-air energy storage, hydrogen storage, flywheels, and thermal storage, among others . Each teGeologic subsurface energy storage, such as porous-media compressed-air energy storage (PM-CAES) and underground hydrogen storage (UHS), involves the multi-phase fluid transport in structurally disordered or heterogeneous porous media  and viscosity ratio (M) to analyze the experimental results [γ is the interfacial surface tension coefficient [mN/m], θ is the contact angle of a solid-fluid–fluid system [deg], V is the velocity of an advancing nonwetting fluid [m/s], and Ca and M values from previous and current PM-CAES projects collected by Zhang et al. [Ca and M are of similar magnitudes in a given operation [We use the capillary number ( results :(1)Ca=V·g et al.  as a benperation ,7.Rc). Our analysis involved examining the distribution of the nonwetting fluid in binary images obtained by applying MATLAB’s  Otsu’s method to convert the original experimental images. cR is defined the same way as in our preceding study [I is the value of a pixel (0 or 1), i is the step of either drainage or imbibition, j is the index of a pixel, N is the total number of pixels, and To quantitatively evaluate the similarity of fluid flow patterns during repetitive drainage and imbibition steps in the micromodels, we utilized the correlation coefficient  can provide information on the distribution and connectivity of fluids in porous media [M0 and M1 in a two-dimensional (2-D) space are related to the measurements of the covered area (F) and boundary length (U) of an object as follows [A, and A. Readers are referred to Zhang et al. [U) in Equation (4) by the covered area (F) in Equation (3) to analyze the flow morphology of the nonwetting fluid more objectively; as such,The Minkowski functionals M0A=∫M0A=∫M0, F and U of the nonwetting fluid were subsequently computed based on the number of pixels representing the nonwetting fluid, utilizing the built-in functions in MATLAB.The Minkowski functionals were calculated using MATLAB’s image processing code. The procedure involved cropping the images of the pore-network domain obtained after each drainage and imbibition step (refer to 2 in 1) was greater than 5 for Type I, while for Type II, it was approximately 1  for the pore-throat width was set to either 0, 0.25, or 0.5. To account for the resolution limit of the fabrication method, the minimum width of the pore throat was set at 20 μm. It was recommended that the height-to-width ratio of the solid part be maintained between 0.2 and 2 to avoid any potential defects during the PDMS-based microfluidic model fabrication [The position of each solid was determined using a MATLAB code, where the width of pore throats followed a log-normal distribution with a mean value of rication ,22. With−12 m2)) and were found to be in the same order.After obtaining the solid coordinates from the code, the information was utilized to replicate the pore structure using AutoCAD 2020 (Autodesk). For this study, two distinct pore structures were designed independently, corresponding to each COV value of 0, 0.25, and 0.5  and 2.3 × 10−5 (log Ca ≈ −4.6) for the applied flow rates of 0.01 and 0.1 mL/min, respectively. The M value in this study was determined to be 2.38 × 10−2 (log M = −1.62). Both the log Ca and log M values closely align with those obtained from the expected PM-CAES operations summarized by Zhang et al. [Ca and M values suggest that the drainage process would either fall within the transition zone between capillary and viscous fingering (at 0.01 mL/min) or within the viscous fingering regime (at 0.1 mL/min) according to Zheng et al. [The wetting and nonwetting fluids used in the experiments were mineral oil  and deionized water, respectively, as indicated in g et al. .Q) of either 0.01 or 0.1 mL/min throughout ten repetitive drainage and imbibition cycles. The effluent fluid from these cycles was collected in an empty container placed at the outlet side. To ensure the percolation of approximately 2.5 pore volumes of the nonwetting or wetting fluid through the tested micromodel, the injection duration for both the drainage and imbibition processes was set at 330 s for Q = 0.01 mL/min and 33 s for Q = 0.1 mL/min.Before each test, the newly fabricated micromodel was saturated with mineral oil. We utilized a syringe  and a high-precision syringe pump (PHD ULTRA) to maintain a constant flow rate  and a high-speed camera (LEICA MIC 190 HD) were utilized. Two pressure transducers  with a pressure range of 0–689 kPa and a sensitivity of 19.96 Pa/kPa were connected to the right and left sides of the micromodel, respectively, to measure the fluid pressure throughout the test. The data acquisition process was automated using a data logger , a computer, and a DC power supply (Keysight E3630A). The complete experimental setup is depicted in To commence the repetitive flow test, the first step involved initiating drainage from the right-hand side of the micromodel. Drainage was sustained for the desired flow duration, starting from the moment the front of the nonwetting fluid passed through the entrance of the tree-like inlet. Subsequently, the syringe pump was halted, and both valves were promptly closed, indicating the completion of the first drainage step (right to left).Subsequently, new outer tubes were introduced, replacing the existing ones. The left-side tube was filled with mineral oil, while the right-side tube was filled with water. The syringe pump, along with a new syringe filled with oil, was moved to the left side, and a fresh empty container was positioned on the right-hand side. The two valves were reopened, and the syringe pump was started, initiating the reoccupation of the micromodel’s pore space with mineral oil through a process known as forced imbibition. This imbibition phase continued for the same duration as the previous drainage step, starting from the moment the oil front passed through the entrance of the tree-like inlet. Once again, the two valves were closed using the same procedure, marking the completion of the first imbibition step (left to right). This entire sequence was repeated until a total of ten cycles were completed.We performed repetitive drainage–imbibition tests for two iterations using a homogeneous model (COV = 0), fabricating a new model each time. Additionally, we conducted tests for four heterogeneous models, with two models having COV = 0.25 and the other two models having COV = 0.5. The Reynolds number for the fluid flow in the tree-like inlet was calculated to be within the range of 0.5–5 for drainage and 0.01–0.1 for imbibition. As a result, all flow experiments in this study fell within the laminar flow regime.Q = 0.01 and 0.1 mL/min). The invasion of the nonwetting fluid at the end of the first drainage occurred primarily in the direction of the pressure gradient  when COV = 0. This trend was more prominent at Q = 0.1 mL/min . On the other hand, the invasion of the nonwetting fluid occurred with more branches as the heterogeneity level increased (COV = 0 → 0.5), which implies that capillarity had a greater influence. Similar to the study results with Type I in Zhang et al. [Q = 0.01 mL/min . Interestingly, such a trend with the cyclic flow became less obvious with Q = 0.1 mL/min (higher flow rate).g et al. , the nonQ = 0.01 mL/min. That is, Rc was larger for the higher COV  with the structural heterogeneity of COV = 0–0.5 in this 2-D environment.In contrast to the observations for Type I, the invasion pattern became more similar between steps in the heterogeneous pore network at Similarly to Rc was larger in the heterogeneous models at Q = 0.01 mL/min  is determined by comparing the volume of the nonwetting fluid (Vnw) to the total pore volume  of the micromodel following drainage. Mathematically, Enw is calculated as Enw = Vnw/Vtotal. Conversely, effective sweep efficiency (eEnw) is determined by comparing the difference between the volume of the nonwetting fluid after drainage (Vnw) and before drainage (Vrnw) to the volume of the wetting fluid (Vw) before drainage. Mathematically, eEnw is calculated as eEnw = (Vnw − Vrnw)/Vw.We utilized sweep efficiency and effective sweep efficiency as quantitative measures to evaluate the invasion of the nonwetting fluid . Sweep eQ = 0.01 mL/min , being consistent with the above observations for drainage . For example, the effective sweep efficiency was increased from eEnw = 0.15–0.3 when Q = 0.01 mL/min to eEnw = 0.35–0.45 when Q = 0.1 mL/min at COV = 0  is determined by comparing the volume of the nonwetting fluid at the end of each imbibition (Vrnw) to the total pore volume  of the micromodel after imbibition. Mathematically, Srnw is calculated as Srnw = Vrnw/Vtotal. Additionally, we defined the effective residual saturation (eSrnw) by comparing the volume of the residual nonwetting fluid (Vrnw) to the volume of the nonwetting fluid just before imbibition (Vnw). Mathematically, eSrnw is calculated as eSrnw = Vrnw/Vnw.In a similar manner to sweep efficiency, we investigated both the absolute and effective residual saturation of the nonwetting fluid. The residual saturation  was lowest in the homogeneous model (COV = 0) when 1 mL/min a,c, whic1 mL/min b,d.Thus, overall, we can infer that the withdrawal efficiency of the nonwetting fluid may be better in the homogeneous pore structure when it belongs to the Type II type. Furthermore, the higher flow rate can yield an improved withdrawal efficiency for the same pore structure compare d, as lonm0) could suggest there are larger numbers of smaller fluid ganglia in the flow domain, as noted by Zhang et al. [m0 decreased as the structural heterogeneity of the pore-network model was intensified (COV = 0 → 0.25 → 0.5) during both drainage and imbibition under the tested flow rates  on m0 also seemed insignificant for any given COV, although the effective sweep efficiency improved substantially with a higher Q , enabled longer and more connected ganglia of the nonwetting fluid, which yielded a smaller value of higher Q .Pmax) during the repetitive drainage–imbibition tests  during both the drainage and imbibition processes. This is the opposite result from that of the Type I study by Zhang et al. [Q increased to 0.1 mL/min .Lastly, we monitored the largest pressure drop across the model .Overall, Δ compare d, which We infer that the randomly distributed residual  nonwetting fluid—and thus a more tortuous travel path for the highly viscous wetting fluid during imbibition—contributed to this observation. Therefore, we may predict that a higher pressure gradient would be required during the withdrawal of compressed air/hydrogen back to the surface unit in operation (like the PM-CAES/UHS), and attention should be paid to this phenomenon to reduce the possibility of the coning problem.Pmax) exhibited a 2-4 times increase when the flow rate (Q) was multiplied by a factor of 10. This can be observed by comparing cR, Enw, eEnw, Srnw, eSrnw, m0, and ΔPmax.Lastly, just like the findings from the Type I study, the maximum pressure drop (ΔQ = 0.01 mL/min in this study (Q = 0.1 mL/min in this study) dwarfs the influence of pore-space heterogeneity; consequently, in association with the small aspect ratio (≈1), this acts as a main factor that decides the sweep efficiency of a nonwetting fluid during drainage. In detail, the higher flow rate, and thus larger capillary pressure, helps to push the nonwetting fluid through more flow channels with smaller widths. Consequently, both injection and withdrawal efficiencies were improved. This was observed for both Type I and II geometries. In addition to this, the square pore shape, and thus small aspect ratios and straight flow paths, results in the most efficient withdrawal of the nonwetting fluid in the homogeneous pore structure for all the tested flow rates and COVs , on the repetitive two-phase fluid flow in general porous media. The influence of pore shape and pore-space heterogeneity on the examined outcomes, including the drainage and imbibition patterns, the similarity of those patterns between repeated steps, the sweep efficiency and residual saturation of nonwetting fluid, and fluid pressure, were observed to be more prominent when the flow rate was low . On the other hand, a higher flow rate  appeared to outweigh those factors for the Type II structure, owing to the low aspect ratio (~1). It was also suggested that the flow morphology, sweep efficiency, residual saturation, and required pressure gradient may not severely fluctuate during the repeated drainage–imbibition processes, becoming stabilized after 4–5 cycles, regardless of the aspect ratio, COV, or Q.As a companion to the study by Zhang et al. , we condQ = 0.01 mL/min . Interestingly, such a trend with the cyclic flow became less obvious with Q = 0.1 mL/min (higher flow rate). The distribution of the residual nonwetting fluid in Type II micromodels was more sporadic and random when COV = 0 (homogeneous). In addition, more of the nonwetting fluid appeared to be stranded in the main domain of the pore space as the COV increased at all tested flow rates.Specifically, similar to the study results with Type I, the nonwetting fluid occupied the pore space more uniformly as the repetitive injection cycle continued at Overall, we can infer that the withdrawal efficiency of the nonwetting fluid may be better in the homogeneous pore structure when it belongs to the Type II geometry. However, a higher flow rate can improve both the injection and withdrawal efficiencies at a given pore structure of Type II. Lastly, a larger pressure gradient may be needed as the COV increases in Type II, which is the opposite of the Type I result. Compared with Type I, the relatively smaller flow resistance in the main pore-network domain during imbibition may imply a higher possibility of the water-coning problem into the wellbore for a short withdrawal time.


Suction devices are frequently used during laparoscopic surgery. However, their cost and limitations can be significant, depending on the clinical case, theatre setting and national health system. Furthermore, the continuous need to reduce the costs of the consumables in minimally invasive surgical procedures and their environmental burden creates extra pressure on the healthcare systems globally. Therefore, we present a new technique for laparoscopic suctioning, the Straw Pressure Gradient and Gravity (SPGG) technique. It is a safe, cost-effective and environmentally friendly technique compared to traditional suction devices.The technique involves using a sterile, single-use Suction Catheter 12-16 French after positioning the patient according to the targeted collection. The catheter is inserted via the laparoscopic port nearest the collection and directed using laparoscopic graspers. The outer end needs to be clamped to avoid fluid spillage, and the catheter tip is placed in the collection. Then after the clamp is released, the fluid will be successfully drained due to the pressure gradient into a pot placed at a lower level than the intra-abdominal collection. Minimal wash can be performed via the gas vent by using a syringe.SPGG is a safe and easy-to-learn technique that requires similar skills as placing an intra-abdominal drain during laparoscopy. It is softer than rigid, traditional suction devices and atraumatic. It can be used for suction, irrigation, collection of fluid for sampling and as a drain in case of an intraoperative indication.SPGG is a cost-effective device as it is cheaper than the average disposable suction device system and has multiple uses, which can significantly decrease the annual cost of laparoscopies. It can also reduce the number of consumables and lighten the environmental burden of laparoscopic procedures. Suction devices are frequently used during laparoscopic surgery. Most of those devices provide suction together with irrigation. They are designed to draw pooled fluid to clear the dissection field and wash out areas of the abdominal cavity to prevent postoperative intra-abdominal abscesses or collections . HoweverFurthermore, the environmental impact of the suboptimal use of disposable consumables in surgery is significant, and there is a need for awareness by all healthcare professionals . TherefoSPGG is a simple technique based on some very well-known principles of Physics; Poiseuille’s Law, Pascal’s Law and the hydrostatic principle of communicating vessels .During a laparoscopic procedure, the patient’s position is crucial for many reasons; One is to have better access and view of the targeted collection. Adjusting the patient, using the Trendelenburg versus the reverse Trendelenburg position with a combination of left or right axial rotation, will allow the intraperitoneal fluid to move along the abdominal quadrants. The gravity will trigger the fluid to move and stay in the lower part of the intra-abdominal cavity. The surgeon will then be able to target it with the SPGG.We use a sterile, single-use Suction Catheter 12-16 French  should be 12-15 mmHg for adequate drainage. Sometimes, intra-abdominal pressure may need to be increased by 2-6 mmHg for a few minutes to facilitate adequate fluid drainage. In that case, communication with the anaesthetic team is necessary to be aware and expect haemodynamic changes that might occur .It is easy to learn and apply, similar to the skill set required to place an intra-abdominal drain during laparoscopic surgery . The sucThe fluid collected via the SPGG stays in the kidney dish and can be sampled separately and sent for analysis. The fluid drained using traditional suction devices ends up in the same canister as the irrigation fluid. This renders the intra-abdominal fluid unsuitable to be used as a sample for further laboratory testing unless another device is used and placed between the suction device and the canister. By using the SPGG, there is no need for extra devices for collection and sampling.Limitations of the technique are that SPGG cannot be used to perform blunt dissection like the traditional rigid suction catheter or to perform rapid suction during critical situations such as managing or attempting to control active bleeding.Surgeons play a pivotal role in reducing operating room expenses . Using tFinally, from the environmental point of view, the SPGG technique uses significantly fewer disposable consumables even compared with the non-disposable suction devices if we take into consideration the use of disposable parts to connect with the negative pressure device.SPGG is a technique which is safe, efficient, cost-effective and easily applicable to all surgical specialities that use laparoscopy in any hospital and health system setting worldwide. Its main advantage is that it can decrease the annual cost of laparoscopies, making SPGG a technique that can be implemented anytime in any setting. In addition, the consumables of the SPGG device exist within the operation theatre, and there is no need for a formal business case or local approvals to obtain and use it. Finally, the technique can reduce the number of consumables and lighten the environmental burden of laparoscopic procedures. There is a need for future studies that will measure the cost-effectiveness of the SPGG compared with the traditional suction devices as well as the outcomes postoperatively.


CD388 is a novel antiviral drug-Fc conjugate (DFC) designed to deliver universal prevention of seasonal and pandemic influenza. Nonclinical studies have shown CD388 was effective prophylactically against a wide range of seasonal and pandemic influenza A and B strains in lethal and nonlethal mouse models of infection.The design of the ongoing blinded CD388 First in Human study, which is being conducted in healthy subjects is shown in Figure 1. CD388 for injection (100 mg/mL) was administered by intramuscular (IM) or subcutaneous (SQ) routes of administration. Single dose pharmacokinetics were determined for subjects in the 50 and 150 mg cohorts of each dosing route at protocol specified nominal time points; alias subject numbers were used to maintain blind. Plasma CD388 concentrations were determined for subjects randomized to receive CD388 using a validated hybrid immunoassay LC-MS method with lower limit of quantitation (LLOQ) of 0.1 µg/mL.max) appearing from 2 to 13 days post dose, and quantifiable concentrations ( >LLOQ) lasting for several weeks to months . Mean apparent half-life of elimination was ∼ 50 days. Increases in CD388 exposures were approximately dose proportional for both routes .CD388 appeared to be well absorbed by IM or SQ routes of administration with maximum plasma concentrations (TNo dose limiting local or systemic safety concerns were noted from review of ongoing blinded safety data and the study is proceeding at doses higher than those reported herein.Plasma concentrations of CD388, were similar following intramuscular or subcutaneous routes of administration and were sustained. No safety concerns have been noted in this ongoing study.Shawn Flanagan, PhD, Cidara Therapeutics: Salaried Employee|Cidara Therapeutics: Stocks/Bonds Voon Ong, Executive Director, Cidara Therapeutics: Stocks/Bonds Tristan Baguet, PharmD, Johnson & Johnson: Employee Roxana E. Rojas, M.D., Ph.D., Janssen: Employee|Janssen: Stocks/Bonds Sy-Shi Wang, Ph.D., Janssen Pharmaceutical: Employee Ozlem Equils, MD, Cidara Therapeutics: Employee|Cidara Therapeutics: Stocks/Bonds


Prostate-Specific Membrane Antigen (PSMA) PET/CT and multiparametric MRI (mpMRI) are well-established modalities for identifying intra-prostatic lesions (IPLs) in localised prostate cancer. This study aimed to investigate the use of PSMA PET/CT and mpMRI for biologically targeted radiation therapy treatment planning by: (1) analysing the relationship between imaging parameters at a voxel-wise level and (2) assessing the performance of radiomic-based machine learning models to predict tumour location and grade.PSMA PET/CT and mpMRI data from 19 prostate cancer patients were co-registered with whole-mount histopathology using an established registration framework. Apparent Diffusion Coefficient (ADC) maps were computed from DWI and semi-quantitative and quantitative parameters from DCE MRI. Voxel-wise correlation analysis was conducted between mpMRI parameters and PET Standardised Uptake Value (SUV) for all tumour voxels. Classification models were built using radiomic and clinical features to predict IPLs at a voxel level and then classified further into high-grade or low-grade voxels.Perfusion parameters from DCE MRI were more highly correlated with PET SUV than ADC or T2w. IPLs were best detected with a Random Forest Classifier using radiomic features from PET and mpMRI rather than either modality alone . The tumour grading model had an overall accuracy ranging from 0.671 to 0.992.Machine learning classifiers using radiomic features from PSMA PET and mpMRI show promise for predicting IPLs and differentiating between high-grade and low-grade disease, which could be used to inform biologically targeted radiation therapy planning.The online version contains supplementary material available at 10.1186/s13550-023-00984-5. Prostate cancer is the second most common cancer in men worldwide after lung cancer and the fourth most common cause of cancer death . TraditiAn alternative focal dose boosting approach is Biologically targeted Radiation Therapy (BiRT) which aims to deliver a non-uniform radiation dose distribution to the tumour according to its spatially defined biological characteristics . The BiRCombining mpMRI and PSMA PET/CT to implement BiRT offers advantages to using either modality alone, as they offer different but complementary information. Multiparametric MRI provides anatomical information from T2-weighted imaging and functional information from diffusion-weighted imaging (DWI) and dynamic contrast enhanced (DCE) MRI which characterise tumour diffusion and perfusion, respectively . PSMA PEDespite these benefits, there are still uncertainties regarding how signals in PSMA PET and mpMRI compare with each other, owing to the limited number of studies and lack of ground truth histology data to quantify their relationships. Furthermore, to facilitate non-uniform IPL dose-distribution approaches such as BiRT, a voxel-wise approach must be taken. Hence, this study aimed to assess the relative value of mpMRI and PSMA PET at a voxel level, by building upon our earlier proof-of-concept study , and by Data from 19 patients diagnosed with localised prostate cancer and recruited as part of a Human Research Ethics Committee (HREC)-approved study (HREC/15/PMCC125) were included. Patients were scheduled for radical prostatectomy at the Peter MacCallum Cancer Center in Melbourne, Australia, and had mpMRI and PET/CT imaging prior to surgery. Patient clinical, imaging, and pathological details are given in Table b-values = 50, 400, 800 and 1200 s/mm2, acquisition matrix = 250 × 250, FOV 250 mm × 250 mm, slice thickness = 4 mm. Apparent Diffusion Coefficient (ADC) maps were computed from DWI images using inline software.In vivo mpMRI was acquired using two 3 T scanners, the first five patients scanned with a Siemens MAGNETOM Trio  and all other patients scanned with a Siemens MAGNETOM Skyra. The imaging protocol followed guidelines from the European Society of Urogenital Radiology (ESUR)  and inclPre-contrast 3D T1-weighted images with variable flip angles  were acquired. DCE-MRI was performed using a 3D spoiled gradient echo with a time-resolved view sharing sequence for high temporal resolution imaging . Each patient received a 10-ml bolus injection of contrast agent Dotarem , followed by a saline flush. Semi-quantitative parameters and pharmacokinetic parameters were computed using Dynamika software  . Semi-quAll patients had PET/CT imaging after injection with a 68 Ga PSMA-HBED-CC PSMA-11) tracer. Five different PET/CT scanners were used, with scanning from the base of the skull or the vertex to the upper thighs. The PET scanning bed steps were acquired starting at the upper thighs to minimise the chance of spatial shifts between PET and CT at the level of the prostate, which can be caused by patient movement, bladder filling or intestinal movements. Further acquisition details are given in Additional file 1 tracer.After prostatectomy, the prostate specimens were embedded in agarose gel in a custom-made sectioning box for ex vivo MRI scanning, after which the specimen was cut into 5-mm sections and then microtomed at 3 µm to obtain whole-mount haematoxylin and eosin (H&E) stained sections. The most apical and basal histology sections obtained were not included in this study, as standard pathology processing required them to be cut in a parasagittal orientation which could not be easily co-registered with ex vivo MRI. Table Co-registration of PET/CT with mpMRI and ground truth histology was carried out using our established framework , 15, whiCorrelation analysis was carried out at a voxel-wise level using the co-registered images to investigate the relationship between signals on mpMRI and PET SUV values. For this and subsequent analyses, DCE MRI parameters Tonset, Twashout and IRW were excluded as the Tonset parameter was inconsistent across the dataset and challenging to reproduce, while Twashout and IRW contained many zero value pixels as the contrast agent had not washed out from the entire prostate during the imaging timeframe. To account for an estimated 3.3 mm average registration uncertainty between histology and in vivo mpMRI and PET/CT computed in our prior study , benign Kolmogorov–Smirnov tests were performed to determine whether PET SUV and mpMRI voxel values for tumour and benign tissue were normally distributed, and whether the benign and tumour voxel values for each imaging parameter exhibited the same distribution. Spearman correlation coefficients were computed to assess the degree of correlation between the PET SUV and the mpMRI parameter tumour voxel values. Bonferroni correction was applied to the correlation cut-off p-values to assess its significance.n = 24), Grey Level Run Length Matrix (GLRLM) (n = 16), Grey Level Size Zone Matrix (GLSZM) (n = 16), Neighbouring Grey Tone Difference Matrix (NGTDM) (n = 5), Grey Level Dependence Matrix (GLDM) (n = 14). Radiomic features were extracted from the original images and after applying filters including wavelet transform, gradient magnitude, Laplacian of Gaussian, 2D local binary pattern (LBP) and 3D LBP. A kernel size of 9 voxels in each direction (7.2 mm in each direction) was chosen, as it was closest to the average tumour radius in the dataset when assumed to be spherical.Voxel-wise radiomic features were extracted from co-registered mpMRI and PET imaging data using the PyRadiomics Library (v.3.0.1)  in the PPatient-based clinical features including age and PSA level, and PET-specific features were added to the model, including SUVmax, SUVmean, radioactive tracer uptake time and tracer radioactivity. Feature reduction was performed to prevent overfitting and reduce model training cost by applying the following strategies: (i) reject all highly correlated features, (ii) retain the top 10% of the features based on the ANOVA test and (iii) retain the top 50 features based on the mean decrease in random forest Gini impurity.Machine learning classifiers were trained to predict tumour location using a radial basis function kernel with the Python scikit-learn library (v0.24.2) . Two claClassifiers were trained to predict tumour location using (1) PET images alone, (2) mpMRI data alone  and (3) mpMRI and PET images combined. For the best performing tumour detection model, a second model was trained to predict the grade of each predicted tumour voxel, by classifying it as either high grade (defined as GG ≥ 3) or low grade (defined as GG ≤ 2). The performance of the tumour grade prediction model was assessed using balanced accuracy and weighted F1 score. Balanced accuracy was computed via the arithmetic mean of the sensitivity and specificity, measuring the combined performance of the tumour detection and tumour grading models. The weighted F1 score was calculated by taking the average F1 score from the high-grade and low-grade grading performance, which were each weighted by the proportion of voxels with the corresponding grade in the patient.Kolmogorov–Smirnov tests showed the distribution of voxel values for each imaging parameter did not follow a normal distribution for either tumour or benign tissue, and the distribution of all parameter values was significantly different between tumour and benign voxels. Figure Overall, the parameter which was the most highly correlated with PET SUV was Ktrans , followed by ME  and then iAUGC60 . For high-grade tumours, Ktrans remained the most highly correlated parameter (mean 0.546), followed by iAUGC60 (mean 0.552) and then ME (mean 0.504), whereas for low-grade tumours, ME was the most highly correlated parameter with PET SUV (mean 0.410), followed by Ktrans (mean 0.386) and then iAUGC60 (mean 0.326). The DCE MRI parameter IRE was positively correlated with PET SUV for all patients, and Ve was positively correlated for most voxels. The DCE MRI parameter TTP mainly showed negative correlations with PET SUV tumour voxel values , whereas T2w and ADC did not show a high degree of correlation with PET SUV.F1) for each tumour detection model: PET alone, mpMRI alone, and PET with mpMRI combined. The RFC performed better than the SVC for all three models, where the best performing model overall was shown by the combined PET and mpMRI RFC, which gave a cross-validation score of 0.757. Each RFC model was then re-trained using a patient-wise leave-one-out scheme to generate tumour location predictions for each patient.Table Table Figure Figure Results from an average performing patient number 64) are shown in Fig. 4 are shoThe top 10 performing features in each of the RFC tumour detection models are listed in Table Tumour classification was carried out for each patient, to classify whether a predicted tumour voxel was high grade or low grade, using output from the combined PET and mpMRI model. This tumour grading model was built using an RF classifier, and a patient-wise leave-one-out cross-validation scheme, to give the performance results as shown in Table In this study we have analysed the voxel-wise relationship between PSMA PET SUV and mpMRI parameters and developed radiomics-based machine learning models to predict tumour location and grade. This builds upon our earlier proof-of-concept study  which haStrengths of this study include the use of a highly controlled dataset, the accurate co-registration of PET/CT and mpMRI with ground truth histology data using an established framework , and theCorrelation analysis was conducted using Spearman rank correlation between imaging parameters rather than Pearson correlation, which had been used in our prior study . The SpeMany artificial intelligence models that use radiomic features have been developed to predict tumour location from prostate mpMRI, with some gaining FDA and CE approval , 25. In The mpMRI and PET radiomics-based models developed in this study, contribute towards this growing research field. The tumour prediction and tumour grading models were developed as two separate classification tasks, with the predicted tumour voxels output by the tumour detection model being further classified into high-grade or low-grade voxels by the grading classifier. A single classifier approach would have resulted in a multi-class classification problem, with three classes: benign, high-grade tumour and low-grade tumour, which would have given a highly imbalanced class split in the dataset requiring a lot of data augmentation to reduce the imbalance and more feature inputs. Hence the two-classifier approach was preferred as it lowered the computational cost and simplified the performance assessment for each task.Results showed that an RFC model for detecting prostate cancer using combined PET and mpMRI radiomic features performed better than RFC models developed using radiomic features from either modality alone. This was not unexpected, due to the aforementioned complementary nature of the imaging modalities as demonstrated in many non-radiomics studies and clinical trials , 33–35. th percentile ADC value. The only other mpMRI parameter with radiomic features in the top 10 list for the mpMRI alone model was the semi-quantitative DCE MRI parameter TTP, demonstrating the importance of perfusion imaging for detecting tumours especially when PSMA PET is unavailable.The top 10 features for each model in our study demonstrated the superior performance of PET radiomic features for tumour detection, compared to mpMRI radiomic features, and their better predictive performance than the commonly used metric SUVmax. When assessing the most predictive features from mpMRI, the ADC map ranked the highest in the combined and the mpMRI alone models, with the NGTDM coarseness texture feature being the top-ranking in both followed by the 10The tumour grading model in our study showed promising results, with high overall accuracy values across patients. The performance of the grading model was limited by the tumour detection model however, because any voxel with undetected tumour would automatically be considered benign and the tumour grading model would not classify them into high grade or low grade at all. Therefore, further development of these models with larger datasets would be required to improve individual patient performance.Several studies aiming to assess tumour grade using PET and mpMRI data can be compared with these findings. Domachevsky et al.  previousThere are limitations to our study, including the small dataset of 19 patients, with imaging performed on two different MRI scanners and five different PET/CT scanners  and maximum 8 slices (covering 3.5 cm). Hence the predictive models may not be as accurate at the apex and base, as they are at the mid-gland. Only a selection of perfusion parameters from DCE MRI was used to develop the classification models; however, incorporating other parameters, such as Ve, may have improved accuracy. In addition, the prostate was not separated into peripheral and transitional zones, which would have allowed the development of zone-specific models as the tissue differs between zones.There are no standardised rules for choosing kernel size, so a large kernel of 9 voxels in each direction was chosen to match the average tumour size on PET imaging; however, a different kernel size may have given better results and improved the detection of small tumours. Studies by Yi et al.  and ZambMachine learning models which utilise radiomic features from both PSMA PET and mpMRI in combination are better for predicting tumour location than using radiomic features from either modality alone. Model predictions could be used to inform voxel-wise dose distributions for biologically targeted radiation therapy treatment planning. Further work is warranted to externally validate such models and to improve their accuracy for classifying predicted tumour voxels into low-grade or high-grade disease to inform appropriate treatment strategies.Additional file 1. Table S1. PET/CT acquisition details. TOF Time of flight, PSF Point spread function, OSEM Ordered-subset expectation maximization. Note the Gaussian filter kernel size was not availablein the DICOM header for GE scanners. Table S2. Spearman correlation coefficients between PET SUV and MRI parameters.


Although effective and appropriate fluid management is a critical aspect of quality care during hospitalization, the widespread adoption of consistent policies that ensure adequate fluid stewardship has been slow and heterogenous. Despite evidence-based guidelines on fluid management being available, clinical opinions continue to diverge on important aspects of care in this setting, and the consistency of guideline implementation is far from ideal. A multidisciplinary panel of leading practitioners and experts convened to discuss best practices for ongoing staff education, intravenous fluid therapy, new training technologies, and strategies to track the success of institutional fluid stewardship efforts. Fluid leads should be identified in every hospital to ensure consistency in fluid administration and monitoring. In this article, strategies to communicate the importance of effective fluid stewardship for the purposes of education, training, institutional support, and improvement of patient outcomes are reviewed and recommendations are summarized. The primary goal of fluid stewardship is to optimize intravenous (IV) fluid administration, minimize the detrimental effects of inappropriate fluid administration, and thereby potentially improve clinical outcomes. Providing consistent educational content and rationalizing available fluids is also important. Successful institutional fluid stewardship practice is therefore a critical aspect of quality hospital care for many patients.Despite the importance of effective fluid management as recognized by the United Kingdom’s National Institute for Health and Care Excellence (NICE)  and otheAppropriate fluid stewardship practices are essential for optimal care in all hospitalized patients. Best practices require adequate clinician awareness, training, and education. The primary aim of this paper therefore was a fresh appraisal and discussion of the available evidence on this topic.A panel of leading practitioners and clinical researchers who are considered effective stewards of fluid management at their home institutions participated in a 2-day virtual Advisory Board meeting  to discuss the current state of fluid management practices in Western Europe and identify gaps in training and education. To ensure an ample diversity of clinical perspectives on this topic, a nurse and a pharmacist were both included on the panel. This was followed by a physical meeting held during the 10th International Fluid Academy Days meeting in Brussels, Belgium . A modified Delphi method was designed to use the collective expertise of the diverse group in answering clinically important questions and achieved consensus on several topics in accordance to the AGREE reporting checklist. This report is a result of the 3-day discussion and reflects our consensus on fluid management based on new evidence and our own individual expertise.The British GIFTASUP describe general rules about how to treat patients before, during, and after surgery . The guiThese recommended amounts of fluid and sodium are rather restrictive and represent about two-thirds of what is contained in the diets of healthy Europeans . HoweverWhile the advantages of fluid therapy are apparent in complicated surgeries, the benefits can be questioned in minor surgical procedures of short duration (< 20 min) and not associated with fluid losses . RandomiBoth balanced and unbalanced crystalloid fluids are used in the perioperative period. Several large trials of more than 1000 patients have not found that saline promotes mortality or major complications  during surgery. Balanced crystalloid fluids are preferable as many small studies of patients and volunteers report a higher number of minor complications when 2 L or more of saline is given, such as metabolic acidosis, low urine flow, nausea, and abdominal pain . The benEnhanced recovery after surgery (ERAS®) programs aim for swift recovery after surgery and recommend either near-zero fluid balance or goal-directed fluid therapy , 20. RecAd hoc groups have also been convened to publish consensus statements about fluid therapy. The POQI consensus statement on fundamental concepts in perioperative fluid management summarizes the goal of fluid therapy and hemodynamic management as providing the conditions that enable normal cellular metabolic function to produce optimal patient outcomes .In the ICU setting, the NICE guidelines recommend that the need for resuscitation fluid is best determined by using individual flow-based hemodynamic measurements . The SurGuidelines on management of massive hemorrhage recommend restricting the use of crystalloids (< 3 L in the first 6 h)  to avoidGuidance from the IFA stresses that perioperative fluid therapy in association with major surgery, as well as fluid therapy in intensive care, should follow the Resuscitation, Optimization, Stabilization and Evacuation (ROSE) conceptual model: liberal therapy is initially of benefit to preserve organ perfusion, and this should be followed by a more conservative or restrictive phase where fluid is withdrawn later in the process , 12. MorAlthough perioperative and ICU fluid management plays a crucial role in optimizing patient outcomes before, during, and after surgery, many knowledge gaps do exist. Guidelines from organizations like NICE, IFA and ERAS provide recommendations on fluid therapy, taking into account factors such as daily physiological needs, patient characteristics, and surgery duration. While the recommended amounts of fluid and sodium may seem restrictive, they are designed to accommodate patients with impaired renal function and altered fluid and electrolyte handling. Frequent monitoring of clinical status and blood tests is essential to ensure adequate fluid provision. In the ICU setting, individual flow-based hemodynamic measurements in combination with assessment of fluid (un)responsiveness are recommended to guide the need for resuscitation fluid. Trials and consensus statements highlight the importance of choosing the appropriate fluid type, with balanced crystalloid solutions being preferable to saline due to a lower incidence of complications. Ongoing trials, such as the ALBIOSS-BALANCED trial, aim to provide further insights into the use of albumin as an alternative fluid. In cases of massive hemorrhage, the use of crystalloids is restricted to avoid hypervolemia. Overall, a personalized and phased approach to fluid therapy, transitioning from liberal to conservative strategies, appears to be beneficial in optimizing patient outcomes.Considering current guidelines and evidence, local institutional practices, and survey findings, our advisory group consensus is that the NICE guidance provides basic knowledge but should be combined with education on clinical assessment and monitoring to guide individualized fluid management within the guideline framework, providing an understanding of fluid requirements in terms of maintenance, replacement, and resuscitation. When monitoring patients receiving fluids, daily body weight, daily and cumulative fluid balance, regular monitoring for renal function and plasma and urinary electrolyte concentrations may be used to assess volume status. Assessment of body fluid composition , and fluid responsiveness  can provide fluid management guidance and other non-invasive hemodynamic monitoring tools may be of help in this setting.In the ICU setting, the largest amount of fluids is not given for resuscitation (6%) but for maintenance (25%), nutrition (33%) and fluid creep (33%), the latter is the inadvertent (and sometimes unavoidable) administration of fluids intravenously when administering drugs or other therapies . NeverthReducing variability in fluid management should improve clinical outcomes particularly in terms of avoiding fluid overload/accumulation and dehydration. However, because fluid therapy is only a small part of patient’s care, studying such outcomes is fraught with difficulty, and relating outcomes such as length of stay and mortality to fluid use may be unrealistic. Fluid management in a critical care setting often involves some monitoring modalities, but even there a simple approach can go far to avoid overload and electrolyte abnormalities. The overarching purpose of fluid therapy in intensive care is to prevent organ dysfunction by maximizing organ perfusion as a tool but not a goal in and of itself.It is important to approach all decision on fluid management and provision with the understanding that fluids are drugs, with indications and contraindications and the potential for both benefits and risks of adverse events. On this note, consistent IV fluid prescribing based on NICE or IFA guidance can be improved throughout an institution with staff education, record keeping and audits to monitor practices. Limiting the availability of specific IV fluid preparations on wards has also been implemented to improve fluid prescription practices. Patient clinical chemistry values can be tracked automatically  as well as the incidence of acute kidney injury before and after the implementation of these measures . Researchttps://www.fluidacademy.org/component/zoo/item/an-introduction-to-fluid-stewardship-a-belgian-perspective.html or https://fluidacademy.mn.co/posts/an-introduction-to-fluid-stewardship-a-belgian-perspective). Variations in case-mix mean that direct comparison may not be relevant, but the resulting data can highlight variations in fluids use that require action [We propose that hospitals conduct small audit studies, focusing on patients with clear evidence of a positive cumulative fluid balance or with clinical indicators consistent with fluid overload, thereby capturing actual practice and the potential harm of fluid mismanagement. If an audit of five to ten patients demonstrates that there is a problem, much larger studies are not required before a solution is sought. At a local level, analysis of the use of fluids, can be of use in monitoring the effect of stewardship initiatives. The Belgian perspective on fluid consumption was recently presented at the 12th IFAD meeting in Brussels Type of fluids delivered Timing of administrationRate of administrationDuration of administrationRoute of administrationPotential factors contributing to fluid dilution Presence and potential role of total salt and fluid loading or accumulationWhen tracking fluid usage for the purpose of assessing appropriate use and patient outcomes, institutions should consider the following metrics:Based on our collective experience in institutions in Western Europe, we agree that there are multiple areas for practice improvements, and that some improvements may be guided by data-driven answers to lingering clinical questions. For instance, in the perioperative setting, there are evidence gaps on whether restrictive or liberal fluid administration policies are more effective, and how closely fluid resuscitation should be tied to mean arterial pressure or organ perfusion pressure. The ideal type of fluid in this setting is also an area of continued debate. In emergency and ICU settings, evidence is still lacking to guide consistent care with respect to fluid type, restrictive versus liberal fluid delivery, and the ideal management of high-risk patients. In the general hospital setting, the most important questions center on best practices for implementing and maintaining effective fluid stewardship programs. Further research is needed to develop comprehensive guidelines that encompass all aspects of fluid management, including clinical assessment of fluid status and fluid (un)responsiveness, body composition and hemodynamic monitoring, and appropriate use. The focus should be on identifying adequate indicators and monitoring strategies for evaluating the success of fluid stewardship programs. Research is required to determine the most effective educational interventions to improve knowledge and understanding of optimal fluid management practices. This includes developing training programs for healthcare professionals at all levels of care and to establish standardized methods for data collection and analysis, including the use of electronic health records, to facilitate benchmarking, quality improvement, and research in fluid stewardship. Finally, in all settings, improved tracking of data on the efficacy of fluid management practices, fluid-associated complications, and the cost-effectiveness of fluid stewardship are needed to confirm and implement best practices.There are several knowledge gaps regarding the costs implicated in current practice on fluid management. Firstly, there is a lack of comprehensive cost-effectiveness studies that evaluate different fluid therapy interventions, hindering our understanding of the economic value of these interventions. Additionally, limited research has been conducted on the actual resource utilization and wastage  associated with fluid management practices, preventing a clear assessment of the financial and environmental implications. The economic burden associated with fluid-related complications, such as fluid accumulation, electrolyte imbalances, and organ dysfunction, has not been extensively studied, making it difficult to quantify the costs involved in managing these complications. Furthermore, there is a need to evaluate the cost implications of adhering to fluid management guidelines, which can provide insights into the potential cost savings and resource optimization. Limited research also exists on the economic impact of implementing fluid stewardship programs in healthcare settings, highlighting the need for studies that assess the costs associated with program implementation and potential return on investment. Lastly, there is a lack of comprehensive health economic models specific to fluid management, which could provide a more holistic understanding of the financial aspects involved. It is important to recognize that while fluid therapy may be perceived as financially insignificant, the reality is that it requires significant resources. Factors such as the cost of fluids, administration sets, infusion pumps, but also training, auditing, and monitoring contribute to the overall financial burden. Raising awareness of these costs may serve as a financial incentive to improve fluid management practices beyond clinical justifications.Wide variations in prices for fluids and consumables in different countries make overall fluid administration cost comparisons challenging. In addition, economic circumstances may influence the degree to which staff, regular audits and pumps can be used to influence good stewardship. However, our collective experience often suggests that substantial financial savings are possible with the effective use of fluids through implementation of consistent fluid stewardship . These sIn summary, outcomes and their associated cost need to be assessed across the full hospital stay, not just in individual wards, and should include data on the use of fluids linked with data relating to adverse outcomes such as increased length of stay and need for further intervention. In addition, consistent definitions are needed for complications and the severity of potential adverse outcomes related to fluid management practices. In many cases, necessary data are already being collected for different purposes. Therefore, new methods of managing such data need to be developed to demonstrate the benefits of appropriate fluid stewardship.https://www.ehden.eu/), which aims to collect Observational Medical Outcomes Partnership-Common Data Model compatible (OMOP-CDM) data from 100 million patient records [Poor fluid management may not directly result in mortality but can impact clinically relevant outcomes such as acid–base and electrolyte disturbances, fluid accumulation and acute kidney injury, that in turn may contribute to morbidity and mortality. Considering the risk for poor outcomes, it is important for institutions to capture the potential cost benefit of good fluid stewardship. To achieve this analysis, institutions will need to rely on big data strategies derived from available data sources as opposed to snapshots of local cost data. These data sources already exist and include prescription data, laboratory records, and patient administration and information systems. In the meantime, without robust data on complication rates, a real cost-effectiveness analysis might be extremely challenging, and the best approach that can be applied is to monitor fluid use and outcomes in specific hospitals. One such example of real-world data is the European Health Data & Evidence Network (EHDEN) project responsiveness.Prescribing IV fluids properly to each patient.Evaluating and documenting changes.Monitoring the response to IV fluids.Taking further action as required.Reporting complications of fluid management or administration as incidents that require investigation, to provide a basis for learning and improvement.The NICE fluid guidelines provide a starting point for an educational program . EducatiWhen planning education, it is essential to provide a platform that meets the needs of all healthcare practitioners at all stages of their careers. In one questionnaire-based analysis of clinicians responsible for fluid management, with experience levels ranging from trainees to experienced clinicians, fluid management knowledge scores were low and most participants reported having experienced unreported fluid-related serious adverse events .To reach all clinicians involved in fluid management with education and training based on evidence-based medicine and guidelines, educators should abandon old ideas that are often based in silo thinking and understand the value of a system approach to education that improves patient care. A systems approach recognizes the roles that all specialties play in patient care and encourages open communication that avoids practices that unnecessarily separate different aspects of the fluid management team into independent component parts. To achieve this systems approach goal and to maximize staff involvement from all relevant specialties involved in fluid management, effective training should take advantage of a variety of innovative educational resources as summarized in Table Better education and training require a transformation in mindset and behavior among both junior and established clinicians. It is important to identify fluid stewards and institutional ambassadors who support not only staff education, but also change and who can obtain necessary buy-in from hospital administrators, such as medical directors. Innovative virtual learning platforms should be considered for staff convenience and flexibility. Targeted education should be provided to all staff responsible for fluid management decisions.Guidelines are valuable tools to ensure consistent fluid delivery practices after regular staff education is established. Any proposed guideline should include assessment, prescription, monitoring, fluid balance charting and regular review of clinical status.There are several opportunities for future research to optimize training and education in fluid stewardship. First, studies are needed to evaluate the effectiveness of different training and education methods and materials such as online courses, in-person training, gamification, virtual and augmented reality technologies and hands-on simulations, to enhance the learning experience and improve retention of information. Second, such studies should assess the effectiveness of training and education programs in promoting long-term behavior change and improved fluid stewardship practices. Third, studies should strive to identify the best practices for implementing training and education programs in different organizational settings and the potential benefits of collaboration and information sharing between organizations for improving fluid stewardship practices. Finally, it is important to better understand the impact of leadership and management on the effectiveness of training and education programs.Current fluid management training and education remains insufficient, and government advocacy may be needed to increase attention to best practice in this area. Adequate staff training requires planning and can be achieved through dedicated learning days or other strategies, but it needs to be ongoing. To ensure that any educational program is truly comprehensive, new technologies and multiple platforms should be employed, both to maximize impact and to provide avenues for attendees to connect and share ideas on best practices. For review, a summary of this panel’s recommendations is available in Table The practice scope of health care providers involved in fluid management varies widely, from the complicated and risky administration and monitoring of fluids in the critical care setting to procurement, quality improvement and evidence-based research projects.Monitoring (daily and cumulative) fluid balance is an integral component of fluid therapy and good patient care; it can identify potential problems and allow for earlier escalation when required. A trigger point on a fluid balance chart that supports fluid delivery decision-making is important for the identification of suboptimal or increased fluid intake or output. These trigger points should be highlighted in an institution-specific educational program that emphasizes the importance of early warning scores (EWS) and strategies for an appropriate response. Patient information leaflets encourage patients and their relatives to be aware of their fluid needs and explain IV therapy. Self-monitoring of intake is possible for some patients.Identify clinical leads within the organization, including medical , nursing, and pharmacy staff Fig. .Obtain agreement from relevant clinical areas of the organization, e.g., anaesthetics and ICU; renal medicine; endocrine medicine; gastro-intestinal medicine and nutrition; other medical specialties; general surgery; orthopedics; and maternity.Obtain agreement and support from organizational management .Agree on choices of standard fluids. Ensure suitable arrangements in pharmacy, and appropriate stock levels on wards.Finalize local guidance with the core fluid team.Educate prescribers, including those who are in training.Educate nursing staff.Establish institutional policies for the use of pumps and other infusion systems, and ensure that adequate equipment is available.Review prescription and monitoring documentation.Clearly communicate local guidance.Implement local guidance.Audit prescribing, administration and clinical issues related to fluids, and provide feedback to relevant staff on all wards.Re-educate relevant staff as appropriate, and consider amending policies in response to observed challenges.A consistent approach to teaching fluid therapy based on established guidelines, as well as strong fluid stewardship, should help reduce prescriber confusion when faced with the need to prescribe fluids in different patient scenarios. As we have discussed, all institutions should consider a commitment to effective fluid stewardship at the local level. Institutions that have yet to implement standardized fluid stewardship should consider the following steps for success Fig. :IdentifyOnce fluid stewardship is implemented, metrics for recording education for relevant staff should include number of learners taught and accessing e-learning modules, assessment results, and ultimately whether prescribers are following guidelines, as determined by information from snapshot audits and fluid usage data Fig. .Does my patient need fluid or is there a benefit of fluid administration? Remember that the best fluid may be the one that has not been administered unnecessarily.If so, why? i.e. Is it for maintenance, replacement of losses, or resuscitation, or do they require fluid restriction? Is there body compartment redistribution?Which fluid should be used in these differing scenarios?How much should I give to the patient, when, and for how long ?For the attending clinician, the process of fluid prescription may be condensed to 4 questions:When to stop IV fluids? When shock has been resolved. This question addresses the risks of ongoing fluid administration.When to start fluid de-escalation? ?When to start fluid removal (deresuscitation)? When the presence of fluid accumulation or global increased permeability syndrome has a negative impact on end-organ function. This question addresses the benefits of fluid removal .When to stop fluid removal? This question addresses the risks of fluid removal After starting an IV fluid, the next questions that should be addressed are:Prescriber: makes a clinical decision regarding fluid management.Prescription: is written, accounting for Drug, Dose and Duration.Pharmacist: checks the prescription for inconsistencies.Preparation: the prescribed fluid is prepared with any necessary additions .Patient: the fluid is administered to the patient; the process, response and follow-up management is handled by fluid stewards.To expand on these questions, consider the “Five P’s” of effective fluid prescriptions:Finally, all staff responsible for fluid management should regularly monitor patients for the appropriateness of fluid prescriptions, including initial patient assessment, decisions on fluid indication, fluid prescription, and regular fluid management. These stages for checking on the appropriateness of IV fluid therapy are summarized in Table https://www.fluidacademy.org/ and also via the new portal https://fluidacademy.mn.co. The IFA offers program details, presentations, and other resources from past and upcoming IFA Day (IFAD) events. IFA also maintains a blog through the site on relevant clinical and practice topics [International Fluid Academy (IFA): e topics , 45.https://criticalcarenorthampton.com/turning-the-tide/. Offered by Critical Care Northampton, this resource offers “useful downloads and resources in order to promote excellence in the field of IV fluid safety. The section is run and managed by various UK experts within the field, hailing from backgrounds within Intensive Care, Anaesthetics, Specialist Nursing and pharmacy, to name a few.” [Turning the Tide:  a few.” .https://elht.nhs.uk/services/pharmacy/fluid-stewardship. This online resource offers brief videos that review the concept of fluid stewardship, fluid resuscitation, routine maintenance fluids, fluid replacement and redistribution, and fluid balance [Fluid Stewardship .  balance .In addition to our recommendations and those we have reviewed, there are multiple resources available to support staff who are responsible for instituting and maintaining effective fluid stewardship practices within their organizations:Multiple researchers and clinicians have embedded physiologic models for fluid management in complex simulation software. The SAPHIR  project approached the integration of renal, cardiac, and respiratory function systems, with circulating volume playing an important role . The worHigh-fidelity simulations and other opportunities for in situ experiences could be valuable tools for fluid management training. As innovation continues and younger populations enter medical training, new options like gaming-based platforms may also be explored to further promote the potential for technology-based education.Gamification or serious games for training has been used in other clinical areas with success, and appears to be at least as effective as traditional learning, and may be more effective for improving knowledge, skills, and trainee satisfaction . HoweverFluid stewardship is defined as a series of coordinated interventions, introduced to select the optimal fluid, dose, rate and duration of therapy that results in the best clinical outcome, prevention of adverse events, and cost-effectiveness. Guidelines are available that standardize fluid management, and effective fluid stewards can be identified for every hospital ward to ensure consistency in delivery and monitoring. Fluid use data and biochemical markers may be used to maintain hospital administration support for training, education, and standardized fluid management protocols. Clinical outcomes data may be used on a small scale to demonstrate the value of fluid prescription. Finally, innovative e-learning and electronic simulation tools are increasingly available to support training and education. Rather than awaiting further research results, it is advisable to take proactive action and immediately initiate and implement fluid stewardship in your hospital and ICU.Recent surveys and evidence of practice gaps suggest additional education is needed on the appropriate administration, timing, dosage, duration, rate, de-escalation, and monitoring of fluids in all hospital settings and wards. Therefore, effective fluid stewards should be identified in every hospital to ensure consistency in fluid administration and monitoring. We describe best practices for fluid stewardship and review strategies to communicate the importance of effective fluid stewardship programs for the purposes of education, training, institutional support, and improvement of patient outcomes.


Heart failure (HF) affects 6.9 million patients in the United States with 700,000 deaths per year. Following an index admission, hospitalization occurs in 24% of patients at 30 days. Despite improvements in medical therapy and the development of services specifically designed to reduce occurrence, the rate of rehospitalization remains high.Volume overload, and the development of hemodynamic and clinical congestion drive symptoms and hospitalizations in patients with HF. Because of altered cardiac physiology and capillary Starling forces in patients with HF, the interstitial compartment and lymphatic system are disproportionately large reservoirs of fluid accumulation, increasing up to 3 to 4 times that of the intravascular compartment.Because of disproportionate reservoir capacity of the interstitial space, measurement of interstitial pressure is attractive for the evaluation of fluid status. In patients with HF, interstitial fluid pressure (IFP) correlates with indicators of impaired cardiac function, including reduced cardiac output and increased right atrial pressure, and the point of transition from sub-atmospheric to supra-atmospheric IFP is marked by rapid accumulation of interstitial fluid.NXT Biomedical is developing IFPx, a minimally invasive remote monitor that adds IFP to the multiparameter data offered by ICMs . This te1-blocker  and 0.9% NaCl  until a central venous pressure of >15 mm Hg was maintained. Fluid was then removed to euvolemia via ultrafiltration (3 hours). Concurrent measurement of IFP using the Guyton capsule and invasive hemodynamics  was made using standard interventional techniques. IFP, measured in the thoracic location, increased, and decreased with invasive hemodynamic measures during fluid loading and ultrafiltration, respectively (We have performed pilot studies with classic Guyton capsules that have shown a relationship between hemodynamic pressures and IFP in acute porcine models of fluid overload (Home Office License: PP1785781). Briefly, under general anesthetic, Yorkshire pigs were implanted with Guyton capsules and housed for 14 days. Under terminal anesthetic, animals were given 12 to 16 mg/kg per hour of a cardio-selective βectively . As suchThe IFPx sensor is a is a minimally invasive, subcutaneously implantable device that has been developed to address deficiencies in current remote HF monitoring technology and provide quantitative, absolute fluid status assessment alongside the multiparameter data offered by insertable cardiac monitors. The IFPx device connects a chronically stabilized Guyton capsule to a commercial, digital, temperature-compensated pressure transducer and is inserted via a small incision in the subcutaneous tissue with an insertion tool, in a 5-minute procedure identical to that of commercial ICMs. The multiparameter dataset includes IFP, heart rate, physical activity, and sleep incline and is relayed to a cloud-based digital health system without patient interaction. Furthermore, the IFPx device is removable in the same fashion as ICMs. The IFPx device will facilitate the provision of proactive, individualized treatments for patients with conditions of significant fluid overload, including HF.