Fluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. We compared the ability of traditional regression techniques and different ML-based modeling approaches to identify clinically meaningful fluid overload predictors. This was a retrospective, observational cohort study of adult patients admitted to an ICU ≥ 72 h between 10/1/2015 and 10/31/2020 with available fluid balance data. Models to predict fluid overload  in the 48–72 h after ICU admission were created. Potential patient and medication fluid overload predictor variables (n = 28) were collected at either baseline or 24 h after ICU admission. The optimal traditional logistic regression model was created using backward selection. Supervised, classification-based ML models were trained and optimized, including a meta-modeling approach. Area under the receiver operating characteristic (AUROC), positive predictive value (PPV), and negative predictive value (NPV) were compared between the traditional and ML fluid prediction models. A total of 49 of the 391 (12.5%) patients developed fluid overload. Among the ML models, the XGBoost model had the highest performance  for fluid overload prediction. The XGBoost model performed similarly to the final traditional logistic regression model . Feature importance analysis revealed severity of illness scores and medication-related data were the most important predictors of fluid overload. In the context of our study, ML and traditional models appear to perform similarly to predict fluid overload in the ICU. Baseline severity of illness and ICU medication regimen complexity are important predictors of fluid overload. Timely de-resuscitation to remove excess fluid is associated with improved outcomes6. While the predictors of volume responsiveness are well-established8, particularly in patients with sepsis10, the predictors for ICU fluid overload remain unclear. Development of rigorous fluid overload prediction algorithms could shorten the time to the implementation of fluid overload mitigation strategies  and improve outcomes.Fluid overload, a frequent and unintended consequence of the resuscitation process in critically ill adults may result in increased rates of acute kidney injury and invasive mechanical ventilation initiation, prolonged intensive care unit (ICU) stay, and mortality11. This score has also been shown to predict mortality and length of stay and also the medication interventions needed to optimize a patient’s pharmacotherapy regimen19. Therefore, quantifying patient-specific, medication-related data is likely an important consideration in the prediction of fluid overload in critically adults21.Non-diuretic ICU medication use may affect fluid overload risk; preliminary data suggests the medication regimen complexity-ICU (MRC-ICU) score is associated with both fluid overload and fluid balance23. Artificial intelligence and machine learning techniques have been proposed as a method to improve ICU clinical outcome prediction given their unique ability to handle multi-dimensional problems and identify novel patterns within the vast troves of continuously-generated patient data26. However, to some ICU clinicians, the use of artificial intelligence/machine learning approaches to predict clinical events may have a ‘black-box effect,’ which can ultimately preclude implementation. The rigorous evaluation of whether artificial intelligence-based approaches predict clinical events better than traditional regression models  remains a key question in critical care practice31.Event prediction in the ICU remains a perennial area of research given the many challenges that exist for clinicians to accurately predict clinical outcomes in the highly complex and dynamic critical care environmentIn this study, we sought to compare the ability of machine learning approaches to traditional regression models to predict fluid overload and the individual predictors for its occurrence in critically ill adults. We hypothesized that advanced machine learning techniques perform better than traditional regression models to predict fluid overload and that the predictors for fluid overload identified through machine learning approaches may be different.32. The reporting of this study adheres to the STrengthening and reporting of OBservational data in Epidemiology statement33.We conducted a retrospective, observational study of adults admitted ICUs at the University of North Carolina Health System (UNCHS), an integrated health system, who had fluid overload data available. The protocol for this study was approved with waivers of informed consent and HIPAA authorization granted by UNHCS Institutional Review Board . Procedures followed in the study were in accordance with the ethical standards of the of the UNHCS Institutional Review Board and the Helsinki Declaration of 1975, as most recently amendedA random sample of 1000 adults (≥ 18 years) admitted to an ICU at UNCHS between October 2015 and October 2020 was generated. Patients on their index ICU admission with fluid balance data available for the first 72 h were included  Fig. 34. For example, a patient with a body weight of 100 kg at ICU admission having a positive fluid balance at 72 h of 12,000 mL (or 12 kg) would be considered to have fluid overload. A secondary outcome was the amount of fluid overload as a function of body weight. For example, the aforementioned patient would have a fluid overload amount of 12%.De-identified UNCHS electronic health record (EHR) data  housed in the Carolina Data Warehouse (CDW) was extracted by a trained CDW data analyst. The primary outcome was the presence of fluid overload at the 48–72 h  after ICU admission. Fluid overload was defined as a positive fluid balance in milliliters (mL) greater than or equal to 10% of the patient’s admission body weight in kilograms (kg)38. A total of 28 potential predictors were identified: 1) ICU baseline: age ≥ 65 years, sex, admission to a medical  ICU, primary ICU admission diagnosis , and select co-morbidities ; 2) 24 h after ICU admission: APACHE II and SOFA score , use of supportive care devices , serum laboratory values , fluid balance (mL), and presence of acute kidney injury ; 3) Medication data at 24 h: MRC-ICU score, vasopressor use in the first 24 h, use of continuous medication infusions, and the number of continuous medication infusions.Following a literature review, and through investigator consensus, potential predictor variables for fluid overload were definedDue to the hypothesis-generating nature of our study and the lack of published data on ICU fluid overload prediction, no attempt was made to estimate a study sample size. The 991 patients were split into training and testing datasets using a 80:20 ratio. We assumed data was missing at random (MAR)  and therefore chose Multiple Imputation by Chained Equation (MICE), rather than complete case analysis or simple imputation, as the most appropriate approach to address missingness. Ten imputations per variable were therefore applied for all missing data in the training and testing datasets to generate multiple imputed training and testing datasets SDC Fig. .41. During the model training on each of the ten imputed training sets, fivefold cross validation was applied for Random Forest, SVM and XGBoost, using their most appropriate R package44, to choose the hyperparameters for these machine learning models that resulted in the highest prediction accuracy. Each of these models was then fitted on the corresponding imputed training set, and predictions for probability of fluid overload were made on each of the ten imputed testing sets using the corresponding optimal model. During this phase, hyperparameters were tuned. For Random Forest, two hyperparameters were tuned (number of trees and number of variables randomly sampled as candidates at each split). For SVM, linear kernel and cost of constraints violation were tuned. For XGBoost, two hyperparameters were tuned (maximum depth of a tree and maximum number of boosting iterations). For each model, ten different predictions were generated on ten different imputed test sets. These predictions of the probability for fluid overload were averaged as the final prediction.We employed Random Forest, SVM, and XGBoost for the task of modeling the presence of fluid overloadFor the degree of fluid overload, we built models with the amount of fluid overload at 72 h. Since this is a continuous variable, we employed their regression of the above machine learning models: Random Forest regression, SVM regression, and XGBoost regression. For XGBoost, feature importance was measured as the frequency a feature was used in the trees. For Random Forest, feature importance was measured by mean decrease in node impurity. Because ten different models were used on each imputed dataset, ten different feature importance lists were generated for each. A subsequent analysis modeling fluid overload as a continuous variable (percent of net milliliters of fluid by body weight) instead of dichotomous presence or absence of fluid overload) was performed  and their 95% confidence intervals (CI) were reported.Subsequently, a full logistic regression model was built for the presence of fluid overload for each of the ten complete training sets. We then applied backward elimination to select the final model. The initial set of variables for the variable selection were determined by the significance of variables in the ten full models by multivariate Wald testingFor each regression model, ten different predictions were generated on ten different imputed test sets as well. These predictions of the probability for fluid overload were averaged as the final prediction. We compared the variables selected through backward selection with the top five variables chosen by Random Forest .A total of 49 12.5%) of the 391 included patients had fluid overload on ICU day 3. The degree of day 3 fluid overload was significantly greater in the fluid overload (vs non overload) patients . Overall, the mean APACHE II score was 15.7 ± 6.6, mean SOFA score was 8.3 ± 3.3, and MRC-ICU score was 11.8 ± 8.7. A significantly greater proportion of fluid overload patients (vs. those without) had an elevated serum lactate ≥ 2 mmol/L  and AKI  at 24 h and positive fluid balance  on ICU day 3. All model covariates are summarized in Table 2.5% of tAmong the machine learning models, XGBoost demonstrated the highest AUROC (0.78) compared to SVM (0.69) and RF (0.76) and was associated with a PPV of 0.27 and NPV of 0.94. Notably, all models tested at relatively poor PPV. In comparison, stepwise logistic regression had an AUROC of 0.70, PPV 0.26, and NPV 0.94. Full results are reported in Table Feature importance graphs were plotted for XGBoost Fig. , RF SDC and SVM When variables selected through backward selection were compared with the variables chosen by the Random Forest model, we found MRC-ICU at 24 h to be highly correlated with sex-male, number of IV continuous infusions to be highly correlated with sex-male and age—≥ 65), fluid balance at 24 h (mL) to be highly correlated with admission diagnosis-sepsis/septic shock, laboratory values-serum bicarbonate, and age—≥ 65 , which includes the number of intravenous medication infusions (the primary method to administer medications in this population and a primary source of fluids to a patient), to be an important contributor to fluid overload. In comparison, in the traditional multivariable regression, the MRC-ICU score was not associated with fluid overload. This may be because machine learning analyses better account for severity of illness and the response of clinicians to respond to this severity by administering more medication infusions leading to a more complex daily medication regimen; however, the methods applied, including feature importance, preclude causal inference at this juncture. As such, our results highlight the unique power of machine learning to identify complex relationships that can be further elucidated via machine-learning based causal inference modeling and other designs aimed at causation20.We report that machine learning and logistic regression analyses demonstrate a similar predictive power to identify patients with fluid overload on day 3 of their ICU stay. Although use of machine learning did not appear to improve predictive performance over regression analysis, it expanded the number of variables critical to fluid overload prediction and highlights the importance of further artificial intelligence-based exploration in this area. This analysis of individual predictors may help bedside clinicians better understand how the machine learning models work and may help overcome their ‘black box’ hesitancy to trust machine learning-generated results35. After an initial 24—48 h period characterized by overt volume resuscitation  and IV medication initiation , and the associated fluid administration, the care priority shifts from volume administration to volume removal. While comprehensive fluid stewardship management strategies including reduced fluid use and diuretic administration can effectively reduce fluid overload and its sequelae, they are often deployed too late2. Interestingly, some reports have indicated ‘hidden fluids’  were significantly associated with the development of fluid overload. During critical illness many of these ‘hidden fluids’ are necessary , given that intravenous medications account for over 40% of total fluid intake in this analysis, interventions such as concentrating intravenous medications, employing oral formulations when feasible, careful evaluation of maintenace fluids, and antibiotic de-escalation are potoentially still viable even in high illness severity that can reduce this complication. However, weighing risks and benefits associated with these interventions in this context may be aided by more quantitative prediction data57. Overall, de-resuscitation and fluid stewardship can be deceptively complex53. In a patient with shock, balancing the dueling forces of volume responsiveness assessment and timely volume resuscitation with the risks associated with fluid overload represents a highly complex Goldilocks scenario that requires clinicians to have high clinical precision, essentially pivoting ‘on a dime’, from a strategy of aggressive volume expansion to one of rapid volume removal55.Optimizing fluid management (or fluid stewardship) has been previously defined by the ROSE model of Resuscitation, Optimization, Stabilization, and dE-resuscitationDespite the complexities of this decision process, limited prediction tools for fluid overload are available to assist clinicians at the ICU bedside. As such, real-time recognition identifying when to make the shift from resuscitation to de-resuscitation has the potential to improve bedside management. However, to go beyond the hourly assessment of ‘Ins and Outs’ would require accurate prediction of future fluid overload risk and the adverse events associated with it, in the time-dependent context of intervention delivery . In such a scenario, an algorithm would be able to accurately interpret a septic patient who is 3 L positive 24 h after fluid resuscitation initiation as being in a ‘green zone’ . However, 24 h later, if the same patient is 4 L positive while off vasopressors and with down-trending sepsis markers the algorithm could alert clinicians that the patient is now in a 'yellow zone' where interventions like diuretic therapy and fluid reductions are required to reduce acute kidney injury and intubation risk. This type of real-time predictive capability could support continuous clinician decision-making but requires evaluation outside the scope of our current study.intervenable event in the ICU. Intervenable events share three key characteristics: they are predictable, preventable, and otherwise associated with poor outcomes. The results of our study, and others, indicate that fluid overload can be predicted with modeling of some kind, especially given its ability to be quantitatively defined58. Fluid overload has been associated with poor outcomes including acute kidney injury, delirium, poor respiratory outcomes, prolonged length of stay, and potentially increasing mortality62. Evidence demonstrates the timely recognition and management of fluid overload is feasible and is associated with reduced mortality and time in the ICU64. Notably, fluid stewardship has been adapted by critical care pharmacists as key component of comprehensive medication management65. As such, these results may support other investigations as they identify patients in whom it is safe to initiate de-resuscitation or importantly never needed that degree of fluid volume initially and at the bedside may prompt clinicians to be more targeted in therapies initiated or aggressive in curtailing early ‘hidden’ fluids to avoid the complications of fluid overload and/or the need for a highly interventional period of de-resuscitation . Artificial intelligence may be particularly well suited to bolster these efforts, and thus while feature importance analyses cannot provide foundation for causal inference, they may guide such future investigations.Fluid overload also presents an important test case for exploring and adapting artificial intelligence methods to ICU problems, particularly those related to ICU medication use. Fluid overload represents a uniquely 66. Future studies applying this approach to alternative, larger datasets  should be considered to examine the external validity of our findings. Although MICE is the established approach to address missingness in cohort studies that includes variables that are a composite of several individual patient-specific values (e.g. SOFA), it is possible that some of the values in the imputed datasets that represented our new ground truth may not have been accurate67. Bias may exist due to which patients had fluid balance data available. Other predictors for fluid overload not included in our models may exist68. By relying on prediction data derived in the first 24 h of ICU admission, we did not fully capture the dynamic nature of critical illness over the entire three day ICU period before fluid overload occurred. Future time-dependent evaluations of changing features employing unsupervised learning techniques may yield novel insights.Our study has limitations. Our patient sample may have been too small to demonstrate superiority of the machine learning approaches compared to traditional regression, and no validation in a separate, external dataset was undertaken at this junctureFluid overload is an important, intervenable event in the ICU population. Incorporation of medication-related variables and artificial intelligence has demonstrated promise to improve prediction that may ultimately guide timely intervention and mitigation of this ICU complication; however, comparative advantages over traditional modeling techniques may remain warranted.Supplementary Information.


The paper presents the application of a strain gauge sensor and a viscous brake filled with an electrorheological (ER) fluid, which is a smart material with controlled rheological properties, by an electric field to the fluid domain. For experimental tests, a cylindrical viscous brake was designed. The tests were carried out on a test stand especially prepared for this purpose and suitable for the examination of the impact of the rotational speed of the input shaft and the value of the electric voltage supplied to the viscous brake on pressing forces, taking into account the ER fluid temperature and brake fluid filling level. On the basis of the experimental research results, a viscous brake control system to exert constant pressing forces with feedback from a strain gauge sensor, based on the programmable logic controller, was designed and implemented. This system, using its own control algorithm, ensured a control pressing force within the assumed range, both during the constant and follow-up control. The measurement results obtained during the tests of the viscous brake designed to exert a force were presented in the form of time courses, showing the changes of the pressing force, the electric voltage applied to the brake and the rotational speed of the brake input shaft. The developed ER fluid brake control system with feedback was tested for constant and follow-up control, taking into account the impact of the working fluid temperature. During the test it was possible to obtain a maximum pressing force equal to 50 N for an electric voltage limited to 2.5 kV. The resultant error was lower than 1 N, wherein the adjustment time after changing the desired value of the force was around 1.5 s. The correct operation of both the brake and the control system, as well as the compatibility of the pressing force value and time adjustment, were determined. The main technical contribution described in this article is the design of a new type of DECPF and a new method for its control with the use of a specifically programmed programmable logic controller which simulates the proportional-integral controllers’ operation. In many technical devices it is necessary to exert a constant pressing force in order to, e.g., make measurements at a constant load or to obtain the required belt tension. The most used components for these purposes are pneumatic cylinders, hydraulic cylinders or mechanisms which use the pressing force of a deflected spring. However, new solutions are being sought due to the disadvantage of these methods of exerting constant pressure . The new solutions include the use of powder brakes , brakes MR and ER fluids are new materials whose rheological properties can be influenced. The fluids change their shear stress in milliseconds under an applied magnetic or electric field. This ability of MR and ER fluids allows for the control of hydraulic devices by means of changing the electric current.The device to exert a constant pressing force (DECPF) presented in this article creates new possibilities of exerting a constant pressing force with the use of a viscous brake with an ER fluid. In this device, the pressing force is exerted on the object by a lever which is attached to a mounted brake housing. The brake’s input shaft is connected to the shaft of the electric motor. Different values of the pressing force can be obtained by changing the angular velocity of the electric motor and by applying the high voltage of the electric current applied to the electrodes of the viscous brake. Thus, the advantage of this solution is a direct use of electricity, which enables the easy implementation of digital control. A significant disadvantage of the device in some applications may be the low durability of the ER fluid. In addition, due to the fact that all the power from the electric motor is converted to heat, it is necessary to take into account the influence of temperature on the properties of the ER fluid during brake control.Consequently, the main technical contribution described in this article is the design of a new type of DECPF and a new method for its control with the use of a specifically programmed programmable logic controller (PLC) which simulates the proportional-integral (PI) controllers’ operation.The ER fluids used in practice are two-phase fluids (heterogenous) consisting of solid particles of the base fluid and different additives ,6,7. SolClutches and brakes containing a working fluid are divided into two basic groups: viscous and hydrodynamic. In viscous clutches and brakes, the driving part and driven part are connected as a result of the shear stress occurring within the working fluid. There are two basic types of viscous clutches and brakes, disc and cylindrical ones, depending on the shape of the driving and driven parts. The driving and driven parts of hydrodynamic clutches and brakes are rotors which contain blades. These parts are connected to each other as a result of the hydrodynamic impact of the working fluid on the rotor blades . Tan et The cylindrical viscous clutch with the ER fluid, which is used to control the rotational speed of a DC electric motor, is described in the publication . The braChoi et al.  reportedOlszak et al.  and ŻuroIn a rehabilitation device with a cylindrical viscous brake with ER fluid ,37, two Choi et al.  reportedThe construction scheme of the mechanical part of the DECPF is shown in The viscous brake operates in such a way that the torque is transferred from the driving part to the driven part by friction, which results from the shear stresses occurring in the ER fluid. The value of the shear stresses can be changed by changing the electric field strength. The electric field is generated between the cylinders  of the driving and driven part. The cylinders are fixed on the shafts by sleeves made of plastic and, thus, are isolated from each other.An increase in the high voltage applied to the electrodes causes an increase in the electric field strength. This, in turn, causes an increase in the shear stress in the ER fluid and, thus, an increase in the transmitted torque. The pressure of the end of the lever on the object increases as a result of the increase in the torque value. The pressing force is measured by a force sensor. The temperature of the ER fluid is measured using a temperature sensor located in the brake filler cap . To maint: the pressing force F, the angular velocity of the electric motor ω, the control voltage U (supplied from the high-voltage power supply), the leakage current gi and the ER fluid temperature T. The measurement results are recorded with the use of a computer measurement system containing a PC with real-time measurement data recording software. The basic data of the electrical and electronic components used to construct the test bench is presented in Experimental research on the DECPF is carried out on a test stand specially built for this purpose. It consists of an electric drive motor, a high-voltage power supply, a PC and a control system using a PLC . The tesF and the angular velocity ω of the input shaft of the viscous brake with ER fluid, for high voltage U = 0 and U ≠ 0. During the study of these relationships, the angular velocity ω and the high voltage U are changed in time linearly and stepwise. Then, tests are carried out to find how the temperature of the ER fluid and electrical breakdowns affect the characteristics of the DECPF.The first stage of the bench tests is to find the relationship between the exerted pressing force ω, the velocity is increased every 5 rad/s and kept constant. This allows the inertia effect to be reduced. Due to a significant increase in the temperature of the working fluid above an angular velocity of 80 rad/s, the tests are carried out in a range from 0 to 80 rad/s. The electric motor is controlled by the inverter so as to obtain the required constant angular velocity ω in a short time.In order to obtain a linear change in the angular velocity F, which occurs for U = 0, depends on the viscosity. t of: the angular velocity ω, the pressing force F and the temperature T of the ER fluid. F increases with increasing angular velocity ω, while the ratio of angular velocity to force ω/F ranges from 15 (rad/s)/N to 20 (rad/s)N. The reason for the deviations in the linear dependence of the force F on the angular velocity ω  is the increasing temperature of the ER fluid.The performance of the DECPF is significantly affected by the dynamic viscosity of the ER fluid. Choosing a low viscosity of ER fluid allows for a wide control range for the DECPF, because the smallest value of the pressing force F for step changes in the angular velocity ω of the input shaft of the viscous brake with ER fluid. Due to the fact that it is not possible to change the angular velocity ω in an infinitely short time, F at 60 rad/s may be caused by phenomena occurring for higher angular velocities during the acceleration of the brake discs in a very short time (0.2 s). Slippage may occur in the boundary layer between the discs and the ER fluid. Then, the angular velocity of the ER fluid close to the disc surface is lower than the disc’s angular velocity. In addition, during the acceleration of the discs to a higher angular velocity, the centrifugal force acting on the solid particles is greater, which may cause local changes in the composition of the ER fluid.ωk and time constant ωT) are determined by treating the waveforms of the force F for individual angular velocities  that the waveforms of the force F shown in UT and steady-state gain factors Uk are determined. The time constants UT are determined on the basis of the time for which the value of the pressing force is 0.63 of the maximum pressing force F value. The values of the identified time constants UT and steady-state gain factors Uk are presented in aT of the electric power supply is 0.18 s for a voltage spike of 2.5 kV, while the steady-stage gain factor is 0.7 kV/V.T on the pressing force F is determined for two temperature ranges: from 25 °C to 38 °C and from 35 °C to 45 °C. Due to the intense heat release during the operation of the viscous brake with ER fluid, it is not possible to maintain a constant temperature for a long time during the measurements. These ranges and the maximum angular velocity at which the tests are conducted (60 rad/s) are determined on the basis of preliminary research. When the ER fluid reaches the lower temperature of the range , the applied high voltage U is increased every 0.1 kV from 0 kV to 3.5 kV. The high voltage U is maintained at a constant level for several seconds in order to stabilize the measurement of the force F. The measurement series is finished when the working fluid temperature reaches the upper value of the range. The dependence of the pressing force F on the electric voltage U for selected temperature ranges is shown in F. This is connected to the increase in shear stresses.The influence of the working fluid temperature F for voltages U over 3 kV, which are caused by electrical breakdowns.gi flowing through the ER fluid within the working gaps of the brake. When the current exceeds the 100 mA limit of the power supply value, there is a decrease in the value of the electric high voltage and, subsequently, a decrease in the electric current intensity. After that, the voltage is automatically restored by the control system of the high-voltage power supply to the desired value. The cycle is repeated, which results in a series of breakdowns. During the initial DECPF research it was noticed that the reason for the increase in frequency of electrical breakdown occurrences may be that the ER fluid does not fill the entire volume of the viscous brake. After emptying the brake, electrical breakdowns occur at a voltage below 3.0 kV. When the brake is filled with the working fluid, the breakdown voltage exceeds 3.5 kV. In order to confirm the occurrence of this phenomenon, the brake is filled to 90 % when operating with the use of working fluid. For a constant angular velocity ω increased every 5 rad/s, the high voltage U is also increased every 0.1 kV up to 3.5 kV. The constant value of the voltage U is maintained for several seconds in order to stabilize the voltage and the measured pressing force.Electrical breakdowns can cause the destruction of solid particles in the ER fluid as a result of a step increase in temperature in the area of the electric spark flashover. Factors which increase the risk of electrical breakdowns might include high temperature of the ER fluid, high surface roughness of the electrodes and humidity and contamination of the ER fluid . OccurreT of the ER fluid is also measured. The course of the voltage U in time t is shown in bU for which the electric breakdowns occurred, as well as their respective pressing forces F for constant angular velocities. The values that have a random nature are read from charts made for 90% brake filling. bU and the pressing force F increase with the increase in rotational speed ω of the input shaft of the viscous brake with ER fluid. The increase in the value of the electric voltage bU (at which breakdowns occur) with the increase in the angular velocity ω can be justified by the centrifugal force. The centrifugal force pushes the air bubbles towards the rotation axis via the ER fluid, whose viscosity is much higher than the viscosity of air. In this way, within the working gaps there is less air which has a resistance lower than the resistance of the ER fluid. That renders it difficult for the spark-over to pass through the gaps. The analysis of the temperature course T does not indicate that the temperature of the ER fluid increases significantly due to electrical breakdowns, which is a result of the high thermal inertia of the viscous brake with ER fluid.The temperature U for selected constant values of the angular velocity Fω. After the preliminary tests, it is assumed that the values of the angular velocity Fω depend on the desired pressing force, as shown in dF partially overlap. This means that the change in the rotational speed Fω from one of the selected values to another is less frequent. In order to limit the inertia effect, it is assumed, based on preliminary tests, that the change in angular velocity from one selected value to another occurs with an acceleration of 20 rad/s2. The scheme of the DECPF control system is shown in Based on the analysis of the literature and the obtained DECPF step responses, it is assumed that the control of the pressing force is implemented in a closed-loop control system by means of changes in the electric high voltage F is measured with the use of a load cell sensor, pressed by a lever connected to the output shaft of the brake. The resultant error e is the difference between the desired force dF and the measured force F. The resultant error e is connected to the input of the PI controller. The output from the PI controller is the control quantity u described by the relation:Pk is the steady-state gain proportional coefficient and Ik is the integral factor.The closed loop pressing force control system uses a digital regulator which contains proportional and integral control terms. The regulator does not contain the differentiator term because, according to publications ,36, its u is applied to the high-voltage power supply’s input. The output signal U of the high-voltage power supply is supplied to the brake, along with the angular velocity ω of the viscous brake shaft.The control quantity Pk, Ik of the PI regulator, two heuristic methods developed by Ziegler and Nichols are taken into account. However, the PI controller settings obtained by these methods needed to be corrected using test-based manual selection [Pk and Ik are presented in While choosing the coefficients election ,44. The dF is increased from 10 N to 60 N (in steps of 5 or 10 N), then kept constant, and then decreased. The time of maintaining a constant value of the set force dF is selected in such a way as to achieve the control goal for an acceptable duration of the experiment. The change in the dF desired value to the next fixed value depends on the regulation time of the measured force F. Pk = 0.05, Ik = 0.2 and T = 50 °C. F is not reached for the angular velocity ω of 60 rad/s. Increasing this force as a result of increasing the high electric voltage U is not possible due to the electric breakdowns occurring more often as the temperature increases . In order to obtain the desired course of the force F, it is assumed, on the basis of the tests, that if the high voltage U ≥ 2.5 kV then the angular velocity Fω is increased with an acceleration of 2.5 rad/s2 from zero up to 10 rad/s. The scheme of the thus corrected control system is shown in dF for ER fluid temperatures over 40 °C, because then the high voltage reaches 2.5 kV for a time not longer than 1 s.In order to assess the operation of this control system, the value of the desired force ω and for identical values of the high voltage U is caused by the random nature of electrorheological phenomena occurring within the ER fluid, and by the temperature changes. These are the reasons why a complex feedback control system is required to maintain a constant pressing force F. ωT and UT have similar values. However, this is due to the fact that the brake power in the tested range does not exceed 1.2 kW, while the power of the test bench drive motor is 5.5 kW. It should be assumed that in the DECPF used in practice, the differences between the brake power and the electric engine power will be significantly smaller. This means that the pressing force F changes would be achieved faster by changing the value of the electric high voltage U than by changing the angular velocity ω. For this reason, the selected control variable is high voltage U.The lack of repeatability of the exerted pressing force for identical values of the angular velocity e (reaching 50 rpm) occur only when the angular velocity Fω is changed. During the adjustments, the time in which the rotational speed reaches the desired angular velocity Fω ranges from 0.8 s to 0.95 s. The stepwise desired angular velocity ω is stabilized by the inverter after ca. 1 s, due to the necessity of limiting the torque shock changes in the electric motor. The average absolute value of the difference between the desired and measured voltage during the adjustment is 17 V, while during the steady-state operation of the power supply it is lower than 0.5 V. During the pressing force control, the resultant error e is lower than 1 N, wherein the adjustment time after changing the desired value of the force dF is around 1.5 s. Considering that in the control system  for the electric voltage limited to 2.5 kV. The applied control adjustment (Fω at the maximum electric voltage (2.5 kV) allowed us to reach the desired pressing force dF, despite the occurrences of electrical breakdowns within the range of higher temperatures. The obtained courses of the measured physical quantities indicate that the components of the electric motor control system and the high-voltage power supply for the DECPF control system were selected correctly.In the developed control system, the maximum values of the resultant error l system  there isjustment  consistiF, in the absence of electric voltage U, is proportional to the angular velocity ω. Its maximal value measured during the tests is 44 N for a rotational speed of 130 rad/s. Applying electric high voltage U to the electrodes of the viscous brake with ER fluid causes an increase in the pressing force F. The lower the angular velocity ω, the greater the increase in the pressing force F. For a rotational speed of 10 rad/s, the increase in the pressing force F caused by applying an electric voltage U = 3.5 kV is 500%, while for an angular velocity of 60 rad/s it is 35%. The temperature of the ER fluid significantly influences the value of the pressing force F. It was found that maintaining the required temperature of the ER fluid in the range of 45 °C to 55 °C is possible for an angular velocity of ω < 60 rad/s.The pressing force value The random nature of changes in shear stresses within the ER fluid, the significant influence of temperature on the value of these stresses and the occurrence of electrical breakdowns are the reasons why it is necessary to use a feedback control system. The developed feedback control system with the PI controller ensured a controlled pressing force in a wide range could be obtained.Due to the intense heat release during the operation of the viscous brake with the ER fluid and due to the increase in the brake temperature (which causes changes in the shear stresses of the ER fluid), it would be beneficial to use an additional brake cooling system in order to stabilize the temperature.When designing the DECPF, it should be taken into consideration how the filling degree of the viscous brake with the ER fluid influences the occurrence of electric breakdowns. All working gaps should be completely filled with ER fluid, which would increase the stability of the control system operation.Further work on the development of the DECPF structure should focus on designing new control methods to shorten the control time in which several applications of ER fluids associated with proper control methods could be realized in practice.On the basis of the results of the DECPF tests, the following conclusions were reached:It is finally worth noting that the number of recent research works on application systems or devices utilizing ER fluid is considerably less than those on application systems or devices using MR fluid. The causes may stem from several issues, including the lack of commercial products using ER fluid, the lower field-dependent yield shear stress of ER fluid itself compared to MR fluid, the high input voltage required to activate chain-like structures of the particles and so forth. This research trend can provide new inspiration or creative insight to many scholars or potential readers for successful practical realization in the near future.


Most childhood meningitis nowadays is caused by viruses in countries with widespread use of conjugate vaccines. There is limited knowledge about outcomes following childhood viral meningitis, necessary to inform clinical guidelines.Children aged 19-42 months in Canterbury New Zealand, who had enterovirus (EV) or human parechovirus (HPEV) meningitis as infants were included. Clinical data was collected. Comprehensive neurodevelopmental assessments were completed using the Bayley Scale for Infant Development (BSID) Third Edition. Mean composite and scaled scores, and proportion below cutoff were assessed in each domain. Normative mean composite scores for any domain is 100. Expected scaled score range is 8-12. Mean scores between EV and HPEV meningitis were compared by t-tests, with P< 0.05 considered significant.BSID assessments were completed for 33 children  of mean age 31 months, from 2019-22, including 23 with EV and 10 with HPEV meningitis. There were 24 children of NZ European, 4 Māori, 4 Asian, and 1 Middle Eastern ethnicity. Mean age at diagnosis was 43 days, and mean length of admission was 2.79 days (CI 1.74-3.15). Of 33 children, 32 (97%) received intravenous or intramuscular antibiotics, 6 (18%) received a fluid bolus on arrival at hospital, and 9 (27%) required high dependency or intensive care. Following viral meningitis, parents reported developmental speech concerns in 6 children, and delayed motor milestones in 1 child. There was no reported sensorineural hearing loss. BSID mean composite scores were in expected range for cognition 102 (CI 98-106), language 96 (93-100) and motor 102 (98-106) domains. Mean scaled scores were in expected range for receptive language 9 (9-10), expressive language 9 (9-10), fine motor 11 (11-12) and gross motor 9 (9-10) domains. Overall, 12/33 (36%) children had below expected scores in one area of development including cognition , receptive language , expressive language , and gross motor . There were no significant differences between BSID scores in EV and HPEV meningitis.Following viral meningitis, 36% percent of preschool children had findings below expected in at least one developmental domain, suggesting targeted follow-up should be considered.Manish Sadarangani, BM BCh, FRCPC, DPhil, GlaxoSmithKline: Grant/Research Support|Merck: Grant/Research Support|Moderna: Grant/Research Support|Pfizer: Grant/Research Support|Sanofi Pasteur: Grant/Research Support|Seqirus: Grant/Research Support|Symvivo: Grant/Research Support|VBI Vaccines: Grant/Research Support


Patient-derived xenograft (PDX) models involve the engraftment of tumour tissue in immunocompromised mice and represent an important pre-clinical oncology research method. A limitation of non-small cell lung cancer (NSCLC) PDX model derivation in NOD-https://github.com/EpiCENTR-Lab/PATHOverview).The immunophenotype of lymphoproliferations arising in the lung TRACERx PDX pipeline were characterised. To present the histology data herein, we developed a Python-based tool for generating patient-level pathology overview figures from whole-slide image files; PATHOverview is available on GitHub . Analysis of immunoglobulin light chain gene rearrangements in three tumours where multiple tumour regions had resulted in lymphoproliferations suggested that each had independent clonal origins. Overall, these data suggest that B cell clones with lymphoproliferative potential are present within primary NSCLC tumours, and that these are under continuous immune surveillance. Since these cells can be expanded following transplantation into NSG mice, our data highlight the value of quality control measures to identify lymphoproliferations within xenograft pipelines and support the incorporation of strategies to minimise lymphoproliferations during the early stages of xenograft establishment pipelines.  UnderWe have recently reported the outcomes from a PDX generation pipeline initiated from multi-regional primary NSCLC tumour tissue  within thttps://clinicaltrials.gov/ct2/show/NCT01888601). Animal studies were approved by the University College London Biological Services Ethical Review Committee and licensed under UK Home Office regulations (P36565407).Ethical approval to generate patient-derived models was obtained through the Tracking Cancer Evolution through Therapy (TRACERx) clinical study  mice were anaesthetised using 2–4% isoflurane, the right flank was shaved and cleaned before tumour tissue in growth factor-reduced Matrigel (BD Biosciences) was injected subcutaneously using a 16G needle. Mice were observed during recovery, then regularly monitored for tumour growth. Mice were kept in individually ventilated cages under specific pathogen-free conditions and had ad libitum access to sterile food and autoclaved water. Tumour monitoring was performed twice per week and tumour measurements taken in two dimensions using callipers. When xenograft tumours formed, mice were terminated before tumours reached 1.5 cm3 in volume. Mice without xenograft tumours were terminated at a median of 306 days (range 37-402 days). Successfully engrafted tumours were propagated by injection of minced xenograft tissue in matrigel into a new host, with banking of FFPE tissue, OCT-embedded tissue and DNA at each generation. Cryopreservation of patient material and xenograft tissue at each tumour transfer was performed in foetal bovine serum plus 10% DMSO.Tissue from patients undergoing surgical resection of non-small cell lung cancers was immediately transported on ice from theatres to a pathology laboratory where it was dissected for diagnostic and then research purposes. Region-specific tumour samples were dissected by a consultant pathologist such that the tissue used to generate patient-derived xenograft (PDX) models was spatially adjacent to the tissue that was sequenced in TRACERx. Individual region-specific tumour samples were transported to the laboratory in transport medium consisting of MEM alpha medium (Gibco) containing 1X penicillin/streptomycin (Gibco), 1X gentamicin (Gibco) and 1X amphotericin B . Tumour tissue was minced with a scalpel rather than dissociated to single cell suspensions to preserve local cytoarchitecture. In some cases, tissue was cryopreserved prior to implantation. To generate PDX tumours, male non-obese diabetic/severe combined immunodeficient (NOD/SCID/IL2Rγhttps://github.com/EpiCENTR-Lab/PATHOverview).Formalin-fixed paraffin‐embedded (FFPE) tissue sections were routinely obtained at PDX passage by fixation of tumour fragments (approximately 3x3x3 mm in size) in 4% paraformaldehyde overnight at 4°C. Samples were stored in 70% ethanol at 4°C before being processed and embedded in paraffin. FFPE tissue sections of PDX tumours and their equivalent primary tumour region were subjected to hematoxylin and eosin (H&E) staining or immunohistochemistry using the following antibodies: anti-human CD45 , anti-keratin , anti-CD3 , anti-CD20 , anti-CD30 , anti-CD56 , anti-CD79A , anti-CD138 , anti-BCL2 , anti-BCL6 , anti-MUM1 , anti-IgL , and anti-IgK . Optimization of the antibodies and staining conditions was carried out on sections of human tonsil. Immunostaining was performed using the automated BOND-III Autostainer  according to protocols described previously . This toIn situ hybridization for detection of the EBV-encoded small RNAs (EBER) was performed using the EBER Probe . The technique was carried out on an automated stainer platform  according to the manufacturer’s instructions.2 and IGL with 2.5mM MgCl2. Cycling conditions were: 95°C for 7 min followed by 35 cycles of 95°C for 30s, 60°C for 30s, 72°C for 30s with a final extension at 72°C for 10min. PCR products were imaged on a 1% agarose gel.DNA was extracted from xenografts using either the PureLink Genomic DNA Mini Kit (Invitrogen) or the DNA/RNA AllPrep Kit (Qiagen). PCR for IGK, Kde and IGL along with a positive control ladder reaction was performed on 35-120 ng DNA utilising the BIOMED-2  protocolCryopreserved xenografts were dissociated by mashing through a 30 μm cell strainer and blocked using 10% foetal bovine serum in PBS. Cells were resuspended in staining buffer consisting of 50% PBS containing 1% bovine serum albumin (BSA) and 50% Brilliant Stain Buffer (BD Biosciences) and incubated for 20 minutes at 4°C in the following antibodies: anti-CD45 , anti-CD3 , anti-CD19 , anti-CD20 , anti-IgK  and anti-IgL . Cells were washed and re-suspended in flow cytometry buffer (PBS + 1% BSA) for flow cytometry. Cells were analysed using a BD LSRFortessa X-20 Cell Analyser  and data were analysed in FlowJo (v10).via standard MTA agreements. PATHOverview, the Python-based tool used to generate histology overview images in this manuscript, has been made available via GitHub (https://github.com/EpiCENTR-Lab/PATHOverview).Biological materials, including xenograft models generated within this study, will be made available to the community for academic non-commercial research purposes via the lung TRACERx study from patients undergoing surgical resections, with no patients having received neoadjuvant therapy  regions generated lymphoproliferations compared to 6/60 lung squamous cell carcinoma (LUSC) regions , while two lymphoproliferations arose from other NSCLC histologies  to assess the EBV status of these lymphoproliferations. All lymphoproliferations were positive for EBER  light chain restriction, two showing lambda light chain restriction and in the remaining five, co-expression of both kappa (IgK) and lambda (IgL) light chains was observed  and at lHistological characterisation showed that lymphoproliferations arising from different spatial regions of the same primary tumour shared similar morphology and immunophenotype in all three cases for which multiple lymphoproliferations were available. However, distinct Ig light chain restrictions in lymphoproliferations arising from the same tumour indicated the likely independent origins of the lymphoproliferative xenografts in two cases for which immunohistochemistry was possible  – a herpes virus which persists as a life-long, asymptomatic latent infection in more than 90% of the human population and exhibits tropism for B lymphocytes  – suggesOne approach to preventing the formation of lymphoproliferations might therefore be to quantify EBV RNA in starting material to filter out regions most likely to contain these B cells. Our finding that cryopreservation has little impact on NSCLC PDX take rate while not selectively favouring either tumour or B cell proliferations, would allow a time window in which to implement this approach . HoweverIn conclusion, post-transplant-like diffuse large B cell proliferations were a frequent outcome in our NSCLC xenograft study using NSG mice. Here we present the characterisation of these lymphoproliferations, including several cases in which lymphoproliferations were derived from spatially distinct regions of the primary tumour. In these cases, we demonstrate that the proliferations likely arise from distinct cells of origin. None of the patients from which these lymphoproliferations were derived suffered haematological malignancy diagnosis with 3.5 years of follow up. This suggests that there are multiple B cell clones with lymphoproliferative potential within a primary NSCLC tumour and that there is continuous surveillance of these cells within the tumour. Our data highlight the value of quality control measures to identify lymphoproliferations within xenograft pipelines and support the incorporation of strategies to minimise lymphoproliferations during the early stages of xenograft establishment pipelines.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by NHS Research Ethics Committee (13/LO/1546). The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by UK Home Office.DP, AA, RD, EK and RH performed experiments. DP, EK, AH, MS, DM, TK and RH performed data analysis. RD, KS, VR and AA provided reagents, protocols and training. VR, DM and TM performed histological analysis. Animal work was performed under SJ’s animal license. NM, TM, CS and RH supervised aspects of the project. DP and RH wrote the first draft of the manuscript. All authors contributed to the article and approved the submitted version.


Guan X, Ma F, Xu B. Pooled analyses of randomized controlled trials on pyrotinib plus capecitabine and a rethink of the first‐line options for HER2‐positive relapsed or metastatic breast cancer. Cancer Innovation.2022;1‐5.In the funding information, the Grant/Award Number of National Nature Science Foundation of China was incorrect. The Grant/Award Number should be “82103634”.We apologize for this error.


These two compounds are long-chained fatty acids, differing solely in the configurations of two double bonds of their conjugated tetraene fluorophore. In this work, we employed all-atom and coarse-grained molecular dynamics simulations to study the behavior of c-PnA and t-PnA in lipid bilayers of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) and 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), representative of the liquid disordered and solid ordered lipid phases, respectively. All-atom simulations indicate that the two probes show similar location and orientation in the simulated systems, with the carboxylate facing the water/lipid interface and the tail spanning the membrane leaflet. The two probes establish interactions with the solvent and lipids to a similar degree in POPC. However, the almost linear t-PnA molecules have tighter lipid packing around them, especially in DPPC, where they also interact more with positively charged lipid choline groups. Probably for these reasons, while both probes show similar partition (assessed from computed free energy profiles across bilayers) to POPC, t-PnA clearly partitions more extensively than c-PnA to the gel phase. t-PnA also displays more hindered fluorophore rotation, especially in DPPC. Our results agree very well with experimental fluorescence data from the literature and allow deeper understanding of the behavior of these two reporters of membrane organization.Fluorescence probes are indispensable tools in biochemical and biophysical membrane studies. Most of them possess extrinsic fluorophores, which often constitute a source of uncertainty and potential perturbation to the host system. In this regard, the few available intrinsically fluorescent membrane probes acquire increased importance. Among them,  Fluorescence-based spectroscopic and microscopic methods are tools of paramount importance in biophysical and biochemical research , and in However, fluorescence emission requires the existence of convenient fluorophores. While most proteins exhibit natural fluorescence due to the presence of aromatic residues, this is not typically the case with membranes, which are, for this reason, often labeled with extrinsic probes . Many sucis- and trans-parinaric acids . While the former two classes of probes have been studied in detail using atomistic MD simulations  and saturated 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), respectively. The structures of both probes and host lipids are shown in Often, extrinsic fluorescent probes possess polar fluorophores, such as nitrobenzoxadiazole or rhodamine-based ones, which do not deeply penetrate lipid bilayers. Even if attached to the end of an alkyl or acyl chain, opposite to the head groups, these polar (and often bulky) fluorescent moieties tend to orient toward the water/lipid interface. Thus, they do not report on the hydrocarbon core where they were supposed to reside and are prone to inducing significant local perturbation of bilayer properties. Apolar fluorescent probes that insert lipid bilayers deeply include pyrene and pyrene-lipids, diphenylhexatriene and its trimethylamino derivative, and polyene probes g., Refs ,11,12,13g., Refs ,15,16,17s [t-PnA . While tc-PnA is a naturally occurring fatty acid, found in the seeds of the Makita tree (Atuna excelsa) from southeast Asia and southwestern Pacific islands, among other sources, while t-PnA may be prepared by sunlight isomerization of c-PnA, catalyzed by iodine [4 M−1cm−1) and broad emission with maximum near 410 nm in methanol [y iodine . As showmethanol . While tmethanol ), it incmethanol .t-PnA exhibits a very long lifetime component in the gel phase , they remain useful probes of lipid order, as evidenced in a recent protocol [The spectroscopic properties of the membrane-inserted parinaric acids include a marked increase in fluorescence quantum yield, lifetime and anisotropy below the main transition temperature of one-component phospholipid systems . Their fat 25 °C ). Time-rprotocol .c-PnA and t-PnA into both lipid phases, to relate to their partition behavior.In this paper, we carry out MD simulations to better understand the behavior of the two parinaric acid probes (and notably their similarities and differences) in both gel and fluid membranes. Two types of MD studies were carried out: all-atom simulations, to obtain insights on location, orientation and dynamics of the probes, as well as their effects on host lipid bilayer and interactions established with lipids, solvent and solution counterions; and complementary coarse-grained (CG) simulations, which aimed to determine the free energy profiles associated with the insertion of As will be further described in Lβ’ phase in evidence) and between the two probes . However, for a thorough comparison, extensive analysis of the different trajectories is required. As a preliminary step, we monitored the temporal variation of the carboxylate groups of each individual probe to verify that they are stable throughout the runs, mostly without systematic variations from the early stages of the simulations  extent to that of one double-chained phospholipid, then an increase of up to 1% could be expected for a membrane with 100 lipid and 2 probe molecules per leaflet. This occurs in all cases, with the marginal exception of c-PnA in POPC .Aside from observation of discrete instant configurations, the most immediate analysis of a membrane MD simulation is the calculation of the average area per lipid ectively a, in vera, its global value is not directly affected by inclusion of few amphiphilic molecules, such as parinaric acids, but may only be changed as a result of local effects of their insertion on the lipid molecules. We calculated the bilayer thickness as the distance between the average planes of the lipid P atoms in opposite leaflets  to the host bilayer. However, the variations are not significant, as expected for this low probe content. In DPPC, both probes increase dPP to values which could be marginally significant. At this stage of analysis, different possible explanations could be raised, such as ordering of the DPPC acyl chains  or a decrease in their tilt.As is well known, the bilayer thickness is highly negatively correlated with the area per lipid. Moreover, unlike ets dPP; b. In POPa and dPP. Still, it should be noted that the effects on all these bulk properties are very slight, confirming that parinaric acids are very mild probes in this respect, as hypothesized since the earliest published membrane studies using them [We also calculated deuterium order parameter profiles, which are depicted in ing them .t-PnA, and at a slightly deeper one for c-PnA. As seen in t-PnA molecules virtually span the entire membrane leaflet, almost reaching the depth of the POPC sn-1 terminal carbon atom . At variance, the cis configuration of the C9–C10 and C15–C16 bonds in c-PnA impedes the chain from reaching the innermost region of the fluid POPC bilayer. Still, the fluorophores of the two probes are located at a similar depth, near that of the C9–C10 double bond in the sn-2 chain of POPC.We now turn from lipid to probe properties. The average transverse locations of different regions of the probes are shown in t-PnA has a slightly shallower location than that of c-PnA. In any case, because of the increased bilayer thickness of DPPC, the carboxylates of both probes are positioned more deeply, in relative terms, than in POPC. Conversely, the opposite ends of the two probes have similarly deep locations. For t-PnA in DPPC, both below and above the main transition temperature, a deep location was inferred experimentally from fluorescence quenching experiments using fatty acids bearing a quencher doxyl spin label attached at different positions along the chain [Because of the considerably less dynamic nature of the DPPC gel phase bilayer, different individual probe molecules tend to display distinct behaviors. This translates into a larger uncertainty in their averaged locations, expressed in he chain . The morAnother way to look at the transverse location of the probes is to examine the mass density profiles across the bilayer normal, shown in c-PnA and t-PnA in POPC are clear, with the latter reaching deeper into the bilayer, similarly to the host lipid  and 32.5° (sn-2)) and more narrow for DPPC  and 32.1° (sn-2), in excellent agreement with the experimental value of (32.0 ± 0.5)°, measured at 19 °C [c-PnA chain adopts an orientation distribution very similar to those of the phospholipids , indicating an easy fitting of this probe within the hydrocarbon region of the membrane. In comparison, the t-PnA chain distribution is displaced to shorter angles , suggesting that this probe is not as easily accommodated in fluid bilayers as c-PnA and that its insertion may induce a slight ordering of the host lipid acyl chains, which is actually compatible with the very small increase in bilayer thickness observed for the t-PnA/POPC system . In POPC system b.t-PnA has a narrow distribution (even more so than those of DPPC), with a peak at 30° and an average of 28.4°. In contrast, c-PnA has a clearly wider distribution, with a slightly higher average (28.9°) but lower peak (27°). For t-PnA, our distributions closely resemble those experimentally obtained in the gel phase [For DPPC, both probes display the orientation distributions which are displaced to lower angles compared to those of the phospholipid. el phase  and calcel phase  by Fernael phase , and difsn-2 chain in the presence of t-PnA (average tilt reduced by 0.5°) in POPC and for both chains in the presence of c-PnA in DPPC . This is expected because of the relatively low probe concentration in the bilayer, which implies that most lipid acyl chains do not come into contact with probe molecules during the simulations. Still, these reductions in overall acyl chain tilt correlate with increases in bilayer thickness for the same systems , those of molecules with no probes in their immediate vicinity (R > 1.2 nm) and those in an intermediate situation (0.7 nm < R < 1.2 nm), as shown in t-PnA, a small extent of induced ordering is apparent , in agreement with the lower average tilt of this probe. Conversely, for c-PnA, the lipid tilts are virtually unaffected , which agrees with the essentially identical chain tilts for this probe and POPC. More significant effects are observed for DPPC bilayers. Although the peak and average tilt angles are not affected by the presence of either c-PnA or t-PnA , a widening of the sn-1 chain distributions is apparent for lipids closest to the probe molecules. This effect is more marked with c-PnA. For this probe, the distribution variance increases from 33.5 deg2 for R > 1.2 nm to 64.4 deg2 for 0.7 nm < R < 1.2 nm and 85.1 deg2 for R < 0.7 nm. In comparison, the corresponding variation for t-PnA ranges from 37.3 deg2 to 49.6 deg2 to 51.4 deg2. This means that the standard deviation of the distribution increases by 59% from DPPC molecules with no c-PnA in their vicinity to those with c-PnA neighbors, while the corresponding increase factor for t-PnA is only 17%. These variations reflect the wider distribution of c-PnA’s own tilt angles compared to t-PnA, which induce a similarly wider range of DPPC acyl chain tilts in nearby lipid molecules.Although, for simplicity, the lipid chain tilt distributions shown in  systems b. For a c-PnA and t-PnA are closely linked with the interactions that probe molecules establish with both the surrounding lipids and solvent. To obtain insight on these interactions and how they may differ for the two probes, we calculated the atom–atom radial distribution functions (RDFs) of several system components around them in POPC . This observable is a measure of the polarization of the emission, which decays over time because of rotation experienced by the fluorophore. In an MD trajectory, rotation can be assessed by calculating rotational autocorrelation functions (ACFs):As mentioned in the Introduction section, one property of particular interest in the behavior of t + ξ, and P2 is the second Legendre polynomial. Averaging is carried out over both ξ and the eight simulated molecules of each species. Because the electronic transition dipole vector of linear conjugated polyenes, such as PnA, is roughly oriented along the direction of the C9–C16 vector [C(t) is expected to be approximately proportional to r(t) [r0, the fundamental anisotropy, which is very close to the maximum value of 0.4 for the PnA probes (see the discussion below).In this equation, θ(ξ) is the angle between the fluorophore long axis (the vector between C9 and C16 atoms of PnA) at times ξ and 6 vector , C(t) is to r(t) ,43, the c-PnA and t-PnA have similar rotational dynamics when inserted in the fluid POPC bilayer. This is not the case in DPPC, especially for c-PnA. The very slow dynamics of the gel phase implies that each molecule undergoes rotation with distinct kinetics, and in this case, averaging over a large number of molecules is required for an accurate calculation. While the relatively small number of molecules simulated here still implies relatively high uncertainty, we are reasonably confident that their average behavior may be compared with the experimental results and discussed, at least qualitatively.The average curves were analyzed with a triexponential function with a finite residual termThe best fit curves are also shown in c-PnA, two exponential terms with similar amplitudes (one in the sub ns and the other in the ns time range) were recovered in POPC, while for DPPC, the exponential terms were in the ns and 100 ns time ranges. In both cases, there was no improvement in the goodness of fit when allowing a third exponential. Such an additional term was, however, recovered in the t-PnA fits. In any case, the most prominent feature of these functions is the residual term, a∞. The existence of such a term is also observed in the anisotropy decay analysis of membrane-inserted fluorophores, and it is commonly interpreted as arising from “wobbling-in-cone”-type hindered rotation [a∞ is relatively low, as expected from the fluid disordered nature of the bilayer. However, in DPPC, a∞ is very high because of the considerable hindrance to rotational motions in this solid ordered membrane. In both bilayers, rotation of the fluorophore of t-PnA is more impeded than that of c-PnA, to the extent that t-PnA is almost rotationally frozen in DPPC, except for a short amplitude tumbling.In all systems, rotational reorientation occurs in the nanosecond timescale. For rotation ,42,44. It-PnA in DPPC, very close to the maximal theoretical value of 2/5, and a residual component r∞ of 0.35 at 30 °C, confirming the limited extent of rotation of this probe in the gel phase. In another study [t-PnA in POPC at 30 °C  and a residual value r∞ = 0.14. A rough comparison of these limiting anisotropies with our a ∞ values may be performed by multiplying the latter by 2/5, yielding estimates of 0.37 and 0.10 for DPPC and POPC, respectively, in fair agreement with the experimental values. Our rotational parameters for t-PnA also agree very well with the Brownian simulation estimates of Fernandes et al. [c-PnA, r∞ values of 0.04–0.07 and 0.30–0.34 have been reported for fluid and gel phase lipids, respectively [a∞ values by 2/5 would yield estimates of 0.01 and 0.28, respectively, somewhat below those values. However, it should be noted that the gel phase value, in particular, is subject to increased uncertainty, as commented above, and the fact that a long correlation time of 120 ns (with amplitude a2 = 0.050) is recovered for this system may justify the small difference, since very long decay times are numerically strongly correlated with limiting asymptotic values. In any case, our calculations seem to confirm the experimental observations of larger rotational hindrance of the fluorophore of t-PnA compared to that of c-PnA in the same systems. The lower residual anisotropy of c-PnA has been attributed to its non-linear shape, namely an extra depolarizing motion consisting of a rotation of the chain around its axis in addition to the other motions that occur [t-PnA and c-PnA have slightly different locations in both lipid systems  position of the center of mass of molecule i of a given species, and the averaging is carried out over all molecules of this kind and time origins t0 using trajectories with fixed center of mass of the monolayer where the solute is located to eliminate noise due to fluctuations in the center of mass of each bilayer leaflet.We also addressed the translational diffusion of both probes in the bilayer plane in comparison to the host lipid in each case. For this purpose, we first calculated the two-dimensional mean squared displacements (MSD), defined bylatD using the Einstein equation.In turn, MSD can be used to estimate the lateral diffusion coefficient latD values of (5.5 ± 1.1) × 10−8 cm2s−1, (11 ± 3) × 10−8 cm2s−1 and (16 ± 1) × 10−8 cm2s−1 for POPC, c-PnA and t-PnA, respectively. It must be stressed that the significance of MSD plots and accurate calculation of lateral diffusion in membranes remains, to a great extent, a controversial problem. It depends largely on the available time window [−8 cm2s−1 from c-PnA compared to t-PnA may be tentatively linked to the kinks in the chain of the former that result from its cis double bonds, which may cause slight hindrance in its lateral translational motion.2s−1 and  ± 1 × 10e window . Sampline window , and thee window , which wlatD values of the order of ~10−10 cm2s−1 for the different species. Still, it must be stressed that a clear linear regime is not obtained for the MSD time variation, and even for DPPC (the species for which sampling is obviously more extensive), the slope seems to be still decreasing for longer times, in qualitative agreement with reports of latD as low as ~10−12–10−13 cm2s−1 for DPPC supported on mica [latD values. Therefore, in this system, we refrain from a quantitative analysis and note only that, while lateral diffusion of c-PnA and DPPC seems to occur on a similar timescale .Turning to DPPC, we observe that diffusion in the gel phase occurs much more slowly than in the fluid phase see , as is w on mica . From thimescale , that ofy slower . This coy slower e and a lr /RT), where ΔG(w→l) is the free energy for solute transfer from the water to the equilibrium location in the lipid bilayer, equal to the difference between the desorption and the insertion energy barriers. These Kp estimates may, in turn, be compared to experimental measurements (sn-glycero-3-phosphocholine in the experiment), but mostly due to different underlying reference states.We can use the barrier heights of urements . It shouc-PnA and t-PnA have similar values of Kp from water to the fluid phase; (ii) the partition coefficient of t-PnA to the gel phase is significantly higher than that of the same probe to the fluid; (iii) t-PnA has a much higher Kp from water to the gel phase (by a full order of magnitude) compared to c-PnA. The only comparison that does not hold well concerns the difference in Kp values of c-PnA between the two phases , but even in this case, there is no gross disagreement. On the whole, MD simulations reproduce the main features of the partition behavior of the two probes between water and both solid ordered and fluid disordered phases.Still, relative comparisons are possible using the PMFs of the ionized form, the one expected to predominate for COO positions in the water medium and the interfacial regions of the bilayer, up to the equilibrium position. For example, both experimental and computational sets of data indicate that: (i) t-PnA and c-PnA were performed using the Ligand Reader & Modeler tool [Ka in water solution lies in the 4.8–5.0 range obtained from the Natural Products Magnetic Resonance Database, NP-MRD  method  with a 1lgorithm , and no t-PnA and c-PnA for Martini 2.2 was carried out by adapting the parameters used for fatty acids. Further details are given in the All CG simulations were carried out using the Martini 2.2 force field , runningThe starting structures, fully hydrated POPC and DPPC bilayers, were built with the insane.py script to set up the Martini bilayer systems . Both bi- bead of the PnA molecules to the center of mass (COM) of the lipid membranes, calculated with a cylinder pull geometry [−1 and a force constant of 500 kJ mol−1 nm−2. From this simulation, the initial snapshots were obtained, separated by 0.1 nm, for a reaction coordinate window from −4 nm to 4 nm for POPC and from −5 nm to 5 nm for DPPC. PMF profiles were obtained for the deprotonated and protonated states of both PnA molecules. The initial frames obtained for the deprotonated PnA were also used for the sampling simulations of the protonated PnA molecules.The PMF profiles were obtained by sampling the behavior of two PnA molecules in each of the POPC and DPPC bilayers. The reaction coordinate was defined as the distance from the COOgeometry . PnA mol−1 nm−2 force constant applied to the COO− bead of the PnA molecules. The set of pulling/sampling simulations was performed in triplicate.For each umbrella window, sampling simulations were run for 200 ns using a harmonic umbrella potential with a 3000 kJ molThe convergence of the PMF profiles was tested as described in . From thFor the visualization of structures and trajectories, VMD software  was used .c-PnA and t-PnA, inserted in both the gel phase (DPPC) and fluid phase (POPC) bilayers. Our CG simulations allowed us to obtain free energy profiles, which reproduced the experimentally observed extensive partition of t-PnA for the solid ordered phase compared to c-PnA. On the other hand, both probes partitioned to fluid bilayers to a similar extent, as also verified experimentally. From the calculated free energy profiles, relatively low energy barriers were inferred for the translocation across the bilayers of both probes in the neutral form, in accordance with their well-known fast equilibration between bilayer leaflets.In this work, we carried out an MD simulation study of fluorescent probes t-PnA molecule than around the more twisted c-PnA isomer. In POPC bilayers, this has little bearing on partition because the fluid phospholipid acyl chains accommodate well the tail of c-PnA, which actually shows an orientational distribution very close to that of the host lipid. However, in the thicker DPPC membrane, c-PnA is forced to adopt a slightly lower position for its carboxylate. Overall, this limits the favorable interactions between c-PnA carboxylates and lipid choline groups and cationic counterions compared to t-PnA, which, combined with the difference in lipid packing around the two probes, may explain the disparity in the partition coefficient of the two probes in this environment.We carried out long all-atom simulations, which were extensively analyzed to provide the clues for this and other known features in the behavior of the two probes, which only differed in the configuration of two double bonds (C9–C10 and C15–C16) along their fatty acid chains. We verified that host lipids are more tightly packed around the almost linear c-PnA and t-PnA exhibited different rotational motion kinetics. Our calculated rotational autocorrelation functions for the fluorophore axis agree well with fluorescence anisotropy decays, namely the higher residual component for t-PnA, especially in DPPC. The linear shape of the t-PnA fluorophore implies a reduction in degrees of rotational freedom, which, conjugated with the tighter lipid packing, justifies the observed difference. Actually, both rotational and translational motions of t-PnA are highly impeded in DPPC, which is also in accordance with the long intensity decay component typical of this probe in gel phases.Experimentally, it was observed that c-PnA and t-PnA are highly valuable fluorescent membrane probes. Absorbance in the ultra-violet and propensity for oxidation, leading to bleaching, are the obvious drawbacks in microscopy studies, but they can be minimized, e.g., by using multiphotonic excitation [c-PnA is a naturally occurring compound, with large relative availability. It is an excellent probe, especially for the fluid phase, where its perturbation of host lipid properties is minimal. While its partition to the gel is less extensive than that of t-PnA, it is still much higher than those of probes with extrinsic fluorophores, which are often excluded from solid ordered phases. On the other hand, t-PnA is simply a distinctive probe in this regard. While a considerable amount of experimental data are available for the two probes, the present study allows an unprecedentedly deep insight on atomic-scale properties, which help explain their behavior when inserted in lipid bilayers. This novel information may contribute to a timely reappraisal of these very useful probes of lipid order. While we chose for this study simple one-component lipid systems, based on representative phosphatidylcholines that exist in stable fluid or solid phases at room temperature , other host lipid compositions, with varying head groups and acyl chain lengths/saturation degrees, as well as containing cholesterol, may be explored in future studies.Overall, our study confirms that citation . c-PnA i


Contemporary data from the UK Get it Right First Time1 suggest high average reoperation rates of around 19 %. Current tumour localization techniques can only guide surgeons to the tumour epicentre, but fail to provide identification of the boundary between tumour and normal tissue. Imaging techniques, such as intraoperative ultrasonography (IOUS), intraoperative MRI (iMRI) or fluorescence-guided surgery (FGS), enable visualization of the tumour in its entirety and may provide improved operative precision2–5.In breast-conserving surgery (BCS), the tumour is removed with the goal of preserving as much healthy breast tissue as possible. Breast conservation comes with a risk of positive resection margins, an independent predictor of ipsilateral tumour recurrence, necessitating reoperationIn April 2022, a literature review was performed exploring localization and identification modalities in BCS. The PubMed electronic database was searched for the highest-quality evidence available for each modality.1. By definition, all localization techniques depend on accurate identification of the target lesion by the radiologist. Challenges include localization and mapping of ductal carcinoma in situ that is not detected by ultrasound imaging6. Similarly, lobular carcinoma is difficult to detect on mammography and ultrasound examination, and thus MRI guidance is often required6.Localization techniques involve radiological insertion of a tumour guidance system, followed by surgical resection. These identify/mark the tumour core, which offers surgeons an approximate geolocation, yet they are unable to provide information about the tumour extent or tumour–normal tissue interface, and are therefore associated with a high average reoperation rate8. Patients, however, find wires uncomfortable, they are prone to displacement and need to be inserted immediately before surgery. This strain on radiology services disrupts patient flow between imaging departments and theatres, delaying operations8.In the standard wire-guided localization (WGL) technique, a wire is placed in the core of the tumour. Bracketing wires can be used to describe the approximate size of the lesion or multiple lesions within proximity8. A Cochrane Review7 confirmed that WGL is comparable to RSL and ROLL in terms of positive margins and reoperation rates, but the latter are associated with improved patient comfort, hospital flow, or surgical precision. More recently, a ferromagnetic seed , a reflector to electromagnetic radar signals , a radiofrequency tag , and an electromagnetic signature for triangulation  have entered the market8–10. Seldom used techniques include cryo-assisted localization (freezing) and carbon track localization , which enable tactile and visual feedback, respectively8. Seeds are somewhat difficult for localization of multifocal lesions in close proximity (less than 2 cm), as independent signals would be indistinguishable2. Thus, localization techniques help pinpoint the tumour core; but they fail to provide a visual representation of the healthy cancer tissue boundary (Table S1).A variety of alternative localization techniques have been developed to address these flaws. Radio-occult lesion localization (ROLL) comprises injection of a radioactive colloid into the tumour, whereas a radioactive seed is implanted in radioactive seed localization (RSL)3–5. IOUS is highly operator-dependent and iMRI  requires the patient to be in the same position as during the preoperative scan. Both delay the operation, while scanning takes place, so FGS might be a more suitable option2 (Table S1).There is an urgent need for techniques that can identify, on a macroscopic scale, tumour location, size, and invasiveness, thus demarcating where disease stops and healthy breast starts. Potential imaging solutions include IOUS, iMRI or FGS12. Furthermore, a lack of fluorescence in the resection cavity could be used to verify the adequacy of resection.During FGS, a near-infrared dye is administered that targets the tumour, and a light source and dual colour and infrared camera system are used to detect the signal, with the images being displayed on a screen. The images are provided in real time and are not dependent on the skill of the operator nor patient position. As infrared light is used, the healthy tissue–tumour border can be visualized without affecting the surgeon’s view of the operative site. Anecdotal evidence suggests a penetration depth of 4 mm in FGS, which enables both intraoperative guidance and provision of an adequate margin11 combining image analysis with texture metrics surpassed results of previous studies, achieving a sensitivity of 66–82% and specificity of 90–93%.There have been multiple studies using US Food and Drug Administration (FDA)-approved non-selective fluorophores, such as indocyanine green (ICG). After injection, ICG leaks into tumours as a result of the enhanced permeability and retention (EPR) effect that capitalizes on the tumour’s porous vasculature and compromised lymphatic outflow. This passive mechanism of action, however, lacks accuracy. The most recent studyFig. 1). Eleven targeting fluorophores (8 targeting receptors and 3 targeting enzymes) are currently undergoing clinical trials, and more are being developed13. Of these, eight are currently undergoing trials in BCS, with favourable results. For example, bevacuzimab-800 , LUM015 (cathepsin), and EC17 (folate) were able to identify 100% of the lesions to be resected, with sensitivities of 88–98, 100, and 100%, respectively14–17. These targeting fluorophores need to gain FDA approval; however, many are already in the latter stages of clinical trials19.The advent of targeting fluorophores has made the delineation between tumour and normal tissue clearer (in vivo. Tumour identification techniques such as FGS are in their infancy, but show promise in enhancing operative precision by improving delineation of the boundary between tumour and healthy tissue. FGS has many barriers to overcome before clinical adoption. Because of the heterogeneity of breast cancer in terms of both genotype and phenotype, it is unlikely that overexpression of one protein will be representative of all cancers. Arguably, the ideal scenario would be to use a combination of multiple targeting fluorophores to target various proteins or combination with a margin assessment technique13.A plethora of options exist to assist tumour localization for BCS, but none have successfully eliminated the risk of positive margins and reoperation, or enabled tumour identification 20. Novel margin assessment technologies include optical and bioimpedance techniques. They provide information on tissue composition or light diffraction, but have proven problematic with regard to either limited depth, probe–tissue contact artefacts or spatial misalignment/misregistration18. Additionally, the majority of them analyse the cavity after resection, provide complex read-outs, and are slow18. Furthermore, as none of these margin assessment techniques were designed to guide resection, but rather to ensure clear margins at a microscopic level, they are of limited use in the initial steps of BCS.Margin assessment techniques cannot guide surgeons to the tumour itself. Rather, they are used to assess the specimen and/or cavity to verify microscopic margins. Immediate cytopathology techniques such as frozen section and imprint cytopathology provide visual demarcation of the tumour–healthy tissue margin. Clinical studies suggest that frozen-section analysis can reduce positive margin rates, but few centres have the resources to facilitate this time-sensitive, resource-intense, and costly techniqueCombining these novel margin assessment methods with FGS, which, unlike other identification modalities, is instantaneous and not dependent on patient position or operator skill level, would very likely enable a satisfactory rim of healthy tissue to be obtained. Not only will surgeons have real-time guidance on a macroscopic scale using FGS, but they will be able to further verify the resection at a microscopic level using these novel margin techniques.znac409_Supplementary_DataClick here for additional data file.


Acinetobacter baumannii infections. We sought to characterize SUL and DUR adsorption and transmembrane clearance (CLTM) in an ex vivo CRRT model.Optimal dosing of antibiotics in patients on CRRT is complicated by factors that can influence drug adsorption and clearance including filter type, CRRT mode, effluent rate (ER), and the drug’s properties itself. SUL-DUR is a novel combination antibiotic under development for management of TM was determined in hemofiltration (CVVH) and hemodialysis (CVVHD) modes using the Prismaflex M100 and HF1400 hemofilter sets. One liter of heparinized bovine blood was allowed to circulate in the Prismaflex CRRT system for 10 minutes. SUL and DUR were then added to achieve a plasma concentration of 30 mg/L, the average maximum concentration achieved in humans following SUL-DUR 1g/1g q6h as a 3h infusion. Pre-filter blood, post-filter blood and effluent samples were collected at 0, 10, 30, and 60 minutes to determine SUL and DUR concentrations. Combinations of filters, modes, and replacement fluid were tested in triplicate at effluent rates of 1, 2 and 3 L/h. The sieving coefficient (SC) for CVVH and the saturation coefficient (SA) for CVVHD were calculated to determine CLTM. Adsorption was measured through a closed loop system bypassing effluent elimination. Multiple linear regression was used to determine SUL and DUR CLTM as a function of ER, filter, and mode.CLTM for both drugs based on the equations: SUL CLTM (L/h) = -0.0123 + (1 x ER), R2 = 0.998; DUR CLTM (L/h) = 0.0346 + (0.91 x ER), R2 = 0.958.SUL and DUR adsorption was minimal at 10% for both drugs. Mean ± standard deviation initial SUL and DUR concentrations were 28.6 ± 2.5 and 27.4 ± 1.9 mg/L, respectively. The overall mean SC/SA across different modes, filters, and ERs was 1.0 and 0.9 for SUL and DUR, respectively. Multiple linear regression demonstrated that the ER was the primary driver (p< 0.001) of CLTM and devise dosing recommendations for SUL-DUR for patients requiring CRRT.SUL and DUR were efficiently cleared by both CVVH and CVVHD through M100 and HF1400 filters. The clearance of both drugs during CRRT was dependent primarily on the ER. When incorporated into established population pharmacokinetic models, these data can be used to estimate CLXiaoyi Ye, MD, Sanofi Genzyme: Honoraria David P. Nicolau, PharmD, Allergan: Advisor/Consultant|Allergan: Grant/Research Support|Cepheid: Advisor/Consultant|Cepheid: Grant/Research Support|Merck: Advisor/Consultant|Merck: Grant/Research Support|Pfizer: Advisor/Consultant|Pfizer: Grant/Research Support|Shionogi: Advisor/Consultant|Shionogi: Grant/Research Support|Tetraphase: Advisor/Consultant|Tetraphase: Grant/Research Support|Venatorx: Advisor/Consultant|Venatorx: Grant/Research Support|Wockhardt: Advisor/Consultant|Wockhardt: Grant/Research Support Joseph L. Kuti, PharmD, bioMeriuex Inc.: Grant/Research Support|Entasis Therapeutics: Grant/Research Support|Merck & Co, Inc: Grant/Research Support|Shionogi Inc: Advisor/Consultant|Shionogi Inc: Grant/Research Support|Shionogi Inc: Honoraria


Post-COVID conditions (PCC) are common, and risk factors include older age and female sex. While high interleukin (IL)-6 and C-reactive protein are associated with more severe disease, it is unclear whether other cytokines are associated with PCC. This study was performed to identify factors associated with PCC development.The Convalescent Plasma to Limit SARS-CoV-2 Associated Complications (CSSC-004) trial was a double-blind, multi-center, randomized, controlled trial comparing the use of COVID-19 convalescent plasma (CCP) to control plasma for the prevention of hospitalization among COVID-19 outpatients. Among 882 individuals participating in the trial with available biospecimens and symptom data, the association between early COVID-19 treatment, cytokine levels and PCC was evaluated. Cytokine and chemokine levels were assessed at baseline, day 14 and day 90 using a multiplexed sandwich immuosassay. Presence of any PCC symptom was assessed at day 90. Associations between COVID-19 treatment, cytokine levels and PCC were examined using multivariate logistic regression models.<5 days after symptom onset) compared to late CCP treatment had statistically significant lower odds of PCC (AOR=0.60 [0.38-0.95]).Baseline characteristics were similar by trial treatment group . At day 90, 292 (33.1%) participants had PCC. The most common symptoms were fatigue (14.5%), anosmia (14.5%), and ageusia (10.0%). Levels of most cytokines decreased over time . Six pro-inflammatory cytokines especially IL-6 were elevated at baseline among those with PCC . In multivariable analysis, female sex (adjusted odds ratio [AOR]=2.70[1.93-3.81]), age 50 or greater (AOR=1.32[1.17-1.50]), and elevated baseline IL-6 (AOR=1.59[1.02-2.47]) were associated with development of PCC, whereas race, obesity, vaccine status and diabetes were not. Those who received early CCP treatment (This study showed high prevalence of PCC symptoms at day 90, particularly among those with higher baseline levels of IL-6 or who did not receive early CCP treatment. The potential utility of IL-6 modulation as a therapeutic intervention among individuals as higher risk for PCC should be studied.Kelly Gebo, MD, MPH, Pfizer: Advisor/Consultant|Spark HealthCare: Advisor/Consultant Sonya L. Heath, MD, Pfizer: Data Monitoring Committee/DSMB Shmuel Shoham, MD, adagio: Advisor/Consultant|Adamis: Advisor/Consultant|ansun: Grant/Research Support|Avir Pharma: Honoraria|cidara: Grant/Research Support|F2G: Grant/Research Support|Immunome: Advisor/Consultant|Karius: Honoraria|Scynexis: Advisor/Consultant|zeteo: Grant/Research Support Judith S. Currier, M.D., MSc, Merck and Company: Advisor/Consultant|Merck and Company: Honoraria Moises A. Huaman, MD, MSc, AN2 Therapeutics Inc: Grant/Research Support|Gilead Sciences Inc: Grant/Research Support|Insmed Inc: Grant/Research Support Jay S. Raval, MD, Sanofi Genzyme: Advisor/Consultant Arturo Casadevall, MD, PhD, Ortho Diagnostics: Speakers Bureau|Sabtherapeutics: Advisor/Consultant