Sufficient confinement can completely suppress the Rayleigh-Taylor instability between two density-inverted miscible fluids. The prevention of hydrodynamic instabilities can lead to important insights for understanding the instabilities’ underlying dynamics. The Rayleigh-Taylor instability that arises when a dense fluid sinks into and displaces a lighter one is particularly difficult to arrest. By preparing a density inversion between two miscible fluids inside the thin gap separating two flat plates, we create a clean initial stationary interface. Under these conditions, we find that the instability is suppressed below a critical plate spacing. With increasing spacing, the system transitions from the limit of stability where mass diffusion dominates over buoyant forces, through a regime where the gap sets the wavelength of the instability, to the unconfined regime governed by the competition between buoyancy and momentum diffusion. Our study, including experiment, simulation, and linear stability analysis, characterizes all three regimes of confinement and opens new routes for controlling mixing processes. The Rayleigh-Taylor instability arises when a dense fluid sinks and displaces a lighter one located below it  simulations to explore the parameters governing this stabilization systematically. We validate our simulations by replicating the three-layer geometry and complex fluid properties of our experimental setup. This results in excellent agreement with the experiments, as shown by the red symbols in D, and fix the viscosity of the fluids such that η = ηH = ηL, where ηH and ηL denote the viscosities of the higher and lower density fluids, respectively.The experimental three-layer fluid profile of the Hele-Shaw cell, replicated in the simulations, creates a configuration with many relevant length scales, including the thickness of each layer. We therefore simplify the geometry to two fluid layers, each occupying half the gap. We use an average concentration-independent mass diffusivity, bc increases with increasing either D or η, indicating that diffusion of mass and momentum act to stabilize the interface. By contrast, increasing Δρ leads to a decrease in bc, indicating that buoyancy forces gΔρ  drive the instability. We therefore normalize the critical plate spacing by a characteristic length formed only from D, η, and gΔρThe simplified simulations again capture the transition to stability at a critical plate spacing, as shown in bc/b* = 15.0 ± 0.9, as shown in bc is determined by a competition between buoyancy and the stabilizing effects of viscosity and mass diffusion.Over six decades of density difference, the data collapse with gl3/ηDt. Here, l is a characteristic length scale and Dt is the coefficient of thermal diffusion 1/3, the data again collapse to bc/b* = 13.8 ± 0.6, as shown in bc.To investigate the case where the fluids have different viscosities, we determine cb, with 0 < c < 1, as shown in cbc/b* has a symmetric minimum at c = 0.5 independent of Δρ. As c approaches either 0 or 1, the confinement-induced stabilization increases, suggesting that, as one might expect, the thinner layer has a greater importance than the thicker one. As cb → 0, cbc/b* plateaus at 4.5 ± 0.4  isA = (ρ1 − ρ2)/(ρ1 + ρ2) is the Atwood number, ν ≡ (η1 + η2)/(ρ1 + ρ2) is the average kinematic viscosity, and ψ is a function of A, k, and the interface thickness kδ ≪ 1 and ψ ≈ 1 for all Atwood numbers. For liquids, it is typical that ν ≫ D. In this limit, 1 = η2. λ∞ corresponds to the plateau of the measured wavelength, as shown in b and λ with λ∞. The onset of the plateau occurs when b ≈ λ∞. Thus, λ∞ sets the scale beyond which the instability no longer feels the confinement. We note that λ∞ is fairly insensitive to mass diffusion and governed only by the competition between momentum diffusion and buoyancy. In the unconfined limit, the instability growth rate is always large enough that the time scale for dynamics is shorter than for mass diffusion.To understand this plateau at large b for b < λ∞. This analysis further shows that decreasing b leads to a decreasing growth rate; when b approaches bc, the time for growth becomes of the same magnitude as the characteristic time for diffusion. At small enough gaps, the interface between the fluids diffuses before the instability can grow by buoyant forces.To account for the confinement-dominated intermediate regime, we add in additional boundary conditions to Chandrasekhar’s unconfined linear stability analysis  λ∞, dominated by momentum diffusion, gives the scale beyond which the system no longer feels the confinement. In between, the wavelength is controlled by b. Varying the plate spacing allows one to approach one or the other limit systematically.The Rayleigh-Taylor instability for miscible fluids in confinement is thus characterized by two independent lengths: (i) In conclusion, the presence of a wall close to a two-fluid interface decreases the most unstable wavelength and slows down the instability growth. Varying confinement could provide a novel means for controlling mixing in miscible fluids; it allows a transition between purely diffusion-dominated mixing to a much faster instability-dominated mixing. Such a transition is similar to the switch from laminar diffusion to convective mixing at a critical value of the Rayleigh number in the Rayleigh-Bénard instability . To differentiate the fluids, we dye one of them with Brilliant Blue G (Alfa Aesar). We first inject an outer fluid through a hole in the center of one of the plates and then inject another inner fluid. We image the patterns from the bottom of the cell with a Prosilica GX3300 camera at frame rates ranging from 0.01 to 14 frames/s.The glass plates used in our Hele-Shaw experiments are of diameter 28 cm and thickness 1.9 cm. A uniform gap between the plates is maintained using spacers of thickness b = 1.14 mm. The inner fluid is dyed with Rhodamine B (Sigma-Aldrich) at a concentration of 2 μM. As a single stack with 31 z slices takes about 30 s to capture, we use higher-viscosity fluids to slow down the dynamics, so that the instability takes 6 min to appear with full development occurring after 15 min. Our acquisition rate is then fast compared to the dynamics that we wish to capture.We use confocal microscopy  to visualize the structure within the gap inside a rectangular Hele-Shaw cell of size 2.5 cm by 7.5 cm and plate spacing i = . Using COMSOL, we solve the Stokes equations for creeping flow coupled with Fick’s law for diffusionu is the flow velocity, p is the pressure, η is the mixture dynamic viscosity, D is the mixture diffusion coefficient, iM is the molar mass of species i, and kx is the molar fraction of species k. The cross section of the Hele-Shaw cell is a rectangular domain of height b and width L = b, large enough for the dynamics to be independent of the domain size. We impose no-slip and no-flux boundary conditions at the top and bottom of the domain and periodic boundary conditions on the left and right. We follow (b and the perturbation amplitude is a ≲ δ. The mesh size Δx is a fraction of a, uniformly spaced around the interface where dynamics are pertinent. With time, the unstable interface chooses a wavelength independent of L.We complement the experiments with 2D numerical simulations of the cross section of the Hele-Shaw cell. We model the two miscible fluids as a mixture with mass concentration ωi. Such care needs to be taken to achieve agreement between the simulations and the experiments.We validate the model by comparing the simulation results to experiments performed under identical conditions, using the same fluid parameters and replicating the three-layer fluid profile within the gap. We use empirical formulas for both the interfluid diffusivity (D, and a viscosity that interpolates linearly with the local concentration, ωi. The empirical relations are asymmetric with respect to ωi, which leads to a drift in the location of the fluid interface as the system diffuses. The simplification of the fluid parameters is necessary to decouple the effect of the interface location from that of the fluid properties.We then run another set of simulations, simplifying the geometry to two fluid layers with a single interface so that we can probe the effect of the interface location systematically. In addition, we simplify the fluid parameters, replacing the concentration-dependent diffusivity and viscosity with a constant average diffusivity,


There is an error in the Correction published on September 12, 2019. The correct grant number is 2019R1A2C2005989. The correct Funding statement is: This work was supported in part by the National Research Foundation of Korea Grant 2019R1A2C2005989 to KJL.


Light microscopy has become an indispensable tool for the life sciences, as it enables the rapid acquisition of three-dimensional images from the interior of living cells/tissues. Over the last decades, super-resolution light microscopy techniques have been developed, which allow a resolution up to an order of magnitude higher than that of conventional light microscopy. Those techniques require labelling of cellular structures with fluorescent probes exhibiting specific properties, which are supplied from outside and therefore have to surpass cell membranes. Currently, major efforts are undertaken to develop probes which can surpass cell membranes and exhibit the photophysical properties required for super-resolution imaging. However, the process of probe development is still based on a tedious and time consuming manual screening. An accurate computer based model that enables the prediction of the cell permeability based on their chemical structure would therefore be an invaluable asset for the development of fluorescent probes. Unfortunately, current models, which are based on multiple molecular descriptors, are not well suited for this task as they require high effort in the usage and exhibit moderate accuracy in their prediction. Here, we present a novel fragment based lipophilicity descriptor DeepFL-LogP, which was developed on the basis of a deep neural network. DeepFL-LogP exhibits excellent correlation with the experimental partition coefficient reference data (R2 = 0.892 and MSE = 0.359) of drug-like substances. Further a simple threshold permeability model on the basis of this descriptor allows to categorize the permeability of fluorescent probes with 96% accuracy. This novel descriptor is expected to largely simplify and speed up the development process for novel cell permeable fluorophores. These, so called, nanoscopes are also integrated into fully automated platforms, which is important for high-throughput screening (HTS) applications in drug discovery and toxicity research4. The direct visualization of intracellular targets in-vivo/in-vitro at this unprecedented resolution requires the use of fluorescent probes with excellent cell permeability, high specificity and low background  models predict the activity/property of potential probes on the basis of molecular descriptors and are increasingly used as prioritization tools for drug/probe development7. The accuracy of the algorithms used to calculate these descriptors is crucial for the reliability of these models, and hence also for the precision of the prioritization tools.Therefore, the development of new methods for prioritizing chemical designs is an attractive alternative. 11. It has been shown that drugs with good cell permeability exhibit moderate LogP values13. The Lipinski rule of five that is used to evaluate the drug-likeness of compounds indicates a moderate LogP range (− 0.5 < LogP < 5) for substances with good cell permeability14. Various LogP descriptors have already been developed that can be used to build models for cell permeability.When it comes to cell permeability the LogP descriptor is the most significant descriptor15. Enhanced/hybrid atomistic algorithms (SLogP/XLOGP3/MLOGP) have been developed to overcome some of the shortcomings of the atomic algorithms and take also the contribution of neighboring atoms and hybridization into account17. Fragment LogP descriptors are based on a different approach. They use the experimentally determined partition coefficient of chemical fragments or compounds as a basis for a QSPR model or a regression technique which then predicts LogP18. Fragment descriptors  take therefore the nuances of electronic or intramolecular interactions into account, which is not the case for atomistic algorithms. Consequently, they tend to perform better for larger molecules or compounds with more complex chemical structures, like fluorescent chemical probes. Another class of algorithms, e.g. MLogP19, calculate LogP by using molecular properties such as 3-D structures or topological indices. The consensus LogP (cLogP) descriptor calculates the average LogP from multiple LogP models with improved accuracy20. In addition to these LogP models based on chemical properties, there exist also physics based algorithms which estimate the LogP from the computed solvation free energy of organic compounds in implicit media, such as the iLOGP descriptor21. Finally, the rapid development of machine learning, e.g. artificial neural networks, has increased the accuracy and speed of many of these LogP descriptors22.Early algorithms calculated the LogP purely on the contribution of single atoms5. In this study it has also been shown that cell-permeable fluorescent molecules tend to exhibit LogP values greater or equal to 1 and that this can be used as a threshold in the design criteria of cell permeant dyes. However, it has not been quantitatively tested whether the LogP descriptor could be solely used to accurately categorize the permeability of this molecule class.Recently, LogP has been used for the first time to build a QSPR model for the development of cell permeant fluorescent probesTo address this important question, we screened several LogP descriptors by analyzing a multitude of more than 100 permeant and impermeant probes using various LogP algorithms and a predefined LogP threshold. Furthermore, we increased the accuracy of the algorithm by introducing a new deep neural network based LogP descriptor (DeepFL-LogP) that can categorize the permeability of fluorescent probes with superior accuracy.23. XLOGP3, MLOGP, cLogP, and the iLOGP have been calculated using the SwissADME web tool20. After calculation, all descriptors were merged into one file for subsequent statistical analysis.SLogP has been calculated using the Mordred Python package24. Corresponding information on additional fluorescent probes was extracted either from literature or commercial catalogues. In cases where the SMILES codes were not accessible, the chemical structures were used to generate SMILES codes.In total 124 fluorescent probes and fluorophores were used for evaluation Table . A diver25, it was considered to be a cell-permeant probe. A probe was considered to be impermeant if it preferentially stains fixed or dead samples (e.g. Propidium iodide and Evans Blue)26.Information on probes’ permeability were manually curated from commercial catalogues or literature. In case of literature search, individual publications were manually explored for permeability data. Only when a fluorescent probe has been applied to live cells and there was clear microscopic evidence about its cellular localization27. The architecture of the neural network is based on a simple sequential model that consists of three main sequentially connected layers: An Input layer, three hidden layers, and an output layer. The input layer consists of 319 neurons, which corresponds exactly to the number of molecular features used. A sigmoid activation function has been added to this layer. The network’s three hidden layers consist of: a first layer of 256 neurons (sigmoid activation function), followed by a second layer of 164 neurons (tanh activation function) and a third layer of 10 neurons (sigmoid activation function). The output layer consists of a single neuron with a single output. Since this is a linear regression-like problem and the network is purposed to predict LogP values based on experimental determined values (i.e. logarithm of measured partition coefficients), an activation function was not added to the final output neuron layer and raw values were directly used.The deep neural network (DNN) has been developed using the Keras library in Python28. For error backpropagation and error minimization during the training process of the neural network, a Stochastic Gradient Descent (SGD) optimizer function with a learning rate and nesterov momentum of 0.01 and 0.9, respectively, has been implemented. For loss monitoring during the training process, the root mean square function was calculated for each epoch (i.e. an epoch corresponds to one learning cycle using the entire training set). A total of 78 epochs was found to be optimal for the pre-defined learning rate.Hyperparameter tuning in neural network-based models is essential for accurate predictionshttps://colab.research.google.com) utilizing the GPU in order to speed up the process. The final trained model architecture and weights were stored in two separate HDF5 files.The batch size controls the size of samples to be used to estimate the error gradient before the model weights are updated and is therefore another important hyperparameter for DNNs. In this study a batch size of 32 was used. To improve the networks performance and to reduce bias, samples were randomly shuffled prior to the start of the training process. Training was performed in Colab (29 (Table The 2D RDKit Electronic State (E-state) fingerprint algorithm was used to determine the atom types as well as the basic fragment descriptors for each molecule29 Table . Descrip29 Table . Overall30. In order to expand the chemical space and to improve the model’s accuracy for fluorescent probes, information on 222 auxiliary molecules together with their experimental LogP or in case of ionizable compounds LogD values were added to the OPERA training set  and the mean square error (MSE) were calculated using built-in functions of the Scikit-learn library31. Pearson and t-test analysis were performed using the SciPy statistics library. To determine statistical significance for the LogP analysis, two-independent sample t-tests with unequal variances were calculated. analysis of the data was performed in Python using the Spyder IDE. The histogram-based analysis of the experimental LogP data and the distribution (boxplot) analysis were performed using the seaborn library. Descriptive statistical measures  of the training and test sets were calculated using basic Python functions. The regression coefficient  in cell culture medium under cell culture conditions. Imaging was performed on an Abberior Instruments Facility Line Microscope using a pulsed excitation laser at 561 nm and detection window between 570 and 680 nm. STED images were recorded using a STED laser at 775 nm.For the labeling of the tubulin network of living U-2 OS cells, the cells were seeded on coverslips one day before the experiment as described before8. Using this threshold several descriptors were tested in order to determine how accurately the permeability of probes can be categorized. For this purpose the LogP values for a test set containing 124 fluorescent probes of known membrane crossing profile were calculated with the six descriptors investigated here and categorized according to the threshold value of 1. The set consists of n = 99 permeant with diverse subcellular localizations and n = 25 impermeant probes  is adequate to distinguish permeant from impermeant compounds, the latter exhibiting lower LogP valueses Table .p value = 1.2e−08, Fig. 33, exhibited a similar difference between the permeant and impermeant probes . The majority (98%) of permeant probes showed a value equal or greater than 1  display high LogP values regardless of the probes’ permeability Fig. A,B. In d8. This could also explain the wrong categorization of 13% of the permeant probes as to be impermeant.Overall, the fragment-based miLogP descriptor shows a good accuracy in correctly categorizing the permeability of permeant as well as impermeant probes. Nevertheless, it tends to underestimate the LogP for some probes of diverse chemistries Table , which m30. It contains high-quality training/validation sets, which were curated from the publicly available PHYSPROP 34 database. The training and the validation set used here consist of the experimental determined LogP values of more than 10,000 and 3,000 molecules, respectively. In order to cover a wider range of LogP values and to enlarge the reference chemical space, the original OPERA training set was enlarged by including additional molecules (n = 222)  has been developed. The DNN has an input layer comprised of 319 neurons, which is sequentially connected to 3 hidden layers and an output layer of a single neuron was used  Table . Twelve 2 of 0.892 and a low mean square error (MSE) of 0.359  for each molecule. The molecular fingerprint is a map that represents the atom types and their bonding information of a particular molecule. In the training step, the E-State indices (bonding information) of the atoms were excluded. The sufficiency of the atom-types information and the expansion of the chemical space of the fragments search   perform better in predicting the permeability of fluorescent chemical probes on basis of a simple LogP threshold model. To verify this presumption quantitatively, the accuracy for all the LogP descriptors was determined and compared utilizing the same test set of probes. Accuracy represents the number of correctly categorized probes. Ranking the descriptors according to the highest accuracy score shows that DeepFl-LogP and miLogP descriptors perform best among all other descriptors Fig. . The DeeThe development of new membrane-permeant fluorescent probes with predefined properties is highly important for applications in diagnostics and research. In a typical development process, improving the permeability of a new probe design is a tedious process which requires many cycles of optimization, in which the binding specificity must be maintained or even additionally optimized further. In the case of fluorescent probes for super-resolution imaging, additional requirements on the photophysical properties, such as photostability, also have to be met. All these properties are sensitive to subtle changes in the chemical structure. Therefore, the development of a robust descriptor or an equivalent QSPR model that can be used to accurately predict one or more of the aforementioned properties could help to speed up the development process and thus reduce the overall development costs.In this study, we developed a simple model, based on thresholding a LogP descriptor, which can predict with good accuracy whether a fluorescent probe is cell permeable or not. Our analysis of different LogP descriptors showed that fragment based LogP descriptors exhibit the best accuracy in categorizing the permeability of fluorescent probes within this model. We also showed that this accuracy can be further improved by using a novel DNN based LogP descriptor.As the development of new permeable fluorescent probes is often limited by the poor permeability and solubility of the precursor fluorophore, in silico approaches are ideally suited as a first screen for predicting permeation. Current LogP descriptors are not computationally expensive, but we showed in our study that they cause a large misclassification error when it comes to predicting the cell permeability of fluorescent probes. This is not the case for the DeepFl-LogP descriptor. It is fast (a few microseconds computing time per molecule) and can therefore easily be used as a permeability prediction tool for fluorescent compounds. Such a LogP-screening of probe designs prior to synthesis is certainly faster and cheaper than the hitherto existing workflow of iterative synthesis, purification and testing of each novel fluorescent compound.36.Finally, we expect that our results as well as the newly developed DeepFl-LogP descriptor will also be beneficial for other types of in silico studies. For example, is LogP an important descriptor in most quantitative toxicity relationship (QSTR) models, which are used to assess the risks of chemical exposureSmall-molecule fluorescent probes are becoming powerful biomedical reagents to advance cell biology and drug discovery research, as well as cancer diagnostics. The majority of applications are bioimaging applications and the design of these probes is usually a two-fold problem: The photophysical properties of the incorporated fluorophore has to be optimum, especially for super-resolution imaging applications, as well as the physicochemical properties, such as the probe permeability. The cell permeability of probes affects both the staining quality and toxicity of the applied molecules. In silico methods for predicting these properties are promising tools for the enhancement of the development of molecules with favorable properties. Nevertheless current permeability models are based on multi-descriptors and statistical models, yet they predict the permeability of fluorescent probes with moderate accuracy. In praxis, to rely on the available tools with moderate accuracy can be counterproductive, especially when searching a wide range of chemical space and at the same time being limited in chemical resources.LogP has been a key molecular descriptor in predicting the cell permeability of molecules. Here, we tested if a simple permeability model that is solely based on this descriptor can accurately predict the cell permeability of complex fluorescent molecules. By screening several standard LogP algorithms, we found that the fragment-based LogP algorithms exhibit a high accuracy in categorizing the permeability of structurally diverse fluorescent probes. Further, we developed an improved deep neural fragment-based LogP descriptor (DeepFl-LogP). The training set of the neural network included additional molecules to those found in the OPERA database. Increasing the reference chemical space and the use of a larger molecular fingerprint in the modeling step enabled us to substantially improve the overall accuracy of the DeepFl-LogP, and more important to categorize permeability of chemically diverse fluorescent probes.www.github.com/k-soliman/DeepFl-LogP).DeepFl-LogP is the first tool, which can be used with confidence as a predictor for one of the most important properties when it comes to the development of cell permeable organic probes. In order to make the DeepFl-LogP tool publicly available, the Python script and the datasets are in our GitHub repository (Supplementary Information 1.Supplementary Information 2.


Page 1: The author byline should appear as given in this correction.Volume 5, no. 5, e00515-20, 2020, Page 17: The following should be added to the end of the first paragraph of Acknowledgments: “…design, S.G.; analysis, S.G.; expertise, S.G.; approval of the final manuscript, S.G.”Page 17: The following should be added to the end of the second paragraph of Acknowledgments: “S.G. was a Michael Smith Foundation for Health Research Scholar funded by a Canadian Association of Diabetes Grant in Aid and an NSERC Discovery Grant.”


Recent advances in fluorescence imaging techniques and super-resolution microscopy have extended the applications of fluorescent probes in studying various cellular processes at the molecular level. Specifically, organelle-targeted probes have been commonly used to detect cellular metabolites and transient chemical messengers with high precision and have become invaluable tools to study biochemical pathways. Moreover, several recent studies reported various labeling strategies and novel chemical scaffolds to enhance target specificity and responsiveness. In this review, we will survey the most recent reports of organelle-targeted fluorescent probes and assess their general strategies and structural features on the basis of their target organelles. We will discuss the advantages of the currently used probes and the potential challenges in their application as well as future directions. Fluorescent chemical probes have been extensively used to study biochemical events within live cells. The advent of super-resolution imaging techniques and the availability of a wide variety of fluorescent probes enable effective subcellular tracking of transient metabolites and signaling molecules that are involved in important physiological processes. Probes targeting specific organelles such as mitochondria and lysosomes have been used routinely to monitor organelle functions and have become invaluable tools for the investigation of disease-relevant pathways. These probes efficiently target subcellular organelles; however, considering the complexity and diversity of biochemical processes inside the cells, probes that are not only specific to target organelles but also tailored to applications are highly desirable.Each subcellular organelle has unique physicochemical and structural characteristics that can be utilized for organelle-specific targeting. For example, mitochondria have a negative membrane potential, which attracts positively charged moieties; lysosomes have an acidic vesicular structure wherein weakly basic compounds can accumulate. Thus, commercially available mitochondria- or lysosome-tracking dyes are cationic amphipathic or weakly basic. An additional targeting moiety can be incorporated to detect specific proteins and chemical species inside the organelles; however, the added moiety may affect cellular permeability as well as organelle specificity. Considering that each organelle plays a different function and mediates diverse pathways, the target of interest must be carefully chosen to provide meaningful observations.In this review, we outline the general strategies and structural characteristics of organelle-targeted fluorescent probes and categorize the most recent examples of these probes on the basis of their target organelles. In particular, we will focus on specific and most up-to-date examples for the extensively studied organelles such as mitochondria and lysosomes, but also cover recent developments associated with much less explored organelles including the endoplasmic reticulum, Golgi-complex, melanosomes, and lipid droplets. We also provide an overview of the most prominent applications and advantages of organelle-targeted probes and discuss potential challenges and future directions.+ and other ions such as Ca2+, Fe2+, Zn2+, and Cl− are the key regulators of lysosomal function; impaired ion homeostasis can lead to defects in lysosomal trafficking and storage, which are associated with neurodegenerative diseases and metabolic disorders . Be. Be28) t29) has a mitochondria-penetrating peptide sequence, viscosity-sensing phenylquinoxaline, and polarity-dependent coumarin 343. The probe visualized changes in mitochondrial viscosity and polarity upon treatment with ionophores and electron transport complex inhibitors. Nam et al. used a peptoid-based mitochondria-targeting group that is conjugated to an activity-based probe (30) to label the active mitochondrial enzyme HTRA2 (the high-temperature requirement A) serine protease in live cells . In. InSMCy,In this review, we discussed recent developments in organelle-targeted fluorescent probes. While most of the reported probes demonstrated excellent organelle specificity as well as high sensitivity, as shown in


Spinal cord stimulation (SCS) paddle leads placed via laminectomy procedures have become common as more data accumulates with regards to their clinical efficacy. In this paper, we describe a case of a 72-year-old male patient with failed back surgery syndrome (FBSS) who underwent a thoracic laminectomy for permanent paddle lead placement. He went on to develop a complication that resulted in a large cerebrospinal fluid leak with a cerebrospinal fluid fistula formation. Spinal cord stimulation (SCS) is a modality used for treating chronic pain syndromes. It involves the delivery of high-dose electrical current to the spinal cord dorsal columns. This modality has been definitively shown to control pain, leading to proposals of multiple postulated mechanisms as to how the stimulation controls pain . It is wA 72-year-old male with failed back surgery syndrome (FBSS) was initially evaluated and treated at an outside hospital. After a successful percutaneous trial lead placement, he underwent thoracic laminectomy for permanent paddle-lead placement. A few weeks after the surgery, he developed a growing painful lump at his thoracic incision site. On physical examination, it appeared to be a fluid-filled collection underneath the thoracic incision site, which seemed well-healed and not leaking. There were no signs of infection or erythema. Lab results were within normal limits. An MRI of the thoracic spine showed a large dorsal spinal fluid collection with a paddle lead floating in the middle of the large spinal fluid pocket Figure , 1B. AddThe patient underwent a thoracic laminectomy for the repair of the cerebrospinal fluid leak and fistula closure with the removal of implanted spinal cord stimulator and lumbar drain placement. During surgery, the paddle was found floating in the middle of a large extradural cerebrospinal fluid collection. Multiple old Prolene sutures  were identified surrounding a large dural defect, indicating an unsuccessful closure attempt in the past. The dural defect was closed with muscle onlay graft, Prolene sutures, and DuraSeal , a polyethylene glycol hydrogel. A lumbar drain was placed and spinal fluid was drained for one week while the thoracic dural repair was healing. After one week of no leaking or fluid collection at the thoracic incision, the lumbar drain was removed, and the patient was discharged home a few days later. Nonabsorbable skin sutures were removed two weeks postoperatively. The patient continues to do well without any signs of cerebrospinal fluid leakage.Pain and its effects on people are complex, multifaceted phenomena, which involve pain nociceptors in the body picking up stimuli that are interpreted as painful. These stimuli are sent through the dorsal columns of the spinal cord, up to the thalamus, and then the somatosensory cortex. As these signals travel along this pathway repeatedly, the strength of this connection solidifies, just as memory does, and can become chronic. Blocking these signals at this level, the dorsal columns initiate a pain control mechanism that can inhibit the actual sensation and body’s experience with pain. A spinal cord stimulator device is composed of a lead and a pulse generator, which are both implanted completely underneath the skin. The impulse produced by the leads effectively blocks the transmission of pain signaling in the dorsal column. Widespread research on the usage of SCS has led to a large body of literature that demonstrates the efficacy of SCS treatment -9. Pain SCS treatment is not without risks and, therefore, is commonly used only after more conservative treatments have failed. Patient screening for contraindications is imperative. Prior to implantation, major points of patient screening include patient history and disease status, coagulative status, presence of a pacemaker or other medical devices, current infection and risk of infection development, and psychological screening . MechaniIn our case, we were unable to determine when the original dural defect occurred, as it could have happened either during the percutaneous trial lead placement or when the patient underwent thoracic laminectomy for permanent paddle lead placement at the outside hospital. In rare cases when pseudomeningoceles do occur, dural repair, lumbar drain, or other cerebrospinal fluid leak intervention must be employed. Pseudomeningocele etiology is based on a dural opening, which leaks cerebrospinal fluid and subsequently forms a fluid-filled pocket. Pseudomeningoceles are extradural collections of cerebrospinal fluid that result from a dural defect and violation of subarachnoid space . SequelaAlthough SCS has been a well-received adjunct for pain control in chronic pain patients, the placement of these mechanical devices is not without risk. Multiple studies have reported SCS as a safe procedure due to its reversibility and minimally invasive nature. We reported a case of a 72-year-old patient with FBSS who underwent a thoracic laminectomy for permanent paddle lead placement and unfortunately developed a pseudomeningocele. We described the details of surgical interventions that were employed to repair this spinal fluid leak, which involved the closure of the dural defect with a muscle onlay graft and placement of a lumbar drain, in order to elicit the best possible outcome in light of this particular SCS complication. This case illustrates how important it is to not understate the risks associated with this treatment modality. A thorough evaluation of the patient’s history can alert providers to potential risks or pitfalls. Attention to surgical details can reduce the rate of complications and improve outcomes in this patient population.


Bioimpedance-based estimates of fluid overload have been widely studied and systematically reviewed in populations of those undergoing dialysis, but data from populations with heart failure or nondialysis chronic kidney disease (CKD) have not.We conducted a systematic review of studies using whole-body bioimpedance from populations with heart failure and nondialysis CKD that reported associations with mortality, cardiovascular outcomes and/or CKD progression. We searched MEDLINE, Embase databases and the Cochrane CENTRAL registry from inception to March 14, 2022. We identified 31 eligible studies: 20 heart failure and 11 CKD cohorts, with 2 studies including over 1000 participants. A wide range of various bioimpedance methods were used across the studies . Studies generally reported positive associations, but between-study differences in bioimpedance methods, fluid overload exposure definitions and modeling approaches precluded meta-analysis. The largest identified study was in nondialysis CKD , which reported adjusted hazard ratios  for phase angle < 5.59 vs ≥ 6.4 of 2.02 (1.67–2.43) for all-cause mortality; 1.80 (1.46–2.23) for heart failure events; and 1.78 (1.56–2.04) for CKD progression.J Cardiac Fail 2022;00:1 – 14)Bioimpedance indices of fluid overload are associated with risk of important cardiorenal outcomes in heart failure and CKD. Facilitation of more widespread use of bioimpedance requires consensus on the optimum device, standardized analytical methods and larger studies, including more detailed characterization of cardiac and renal phenotypes. ( Heart failure increases the risk of CKD due to impaired perfusion of the kidneys and neurohormonal activation,5 and there are a number of pathophysiological changes associated with advancing CKD that contribute to heart failure. These include chronic hypertension and fluid overload as well as the possibility of direct uremia-related cardiotoxicity.4 Fluid overload is a common manifestation in both disease states and has clinical and prognostic implications. Treatment of both diseases has progressed with the advent of sodium-glucose cotrans-porter-2 inhibitors and mineralocorticoid receptor antagonists; both drug classes have diuretic mechanisms, and large trials have demonstrated benefits for cardiovascular risk and CKD progression.9Heart failure and chronic kidney disease (CKD) commonly coexist but are often considered separately in research and clinical practice. The burden of heart failure increases with advancing CKD; the estimated prevalence of clinical heart failure is around 40% in patients requiring dialysis.10 Bedside medical devices to standardize and more precisely quantify fluid status in both heart failure and CKD have been developed using ultrasound and bioimpedance technology. Both methods can be employed with relatively little training and allow rapid clinic-based measurements. Ultrasound modalities include lung ultrasonography13 and, less commonly, vascular ultrasound of the inferior vena cava and internal jugular veins.14 Bioimpedance methods include both bioimpedance analysis (BIA)  and bioimpedance spectroscopy (BIS), both of which have been demonstrated to be highly reproducible and have been validated against gold-standard techniques15 more extensively than ultrasound approaches. Bioimpedance is a noninvasive measure of resistance and reactance of body tissues quantified by application of an electrical current via electrodes attached to the skin from which fluid compartment volumes and body composition can be estimated. Fluid overload is traditionally assessed nonquantitatively by clinical examination.16 provide more specific estimates of fluid overload, independent of body composition (19 These studies used relative fluid overload derived by indexing absolute excess fluid volume to the volume of the extracellular water (ECW) compartment as the exposure, thereby allowing for comparisons between individuals.20 A threshold of > 15% relative fluid overload, equivalent to approximately+2.5 L absolute fluid overload,20 has often been used in analyses,19 whereas other studies have employed the more modest threshold of > 7% relative fluid overload .21 These dialysis studies have been subject to systematic reviews,25 but reviews of studies of heart failure and nondialysis CKD have not been reported. We sought to assess whether similar positive associations exist between fluid overload and adverse cardiorenal outcomes in other at-risk populations, where fluid overload may be less marked than in dialysis cohorts but may still represent a key modifiable cause of morbidity and mortality. A secondary aim was to identify a threshold of fluid overload, which is associated with adverse cardiorenal outcomes and which could be used as a surrogate marker of clinically significant fluid overload in both research studies and clinical care in heart failure and nondialysis CKD.Although both BIA and BIS have been applied in heart failure and CKD, the Fresenius Medical Care (FMC) Body Composition Monitor (BCM), which uses BIS, is the most widely employed in patients with kidney disease because secondary calculations taking account of estimates of lean and adipose tissue mass (by applying a 3-compartment model)position . WidesprThe Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were followed, and the review was registered via PROSPERO international prospective register of systematic reviews (CRD42022316312). This report focuses on observational and interventional studies of adult populations with heart failure and/or CKD which have assessed the association between whole-body bioimpedance indices of fluid overload with risk of cardiorenal outcomes. Studies of patients with kidney failure requiring maintenance kidney replacement therapy were excluded. Studies exclusively of acute kidney injury and other acute disease states were also excluded , with the exception of acute decompensated heart failure. Studies of other chronic disease in which fluid overload may manifest  were also excluded.26 when both were reported.All whole-body bioimpedance indices of fluid overload were considered relevant, including absolute and relative fluid overload (or overhydration), ratios of body water compartments, phase angle, vector length, and bioimpedance vector analysis (BIVA) hydration index, whether reported as continuous or categorical exposures. We tabulated results of both absolute fluid overload in liters and the related relative fluid overload parameter 27The primary outcome of interest was mortality . Secondarily, we included studies reporting cardiovascular and kidney disease progression outcomes. For populations with heart failure, composite outcomes comprising all-cause death and hospitalization were included as a cardiovascular outcome on the presumption that a large proportion of deaths in these composite outcomes reflects cardiovascular disease in the included populations (and particularly in populations with heart failure).The systematic search was conducted within MEDLINE (Ovid), Embase (Ovid) and Cochrane Central Register of Controlled Trials (CENTRAL) from inception to March 14, 2022 ; it included data fields for study design, funding, population characteristics, measures of kidney function and cardiac status, blood pressure and other laboratory parameters relevant to fluid overload at recruitment, as well as bioimpedance-outcome associations. Risk of bias was independently assessed by both reviewers using the Quality In Prognosis Studies (QUIPS) tool.  than those in populations with heart failure (51–706 participants) and had longer durations of follow-up (range 1.0-8.6 years for CKD vs 0.02–3.0 years for heart failure cohorts). Heart failure studies more commonly studied participants with acute decompensated heart failure compared with stable chronic disease ; they are described in 59 than in CKD studies (3 studies [27%])70; serial measurements were commonly recorded over short timeframes during admissions due to heart failure. Reports tended to select preferentially a single exposure time point for observational analyses, relating fluid overload to future risk of outcomes, rather than considering time-updated exposures or applying adjustment for regression dilution bias.The majority of studies (21 studies [68%]) reported single baseline measurements as opposed to serial measurements. Serial measurements were slightly more common in heart failure (7 studies [35%])Associations between fluid overload and specific causes of death were not widely reported in either heart failure or CKD cohorts, limiting the review to all-cause mortality. Associations between fluid overload with risk of death from any cause were presented in 13 studies, 10 of which reported estimates from multivariable models. Significant between-study differences in exposures and model approaches precluded meta-analysis.49 Of note, Massari et al. included heart failure status (acute vs chronic), brain natriuretic peptide (BNP) and estimated plasma volume status  in the multivariable model alongside the BIVA hydration index,49 meaning that models were estimating the relevance of total body fluid overload for a given level of intravascular status. The associations, therefore, estimate the relevance of excess extravascular fluid, rather than total body fluid overload, with risk. Similarly sized cohorts of populations with stable chronic heart failure41 and acute heart failure51 were studied for markers of total body fluid overload, and strong positive associations with risk of all-cause mortality were found, whether estimated by phase angle41 or BIVA hydration index51  and Vega et al. (356 participants) using BCM-derived parameters were much smaller and, perhaps as a consequence, were unable to confirm statistically significant associations consistently , demonstrated that phase angle < 5.59° (where lower phase angles represent higher degrees of fluid overload) vs ≥ 6.4° was associated with double the risk of all-cause mortality  after adjustment for age, sex, ethnicity, and clinical site. deaths] .68,69Associations with composite cardiovascular outcomes were reported in 16 heart failure studies, 7 of which reported multivariable Cox regression analyses; a further 4 reported other multivariable regression analyses, and 5 reported only univariable associations . Five CK59; ECW volume/ratio in 2 studies53; and relative fluid overload in 1 study46) and risk of these cardiovascular outcomes. The seventh study  reported on a composite of death, urgent transplant or ventricular assist device implantation and found no significant association between the ratio of ECW-to-total body water ≤ 0.39 vs ≤ 0.39 . Adjustment for BNP and heart failure symptoms in this and other studies may result in models underestimating any causal relevance of associations, and for the majority of studies, we were unable to find less adjusted models, which are more relevant to the etiological scientific focus of this systematic review 68  but not for the smaller number of cardiovascular outcomes (18 outcomes).Of the 5 CKD studies reporting relevant cardiovascular/composite outcomes, the largest study reported a 48%  increased risk of atherosclerotic cardiovascular disease  and an 80%  increased risk of heart failure events 68 . Vega et68 The largest studies consistently report increased risk of composite kidney outcomes associated with fluid overload as defined by absolute/relative fluid overload or phase angle70  decline . Two stuWhole-body bioimpedance is frequently used and well-studied in populations undergoing dialysis. In order to address the potential role of bioimpedance in heart failure and nondialysis CKD populations, we conducted a systematic review to summarize existing evidence and determine a threshold value of clinically significant fluid overload for use in research and clinical practice. We identified 31 eligible studies (20 heart failure and 11 CKD cohorts) which used 10 different fluid overload parameters derived from bioimpedance analysis or spectroscopy to assess associations with cardiorenal outcomes. Studies also varied greatly in size, duration, approaches to model construction, and outcome definitions, which precluded statistical aggregation of results by meta-analysis. Nevertheless, there was convincing evidence from individual studies that bioimpedance indices of fluid overload were associated with an increased risk of death in populations with both heart failure and CKD. Similarly, significant positive associations were observed with study-defined cardiovascular outcomes across the majority of studies. These associations appeared clearest for heart failure hospitalization outcomes, whereas evidence of a link with ischemic events were limited to CKD cohorts.24 Such data are based largely on the Fresenius BCM device used in 7/11 [64%] CKD cohorts and 2/20 [10%] heart failure cohorts in our review. Dialysis studies have assessed a variety of threshold values of BCM-derived fluid overload. Wizemann et al. first established a 15% threshold value of relative fluid overload based upon the highest quartile of a reference hemodialysis population ,20 which was followed by studies of a > 7% threshold, derived from the 90th percentile of a healthy reference population.71 Both thresholds  have been consistently linked to lower survival rates.71 Studies not using these thresholds selected cut-offs based upon quantiles of the study population, ranging between ≥ 4% and > 17.4%.35 In our review, we found no studies of heart failure or nondialysis CKD reporting associations with the 15% threshold value, perhaps because this degree of fluid overload is uncommon in earlier stages of CKD and heart failure compared with the extreme phenotype of fluid overload, which manifests in kidney failure requiring kidney replacement therapy. The 7% relative fluid overload threshold was applied in two CKD cohorts68 and 1 heart failure cohort46 and was positively associated with cardiorenal outcomes 74 of ~ 650 participants with serial BCM measurements .The findings from this systematic review are qualitatively consistent with the much larger body of evidence from dialysis populations.outcomes . We, the16 It is not possible to equate BCM-derived fluid overload with other bioimpedance parameters, such as phase angle or BIVA hydration index, which were more commonly employed in heart failure cohorts (75) were applied in the identified heart failure studies but, like phase angle and ECW ratios, this parameter may reflect differences in fluid volume, body composition or a combination of both. Multivariable analysis adjusted for body composition and nutritional factors may not completely address this limitation and is not practical for clinical application. For now, we propose that BCM measures are the optimum method to assess fluid overload for patients with heart failure and/or CKD.A key advantage of the BCM over all other commercially available bioimpedance devices is its ability to quantify fluid overload independent of body composition  by application of a 3-compartment model described by Chamney et al. cohorts . Establi78 This has yet to be shown to impact risk of hard clinical outcomes; randomized trials comparing bioimpedance added to standard care vs standard of care alone have not demonstrated meaningful impact on hospitalizations,78 preservation of residual kidney function,80 cardiovascular outcomes, or death,82 but numbers of outcomes in completed trials are generally small.83 Existing national clinical guidelines support the use of bioimpedance devices in dialysis patients when clinical assessment is challenging and suggest further consideration of the role of devices,85 though cost-effectiveness has not yet been demonstrated.25 Bioimpedance devices could be employed with a slightly different clinical aim in patients with earlier stage CKD not requiring dialysis. Fluid overload measured by bioimpedance is evident in very early CKD86 and has been associated with diastolic dysfunction87 and left ventricular hypertrophy on echocardiography.88 Identifying this subclinical diastolic dysfunction is challenging in CKD because NTpro-BNP is an imperfect diagnostic marker in those with decreased kidney function.89 Bioimpedance techniques may, therefore, represent an attractive tool for identification of patients with CKD who might benefit from screening echocardiographic assessments.Randomized evidence using whole-body bioimpedance indices to support clinical care have emerged from dialysis populations but there are limited randomized data from heart failure and nondialysis CKD populations. For example, in dialysis trials, bioimpedance-based assessment of fluid status vs standard clinical assessment improved parameters, such as blood pressure, left ventricular mass and arterial stiffness.91 Bioimpedance devices have been shown to detect subclinical fluid overload92 which, in people with heart failure, is associated with increased risk of death or need for cardiac transplant.93 Bioimpedance may, therefore, support clinical decisions about when to intensify diuretic therapy so as to modify risk. Bioimpedance devices are generally portable and could be used in outpatient heart failure and CKD clinic assessments and even in patients’ homes. This strategy is being assessed in a small Korean pilot randomized trial assessing the impact of diuretic adjustment guided by home bioimpedance measurements vs standard care on change in NTpro-BNP and, secondarily, on risk of hospitalization for heart failure (NCT05177081). Segmental or localized impedance methods have also been tested and can be measured via implanted cardiac devices that quantify lung impedance. There is some evidence that fluid overload indicated by thoracic impedance predicts hospitalization and has the clinical potential to monitor diuresis.96 Nevertheless, we remain proponents of more widespread study and use of wholebody bioimpedance in a wider range of populations. There is a theoretical concern that whole-body bioimpedance devices may inhibit unipolar pacing in patients dependent on pacemakers, but the majority of pacemakers are now bipolar, and overall risk is considered low.Bioimpedance technology has the potential to support clinical heart failure management by providing serial and objective assessments of fluid status with minimal between-operator differences, yet its use is not featured in recent international clinical guidelines.Our systematic review is the first to assess associations between bioimpedance indices of fluid overload and cardiorenal outcomes reported from heart failure and nondialysis CKD cohorts. The review has a number of limitations largely dictated by the nature of existing studies. First, the observational nature of the studies precludes causal inferences. Second, as described in Results, significant between-study differences in the fluid overload parameters and definitions of clinical outcomes precluded quantitative aggregation of results by meta-analysis. Furthermore, the wide range of reported models each considered a different set of covariates, often adjusting for combinations of potential confounders and mediators of associations simultaneously. This means models often addressed somewhat different research questions. Consequently, our review is limited to qualitative conclusions. Availability of individual participant data from included studies could address some of these limitations but would not address the differing approaches to fluid overload assessment or the relatively small size of completed studies. Third, studies commonly reported only single baseline bioimpedance measurements, which do not account for fluctuation in fluid status resulting in regression-dilution bias and reported associations underestimating the full importance of fluid overload in relation to outcomes. Last, studies rarely characterized both baseline and follow-up cardiac and CKD phenotypes, precluding the joint consideration of these overlapping populations.In summary, whole-body bioimpedance indices of fluid overload appear to be consistently and positively associated with risk of death and adverse cardiovascular outcomes in populations with heart failure and nondialysis CKD, but there are limitations to the currently available evidence. Bioimpedance has several potential roles in clinical management and in clinical research in heart failure and nondialysis CKD. Its further development for these populations would benefit from consensus on the optimum device and standardization of analytical methods for such patients. Large studies recording serial measurements and more detailed baseline and follow-up characterization of both cardiac and renal phenotypes in a range of patients with heart failure and CKD are then needed to quantify more precisely and definitively any threshold above which fluid overload is associated with cardiorenal risks. Such studies could quantify the full extent and shape of associations and investigate the key potential mechanisms by which these associations are mediated.10.1016/j.cardfail.2022.08.005.Supplementary material associated with this article can be found in the online version at doi:Supplementary material


Twitter is one of the most popular social media platforms, with over 320 million active users as of February 2019. Twitter users can enjoy free content delivered by other users whom they actively decide to follow. However, unlike in other areas where Twitter is used passively , in science it can be used in a much more active, collaborative way: to ask for advice, to form new bonds and scientific collaborations, to announce jobs and find employees, to find new mentors and jobs. This is particularly important in the early stages of a scientific career, during which lack of collaboration or delayed access to information can have the most impact.For these reasons, using Twitter appropriately  can be mThis article is written by a group of researchers who have a strong feeling that they have personally benefited from using Twitter, both research-wise and network-wise. We  share our personal experience and advice in the form of ten simple rules, and we hope that this material will help a number of researchers who are planning to start their journey on Twitter to take their first steps and advance their careers using Twitter.Before we start with the rules, inspired by  we briefTimeline—The tweets from the people you follow.Retweet (RT)—sharing a tweet that was originally made by someone else.Quote-tweet—sharing a tweet by someone else in a quote, while adding your own comments.Like (♡), used for showing you like a tweet—a fast way to give feedback without replying. There is no similar function for disliking a tweet.Notifications—Tweets that mention you and replies, retweets and likes for your tweets.Mentioning (@)—If you mention someone with their handle (“This paper by @CaAl is great”), your tweet will show up in their notifications.Direct message (DM)—A private message that is only visible to the sender and the specifically identified recipients. By default, regular Twitter messages are visible to the whole world, including (via search engines such as Google) people who do not have a Twitter account.Hashtag (#)—used to make it easier to find tweets with a common theme by defining ad hoc keywords, for instance tweets about a conference (#ICA19) or career talks (#PhDChat).List—a list of Twitter users that can be public (followed by anyone) or private. Lists can be used to follow accounts that tweet about specific topics, but which you don’t want to follow yourself.Hat Tip or Heard Through (HT), used for thanking the source of a tweet.Subtweeting—Tweeting about somebody without explicitly mentioning their handle, so that they are not informed of your comment (see ‘Mentioning’).Live-tweeting—Tweeting short summaries of an event, for example of a conference talk, as it is happening.Thread—A series of tweets on one subject, for instance ten tweets about a new research paper.To get into the habit of using Twitter, you need to do just that: start. You can do this even before you finish reading this article! You don’t need to know everything about Twitter before posting a tweet, just like you don’t need to know everything about running a marathon before going for a jog. Create an account (you can change your username later) and tweet something—for example, a comment about a paper you are writing or a conference you are going to. This way you can use ideas that you already have—but put them into the form of a tweet, which aligns with the advice of not treating research and outreach as separate entities .A bigger challenge is to continue tweeting regularly once you have started, which is essential to habit change , similarTo Early Career Researchers (ECRs), Twitter has become an invaluable source of information ,5. One cBut even more importantly, Twitter culture has exposed a part of academia that has traditionally always been hidden from view, namely the inception of new research activities. Now, ECRs can observe and even join the process of creating national or international research projects  and logistic (“How do you handle requests for letters of recommendation?”).Interact with others by asking them their opinion on your (re)tweet. Interactivity can be increased by using (simple) polls. Do not hesitate to ask scientists questions about their work : they signed up to Twitter because they intend to interact. Also, feel free to ask questions, both content-related  and their username. Be careful about subtweeting people . It is like gossiping, but then clearly visible to everyone. This is especially problematic if you are in a position of privilege with respect to the person you are quoting. Repeatedly tweeting at somebody who is not interested is another behavior to avoid. This is called “sealioning”, and is one of the negative sides of Twitter experienced at a higher rate by underrepresented groups on Twitter.Use hashtags when appropriate. You can give tips for people who are interesting to follow on Friday with: #FollowFriday or share stories of colleagues who are helping others with #AcademicKindness. Next to hashtags, there are many memes that are like little inside jokes; use those only when you really understand them. One of our favorite examples is “Asking for a friend” used in tweets where you are asking something for yourself that is a bit embarrassing. One example is: “Why is Reviewer 2 always the one who says you should have written on a different topic using a different method? Asking for a friend” .Also, do not respond impulsively if someone is critical towards your research on Twitter. Diplomacy is one of the key components to building a scientific reputation .https://ucomm.stanford.edu/policies/social-media-guidelines/) and the University of Oxford warns employees to be mindful of what pictures might reveal in the background . Some employers, for example Leiden University  may ask you to add a disclaimer that opinions are your own.It might be good to check whether your institute has any social media guidelines (and stick to those). For example, Stanford University tells researchers not to endorse commercial partners , since these are personal remarks and questions just for you.Never feel pressured to read everything on Twitter. It is perfectly okay to In fact, using Twitter irresponsibly can yield negative impact on mental health. One should remember that tweeting can be addictive and should therefore be self-managed with care. Getting notifications on social media activates the same dopamine loop as gambling . When yoA nice feature of Twitter is its asymmetry: you do not have to follow everyone who follows you and vice versa. You can follow big names you have never met and should not be afraid to join a conversation with more senior researchers or people you do not know. Following diverse voices will broaden your horizon.To a certain extent, you can also determine the kind of community your followers form. Twitter offers the possibility for communicating directly with people who would otherwise not find out about your work and field. This is a very relevant form of science communication, since it is a transaction of ideas and not just a transmission . If thisTwitter is a great way to make networking more easy and fun and less scary. For example, it can change your experience in attending a conference . Firstlyafter the conference too. Twitter is a good way to stay in touch after a conference, aa it provides a low-cost way of tracking what people are working on or interested in. Knowing a bit about their interests in research and in life will make it a lot easier and less scary to say “hi” to people the year after!Following people from conferences has value This advice also holds outside of conferences: if you are approaching people you do not know, for example, to ask for a committee membership or a collaboration—you can check out their Twitter account and get an idea, not just of what they work on, but also their style of communication in general. Finally, when you are visiting a conference or university, you can advertise your visit in advance and perhaps plan extra meetings or talks.There are also possibilities to make your Twitter network more extensive and more interesting by interfacing with real life. Put your Twitter handle on slides see  and otheWhenever you have some scientific accomplishment, you can share this information by, for instance, sharing a link to a preprint or vacancy notice. In order to get the best exposure, summarize the content and include an image and an appropriate hashtag . You can “tag”  scientists if you think your tweet is of special interest to them, but do not overdo this. Retweeting can also be a way to put scientists whom you feel deserve more attention in the spotlight.Adding humor to your tweets e.g., , 19) can can19]) Although Twitter is accessible worldwide, it is useful to consider the time of tweeting things, especially when your tweet has more of a local audience. Many people do not read the whole of their timeline and so may never see tweets that were sent while they were asleep. Also keep in mind that not all scientists work at weekends, so science Twitter is a bit quieter then than on weekdays. Tweets for global audiences can be retweeted at a later moment in order to reach different time zones.Even if you use Twitter only for professional purposes, consider opening up a little bit to show your followers you are a real person. People outside your field are not likely to follow you if your tweets are only about sharing events, articles, and positions in your own field. You need to add an extra ingredient—your opinions, or something personal—to what you share. One way to do this is through sharing failures: a rejected paper or job application, or even a spilt coffee. This is a great way to give and receive moral support from other academics.Beyond academia, sharing something personal shows non-academics that scientists are just people too. You don’t need to go into details of your life to do this—think about a nice book you have read recently, a concert you visited, or your cat, as long as you keep the vibe positive. Similar to , we thinOnce you reach a substantial number of followers, let’s say around 1,000, Twitter changes a bit from being a place where you go to share to a place where other people go to learn from you! This change can be a bit similar to going from PhD student to staff: suddenly you are (part of) defining what is and what isn’t appropriate, interesting or cool.This responsibility means that you will need to spend a bit more time to think about what you tweet. There are cases of people getting in trouble for things they have shared through Twitter—going as far as being taken to court for retweeting somebody else’s opinion . 8] als als8] alOn the other hand, a large Twitter network also helps you to spread important ideas, and help people learn about opportunities you know about. To a certain extent, Twitter is an equalizer, whether you are a full professor or an undergraduate student.You can also use Twitter to lift deserving people up. Ensure that you properly credit collaborators, mentors, and especially students to increase their visibility. A great hashtag to promote researchers is #ScholarSunday, an academic version of #FollowFriday created by Dr. Raul Pacheco-Vega.In this article, we have introduced the way in which researchers can benefit from using Twitter, and we listed a number of tips for effective tweeting, both for people who are considering joining Twitter and for those who are already ardent Twitter users.As with any other aspect of human life, Twitter has trade-offs between quantity and quality. It is, unfortunately, a frequent practice to retweet any content spread by one’s collaborators or friends without even reading it. This allows users with high numbers of followers to gain further attention with minimal effort in a kind of “snowball” effect. But chasing after numbers in this or other ways (e.g. tweeting with lots of hashtags in order to maximize your number of followers) is typically a short-sighted strategy, as Twitter is also inhabited by a large number of bots (automated accounts) which will tend to follow you and instead of contributing to your networking efforts, distract you by flooding you with a lot of unwanted content.Twitter is also a public platform and one should be careful about spreading any content that is judgmental about other people, including their personality, their beliefs and their work. Some people have an inborn sense of diplomacy while others need to learn it the hard way. You might find it prudent to observe other researchers’ interactions on Twitter for a while before you indulge in heated conversations on any topic.Furthermore, one should remember that Twitter’s business model is based on advertising. Therefore, the users need to be aware that some of the content is sponsored and aims solely at selling products to the user. As mentioned earlier, Twitter is also populated by bots which are designed to enable certain accounts to increase their numbers of followers. This often takes the form of a certain users following you and then unfollowing you immediately after you follow them back.Regardless of these potential downsides, Twitter is still strongly recommended for anyone who needs to develop themselves in academia, to learn and to teach, to develop and tighten bonds with researchers overseas, and to join the #AcademicTwitter community.


The indwelling pleural catheter (IPC) is an established treatment for recurrent pleural effusion. Fluid leakage through the IPC insertion tract has been reported, but its occurrence is only limited to a short period after the procedure. Besides, the drainage efficacy of IPC may be limited by the presence of loculation in the pleural space, especially when intrapleural fibrinolytic is contraindicated. We report a case of fluid leakage through the healed entry site of IPC due to high pressure built from undrained pleural fluid locules, which was successfully treated with an additional drain targeting the largest undrained locule. This is the first case report for fluid leakage through a healed entry site of the indwelling pleural catheter due to high pressure built from undrained pleural fluid locules. The condition was successfully treated with an additional drain targeting the largest undrained locule. Malignant pleural effusion (MPE) is a common condition in metastatic malignancy and necessitates repeated pleural drainages. The indwelling pleural catheter (IPC) is an established treatment for MPE to minimize the need for repeated pleural intervention. However, its drainage effectiveness can be limited by fluid loculation , and varOur patient, an 88‐year‐old gentleman, presented to the Department of Oncology for recurrent right‐sided MPE due to metastatic lung adenocarcinoma. This was his third episode of recurrent symptomatic MPE in the past two months and all prior episodes were treated by simple pleural drainages only. The pleural fluid became more loculated on the chest X‐ray (CXR) after repeated procedures over these two months.The Respiratory Division was consulted for the placement of IPC. Pre‐insertion thoracic ultrasound (TUS) revealed a heavily septated right pleural space containing multiple fluid locules. The patient consented for the placement of IPC and acknowledged the possibility of incomplete pleural fluid drainage due to septated pleural space. IPC was inserted successfully to the largest locule at the laterobasal pleural space and allowed smooth fluid drainage. However, the right hemithorax remained partially opacified on imaging due to non‐drainable fluid locules Fig. . A trialAt the eighth week, there was a reduction of output to less than a 50 mL per day, together with pain at the healed IPC entry site and progressive increase in shortness of breath. Examination revealed an area of erythematous oedema at the healed entry site of IPC, while sparing the exit site Fig. . There wThe patient returned to the clinic four weeks later and demonstrated complete healing of the IPC entry and exit sites Fig. . The newPersistent fluid leakage through the IPC entry site shortly after insertion has been reported , 4. It iThe occurrence of erythematous induration around the entry site should raise the suspicion of CTM and wound infection. However, the rapid onset of symptoms argued against the possibility of CTM, while the delayed occurrence at a healed wound and a failed response to antibiotic also excluded the diagnosis of wound infection. Combining the prior drainage history and radiological findings, this rare complication of leakage through a healed tract should be considered and an additional drain can relieve both the symptoms and leakage.Appropriate written informed consent was obtained for publication of this case report and accompanying images.Video S1. Spurting of fluid from a bulla at the entry site of indwelling pleural catheter (IPC). The bulla situated at the entry site of IPC with a surrounding area of erythema and subcutaneous oedema. Spurting and continuous oozing of fluid from the bulla due to high pressure built inside it.Click here for additional data file.