The significance of resection margins on R0 results in intrahepatic cholangiocarcinoma.

BACKGROUND: Intrahepatic cholangiocarcinoma is the second most common primary liver cancer after hepatocellular carcinoma with an increasing incidence worldwide. Surgical resection is still the only potential cure, and survival rates are dismal due to disease relapse after resection and/or metastatic disease. Positive resection margins are associated with recurrence, with conflicting studies regarding the benefits of wide resection margins to reduce recurrence rates. METHODS: 126 patients with an R0 resection treated with hepatic surgery for intrahepatic cholangiocarcinoma at the Surgical Department at the Medical University Centre Essen, Germany were identified in a database and retrospectively analysed. Patients were grouped into three groups according to margin width, <1 mm (very narrow margin width) 1-5 mm (narrow margin width) and >5 mm (wide margin width). Epidemiological as well as perioperative data was analysed, and a univariate analysis as well as Kaplan-Meier plots carried out to investigate recurrence-free and overall survival. RESULTS: Wider resection margins did not lead to better recurrence-free survival. A wider resection margin >5 mm was not significantly associated with improved overall survival. Positive lymph nodes (HR 2.50, 95% CI 1.11-5.61, p=0.027) and non-anatomic resections (HR 2.06, 95% CI 1.13-3.75, p=0.019) are significantly associated with poorer overall survival. Regarding recurrence-free survival, V2 vascular invasion was the only risk factor statistically significantly associated with poorer recurrence-free survival (HR 8.83, 95% CI 0.85-2.83, p=0.005). CONCLUSION: Resection margins did not have a significant impact on disease free survival or overall survival following hepatic resection for intrahepatic cholangiocarcinoma. Non-anatomical resections, lymph node and vascular invasion all significantly impacted oncological outcomes.

Comparative Evaluation of Specific Antibody against Mycobacterium tuberculosis ESAT-6 Recombinant Antigen in Healthy Subject with Positive and Negative Skin Test.

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (M. tuberculosis). The laboratory diagnosis of the disease includes various bacteriologic and immunologic methods. Despite the effectiveness of many of these methods in diagnosing active TB, their high cost and time-consuming nature have led researchers to adopt more accurate and rapid screening methods based on specific antigens for M. tuberculosis. The present study aimed to measure specific antibody serum levels against the early secretory antigenic target 6 kDa (ESAT-6) recombinant protein in healthy people and compare it to TB patients. The target population included 27 TB patients and 87 healthy individuals with no clinical TB symptoms. The healthy population was divided into two groups, including positive purified protein derivative (PPD) and negative PPD (35 and 52 people, respectively), using the Tuberculin skin test. The specific antibody level against the ESAT-6 recombinant antigen and the PPD protein was measured using an indirect Enzyme-Linked Immunosorbent Assay (ELISA) test. The results of the study showed that the majority of the healthy population with no symptoms of clinical TB and having negative skin test results did not have antibodies against the recombinant ESAT-6 (98%) and PPD (96%) antigens. On the other hand, there was a high level of the specific antibody of the ESAT-6 recombinant and PPD antigens in TB patients (77%). It is notable that in people with positive skin test results, the level of the antibody against the ESAT-6 recombinant antigen and PPD antigen was 94%. The results demonstrated that the ELISA method based on the measurement of antibodies against the ESAT-6 recombinant antigen can be a proper diagnostic method for rapid and accurate screening of healthy from infected people.

Noninvasive Colorimetric Detection of Acetic Acid in Human Breath Based on an Alginate/Ni-Al-LDH/Dye Composite Film.

Alginate/Ni-Al-layered double hydroxide/dye (Alg/Ni-Al-LDH/dye) composite films were fabricated using the solution casting method. The dyes used included methyl red, phenol red, thymol blue, bromothymol blue, m-cresol purple, methyl orange, bromocresol purple (BP), and bromocresol green (BG) in the overall pH range of 3.8 to 9.6. The chemical composition and morphology of the Alg/Ni-Al-LDH/dye composite film structure were investigated by Fourier transform infrared spectroscopy, FESEM, atomic force microscopy, and X-ray diffraction. The Alg/Ni-Al-LDH/dye composite films were semitransparent and mechanically flexible. Acetic acid was investigated as a respiratory biomarker related to gastrointestinal diseases. The parameters studied included color volume, response time, Ni-Al-LDH nanosheet volume, reusability, and drawing of the calibration curve along with statistical features including standard deviation, relative standard deviation, limit of detection, and limit of quantitation. Colorimetric indicators BP and BG in the presence of acetic acid produce color changes that are almost visible to the naked eye. However, other used indicators have shown almost no change. Therefore, it can be reported that the sensors made in the presence of BP and BG act selectively in relation to acetic acid.

Arrested Dynamics of Droplet Spreading on Ice.

We investigate the arrested spreading of room temperature droplets impacting flat ice. The use of an icy substrate eliminates the nucleation energy barrier, such that a freeze front can initiate as soon as the droplet's temperature cools down to 0 °C. We employ scaling analysis to rationalize distinct regimes of arrested hydrodynamics. For gently deposited droplets, capillary-inertial spreading is halted at the onset of contact line freezing, yielding a 1/7 scaling law for the arrested diameter. At low impact velocities (We≲100), inertial effects result in a 1/2 scaling law. At higher impact velocities (We>100), inertio-viscous spreading can spill over the frozen base of the droplet until its velocity matches that of a kinetic freeze front caused by local undercooling, resulting in a 1/5 scaling law.

Semi-automated digital workflow to design and evaluate patient-specific mandibular reconstruction implants.

The reconstruction of large mandibular defects with optimal aesthetic and functional outcomes remains a major challenge for maxillofacial surgeons. The aim of this study was to design patient-specific mandibular reconstruction implants through a semi-automated digital workflow and to assess the effects of topology optimization on the biomechanical performance of the designed implants. By using the proposed workflow, a fully porous implant (LA-implant) and a topology-optimized implant (TO-implant) both made of Ti-6Al-4V ELI were designed and additively manufactured using selective laser melting. The mechanical performance of the implants was predicted by performing finite element analysis (FEA) and was experimentally assessed by conducting quasi-static and cyclic biomechanical tests. Digital image correlation (DIC) was used to validate the FE model by comparing the principal strains predicted by the FEM model with the measured distribution of the same type of strain. The numerical predictions were in good agreement with the DIC measurements and the predicted locations of specimen failure matched the actual ones. No statistically significant differences (p < 0.05) in the mean stiffness, mean ultimate load, or mean ultimate displacement were detected between the LA- and TO-implant groups. No implant failures were observed during quasi-static or cyclic testing under masticatory loads that were substantially higher (>1000 N) than the average maximum biting force of healthy individuals. Given its relatively lower weight (16.5%), higher porosity (17.4%), and much shorter design time (633.3%), the LA-implant is preferred for clinical application. This study clearly demonstrates the capability of the proposed workflow to develop patient-specific implants with high precision and superior mechanical performance, which will greatly facilitate cost- and time-effective pre-surgical planning and is expected to improve the surgical outcome.

Development of a Translational Model to Assess the Impact of Opioid Overdose and Naloxone Dosing on Respiratory Depression and Cardiac Arrest.

In response to a surge of deaths from synthetic opioid overdoses, there have been increased efforts to distribute naloxone products in community settings. Prior research has assessed the effectiveness of naloxone in the hospital setting; however, it is challenging to assess naloxone dosing regimens in the community/first-responder setting, including reversal of respiratory depression effects of fentanyl and its derivatives (fentanyls). Here, we describe the development and validation of a mechanistic model that combines opioid mu receptor binding kinetics, opioid agonist and antagonist pharmacokinetics, and human respiratory and circulatory physiology, to evaluate naloxone dosing to reverse respiratory depression. Validation supports our model, which can quantitatively predict displacement of opioids by naloxone from opioid mu receptors in vitro, hypoxia-induced cardiac arrest in vivo, and opioid-induced respiratory depression in humans from different fentanyls. After validation, overdose simulations were performed with fentanyl and carfentanil followed by administration of different intramuscular naloxone products. Carfentanil induced more cardiac arrest events and was more difficult to reverse than fentanyl. Opioid receptor binding data indicated that carfentanil has substantially slower dissociation kinetics from the opioid receptor compared with nine other fentanyls tested, which likely contributes to the difficulty in reversing carfentanil. Administration of the same dose of naloxone intramuscularly from two different naloxone products with different formulations resulted in differences in the number of virtual patients experiencing cardiac arrest. This work provides a robust framework to evaluate dosing regimens of opioid receptor antagonists to reverse opioid-induced respiratory depression, including those caused by newly emerging synthetic opioids.

Electrochemical distinction of neuronal and neuroblastoma cells via the phosphorylation of the cellular extracellular membrane.

In this contribution we establish a proof of concept method for monitoring, quantifying and differentiating the extracellular phosphorylation of Human SHSY5Y undifferentiated neuronal cells and neuroblastoma cells by three prominent ectokinases PKA, PKC and Src. Herein it is demonstrated that a combination of different experimental techniques, including fluorescence microscopy, quartz crystal microscopy (QCM) and electrochemistry, can be used to detect extracellular phosphorylation levels of neuronal and neuroblastoma cells. Phosphorylation profiles of the three ectokinases, PKA, PKC and Src, were investigated using fluorescence microscopy and the number of phosphorylation sites per kinase was estimated using QCM. Finally, the phosphorylation of the extracellular membrane was determined using electrochemistry. Our results clearly demonstrate that the extracellular phosphorylation of neuronal cells differs significantly in terms of its phosphorylation profile from diseased neuroblastoma cells and the strength of surface electrochemical techniques in the differentiation process. We reveal that using electrochemistry, the percent compositions of neuronal and neuroblastoma cells can also be identified.

Diagnosis of COVID-19 in symptomatic patients: An updated review.

A group of pneumonia patients was detected in Hubei Province, in China in December 2019. The etiology of the disease was unknown. Later, the researchers diagnosed the novel Coronavirus as the causal agent of this respiratory disease. On February 12th 2020, the World Health Organization (WHO) officially named this disease Coronavirus disease 2019 (COVID-19). Consequently, the disease spread globally and became a pandemic. As there is no specific treatment for the symptomatic patients and several vaccines are approved by WHO, the efficacy and effectiveness of these vaccines are not fully understood yet and the availability of these vaccines are very limited. In addition, new variants and mutants of SARS-CoV-2 are thought to be able to evade the immune system of the host. So, diagnosis and isolation of infected individuals is advised. Currently, real-time reverse transcription-polymerase chain reaction (RT-PCR) is considered the gold standard method to detect novel Coronavirus, however, there are few limitations associated with RT-PCR such as false-negative results. This demanded another diagnostic tool to detect and isolate COVID-19 early and accurately. Chest computed tomography (CT) became another option to diagnose COVID-19 patients accurately (about 98% sensitivity). However, it did not apply to the asymptomatic carriers and sometimes the results were misinterpreted as from other groups of Coronavirus infection. The combination of RT-PCR and chest CT might be the best option in detecting novel Coronavirus infection early and accurately thereby allowing adaptation of measures for the prevention and control of the COVID-19.

En diciembre de 2019 se detectó un grupo de pacientes con neumonía en la provincia de Hubei, China, desconociéndose la etiología de la enfermedad. Posteriormente, los investigadores señalaron al nuevo coronavirus como agente causal de esta enfermedad respiratoria. El 12 de febrero de 2020, la Organización Mundial de la Salud (OMS) la designó oficialmente como enfermedad por coronavirus de 2019 (COVID-19). A continuación, dicha enfermedad se propagó a nivel global, y se convirtió en una pandemia. No existe tratamiento específico para los pacientes sintomáticos, y la OMS ha aprobado diversas vacunas. Sin embargo, la eficacia y la efectividad de las mismas no se comprende plenamente aún, siendo muy limitada su disponibilidad. Además, se piensa que las diferentes variantes y mutaciones del SARS-CoV-2 son capaces de evadir el sistema inmune del huésped. Por tanto, se recomienda el diagnóstico y aislamiento de las personas infectadas. Actualmente se considera la reacción en cadena de la polimerasa con transcriptasa inversa (RT-PCR) a tiempo real el método de referencia para detectar el nuevo coronavirus. Sin embargo, existen algunas limitaciones asociadas a RT-PCR tales como los resultados falso-negativos. En consecuencia, ello ha demandado otra herramienta diagnóstica para detectar y aislar la COVID-19 de manera temprana y precisa. La tomografía computarizada (TC) de tórax se ha convertido en otra opción para diagnosticar de manera precisa a los pacientes con COVID-19 (cerca del 98% de sensibilidad). Sin embargo no se aplica a los portadores asintomáticos, y a veces se han malinterpretado los resultados como en el caso de otros grupos de infección por coronavirus. La combinación de RT-PCR y TC de tórax podría ser la mejor opción para detectar la nueva infección por coronavirus de manera temprana y precisa, permitiendo, por tanto, la adaptación de las medidas para la prevención y el control de la COVID-19.

CORAL: Monte Carlo based global QSAR modelling of Bruton tyrosine kinase inhibitors using hybrid descriptors.

Global QSAR modelling was performed to predict the pIC50 values of 233 diverse heterocyclic compounds as BTK inhibitors with the Monte Carlo algorithm of CORAL software using the DCW hybrid descriptors extracted from SMILES notations of molecules. The dataset of 233 BTK inhibitors was randomly split into training, invisible training, calibration and validation sets. The index of ideality of correlation was also applied to build and judge the predictability of the QSAR models. Eight global QSAR models based on the hybrid optimal descriptor using two target functions, i.e. TF1 (WIIC = 0) and TF2 (WIIC = 0.2) have been constructed. The statistical parameters of QSAR models computed by TF2 are more reliable and robust and were used to predict the pIC50 values. The model constructed for split 4 via TF2 is regarded as the best model and the numerical values of r2Train, r2Valid, Q2Train and Q2Valid are equal to 0.7981, 0.7429, 0.7898 and 0.6784, respectively. By internal and external validation techniques, the predictability and reliability of the designed models have been assessed. The structural attributes responsible for the increase and decrease of pIC50 of BTK inhibitors were also identified.

Using Frost to Promote Cassie Ice on Hydrophilic Pillars.

We develop a novel approach to suspend ice in the air-trapping Cassie state without requiring any fragile hydrophobic coatings or nanostructures. First, frost was preferentially grown on the tops of hydrophilic aluminum pillars due to their sharp corners and elevation over the noncondensable gas barrier. Subsequently, Cassie ice was formed by virtue of the impacting droplets getting arrested by the upper frost tips. A scaling model reveals that the dynamic pressure of an impacting droplet causes the water to wick inside the porous frost faster than the timescale to impale between the pillars.

The index of ideality of correlation: QSAR studies of hepatitis C virus NS3/4A protease inhibitors using SMILES descriptors.

Robust and reliable QSAR models were developed to predict half-maximal inhibitory concentration (IC50) values of hepatitis C virus NS3/4A protease inhibitors from the Monte Carlo technique. 524 HCV NS3/4A protease inhibitors were extracted from the scientific literature to create a reasonably large set. The models were developed using CORAL software by using two target functions namely target function 1 (TF1) without applying the index of ideality of correlation (IIC) and target function 2 (TF2) that uses IIC. The constructed models based on TF2 were statistically more significant and robust than the models based on TF1. The determination coefficients (r2) of training and test sets were 0.86 and 0.88 for the best split based on TF2. The promoters of the increase/decrease of activity were also extracted and interpreted in detail. The model interpretation results explain the role of different structural attributes in predicting the pIC50 values of hepatitis C virus NS3/4A protease inhibitors. Based on the mechanistic model interpretation results, eight new compounds were designed and their pIC50 values were predicted based on the average prediction of ten models.

How Multilayered Feathers Enhance Underwater Superhydrophobicity.

Inspired by ducks, we demonstrate that air pockets within stacked layers of porous superhydrophobic feathers can withstand up to five times more water pressure compared to a single feather. In addition to natural duck feathers, this "layer effect" was replicated with synthetic feathers created by laser cutting micrometric slots into aluminum foil and imparting a superhydrophobic nanostructure. It was revealed that adding layers promotes an increasingly redundant pathway for water impalement, which serves to pressurize the enclosed air pockets. This was validated by creating a probabilistic pore impalement model and also by filling the feathers with an incompressible oil, rather than air, to suppress the layer effect. In addition to revealing a utility of natural duck feathers, our findings suggest that multilayered engineered surfaces can maintain air pockets at high pressures, useful for reducing the drag and fouling of marine structures or enhancing desalination membranes.

Electrostatic Jumping of Frost.

The electrification of ice has been a subject of research since 1940, mostly in the context of charge generation in thunderstorms. This generation of electric charge is spontaneous, distinct from applying an external electric field to affect the diffusive growth of ice crystals. Here, we exploit the spontaneous electrification of ice to reveal a surprising phenomenon of jumping frost dendrites. We use side-view high-speed imaging to experimentally observe frost dendrites breaking off from mother dendrites and/or the substrate to jump out-of-plane toward an opposing polar liquid. Analytical and numerical models are then developed to estimate the attractive force between the frost dendrites and liquid, in good agreement with the experimental results. These models estimate the extent of charge separation within a growing sheet of frost, which is caused by mismatches in the mobilities of the charge carriers in ice. Our findings show that the unexpected jumping frost event can serve as a model system for resolving long-standing questions in atmospheric physics regarding charge separation in ice, while also having potential as a deicing construct.

Suppressing Condensation Frosting Using an Out-of-Plane Dry Zone.

The vapor pressure above ice is lower than that above supercooled water at the same temperature. This inherent hygroscopic quality of ice has recently been exploited to suppress frost growth by patterning microscopic ice stripes along a surface. These vapor-attracting ice stripes prevented condensation frosting from occurring in the intermediate regions; however, the required presence of the sacrificial ice stripes made it impossible to achieve the ideal case of a completely dry surface. Here, we decouple the sacrificial ice from the antifrosting surface by holding an uncoated aluminum surface in parallel with a prefrosted surface. By replacing the overlapping in-plane dry zones with a uniform out-of-plane dry zone, we show that even an uncoated aluminum surface can stay almost completely dry in chilled and supersaturated conditions. Using a blend of experiments and numerical simulations, we show that the critical separation required to keep the surface dry is a function of the ambient supersaturation.

Toward Antibacterial Coatings for Personalized Implants.

The complex reconstructive surgeries for which patient-specific orthopedic, maxillofacial, or dental implants are used often necessitate wounds that are open for a considerable amount of time. Unsurprisingly, this allows bacteria to establish implant-associated infection, despite the scrupulous sterilization efforts made during surgery. Here, we developed a prophylactic bactericidal coating via electrophoretic deposition technology for two 3D-printed porous titanium implant designs. The surface characteristics, antibiotic release behavior, antibacterial properties, and impact on osteoblast cell proliferation of the optimized coatings were investigated. The results unequivocally confirmed the biofunctionality of the implants in vitro. This study reveals a new avenue for future antibacterial patient-specific implants.

A Monte Carlo method based QSPR model for prediction of reaction rate constants of hydrated electrons with organic contaminants.

The Monte Carlo algorithm was applied to formulate a robust quantitative structure-property relationship (QSPR) model to compute the reactions rate constants of hydrated electron values for a data set of 309 water contaminants containing 125 aliphatic and 184 phenyl-based chemicals. The QSPR models were computed with the hybrid optimal descriptors which were procured by combining the SMILES and hydrogen-suppressed molecular graph for both classes of compounds. Approximately 75% of the total experimental data set was randomly divided into training and invisible training sets, while approximately 25% was divided into calibration and validation sets. The authenticity and robustness of the developed QSPR models were also judged by the Index of Ideality of Correlation. In QSPR modelling of aliphatic compounds, the numerical values of r T r a i n i n g 2 , r V a l i d a t i o n 2 , Q T r a i n i n g 2 and Q V a l i d a t i o n 2 were in the range of 0.852-0.905, 0.815-0.894, 0.839-0.897 and 0.737-0.867, respectively. Whereas, in the QSPR modelling of phenyl-based compounds, the numerical values of r T r a i n i n g 2 , r V a l i d a t i o n 2 , Q T r a i n i n g 2 and Q V a l i d a t i o n 2 were in the range of 0.867-0.896, 0.852-0.865, 0.816-0.850 and 0.760-0.762, respectively. The structural attributes, which are promoters of l o g K e a q - increase/decrease are also extracted from the SMILES notation for mechanistic interpretation. These QSPR models can also be applied to compute the reaction rate constants of organic contaminants.

Biomedical soft robots: current status and perspective.

This paper reviews the current status of soft robots in biomedical field. Soft robots are made of materials that have comparable modulus of elasticity to that of biological systems. Several advantages of soft robots over rigid robots are safe human interaction, ease of adaptation with wearable electronics and simpler gripping. We review design factors of soft robots including modeling, controls, actuation, fabrication and application, as well as their limitations and future work. For modeling, we survey kinematic, multibody and numerical finite element methods. Finite element methods are better suited for the analysis of soft robots, since they can accurately model nonlinearities in geometry and materials. However, their real-time integration with controls is challenging. We categorize the controls of soft robots as model-based and model-free. Model-free controllers do not rely on an explicit analytical or numerical model of the soft robot to perform actuation. Actuation is the ability to exert a force using actuators such as shape memory alloys, fluid gels, elastomers and piezoelectrics. Nonlinear geometry and materials of soft robots restrict using conventional rigid body controls. The fabrication techniques used for soft robots differ significantly from that of rigid robots. We survey a wide range of techniques used for fabrication of soft robots from simple molding to more advanced additive manufacturing methods such as 3D printing. We discuss the applications and limitations of biomedical soft robots covering aspects such as functionality, ease of use and cost. The paper concludes with the future discoveries in the emerging field of soft robots.

QSAR modelling of larvicidal phytocompounds against Aedes aegypti using index of ideality of correlation.

Aedes aegypti is the primary vector of several infectious viruses that cause yellow, dengue, chikungunya, and Zika fevers. Recently, plant-derived products have been tested as safe and eco-friendly larvicides against Ae. aegypti. The present study aimed to improve QSAR models for 62 larvicidal phytocompounds against Ae. aegypti via the Monte Carlo method based on the index of the ideality of correlation (IIC) criterion. The representation of structures was done with SMILES. Three splits were prepared randomly and three QSAR models were constructed using IIC target function. The molecular descriptors were selected from SMILES descriptors and the hydrogen-filled molecular graphs. The predictability of three models was evaluated on the validation sets, the r 2 of which was 0.9770, 0.8660, and 0.8565 for models 1 to 3, respectively. The statistical results of three randomized splits indicated that robust, simple, predictive, and reliable models were obtained for different sets. From the modelling results, important descriptors were identified to enhance and reduce the larvicidal activity of compounds. Based on the identified important descriptors, some new structures of larvicidal compounds were proposed. The larvicidal activity of novel molecules designed further was supported by docking studies. Using the simple QSAR model, one can predict pLC50 of new similarity larvicidal phytocompounds.

Electrochemical detection of neuronal extracellular phosphorylation by PKA, PKC and Src.

The focus of this work described here is to establish a method for monitoring and quantifying the extracellular phosphorylation of Human SHSY5Y undifferentiated neuronal cells by three ectokinases PKA, PKC and Src; these are kinases that are known to be present in the extracellular matrix. Here is demonstrated that a combination of different experimental techniques, including microscopy and electrochemistry, can be used to detect extracellular phosphorylations. Phosphorylation profiles of the three ectokinases, PKA, PKC and Src, were investigated using fluorescence microscopy and the number of phosphorylation sites per kinase was estimated using QCM. Finally, the phosphorylation of the extracellular membrane was determined using electrochemistry. Our results clearly demonstrate the extracellular phosphorylation of neuronal cells and the strength of surface electrochemical techniques in the investigation of cellular phosphorylation.

VOLT: a novel open-source pipeline for automatic segmentation of endolymphatic space in inner ear MRI.

BACKGROUND: Objective and volumetric quantification is a necessary step in the assessment and comparison of endolymphatic hydrops (ELH) results. Here, we introduce a novel tool for automatic volumetric segmentation of the endolymphatic space (ELS) for ELH detection in delayed intravenous gadolinium-enhanced magnetic resonance imaging of inner ear (iMRI) data. METHODS: The core component is a novel algorithm based on Volumetric Local Thresholding (VOLT). The study included three different data sets: a real-world data set (D1) to develop the novel ELH detection algorithm and two validating data sets, one artificial (D2) and one entirely unseen prospective real-world data set (D3). D1 included 210 inner ears of 105 patients (50 male; mean age 50.4 ± 17.1 years), and D3 included 20 inner ears of 10 patients (5 male; mean age 46.8 ± 14.4 years) with episodic vertigo attacks of different etiology. D1 and D3 did not differ significantly concerning age, gender, the grade of ELH, or data quality. As an artificial data set, D2 provided a known ground truth and consisted of an 8-bit cuboid volume using the same voxel-size and grid as real-world data with different sized cylindrical and cuboid-shaped cutouts (signal) whose grayscale values matched the real-world data set D1 (mean 68.7 ± 7.8; range 48.9-92.8). The evaluation included segmentation accuracy using the Sørensen-Dice overlap coefficient and segmentation precision by comparing the volume of the ELS. RESULTS: VOLT resulted in a high level of performance and accuracy in comparison with the respective gold standard. In the case of the artificial data set, VOLT outperformed the gold standard in higher noise levels. Data processing steps are fully automated and run without further user input in less than 60 s. ELS volume measured by automatic segmentation correlated significantly with the clinical grading of the ELS (p < 0.01). CONCLUSION: VOLT enables an open-source reproducible, reliable, and automatic volumetric quantification of the inner ears' fluid space using MR volumetric assessment of endolymphatic hydrops. This tool constitutes an important step towards comparable and systematic big data analyses of the ELS in patients with the frequent syndrome of episodic vertigo attacks. A generic version of our three-dimensional thresholding algorithm has been made available to the scientific community via GitHub as an ImageJ-plugin.