The BMDC model, a performant cell-based test to assess the sensitizing potential and potency of chemicals including pre/pro-haptens.

BACKGROUND: Chemical-induced allergies at workplace represent a significant occupational health issue. These substances must be properly identified as sensitizers. In previous studies, an original model using mouse bone marrow-derived dendritic cells (BMDC) was developed for this purpose. OBJECTIVES: The aim of this study was to evaluate the predictive capacity of the BMDC model with a large panel of sensitizers (including pre- and pro-haptens) and non-sensitizers. METHODS: The readout from the BMDC model is based on expression levels of six phenotypic markers measured by flow cytometry. RESULTS: The results indicate that 29 of the 37 non-sensitizers, and 81 of the 86 sensitizers were correctly classified compared to the Local Lymph Node Assay (LLNA). Statistical analysis revealed the BMDC model to have a sensitivity of 94%, a specificity of 78%, and an accuracy of 89%. The EC2 (Effective Concentration) values calculated with this model allow sensitizers to be categorized into four classes: extreme, strong, moderate and weak. CONCLUSIONS: These excellent predictive performances show that the BMDC model discriminates between sensitizers and non-sensitizers with outstanding precision equal to or better than existing validated alternative models. Moreover, this model allows to predict sensitization potency of chemicals. The BMDC test could therefore be proposed as an additional tool to assess the sensitizing potential and potency of chemicals.

Identification of the allergenic sensitizing potential of bisphenol A substitutes used in the industry.

BACKGROUND: Bisphenol (BP-)A is a chemical used in Europe to produce polycarbonate plastics and epoxy resin or as colour developer in thermal paper. Due to its toxicity, BPA presence was restricted by European regulations. Therefore, substitute chemicals are replacing BPA. OBJECTIVE: To assess the allergenic sensitizing potential of 27 substitutes to BPA used in the industry. METHODS: The expression of two costimulatory molecules and six cytokines were analysed by flow cytometry in mouse bone marrow-derived dendritic cells (BMDCs) exposed to the chemicals. RESULTS: All substances except one induced overexpression of at least one receptor and were thus identified as having allergenic sensitizing potential. Based on the BMDC model, they were classified as extreme (1 out of 27), strong (20 out of 27) and moderate (5 out of 27) sensitizers. BPA was classified as a moderate sensitizer and BPF was the only substitute classified as a non-sensitizer. The more potent substitutes induced more than 2-fold secretion of CCL3, CCL4 and/or CCL5 by dendritic cells. CONCLUSION: Most of the BPA substitutes tested in this study have an allergenic sensitizing potential; 24 of them being more potent than BPA itself. Only BPE, BPF and 2,4-BPS appeared to be weaker sensitizers than BPA.

A Multi-Step Precision Pathway for Predicting Allograft Survival in Heterogeneous Cohorts of Kidney Transplant Recipients.

Accurate prediction of allograft survival after kidney transplantation allows early identification of at-risk recipients for adverse outcomes and initiation of preventive interventions to optimize post-transplant care. Many prediction algorithms do not model cohort heterogeneity and may lead to inaccurate assessment of longer-term graft outcomes among minority groups. Using data from a national Australian kidney transplant cohort (2008-2017) as the derivation set, we developed P-Cube, a multi-step precision prediction pathway model for predicting overall graft survival in three ethnic subgroups: European Australians, Asian Australians and Aboriginal and Torres Strait Islander Peoples. The concordance index for the European Australians, Asian Australians, and Aboriginal and Torres Strait Islander Peoples subpopulations were 0.99 (0.98-0.99), 0.93 (0.92-0.94) and 0.92 (0.91-0.93), respectively. Similar findings were observed when validating P-cube using an external dataset [Scientific Registry of Transplant Recipient Registry (2006-2020)]. Six sub-categories of recipients with distinct risk factor profiles were identified. Some factors such as blood group compatibility were considered important across the entire transplant population. Other factors such as human leukocyte antigen (HLA)-DR mismatches were unique to older recipients. The P-cube model identifies allograft survival specific risk factors within a heterogenous population and offers personalized survival predictions in a diverse cohort.

simKAP: simulation framework for the kidney allocation process with decision making model.

Organ shortage is a major barrier in transplantation and rules guarding organ allocation decisions should be robust, transparent, ethical and fair. Whilst numerous allocation strategies have been proposed, it is often unrealistic to evaluate all of them in real-life settings. Hence, the capability of conducting simulations prior to deployment is important. Here, we developed a kidney allocation simulation framework (simKAP) that aims to evaluate the allocation process and the complex clinical decision-making process of organ acceptance in kidney transplantation. Our findings have shown that incorporation of both the clinical decision-making and a dynamic wait-listing process resulted in the best agreement between the actual and simulated data in almost all scenarios. Additionally, several hypothetical risk-based allocation strategies were generated, and we found that these strategies improved recipients' long-term post-transplant patient survival and reduced wait time for transplantation. The importance of simKAP lies in its ability for policymakers in any transplant community to evaluate any proposed allocation algorithm using in-silico simulation.

Preoperative Transfusion in Sickle Cell Disease Children Undergoing Adenotonsillectomy.

Objective: To perform a systematic review and meta-analysis comparing two pre-operative transfusion regimens (conservative versus aggressive) in children with sickle cell disease(SCD) undergoing adenotonsillectomy in terms of post-operative complications, complications related to SCD and transfusion related complications. Data Sources and Review Methods: A literature review was performed through PubMed, EMBASE, Cochrane, and Ovid databases using the following phrases: (Adenotonsillectomy OR Tonsillectomy) AND (Sickle Cell Disease OR Sickle Cell Trait). Using predetermined inclusion and exclusion criteria, seven articles were selected for systemic review and two control trials were included in meta-analysis. Results: Out of a total of 3,146 results, seven articles were selected for review and two controlled trials were included in the meta-analysis. There was no statistically significant difference in the rate of primary and secondary hemorrhage between the aggressive and conservative transfusion regimens (RR = 3.1, CI = 0.84-11.4, p-value = 0.089). The rate of sickle cell disease related complications including vaso-occlusive crisis and acute chest syndrome was also not statistically significant between the two transfusion groups (RR = 1.4, CI = 0.7-2.8, p-value = 0.339). Even though, the transfusion related complications did not reach statistical significance, there was a higher complication rate in the group receiving aggressive blood transfusion. Conclusion: In SCD children undergoing adenotonsillectomy, an aggressive transfusion regimen that focuses on reducing the Hemoglobin S ratio to below 30% has not been shown to be more effective in reducing post-operative complications when compared to a conservative transfusion regimen. Therefore, it is reasonable to utilize a conservative transfusion regimen, thereby reducing the transfusion-associated risks.

NEMoE: a nutrition aware regularized mixture of experts model to identify heterogeneous diet-microbiome-host health interactions.

BACKGROUND: Unrevealing the interplay between diet, the microbiome, and the health state could enable the design of personalized intervention strategies and improve the health and well-being of individuals. A common approach to this is to divide the study population into smaller cohorts based on dietary preferences in the hope of identifying specific microbial signatures. However, classification of patients based solely on diet is unlikely to reflect the microbiome-host health relationship or the taxonomic microbiome makeup. RESULTS: We present a novel approach, the Nutrition-Ecotype Mixture of Experts (NEMoE) model, for establishing associations between gut microbiota and health state that accounts for diet-specific cohort variability using a regularized mixture of experts model framework with an integrated parameter sharing strategy to ensure data-driven diet-cohort identification consistency across taxonomic levels. The success of our approach was demonstrated through a series of simulation studies, in which NEMoE showed robustness with regard to parameter selection and varying degrees of data heterogeneity. Further application to real-world microbiome data from a Parkinson's disease cohort revealed that NEMoE is capable of not only improving predictive performance for Parkinson's Disease but also for identifying diet-specific microbial signatures of disease. CONCLUSION: In summary, NEMoE can be used to uncover diet-specific relationships between nutritional-ecotype and patient health and to contextualize precision nutrition for different diseases. Video Abstract.

Quantification of global orbital shape variation.

The complex anatomy of the orbit generates a complex orbital shape that can only be quantified approximatively by classic linear measurements such as maximum width and height. There is no global three-dimensional quantification of variations in orbital shape. The purpose of this study was to develop a method to quantify a global three-dimensional orbital shape variation in a healthy population and to test a series of explanatory factors. We investigated the hypotheses that orbital shape is related to gender(H1), orbital size(H2) and/or age(H3). Medical computed tomography(CT) images of 60 adult individuals were studied. The study sample consisted of 30 males and 30 females with a mean age of 25.1 years. Four anatomical landmarks and 140 semi-landmarks were measured on both positive and negative 3D reconstructed orbits and analyzed with geometric morphometrics. A principal component analysis(PCA) was computed to define a morphological space. Shape variation was visualized using vector distance maps and diagrams. The greatest variation was seen in the length of the superior orbital fissure. There was a gradient in terms of orbital shape ranging from short, wide orbits to tall, narrow orbits. The analysis did not highlight any significant age-, gender- or size-related impact in terms of orbital shape variation. Future avenues to explore include the study of other potential explanatory factors such as the different embryological origins of the orbital bones, the passage of vessels and nerves, and ethnic origins. This method can also be applied to the study of pathological orbits.

Screening methods for linear errors-in-variables models in high dimensions.

Microarray studies, in order to identify genes associated with an outcome of interest, usually produce noisy measurements for a large number of gene expression features from a small number of subjects. One common approach to analyzing such high-dimensional data is to use linear errors-in-variables (EIV) models; however, current methods for fitting such models are computationally expensive. In this paper, we present two efficient screening procedures, namely, corrected penalized marginal screening (PMSc) and corrected sure independence screening (SISc), to reduce the number of variables for final model building. Both screening procedures are based on fitting corrected marginal regression models relating the outcome to each contaminated covariate separately, which can be computed efficiently even with a large number of features. Under mild conditions, we show that these procedures achieve screening consistency and reduce the number of features substantially, even when the number of covariates grows exponentially with sample size. In addition, if the true covariates are weakly correlated, we show that PMSc can achieve full variable selection consistency. Through a simulation study and an analysis of gene expression data for bone mineral density of Norwegian women, we demonstrate that the two new screening procedures make estimation of linear EIV models computationally scalable in high-dimensional settings, and improve finite sample estimation and selection performance compared with estimators that do not employ a screening stage.

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data.

Survival analysis is a branch of statistics that deals with both the tracking of time and the survival status simultaneously as the dependent response. Current comparisons of survival model performance mostly center on clinical data with classic statistical survival models, with prediction accuracy often serving as the sole metric of model performance. Moreover, survival analysis approaches for censored omics data have not been thoroughly investigated. The common approach is to binarize the survival time and perform a classification analysis. Here, we develop a benchmarking design, SurvBenchmark, that evaluates a diverse collection of survival models for both clinical and omics data sets. SurvBenchmark not only focuses on classical approaches such as the Cox model but also evaluates state-of-the-art machine learning survival models. All approaches were assessed using multiple performance metrics; these include model predictability, stability, flexibility, and computational issues. Our systematic comparison design with 320 comparisons (20 methods over 16 data sets) shows that the performances of survival models vary in practice over real-world data sets and over the choice of the evaluation metric. In particular, we highlight that using multiple performance metrics is critical in providing a balanced assessment of various models. The results in our study will provide practical guidelines for translational scientists and clinicians, as well as define possible areas of investigation in both survival technique and benchmarking strategies.

Cross-Platform Omics Prediction procedure: a statistical machine learning framework for wider implementation of precision medicine.

In this modern era of precision medicine, molecular signatures identified from advanced omics technologies hold great promise to better guide clinical decisions. However, current approaches are often location-specific due to the inherent differences between platforms and across multiple centres, thus limiting the transferability of molecular signatures. We present Cross-Platform Omics Prediction (CPOP), a penalised regression model that can use omics data to predict patient outcomes in a platform-independent manner and across time and experiments. CPOP improves on the traditional prediction framework of using gene-based features by selecting ratio-based features with similar estimated effect sizes. These components gave CPOP the ability to have a stable performance across datasets of similar biology, minimising the effect of technical noise often generated by omics platforms. We present a comprehensive evaluation using melanoma transcriptomics data to demonstrate its potential to be used as a critical part of a clinical screening framework for precision medicine. Additional assessment of generalisation was demonstrated with ovarian cancer and inflammatory bowel disease studies.

The Gut Microbiome in Parkinson's Disease: A Longitudinal Study of the Impacts on Disease Progression and the Use of Device-Assisted Therapies.

Background: Altered gut microbiome (GM) composition has been established in Parkinson's disease (PD). However, few studies have longitudinally investigated the GM in PD, or the impact of device-assisted therapies. Objectives: To investigate the temporal stability of GM profiles from PD patients on standard therapies and those initiating device-assisted therapies (DAT) and define multivariate models of disease and progression. Methods: We evaluated validated clinical questionnaires and stool samples from 74 PD patients and 74 household controls (HCs) at 0, 6, and 12 months. Faster or slower disease progression was defined from levodopa equivalence dose and motor severity measures. 19 PD patients initiating Deep Brain Stimulation or Levodopa-Carbidopa Intestinal Gel were separately evaluated at 0, 6, and 12 months post-therapy initiation. Results: Persistent underrepresentation of short-chain fatty-acid-producing bacteria, Butyricicoccus, Fusicatenibacter, Lachnospiraceae ND3007 group, and Erysipelotrichaceae UCG-003, were apparent in PD patients relative to controls. A sustained effect of DAT initiation on GM associations with PD was not observed. PD progression analysis indicated that the genus Barnesiella was underrepresented in faster progressing PD patients at t = 0 and t = 12 months. Two-stage predictive modeling, integrating microbiota abundances and nutritional profiles, improved predictive capacity (change in Area Under the Curve from 0.58 to 0.64) when assessed at Amplicon Sequence Variant taxonomic resolution. Conclusion: We present longitudinal GM studies in PD patients, showing persistently altered GM profiles suggestive of a reduced butyrogenic production potential. DATs exerted variable GM influences across the short and longer-term. We found that specific GM profiles combined with dietary factors improved prediction of disease progression in PD patients.

Large-magnitude (VEI ≥ 7) 'wet' explosive silicic eruption preserved a Lower Miocene habitat at the Ipolytarnóc Fossil Site, North Hungary.

During Earth's history, geosphere-biosphere interactions were often determined by momentary, catastrophic changes such as large explosive volcanic eruptions. The Miocene ignimbrite flare-up in the Pannonian Basin, which is located along a complex convergent plate boundary between Europe and Africa, provides a superb example of this interaction. In North Hungary, the famous Ipolytarnóc Fossil Site, often referred to as "ancient Pompeii", records a snapshot of rich Early Miocene life buried under thick ignimbrite cover. Here, we use a multi-technique approach to constrain the successive phases of a catastrophic silicic eruption (VEI ≥ 7) dated at 17.2 Ma. An event-scale reconstruction shows that the initial PDC phase was phreatomagmatic, affecting ≥ 1500 km2 and causing the destruction of an interfingering terrestrial-intertidal environment at Ipolytarnóc. This was followed by pumice fall, and finally the emplacement of up to 40 m-thick ignimbrite that completely buried the site. However, unlike the seemingly similar AD 79 Vesuvius eruption that buried Pompeii by hot pyroclastic density currents, the presence of fallen but uncharred tree trunks, branches, and intact leaves in the basal pyroclastic deposits at Ipolytarnóc as well as rock paleomagnetic properties indicate a low-temperature pyroclastic event, that superbly preserved the coastal habitat, including unique fossil tracks.

Nutritional Intake and Gut Microbiome Composition Predict Parkinson's Disease.

Background: Models to predict Parkinson's disease (PD) incorporating alterations of gut microbiome (GM) composition have been reported with varying success. Objective: To assess the utility of GM compositional changes combined with macronutrient intake to develop a predictive model of PD. Methods: We performed a cross-sectional analysis of the GM and nutritional intake in 103 PD patients and 81 household controls (HCs). GM composition was determined by 16S amplicon sequencing of the V3-V4 region of bacterial ribosomal DNA isolated from stool. To determine multivariate disease-discriminant associations, we developed two models using Random Forest and support-vector machine (SVM) methodologies. Results: Using updated taxonomic reference, we identified significant compositional differences in the GM profiles of PD patients in association with a variety of clinical PD characteristics. Six genera were overrepresented and eight underrepresented in PD patients relative to HCs, with the largest difference being overrepresentation of Lactobacillaceae at family taxonomic level. Correlation analyses highlighted multiple associations between clinical characteristics and select taxa, whilst constipation severity, physical activity and pharmacological therapies associated with changes in beta diversity. The random forest model of PD, incorporating taxonomic data at the genus level and carbohydrate contribution to total energy demonstrated the best predictive capacity [Area under the ROC Curve (AUC) of 0.74]. Conclusion: The notable differences in GM diversity and composition when combined with clinical measures and nutritional data enabled the development of a predictive model to identify PD. These findings support the combination of GM and nutritional data as a potentially useful biomarker of PD to improve diagnosis and guide clinical management.

A new cytometry-based method reveals an accumulation of Nrf2 in dendritic cells exposed to two respiratory sensitizers.

The mechanisms underlying chemical respiratory sensitization are incompletely understood. One of the major cell types involved in this pathology are dendritic cells. In this study, the mechanisms of the NRF2-Keap1 pathway were studied using a bone marrow-derived dendritic cell model exposed to two respiratory sensitizers: ammonium hexachloroplatinate (HCP) and ammonium tetrachloroplatinate (ATCP). Expression levels for two Nrf2-regulated genes, hmox1 and srxn1, were analyzed by real time-quantitative polymerase chain reaction. A flow cytometry-based method was also developed to measure intracellular Nrf2 accumulation in dendritic cells following exposure. Exposure to HCP and ATCP increased both hmox1 and srxn1 gene expression, and was associated with accumulation of Nrf2 protein in cells. Overall, these results show that the respiratory sensitizers, in addition to skin sensitizers, can also induced markers associated with NRF2-Keap1 pathway activation in dendritic cells. This study contributes to a better understanding of the adverse outcome of respiratory sensitization.

svReg: Structural varying-coefficient regression to differentiate how regional brain atrophy affects motor impairment for Huntington disease severity groups.

For Huntington disease, identification of brain regions related to motor impairment can be useful for developing interventions to alleviate the motor symptom, the major symptom of the disease. However, the effects from the brain regions to motor impairment may vary for different groups of patients. Hence, our interest is not only to identify the brain regions but also to understand how their effects on motor impairment differ by patient groups. This can be cast as a model selection problem for a varying-coefficient regression. However, this is challenging when there is a pre-specified group structure among variables. We propose a novel variable selection method for a varying-coefficient regression with such structured variables and provide a publicly available R package svreg for implementation of our method. Our method is empirically shown to select relevant variables consistently. Also, our method screens irrelevant variables better than existing methods. Hence, our method leads to a model with higher sensitivity, lower false discovery rate and higher prediction accuracy than the existing methods. Finally, we found that the effects from the brain regions to motor impairment differ by disease severity of the patients. To the best of our knowledge, our study is the first to identify such interaction effects between the disease severity and brain regions, which indicates the need for customized intervention by disease severity.

Prediction modeling-part 2: using machine learning strategies to improve transplantation outcomes.

Kidney transplant recipients and transplant physicians face important clinical questions where machine learning methods may help improve the decision-making process. This mini-review explores potential applications of machine learning methods to key stages of a kidney transplant recipient's journey, from initial waitlisting and donor selection, to personalization of immunosuppression and prediction of post-transplantation events. Both unsupervised and supervised machine learning methods are presented, including k-means clustering, principal components analysis, k-nearest neighbors, and random forests. The various challenges of these approaches are also discussed.

Surgical benefit of mandibular morphometric analysis: A new tool to standardize mandibular reconstruction.

PURPOSE: The gold-standard for reconstruction of large mandibular defects is the use of free flaps of vascularized autologous bone with the fibula as the preferred donor site. The use of "custom cutting guides" for this indication is becoming increasingly prevalent. But cost of the procedure averages around 2,500 dollars per patient excluding treatment and entails selection criteria. We think it is possible to standardize mandibular reconstructions from an anatomical mean. The objective of this study was to perform a mandibular morphometric analysis in order to obtain a set of "mean" measurements, which can be used by all surgeons interested in mandibular reconstruction. METHODS: We performed a morphometric analysis consisting of three-dimensional mandibular reconstructions of 30 men and 30 women. Several reference points were set and defined to evaluate specific lengths and angles of interest. We conducted an intra and inter-sexual descriptive analysis of measurements obtained. RESULTS: We did not identify any major intra-sexual differences within each group. The gonial angle is more open in women and the measurements characterizing the basilar contour are more prominent in men. We did not identify any differences in alveolar region parameters. CONCLUSION: The results of this study constitute a morphological tool for surgeons, from bone graft to free flap. These results also confirm us that the use of «custom cutting guides¼ for mandibular reconstruction may be excessive. It is pertinent to examine the value of "custom made" mandibular reconstructions since the differences observed are of the order of millimeters.

Effects of passion fruit peel flour (Passiflora edulis f. flavicarpa O. Deg.) in cafeteria diet-induced metabolic disorders.

ETHNOPHARMACOLOGICAL RELEVANCE: Passiflora edulis f. flavicarpa O. Deg. is a native Brazilian fruit known as sour or yellow passion fruit. From its peel, mainly in the northeast of Brazil, is produced a flour that is largely used as folk medicine to treat diabetes and other metabolic conditions. AIM OF THE STUDY: The aim of the study was to show the effects of P. edulis peel flour (PEPF) in metabolic disorders caused by cafeteria diet in mice. MATERIAL AND METHODS: The antioxidant activity in vitro of PEPF extract was determined by ferric reducing/antioxidant power, ß-carotene/linoleic acid system and nitric oxide scavenging activity assay. C57BL/6 mice divided in 3 groups: Control group, fed on a standard diet (AIN); Cafeteria diet (CAF) group, fed on a cafeteria diet, and PEPF group, fed on a cafeteria diet containing 15% of PEPF, during 16 weeks. The glucose tolerance and insulin sensitivity were evaluated through the glucose tolerance test (GTT) and the insulin tolerance test (ITT). After the intervention period, blood, hepatic, pancreatic and adipose tissues were collected for biochemical and histological analysis. Cholesterol, triglyceride, interleukins and antioxidant enzymes were measured in the liver tissue. RESULTS: PEPF extract presented antioxidant activity in the higher concentrations in the performed assays. The PEPF intake decreased the body weight gain, fat deposition, predominantly in the liver, improved the glucose tolerance and insulin sensitivity in metabolic changes caused by cafeteria diet. CONCLUSION: Together, the data herein obtained points out that P. edulis peel flour supplementation in metabolic syndrome condition induced by CAF-diet, prevents insulin and glucose resistance, hepatic steatosis and adiposity.

Intra-erythrocyte chromium as an indicator of exposure to hexavalent chromium: An in vivo evaluation in intravenous administered rat.

Hexavalent chromium (Cr(VI)) compounds are classified as carcinogenic to humans. Whereas chromium measurements in urine and plasma attest to the last few hours of total chromium exposure (all oxidation states of chromium), chromium in red blood cells (RBC) is attributable specifically to Cr(VI) exposure over the last few days. Before recommending Cr in RBC (CrIE) as a biological indicator of Cr(VI) exposure, in vivo studies must be undertaken to assess its reliability. The present study examines the kinetics of Cr(VI) in rat after a single intravenous dose of ammonium dichromate. Chromium levels were measured in plasma, red blood cells and urine. The decay of the chromium concentration in plasma is one-phase-like (with half-life time of 0.55 day) but still measurable two days post injection. The excretion of urinary chromium peaks between five and six hours after injection and shows large variations. Intra-erythrocyte chromium (CrIE) was very constant up to a minimum of 2 days and half-life time was estimated to 13.3 days. Finally, Cr(III) does not interfere with Cr(VI) incorporation in RBC. On the basis of our results, we conclude that, unlike urinary chromium, chromium levels in RBC are indicative of the amount of dichromate (Cr(VI)) in blood.

Workflow assessment as a preclinical development tool : Surgical process models of three techniques for minimally invasive cochlear implantation.

PURPOSE: Minimally invasive cochlear implant surgery is a challenging procedure due to high demands on accuracy. For clinical success, an according assistance system has to compete against the traditional approach in terms of risk, operating time and cost. It has not yet been determined what kind of system is the most suited. The purpose of this study is a proof of concept of surgical process modeling as a preclinical development tool and the comparison of workflow concepts for this new approach. METHODS: Three preclinical systems (two stereotactic and one robotic) for minimally invasive cochlear implant surgery are compared using the method of surgical process modeling. All three systems were successfully tested with ex vivo human specimen to create minimally invasive surgical access to the cochlea. Those systems where chosen for comparison, because they represent three diverse approaches with different corresponding workflows for the same intervention. The experiments were used to create a process model for each system by recording the interventions. RESULTS: All three conceptual systems developed by our group have shown their eligibility. The recorded process models provide a convenient method for direct comparison. Reduction in the surgical time has a higher impact on the process, than time that is needed for setting up a system beforehand. The stereotactic approaches have little preparation effort and are low cost in terms of hardware compared to the robotic approach, which in return is beneficial in terms of workload reduction for the surgeon. CONCLUSION: Surgical process modeling is suitable for comparison of different assistant systems for minimally invasive cochlear implantation. The benefit of reduced trauma, compared to the traditional mastoidectomy, can now be assessed with consideration of the workflow of each technique. The process models enable an assessment in the regard of surgical time and workload.