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IntroductionAnti-Xa serves as a clinical surrogate for assessing the efficacy and bleeding risk in patients treated with enoxaparin for thromboembolic events. Evidence from the literature and empirical observations suggest that patients are underdosed in clinical practice to avoid bleeding complications. This study aimed to investigate such underdosing of enoxaparin and its potential impact on achieving therapeutic anti-Xa levels.MethodsThis multicentric, retrospective, observational study included patients with acute ischemic stroke due to atrial fibrillation. All patients received enoxaparin in the therapeutic setting with subsequent anti-Xa measurements. The one-sample, one-tailed Wilcoxon signed-rank test was used to identify a significant difference between the doses administered and the recommended daily dose. Logistic regression model analysis was performed to identify additional predictors affecting achievement of the therapeutic anti-Xa target range. Stepwise forward-backward selection with Akaike’s information criterion as metric was applied to refine the logistic regression model.ResultsA total of 145 patients from the university hospitals of St. Pölten and Tulln in Lower Austria were included. The median daily enoxaparin dose administered was 1.23 mg/kg, resulting in an overall target range achievement rate of 66%. As compared to recommended therapeutic doses, significant underdosing of enoxaparin was evident in both participating centers (p < 0.001). The calculated threshold dose to achieve the therapeutic target range with a 90% probability was 1.5 mg/kg enoxaparin daily. Female sex was found to be a strong independent predictor of achieving a therapeutic target range (OR 9.44; 95% CI 3.40–30.05, p < 0.001).ConclusionDespite the underdosing observed in both centers, therapeutic anti-Xa levels were achieved with lower than recommended doses of enoxaparin, and women required even lower doses than men. These findings warrant further confirmation by prospective studies.

IntroductionAdvances in molecular targeting of ion channels may open up new avenues for therapeutic approaches in cancer based on the cells’ bioelectric properties. In addition to in-vitro or in-vivo models, in silico models can provide deeper insight into the complex role of electrophysiology in cancer and reveal the impact of altered ion channel expression and the membrane potential on malignant processes. The A549 in silico model is the first computational cancer whole-cell ion current model that simulates the bioelectric mechanisms of the human non-small cell lung cancer cell line A549 during the different phases of the cell cycle. This work extends the existing model with a detailed mathematical description of the store-operated Ca2+ entry (SOCE) and the complex local intracellular calcium dynamics, which significantly affect the entire electrophysiological properties of the cell and regulate cell cycle progression.MethodsThe initial model was extended by a multicompartmental approach, addressing the heterogenous calcium profile and dynamics in the ER-PM junction provoked by local calcium entry of store-operated calcium channels (SOCs) and uptake by SERCA pumps. Changes of cytosolic calcium levels due to diffusion from the ER-PM junction, release from the ER by RyR channels and IP3 receptors, as well as corresponding PM channels were simulated and the dynamics evaluated based on calcium imaging data. The model parameters were fitted to available data from two published experimental studies, showing the function of CRAC channels and indirectly of IP3R, RyR and PMCA via changes of the cytosolic calcium levels.ResultsThe proposed calcium description accurately reproduces the dynamics of calcium imaging data and simulates the SOCE mechanisms. In addition, simulations of the combined A549-SOCE model in distinct phases of the cell cycle demonstrate how Ca2+ - dynamics influence responding channels such as KCa, and consequently modulate the membrane potential accordingly.DiscussionLocal calcium distribution and time evolution in microdomains of the cell significantly impact the overall electrophysiological properties and exert control over cell cycle progression. By providing a more profound description, the extended A549-SOCE model represents an important step on the route towards a valid model for oncological research and in silico supported development of novel therapeutic strategies.

Abstract Background The introduction of functional in-silico models, in addition to in-vivo tumor models, opens up new and unlimited possibilities in cancer research and drug development. The world's first digital twin of the A549 cell's electrophysiology in the human lung adenocarcinoma, unveiled in 2021, enables the investigation and evaluation of new research hypotheses about modulating the function of ion channels in the cell membrane, which are important for better understanding cancer development and progression, as well as for developing new drugs and predicting treatments. Main body The developed A549 in-silico model allows virtual simulations of the cell’s rhythmic oscillation of the membrane potential, which can trigger the transition between cell cycle phases. It is able to predict the promotion or interruption of cell cycle progression provoked by targeted activation and inactivation of ion channels, resulting in abnormal hyper- or depolarization of the membrane potential, a potential key signal for the known cancer hallmarks. For example, model simulations of blockade of transient receptor potential cation channels (TRPC6), which are highly expressed during S-G2/M transition, result in a strong hyperpolarization of the cell’s membrane potential that can suppress or bypass the depolarization required for the S-G2/M transition, allowing for possible cell cycle arrest and inhibition of mitosis. All simulated research hypotheses could be verified by experimental studies. Short conclusion Functional, non-phenomenological digital twins, ranging from single cells to cell–cell interactions to 3D tissue models, open new avenues for modern cancer research through "dry lab" approaches that optimally complement established in-vivo and in-vitro methods.

The supercurrent diode effect was recently observed in a Nb/V/Ta superlattice thin film with Rashba-type spin-orbit coupling. Here, the authors observe this effect in few-layer NbSe2 crystals driven by valley-Zeeman-type spin-orbit coupling and find that the effect is proportional to out-of-plane magnetic field.

Abstract Background Though Internet- and mobile-based interventions (IMIs) and mindfulness-based interventions (generally delivered in-situ) appear effective for people with substance use disorders, IMIs incorporating mindfulness are largely missing, including those targeting frequent cannabis use. Methods This paper details the protocol for a three-arm randomized controlled trial comparing a mindfulness-based self-help IMI (arm 1) and cognitive-behavioral therapy (CBT)-based self-help IMI (arm 2) versus being on a waiting list (arm 3) in their effectiveness reducing cannabis use in frequent cannabis users. Predictors of retention, adherence and treatment outcomes will be identified and similarities between the two active intervention arms explored. Both active interventions last six weeks and consist of eight modules designed to reduce cannabis use and common mental health symptoms. With a targeted sample size of n = 210 per treatment arm, data will be collected at baseline immediately before program use is initiated; at six weeks, immediately after program completion; and at three and six months post baseline assessment to assess the retention of any gains achieved during treatment. The primary outcome will be number of days of cannabis use over the preceding 30 days. Secondary outcomes will include further measures of cannabis use and use of other substances, changes in mental health symptoms and mindfulness, client satisfaction, intervention retention and adherence, and adverse effects. Data analysis will follow ITT principles and primarily employ (generalized) linear mixed models. Discussion This RCT will provide important insights into the effectiveness of an IMI integrating mindfulness to reduce cannabis use in frequent cannabis users. Trial registration International Standard Randomized Controlled Trial Number Registry: ISRCTN14971662 ; date of registration: 09/09/2021.

Background: Surgical interventions can cause severe fluid imbalances in patients undergoing cardiac surgery, affecting length of hospital stay and survival. Therefore, appropriate management of daily fluid goals is a key element of postoperative intensive care in these patients. Because fluid balance is influenced by a complex interplay of patient-, surgery- and intensive care unit (ICU)-specific factors, fluid prediction is difficult and often inaccurate.Methods: A novel system theory based digital model for cumulative fluid balance (CFB) prediction is presented using recorded patient fluid data as the sole parameter source by applying the concept of a transfer function. Using a retrospective dataset of n = 618 cardiac intensive care patients, patient-individual models were created and evaluated. RMSE analyses and error calculations were performed for reasonable combinations of model estimation periods and clinically relevant prediction horizons for CFB.Results: Our models have shown that a clinically relevant time horizon for CFB prediction with the combination of 48 h estimation time and 8–16 h prediction time achieves high accuracy. With an 8-h prediction time, nearly 50% of CFB predictions are within ±0.5 L, and 77% are still within the clinically acceptable range of ±1.0 L.Conclusion: Our study has provided a promising proof of principle and may form the basis for further efforts in the development of computational models for fluid prediction that do not require large datasets for training and validation, as is the case with machine learning or AI-based models. The adaptive transfer function approach allows estimation of CFB course on a dynamically changing patient fluid balance system by simulating the response to the current fluid management regime, providing a useful digital tool for clinicians in daily intensive care.

Given prevalent alcohol misuse-emotional comorbidities among young adults, we developed an internet-based integrated treatment called Take Care of Me. Although the treatment had an impact on several secondary outcomes, effects were not observed for the primary outcome. Therefore, the goal of the current study was to examine heterogeneity in treatment responses. The initial RCT randomized participants to either a treatment or psychoeducational control condition. We conducted an exploratory latent class analysis to distinguish individuals based on pre-treatment risk and then used moderated regressions to examine differential treatment responses based on class membership. We found evidence for three distinct groups. Most participants fell in the “low severity” group (n = 123), followed by the “moderate severity” group (n = 57) who had a higher likelihood of endorsing a previous mental health diagnosis and treatment and higher symptom severity than the low group. The “high severity” group (n = 42) endorsed a family history of alcoholism, and the highest symptom severity and executive dysfunction. Moderated regressions revealed significant class differences in treatment responses. In the treatment condition, high severity (relative to low) participants reported higher alcohol consumption and hazardous drinking and lower quality of life at follow-up, whereas moderate severity (relative to low) individuals had lower alcohol consumption at follow-up, and lower hazardous drinking at end-of-treatment. No class differences were found for participants in the control group. Higher risk individuals in the treatment condition had poorer responses to the program. Tailoring interventions to severity may be important to examine in future research.

BackgroundPrevalence rates for lifetime cannabis use and cannabis use disorder are much higher in people with attention deficit/hyperactivity disorder than in those without. CANreduce 2.0 is an intervention that is generally effective at reducing cannabis use in cannabis misusers. This self-guided web-based intervention (6-week duration) consists of modules grounded in motivational interviewing and cognitive behavioral therapy. ObjectiveWe aimed to evaluate whether the CANreduce 2.0 intervention affects cannabis use patterns and symptom severity in adults who screen positive for attention deficit/hyperactivity disorder more than in those who do not. MethodsWe performed a secondary analysis of data from a previous study with the inclusion criterion of cannabis use at least once weekly over the last 30 days. Adults with and without attention deficit/hyperactivity disorder (based on the Adult Attention deficit/hyperactivity disorder Self-Report screener) who were enrolled to the active intervention arms of CANreduce 2.0 were compared regarding the number of days cannabis was used in the preceding 30 days, the cannabis use disorder identification test score (CUDIT) and the severity of dependence scale score (SDS) at baseline and the 3-month follow-up. Secondary outcomes were Generalized Anxiety Disorder score, Center for Epidemiological Studies Depression scale score, retention, intervention adherence, and safety. ResultsBoth adults with (n=94) and without (n=273) positive attention-deficit/hyperactivity disorder screening reported significantly reduced frequency (reduction in consumption days: with: mean 11.53, SD 9.28, P

Objective: Many young adults struggle with comorbid alcohol misuse and emotional problems (i.e., depression and anxiety). However, there is currently a paucity of evidence-based, integrated, accessible treatment options for individuals with these comorbidities. The main goal of this study was to examine efficacy of a novel online, minimally guided, integrated program for comorbid alcohol misuse and emotional problems in young adults. Method: The study was an open-label two-arm RCT. Participants (N = 222, Mage = 24.6, 67.6% female) were randomized to one of two conditions: the Take Care of Me program (an 8-week, online integrated treatment condition consisting of 12 modules), or an online psychoeducational control condition. Intervention modules incorporated content based on principles of cognitive behavioral therapy and motivational interviewing. Participants completed assessment data at baseline, at the end of treatment (i.e., 8 weeks), and at follow-up (i.e., 24 weeks). Data were analyzed using generalized linear mixed models. Results: We observed that participants in the treatment condition showed larger reductions in depression, hazardous drinking, as well as increases in psychological quality of life and confidence at the end of treatment. We did not find group differences on total alcohol use at follow-up, but participants in the treatment group reduced their hazardous drinking and improved their quality of life at 24-week follow-up. Conclusions: Our study provides promising initial evidence for the first iteration of the comorbid alcohol misuse and emotional problems online program.

BackgroundGiven the scarcity of alcohol prevention and use disorder treatments in many low- and middle-income countries (LMICs), the World Health Organization has launched an eHealth portal that includes the web-based self-help program “Alcohol e-Health.” ObjectiveWe aimed to test the effectiveness of the Alcohol e-Health program in a randomized controlled trial. MethodsThis was a two-arm, individually randomized, and controlled trial across four LMICs comparing the self-help program and a psychoeducation and internet access as usual waiting list. Participants were broadly recruited from community samples in Belarus, Brazil, India, and Mexico from January 2016 through January 2019. The primary outcome measure was change in the Alcohol Use Disorders Identification Test (AUDIT) score with a time frame of 6 months between baseline and follow-up. Secondary outcomes included self-reported numbers of standard drinks over the previous week and cessation of harmful or hazardous drinking (AUDIT score

Abstract Background Demographic changes, increased life expectancy and the associated rise in chronic diseases pose challenges to public health care systems. Optimized treatment methods and integrated concepts of care are potential solutions to overcome increasing financial burdens and improve quality of care. In this context modeling is a powerful tool to evaluate potential benefits of different treatment procedures on health outcomes as well as health care budgets. This work presents a novel modeling approach for simulating different treatment procedures of heart failure patients based on extensive data sets from outpatient and inpatient care. Methods Our hybrid heart failure model is based on discrete event and agent based methodologies and facilitates the incorporation of different therapeutic procedures for outpatient and inpatient care on patient individual level. The state of health is modeled with the functional classification of the New York Heart Association (NYHA), strongly affecting discrete state transition probabilities alongside age and gender. Cooperation with Austrian health care and health insurance providers allowed the realization of a detailed model structure based on clinical data of more than 25,000 patients. Results Simulation results of conventional care and a telemonitoring program underline the unfavorable prognosis for heart failure and reveal the correlation of NYHA classes with health and economic outcomes. Average expenses for the treatment of NYHA class IV patients of €10,077 ± €165 were more than doubled compared to other classes. The selected use case of a telemonitoring program demonstrated potential cost savings within two years of application. NYHA classes II and III revealed most potential for additional treatment measures. Conclusion The presented model allows performing extensive simulations of established treatment procedures for heart failure patients and evaluating new holistic methods of care and innovative study designs. This approach offers health care providers a unique, adaptable and comprehensive tool for decision making in the complex and socioeconomically challenging field of cardiovascular diseases.

Lung cancer is still a leading cause of death worldwide. In recent years, knowledge has been obtained of the mechanisms modulating ion channel kinetics and thus of cell bioelectric properties, which is promising for oncological biomarkers and targets. The complex interplay of channel expression and its consequences on malignant processes, however, is still insufficiently understood. We here introduce the first approach of an in-silico whole-cell ion current model of a cancer cell, in particular of the A549 human lung adenocarcinoma, including the main functionally expressed ion channels in the plasma membrane as so far known. This hidden Markov-based model represents the electrophysiology behind proliferation of the A549 cell, describing its rhythmic oscillation of the membrane potential able to trigger the transition between cell cycle phases, and it predicts membrane potential changes over the cell cycle provoked by targeted ion channel modulation. This first A549 in-silico cell model opens up a deeper insight and understanding of possible ion channel interactions in tumor development and progression, and is a valuable tool for simulating altered ion channel function in lung cancer electrophysiology.

The use of nanoelectromechanical systems or nanorobots offers a new concept for sensing and controlling subcellular structures, such as ion channels. We present here a novel method for mathematical modeling of ion channels based on control system theory and system identification. We investigated the use of nanoelectromechanical devices to control the activity of ion channels, particularly the activity of the voltage-gated ion channel Kv10.1, an important channel in cancer development and progression. A mathematical model of the dynamic behavior of the selected ion channel Kv10.1 in the Laplace (s) domain was developed, which is given in the representation of a transfer function. In addition, we addressed the possibilities of controlling ion channel activity by nanoelectromechanical devices and nanorobots and finally presented a control algorithm for the Kv10.1 as a control object. A use case demonstrates the potential of a Kv10.1 controlled nanorobot for cancer treatment at a single-cell level.

For cardiac defibrillator testing and design purposes, the range and limits of the human TTI is of high interest. Potential influencing factors regarding the electronic configurations, the electrode/tissue interface and patient characteristics were identified and analyzed. A literature survey based on 71 selected articles was used to review and assess human TTI and the influencing factors found. The human TTI extended from 12 to 212 Ω in the literature selected. Excluding outliers and pediatric measurements, the mean TTI recordings ranged from 51 to 112 Ω with an average TTI of 76.7 Ω under normal distribution. The wide range of human impedance can be attributed to 12 different influencing factors, including shock waveforms and protocols, coupling devices, electrode size and pressure, electrode position, patient age, gender, body dimensions, respiration and lung volume, blood hemoglobin saturation and different pathologies. The coupling device, electrode size and electrode pressure have the greatest influence on TTI.

The mathematical modeling of ion channel kinetics is an important tool for studying the electrophysiological mechanisms of the nerves, heart, or cancer, from a single cell to an organ. Common approaches use either a Hodgkin–Huxley (HH) or a hidden Markov model (HMM) description, depending on the level of detail of the functionality and structural changes of the underlying channel gating, and taking into account the computational effort for model simulations. Here, we introduce for the first time a novel system theory-based approach for ion channel modeling based on the concept of transfer function characterization, without a priori knowledge of the biological system, using patch clamp measurements. Using the shaker-related voltage-gated potassium channel Kv1.1 (KCNA1) as an example, we compare the established approaches, HH and HMM, with the system theory-based concept in terms of model accuracy, computational effort, the degree of electrophysiological interpretability, and methodological limitations. This highly data-driven modeling concept offers a new opportunity for the phenomenological kinetic modeling of ion channels, exhibiting exceptional accuracy and computational efficiency compared to the conventional methods. The method has a high potential to further improve the quality and computational performance of complex cell and organ model simulations, and could provide a valuable new tool in the field of next-generation in silico electrophysiology.

Introduction The past-year prevalence of problem gambling worldwide averages 2.3%. Switzerland exhibits a slightly lower past-year prevalence rate, of 1.1%, among adults. Only a minority of these adults attend outpatient treatment. Surveyed problem gamblers have explained that they wanted to handle the problem on their own. The option of a web-based self-help programme could potentially reach those users who hesitate to approach treatment centres and help them to reduce or stop their problem gambling. The effectiveness of such web-based interventions has been shown in other countries.Methods and analysis This two-armed randomised controlled trial (RCT) will examine the efficacy of a web-based self-help intervention, relative to an active control condition with a self-help manual, at reducing problem gambling. The active intervention programme, spanning 8 weeks, consists of nine modules developed to reduce gambling and attenuate psychopathological comorbidity, including depression, anxiety and stress-related disorder symptoms, relying on motivational interviewing and cognitive behavioural therapy. With a target sample size of 352, questionnaire data will be collected at baseline, and at 8 and 24 weeks after baseline. Primary outcomes will be the number of days one has gambled in the last 30 days. Secondary outcomes will include money and time spent on gambling activities, changes in gambling-related problems (Problem Gambling Severity Index, Gambling Symptom Assessment Scale), use of alcohol and cigarettes, and psychopathological comorbidity. All data analysis will comply with the intention-to-treat principle.Ethics and dissemination The RCT will be conducted in accordance with the Declaration of Helsinki; the consort eHealth Guidelines for studies on medical devices; the European Directive on medical devices 93/42/EEC, Swiss Law and Swiss Regulatory Authority requirements. The study was approved by the ethics committee of the Canton of Zurich. Results will be published in a scientific peer-reviewed journal. Participants will be informed via e-mail about study results via a lay-person-friendly summary of trial findings.Trial registration number Current Controlled Trials registry (ISRCTN16339434).

Background: In recent years, cocaine use has increased in many countries, but only a minority of users seek treatment. Cognitive behavioral therapy (CBT) is seen as first-choice face-to-face treatment. However, a web-based intervention might serve as an alternative. Aims: To test the efficacy of a web-based self-help intervention, with and without chat counseling, grounded in CBT, at reducing cocaine use in cocaine misusers not in treatment for a substance use disorder. Methods: Subjects were randomly assigned to (1) a self-help intervention with chat support, (2) a self-help intervention without chat support, or (3) a waiting list control group. The fully-automated self-help program consisted of eight modules based on motivational interviewing, self-control practices and CBT. The primary outcome was the quantity of cocaine use per week. Secondary outcomes included frequency of cocaine and other substance use and mental health symptoms. Linear regression analysis was performed to investigate changes in primary and secondary outcomes. Results: In total, 416 users registered online for the trial, of whom 311 completed the baseline assessment. Participants were predominantly male (73%) and averaged 33 years old (SD = 7.6). Despite considerable efforts on our part, only 47 of 311 (15.1%) subjects completed the 6-month follow-up assessment. Frequency of cocaine use and severity of cocaine dependence decreased only in the intervention groups. No significant difference in the primary outcome was observed between the study arms, but several differences in secondary outcomes were observed by complete case analyses. Conclusions: Many cocaine misusers from the general population and not otherwise in treatment could be reached and decreased their cocaine use utilizing a CBT-based online intervention. However, due to the high percentage of dropouts and serious difficulties reaching subjects for follow-up assessments, no conclusions can be drawn regarding study arm differences. Implications for future studies are discussed. Keywords: Cocaine, Internet, Chat, Self-help, Cognitive Behavioral Therapy, Motivational Interviewing

A dysfunctional vestibular system can be a severe detriment to the quality of life of a patient. Recent studies have shown the feasibility for a vestibular implant to restore rotational sensation via electrical stimulation of vestibular ampullary nerves. However, the optimal stimulation site for selective elicitation of the desired nerve is still unknown. We realized a finite element model on the basis of μCT scans of a human inner ear and incorporated naturally distributed, artificial neural trajectories. A well-validated neuron model of myelinated fibers was incorporated to predict nerve responses to electrical stimulation. Several virtual electrodes were placed in locations of interest inside the bony labyrinth (intra-labyrinthine) and inside the temporal bone, near the target nerves (extra-labyrinthine), to determine preferred stimulation sites and electrode insertion depths. We investigated various monopolar and bipolar electrode configurations as well as different pulse waveform shapes for their ability to selectively stimulate the target nerve and for their energy consumption. The selectivity was evaluated with an objective measure of the fiber recruitment. Considerable differences of required energy and achievable selectivity between the configurations were observed. Bipolar, intra-labyrinthine electrodes provided the best selectivities but also consumed the highest amount of energy. Bipolar, extra-labyrinthine configurations did not offer any advantages compared to the monopolar approach. No selective stimulation could be performed with the monopolar, intra-labyrinthine approach. The monopolar, extra-labyrinthine electrodes required the least energy for satisfactory selectivities, making it the most promising approach for functional vestibular implants. Different pulse waveform shapes did not affect the achieved selectivity considerably but shorter pulse durations showed consistently a more selective activation of the target nerves. A cathodic, centered triangular waveform shape was identified as the most energy-efficient of the tested shapes. Based on these simulations we are able to recommend the monopolar, extra-labyrinthine stimulation approach with cathodic, centered triangular pulses as good trade-off between selectivity and energy consumption. Future implant designs could benefit from the findings presented here.

Metabolic biomarkers may play an important role in the diagnosis, prognostication and assessment of response to pharmacological therapy in complex diseases. The process of discovering new metabolic biomarkers is a non-trivial task which involves a number of bioanalytical processing steps coupled with a computational approach for the search, prioritization and verification of new biomarker candidates. Kinetic analysis provides an additional dimension of complexity in time-series data, allowing for a more precise interpretation of biomarker dynamics in terms of molecular interaction and pathway modulation. A novel network-based computational strategy for the discovery of putative dynamic biomarker candidates is presented, enabling the identification and verification of unexpected metabolic signatures in complex diseases such as myocardial infarction. The novelty of the proposed method lies in combining metabolic time-series data into a superimposed graph representation, highlighting the strength of the underlying kinetic interaction of preselected analytes. Using this approach, we were able to confirm known metabolic signatures and also identify new candidates such as carnosine and glycocholic acid, and pathways that have been previously associated with cardiovascular or related diseases. This computational strategy may serve as a complementary tool for the discovery of dynamic metabolic or proteomic biomarkers in the field of clinical medicine.

Our sense of balance and spatial orientation strongly depends on the correct functionality of our vestibular system. Vestibular dysfunction can lead to blurred vision and impaired balance and spatial orientation, causing a significant decrease in quality of life. Recent studies have shown that vestibular implants offer a possible treatment for patients with vestibular dysfunction. The close proximity of the vestibular nerve bundles, the facial nerve and the cochlear nerve poses a major challenge to targeted stimulation of the vestibular system. Modeling the electrical stimulation of the vestibular system allows for an efficient analysis of stimulation scenarios previous to time and cost intensive in vivo experiments. Current models are based on animal data or CAD models of human anatomy. In this work, a (semi-)automatic modular workflow is presented for the stepwise transformation of segmented vestibular anatomy data of human vestibular specimens to an electrical model and subsequently analyzed. The steps of this workflow include (i) the transformation of labeled datasets to a tetrahedra mesh, (ii) nerve fiber anisotropy and fiber computation as a basis for neuron models, (iii) inclusion of arbitrary electrode designs, (iv) simulation of quasistationary potential distributions, and (v) analysis of stimulus waveforms on the stimulation outcome. Results obtained by the workflow based on human datasets and the average shape of a statistical model revealed a high qualitative agreement and a quantitatively comparable range compared to data from literature, respectively. Based on our workflow, a detailed analysis of intra- and extra-labyrinthine electrode configurations with various stimulation waveforms and electrode designs can be performed on patient specific anatomy, making this framework a valuable tool for current optimization questions concerning vestibular implants in humans.

Movement science investigating muscle and tendon functions during locomotion utilizes commercial ultrasound imagers built for medical applications. These limit biomechanics research due to their form factor, range of view, and spatio-temporal resolution. This review systematically investigates the technical aspects of applying ultrasound as a research tool to investigate human and animal locomotion. It provides an overview on the ultrasound systems used and of their operating parameters. We present measured fascicle velocities and discuss the results with respect to operating frame rates during recording. Furthermore, we derive why muscle and tendon functions should be recorded with a frame rate of at least 150 Hz and a range of view of 250 mm. Moreover, we analyze why and how the development of better ultrasound observation devices at the hierarchical level of muscles and tendons can support biomechanics research. Additionally, we present recent technological advances and their possible application. We provide a list of recommendations for the development of a more advanced ultrasound sensor system class targeting biomechanical applications. Looking to the future, mobile, ultrafast ultrasound hardware technologies create immense opportunities to expand the existing knowledge of human and animal movement.

Hypothermia has a profound impact on the electrophysiological mechanisms of the heart. Experimental investigations provide a better understanding of electrophysiological alterations associated with cooling. However, there is a lack of computer models suitable for simulating the effects of hypothermia in cardio-electrophysiology. In this work, we propose a model that describes the cooling-induced electrophysiological alterations in ventricular tissue in a temperature range from 27°C to 37°C. To model the electrophysiological conditions in a 3D left ventricular tissue block it was essential to consider the following anatomical and physiological parameters in the model: the different cell types (endocardial, M, epicardial), the heterogeneous conductivities in longitudinal, transversal and transmural direction depending on the prevailing temperature, the distinct fiber orientations and the transmural repolarization sequences. Cooling-induced alterations on the morphology of the action potential (AP) of single myocardial cells thereby are described by an extension of the selected Bueno-Orovio model for human ventricular tissue using Q10 temperature coefficients. To evaluate alterations on tissue level, the corresponding pseudo electrocardiogram (pECG) was calculated. Simulations show that cooling-induced AP and pECG-related parameters, i.e. AP duration, morphology of the notch of epicardial AP, maximum AP upstroke velocity, AP rise time, QT interval, QRS duration and J wave formation are in good accordance with literature and our experimental data. The proposed model enables us to further enhance our knowledge of cooling-induced electrophysiological alterations from cellular to tissue level in the heart and may help to better understand electrophysiological mechanisms, e.g. in arrhythmias, during hypothermia.

Reliable bonding of microstructured polymer parts is one of the major challenges in industrial fabrication of microfluidic devices. In the present work, the effects of a UV/ozone surface activation on the bonding process were investigated for the combination of a commonly used thermoplastic cyclic olefin copolymer (COC) with an elastomeric COC (eCOC) as a new thermoplastic elastomer material. Bonding was studied using two-component injection molded parts of COC and eCOC, together with microfluidic COC chips. Surface activation and bonding process parameters were optimized and bond strengths were characterized by the wedge test method. The results showed that strong bonding of this polymer materials combination can be achieved at temperatures significantly below the bulk glass transition temperature of COC.

The objectives of this work were the classification of dynamic metabolic biomarker candidates and the modeling and characterization of kinetic regulatory mechanisms in human metabolism with response to external perturbations by physical activity. Longitudinal metabolic concentration data of 47 individuals from 4 different groups were examined, obtained from a cycle ergometry cohort study. In total, 110 metabolites (within the classes of acylcarnitines, amino acids, and sugars) were measured through a targeted metabolomics approach, combining tandem mass spectrometry (MS/MS) with the concept of stable isotope dilution (SID) for metabolite quantitation. Biomarker candidates were selected by combined analysis of maximum fold changes (MFCs) in concentrations and P-values resulting from statistical hypothesis testing. Characteristic kinetic signatures were identified through a mathematical modeling approach utilizing polynomial fitting. Modeled kinetic signatures were analyzed for groups with similar behavior by applying hierarchical cluster analysis. Kinetic shape templates were characterized, defining different forms of basic kinetic response patterns, such as sustained, early, late, and other forms, that can be used for metabolite classification. Acetylcarnitine (C2), showing a late response pattern and having the highest values in MFC and statistical significance, was classified as late marker and ranked as strong predictor (MFC = 1.97, P < 0.001). In the class of amino acids, highest values were shown for alanine (MFC = 1.42, P < 0.001), classified as late marker and strong predictor. Glucose yields a delayed response pattern, similar to a hockey stick function, being classified as delayed marker and ranked as moderate predictor (MFC = 1.32, P < 0.001). These findings coincide with existing knowledge on central metabolic pathways affected in exercise physiology, such as β-oxidation of fatty acids, glycolysis, and glycogenolysis. The presented modeling approach demonstrates high potential for dynamic biomarker identification and the investigation of kinetic mechanisms in disease or pharmacodynamics studies using MS data from longitudinal cohort studies.

This article presents a new optical, multi-functional, high-resolution 3-axis sensor which serves to navigate and can, for example, replace standard joysticks in medical devices such as electric wheelchairs, surgical robots or medical diagnosis devices. A light source, e.g., a laser diode, is affixed to a movable axis and projects a random geometric shape on an image sensor (CMOS or CCD). The downstream microcontroller’s software identifies the geometric shape’s center, distortion and size, and then calculates x, y, and z coordinates, which can be processed in attached devices. Depending on the image sensor in use (e.g., 6.41 megapixels), the 3-axis sensor features a resolution of 1544 digits from right to left and 1038 digits up and down. Through interpolation, these values rise by a factor of 100. A unique feature is the exact reproducibility (deflection to coordinates) and its precise ability to return to its neutral position. Moreover, optical signal processing provides a high level of protection against electromagnetic and radio frequency interference. The sensor is adaptive and adjustable to fit a user’s range of motion (stroke and force). This recommendation aims to optimize sensor systems such as joysticks in medical devices in terms of safety, ease of use, and adaptability.