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Cancer cells typically exhibit altered ion channel expression and membrane potential, wich plays an important role in various processes such as proliferation or apoptosis Hence, therapeutic strategies that modulate ion channel activity or shift the membrane potential prove to be promising for cancer therapy. Since temperature significantly affects ion channel function, deviation from normal body temperture might provide akey factor in electrophysiological progression. In this pilot study, we focused on a first insight into the temperature-induced modulation of A549 cells using an automated patch clamp system. Measurements of 14 cells at hypo-, normo- and hypertermia were accomplished to investigate the temperature-dependent electophysiological behavior. The results revealed that both, temperatures below and above normal body temperature affect the current and potential of A549 cells. In hyperthermia the potential tends to more hyperpolarized values, while conversely, in hypothermic conditions we observer mainly a lower current reponse and thus a depolarization of the membrane potential.

In this paper, we present a control system theory based modeling approach for the voltage-gated ion-channel Kv10.1 at different temperatures. Kv10.1 is expressed in the central nervous system and the brain of healthy humans. The study of the activity of this ion channel is important, because its activity is associated with the occurrence of cancer in different organs or tissues. According to systems and control theory, the voltage-gated channel Kv10.1 was assumed to be a linear time-invariant system and, as such, exhibits dynamic behavior. The experimental results show that Kv10.1 operates as a first-order model based on the input voltage step protocol and the measured macroscopic ion current output.

Capacitive extracellular stimulation is a common method in implanted stimulation electrodes. The basis for investigating the transmission of stimuli from an electrode to adhered cells are in vitro experiments using calcium imaging or patch clamp measurements. Computational spatial models are used to simulate the mechanism of signal transmission at the cell-electrode interface but require high computing power. In this work, the Stern model to characterize the electrochemical double-layer (EDL) formation and a modified two-domain model are combined into a holistic equivalent circuit modelling capacitive cell stimulation. The described parameters can be directly associated with physicochemical effects. A simulation of the involved control and measurement systems allows the validation of the model with in vitro patch clamp recordings. The relationship of the cell’s distance to the electrode and efficacy of signal transmission could be observed. With this concept we aim to convert different complex approaches into a simple model and thus give an overview of the mechanisms of stimulation. We want to facilitate the interpretation of measured signals especially in voltage clamp measurements.

While deep neural network models offer unmatched classification performance, they are prone to learning spurious correlations in the data. Such dependencies on confounding information can be difficult to detect using performance metrics if the test data comes from the same distribution as the training data. Interpretable ML methods such as post-hoc explanations or inherently interpretable classifiers promise to identify faulty model reasoning. However, there is mixed evidence whether many of these techniques are actually able to do so. In this paper, we propose a rigorous evaluation strategy to assess an explanation technique's ability to correctly identify spurious correlations. Using this strategy, we evaluate five post-hoc explanation techniques and one inherently interpretable method for their ability to detect three types of artificially added confounders in a chest x-ray diagnosis task. We find that the post-hoc technique SHAP, as well as the inherently interpretable Attri-Net provide the best performance and can be used to reliably identify faulty model behavior.

BACKGROUND ChatGPT, an artificial intelligence chatbot developed by OpenAI has ushered in a new era of possibilities, offering exciting opportunities for natural language processing in healthcare; it marks a clear paradigm shift in academic research, patient care, and clinical communication. However, a hurried and uninformed implementation of ChatGPT in healthcare settings can have unforeseen results. OBJECTIVE The objective of this study is to discuss and provide a comprehensive summary of the existing literature on ChatGPT based on insights derived from different research teams across diverse disciplines, focusing on the main applications in medicine as conceived by authors who have published on the subject. Furthermore, we seek to address potential concerns associated with the integration of ChatGPT in the medical field, while simultaneously opening up unexplored paths. METHODS The corresponding author selected a group of authors through an initial screening of papers published on ChatGPT from various databases. Based on the replies and their level of cooperation, a dozen authors were chosen for the study and included based on their cooperation and geographic representation. We conducted a comprehensive search across databases, including Web of Science, PubMed, and Scopus. The study followed the PRISMA guidelines for screening and selection. Analysis was performed on the selected articles (n=223), categorizing them based on article type, specialty, and specific role discussed. Descriptive statistics were calculated, and bibliometric analyses were conducted using VOSviewer. RESULTS We identified 223 peer-reviewed articles. Writing assistance emerged as the most widely discussed role of ChatGPT. The rest of the roles included: general use (16%, n=35), education tool (17%, n=37), solution and discussion of clinical vignettes (15%, n=34), valuing and predicting future impact (7%, n=15), information source (5%, n=12), and as a discussion forum about ethical issues (6%, n=14). To detect any difference in usage across continents, a chi-square test for distribution of categorical variables was carried out and yielded a p-value of P=0.56, in article type. Regarding specialty and the role of ChatGPT, the p-values were P=0.86 and P=0.87, respectively. We found no significant association between a specific geographic location and the distribution of any of the variables. CONCLUSIONS This study provides a comprehensive overview of the literature on ChatGPT, revealing its diverse range of applications as observed across different disciplines. By examining the concerns surrounding the integration of ChatGPT in medicine, we underscore the importance of responsible implementation and highlight the need for ongoing research and development in this area. Ultimately, this work opens new perspectives for the detection of unexplored paths and the realization of ChatGPT's potential in enhancing various domains, including medicine.

The Anthropocene, the strongly debated potential new unit of the Geological Time Scale, describes the intensified anthropogenic influence on the environment and geological processes, and its traces in geological archives. Regional studies characterizing the growth of human impact, the Anthropocene transformation, are scarce, especially for urban or per-urban environments.In this project, we investigate the anthropogenic impact of the metropolis Vienna on its peri-urban environment and the proposed beginning of the Anthropocene epoch in the 1950s CE by applying sedimentological and geochemical methods. In previous studies (Wagreich et al. 2022), the authors were able to successfully detect the human influence in urban sedimentary archives of Vienna (anthropogenic coarse sediments) using artificial isotopes and anthropogenic trace metals. For our project, we extend the study area from Vienna to the city of Hainburg to investigate Vienna’s anthropogenic impact in both anthropogenic and natural sediments downstream the Danube river. In this area, direct human intervention in the environment, such as ground excavations, backfill and damming, is highly variable, from locally strong (e.g., hydro-power dams, airport constructions), to not existing (National Park Donau-Auen), thus offering a suitable location to trace and quantify the extent of anthropogenic impact.Within petrographic facies, sedimentological and geochemical markers are applied to characterize the anthropogenic strata in this area: The archive of fine-grained natural Danube deposits, i.e. erosional profiles and sediment cores, is analysed for trace metals, artificial radiogenic isotopes, and microplastics with the aim (i) to disentangle the anthropogenic fingerprint of Vienna from the sediment, (ii) to identify and evaluate the proposed Anthropocene geological boundary around 1950 CE, and (iii) to evaluate a potential correlative stratigraphic reference section section/ point for the Anthropocene downstream of Vienna. Finally, the Carnuntum-Vienna Anthropocene field lab offers the opportunity to integrate environmental systems modelling with an Anthropocene equation approach for the temporal and spatial growth of the anthropogenic layers (iv).

The project »From Romans to the Anthropocene, from Carnuntum to Vienna: An Urban Anthropocene Field Lab« (WWTF ESR20-027) focuses on the urban transformation from the Roman legionary camps of Carnuntum and Vindobona to Vienna's periurban areas. Combining historical and geoarchaeological methods, we investigate the diverging development of the two sites and their manifold relations over time. The Danube river crosses the mountain ranges of the Wienerwald and Malé Karpaty, forming the eastern and western limits of the central Vienna Basin. For many centuries, the river was both a barrier and a transportation route. The floodplains and river terraces along the Danube served as concentration areas and battlegrounds during countless conflicts between central Europe and its enemies. The legionary camps of Carnuntum and Vindobona were built during the first century AD at the rim of glacial river terraces, next to the shortest passages across the Danube. During late antiquity, the former provincial capital Carnuntum lost importance. However, Vindobona became first a local center and later the capital of the Austrian rulers. After a sudden Turkish siege in 1529, the fortifications of Vienna were strengthened and maintained until the middle of the 19th century.Urban development of the region over the centuries was limited not only by permanent military threats. North of the Danube, agriculture was always restricted climatically by aridity and sand drift. Most settlements on the riverbanks of the Danube and its tributaries were affected frequently by floods and erosion. Many villages vanished completely as a consequence of such natural hazards. Since some decades, natural river dynamics have been restricted technically, and many areas under cultivation are now irrigated artificially. The analysis of the sedimentary record downstream of Vienna clearly shows the anthropogenic impact on sedimentation processes. Contrary to Carnuntum, Vienna could evolve from a legionary camp step by step into a capital and a strong fortress. The demolition of the city walls after the middle of the 19th century, the following long period of peace, and, in particular, the consequent river engineering, were preconditions for the development into a modern metropolis.

Biomechanical and clinical gait research observes muscles and tendons in limbs to study their functions and behaviour. Therefore, movements of distinct anatomical landmarks, such as muscle-tendon junctions, are frequently measured. We propose a reliable and time efficient machine-learning approach to track these junctions in ultrasound videos and support clinical biomechanists in gait analysis. In order to facilitate this process, a method based on deep-learning was introduced. We gathered an extensive dataset, covering 3 functional movements, 2 muscles, collected on 123 healthy and 38 impaired subjects with 3 different ultrasound systems, and providing a total of 66864 annotated ultrasound images in our network training. Furthermore, we used data collected across independent laboratories and curated by researchers with varying levels of experience. For the evaluation of our method a diverse test-set was selected that is independently verified by four specialists. We show that our model achieves similar performance scores to the four human specialists in identifying the muscle-tendon junction position. Our method provides time-efficient tracking of muscle-tendon junctions, with prediction times of up to 0.078 seconds per frame (approx. 100 times faster than manual labeling). All our codes, trained models and test-set were made publicly available and our model is provided as a free-to-use online service on https://deepmtj.org/., Comment: in IEEE Transactions on Biomedical Engineering

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.

Background and aimsProblem gambling and tobacco use are highly comorbid among adults. However, there are few treatment frameworks that target both gambling and tobacco use simultaneously (i.e., an integrated approach), while also being accessible and evidence-based. The aim of this two-arm open label RCT was to examine the efficacy of an integrated online treatment for problem gambling and tobacco use.MethodsA sample of 209 participants (Mage = 37.66, SD = 13.81; 62.2% female) from North America were randomized into one of two treatment conditions (integrated [n = 91] or gambling only [n = 118]) that lasted for eight weeks and consisted of seven online modules. Participants completed assessments at baseline, after treatment completion, and at 24-week follow-up.ResultsWhile a priori planned generalized linear mixed models showed no condition differences on primary (gambling days, money spent, time spent) and secondary outcomes, both conditions did appear to significantly reduce problem gambling and smoking behaviours over time. Post hoc analyses showed that reductions in smoking and gambling craving were correlated with reductions in days spent gambling, as well as with gambling disorder symptoms. Relatively high (versus low) nicotine replacement therapy use was associated with greater reductions in gambling behaviours in the integrated treatment condition.Discussion and conclusionsWhile our open label RCT does not support a clear benefit of integrated treatment, findings suggest that changes in smoking and gambling were correlated over time, regardless of treatment condition, suggesting that more research on mechanisms of smoking outcomes in the context of gambling treatment may be relevant.

Interpreting deep learning models typically relies on post-hoc saliency map techniques. However, these techniques often fail to serve as actionable feedback to clinicians, and they do not directly explain the decision mechanism. Here, we propose an inherently interpretable model that combines the feature extraction capabilities of deep neural networks with advantages of sparse linear models in interpretability. Our approach relies on straight-forward but effective changes to a deep bag-of-local-features model (BagNet). These modifications lead to fine-grained and sparse class evidence maps which, by design, correctly reflect the model’s decision mechanism. Our model is particularly suited for tasks which rely on characterising regions of interests that are very small and distributed over the image. In this paper, we focus on the detection of Diabetic Retinopathy, which is characterised by the progressive presence of small retinal lesions on fundus images. We observed good classification accuracy despite our added sparseness constraint. In addition, our model precisely highlighted retinal lesions relevant for the disease grading task and excluded irrelevant regions from the decision mechanism. The results suggest our sparse BagNet model can be a useful tool for clinicians as it allows efficient inspection of the model predictions and facilitates clinicians’ and patients’ trust.

Interpretability is essential for machine learning algorithms in high-stakes application fields such as medical image analysis. However, high-performing black-box neural networks do not provide explanations for their predictions, which can lead to mistrust and suboptimal human-ML collaboration. Post-hoc explanation techniques, which are widely used in practice, have been shown to suffer from severe conceptual problems. Furthermore, as we show in this paper, current explanation techniques do not perform adequately in the multi-label scenario, in which multiple medical findings may co-occur in a single image. We propose Attri-Net, an inherently interpretable model for multi-label classification. Attri-Net is a powerful classifier that provides transparent, trustworthy, and human-understandable explanations. The model first generates class-specific attribution maps based on counterfactuals to identify which image regions correspond to certain medical findings. Then a simple logistic regression classifier is used to make predictions based solely on these attribution maps. We compare Attri-Net to five post-hoc explanation techniques and one inherently interpretable classifier on three chest X-ray datasets. We find that Attri-Net produces high-quality multi-label explanations consistent with clinical knowledge and has comparable classification performance to state-of-the-art classification models.

Magnetic Resonance Imaging (MRI) offers strong soft tissue contrast but suffers from long acquisition times and requires tedious annotation from radiologists. Traditionally, these challenges have been addressed separately with reconstruction and image analysis algorithms. To see if performance could be improved by treating both as end-to-end, we hosted the K2S challenge, in which challenge participants segmented knee bones and cartilage from 8× undersampled k-space. We curated the 300-patient K2S dataset of multicoil raw k-space and radiologist quality-checked segmentations. 87 teams registered for the challenge and there were 12 submissions, varying in methodologies from serial reconstruction and segmentation to end-to-end networks to another that eschewed a reconstruction algorithm altogether. Four teams produced strong submissions, with the winner having a weighted Dice Similarity Coefficient of 0.910 ± 0.021 across knee bones and cartilage. Interestingly, there was no correlation between reconstruction and segmentation metrics. Further analysis showed the top four submissions were suitable for downstream biomarker analysis, largely preserving cartilage thicknesses and key bone shape features with respect to ground truth. K2S thus showed the value in considering reconstruction and image analysis as end-to-end tasks, as this leaves room for optimization while more realistically reflecting the long-term use case of tools being developed by the MR community.

Understanding the interactions of different cell types inside the immune tumor microenvironment (iTME) is crucial for the development of immunotherapy treatments as well as for predicting their outcomes. Highly multiplexed tissue imaging (HMTI) technologies offer a tool which can capture cell properties of tissue samples by measuring expression of various proteins and storing them in separate image channels. HMTI technologies can be used to gain insights into the iTME and in particular how the iTME differs for different patient outcome groups of interest (e.g., treatment responders vs. non-responders). Understanding the systematic differences in the iTME of different patient outcome groups is crucial for developing better treatments and personalising existing treatments. However, such analyses are inherently limited by the fact that any two tissue samples vary due to a large number of factors unrelated to the outcome. Here, we present CF-HistoGAN, a machine learning framework that employs generative adversarial networks (GANs) to create artificial counterfactual tissue samples that resemble the original tissue samples as closely as possible but capture the characteristics of a different patient outcome group. Specifically, we learn to "translate" HMTI samples from one patient group to create artificial paired samples. We show that this approach allows to directly study the effects of different patient outcomes on the iTMEs of individual tissue samples. We demonstrate that CF-HistoGAN can be employed as an explorative tool for understanding iTME effects on the pixel level. Moreover, we show that our method can be used to identify statistically significant differences in the expression of different proteins between patient groups with greater sensitivity compared to conventional approaches.

The fabrication of a fully printed, lead-free, polymer piezoelectric transducer is presented and the characterization of its structural, dielectric, and ferroelectric properties at different processing stages is demonstrated. The performance of poly(vinylidene fluoride-trifluoroethylene) transducers with resonance frequency analyses, acoustic power measurements, and pulse-echo experiments is evaluated. Notably, for the first time for a fully printed transducer, an optimal performance in the medical ultrasound range (1 W cm(-2), which is promising for applications in epidermal and wearable electronics. Overall, the findings provide a strong foundation for future research in the area of flexible ultrasound transducers., Advanced Materials Technologies, ISSN:2365-709X

Generative modeling of 3D brain MRIs presents difficulties in achieving high visual fidelity while ensuring sufficient coverage of the data distribution. In this work, we propose to address this challenge with composable, multiscale morphological transformations in a variational autoencoder (VAE) framework. These transformations are applied to a chosen reference brain image to generate MRI volumes, equipping the model with strong anatomical inductive biases. We structure the VAE latent space in a way such that the model covers the data distribution sufficiently well. We show substantial performance improvements in FID while retaining comparable, or superior, reconstruction quality compared to prior work based on VAEs and generative adversarial networks (GANs)., Comment: Accepted to the NeurIPS 2022 Workshop on Medical Imaging meets NeurIPS

Distribution shifts remain a fundamental problem for the safe application of machine learning systems. If undetected, they may impact the real-world performance of such systems or will at least render original performance claims invalid. In this paper, we focus on the detection of subgroup shifts, a type of distribution shift that can occur when subgroups have a different prevalence during validation compared to the deployment setting. For example, algorithms developed on data from various acquisition settings may be predominantly applied in hospitals with lower quality data acquisition, leading to an inadvertent performance drop. We formulate subgroup shift detection in the framework of statistical hypothesis testing and show that recent state-of-the-art statistical tests can be effectively applied to subgroup shift detection on medical imaging data. We provide synthetic experiments as well as extensive evaluation on clinically meaningful subgroup shifts on histopathology as well as retinal fundus images. We conclude that classifier-based subgroup shift detection tests could be a particularly useful tool for post-market surveillance of deployed ML systems., Comment: Under review

Objectives: In this synopsis, we identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2021. Methods: A broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and imaging informatics. Except for the sensor section, we only consider papers that have been published in journals providing at least three articles in the query response. Using a three-point Likert scale (1=not include, 2=maybe include, and 3=include), we reviewed the titles and abstracts of all database returns. Only those papers which reached two times three points were further considered for full paper review using the same Likert scale. Again, we only considered works with two times three points and provided these for external reviews. Based on the external reviews, we selected three best papers, as it happens that the three highest ranked papers represent works from all three parts of this section: sensors, signals, and imaging informatics. Results: The search for papers was executed in January 2022. After removing duplicates and conference proceedings, the query returned a set of 88, 376, and 871 papers for sensors, signals, and imaging informatics, respectively. For signals and images, we filtered out journals that had less than three papers in the query results, reducing the number of papers to 215 and 512, respectively. From this total of 815 papers, the section co-editors identified 35 candidate papers with two times three Likert points, from which nine candidate best papers were nominated after full paper assessment. At least three external reviewers then rated the remaining papers and the three best-ranked papers were selected using the composite rating of all external reviewers. By accident, these three papers represent each of the three fields of sensor, signal, and imaging informatics. They were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. Deep and machine learning techniques are still a dominant topic as well as concepts beyond the state-of-the-art. Conclusions: Sensors, signals, and imaging informatics is a dynamic field of intense research. Current research focuses on creating and processing heterogeneous sensor data towards meaningful decision support in clinical settings.

Summary Objective: To identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2020. Method: A broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and image informatics. We only considered papers that have been published in journals providing at least three articles in the query response. Section editors then independently reviewed the titles and abstracts of preselected papers assessed on a three-point Likert scale. Papers were rated from 1 (do not include) to 3 (should be included) for each topical area (sensors, signals, and imaging informatics) and those with an average score of 2 or above were subsequently read and assessed again by two of the three co-editors. Finally, the top 14 papers with the highest combined scores were considered based on consensus. Results: The search for papers was executed in January 2021. After removing duplicates and conference proceedings, the query returned a set of 101, 193, and 529 papers for sensors, signals, and imaging informatics, respectively. We filtered out journals that had less than three papers in the query results, reducing the number of papers to 41, 117, and 333, respectively. From these, the co-editors identified 22 candidate papers with more than 2 Likert points on average, from which 14 candidate best papers were nominated after intensive discussion. At least five external reviewers then rated the remaining papers. The four finalist papers were found using the composite rating of all external reviewers. These best papers were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. Conclusions. Sensors, signals, and imaging informatics is a dynamic field of intense research. The four best papers represent advanced approaches for combining, processing, modeling, and analyzing heterogeneous sensor and imaging data. The selected papers demonstrate the combination and fusion of multiple sensors and sensor networks using electrocardiogram (ECG), electroencephalogram (EEG), or photoplethysmogram (PPG) with advanced data processing, deep and machine learning techniques, and present image processing modalities beyond state-of-the-art that significantly support and further improve medical decision making.

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.

Implementation of the principles of “wise” online, social psychological interventions using the content of cognitive-behavioral therapy (CBT) to reduce problematic pornography use.

Deep Learning (DL) methods have shown promising results for solving ill-posed inverse problems such as MR image reconstruction from undersampled $k$-space data. However, these approaches currently have no guarantees for reconstruction quality and the reliability of such algorithms is only poorly understood. Adversarial attacks offer a valuable tool to understand possible failure modes and worst case performance of DL-based reconstruction algorithms. In this paper we describe adversarial attacks on multi-coil $k$-space measurements and evaluate them on the recently proposed E2E-VarNet and a simpler UNet-based model. In contrast to prior work, the attacks are targeted to specifically alter diagnostically relevant regions. Using two realistic attack models (adversarial $k$-space noise and adversarial rotations) we are able to show that current state-of-the-art DL-based reconstruction algorithms are indeed sensitive to such perturbations to a degree where relevant diagnostic information may be lost. Surprisingly, in our experiments the UNet and the more sophisticated E2E-VarNet were similarly sensitive to such attacks. Our findings add further to the evidence that caution must be exercised as DL-based methods move closer to clinical practice., Accepted at the MICCAI-2022 workshop: Machine Learning for Medical Image Reconstruction

Maximal oxygen uptake (VO2max) is a direct measure of human cardiorespiratory fitness and is associated with health. However, the molecular determinants of interindividual differences in baseline (intrinsic) VO2max, and of increases of VO2max in response to exercise training (ΔVO2max), are largely unknown. Here, we measure ~5,000 plasma proteins using an affinity-based platform in over 650 sedentary adults before and after a 20-week endurance-exercise intervention and identify 147 proteins and 102 proteins whose plasma levels are associated with baseline VO2max and ΔVO2max, respectively. Addition of a protein biomarker score derived from these proteins to a score based on clinical traits improves the prediction of an individual’s ΔVO2max. We validate findings in a separate exercise cohort, further link 21 proteins to incident all-cause mortality in a community-based cohort and reproduce the specificity of ~75% of our key findings using antibody-based assays. Taken together, our data shed light on biological pathways relevant to cardiorespiratory fitness and highlight the potential additive value of protein biomarkers in identifying exercise responsiveness in humans. Plasma proteomic profiles from 650 adult humans are measured before and after a 20-week exercise regimen to determine proteins associated with baseline cardiorespiratory fitness and improvements in response to exercise.

Background Cannabis is the most-frequently used illicit drug in Europe. Over the last few years in Spain, treatment demand has increased, yet most cannabis users do not seek treatment despite the related problems. A web-based self-help tool, like CANreduce 2.0, could help these users to control their consumption. Methods This study protocol describes a three-arm randomized controlled trial (RCT) comparing the effectiveness of three approaches, in terms of reducing cannabis use among problematic cannabis users, the first two treatment arms including the Spanish version of CANreduce 2.0 (an adherence-focused, guidance-enhanced, web-based self-help tool) (1) with and (2) without psychological support; and the third group (3) treatment as usual (TAU). Study hypotheses will be tested concerning the primary outcome: change in the number of days of cannabis use over the previous week, comparing assessments at 6 weeks and 3 and 6 months follow-up between groups and against baseline. Secondary outcomes related to cannabis use will be tested similarly. Mental disorders will be explored as predictors of adherence and outcomes. Analyses will be performed on an intention-to-treat basis, then verified by complete case analyses. Discussion This study will test how effective the Spanish version of CANreduce 2.0 (CANreduce-SP) is at reducing both the frequency and quantity of cannabis use in problematic users and whether adding psychological support increases its effectiveness. Trial registration This trial is registered with the Clinical Trials Protocol Registration and Results System (PRS) number: NCT04517474. Registered 18 August 2020, (Archived by archive.is https://archive.is/N1Y64). The project commenced in November 2020 and recruitment is anticipated to end by November 2022.

Zusammenfassung Ziel der Studie Zahlreiche Studien weisen auf ein breites Spektrum häufig gleichzeitig auftretender psychischer Störungen wie jene der Generalisierten Angststörung bei der Behandlung von Personen mit Cannabiskonsumstörungen hin (GAD). Bisherige Internet-basierte Studien konnten zwar vereinzelt die Reduktion von Cannabiskonsum zeigen, berücksichtigten aber keine komorbide Störungen. Ziel des E-Coach gestützten Online-Selbsthilfeprogramm CANreduce 2.0 ist die Reduzierung des Cannabiskonsums und der gegebenenfalls vorhandenen GAD. Methodik Sekundäranalyse der in der Allgemeinbevölkerung rekrutierten Cannabiskonsumierenden der CANreduce 2.0 Studie der beiden aktiven Interventionsgruppen (N=367). Für die Analysen wurden diese Teilnehmende aufgrund ihres erreichten Symptomatik-Wertes der General Anxiety Disorder Scale (GAD-7) in 4 Gruppen eingeteilt: Minimale (GAD-7 Werte: 0–4; N=102), Milde (5–9; N=151), Moderate (10–14; N=78) und Schwere (15–21; N=36) Angststörung und anschliessend ihre Behandlungsverläufe mittels Intention-to-Treat Analysen bezüglich ihrer Cannabiskonsumtage und ihrer Werte auf dem Cannabis Use Disorder Identification Test (CUDIT), der Severity of Dependence Scale (SDS), der GAD-7, der Center of Epidemiologic Studies of Depression Scale (CES-D) sowie bezüglich ihrer Adhärenz verglichen. Ergebnisse Alle 4 Gruppen reduzierten unabhängig von ihrer GAD-Symptomatik die Anzahl ihrer Cannabiskonsumtage, ihren CUDIT und SDS Score sowohl 6 Wochen als auch 3 Monate nach der Baselineerhebung. Weiter haben mit Ausnahme von der minimalen GAD-7-Gruppe alle Gruppen eine Reduktion der Angstsymptomatik nach 3 Monaten aufgewiesen. Analog zeigten mit Ausnahme der minimalen GAD-7-Gruppe alle GAD-7-Gruppen eine deutliche Reduktion in der depressiven Symptomatik. Bezüglich der gemessenen Adhärenz zeigten sich keine Unterschiede zwischen den GAD-7-Gruppen. Schlussfolgerungen CANreduce 2.0 scheint sowohl den Cannabiskonsum, die Ausprägung der Cannabiskonsumstörung bzw. die Cannabisabhängigkeit wie auch eine gegebenenfalls vorhandene GAD-Symptomatik zu reduzieren.

ZusammenfassungZiel Untersuchung von Zusammenhängen zwischen der Haptoglobinkonzentration im Blut und dem Auftreten einer Ketose, ausgewählten klinischen Parametern und Lahmheit bei Milchkühen.Material und Methoden Die Datenerhebung fand in 39 bayerischen Milchviehbetrieben über 8 Monate statt. Bei den einbezogenen 712 Fleckvieh- und Braunviehkühen, deren Kalbung 10–30 Tage zurücklag, wurde eine klinische Untersuchung durchgeführt sowie eine Milch- und eine Blutprobe entnommen. Im Blut wurden die Konzentrationen von freien Fettsäuren (non-esterified fatty acids, NEFA), β-Hydroxybutyrat (BHB) und Haptoglobin (Hp) bestimmt, in der Milch die Milchinhaltsstoffe (Fett, Eiweiß, Harnstoff, Laktose, Azeton), BHB, NEFA und die somatische Zellzahl (SCC).Ergebnisse Signifikante Korrelationen ergaben sich zwischen erhöhten Hp-Konzentrationen einerseits und erhöhten NEFA-Konzentrationen in Blut und Milch (p < 0,001), erhöhter SCC (p < 0,001), Lahmheit (p < 0,001), vermindertem Laktosegehalt (p < 0,001) sowie vermindertem Proteingehalt der Milch (p = 0,001) andererseits. In den wärmeren Sommermonaten beprobte Tiere hatten signifikant höhere Hp-Konzentrationen (p < 0,001). Erstkalbinnen wiesen signifikant höhere Hp-Werte auf als multipare Tiere (p < 0,001). Durch die Einteilung der Tiere in 4 Cluster ließ sich ein Hp-Grenzwert bestimmen, der 0,18 mg/ml betrug. Kombiniert mit einem SCC-Grenzwert von 40 500 Zellen/ml Milch konnte der Großteil der subklinisch und klinisch auffälligen Tiere ermittelt werden.Schlussfolgerung und klinische Relevanz Die Messung der Hp-Konzentration im Blut stellt einen praktikablen Ansatz zur Unterstützung des Tiergesundheitsmonitorings im postpartalen Zeitraum dar. Zusammen mit der Auswertung der Milchmenge und der Milchinhaltsstoffe können Abweichungen von physiologischen Zuständen erkannt und betroffene Tiere zeitnah behandelt werden. Haptoglobin kann für die Bewertung des Gesundheitsstatus des Einzeltieres wie auch als Indikator für die Herdengesundheit genutzt werden.

Author(s): Hsu, William; Baumgartner, Christian; Deserno, Thomas M; Section Editors for the IMIA Yearbook Section on Sensors, Signals, and Imaging Informatics | Abstract: ObjectiveTo highlight noteworthy papers that are representative of 2019 developments in the fields of sensors, signals, and imaging informatics.MethodA broad literature search was conducted in January 2020 using PubMed. Separate predefined queries were created for sensors/signals and imaging informatics using a combination of Medical Subject Heading (MeSH) terms and keywords. Section editors reviewed the titles and abstracts of both sets of results. Papers were assessed on a three-point Likert scale by two co-editors, rated from 3 (do not include) to 1 (should be included). Papers with an average score of 2 or less were then read by all three section editors, and the group nominated top papers based on consensus. These candidate best papers were then rated by at least six external reviewers.ResultsThe query related to signals and sensors returned a set of 255 papers from 140 unique journals. The imaging informatics query returned a set of 3,262 papers from 870 unique journals. Based on titles and abstracts, the section co-editors jointly filtered the list down to 50 papers from which 15 candidate best papers were nominated after discussion. A composite rating after review determined four papers which were then approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. These best papers represent different international groups and journals.ConclusionsThe four best papers represent state-of-the-art approaches for processing, combining, and analyzing heterogeneous sensor and imaging data. These papers demonstrate the use of advanced machine learning techniques to improve comparisons between images acquired at different time points, fuse information from multiple sensors, and translate images from one modality to another.

Aims: Modern pharmaceutical product development is a long and complex process associated with significant investments by pharmaceutical companies. The innovative pharmaceutical industry accounts for the vast majority of expenditures in clinical trials of potential new pharmaceuticals and therefore generates economic activity within a country. The aim was to assess the far-reaching economic impact of industry-sponsored clinical-trials (ISCTs) of pharmaceutical products for the healthcare system and the national economy. Materials and methods: The study approach was based on three analytical steps. First, a survey among 15 pharmaceutical companies in Austria was conducted to evaluate the annual number of ISCTs subdivided according to trial phase, therapeutic areas and associated employees. Second, the monetary value of treatments performed in ISCTs was calculated based on a sample of clinical-trial protocols. Finally, the macroeconomic impact, measured in terms of value-added and jobs created by the conducted ISCTs, was calculated using Input–Output analysis by applying an extended Leontief-model. Results: The study demonstrated that €116.22 million spent in ISCTs generated a total value added of €144 million, €74 million direct, in 2018. Each year a medical treatment value of €100 million was financed through 463 ISCTs, with an average value of medical treatment of €37,068 per recruited patient. This represents a significant 0.3% of annual current health-expenditures. In summary, each Euro invested by the pharmaceutical industry in ISCTs generates €1.95 for the Austrian economy. ISCTs also created and secured employment in the extent of 2,021 full-time-equivalents, thus resulting in an employment multiplier of 1.66. Conclusions: In conclusion, conducting clinical-trials by pharmaceutical industry—beside its importance in its own domain—results in tangible benefits and a positive macroeconomic impact that contribute to the sustainability of the Austrian healthcare system by complementing its limited resources. Furthermore, it is a non-negligible factor in locational and industrial policy.

Given prevalent alcohol misuse-emotional comorbidities among young adults, we developed an internet-based integrated treatment called

Undersampling the k-space during MR acquisitions saves time, however results in an ill-posed inversion problem, leading to an infinite set of images as possible solutions. Traditionally, this is tackled as a reconstruction problem by searching for a single "best" image out of this solution set according to some chosen regularization or prior. This approach, however, misses the possibility of other solutions and hence ignores the uncertainty in the inversion process. In this paper, we propose a method that instead returns multiple images which are possible under the acquisition model and the chosen prior to capture the uncertainty in the inversion process. To this end, we introduce a low dimensional latent space and model the posterior distribution of the latent vectors given the acquisition data in k-space, from which we can sample in the latent space and obtain the corresponding images. We use a variational autoencoder for the latent model and the Metropolis adjusted Langevin algorithm for the sampling. We evaluate our method on two datasets; with images from the Human Connectome Project and in-house measured multi-coil images. We compare to five alternative methods. Results indicate that the proposed method produces images that match the measured k-space data better than the alternatives, while showing realistic structural variability. Furthermore, in contrast to the compared methods, the proposed method yields higher uncertainty in the undersampled phase encoding direction, as expected. ISSN:0278-0062 ISSN:1558-254X

BackgroundDespite increasing demand for treatment among cannabis users in many countries, most users are not in treatment. Internet-based self-help offers an alternative for those hesitant to seek face-to-face therapy, though low effectiveness and adherence issues often arise. ObjectiveThrough adherence-focused guidance enhancement, we aimed to increase adherence to and the effectiveness of internet-based self-help among cannabis users. MethodsFrom July 2016 to May 2019, cannabis users (n=775; male: 406/575, 70.6%, female: 169/575, 29.4%; age: mean 28.3 years) not in treatment were recruited from the general population and were randomly assigned to (1) an adherence-focused guidance enhancement internet-based self-help intervention with social presence, (2) a similar intervention with an impersonal service team, and (3) access to internet as usual. Controls who were placed on a waiting list for the full intervention after 3 months underwent an assessment and had access to internet as usual. The primary outcome measurement was cannabis-use days over the preceding 30 days. Secondary outcomes included cannabis-dependence severity, changes in common mental disorder symptoms, and intervention adherence. Differences between the study arms in primary and secondary continuous outcome variables at baseline, posttreatment, and follow-up were tested using pooled linear models. ResultsAll groups exhibited reduced cannabis-use days after 3 months (social presence: –8.2 days; service team: –9.8 days; internet as usual: –4.2 days). The participants in the service team group (P=.01, d=.60) reported significantly fewer cannabis-use days than those in the internet as usual group; the reduction of cannabis use in the social presence group was not significant (P=.07, d=.40). There was no significant difference between the 2 intervention groups regarding cannabis-use reduction. The service team group also exhibited superior improvements in cannabis-use disorder, cannabis-dependence severity, and general anxiety symptoms after 3 months to those in the internet as usual group. ConclusionsThe adherence-focused guidance enhancement internet-based self-help intervention with an impersonal service team significantly reduced cannabis use, cannabis-use disorder, dependence severity, and general anxiety symptoms. Trial RegistrationISRCTN Registry ISRCTN11086185; http://www.isrctn.com/ISRCTN11086185

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.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.This RCT will provide important insights into the effectiveness of an IMI integrating mindfulness to reduce cannabis use in frequent cannabis users.International Standard Randomized Controlled Trial Number Registry: ISRCTN14971662 ; date of registration: 09/09/2021.

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.

BACKGROUND Prevalence 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. OBJECTIVE We 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. METHODS We 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. RESULTS Both 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

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.

Nongenetic optical control of neurons is a powerful technique to study and manipulate the function of the nervous system. This research has benchmarked the performance of organic electrolytic photocapacitor (OEPC) optoelectronic stimulators at the level of single mammalian cells: human embryonic kidney (HEK) cells with heterologously expressed voltage-gated K+ channels and hippocampal primary neurons. OEPCs act as extracellular stimulation electrodes driven by deep red light. The electrophysiological recordings show that millisecond light stimulation of OEPC shifts conductance-voltage plots of voltage-gated K+ channels by ≈30 mV. Models are described both for understanding the experimental findings at the level of K+ channel kinetics in HEK cells, as well as elucidating interpretation of membrane electrophysiology obtained during stimulation with an electrically floating extracellular photoelectrode. A time-dependent increase in voltage-gated channel conductivity in response to OEPC stimulation is demonstrated. These findings are then carried on to cultured primary hippocampal neurons. It is found that millisecond time-scale optical stimuli trigger repetitive action potentials in these neurons. The findings demonstrate that OEPC devices enable the manipulation of neuronal signaling activities with millisecond precision. OEPCs can therefore be integrated into novel in vitro electrophysiology protocols, and the findings can inspire in vivo applications.

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.