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This study presents a finite element model (FEM) simulation of optoelectronic stimulation in the rat brain, designed to investigate the effects of this emerging neural stimulation technique. A detailed 3D model of the rat brain and an equivalent circuit representation of the organic semiconductor-based stimulation device was used to simulate the subdural application of optoelectronic stimuli to the rat brain. The model is based on realistic electrode geometries and material properties. The stimulated brain volume is approximately 38 mm3 with a maximal stimulation depth of 2.6 mm. Our results indicate that optoelectronic stimulation can achieve targeted activation of cortical neurons. Further studies will focus on optimizing device parameters, exploring long-term effects, and expand this approach for specific neural bioelectronics to fully exploit its potential in clinical applications. [ABSTRACT FROM AUTHOR]

The Gambling Craving Scale (GACS) is a multifaceted measure of gambling craving. Initial validation work by Young and Wohl (2009) in university student samples showed that the GACS had a three-factor structure capturing dimensions of Desire, Anticipation, and Relief. Despite its potential clinical utility as a measure of craving, the GACS has yet to be validated in people seeking treatment for gambling problems. Accordingly, we examined the psychometric properties in a sample of people (N = 209; Mage = 37.66; 62.2% female) participating in a randomized controlled trial testing a novel online treatment for problem gambling. We predicted the GACS would have a three-factor structure. In addition, we also examined measurement invariance across sex and problem gambling risk status. Finally, we assessed concurrent validity of the factors with other measures of problem gambling severity and involvement. Exploratory structural equation modeling findings supported a three-factor structure that was invariant across the groups tested. Each of the Desire, Anticipation, and Relief subscales were significant positive predictors of problem gambling severity and symptoms, and some form of gambling behaviour. Findings show the GACS is a promising scale to assess multidimensional craving experiences among people in treatment for gambling problems. [ABSTRACT FROM AUTHOR]

Introduction: Anti-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. Methods: This 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. Results: A 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). Conclusion: Despite 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. [ABSTRACT FROM AUTHOR]

Distribution shifts remain a problem for the safe application of regulated medical AI systems, and may impact their real-world performance if undetected. Postmarket shifts can occur for example if algorithms developed on data from various acquisition settings and a heterogeneous population are predominantly applied in hospitals with lower quality data acquisition or other centre-specific acquisition factors, or where some ethnicities are over-represented. Therefore, distribution shift detection could be important for monitoring AI-based medical products during postmarket surveillance. We implemented and evaluated three deep-learning based shift detection techniques (classifier-based, deep kernel, and multiple univariate kolmogorov-smirnov tests) on simulated shifts in a dataset of 130'486 retinal images. We trained a deep learning classifier for diabetic retinopathy grading. We then simulated population shifts by changing the prevalence of patients' sex, ethnicity, and co-morbidities, and example acquisition shifts by changes in image quality. We observed classification subgroup performance disparities w.r.t. image quality, patient sex, ethnicity and co-morbidity presence. The sensitivity at detecting referable diabetic retinopathy ranged from 0.50 to 0.79 for different ethnicities. This motivates the need for detecting shifts after deployment. Classifier-based tests performed best overall, with perfect detection rates for quality and co-morbidity subgroup shifts at a sample size of 1000. It was the only method to detect shifts in patient sex, but required large sample sizes ( > 3 0 ′ 000 ). All methods identified easier-to-detect out-of-distribution shifts with small (≤300) sample sizes. We conclude that effective tools exist for detecting clinically relevant distribution shifts. In particular classifier-based tests can be easily implemented components in the post-market surveillance strategy of medical device manufacturers. [ABSTRACT FROM AUTHOR]

Fluid flow in the human body can be modeled using a water-level tank, a commonly used mechanistic approach in mechanical engineering for fluid transport processes. Postoperative fluid data from patients undergoing cardiac surgery is used to estimate fluid flow dynamics and total body water in the human body. This simplified model provide a basic understanding of the dynamics of fluid flow processes in the human body and could aid in modeling distribution of fluids in compartments. [ABSTRACT FROM AUTHOR]

In this study, we employed calcium imaging to investigate the dynamics of intracellular calcium levels in embryonic chicken cardiomyocytes upon extracellular, optoelectronic stimulation. A photovoltaic layer of donoracceptor pigments on a flexible PET substrate was used as a wireless stimulation electrode. Our findings revealed a distinct change in their spontaneous activity pattern in cardiac cells following asynchronous light stimulation. A short pause in cellular activity, indicative of overdrive suppression, was observed in recordings from several different cells. The pause in activity signifies a transient refractory period induced by stimulation of the photovoltaic device with red light. These findings suggest that photovoltaic electrodes can be used to effectively modulate the electrical activity of cardiac cells in a wireless, non-pharmacological manner. This opens new avenues for non-invasive and precise light-modulated control of cellular electrophysiology as well as potential therapeutic applications for cardiac rhythm disorders. [ABSTRACT FROM AUTHOR]

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 (<15 MHz) is demonstrated with acoustic power >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. [ABSTRACT FROM AUTHOR]

Background and Aims: Problem gambling constitutes a public health concern associated with psychopathological comorbidity, substance use, and financial difficulties. Most individuals with gambling problems avoid counseling services due to perceived stigma and their preference for self-reliance. Treatment accessibility could be improved through web-based interventions. Methods: We recruited 360 individuals with gambling problems and randomized them to a web-based intervention (n 5 185) or an active control group consisting of a self-help manual for problem gambling (n 5 175). The primary outcome was the number of days of gambling in the last 30 days. Secondary outcomes included money spent in the last 30 days, time gambling in the last 7 days, gambling-related problems, consumption of alcohol and cigarettes, and psychopathological comorbidity measured at posttreatment and 6-month follow-up. Results: The primary outcome decreased significantly for both groups, with no significant difference between the groups. There were significant group 3time interactions according to the Gambling Symptom Assessment Scale (F 5 8.83, p <0 .001), the Problem Gambling Severity Index (F 5 3.54, p 5 0.030), for cigarettes smoked in the last 7 days (F 5 26.68, p < 0.001), the Patient Health Questionnaire-9 (F 5 19.41, p <0 .001), and the Generalized Anxiety Disorder-7 (F 5 41.09, p <0 .001) favoring the intervention group. We experienced an overall high dropout rate (76%). Conclusions: Win Back Control seems to be an effective low-threshold treatment option for individuals with gambling problems that might otherwise be unapproachable for outpatient treatment services. Nevertheless, the high dropout rate should be considered when interpreting the study results, as they may have introduced a degree of variability. [ABSTRACT FROM AUTHOR]

Artificial intelligence, ChatGPT, healthcare decisions, natural language processing (NLP), regulatory approval, software as medical device (SaMD) A regulatory challenge for natural language processing (NLP)-based tools such as ChatGPT to be legally used for healthcare decisions. However, if the intended use of NLP-based tools, beyond ChatGPT, for clinical purposes falls within the definition of an AI/ML-based SaMD, regulatory approval is required. [Extracted from the article]

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, patientindividual 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. [ABSTRACT FROM AUTHOR]

This paper discusses possibilities to explain which Kv channels can be considered as first-order systems. This means that the dynamic behavior of these channels can be described with a first-order differential equation and are the most straightforward dynamical systems. For example, a typical first-order system is an RC circuit, and analogously, Kv channels can be considered with the dynamic behavior of first-order systems following RC circuits. This work represents a family of KV channels that can be described as first-order systems, providing an efficient method of ion channel modeling in computational electrophysiology. [ABSTRACT FROM AUTHOR]

Background and aims: Problem 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. Methods: A 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. Results: While 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 conclusions: While 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. [ABSTRACT FROM AUTHOR]

ChatGPT has given the following answer: I "ChatGPT, a state-of-the-art language model developed by OpenAI, has the potential to revolutionize the way medical information is disseminated and processed in the clinical and translational medicine fields. "The Ultimate Computer", an episode of the legendary 1968 series Star Trek: The Original Series, in retrospect, put the spotlight on the unprecedented development of artificial intelligence (AI), with its enormous possibilities, but also limitations and dangers, without knowledge of the modern computational methods of the 21st century. I Personalized medicine: ChatGPT has the potential to provide personalized medical advice based on individual patient data and health histories i . [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

High throughput technologies in genomics enable the analysis of small alterations in gene expression levels. Patterns of such deviations are an important starting point for the discovery and verification of new biomarker candidates. Identifying such patterns is a challenging task that requires sophisticated machine learning approaches. Currently, there are a variety of classification models, and a common approach is to compare the performance and select the best one for a given classification problem. Since the association between the features of a data set and the performance of a particular classification method is still not fully understood, the main contribution of this work is to provide a new methodology for predicting the prediction results of different classifiers in the field of biomarker discovery. We propose here a three-steps computational workflow that includes an analysis of the data set characteristics, the calculation of the classification accuracy and, finally, the prediction of the resulting classification error. The experiments were carried out on synthetic and microarray datasets. Using this method, we showed that the predictability strongly depends on the discriminatory ability of the features, e.g., sets of genes, in two or multi-class datasets. If a dataset has a certain discriminatory ability, this method enables prediction of the classification performance before applying a learning model. Thus, our results contribute to a better understanding of the relationship between dataset characteristics and the corresponding performance of a machine learning method, and suggest the optimal classification method for a given dataset based on its discriminatory ability. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The incidence of diabetes is increasing globally. Short and long-term effects of diabetes can negatively affect the lives of a person and their relatives. A good glycaemic control is important. Compared to conventional blood glucose measurement with capillary blood, which is obtained by finger pricking, it is now possible to continuously measure glucose using sensors that are placed in the subcutaneous tissue and remain there for several days to weeks. This means that the "tissue sugar" and not the blood sugar directly is determined. For this reason, it is important to determine the accuracy of these sensors. Furthermore, such systems can influence not only the physical but also the psychological/mental health of people with diabetes and their relatives. Reimbursement of sensors differs in Europe. This overview is based on the Austrian health care system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Nonreciprocal transport refers to charge transfer processes that are sensitive to the bias polarity. Until recently, nonreciprocal transport was studied only in dissipative systems, where the nonreciprocal quantity is the resistance. Recent experiments have, however, demonstrated nonreciprocal supercurrent leading to the observation of a supercurrent diode effect in Rashba superconductors. Here we report on a supercurrent diode effect in NbSe2 constrictions obtained by patterning NbSe2 flakes with both even and odd layer number. The observed rectification is a consequence of the valley-Zeeman spin-orbit interaction. We demonstrate a rectification efficiency as large as 60%, considerably larger than the efficiency of devices based on Rashba superconductors. In agreement with recent theory for superconducting transition metal dichalcogenides, we show that the effect is driven by the out-of-plane component of the magnetic field. Remarkably, we find that the effect becomes field-asymmetric in the presence of an additional in-plane field component transverse to the current direction. Supercurrent diodes offer a further degree of freedom in designing superconducting quantum electronics with the high degree of integrability offered by van der Waals materials. 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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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/. [ABSTRACT FROM AUTHOR]

Objective: To study the longitudinal performance of fully automated cartilage segmentation in knees with radiographic osteoarthritis (OA), we evaluated the sensitivity to change in progressor knees from the Foundation for the National Institutes of Health OA Biomarkers Consortium between the automated and previously reported manual expert segmentation, and we determined whether differences in progression rates between predefined cohorts can be detected by the fully automated approach. Methods: The OA Initiative Biomarker Consortium was a nested case–control study. Progressor knees had both medial tibiofemoral radiographic joint space width loss (≥0.7 mm) and a persistent increase in Western Ontario and McMaster Universities Osteoarthritis Index pain scores (≥9 on a 0–100 scale) after 2 years from baseline (n = 194), whereas non‐progressor knees did not have either of both (n = 200). Deep‐learning automated algorithms trained on radiographic OA knees or knees of a healthy reference cohort (HRC) were used to automatically segment medial femorotibial compartment (MFTC) and lateral femorotibial cartilage on baseline and 2‐year follow‐up magnetic resonance imaging. Findings were compared with previously published manual expert segmentation. Results: The mean ± SD MFTC cartilage loss in the progressor cohort was –181 ± 245 μm by manual segmentation (standardized response mean [SRM] –0.74), –144 ± 200 μm by the radiographic OA–based model (SRM –0.72), and –69 ± 231 μm by HRC‐based model segmentation (SRM –0.30). Cohen's d for rates of progression between progressor versus the non‐progressor cohort was –0.84 (P < 0.001) for manual, –0.68 (P < 0.001) for the automated radiographic OA model, and –0.14 (P = 0.18) for automated HRC model segmentation. Conclusion: A fully automated deep‐learning segmentation approach not only displays similar sensitivity to change of longitudinal cartilage thickness loss in knee OA as did manual expert segmentation but also effectively differentiates longitudinal rates of loss of cartilage thickness between cohorts with different progression profiles. [ABSTRACT FROM AUTHOR]

Featured Application: Nanoelectromechanical systems and nanorobots can be used to treat cancers associated with the Kv10.1 voltage-gated ion channel activity. The Kv10.1 model was developed by applying the control engineering theory. Nanoelectromechanical systems play the role of a PID regulator. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background and Aims: Despite problematic pornography use (PPU) being prevalent, no previous study has examined the effectiveness of evidence-based interventions for PPU, using rigorous methods. Using a two-armed randomized controlled trial study design, we examined the feasibility and initial effectiveness of a six-week online PPU intervention. Methods: We recruited 264 participants (3.8% women, Mage = 33.2, SD = 10.6) who were randomized and assigned to either the self-help intervention (n = 123) or waitlist control condition (n = 141), and completed self-report questionnaires at baseline and after the end of the intervention (six-week follow-up). Multivariable linear regression models were generated and tested on a complete case basis to investigate possible treatment effects. Participants provided quantitative and qualitative feedback regarding the intervention's content and appearance. Results: Participants evaluated all modules positively in the intervention in general. There were differential dropout rates (89.4% in intervention vs. 44.7% in control group) with an overall follow-up rate of 34.5%. The intervention group reported significantly lower levels of PPU (P < 0.001, d = 1.32) at the six-week follow-up. Moreover, they reported lower pornography use frequency (P < 0.001, d = 1.65), self-perceived pornography addiction (P = 0.01, d = 0.85), pornography craving (P = 0.02, d = 0.40), and higher pornography avoidance self-efficacy (P = 0.001, d = 0.87) at the six-week follow-up. Discussion and Conclusions: The present study was only a first step in rigorous treatment studies for PPU, but the findings are promising and suggest that online interventions for PPU might help reduce PPU in some cases, even without the guidance of therapists, by reducing treatment barriers. [ABSTRACT FROM AUTHOR]

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. Author summary: Advances in the understanding of functional alterations at genetic, epigenetic or protein expression and the expanding knowledge in mechanisms modulating ion channel kinetics and thus the cells' bioelectric properties have arisen as promising cancer biomarkers and oncological targets. Our hidden Markov-based in-silico cell model represents the electrophysiology behind proliferation of the A549 cell line, explaining the cell's rhythmic oscillation from hyperpolarized to depolarized states of the membrane potential, able to trigger the transition between cell cycle phases. The model enables the prediction of membrane potential changes over the cell cycle provoked by targeted modulation of specific ion channels, leading to cell cycle promotion or interruption. We are encouraged that the availability of this first cancer cell model will provide profound insight into possible roles and interactions of ion channels in tumor development and progression, and may aid in the testing of research hypotheses in lung cancer electrophysiology. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the agreement, accuracy, and longitudinal reproducibility of quantitative cartilage morphometry from 2D U-Net-based automated segmentations for 3T coronal fast low angle shot (corFLASH) and sagittal double echo at steady-state (sagDESS) MRI. Methods: 2D U-Nets were trained using manual, quality-controlled femorotibial cartilage segmentations available for 92 Osteoarthritis Initiative healthy reference cohort participants from both corFLASH and sagDESS (n = 50/21/21 training/validation/test-set). Cartilage morphometry was computed from automated and manual segmentations for knees from the test-set. Agreement and accuracy were evaluated from baseline visits (dice similarity coefficient: DSC, correlation analysis, systematic offset). The longitudinal reproducibility was assessed from year-1 and -2 follow-up visits (root-mean-squared coefficient of variation, RMSCV%). Results: Automated segmentations showed high agreement (DSC 0.89–0.92) and high correlations (r ≥ 0.92) with manual ground truth for both corFLASH and sagDESS and only small systematic offsets (≤ 10.1%). The automated measurements showed a similar test–retest reproducibility over 1 year (RMSCV% 1.0–4.5%) as manual measurements (RMSCV% 0.5–2.5%). Discussion: The 2D U-Net-based automated segmentation method yielded high agreement compared with manual segmentation and also demonstrated high accuracy and longitudinal test–retest reproducibility for morphometric analysis of articular cartilage derived from it, using both corFLASH and sagDESS. [ABSTRACT FROM AUTHOR]

Objective: Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a fully automated approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study. Materials and methods: The segmentation method is based on U-Net architecture trained on 250 manually segmented thighs from the Osteoarthritis Initiative (OAI). The clinical evaluation is performed on a hold-out test set bilateral thighs of 48 subjects with unilateral knee pain. Results: The segmentation time of the method is < 1 s and demonstrated high agreement with the manual method (dice similarity coeffcient: 0.96 ± 0.01). In the clinical study, the automated method shows that similar to manual segmentation (− 5.7 ± 7.9%, p < 0.001, effect size: 0.69), painful knees display significantly lower quadriceps CSAs than contralateral painless knees (− 5.6 ± 7.6%, p < 0.001, effect size: 0.73). Discussion: Automated segmentation of thigh muscle and adipose tissues has high agreement with manual segmentations and can replicate the effect size seen in a clinical study on osteoarthritic pain. [ABSTRACT FROM AUTHOR]

Optoelectronic neurostimulation is a promising, minimally invasive treatment modality for neuronal damage, in particular for patients with traumatic brain injury. In this work, a newly developed optoelectronic device, a so-called photocap, based on light-activated organic semiconductor structures with high spatial and temporal resolution is investigated. To prove and verify the feasibility of this new technology, a mathematical model was developed, simulating the electrical response of excitable cells to photocap stimulation. In the first step, a comprehensive technical review of the device concept was performed, building the basis for setting up the simulation model. The simulations demonstrate that photocaps may serve as a stimulation device, triggering action potentials in neural or cardiac cells. Our first results show that the model serves as a perfect tool for evaluating and further developing this new technology, showing high potential for introducing new and innovative therapy methods in the field of optoelectronic cell stimulation. [ABSTRACT FROM AUTHOR]

Ultrasound imaging enables in-vivo investigations of muscle and tendon behaviour during human movement. Individual contributions of muscles and tendons to the behaviour of the whole muscle-tendon unit during locomotion are versatile. Therefore, movements of distinct landmarks, such as muscle tendon junctions are recorded and tracked in order to investigate internal dynamics of the muscle-tendon complex. In this study, we use a semi-automatic tracking method based on image segmentation and investigate how tracking accuracy can be improved using a sticks filter. We demonstrate that a speckle reduction decreases the root-mean-square error of the tracking result by up to 78.1%, depending on the chosen window size of the sticks filter. [ABSTRACT FROM AUTHOR]

The past-year prevalence of problematic pornography use (PPU) was 1–6% in adult populations. As a result of treatment obstacles and barriers, such as unaffordable treatments, only a minority of problematic pornography users may seek treatment. Having a free, online, self-help program may overcome treatment barriers and may help those individuals who cannot receive traditional or offline treatment for PPU. Although the effectiveness of such online programs reducing substance use and problematic gambling have been reported, no prior study has examined the efficacy of an online self-help intervention aiming to reduce PPU. This two-armed randomized controlled trial (RCT) will examine the effectiveness of an online self-help program (Hands-off) to reduce PPU, while also considering psychopathological comorbidities. The six-week intervention condition includes six core modules developed to reduce PPU based on motivational interviewing, cognitive behavioral therapy, mindfulness, and wise social-psychological intervention techniques. The target sample size is 242 participants. Self-report questionnaires will be administered at baseline, right after the end of the intervention, at one-month, and three-month follow-ups after the end of the intervention. The primary outcome will be the level of PPU. Secondary outcomes will include pornography use frequency, pornography craving, pornography use-avoidance self-efficacy, sex mindset, sexual satisfaction, negative and positive emotions, and life satisfaction. Data will be analyzed on an intention-to-treat basis using linear mixed models. Results will be reported at conferences and published in a scientific peer-reviewed journal. The participants will be sent a lay-person-friendly summary of the results via e-mail. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Carpal tunnel syndrome (CTS) and spinal canal stenosis can be frequently observed in the medical history of patients with transthyretin amyloidosis (ATTR), both in the hereditary (mt-ATTR) and wild-type (wt-ATTR) form. The aim of this retrospective single-center analysis was to determine the prevalence of these findings, delay to diagnosis of systemic amyloidosis and the prognostic value in a large cohort of patients with wt-ATTR and mt-ATTR amyloidosis. Methods: Medical records of 253 patients diagnosed with wt-ATTR, 136 patients with mt-ATTR and 77 asymptomatic gene carriers were screened for history of CTS and spinal canal stenosis and laboratory analysis, electrocardiography and echocardiographic results, respectively. Clinical follow-up was performed by phone assessment. Results: History of CTS was present in 77 patients (56%) with mt-ATTR, in 152 patients (60%) with wt-ATTR and even in 10 of the asymptomatic gene carriers (13%). Latency between carpal tunnel surgery and first diagnosis of systemic amyloidosis was significantly longer in wt-ATTR compared to mt-ATTR (117 ± 179 months vs. 66 ± 73 months; p = 0.02). In total, 36 patients (14%) with wt-ATTR and 7 patients (5%) with mt-ATTR had a history of clinically significant spinal canal stenosis. In the subgroup of mt-ATTR, patients with CTS had thicker IVS (19 ± 5 mm vs. 16 ± 5 mm, p < 0.05), higher LV mass index (225 ± 78 g vs. 193 ± 98 g, p < 0.05), lower Karnofsky index (78 ± 15% vs. 83 ± 17%, p < 0.05), and lower mitral annular plane systolic excursion (MAPSE; 9 ± 4 mm vs. 11 ± 5 mm, p < 0.05) compared to patients without CTS, whereas in wt-ATTR no significant differences could be observed. No significant difference in survival was observed between patients with and without CTS (wt-ATTR: 67 vs. 63 months, p = 0.45; mt-ATTR: 74 vs. 63 months, p = 0.60). A combination of CTS and spinal stenosis was present in 32 wt-ATTR patients (12%) and 3 mt-ATTR patients (2.2%). Conclusions: The prevalence of CTS is high and the latency between CTS surgery and diagnosis of amyloidosis is long among patients with wt-ATTR and mt-ATTR. CTS might be predictive for future occurrence of systemic (predominantly cardiac) ATTR amyloidosis. [ABSTRACT FROM AUTHOR]

Algorithms for magnetic resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstruction. Leveraging this, recent methods employed DL to learn mappings from undersampled to fully sampled images using paired datasets, including undersampled and corresponding fully sampled images, integrating prior knowledge implicitly. In this letter, we propose an alternative approach that learns the probability distribution of fully sampled MR images using unsupervised DL, specifically variational autoencoders (VAE), and use this as an explicit prior term in reconstruction, completely decoupling the encoding operation from the prior. The resulting reconstruction algorithm enjoys a powerful image prior to compensate for missing k-space data without requiring paired datasets for training nor being prone to associated sensitivities, such as deviations in undersampling patterns used in training and test time or coil settings. We evaluated the proposed method with T1 weighted images from a publicly available dataset, multi-coil complex images acquired from healthy volunteers (${N}=8$), and images with white matter lesions. The proposed algorithm, using the VAE prior, produced visually high quality reconstructions and achieved low RMSE values, outperforming most of the alternative methods on the same dataset. On multi-coil complex data, the algorithm yielded accurate magnitude and phase reconstruction results. In the experiments on images with white matter lesions, the method faithfully reconstructed the lesions. [ABSTRACT FROM AUTHOR]

Background: Machine learning is one important application in the area of health informatics, however classification methods for longitudinal data are still rare. Objectives: The aim of this work is to analyze and classify differences in metabolite time series data between groups of individuals regarding their athletic activity. Methods: We propose a new ensemble-based 2-tier approach to classify metabolite time series data. The first tier uses polynomial fitting to generate a class prediction for each metabolite. An induced classifier (k-nearest-neighbor or naïve bayes) combines the results to produce a final prediction. Metabolite levels of 47 individuals undergoing a cycle ergometry test were measured using mass spectrometry. Results: In accordance with our previous work the statistical results indicate strong changes over time. We found only small but systematic differences between the groups. However, our proposed stacking approach obtained a mean accuracy of 78% using 10-fold cross-validation. Conclusion: Our proposed classification approach allows a considerable classification performance for time series data with small differences between the groups. [ABSTRACT FROM AUTHOR]