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Objective: A fully automated laminar cartilage composition (MRI-based T2) analysis method was technically and clinically validated by comparing radiographically normal knees with (CL-JSN) and without contra-lateral joint space narrowing or other signs of radiographic osteoarthritis (OA, CL-noROA)., Materials and Methods: 2D U-Nets were trained from manually segmented femorotibial cartilages (n = 72) from all 7 echoes (All E ), or from the 1st echo only (1 st E ) of multi-echo-spin-echo (MESE) MRIs acquired by the Osteoarthritis Initiative (OAI). Because of its greater accuracy, only the All E U-Net was then applied to knees from the OAI healthy reference cohort (n = 10), CL-JSN (n = 39), and (1:1) matched CL-noROA knees (n = 39) that all had manual expert segmentation, and to 982 non-matched CL-noROA knees without expert segmentation., Results: The agreement (Dice similarity coefficient) between automated vs. manual expert cartilage segmentation was between 0.82 ± 0.05/0.79 ± 0.06 (All E /1 st E) and 0.88 ± 0.03/0.88 ± 0.03 (All E /1 st E ) across femorotibial cartilage plates. The deviation between automated vs. manually derived laminar T2 reached up to - 2.2 ± 2.6 ms/ + 4.1 ± 10.2 ms (All E /1 st E ). The All E U-Net showed a similar sensitivity to cross-sectional laminar T2 differences between CL-JSN and CL-noROA knees in the matched (Cohen's D ≤ 0.54) and the non-matched (D ≤ 0.54) comparison as the matched manual analyses (D ≤ 0.48). Longitudinally, the All E U-Net also showed a similar sensitivity to CL-JSN vs. CS-noROA differences in the matched (D ≤ 0.51) and the non-matched (D ≤ 0.43) comparison as matched manual analyses (D ≤ 0.41)., Conclusion: The fully automated T2 analysis showed a high agreement, acceptable accuracy, and similar sensitivity to cross-sectional and longitudinal laminar T2 differences in an early OA model, compared with manual expert analysis., Trial Registration: Clinicaltrials.gov identification: NCT00080171., (© 2024. The Author(s).)

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; M age  = 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., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Tinchon, Brait, Klee, Graichen, Baumgartner, Friedrich, Freydl, Oberndorfer, Struhal, Hain, Waiß and Stoiber.)

Background: Antibiotic residues in milk are a well-known hazard in the dairy food chain. Detection methods for these residues, such as nonspecific microbiological inhibitor tests or group-specific receptor tests, are relatively inexpensive, easy to use, and widely applied to ensure food safety. In contrast, specific detection by liquid chromatography-tandem mass spectrometry (LC-MS/MS)-although a critical, complimentary method to confirm the results of nonspecific testing-is relatively costly, time-consuming, and laborious. Furthermore, sample processing before LC-MS/MS analysis requires unique preparation procedures for different groups of antibiotic compounds., Objective: To simplify and speed up specific antibiotic residue detection, a low-cost, passive, and single-step method to fractionate analytes in raw milk was developed., Methods: Untreated raw milk was fractionated into its water and fat/protein phases using a Fractionation of Milk for Trace Analysis of Contaminants and Residues for Antibiotics (FraMiTrACR® AB) fractionation unit. The water fraction was then analyzed by LC-MS/MS. The analyte fractionation method was evaluated against a Quick Easy Cheap Effective Rugged and Safe (QuEChERS)-based method for sample preparation., Results: Our method allows qualitative and quantitative detection of substances from the penicillin, cephalosporin, macrolide, lincosamide, sulfonamide, tetracycline, and fluoroquinolone groups of antibiotics. Detection limits are below the legally prescribed maximum residue levels, allowing reliable, specific, and rapid validation of a positive result in nonspecific microbiological inhibitor tests., Conclusion: Analyte fractionation by FraMiTrACR AB is a faster alternative to QuEChERS-based sample preparation for the detection of antibiotic substances in milk., Highlight: This method describes a low-cost, environmentally friendly, passive, and single-step milk analyte fractionation. As an alternative to QuEChERS-based preparation, this fractionation method simplifies and speeds up the process for specific antibiotic residue detection., (© The Author(s) 2024. Published by Oxford University Press on behalf of AOAC INTERNATIONAL. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: The results of this study describe the relationship between the body condition of dairy cows and selected metabolic parameters during the peri- and post-partum period with special consideration of 3 local dairy cow breed in Upper Bavaria and the Allgau., Material and Methods: Three local dairy cattle breeds (Swiss Brown (BV), Simmental (FL), Holstein Friesian (HF)) were examined on 68 farms in southern Germany for 7 consecutive weeks. In dry cows as well as lactating cows (5.-65. day in milk), following blood parameters were investigated: beta-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), creatinine, aspartate aminotransferase, gamma-glutamyltransferase, glutamate dehydrogenase, total protein, albumin, creatine kinase. In addition, body condition (body condition score [BCS] and back fat thickness [BFT]) were recorded. Exploratory and descriptive statistics were used for data analysis., Results: Concerning the difference in condition before and after calving, the FL showed the smallest difference in RFD. For FL and BV a trend towards higher BFT values could be seen in first lactating cows. For FL and HF, the NEFA values of the later lactating cows were below those of the first lactating cows. The higher lactating cows of BV and FL had higher BHB values. The correlation between BFT and BCS showed the highest R2 (0.53) in the HF cows. BV and FL were below at 0.42 and 0.37. BCS and BFT could not be predicted by the variables NEFA, BHB and liver enzymes. BHB levels of all 3 breeds increased at weeks 2-4 post-partum. The NEFA values for all 3 breeds increased primarily in the 1st-3rd week p.p. in parallel to when the BFT p.p. decreased. NEFA values were highest when body condition declined and therefore when fat mobilization peaked. In BV and HF, there was a constant increase in GLDH when the p.p. BCS difference was there., Conclusion and Clinical Relevance: Body condition assessment (BCS at herd and animals` level, BFT at animal level) is an important tool for animal health monitoring. Due to the recognizable breed specificity, the dairy herds can be dealt with more explicitly. The aim is to optimally influence the energy balance of the cow during early lactation in order maintain the health of the animal and its organ systems., Competing Interests: Die Autor*innen bestätigen, dass kein Interessenkonflikt besteht., (Thieme. All rights reserved.)

Objective: This study aims to explore the potential of organic electrolytic photocapacitors (OEPCs), an innovative photovoltaic device, in mediating the activation of native voltage-gated Cav1.2 channels (I Ca,L ) in Guinea pig ventricular cardiomyocytes., Methods: Whole-cell patch-clamp recordings were employed to examine light-triggered OEPC mediated I Ca,L activation, integrating the channel's kinetic properties into a multicompartment cell model to take intracellular ion concentrations into account. A multidomain model was additionally incorporated to evaluate effects of OEPC-mediated stimulation. The final model combines external stimulation, multicompartmental cell simulation, and a patch-clamp amplifier equivalent circuit to assess the impact on achievable intracellular voltage changes., Results: Light pulses activated I Ca,L , with amplitudes similar to voltage-clamp activation and high sensitivity to the L-type Ca 2+ channel blocker, nifedipine. Light-triggered I Ca,L inactivation exhibited kinetic parameters comparable to voltage-induced inactivation., Conclusion: OEPC-mediated activation of I Ca,L demonstrates their potential for nongenetic optical modulation of cellular physiology potentially paving the way for the development of innovative therapies in cardiovascular health. The integrated model proves the light-mediated activation of I Ca,L and advances the understanding of the interplay between the patch-clamp amplifier and external stimulation devices., Significance: Treating cardiac conduction disorders by minimal-invasive means without genetic modifications could advance therapeutic approaches increasing patients' quality of life compared with conventional methods employing electronic devices.

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., (© 2024. The Author(s).)

Introduction: Advances in molecular targeting of ion channels may open up new avenues for therapeutic approaches in cancer based on the cells' bioelectric properties. In addition to in-vitro or in-vivo models, in silico models can provide deeper insight into the complex role of electrophysiology in cancer and reveal the impact of altered ion channel expression and the membrane potential on malignant processes. The A549 in silico model is the first computational cancer whole-cell ion current model that simulates the bioelectric mechanisms of the human non-small cell lung cancer cell line A549 during the different phases of the cell cycle. This work extends the existing model with a detailed mathematical description of the store-operated Ca 2+ entry (SOCE) and the complex local intracellular calcium dynamics, which significantly affect the entire electrophysiological properties of the cell and regulate cell cycle progression., Methods: The initial model was extended by a multicompartmental approach, addressing the heterogenous calcium profile and dynamics in the ER-PM junction provoked by local calcium entry of store-operated calcium channels (SOCs) and uptake by SERCA pumps. Changes of cytosolic calcium levels due to diffusion from the ER-PM junction, release from the ER by RyR channels and IP3 receptors, as well as corresponding PM channels were simulated and the dynamics evaluated based on calcium imaging data. The model parameters were fitted to available data from two published experimental studies, showing the function of CRAC channels and indirectly of IP3R, RyR and PMCA via changes of the cytosolic calcium levels., Results: The proposed calcium description accurately reproduces the dynamics of calcium imaging data and simulates the SOCE mechanisms. In addition, simulations of the combined A549-SOCE model in distinct phases of the cell cycle demonstrate how Ca 2+ - dynamics influence responding channels such as KCa, and consequently modulate the membrane potential accordingly., Discussion: Local calcium distribution and time evolution in microdomains of the cell significantly impact the overall electrophysiological properties and exert control over cell cycle progression. By providing a more profound description, the extended A549-SOCE model represents an important step on the route towards a valid model for oncological research and in silico supported development of novel therapeutic strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Langthaler, Zumpf, Rienmüller, Shrestha, Fuchs, Zhou, Pelzmann, Zorn-Pauly, Fröhlich, Weinberg and Baumgartner.)

Background: TLD-1 is a novel liposomal doxorubicin that compared favorably to conventional doxorubicin liposomal formulations in preclinical models. This phase I first-in-human study aimed to define the maximum tolerated dose (MTD), recommended phase 2 dose (RP2D), safety and preliminary activity of TLD-1 in patients with advanced solid tumors., Patients and Methods: We recruited patients with advanced solid tumors who failed standard therapy and received up to 3 prior lines of palliative systemic chemotherapy. TLD-1 was administered intravenously every 3 weeks up to a maximum of 9 cycles (6 for patients with prior anthracyclines) from a starting dose of 10 mg/m 2 , according to an accelerated titration design followed by a modified continual reassessment method., Results: 30 patients were enrolled between November 2018 and May 2021. No dose-limiting toxicities (DLT) were observed. Maximum administered dose of TLD-1 was 45 mg/m 2 , RP2D was defined at 40 mg/m 2 . Most frequent treatment-related adverse events (TRAE) of any grade included palmar-plantar erythrodysesthesia (PPE) (50% of patients), oral mucositis (50%), fatigue (30%) and skin rash (26.7%). Most common G3 TRAE included PPE in 4 patients (13.3%) and oral mucositis in 2 (6.7%). Overall objective response rate was 10% in the whole population and 23.1% among 13 patients with breast cancer; median time-to-treatment failure was 2.7 months. TLD-1 exhibit linear pharmacokinetics, with a median terminal half-life of 95 h., Conclusions: The new liposomal doxorubicin formulation TLD-1 showed a favourable safety profile and antitumor activity, particularly in breast cancer. RP2D was defined at 40 mg/m 2 administered every 3 weeks. (NCT03387917)., Competing Interests: Declaration of Competing Interest Ilaria Colombo: Travel grants: Tesaro, Janssen, AZ, GSK. Honoraria for consultancy or expert opinion: AZ, GSK, Novartis, MSD, BionTech. Institutional grants for clinical trials (PI): MSD, Bayer, Vivesto, Incyte, AZ, Orion. Kira-Lee Koster: Travel grants for congress visits from Takeda Pharma AG und Janssen-Cilag AG (paid to institution). Lisa Holer: no conflict of interest. Simon Haefliger: no conflict of interest. Manuela Rabaglio: no conflict of interest. Sara Bastian: no conflict of interest. Michael Schwitter: no conflict of interest. Katrin Eckhardt: no conflict of interest. Stefanie Hayoz: no conflict of interest. Anna M. Mc Laughlin: current employee of Pharmetheus AB and a paid consultant to multiple pharmaceutical companies. Charlotte Kloft: no conflict of interest. Marian Klose: no conflict of interest. Stefan Halbherr: Stefan Halbherr owns shares of InnoMedica Holding AG and functions as part of the executive management of the firm. Christian Baumgartner: Christian Baumgartner hold shares of Innomedica and is a former employee of the firm. He has received advisory fees from Boehringer Ingelheim. Cristiana Sessa: no conflict of interest. Anastasios Stathis: Institutional funding for clinical trials: Innomedica, Abbvie; ADC Therapeutics; Amgen, Astra Zeneca; Bayer; Cellestia; Incyte, Loxo Oncology; Merck MSD; Novartis; Pfizer; Philogen; Roche. Consultant/expert testimony/advisory board (institutional): Debiopharm, Janssen, AstraZeneca, Incyte, Eli Lilly, Novartis, Roche, Loxo Oncology. Travel grant: Incyte; Astra Zeneca. Dagmar Hess: no conflict of interest. Markus Joerger: Advisory role (institutional): Novartis, Astra Zeneca, Basilea Pharmaceutica, Bayer, BMS, Debiopharm, MSD, Roche, Sanofi; research funding: Swiss Cancer Research; travel grants: Roche, Sanofi, Takeda., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: The aim of the present study was to investigate relationships between elevated haptoglobin concentrations in milk and clinical as well as laboratory parameters in early lactating dairy cows. Furthermore, cut-off values should be identified for the differentiation of healthy and affected animals., Material and Methods: 1462 dairy cows between 5.-65. days in milk were examined on 68 Bavarian farms. Milk and blood samples were taken once a week for a 7-week period per farm and body-condition-scoring, backfat thickness measurement and Metricheck examination, to evaluate uterine health, were performed. Milk samples were analysed for milk fat, milk protein, lactose, urea, ß-hydroxybutyrate and non-esterified fatty acids (indirect measurement, based on IR spectra), cell count, and milk haptoglobin. Blood samples were analysed for creatinine, aspartate aminotransferase, gamma-glutamyl transferase, glutamate dehydrogenase, total protein, albumin, creatine kinase, ß-hydroxybutyrate, non-esterified fatty acids, and blood haptoglobin.Cluster analyses were performed to determine cut-off values for haptoglobin., Results: Besides milk haptoglobin (µg/ml) and blood haptoglobin (µg/ml), cell count (cells/ml milk), milk fat (%), milk protein (%), non-esterified fatty acids in blood (mmol/l), lactation number, days in milk, breed, season, and milk yield (kg) were included as significant input variables (p<0.005) in the cluster analyses. Cluster analysis, using k-means resp. k-prototypes algorithms, resulted in 5 (clusters 1-5 M 1 ) resp. 4 different clusters (clusters 0-3 M 2 and 0-3 B).A cut-off value of 0.5 µg/ml haptoglobin in milk was determined for the differentiation of healthy and affected animals., Conclusion and Clinical Relevance: As milk is an easily available substrate, routine determination of haptoglobin in milk might be a suitable parameter for animal health monitoring. Using the detected cut-off value, apparently healthy animals with subclinical inflammatory diseases can be identified more quickly., Competing Interests: Die Autoren bestätigen, dass kein Interessenkonflikt besteht., (Thieme. All rights reserved.)

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 = 185) or an active control group consisting of a self-help manual for problem gambling (n = 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 × time interactions according to the Gambling Symptom Assessment Scale (F = 8.83, p <0 .001), the Problem Gambling Severity Index (F = 3.54, p = 0.030), for cigarettes smoked in the last 7 days (F = 26.68, p < 0.001), the Patient Health Questionnaire-9 (F = 19.41, p <0 .001), and the Generalized Anxiety Disorder-7 (F = 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.

Objectives: This review presents research papers highlighting notable developments and trends in sensors, signals, and imaging informatics (SSII) in 2022., Method: We performed a bibliographic search in PubMed combining Medical Subject Heading (MeSH) terms and keywords to create particular queries for sensors, signals, and imaging informatics. Only papers published in journals containing greater than three articles in the search query were considered. Using a three-point Likert scale (1 = not include, 2 = perhaps include, 3 = include), we reviewed the titles and abstracts of all database results. Only articles that scored three times Likert scale 3, or two times Likert scale 3, and one time Likert scale 2 were considered for full paper review. On this pre-selection, only papers with a total of at least eight points of the three section co-editors were considered for external review. Based on the external reviewers, we selected the top two papers representing significant research in SSII., Results: Among the 469 returned papers published in 2022 in the various areas of SSII, 90, 31, and 348 papers for sensors, signals, and imaging informatics, and then, the full review process selected the two best papers. From the 469 papers, the section co-editors identified 29 candidate papers with at least 8 Likert points in total, of which 9 were nominated as the best contributions after a full paper assessment. Five external reviewers evaluated the nominated papers, and the two highest-scoring papers were selected based on the overall scores of all external reviewers. A consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board finally approved the nominated papers. Machine and deep learning-based techniques continue to be the dominant theme in this field., Conclusions: Sensors, signals, and imaging informatics is a dynamic field of intensive research with increasing practical applications to support medical decision-making on a personalized basis., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Polz, Bergmoser, Horn, Schörghuber, Lozanović, Rienmüller and Baumgartner.)

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.

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.

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Netzer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., Competing Interests: The authors declare no conflict of interest., (© 2022 The Authors. Advanced Materials Technologies published by Wiley‐VCH GmbH.)

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., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

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 NbSe 2 constrictions obtained by patterning NbSe 2 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., (© 2022. The Author(s).)

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.

Given prevalent alcohol misuse-emotional comorbidities among young adults, we developed an internet-based integrated treatment called Take Care of Me . Although the treatment had an impact on several secondary outcomes, effects were not observed for the primary outcome. Therefore, the goal of the current study was to examine heterogeneity in treatment responses. The initial RCT randomized participants to either a treatment or psychoeducational control condition. We conducted an exploratory latent class analysis to distinguish individuals based on pre-treatment risk and then used moderated regressions to examine differential treatment responses based on class membership. We found evidence for three distinct groups. Most participants fell in the "low severity" group (n = 123), followed by the "moderate severity" group (n = 57) who had a higher likelihood of endorsing a previous mental health diagnosis and treatment and higher symptom severity than the low group. The "high severity" group (n = 42) endorsed a family history of alcoholism, and the highest symptom severity and executive dysfunction. Moderated regressions revealed significant class differences in treatment responses. In the treatment condition, high severity (relative to low) participants reported higher alcohol consumption and hazardous drinking and lower quality of life at follow-up, whereas moderate severity (relative to low) individuals had lower alcohol consumption at follow-up, and lower hazardous drinking at end-of-treatment. No class differences were found for participants in the control group. Higher risk individuals in the treatment condition had poorer responses to the program. Tailoring interventions to severity may be important to examine in future research., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

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/.

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., (© 2020 The Authors. Arthritis Care & Research published by Wiley Periodicals LLC on behalf of American College of Rheumatology.)

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<.001; without: mean 8.53, SD 9.4, P<.001) and severity of cannabis use (SDS: with: mean 3.57, SD 3.65, P<.001; without: mean 2.47, SD 3.39, P<.001; CUDIT: with: mean 6.38, SD 5.96, P<.001; without: mean 5.33, SD 6.05, P<.001), as well as anxiety (with: mean 4.31, SD 4.71, P<.001; without: mean 1.84, SD 4.22, P<.001) and depression (with: mean 10.25, SD 10.54; without: mean 4.39, SD 10.22, P<.001). Those who screened positive for attention deficit/hyperactivity disorder also reported significantly decreased attention deficit/hyperactivity disorder scores (mean 4.65, SD 4.44, P<.001). There were no significant differences in change in use (P=.08), dependence (P=.95), use disorder (P=.85), attention deficit/hyperactivity disorder status (P=.84), depression (P=.84), or anxiety (P=.26) between baseline and final follow-up, dependent on positive attention-deficit/hyperactivity disorder screening. Attention deficit/hyperactivity disorder symptom severity at baseline was not associated with reduced cannabis use frequency or severity but was linked to greater reductions in depression (Spearman ρ=.33) and anxiety (Spearman ρ=.28). Individuals with positive attention deficit/hyperactivity disorder screening were significantly less likely to fill out the consumption diary (P=.02), but the association between continuous attention deficit/hyperactivity disorder symptom severity and retention (Spearman ρ=-0.10, P=.13) was nonsignificant. There also was no significant intergroup difference in the number of completed modules (with: mean 2.10, SD 2.33; without: mean 2.36, SD 2.36, P=.34), and there was no association with attention deficit/hyperactivity disorder symptom severity (Spearman ρ=-0.09; P=.43). The same was true for the rate of adverse effects (P=.33)., Conclusions: Cannabis users screening positive for attention deficit/hyperactivity disorder may benefit from CANreduce 2.0 to decrease the frequency and severity of cannabis dependence and attenuate symptoms of depression and attention deficit/hyperactivity disorder-related symptoms. This web-based program's advantages include its accessibility for remote users and a personalized counselling option that may contribute to increased adherence and motivation to change among program users., Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN) 11086185; http://www.isrctn.com/ISRCTN11086185., (©Joachim Ahlers, Christian Baumgartner, Mareike Augsburger, Andreas Wenger, Doris Malischnig, Nikolaos Boumparis, Thomas Berger, Lars Stark, David D Ebert, Severin Haug, Michael P Schaub. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.04.2022.)

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.

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., (© 2022. The Author(s).)

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.

Transport is non-reciprocal when not only the sign, but also the absolute value of the current depends on the polarity of the applied voltage. It requires simultaneously broken inversion and time-reversal symmetries, for example, by an interplay of spin-orbit coupling and magnetic field. Hitherto, observation of nonreciprocity was tied to resistivity, and dissipationless non-reciprocal circuit elements were elusive. Here we engineer fully superconducting non-reciprocal devices based on highly transparent Josephson junctions fabricated on InAs quantum wells. We demonstrate supercurrent rectification far below the transition temperature. By measuring Josephson inductance, we can link the non-reciprocal supercurrent to an asymmetry of the current-phase relation, and directly derive the supercurrent magnetochiral anisotropy coefficient. A semiquantitative model explains well the main features of our experimental data. Non-reciprocal Josephson junctions have the potential to become for superconducting circuits what pn junctions are for traditional electronics, enabling new non-dissipative circuit elements., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

This paper presents applications of control system theory in biomedical engineering. These methodologies are used in engineering sciences to obtain a mathematical model of systems, but system identification as scientific methodology is rarely used in biomedical engineering. The paper presents exemplarily control theory and system identification as methods for obtaining a mathematical model of the spread SARS-CoV-2 virus. The models obtained in the course of this are data-driven and strongly data-dependent. The available dataset allowed us to consider a model of a pandemic spread in the context of both the number of tested individuals and the number of infected individuals and with a resultant model that is nonlinear. We also considered a mathematical model for the dependence between the number of confirmed infected individuals and the number of deaths caused by the disease. The resulting model is linear given with the transfer function corresponding to the second-order differential equation. The mathematical models developed were additionally analyzed in accordance with controllability and observability., (© 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd.)

Human machine interfaces follow machine learning approaches to interpret muscles states, mainly from electrical signals. These signals are easy to collect with tiny devices, on tight power budgets, interfaced closely to the human skin. However, natural movement behavior is not only determined by muscle activation, but it depends on an orchestration of several subsystems, including the instantaneous length of muscle fibers, typically inspected by means of ultrasound (US) imaging systems. This work shows for the first time an ultra-lightweight (7 g) electromyography (sEMG) system transparent to ultrasound, which enables the simultaneous acquisition of sEMG and US signals from the same location. The system is based on ultrathin and skin-conformable temporary tattoo electrodes (TTE) made of printed conducting polymer, connected to a tiny, parallel-ultra-low power acquisition platform (BioWolf). US phantom images recorded with the TTE had mean axial and lateral resolutions of 0.90±0.02 mm and 1.058±0.005 mm, respectively. The root mean squares for sEMG signals recorded with the US during biceps contractions were at 57±10 µV and mean frequencies were at 92±1 Hz. We show that neither ultrasound images nor electromyographic signals are significantly altered during parallel and synchronized operation.Clinical relevance- Modern prosthetic engineering concepts use interfaces connected to muscles or nerves and employ machine learning models to infer on natural movement behavior of amputated limbs. However, relying only on a single data source (e.g., electromyography) reduces the quality of a fine-grained motor control. To address this limitation, we propose a new and unobtrusive device capable of capturing the electrical and mechanical behavior of muscles in a parallel and synchronized fashion. This device can support the development of new prosthetic control and design concepts, further supporting clinical movement science in the configuration of better simulation models.

Many young adults struggle with comorbid alcohol misuse and emotional problems (i.e., depression and anxiety). However, there is currently a paucity of evidence-based, integrated, accessible treatment options for individuals with these comorbidities. The main goal of this study was to examine efficacy of a novel online, minimally guided, integrated program for comorbid alcohol misuse and emotional problems in young adults. Method: The study was an open-label two-arm RCT. Participants ( N =  222, M age  = 24.6, 67.6% female) were randomized to one of two conditions: the Take Care of Me program (an 8-week, online integrated treatment condition consisting of 12 modules), or an online psychoeducational control condition. Intervention modules incorporated content based on principles of cognitive behavioral therapy and motivational interviewing. Participants completed assessment data at baseline, at the end of treatment (i.e., 8 weeks), and at follow-up (i.e., 24 weeks). Data were analyzed using generalized linear mixed models. Results: We observed that participants in the treatment condition showed larger reductions in depression, hazardous drinking, as well as increases in psychological quality of life and confidence at the end of treatment. We did not find group differences on total alcohol use at follow-up, but participants in the treatment group reduced their hazardous drinking and improved their quality of life at 24-week follow-up. Conclusions: Our study provides promising initial evidence for the first iteration of the comorbid alcohol misuse and emotional problems online program., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors. Published by Elsevier Ltd.)

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, M age = 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.

Background: Given the scarcity of alcohol prevention and use disorder treatments in many low- and middle-income countries (LMICs), the World Health Organization has launched an eHealth portal that includes the web-based self-help program "Alcohol e-Health.", Objective: We aimed to test the effectiveness of the Alcohol e-Health program in a randomized controlled trial., Methods: This was a two-arm, individually randomized, and controlled trial across four LMICs comparing the self-help program and a psychoeducation and internet access as usual waiting list. Participants were broadly recruited from community samples in Belarus, Brazil, India, and Mexico from January 2016 through January 2019. The primary outcome measure was change in the Alcohol Use Disorders Identification Test (AUDIT) score with a time frame of 6 months between baseline and follow-up. Secondary outcomes included self-reported numbers of standard drinks over the previous week and cessation of harmful or hazardous drinking (AUDIT score <8)., Results: For this study, we recruited 1400 predominantly male (n=982, 70.1%) at least harmful or hazardous alcohol drinkers. The mean age was 37.6 years (SD 10.5). The participants were recruited from Brazil (n=587), Mexico (n=509), India (n=212), and Belarus (n=92). Overall, complete case analysis identified higher AUDIT changes in the intervention group (B=-4.18, 95% CI -5.42 to -2.93, P<.001, d=0.56) that were mirrored by changes in weekly standard drinks (B=-9.34, 95% CI -15.90 to -2.77, P=.005, d=0.28) and cessation rates for harmful or hazardous drinking (χ 2 1 =14.56, N=561, P<.001). The supplementary intention-to-treat analyses largely confirmed these initial results., Conclusions: The expansion of the Alcohol e-Health program to other LMICs with underdeveloped alcohol prevention and treatment systems for alcohol use disorders should be considered after successful replication of the present results., Trial Registration: ISRCTN ISRCTN14037475; https://www.isrctn.com/ISRCTN14037475., International Registered Report Identifier (irrid): RR2-10.1111/add.14034., (©Michael P Schaub, Marcela Tiburcio, Nora Martínez-Vélez, Atul Ambekar, Roshan Bhad, Andreas Wenger, Christian Baumgartner, Dzianis Padruchny, Sergey Osipchik, Vladimir Poznyak, Dag Rekve, Fabricio Landi Moraes, André Luiz Monezi Andrade, Maria Lucia Oliveira Souza-Formigoni, WHO E-Health Project On Alcohol And Health Investigators Group. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.08.2021.)

Background: Depression and harmful alcohol use are two of the top five leading causes of years of life lost to disability in high-income countries. Integrated treatment targeting both at the same time is often considered more complicated and difficult and, therefore, more expensive. Consequently, integrated internet-based interventions could be a valuable addition to traditional care., Methods: A three-arm randomized controlled trial was conducted comparing the effectiveness of (1) an integrated, minimal-guidance, adherence-focused self-help intervention designed to reduce both alcohol use and depression symptoms (AFGE-AD); (2) a similar intervention designed to reduce alcohol use only (AFGE-AO), and (3) internet access as usual (IAU) as a control condition, in at least moderately depressed alcohol misusers from February 2016-March 2020. We recruited 689 alcohol misusers (51.6 % males, mean age = 42.8 years) with at least moderate depression symptoms not otherwise in treatment from the general population. Six months after baseline, 288 subjects (41.8 %) were reachable for the final assessment., Results: All interventions yielded reduced alcohol-use after six months (AFGE-AD: -16.6; AFGE-AO: -19.8; IAU: -13.2). Those who undertook active-interventions reported significantly fewer standard drinks than controls (AFGE-AD: p = .048, d=0.10; AFGE-AO: p = .004, d=0.20). The two active-intervention groups also reported significantly less severe depression symptoms than controls (AFGE-AD: p = .006, d=0.41; AFGE-AO: p = .008, d=0.43). Testing revealed noninferiority between the two interventions., Conclusions: This study documented sustained effectiveness of the first integrated, fully internet-based self-help intervention developed for the reduction of both alcohol use and depression symptoms in at least moderately depressed adult alcohol misusers recruited from the general population., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

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., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

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., Competing Interests: The authors have declared that no competing interests exist.

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.

Maximal oxygen uptake (VO 2 max) is a direct measure of human cardiorespiratory fitness and is associated with health. However, the molecular determinants of interindividual differences in baseline (intrinsic) VO 2 max, and of increases of VO 2 max in response to exercise training (ΔVO 2 max), 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 VO 2 max and ΔVO 2 max, 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 ΔVO 2 max. 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.

Background: Despite 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., Objective: Through adherence-focused guidance enhancement, we aimed to increase adherence to and the effectiveness of internet-based self-help among cannabis users., Methods: From 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., Results: All 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., Conclusions: The 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 Registration: ISRCTN Registry ISRCTN11086185; http://www.isrctn.com/ISRCTN11086185., (©Christian Baumgartner, Michael Patrick Schaub, Andreas Wenger, Doris Malischnig, Mareike Augsburger, Marc Walter, Thomas Berger, Lars Stark, David Daniel Ebert, Matthew T Keough, Severin Haug. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.04.2021.)

The use of different cardiac imaging modalities such as MRI, CT or ultrasound enables the visualization and interpretation of altered morphological structures and function of the heart. In recent years, there has been an increasing interest in AI and deep learning that take into account spatial and temporal information in medical image analysis. In particular, deep learning tools using temporal information in image processing have not yet found their way into daily clinical practice, despite its presumed high diagnostic and prognostic value. This review aims to synthesize the most relevant deep learning methods and discuss their clinical usability in dynamic cardiac imaging using for example the complete spatiotemporal image information of the heart cycle. Selected articles were categorized according to the following indicators: clinical applications, quality of datasets, preprocessing and annotation, learning methods and training strategy, and test performance. Clinical usability was evaluated based on these criteria by classifying the selected papers into (i) clinical level, (ii) robust candidate and (iii) proof of concept applications. Interestingly, not a single one of the reviewed papers was classified as a "clinical level" study. Almost 39% of the articles achieved a "robust candidate" and as many as 61% a "proof of concept" status. In summary, deep learning in spatiotemporal cardiac imaging is still strongly research-oriented and its implementation in clinical application still requires considerable efforts. Challenges that need to be addressed are the quality of datasets together with clinical verification and validation of the performance achieved by the used method., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Supervised learning-based segmentation methods typically require a large number of annotated training data to generalize well at test time. In medical applications, curating such datasets is not a favourable option because acquiring a large number of annotated samples from experts is time-consuming and expensive. Consequently, numerous methods have been proposed in the literature for learning with limited annotated examples. Unfortunately, the proposed approaches in the literature have not yet yielded significant gains over random data augmentation for image segmentation, where random augmentations themselves do not yield high accuracy. In this work, we propose a novel task-driven data augmentation method for learning with limited labeled data where the synthetic data generator, is optimized for the segmentation task. The generator of the proposed method models intensity and shape variations using two sets of transformations, as additive intensity transformations and deformation fields. Both transformations are optimized using labeled as well as unlabeled examples in a semi-supervised framework. Our experiments on three medical datasets, namely cardiac, prostate and pancreas, show that the proposed approach significantly outperforms standard augmentation and semi-supervised approaches for image segmentation in the limited annotation setting. The code is made publicly available at https://github.com/krishnabits001/task&#95;driven&#95;data&#95;augmentation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier B.V.)

We present simultaneous measurements of Josephson inductance and dc transport characteristics of ballistic Josephson junctions based upon an epitaxial Al-InAs heterostructure. The Josephson inductance at finite current bias directly reveals the current-phase relation. The proximity-induced gap, the critical current and the average value of the transparency τ[over ¯] are extracted without need for phase bias, demonstrating, e.g., a near-unity value of τ[over ¯]=0.94. Our method allows us to probe the devices deeply in the nondissipative regime, where ordinary transport measurements are featureless. In perpendicular magnetic field the junctions show a nearly perfect Fraunhofer pattern of the critical current, which is insensitive to the value of τ[over ¯]. In contrast, the signature of supercurrent interference in the inductance turns out to be extremely sensitive to τ[over ¯].

Background: Tissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Native T 1 mapping in particular has shown promise as a useful biomarker to support diagnostic, therapeutic and prognostic decision-making in ischaemic and non-ischaemic cardiomyopathies., Methods: Convolutional neural networks (CNNs) with Bayesian inference are a category of artificial neural networks which model the uncertainty of the network output. This study presents an automated framework for tissue characterisation from native shortened modified Look-Locker inversion recovery ShMOLLI T 1 mapping at 1.5 T using a Probabilistic Hierarchical Segmentation (PHiSeg) network (PHCUMIS 119-127, 2019). In addition, we use the uncertainty information provided by the PHiSeg network in a novel automated quality control (QC) step to identify uncertain T 1 values. The PHiSeg network and QC were validated against manual analysis on a cohort of the UK Biobank containing healthy subjects and chronic cardiomyopathy patients (N=100 for the PHiSeg network and N=700 for the QC). We used the proposed method to obtain reference T 1 ranges for the left ventricular (LV) myocardium in healthy subjects as well as common clinical cardiac conditions., Results: T 1 values computed from automatic and manual segmentations were highly correlated (r=0.97). Bland-Altman analysis showed good agreement between the automated and manual measurements. The average Dice metric was 0.84 for the LV myocardium. The sensitivity of detection of erroneous outputs was 91%. Finally, T 1 values were automatically derived from 11,882 CMR exams from the UK Biobank. For the healthy cohort, the mean (SD) corrected T 1 values were 926.61 (45.26), 934.39 (43.25) and 927.56 (50.36) for global, interventricular septum and free-wall respectively., Conclusions: The proposed pipeline allows for automatic analysis of myocardial native T 1 mapping and includes a QC process to detect potentially erroneous results. T 1 reference values were presented for healthy subjects and common clinical cardiac conditions from the largest cohort to date using T 1 -mapping images.

Objective: To investigate the association between haptoglobin concentration in the blood and the occurrence of ketosis, selected clinical parameters as well as lameness in dairy cows., Material and Methods: The data was collected on 39 dairy farms in Bavaria over a period of 8 months. In 712 Simmental and Brown Swiss cows, clinical examinations as well as milk and blood samplings were performed between 10 and 30 days after calving. In these blood samples, the concentrations of non-esterified fatty acids (NEFA), β-hydroxybutyric acid (BHBA) and haptoglobin (Hp) were determined. Analysis of the milk included milk constituents (fat, protein, urea, lactose and acetone), BHBA, NEFA and the somatic cell count (SCC)., Results: Significant correlations were found between increased Hp-concentration on the one hand and increased NEFA levels in blood and milk (p < 0.001), increased somatic cell count (p < 0.001), lameness (p < 0.001), as well as reduced lactose content (p < 0.001) and protein content in the milk (p = 0.001) on the other hand. Animals sampled during the warmer summer months showed significantly higher serum Hp-concentrations (p < 0,001). Heifers exhibited significantly higher Hp-values than multiparous individuals (p < 0.001). By dividing the examined cows into 4 clusters, a Hp-threshold value could be determined at 0.18 mg/ml. Combined with a SCC threshold of 40 500 cells/ml milk, the majority of animals with subclinical and clinical abnormalities could be identified., Conclusion and Clinical Relevance: Measurement of the Hp-concentration in blood is a pertinent approach in animal health monitoring during the postpartum period. In combination with evaluations of milk amount and contents, deviations from the physiological status may be recognized and affected individuals treated early on. Haptoglobin may be used to assess the health status of the individual animal as well as an indicator of herd health in the context of animal health monitoring., Competing Interests: Die Autoren bestätigen, dass kein Interessenkonflikt besteht., (© Georg Thieme Verlag KG Stuttgart · New York.)

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.