Psychometric Validation of the Gambling Craving Scale in a Treatment-Seeking Sample.

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.

A549 in-silico 1.0: A first computational model to simulate cell cycle dependent ion current modulation in the human lung adenocarcinoma.

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.

Comparing a mindfulness- and CBT-based guided self-help Internet- and mobile-based intervention against a waiting list control condition as treatment for adults with frequent cannabis use: a randomized controlled trial of CANreduce 3.0.

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.

Cannabis Use in Adults Who Screen Positive for Attention Deficit/Hyperactivity Disorder: CANreduce 2.0 Randomized Controlled Trial Subgroup Analysis.

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.

A Systematic Review of the Transthoracic Impedance during Cardiac Defibrillation.

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.

Josephson Inductance as a Probe for Highly Ballistic Semiconductor-Superconductor Weak Links.

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 ¯].

MEK1 is required for PTEN membrane recruitment, AKT regulation, and the maintenance of peripheral tolerance.

The Raf/MEK/ERK and PI3K/Akt pathways are prominent effectors of oncogenic Ras. These pathways negatively regulate each other, but the mechanism involved is incompletely understood. We now identify MEK1 as an essential regulator of lipid/protein phosphatase PTEN, through which it controls phosphatidylinositol-3-phosphate accumulation and AKT signaling. MEK1 ablation stabilizes AKT activation and, in vivo, causes a lupus-like autoimmune disease and myeloproliferation. Mechanistically, MEK1 is necessary for PTEN membrane recruitment as part of a ternary complex containing the multidomain adaptor MAGI1. Complex formation is independent of MEK1 kinase activity but requires phosphorylation of T292 on MEK1 by activated ERK. Thus, inhibiting the ERK pathway reduces PTEN membrane recruitment, increasing phosphatidylinositol-3-phosphate accumulation and AKT activation. Our data offer a conceptual framework for the observation that activation of the PI3K pathway frequently mediate resistance to MEK inhibitors and for the promising results obtained by combined MEK/PI3K inhibition in preclinical cancer models.

Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort.

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.

CANreduce 2.0 Adherence-Focused Guidance for Internet Self-Help Among Cannabis Users: Three-Arm Randomized Controlled Trial.

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.

The Effectiveness of a Web-Based Self-Help Program to Reduce Alcohol Use Among Adults With Drinking Patterns Considered Harmful, Hazardous, or Suggestive of Dependence in Four Low- and Middle-Income Countries: Randomized Controlled Trial.

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 (χ21=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.

Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with uncertainty-based quality-control.

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 T1 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 T1 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 T1 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 T1 ranges for the left ventricular (LV) myocardium in healthy subjects as well as common clinical cardiac conditions. RESULTS: T1 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, T1 values were automatically derived from 11,882 CMR exams from the UK Biobank. For the healthy cohort, the mean (SD) corrected T1 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 T1 mapping and includes a QC process to detect potentially erroneous results. T1 reference values were presented for healthy subjects and common clinical cardiac conditions from the largest cohort to date using T1-mapping images.

Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain.

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.

A novel hybrid modeling approach for the evaluation of integrated care and economic outcome in heart failure treatment.

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

Ultrasound as a Tool to Study Muscle-Tendon Functions during Locomotion: A Systematic Review of Applications.

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

Poikilothermic Animals as a Previously Unrecognized Source of Fecal Indicator Bacteria in a Backwater Ecosystem of a Large River.

Quantitative information regarding the presence of Escherichia coli, intestinal enterococci, and Clostridium perfringens in poikilotherms is notably scarce. Therefore, this study was designed to allow a systematic comparison of the occurrence of these standard fecal indicator bacteria (SFIB) in the excreta of wild homeothermic (ruminants, boars, carnivores, and birds) and poikilothermic (earthworms, gastropods, frogs, and fish) animals inhabiting an alluvial backwater area in eastern Austria. With the exception of earthworms, the average concentrations of E. coli and enterococci in the excreta of poikilotherms were equal to or only slightly lower than those observed in homeothermic excreta and were 1 to 4 orders of magnitude higher than the levels observed in the ambient soils and sediments. Enterococci reached extraordinarily high concentrations in gastropods. Additional estimates of the daily excreted SFIB (E. coli and enterococcus) loads (DESL) further supported the importance of poikilotherms as potential pollution sources. The newly established DESL metric also allowed comparison to the standing stock of SFIB in the sediment and soil of the investigated area. In agreement with its biological characteristics, the highest concentrations of C. perfringens were observed in carnivores. In conclusion, the long-standing hypothesis that only humans and homeothermic animals are primary sources of SFIB is challenged by the results of this study. It may be necessary to extend the fecal indicator concept by additionally considering poikilotherms as potential important primary habitats of SFIB. Further studies in other geographical areas are needed to evaluate the general significance of our results. We hypothesize that the importance of poikilotherms as sources of SFIB is strongly correlated with the ambient temperature and would therefore be of increased significance in subtropical and tropical habitats and water resources.IMPORTANCE The current fecal indicator concept is based on the assumption that the standard fecal indicator bacteria (SFIB) Escherichia coli, intestinal enterococci, and Clostridium perfringens multiply significantly only in the guts of humans and other homeothermic animals and can therefore indicate fecal pollution and the potential presence of pathogens from those groups. The findings of the present study showed that SFIB can also occur in high concentrations in poikilothermic animals (i.e., animals with body temperatures that vary with the ambient environmental temperature, such as fish, frogs, and snails) in an alluvial backwater area in a temperate region, indicating that a reconsideration of this long-standing indicator paradigm is needed. This study suggests that poikilotherms must be considered to be potential primary sources of SFIB in future studies.

A New, Adaptable, Optical High-Resolution 3-Axis Sensor.

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

A New Input Device for Spastics Based on Strain Gauge.

This article presents a new sensor for use by people with spastic disorders and similar conditions and enables them to steer and control medical devices such as electric powered wheelchairs. As spastic patients often suffer from cramping of their extremities, which can then no longer be controlled, using a standard joystick while operating a powered wheelchair can lead to dangerous situations. To prevent this, we designed a sensor based on strain gauges, which is shaped like a flat disc that can be operated using any body part. By shifting weight along the x- and y-axis, the disc tilts in all directions thereby generating proportionate output signals. The disc can also be pressed downward (z-axis), for example, to open a wheelchair's menu. Thanks to the sensor's flat disc-like construction and the option of mounting it into a control panel, users are not in danger of becoming stuck on the disc during spastic episodes. In the event of a spasm, body parts simply slide over the disc reducing risk of unintended actions. The sensor is adaptive and adjustable enabling it to fit a user's range of strength and motion at any time. It was developed to ensure users can operate sensitive systems safely.

MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm.

BACKGROUND: This work presents a machine learning strategy to increase sensitivity in tandem mass spectrometry (MS/MS) data analysis for peptide/protein identification. MS/MS yields thousands of spectra in a single run which are then interpreted by software. Most of these computer programs use a protein database to match peptide sequences to the observed spectra. The peptide-spectrum matches (PSMs) must also be assessed by computational tools since manual evaluation is not practicable. The target-decoy database strategy is largely used for error estimation in PSM assessment. However, in general, that strategy does not account for sensitivity. RESULTS: In a previous study, we proposed the method MUMAL that applies an artificial neural network to effectively generate a model to classify PSMs using decoy hits with increased sensitivity. Nevertheless, the present approach shows that the sensitivity can be further improved with the use of a cost matrix associated with the learning algorithm. We also demonstrate that using a threshold selector algorithm for probability adjustment leads to more coherent probability values assigned to the PSMs. Our new approach, termed MUMAL2, provides a two-fold contribution to shotgun proteomics. First, the increase in the number of correctly interpreted spectra in the peptide level augments the chance of identifying more proteins. Second, the more appropriate PSM probability values that are produced by the threshold selector algorithm impact the protein inference stage performed by programs that take probabilities into account, such as ProteinProphet. Our experiments demonstrate that MUMAL2 reached around 15% of improvement in sensitivity compared to the best current method. Furthermore, the area under the ROC curve obtained was 0.93, demonstrating that the probabilities generated by our model are in fact appropriate. Finally, Venn diagrams comparing MUMAL2 with the best current method show that the number of exclusive peptides found by our method was nearly 4-fold higher, which directly impacts the proteome coverage. CONCLUSIONS: The inclusion of a cost matrix and a probability threshold selector algorithm to the learning task further improves the target-decoy database analysis for identifying peptides, which optimally contributes to the challenging task of protein level identification, resulting in a powerful computational tool for shotgun proteomics.

Modeling and Classification of Kinetic Patterns of Dynamic Metabolic Biomarkers in Physical Activity.

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

Simulation and evaluation of freeze-thaw cryoablation scenarios for the treatment of cardiac arrhythmias.

BACKGROUND: Cardiac cryoablation is a minimally invasive procedure to treat cardiac arrhythmias by cooling cardiac tissues responsible for the cardiac arrhythmia to freezing temperatures. Although cardiac cryoablation offers a gentler treatment than radiofrequency ablation, longer interventions and higher recurrence rates reduce the clinical acceptance of this technique. Computer models of ablation scenarios allow for a closer examination of temperature distributions in the myocardium and evaluation of specific effects of applied freeze-thaw protocols in a controlled environment. METHODS: In this work multiple intervention scenarios with two freeze-thaw cycles were simulated with varying durations and starting times of the interim thawing phase using a finite element model verified by in-vivo measurements and data from literature. To evaluate the effects of different protocols, transmural temperature distributions and iceball dimensions were compared over time. Cryoadhesion durations of the applicator were estimated in the interim thawing phase with varying thawing phase starting times. In addition, the increase of cooling rates was compared between the freezing phases, and the thawing rates of interim thawing phases were analyzed over transmural depth. RESULTS: It could be shown that the increase of cooling rate, the regions undergoing additional phase changes and depths of selected temperatures depend on the chosen ablation protocol. Only small differences of the estimated cryoadhesion duration were found for ablation scenarios with interim thawing phase start after 90 s freezing. CONCLUSIONS: By the presented model a quantification of effects responsible for cell death is possible, allowing for the analysis and optimization of cryoablation scenarios which contribute to a higher clinical acceptance of cardiac cryoablation.