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Frictional properties of cartilage resurfacing implants should be sufficiently low to limit damaging of the opposing cartilage during articulation. The present study determines if native lubricious molecule proteoglycan 4 (PRG4) can adsorb onto a layer-by-layer bioinspired coating composed of poly-l-lysine (PLL) and dopamine modified hyaluronic acid (HADN) and thereby can reduce the friction between implant and articular cartilage. An ELISA was developed to quantify the amount of immobilized human recombinant (rh)PRG4 after exposure to the PLL-HADN coating. The effect on lubrication was evaluated by comparing the coefficient of friction (CoF) of bare polycaprolactone (PCL) disks to that of PLL-HADN coated PCL disks while articulated against cartilage using a ring-on-disk geometry and a lubricant solution consisting of native synovial fluid components including rhPRG4. The PLL-HADN coating effectively immobilized rhPRG4. The surface roughness of PCL disks significantly increased while the water contact angle significantly decreased after application of the coating. The average CoF measured during the first minute of bare PCL against cartilage exceeded twice the CoF of the PLL-HADN coated PCL against cartilage. After 60 min, the CoF reached equilibrium values which were still significantly higher for bare PCL compared to coated PCL. The present study demonstrated that PCL can effectively be coated with PLL-HADN. Additionally, this coating reduces the friction between PCL and cartilage when a PRG4-rich lubricant is used, similar to the lubricating surface of native cartilage. This makes PLL-HADN coating a promising application to improve the clinical success of PCL-based cartilage resurfacing implants. ispartof: Journal Of Biomedical Materials Research Part B-Applied Biomaterials vol:111 issue:8 ispartof: location:United States status: published

The transition in the field of bone tissue engineering from bone regeneration to in vitro models has come with the challenge of recreating a dense and anisotropic bone-like extracellular matrix (ECM). Although the mechanism by which bone ECM gains its structure is not fully understood, mechanical loading and curvature have been identified as potential contributors. Here, guided by computational simulations, we evaluated cell and bone-like tissue growth and organization in a concave channel with and without directional fluid flow stimulation. Human mesenchymal stromal cells were seeded on donut-shaped silk fibroin scaffolds and osteogenically stimulated for 42 days statically or in a flow perfusion bioreactor. After 14, 28, and 42 days, constructs were investigated for cell and tissue growth and organization. As a result, directional fluid flow was able to improve organic tissue growth but not organization. Cells tended to orient in the tangential direction of the channel, possibly attributed to its curvature. Based on our results, we suggest that organic ECM production but not anisotropy can be stimulated through the application of fluid flow. With this study, an initial attempt in three-dimensions was made to improve the resemblance of in vitro produced bone-like ECM to the physiological bone ECM.

To improve abdominal aortic aneurysm (AAA) rupture risk assessment, a large, longitudinal study on AAA hemodynamics and biomechanics is necessary, using personalized fluid-structure interaction (FSI) modeling. 3-dimensional, time-resolved ultrasound (3D+t US) is the preferred image modality to obtain the patient-specific AAA geometry for such a study, since it is safe, affordable and provides temporal information. However, the 3D+t US field-of-view (FOV) is limited and therefore often fails to capture the inlet and aorto-iliac bifurcation geometry. In this study, a framework was developed to add parametric inlet and bifurcation geometries to the abdominal aortic aneurysm geometry by employing dataset statistics and parameters of the AAA geometry. The impact of replacing the patient-specific inlet and bifurcation geometries, acquired using computed tomography (CT) scans, by parametric geometries was evaluated by examining the differences in hemodynamics (systolic and time-averaged wall shear stress and oscillatory shear index) in the aneurysm region. The results show that the inlet geometry has a larger effect on the AAA hemodynamics (median differences of 7.5 to 18.8%) than the bifurcation geometry (median differences all below 1%). Therefore, it is not feasible to replace the patient-specific inlet geometry by a generic one. Future studies should investigate the possibilities of extending the proximal FOV of 3D+t US. However, this study did show the feasibility of adding a parametric bifurcation geometry to the aneurysm geometry. After extending the proximal FOV, the obtained framework can be used to extract AAA geometries from 3D+t US for FSI simulations, despite the absence of the bifurcation geometry.

Lignin is a potential resource for biobased aromatics with applications in the field of fuel additives, resins, and bioplastics. Via a catalytic depolymerization process using supercritical ethanol and a mixed metal oxide catalyst (CuMgAlOx), lignin can be converted into a lignin oil, containing phenolic monomers that are intermediates to the mentioned applications. Herein, we evaluated the viability of this lignin conversion technology through a stage-gate scale-up methodology. Optimization was done with a day-clustered Box-Behnken design to accommodate the large number of experimental runs in which five input factors (temperature, lignin-to-ethanol ratio, catalyst particle size, catalyst concentration, and reaction time) and three output product streams (monomer yield, yield of THF-soluble fragments, and yield of THF-insoluble fragments and char) were considered. Qualitative relationships between the studied process parameters and the product streams were determined based on mass balances and product analyses. Linear mixed models with random intercept were employed to study quantitative relationships between the input factors and the outcomes through maximum likelihood estimation. The response surface methodology study reveals that the selected input factors, together with higher order interactions, are highly significant for the determination of the three response surfaces. The good agreement between the predicted and experimental yield of the three output streams is a validation of the response surface methodology analysis discussed in this contribution.

Background and PurposeThe lack of a robust diagnostic biomarker makes understanding depression from a neurobiological standpoint an important goal, especially in the context of brain imaging.MethodsIn this study, we aim to create novel image-based features for objective diagnosis of depression. Resting-state network time series are used to investigate neurodynamics with the help of wavelet coherence and Granger causality (G-causality). Three new features are introduced: total wavelet coherence, wavelet lead coherence, and wavelet coherence blob analysis. The fourth feature, pair-wise conditional G-causality, is used to establish the causality between resting-state networks. We use the proposed features to classify depression in adult subjects.ResultsWe obtained an accuracy of 86% in the wavelet lead coherence, 80% in Granger causality, and 86% in wavelet coherence blob analysis. Subjects with depression showed hyperconnectivity between the dorsal attention network and the auditory network as well as between the posterior default mode network and the dorsal attention network. Hypoconnectivity was found between the anterior default mode network and the auditory network as well as the right frontoparietal network and the lateral visual network. An abnormal co-activation pattern was found between cerebellum and the lateral motor network according to the wavelet coherence blob analysis.ConclusionBased on abnormal functional dynamics between brain networks, we were able to identify subjects with depression with high accuracy. The findings of this study contribute to the understanding of the impaired emotional and attention processing associated with depression, as well as decreased motor activity., BACKGROUND AND PURPOSE: The lack of a robust diagnostic biomarker makes understanding depression from a neurobiological standpoint an important goal, especially in the context of brain imaging.METHODS: In this study, we aim to create novel image-based features for objective diagnosis of depression. Resting-state network time series are used to investigate neurodynamics with the help of wavelet coherence and Granger causality (G-causality). Three new features are introduced: total wavelet coherence, wavelet lead coherence, and wavelet coherence blob analysis. The fourth feature, pair-wise conditional G-causality, is used to establish the causality between resting-state networks. We use the proposed features to classify depression in adult subjects.RESULTS: We obtained an accuracy of 86% in the wavelet lead coherence, 80% in Granger causality, and 86% in wavelet coherence blob analysis. Subjects with depression showed hyperconnectivity between the dorsal attention network and the auditory network as well as between the posterior default mode network and the dorsal attention network. Hypoconnectivity was found between the anterior default mode network and the auditory network as well as the right frontoparietal network and the lateral visual network. An abnormal co-activation pattern was found between cerebellum and the lateral motor network according to the wavelet coherence blob analysis.CONCLUSION: Based on abnormal functional dynamics between brain networks, we were able to identify subjects with depression with high accuracy. The findings of this study contribute to the understanding of the impaired emotional and attention processing associated with depression, as well as decreased motor activity.

BACKGROUND: Heart failure (HF) biomarkers have prognostic value. The aim of this study was to combine HF biomarkers into an objective classification system for risk stratification of patients with HF.METHODS: HF biomarkers were analyzed in a population of HF outpatients and expressed relative to their cut-off values (N-terminal pro-B-type natriuretic peptide [NT-proBNP] >1,000 pg/mL, soluble suppression of tumorigenesis-2 [ST2] >35 ng/mL, growth differentiation factor-15 [GDF-15] >2,000 pg/mL, and fibroblast growth factor-23 [FGF-23] >95.4 pg/mL). Biomarkers that remained significant in multivariable analysis were combined to devise the Heartmarker score. The performance of the Heartmarker score was compared to the widely used New York Heart Association (NYHA) classification based on symptoms during ordinary activity.RESULTS: HF biomarkers of 245 patients were analyzed, 45 (18%) of whom experienced the composite endpoint of HF hospitalization, appropriate implantable cardioverter-defibrillator shock, or death. HF biomarkers were elevated more often in patients that reached the composite endpoint than in patients that did not reach the endpoint. NT-proBNP, ST2, and GDF-15 were independent predictors of the composite endpoint and were thus combined as the Heartmarker score. The event-free survival and distance covered in 6 minutes of walking decreased with an increasing Heartmarker score. Compared with the NYHA classification, the Heartmarker score was better at discriminating between different risk classes and had a comparable relationship to functional capacity.CONCLUSIONS: The Heartmarker score is a reproducible and intuitive model for risk stratification of outpatients with HF, using routine biomarker measurements.

Microbial cell factories face changing environments during industrial fermentations. Kinetic metabolic models enable the simulation of the dynamic metabolic response to these perturbations, but their development is challenging due to model complexity and experimental data requirements. An example of this is the well-established microbial cell factory Saccharomyces cerevisiae, for which no consensus kinetic model of central metabolism has been developed and implemented in industry. Here, we aim to bring the academic and industrial communities closer to this consensus model. We developed a physiology informed kinetic model of yeast glycolysis connected to central carbon metabolism by including the effect of anabolic reactions precursors, mitochondria and the trehalose cycle. To parametrize such a large model, a parameter estimation pipeline was developed, consisting of a divide and conquer approach, supplemented with regularization and global optimization. Additionally, we show how this first mechanistic description of a growing yeast cell captures experimental dynamics at different growth rates and under a strong glucose perturbation, is robust to parametric uncertainty and explains the contribution of the different pathways in the network. Such a comprehensive model could not have been developed without using steady state and glucose perturbation data sets. The resulting metabolic reconstruction and parameter estimation pipeline can be applied in the future to study other industrially-relevant scenarios. We show this by generating a hybrid CFD-metabolic model to explore intracellular glycolytic dynamics for the first time. The model suggests that all intracellular metabolites oscillate within a physiological range, except carbon storage metabolism, which is sensitive to the extracellular environment.

OBJECTIVES: Pathologic subtyping of tissue biopsies is the gold standard for the diagnosis of lung cancer (LC), which could be complicated in cases of e.g. inconclusive tissue biopsies or unreachable tumors. The diagnosis of LC could be supported in a minimally invasive manner using protein tumor markers (TMs) and circulating tumor DNA (ctDNA) measured in liquid biopsies (LBx). This study evaluates the performance of LBx-based decision-support algorithms for the diagnosis of LC and subtyping into small- and non-small-cell lung cancer (SCLC and NSCLC) aiming to directly impact clinical practice.MATERIALS AND METHODS: In this multicenter prospective study (NL9146), eight protein TMs (CA125, CA15.3, CEA, CYFRA 21-1, HE4, NSE, proGRP and SCCA) and ctDNA mutations in EGFR, KRAS and BRAF were analyzed in blood of 1096 patients suspected of LC. The performance of individual and combined TMs to identify LC, NSCLC or SCLC was established by evaluating logistic regression models at pre-specified positive predictive values (PPV) of ≥95% or ≥98%. The most informative protein TMs included in the multi-parametric models were selected by recursive feature elimination.RESULTS: Single TMs could identify LC, NSCLC and SCLC patients with 46%, 25% and 40% sensitivity, respectively, at pre-specified PPVs. Multi-parametric models combining TMs and ctDNA significantly improved sensitivities to 65%, 67% and 50%, respectively.CONCLUSION: In patients suspected of LC, the LBx-based decision-support algorithms allowed identification of about two-thirds of all LC and NSCLC patients and half of SCLC patients. These models therefore show clinical value and may support LC diagnostics, especially in patients for whom pathologic subtyping is impossible or incomplete.

Objective. Fetal heart rate (fHR) evaluation is fundamental to guarantee timely medical intervention in case of pregnancy complications. Due to the limitations of traditional cardiotocography, multichannel electrophysiological recording was proposed as a viable alternative, which requires Blind Source Separation (BSS) techniques. Yet effective and reliable separation of the fetal ECG remains challenging due to multiple noise sources and the effects of varying fetal position. In this work, we demonstrate that the adopted electrode configuration plays a key role in the effectiveness of BSS and propose guidelines for optimal electrode positioning. Moreover, a model is proposed to automatically predict the most suited configuration for accurate BSS-based fHR estimation with a minimal number of leads, to facilitate practical implementation. Methods. We compared fHR estimation accuracy with different electrode configurations on in-silico data, identifying the optimal configuration for a recent BSS method. Based on features extracted from raw signals, we proposed a support vector regression model to automatically identify the best electrode configuration in terms of fHR estimation accuracy and to dynamically adjust it to varying fetal presentation. Evaluation was performed on real and synthetic data. Results. Guidelines for the optimal electrode configuration are proposed by using 4 leads. Prediction of configuration quality shows 80.9% accuracy; the optimal configuration is recognized in 92.2% of the subjects. Conclusion. The proposed method successfully predicts the quality of the configurations, demonstrating the impact of the electrode configuration on the BSS performance. Significance. The method holds potential for long-term fetal monitoring, by dynamically choosing the optimal configuration.

PURPOSE: The corrected carotid flow time (ccFT) is derived from a pulsed-wave Doppler signal at the common carotid artery. Several equations are currently used to calculate ccFT. Its ability to assess the intravascular volume status non-invasively has recently been investigated. The purpose of this study was to evaluate the correlation and trending ability of ccFT with invasive cardiac output (CO) and stroke volume (SV) measurements.METHODS: Eighteen cardiac surgery patients were included in this prospective observational study. ccFT measurements were obtained at three time points: after induction of anesthesia (T1), after a passive leg raise (T2), and post-bypass (T3). Simultaneously, CO and SV were measured by calibrated pulse contour analysis. Three different equations (Bazett, Chambers, and Wodey) were used to calculate ccFT. The correlation and percentage change in time (concordance) between ccFT and CO and between ccFT and SV were evaluated.RESULTS: Mean ccFT values differed significantly for the three equations (p CONCLUSION: The use of different ccFT equations leads to variable correlation and concordance rates between ccFT and CO/SV measurements. Bazett's equation acceptably tracked CO changes in time, while the trending capability of SV was poor.

In the last decade, many advances have been made in high frame rate 3-D ultrasound imaging, including more flexible acquisition systems, transmit (TX) sequences, and transducer arrays. Compounding multiangle transmits of diverging waves has shown to be fast and effective for 2-D matrix arrays, where heterogeneity between transmits is key in optimizing the image quality. However, the anisotropy in contrast and resolution remains a drawback that cannot be overcome with a single transducer. In this study, a bistatic imaging aperture is demonstrated that consists of two synchronized matrix ( 32\times32 ) arrays, allowing for fast interleaved transmits with a simultaneous receive (RX). First, for a single array, the aperture efficiency for high volume rate imaging was evaluated between sparse random arrays and fully multiplexed arrays. Second, the performance of the bistatic acquisition scheme was analyzed for various positions on a wire phantom and was showcased in a dynamic setup mimicking the human abdomen and aorta. Sparse array volume images were equal in resolution and lower in contrast compared to fully multiplexed arrays but can efficiently minimize decorrelation during motion for multiaperture imaging. The dual-array imaging aperture improved the spatial resolution in the direction of the second transducer, reducing the average volumetric speckle size with 72% and the axial-lateral eccentricity with 8%. In the aorta phantom, the angular coverage increased by a factor of 3 in the axial-lateral plane, raising the wall-lumen contrast with 16% compared to single-array images, despite accumulation of thermal noise in the lumen.

By exploiting the complementary information of RGB modality and thermal modality, RGB-thermal (RGB-T) semantic segmentation is robust to adverse lighting conditions. When fusing features from RGB images and thermal images, the existing methods design different feature fusion strategies, but most of these methods overlook the modality differences caused by different imaging mechanisms. This may result in insufficient usage of complementary information. To address this issue, we propose a novel Mask-guided Modality Difference Reduction Network (MMDRNet), where the mask is utilized in the image reconstruction to ensure that the modality discrepancy within foreground regions is minimized. Doing so enables the generation of more discriminative representations for foreground pixels, thus facilitating the segmentation task. On top of this, we present a Dynamic Task Balance (DTB) method to balance the modality difference reduction task and semantic segmentation task dynamically. The experimental results on the MFNet dataset and the PST900 dataset demonstrate the superiority of the proposed mask-guided modality difference reduction strategy and the effectiveness of the DTB method.

The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by its unnormalized (log-)probabilities. Over the past years, the machine learning community has proposed several extensions of this trick to facilitate, e.g., drawing multiple samples, sampling from structured domains, or gradient estimation for error backpropagation in neural network optimization. The goal of this survey article is to present background about the Gumbel-max trick, and to provide a structured overview of its extensions to ease algorithm selection. Moreover, it presents a comprehensive outline of (machine learning) literature in which Gumbel-based algorithms have been leveraged, reviews commonly-made design choices, and sketches a future perspective., Comment: Accepted as a survey article in IEEE TPAMI

In the analysis of binary disease classification, single biomarkers might not have significant discriminating power and multiple biomarkers from a large set of biomarkers should be selected. Numerous approaches exist, but they merely work well for mean differences in biomarkers between cases and controls. Biological processes are however much more heterogeneous, and differences could also occur in other distributional characteristics (e.g. variances, skewness). Many machine learning techniques are better capable of utilizing these higher order distributional differences, sometimes at cost of explainability. In this study we propose quantile based prediction (QBP), a binary classification method that is based on the selection of multiple continuous biomarkers. QBP generates a single score using the tails of the biomarker distributions for cases and controls. This single score can then be evaluated by ROC analysis to investigate its predictive power. The performance of QBP is compared to supervised learning methods using extensive simulation studies, and two case studies: major depression disorder and trisomy. Simultaneously, the classification performance of the existing techniques in relation to each other is assessed. The key strengths of QBP are the opportunity to select relevant biomarkers and the outstanding classification performance in the case biomarkers predominantly show variance differences between cases and controls. When only shifts in means were present in the biomarkers, QBP obtained an inferior performance. Lastly, QBP proved to be unbiased in case of absence of disease relevant biomarkers and outperformed the other methods on the MDD case study. More research is needed to further optimize QBP, since it has several opportunities to improve its performance. Here we wanted to introduce the principle of QBP and show its potential., Comment: 26 pages, 5 figures

Research targeting the development of on-body sensors has been significantly growing in recent years - an example is on-skin sweat sensing. However, the wide inter and intra person variability of skin characteristics make in vivo testing of these sensors and included materials such as skin adhesives difficult, which hampers especially the initial development phase of such wearables. Besides the development of wearable sweat sensors, companies developing deodorants, cosmetics, medical adhesives and wearable textiles now need to perform expensive human subjects testing with little control over the exact sweat mechanisms. Hence, there is a need for a realistic, adaptable and stable test platform, or artificial skin. We present a versatile artificial skin platform that faithfully recapitulates skin topography, active sweat pores, skin wetting behaviour and sweat rate, and that can be tuned to mimic the specifications of the targeted body location and sweating characteristics. The developed artificial skin is capable of generating sweat rates as low as 0.1 nL min−1 pore−1 and as high as 100 nL min−1 pore−1, spanning the whole range of physiological sweat rates. Specifically, the platform can be used for the development of sweat sensors for sedentary persons whose sweat rates are commonly lower than currently delivered by any other artificial skin platform.

There is an increased desire for miniature ultrasound probes with small apertures to provide volumetric images at high frame rates for in-body applications. Satisfying these increased requirements makes simultaneous achievement of a good lateral resolution a challenge. As micro-beamforming is often employed to reduce data rate and cable count to acceptable levels, receive processing methods that try to improve spatial resolution will have to compensate the introduced reduction in focusing. Existing beamformers do not realize sufficient improvement and/or have a computational cost that prohibits their use. Here we propose the use of adaptive beamforming by deep learning (ABLE) in combination with training targets generated by a large aperture array, which inherently has better lateral resolution. In addition, we modify ABLE to extend its receptive field across multiple voxels. We illustrate that this method improves lateral resolution both quantitatively and qualitatively, such that image quality is improved compared with that achieved by existing delay-and-sum, coherence factor, filtered-delay-multiplication-and-sum and Eigen-based minimum variance beamformers. We found that only in silica data are required to train the network, making the method easily implementable in practice.

With a typical 100 - 500 nm diameter, nanobubbles are a promising new-generation ultrasound contrast agent that paves ways for several applications, such as efficient drug delivery, molecular imaging, and assessment of vascular permeability. Due to their unique physical properties, nanobubbles exhibit distinct in vivo pharmacokinetics. We have shown that the first pass of the nanobubble bolus is usually accompanied by the appearance of a second bolus (wave) within a time range of about 15 minutes. Such phenomenon, to the best of our knowledge, has never been observed with conventional microbubbles and smaller molecular contrast agents used in MRI and CT. In a previous study, we showed the potential of this phenomenon in supporting cancer diagnosis. This study focuses on developing a new compartmental pharmacokinetic model that can be used to interpret the second-wave phenomenon. With this model, we can analyze more in-depth the roles of several physiological factors affecting the characteristics of the second-wave phenomenon.

Background/purpose: Matching phasic pressure tracings between a fluid-filled catheter and high-fidelity pressure wire has received limited attention, although each part contributes half of the information to clinical decisions. We aimed to study the impact of a novel and automated method for improving the phasic calibration of a fluid-filled catheter by accounting for its oscillatory behavior. Methods/materials: Retrospective analysis of drift check tracings was performed using our algorithm that corrects for mean difference (offset), temporal delays (timing), differential sensitivity of the manifold transducer and pressure wire sensor (gain), and the oscillatory behavior of the fluid-filled catheter described by its resonant frequency and damping factor (how quickly oscillations disappear after a change in pressure). Results: Among 2886 cases, correcting for oscillations showed a large improvement in 28 % and a medium improvement in 41 % (decrease in root mean square error >0.5 mmHg to

BACKGROUND: The value of ST analysis of the fetal electrocardiogram during labor to lower asphyxia and cesarean section rates is uncertain. Physiological variation of the electrical heart axis between fetuses may explain false alarms in conventional ST analysis (absolute ST analysis). ST events (alarms) based on relative T/QRS rises (relative ST analysis) correct for this variation and may improve diagnostic accuracy of ST analysis.AIMS: To compare the diagnostic accuracy of absolute and relative ST analysis with regard to fetal acidemia.STUDY DESIGN: Retrospective case-control study.SUBJECTS: 20 healthy women with an uncomplicated pregnancy monitored with ST analysis during labor: 10 cases (umbilical cord artery pH < 7.05) and 10 controls (pH > 7.20).OUTCOME MEASURES: Sensitivity, specificity, positive and negative likelihood ratio.RESULTS: In 16 of the 20 patients a total of 54 absolute ST events were reported. Two reviewers classified the cardiotocograms; in cases 29% of the absolute ST events were significant, in the controls it was 19%. Relative ST analysis versus absolute ST analysis showed a sensitivity of 90% (55-100%) vs. 70% (35-93%), a specificity of 100% (69-100%) vs. 70% (35-93%), a positive likelihood ratio of infinity vs. 2.3 (0.8-6.5), a negative likelihood ratio of 0.1 (0.0-0.6) vs. 0.4 (0.2-1.2), and diagnostic odds ratio of infinity vs. 5.4 (0.8-36.9). McNemar showed no statistical significant difference between the sensitivity and specificity of the methods.CONCLUSIONS: We observed higher positive and lower negative likelihood ratios for relative ST analysis in comparison to absolute ST analysis. In this small study we found no statistical difference. Relative ST analysis should be studied in a larger study.

AimsDespite general awareness that screening for atrial fibrillation (AF) could reduce health hazards, large-scale implementation is lagging behind technological developments. As the successful implementation of a screening programme remains challenging, this study aims to identify facilitating and inhibiting factors from healthcare providers’ perspectives.Methods and resultsA mixed-methods approach was used to gather data among practice nurses in primary care in the southern region of the Netherlands to evaluate the implementation of an ongoing single-lead electrocardiogram (ECG)-based AF screening programme. Potential facilitating and inhibiting factors were evaluated using online questionnaires (N = 74/75%) and 14 (of 24) semi-structured in-depth interviews (58.3%). All analyses were performed using SPSS 26.0. In total, 16 682 screenings were performed on an eligible population of 64 000, and 100 new AF cases were detected. Facilitating factors included ‘receiving clear instructions’ (mean ± SD; 4.12 ± 1.05), ‘easy use of the ECG-based device’ (4.58 ± 0.68), and ‘patient satisfaction’ (4.22 ± 0.65). Inhibiting factors were ‘time availability’ (3.20 ± 1.10), ‘insufficient feedback to the practice nurse’ (2.15 ± 0.89), ‘absence of coordination’ (54%), and the ‘lack of fitting policy’ (32%).ConclusionLarge-scale regional implementation of an AF screening programme in primary care resulted in a low participation of all eligible patients. Based on the perceived barriers by healthcare providers, future AF screening programmes should create preconditions to fit the intervention into daily routines, appointing an overall project lead and a General Practitioner (GP) as a coordinator within every GP practice.

The energy efficiency of an Internet-of-Things (IoT) receiver can be improved by introducing an adjustable trade-off between signal-quality and energy consumption. In good channel conditions, the receiver can be set to consume less energy per bit, without compromising signal quality in bad channel conditions. We propose a system-level receiver design that enables adequate configuration and combination of signal-quality and energy trade-offs in multiple receiver components. Co-design of all components is essential. We identify the most energy-efficient configurations in our system-level design under different channel conditions. With those configurations, the proposed receiver outperforms a state-of-the-art adjustable receiver with only an adjustable analog front end by several tens of percent in energy per successfully received bit and by 2x in energy-sensitivity configuration range. To show the efficacy of the proposed approach, we integrate a model of the proposed design into the OMNeT++ simulator and show the benefits on an environmental monitoring scenario. In this scenario, we report up to 6x energy savings for the entire transceiver compared to the conventional transceiver design without adjustable receiver.

INTRODUCTION: Worldwide, cardiotocography is used for continuous monitoring of fetal heart rate (FHR) and uterine contractions during labour. Different methods for FHR registration and registration of contractions are available. Literature about the frequency of use of different fetal monitoring methods is lacking.OBJECTIVE: To evaluate the use of and preferences for fetal monitoring methods for intrapartum fetal monitoring among Dutch obstetric care providers.STUDY DESIGN: Between October and November 2020 the Dutch Society of Obstetrics and Gynaecology sent an email invitation to all secondary care midwives and gynaecologists (in training) in the Netherlands to complete an online survey regarding the use and personal experience with fetal monitoring methods. The survey mainly consisted of multiple choice questions. Descriptive statistics are reported. Continuous variables were presented as median with interquartile ranges (IQR). Categorical variables were expressed as numbers with percentages.RESULTS: The response rate was 29 % (n/N = 510/1748). All Dutch hospitals were represented. The respondents estimated the use of fetal scalp electrode (FSE) at 71 % (IQR 58-85 %) of deliveries. The most common indication for use of the FSE was inadequate external FHR registration (94 %). More than half (54 %) of the respondents reported to use intrauterine pressure catheter with an estimated use of 5 % (IQR 2-8 %) of deliveries. The most common indication for use of intrauterine pressure catheter was inadequate external contraction registration (75 %). The use of ST-analysis was reported in 25 % of the respondents with an estimated use of 60 % (IQR 30-72 %) of deliveries. Almost all respondents (99 %) reported to use fetal blood sampling with an estimated use of 15 % (IQR 10-23 %) of deliveries. Ninety percent of respondents would prefer a valid and reliable external monitoring technique during labour. Thirty-one percent of respondents assume that external fetal monitoring with non-invasive fetal electrocardiography and electrohysterography will become standard care within the next 5 years.CONCLUSIONS: Currently, the FSE is the most used technique for FHR monitoring during labour in the Netherlands. The most common indication for use of FSE is inadequate external FHR registration. Obstetric care providers would prefer a non-invasive external registration method that provides reliable data.

Importance: Maternal anemia in pregnancy is a common condition worldwide and is considered a risk factor for adverse neonatal and maternal outcome. Also high hemoglobin (Hb) levels are associated with adverse pregnancy outcomes. However, studies regarding the influence of intrapartum maternal Hb on mode of delivery and short-term neonatal outcome are limited and contradicting.Objective: The aim of this study was to provide an overview of current evidence regarding associations between intrapartum maternal Hb levels and mode of delivery and short-term neonatal outcome. In addition, we propose directions for future research.Evidence Acquisition: We systematically searched the electronic PubMed, EMBASE, and Cochrane databases for studies on maternal Hb levels and mode of delivery maternal and short-term neonatal outcome until January 2021. Eligible articles and their references were independently reviewed by 2 authors. Assessment was based on methodological quality and study results.Results: We included 14 studies that evaluated the level of maternal pH in relation to clinical outcome, 6 studies on mode of delivery, 10 studies on Apgar score, 1 study on fetal distress, 2 studies on neonatal intensive care unit admission, 1 study on umbilical cord pH, and 5 studies on perinatal mortality.Conclusions and Relevance: We found a trend toward an increased risk of cesarean delivery in anemic woman. Concerning the short-term neonatal outcomes, the evidence is conflicting, and included studies are too heterogenic to compare. Furthermore, various studies indicated a relation between high Hb levels and increased perinatal mortality. Therefore, we especially recommend attention to elevated Hb levels.

AIM: To reduce detrimental opioid-related side effects, minimising the postoperative opioid consumption is needed, especially in older patients. Continuous wound infusion (CWI) with local analgesics seems an effective opioid-sparing alternative. However, the added value of CWI to an enhanced recovery protocol after colorectal cancer (CRC) surgery is unclear. The aim of this study was to evaluate the outcomes of CWI after CRC surgery within a strictly adhered enhanced recovery protocol.METHOD: In this multicentre prospective observational cohort study, patients who underwent CRC surgery between May 2019 and January 2021 were included. Patients were treated with CWI as adjunct to multimodal pain management within an enhanced recovery protocol. Postoperative opioid consumption, pain scores and outcomes regarding functional recovery were evaluated.RESULTS: A cohort of 130 consecutive patients were included, of whom 36.2% were ≥75 years. Postoperative opioids were consumed by 80 (61.5%) patients on postoperative day 0, and by 28 (21.5%), 27 (20.8%), and 18 (13.8%) patients on postoperative day 1, 2, and 3, respectively. Median pain scores were CONCLUSION: In patients treated with CWI, low amounts of postoperative opioid consumption, adequate postoperative pain control, and enhanced recovery were observed. CWI seems a beneficial opioid-sparing alternative and may further improve the outcomes of an enhanced recovery protocol after CRC surgery, which seems especially valuable for older patients.

The clinical success of osteochondral implants depends significantly on their surface properties. In vivo, an implant may roughen over time which can decrease its performance. The present study investigates whether changes in the surface texture of metal and two types of polycarbonate urethane (PCU) focal knee resurfacing implants (FKRIs) occurred after 6 and 12 months of in vivo articulation with native goat cartilage. PCU implants which differed in stem stiffness were compared to investigate whether the stem fixating the implant in the bone influences surface topography. Using optical profilometry, 19 surface texture parameters were evaluated, including spatial distribution and functional parameters obtained from the material ratio curve. For metal implants, wear during in vivo articulation occurred mainly via material removal, as shown by the significant decrease of the core-valley transition from 91.5% in unused implants to 90% and 89.6% after 6 and 12 months, respectively. Conversely, for PCU implants, the wear mechanism consisted in either filling of the valleys or flattening of the surface by dulling of sharp peaks. This was illustrated in the change in roughness skewness from negative to positive values over 12 months of in vivo articulation. Implants with a softer stem experienced the most deformation, shown by the largest change in material ratio curve parameters. We therefore showed, using a detailed surface profilometry analysis, that the surface texture of metal and two different PCU FKRIs changes in a different way after articulation against cartilage, revealing distinct wear mechanisms of different implant materials.

Cerclages can be used to prevent preterm birth, although their effectiveness and safety is disputed. We aimed to describe obstetric outcomes after cerclage procedures. We included 156 singleton pregnancies and six multiple pregnancies. In singleton pregnancies with history-indicated, short cervix-indicated and emergency cerclages, respectively 84.6, 76.5 and 43.8% resulted in late preterm or term deliveries. In singletons, the following complications were reported: excessive bleeding in one emergency cerclage procedure and three re-cerclage procedures in the history-indicated cerclage group. No perioperative rupture of membranes occurred in singletons. When comparing results of experienced and less-experienced gynaecologists, a remarkably smaller take home child rate was observed for singletons treated by less-experienced gynaecologists: 90.7% and 94.4% for the two experienced gynaecologist as compared to 85.0% for the group of less-experienced gynaecologists. In conclusion, cerclages in singletons result in few cerclage-associated complications and a high take home child rate, when performed by experienced gynaecologists. Impact statementWhat is already known on this subject? Prematurity is the leading cause of perinatal and neonatal mortality and morbidity worldwide. Cervical cerclages can be used to prevent preterm birth, although their effectiveness and safety is disputed.What the results of this study add? In our cohort study, singleton pregnancies with cerclages seem to have satisfactory obstetric outcomes. We found a very low prevalence of cerclage-associated complications in singleton pregnancies, for both history-indicated, short cervix-indicated and emergency cerclages. Additionally, take home child rates in singleton pregnancies were remarkably higher when cerclage procedures were performed by experienced gynaecologists, compared to less experienced gynaecologists.What the implications are of these findings for clinical practice and/or further research? Based on the observed difference in take home child rates, we advise all cerclage procedures to be performed by experienced gynaecologists only. This may mean that women with an indication for cerclage will be referred to a more experienced colleague, either in the same, or in another hospital. To ensure treatment by an experienced gynaecologist, simulation-based training could also provide a solution. What is already known on this subject? Prematurity is the leading cause of perinatal and neonatal mortality and morbidity worldwide. Cervical cerclages can be used to prevent preterm birth, although their effectiveness and safety is disputed. What the results of this study add? In our cohort study, singleton pregnancies with cerclages seem to have satisfactory obstetric outcomes. We found a very low prevalence of cerclage-associated complications in singleton pregnancies, for both history-indicated, short cervix-indicated and emergency cerclages. Additionally, take home child rates in singleton pregnancies were remarkably higher when cerclage procedures were performed by experienced gynaecologists, compared to less experienced gynaecologists. What the implications are of these findings for clinical practice and/or further research? Based on the observed difference in take home child rates, we advise all cerclage procedures to be performed by experienced gynaecologists only. This may mean that women with an indication for cerclage will be referred to a more experienced colleague, either in the same, or in another hospital. To ensure treatment by an experienced gynaecologist, simulation-based training could also provide a solution.

Objective: To assess the diagnostic accuracy and intra-observer agreement of endoscopic stone recognition (ESR) compared with formal stone analysis. Introduction: Stone analysis is a corner stone in the prevention of stone recurrence. Although X-ray diffraction (XRD) and infrared spectroscopy are the recommended techniques for reliable formal stone analysis, this is not always possible, and the process takes time and is costly. ESR could be an alternative, as it would give immediate information on stone composition. Materials and Methods: Fifteen endourologists predicted stone composition based on 100 videos from ureterorenoscopy. Diagnostic accuracy was evaluated by comparing the prediction from visual assessment with stone analysis by XRD. After 30 days, the videos were reviewed again in a random order to assess intra-observer agreement. Results: The median diagnostic accuracy for calcium oxalate monohydrate was 54% in questionnaire 1 (Q1) and 59% in questionnaire 2 (Q2), whereas calcium oxalate dihydrate had a median diagnostic accuracy of 75% in Q1 and 50% in Q2. The diagnostic accuracy for calcium hydroxyphosphate was 10% in Q1 and 13% in Q2. The median diagnostic accuracy for calcium hydrogen phosphate dihydrate and calcium magnesium phosphate was 0% in both questionnaires. The median diagnostic accuracy for magnesium ammonium phosphate was 20% in Q1 and 40% in Q2. The median diagnostic accuracy for uric acid was 22% in both questionnaires. Finally, there was a diagnostic accuracy of 60% in Q1 and 80% in Q2 for cystine. The intra-observer agreement ranged between 45% and 72%. Conclusion: Diagnostic accuracy of ESR is limited and intra-observer agreement is below the threshold of acceptable agreement.

Background The metabolic health index (MHI) is a biomarker-based model that objectively assesses the cumulative impact of comorbidities type 2 diabetes mellitus, hypertension and dyslipidemia on the health state of bariatric patients. The MHI was developed on a single-center cohort using a fully laboratory data-driven approach, resulting in a MHI score on a range from 1 to 6. To show universal applicability in clinical care, the MHI was validated externally and potential laboratory-related shortcomings were evaluated. Methods Retrospective laboratory and national bariatric quality registry data were collected from five Dutch renowned bariatric centers (n = 11 501). MHI imprecision was derived from the cumulative effect of biological and analytical variance of the individual input variables of the MHI model. The performance of the MHI (model) was assessed in terms of discrimination and calibration. Results The cumulative imprecision in MHI was 0.25 MHI points. Calibration of the MHI model diverged over the different centers but was accounted for by misregistration of comorbidity after cross-checking the data. Discriminative performance of the MHI model was consistent across the different centers. Conclusions The MHI model can be applied in clinical practice of bariatric centers, regardless of patient mix and analytical platform. Because the MHI is based on objective parameters, it is insensitive to diverging clinical definitions of comorbidities. Therefore, the MHI can be used to objectify severity of metabolic comorbidities in bariatric patients. The MHI can support the patient-selection process for surgery and objectively assessing the effect of surgery on the metabolic health state.

In this paper, we extend the work of Pimentel et al. (2015) and propose an adjusted estimator of Kendall's $\tau$ for bivariate zero-inflated count data. We provide achievable lower and upper bounds of our proposed estimator and show its relationship with current literature. In addition, we also suggest an estimator of the achievable bounds, thereby helping practitioners interpret the results while working with real data. The performance of the proposed estimator for Kendall's $\tau$ is unbiased with smaller mean squared errors compared to the unadjusted estimator of Pimentel et al. (2015). Our results also show that the bound estimator can be used when knowledge of the marginal distributions is lacking.

Healthy bone is maintained by the process of bone remodeling. An unbalance in this process can lead to pathologies such as osteoporosis which are often studied with animal models. However, data from animals have limited power in predicting the results that will be obtained in human clinical trials. In search for alternatives to animal models, human in vitro models are emerging as they address the principle of reduction, refinement, and replacement of animal experiments (3Rs). At the moment, no complete in vitro model for bone-remodeling exists. Microfluidic chips offer great possibilities, particularly because of the dynamic culture options, which are crucial for in vitro bone formation. In this study, a scaffold free, fully human, 3D microfluidic coculture model of bone remodeling is presented. A bone-on-a-chip coculture system was developed in which human mesenchymal stromal cells differentiated into the osteoblastic lineage and self-assembled into scaffold free bone-like tissues with the shape and dimensions of human trabeculae. Human monocytes were able to attach to these tissues and to fuse into multinucleated osteoclast-like cells, establishing the coculture. Computational modeling was used to determine the fluid flow induced shear stress and strain in the formed tissue. Furthermore, a set-up was developed allowing for long-term (35 days) on-chip cell culture with benefits including continuous fluid-flow, low bubble formation risk, easy culture medium exchange inside the incubator and live cell imaging options. This on-chip coculture is a crucial advance towards developing in vitro bone remodeling models to facilitate drug testing.

Objective: Tissue mechanical properties are valuable markers for tissue characterization, aiding in the detection and staging of pathologies. Shear wave elastography (SWE) offers a quantitative assessment of tissue mechanical characteristics based on the SW propagation profile, which is derived from the SW particle motion. Improving the signal-to-noise ratio (SNR) of the SW particle motion would directly enhance the accuracy of the material property estimates such as elasticity or viscosity.Methods: In this paper, we present a 3-D multi-resolution convolutional neural network (MRCNN) to perform improved estimation of the SW particle velocity V. Additionally, we propose a novel approach to generate training data from real acquisitions, providing high SNR ground truth target data, one-to-one paired to inputs that are corrupted with real-world noise and disturbances.Discussion: By testing the network on in vitro data acquired from a commercial breast elastography phantom, we show that the MRCNN outperforms Loupas’ autocorrelation algorithm with an improved SNR of 4.47 dB for the V signals, a two-fold decrease in the standard deviation of the downstream elasticity estimates, and a two-fold increase in the contrast-to-noise ratio of the elasticity maps. The generalizability of the network was further demonstrated with a set of ex vivo porcine liver data.Conclusion: The proposed MRCNN outperforms the standard autocorrelation method, in particular in low SNR regimes.

This study describes a computationally efficient algorithm for 4-class sleep staging based on cardiac activity and body movements. Using an accelerometer to calculate gross body movements and a reflective photoplethysmographic (PPG) sensor to determine interbeat intervals and a corresponding instantaneous heart rate signal, a neural network was trained to classify between wake, combined N1 and N2, N3 and REM sleep in epochs of 30 s. The classifier was validated on a hold-out set by comparing the output against manually scored sleep stages based on polysomnography (PSG). In addition, the execution time was compared with that of a previously developed heart rate variability (HRV) feature-based sleep staging algorithm. With a median epoch-per-epoch κ of 0.638 and accuracy of 77.8% the algorithm achieved an equivalent performance when compared to the previously developed HRV-based approach, but with a 50-times faster execution time. This shows how a neural network, without leveraging any a priori knowledge of the domain, can automatically “discover” a suitable mapping between cardiac activity and body movements, and sleep stages, even in patients with different sleep pathologies. In addition to the high performance, the reduced complexity of the algorithm makes practical implementation feasible, opening up new avenues in sleep diagnostics.

The introduction of rapid exome sequencing (rES) for critically ill neonates admitted to the neonatal intensive care unit has made it possible to impact clinical decision-making. Unbiased prospective studies to quantify the impact of rES over routine genetic testing are, however, scarce. We performed a clinical utility study to compare rES to conventional genetic diagnostic workup for critically ill neonates with suspected genetic disorders. In a multicenter prospective parallel cohort study involving five Dutch NICUs, we performed rES in parallel to routine genetic testing for 60 neonates with a suspected genetic disorder and monitored diagnostic yield and the time to diagnosis. To assess the economic impact of rES, healthcare resource use was collected for all neonates. rES detected more conclusive genetic diagnoses than routine genetic testing (20% vs. 10%, respectively), in a significantly shorter time to diagnosis (15 days (95% CI 10–20) vs. 59 days (95% CI 23–98, p < 0.001)). Moreover, rES reduced genetic diagnostic costs by 1.5% (€85 per neonate). Conclusion: Our findings demonstrate the clinical utility of rES for critically ill neonates based on increased diagnostic yield, shorter time to diagnosis, and net healthcare savings. Our observations warrant the widespread implementation of rES as first-tier genetic test in critically ill neonates with disorders of suspected genetic origin.What is Known:• Rapid exome sequencing (rES) enables diagnosing rare genetic disorders in a fast and reliable manner, but retrospective studies with neonates admitted to the neonatal intensive care unit (NICU) indicated that genetic disorders are likely underdiagnosed as rES is not routinely used.• Scenario modeling for implementation of rES for neonates with presumed genetic disorders indicated an expected increase in costs associated with genetic testing.What is New:• This unique prospective national clinical utility study of rES in a NICU setting shows that rES obtained more and faster diagnoses than conventional genetic tests.• Implementation of rES as replacement for all other genetic tests does not increase healthcare costs but in fact leads to a reduction in healthcare costs.

The aim of the present study was to further improve an in vitro 3D osteoblast (OB) – osteoclast (OC) co-culture model of bone by tuning it towards states of formation, resorption, and equilibrium for their future applications in fundamental research, drug development and personalized medicine. This was achieved by varying culture medium composition and monocyte seeding density, the two external parameters that affect cell behavior the most. Monocytes were seeded at two seeding densities onto 3D silk-fibroin constructs pre-mineralized by MSC-derived OBs and were co-cultured in one of three different media (OC stimulating, Neutral and OB stimulating medium) for three weeks. Histology showed mineralized matrix after co-culture and OC markers in the OC medium group. Scanning Electron Microscopy showed large OC-like cells in the OC medium group. Micro-computed tomography showed increased formation in the OB medium group, equilibrium in the Neutral medium group and resorption in the OC medium group. Culture supernatant samples showed high early TRAP release in the OC medium group, a later and lower release in the Neutral medium group, and almost no release in the OB medium group. Increased monocyte seeding density showed a less-than-proportional increase in TRAP release and resorption in OC medium, while it proportionally increased TRAP release in Neutral medium without affecting net resorption. The 3D OB-OC co-culture model was effectively used to show an excess of mineral deposition using OB medium, resorption using OC medium, or an equilibrium using Neutral medium. All three media applied to the model may have their own distinct applications in fundamental research, drug development, and personalized medicine.

STUDY OBJECTIVES: Obstructive sleep apnea (OSA) and insomnia frequently co-occur, making diagnosis and treatment challenging. We investigated differences in sleep structure between patients with OSA, insomnia, and comorbid insomnia and sleep apnea (COMISA) to identify characteristics that can be used to improve the diagnosis of COMISA.METHODS: We obtained polysomnography data of 326 patients from the Sleep and OSA Monitoring with Non-Invasive Applications database. The group included patients with OSA (n = 199), insomnia (n = 47), and COMISA (n = 80). We compared statistics related to sleep structure between the 3 patient groups.RESULTS: Wake after sleep onset was significantly shorter for the OSA group (median: 60.0 minutes) compared to the COMISA (median: 83.3 minutes, P < .01) and the insomnia (median: 83.5 minutes, P = .01) groups. No significant differences were found in the total number of awakenings and the number of short (up to and including 2 minutes) and medium-length awakenings (2.5 up to and including 4.5 minutes). However, the number of long awakenings (5 minutes or longer) and wake after sleep onset containing only long awakenings was significantly lower for patients with OSA (median: 2 awakenings and 25.5 minutes) compared to patients with COMISA (median: 3 awakenings, P < .01 and 43.3 minutes, P < .001) or with insomnia (median: 3 awakenings, P < .01 and 56.0 minutes, P < .001). Total sleep time was significantly longer and sleep efficiency was significantly higher for the OSA group (median: 418.5 minutes and 84.4%) compared to both the COMISA (median: 391.5 minutes, P < .001 and 77.3%, P < .001) and the insomnia (median: 381.5 minutes, P < .001 and 78.2%, P < .001) groups. The number of sleep-stage transitions during the night for patients with COMISA (median: 194.0) was lower compared to that for patients with OSA (median: 218.0, P < .01) and higher compared to that for patients with insomnia (median: 156.0, P < .001). Other sleep architectural parameters were not discriminative between the groups. CONCLUSIONS: Patients with COMISA show specific characteristics of insomnia, including prolonged awakenings. This variable is distinctive in comparison to patients with OSA. The combination of prolonged awakenings and the presence of sleep-disordered breathing leads to increased sleep disturbance compared to patients having only 1 of the sleep disorders.

Objective: To assess the performance of a multimodal seizure detection device, first tested in adults (sensitivity 86%, PPV 49%), in a pediatric cohort living at home or residential care. Methods: In this multicenter, prospective, video-controlled cohort-study, nocturnal seizures were detected by heartrate and movement changes in children with epilepsy and intellectual disability. Participants with a history of >1 monthly major motor seizure wore Nightwatch bracelet at night for 3 months. Major seizures were defined as tonic–clonic, generalized tonic >30 s, hyperkinetic, or clusters (>30 min) of short myoclonic or tonic seizures. The video of all events (alarms and nurse diaries) and about 10% of whole nights were reviewed to classify major seizures, and minor or no seizures. Results: Twenty-three participants with focal or generalized epilepsy and nightly motor seizures were evaluated during 1511 nights, with 1710 major seizures. First 1014 nights, 4189 alarms occurred with average of 1.44/h, showing average sensitivity of 79.9% (median 75.4%) with mean PPV of 26.7% (median 11.1%) and false alarm rate of 0.2/hour. Over 90% of false alarms in children was due to heart rate (HR) part of the detection algorithm. To improve this rate, an adaptation was made such that the alarm was only triggered when the wearer was in horizontal position. For the remaining 497 nights, this was tested prospectively, 384 major seizures occurred. This resulted in mean PPV of 55.5% (median 58.1%) and a false alarm rate 0.08/h while maintaining a comparable mean sensitivity of 79.4% (median 93.2%). Significance: Seizure detection devices that are used in bed which depend on heartrate and movement show similar sensitivity in children and adults. However, children do show general higher false alarm rate, mostly triggered while awake. By correcting for body position, the false alarms can be limited to a level that comes close to that in adults.

Contains fulltext : 287686.pdf (Publisher’s version ) (Open Access) OBJECTIVE: To determine the prevalence of depression, anxiety, and posttraumatic stress disorder (PTSD) years after hyperemesis gravidarum (HG) and its association with HG severity. MATERIAL AND METHODS: This prospective cohort study consisted of a follow-up of 215 women admitted for HG, who were eligible to participate in a randomized controlled trial and either declined or agreed to be randomized between 2013 and 2016 in 19 hospitals in the Netherlands. Participants completed the Hospital Anxiety and Depression Scale (HADS) six weeks postpartum and during follow-up and the PTSD checklist for DSM-5 (PCL-5) during follow-up. An anxiety or depression score ≥8 is indicative of an anxiety or depression disorder and a PCL-5 ≥ 31 indicative of PTSD. Measures of HG severity were symptom severity (PUQE-24: Pregnancy Unique Quantification of Emesis), weight change, duration of admissions, readmissions, and admissions after the first trimester. RESULTS: About 54/215 participants completed the HADS six weeks postpartum and 73/215 participants completed the follow-up questionnaire, on average 4.5 years later. Six weeks postpartum, 13 participants (24.1%) had an anxiety score ≥8 and 11 participants (20.4%) a depression score ≥8. During follow-up, 29 participants (39.7%) had an anxiety score ≥8, 20 participants (27.4%) a depression score ≥8, and 16 participants (21.9%) a PCL-5 ≥ 31.Multivariable logistic regression analysis showed that for every additional point of the mean PUQE-24 three weeks after inclusion, the likelihood of having an anxiety score ≥8 and PCL-5 ≥ 31 at follow-up increased with OR 1.41 (95% CI: 1.10;1.79) and OR 1.49 (95% CI: 1.06;2.10) respectively. CONCLUSION: Depression, anxiety, and PTSD symptoms are common years after HG occurred.

Many application domains, spanning from computational photography to medical imaging, require recovery of high-fidelity images from noisy, incomplete or partial/compressed measurements. State-of-the-art methods for solving these inverse problems combine deep learning with iterative model-based solvers, a concept known as deep algorithm unfolding or unrolling. By combining a-priori knowledge of the forward measurement model with learned proximal image-to-image mappings based on deep networks, these methods yield solutions that are both physically feasible (data-consistent) and perceptually plausible (consistent with prior belief). However, current proximal mappings based on (predominantly convolutional) neural networks only implicitly learn such image priors. In this paper, we propose to make these image priors fully explicit by embedding deep generative models in the form of normalizing flows within the unfolded proximal gradient algorithm, and training the entire algorithm end-to-end for a given task. We demonstrate that the proposed method outperforms competitive baselines on various image recovery tasks, spanning from image denoising to inpainting and deblurring, effectively adapting the prior to the restoration task at hand

Computational modeling is increasingly used to design charging systems for implanted medical devices. The design of these systems must often satisfy conflicting requirements, such as charging speed, specific absorption rate (SAR) and coil size. Fast electromagnetic solvers are pivotal for enabling multi-criteria optimization. In this paper, we present an analytical model based on the quasi-static approximation as a fast, yet sufficiently accurate tool for optimizing inductive charging systems.The approximate model was benchmarked against full-wave simulations to validate accuracy and improvement in computation time. The coupling factor of two test coils was measured for lateral and axial displacements and the SAR was measured experimentally in a PAA phantom.The approximate model takes only 11 seconds to compute a single iteration, while the full-wave model takes 5 hours to compute the same case. The maximum difference with full-wave simulations was less than 24% and the mean difference less than 2%. Adding a novel figure of merit into the multi-criterion optimization resulted in a 16% higher charging speed. The measured results of the SAR and coupling factor are within a 5 mm coil offset margin.The proposed approximate model succeeds as a rapid prototyping tool, enabling fast and sufficiently accurate optimization for wireless charging systems.The approximate model is the first of its kind to compute both the coupling factor and the SAR near conducting structures fast enough to enable optimization of charging speed.

This literature review presents a comprehensive overview of machine learning (ML) applications in proton magnetic resonance spectroscopy (MRS). As the use of ML techniques in MRS continues to grow, this review aims to provide the MRS community with a structured overview of the state-of-the-art methods. Specifically, we examine and summarize studies published between 2017 and 2023 from major journals in the magnetic resonance field. We categorize these studies based on a typical MRS workflow, including data acquisition, processing, analysis, and artificial data generation. Our review reveals that ML in MRS is still in its early stages, with a primary focus on processing and analysis techniques, and less attention given to data acquisition. We also found that many studies use similar model architectures, with little comparison to alternative architectures. Additionally, the generation of artificial data is a crucial topic, with no consistent method for its generation. Furthermore, many studies demonstrate that artificial data suffers from generalization issues when tested on in-vivo data. We also conclude that risks related to ML models should be addressed, particularly for clinical applications. Therefore, output uncertainty measures and model biases are critical to investigate. Nonetheless, the rapid development of ML in MRS and the promising results from the reviewed studies justify further research in this field.

Neuron–specific enolase (NSE) is a promising small–cell lung cancer (SCLC) biomarker composed of αγ and γγ isozyme dimers. As the conventional immunoassays are prone to interferences and cannot differentiate between the isozymes, we developed a multiplex immunoaffinity (IA) liquid chromatography–tandem mass spectrometry (LC–MS/MS) assay for the quantification of NSEα and NSEγ in human serum. A calibrator was prepared by performing cold denaturation of recombinantly expressed αα and γγ enolase dimers to induce a new dimer equilibrium that was determined to be approximately 1αγ:1γγ:1αα. Selective sample purification was achieved by performing IA extraction using an antibody specific towards NSEγ. The isolated αγ and γγ dimers were denatured and trypsin digested to allow quantification of the selected signature peptides and their corresponding isotopically labelled peptide internal standard. The obtained linear dynamic ranges were determined to be 1.5–56 ng/mL and 0.64–167 ng/mL for NSEα and NSEγ (R2 = 0.88 and 0.97 respectively). Validation of the assay showed acceptable accuracy and precision for NSEα and NSEγ. The method was successfully applied to patient serum in which both isozymes were detected. Compared to the conventional immunoassay, substantially lower total NSE concentrations were measured in IA LC–MS/MS. With this multiplex IA LC-MS/MS assay, the clinical value of quantifying the individual isozymes can be explored. In addition, together with the calibrator described here, it may be applied to standardize NSE immunoassays across different platforms.

Abdominal ultrasound image quality is hampered by phase aberration, that is mainly caused by the large speed-of-sound (SoS) differences between fat and muscle tissue in the abdominal wall. The mismatch between the assumed and actual SoS distribution introduces general blurring of the ultrasound images, and acoustic refraction can lead to geometric distortion of the imaged features. Large aperture imaging or dual-transducer imaging can improve abdominal imaging at deep locations by providing increased contrast and resolution. However, aberration effects for large aperture imaging can be even more severe, which limits its full potential. In this study, a model-based aberration correction method for arbitrary acquisition schemes is introduced for delay-and-sum (DAS) beamforming and its performance was analyzed for both single- and dual-transducer ultrasound imaging. The method employs aberration corrected wavefront arrival times, using manually assigned local SoS values. Two wavefront models were compared. The first model is based on a straight ray approximation, and the second model on the Eikonal equation, which is solved by a multi-stencils fast marching method. Their accuracy for abdominal imaging was evaluated in acoustic simulations and phantom experiments involving tissue-mimicking and porcine material with large SoS contrast (∼100 m/s). The lateral resolution was improved by up to 90% in simulations and up to 65% in experiments compared to standard DAS, in which the use of Eikonal beamforming generally outperformed straight ray beamforming. Moreover, geometric distortions were mitigated in multi-aperture imaging, leading to a reduction in position error of around 80%. A study on the sensitivity of the aberration correction to shape and SoS of aberrating layers was performed, showing that even with imperfect segmentations or SoS values, aberration correction still outperforms standard DAS.

INTRODUCTION: Palpitations occurring in specific body positions are often reported by patients, but the effect of body position on arrhythmia has received little research attention. We hypothesize that resting body position can exert pro-arrhythmogenic effects in various ways. For example, lateral body position is known to increase change atrial and pulmonary vein dimensions.METHODS: This observational study capitalizes on overnight polysomnography (PSG) recordings from a tertiary sleep clinic. PSGs were retrieved based on any mention of cardiac arrhythmia in the clinical report, irrespective of primary sleep diagnosis or (cardiac) comorbidities. Every instance of atrial ectopy was annotated and subgroups with a homogenous rate of atrial ectopy were created based on the Dunn index. A generalized linear mixed-effects model using age, sex, gender, sleep stage and body position was used to analyse the total amount of atrial ectopy in each combination of sleep stage and body position. Backward elimination was then performed to select the best subset of variables for the model. Presence of a respiratory event was then added to the model for the subgroup with a high atrial ectopy rate.RESULTS: PSGs of 22 patients (14% female, mean age 61y) were clustered and analysed. Body position, sleep stage, age or sex did not have a significant effect on atrial ectopy in the subgroup with a low rate of atrial ectopy (N = 18). However, body position did significantly affect the rate of atrial ectopy in the subgroup with a high rate of atrial ectopy (N = 4; 18%). Respiratory events significantly altered the atrial ectopy rate in only three body positions across two patients.DISCUSSION: In each individual with a high rate of atrial ectopy, the rate of atrial ectopy was significantly higher in either left or right decubital or supine position. Increase in atrial wall stretch in lateral decubital position and obstructive respiratory events in positional sleep apnea are two possible pathophysiological mechanisms, while avoidance of a body position due to symptomatic atrial ectopy in that position is an important limitation.CONCLUSION: In a selected cohort of patients with a high rate of atrial ectopy during overnight polysomnography, the occurrence of atrial ectopy is related to resting body position.

Background: Currently, in the majority of patients with stable angina pectoris (SAP) treatment consists of optimal medical treatment, potentially followed by coronary angiography and subsequent coronary revascularisation if necessary”. Recent work questioned the effectiveness of these invasive procedures in reducing re-events and improving prognosis. The potential of exercise-based cardiac rehabilitation on clinical outcomes in patients with coronary artery disease is well-known. However, in the modern era, no studies compared the effects of cardiac rehabilitation versus coronary revascularisation in patients with SAP. Methods: In this multicentre randomised controlled trial, 216 patients with stable angina pectoris and residual anginal complaints under optimal medical treatment will be randomised to: 1) usual care (i.e., coronary revascularisation), or 2) a 12-month cardiac rehabilitation (CR) programme. CR consists of a multidisciplinary intervention, including education, exercise training, lifestyle coaching and a dietary intervention with a stepped decline in supervision. The primary outcome will be anginal complaints (Seattle Angina Questionnaire-7) following the 12-month intervention. Secondary outcomes include cost-effectiveness, ischemic threshold during exercise, cardiovascular events, exercise capacity, quality of life and psychosocial wellbeing. Discussion: In this study, we will examine the hypothesis that multidisciplinary CR is at least equally effective in reducing anginal complaints as the contemporary invasive approach at 12-months follow-up for patients with SAP. If proven successful, this study will have significant impact on the treatment of patients with SAP as multidisciplinary CR is a less invasive and potentially less costly and better sustainable treatment than coronary revascularisations. Trial registration: Netherlands Trial Register, NL9537. Registered 14 June 2021.

Assessing post-operative recovery is a significant component of perioperative care, since this assessment might facilitate detecting complications and determining an appropriate discharge date. However, recovery is difficult to assess and challenging to predict, as no universally accepted definition exists. Current solutions often contain a high level of subjectivity, measure recovery only at one moment in time, and only investigate recovery until the discharge moment. For these reasons, this research aims to create a model that predicts continuous recovery scores in perioperative care in the hospital and at home for objective decision making. This regression model utilized vital signs and activity metrics measured using wearable sensors and the XGBoost algorithm for training. The proposed model described continuous recovery profiles, obtained a high predictive performance, and provided outcomes that are interpretable due to the low number of features in the final model. Moreover, activity features, the circadian rhythm of the heart, and heart rate recovery showed the highest feature importance in the recovery model. Patients could be identified with fast and slow recovery trajectories by comparing patient-specific predicted profiles to the average fast- and slow-recovering populations. This identification may facilitate determining appropriate discharge dates, detecting complications, preventing readmission, and planning physical therapy. Hence, the model can provide an automatic and objective decision support tool.

Medical instrument detection is essential for computer-assisted interventions, since it facilitates clinicians to find instruments efficiently with a better interpretation, thereby improving clinical outcomes. This article reviews image-based medical instrument detection methods for ultrasound-guided (US-guided) operations. Literature is selected based on an exhaustive search in different sources, including Google Scholar, PubMed, and Scopus. We first discuss the key clinical applications of medical instrument detection in the US, including delivering regional anesthesia, biopsy taking, prostate brachytherapy, and catheterization. Then, we present a comprehensive review of instrument detection methodologies, including non-machine-learning and machine-learning methods. The conventional non-machine-learning methods were extensively studied before the era of machine learning methods. The principal issues and potential research directions for future studies are summarized for the computer-assisted intervention community. In conclusion, although promising results have been obtained by the current (non-) machine learning methods for different clinical applications, thorough clinical validations are still required.