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Background: Trauma-focused treatments for post-traumatic stress disorder (PTSD) are effective for many patients. However, relapse may occur when acquired extinction memories fail to generalize beyond treatment contexts. A subgroup of PTSD patients – potentially with substantial exposure to early-life adversity (ELA) – show dysregulation of the hypothalamic–pituitary–adrenal (HPA) axis, which results in lower cortisol levels. Glucocorticoids, including cortisol, appear to facilitate strength and generalization of emotional memories.Objective: We describe the protocol of an integrated PTSD study. We investigate (A) associations between HPA-axis dysregulation, ELA, epigenetic markers, and PTSD treatment outcome (observational study); and (B) effects of exogenous glucocorticoids on strength and generalization of extinction memories and associated neural mechanisms [pharmacological intervention study with functional magnetic resonance imaging (fMRI)]. The objective is to provide proof of concept that PTSD patients with HPA-axis dysregulation often experienced ELA and may show improved strength and generalization of extinction learning after glucocorticoid administration.Method: The observational study (n = 160 PTSD group, n = 30 control group) assesses ELA, follow-up PTSD symptoms, epigenetic markers, and HPA-axis characteristics (salivary cortisol levels during low-dose dexamethasone suppression test and socially evaluated cold-pressor test). The pharmacological intervention study (n = 80 PTSD group, with and without HPA-axis dysregulation) is a placebo-controlled fMRI study with a crossover design. To investigate strength and generalization of extinction memories, we use a differential fear acquisition, extinction, and extinction recall task with spatial contexts within a virtual environment. Prior to extinction learning, 20 mg hydrocortisone or placebo is administered. During next-day recall, strength of the extinction memory is determined by recovery of skin conductance and pupil dilation differential responding, whereas generalization is assessed by comparing responses between different spatial contexts.Conclusion: The integrated study described in the current protocol paper could inform a personalized treatment approach in which these PTSD patients may receive glucocorticoids as a treatment enhancer in trauma-focused therapies.Trial registration: The research project is registered in the European Union Drug Regulating Authorities Clinical Trials (EudraCT) database, https://eudract.ema.europa.eu/, EudraCT number 2020-000712-30.

Abstract Background We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM). Methods In this prospective cohort study, pre- and post-systemic treatment computed tomography (CT) scans of 259 patients with initially unresectable CRLM of the CAIRO5 trial (NCT02162563) were included. In total, 595 CT scans comprising 8,959 CRLM were divided into training (73%), validation (6.5%), and test sets (21%). Deep learning models were trained with ground truth segmentations of the liver and CRLM. TTV was calculated based on the CRLM segmentations. An external validation cohort was included, comprising 72 preoperative CT scans of patients with 112 resectable CRLM. Image segmentation evaluation metrics and intraclass correlation coefficient (ICC) were calculated. Results In the test set (122 CT scans), the autosegmentation models showed a global Dice similarity coefficient (DSC) of 0.96 (liver) and 0.86 (CRLM). The corresponding median per-case DSC was 0.96 (interquartile range [IQR] 0.95–0.96) and 0.80 (IQR 0.67–0.87). For tumor segmentation, the intersection-over-union, precision, and recall were 0.75, 0.89, and 0.84, respectively. An excellent agreement was observed between the reference and automatically computed TTV for the test set (ICC 0.98) and external validation cohort (ICC 0.98). In the external validation, the global DSC was 0.82 and the median per-case DSC was 0.60 (IQR 0.29–0.76) for tumor segmentation. Conclusions Deep learning autosegmentation models were able to segment the liver and CRLM automatically and accurately in patients with initially unresectable CRLM, enabling automatic TTV assessment in such patients. Relevance statement Automatic segmentation enables the assessment of total tumor volume in patients with colorectal liver metastases, with a high potential of decreasing radiologist’s workload and increasing accuracy and consistency. Key points • Tumor response evaluation is time-consuming, manually performed, and ignores total tumor volume. • Automatic models can accurately segment tumors in patients with colorectal liver metastases. • Total tumor volume can be accurately calculated based on automatic segmentations. Graphical Abstract

Future sea-level rise projections are characterized by both quantifiable uncertainty and unquantifiable structural uncertainty. Thorough scientific assessment of sea-level rise projections requires analysis of both dimensions of uncertainty. Probabilistic sea-level rise projections evaluate the quantifiable dimension of uncertainty; comparison of alternative probabilistic methods provides an indication of structural uncertainty. Here we describe the Framework for Assessing Changes To Sea-level (FACTS), a modular platform for characterizing different probability distributions for the drivers of sea-level change and their consequences for global mean, regional, and extreme sea-level change. We demonstrate its application by generating seven alternative probability distributions under multiple emissions scenarios for both future global mean sea-level change and future relative and extreme sea-level change at New York City. These distributions, closely aligned with those presented in the Intergovernmental Panel on Climate Change Sixth Assessment Report, emphasize the role of the Antarctic and Greenland ice sheets as drivers of structural uncertainty in sea-level change projections.

Abstract When different flooding drivers co‐occur, they can cause compound floods. Despite the potential impact of compound flooding, few studies have projected how the joint probability of flooding drivers may change. Furthermore, existing projections may not be very robust, as they are based on only 5 to 6 climate model simulations. Here, we use a large ensemble of simulations from the Coupled Model Intercomparison Project 6 (CMIP6) to project changes in the joint probability of extreme storm surges and precipitation at European tide gauges under a medium and high emissions scenario, enabled by data‐proximate cloud computing and statistical storm surge modeling. We find that the joint probability will increase in the northwest and decrease in most of the southwest of Europe. Averaged over Europe, the absolute magnitude of these changes is 36%–49% by 2080, depending on the scenario. The large‐scale changes in the joint probability of extreme storm surges and precipitation are similar to those in the joint probability of extreme wind speeds and precipitation, but locally, differences can exceed the changes themselves. Due to internal climate variability and inter‐model differences, projections based on simulations of only 5 to 6 randomly chosen CMIP6 models have a probability of higher than 10% to differ qualitatively from projections based on all CMIP6 simulations in multiple regions, especially under the medium emissions scenario and earlier in the twenty‐first century. Therefore, our results provide a more robust and less uncertain representation of changes in the potential for compound flooding in Europe than previous projections.

Abstract Background Stress-related disorders such as anxiety and depression are highly prevalent and cause a tremendous burden for affected individuals and society. In order to improve prevention strategies, knowledge regarding resilience mechanisms and ways to boost them is highly needed. In the Dynamic Modelling of Resilience – interventional multicenter study (DynaM-INT), we will conduct a large-scale feasibility and preliminary efficacy test for two mobile- and wearable-based just-in-time adaptive interventions (JITAIs), designed to target putative resilience mechanisms. Deep participant phenotyping at baseline serves to identify individual predictors for intervention success in terms of target engagement and stress resilience. Methods DynaM-INT aims to recruit N = 250 healthy but vulnerable young adults in the transition phase between adolescence and adulthood (18–27 years) across five research sites (Berlin, Mainz, Nijmegen, Tel Aviv, and Warsaw). Participants are included if they report at least three negative burdensome past life events and show increased levels of internalizing symptoms while not being affected by any major mental disorder. Participants are characterized in a multimodal baseline phase, which includes neuropsychological tests, neuroimaging, bio-samples, sociodemographic and psychological questionnaires, a video-recorded interview, as well as ecological momentary assessments (EMA) and ecological physiological assessments (EPA). Subsequently, participants are randomly assigned to one of two ecological momentary interventions (EMIs), targeting either positive cognitive reappraisal or reward sensitivity. During the following intervention phase, participants' stress responses are tracked using EMA and EPA, and JITAIs are triggered if an individually calibrated stress threshold is crossed. In a three-month-long follow-up phase, parts of the baseline characterization phase are repeated. Throughout the entire study, stressor exposure and mental health are regularly monitored to calculate stressor reactivity as a proxy for outcome resilience. The online monitoring questionnaires and the repetition of the baseline questionnaires also serve to assess target engagement. Discussion The DynaM-INT study intends to advance the field of resilience research by feasibility-testing two new mechanistically targeted JITAIs that aim at increasing individual stress resilience and identifying predictors for successful intervention response. Determining these predictors is an important step toward future randomized controlled trials to establish the efficacy of these interventions.

Abstract Background Stress-related disorders are a growing public health concern. While stress is a natural and adaptive process, chronic exposure to stressors can lead to dysregulation and take a cumulative toll on physical and mental well-being. One approach to coping with stress and building resilience is through Mindfulness-Based Stress Reduction (MBSR). By understanding the neural mechanisms of MBSR, we can gain insight into how it reduces stress and what drives individual differences in treatment outcomes. This study aims to establish the clinical effects of MBSR on stress regulation in a population that is susceptible to develop stress-related disorders (i.e., university students with mild to high self-reported stress), to assess the role of large-scale brain networks in stress regulation changes induced by MBSR, and to identify who may benefit most from MBSR. Methods This study is a longitudinal two-arm randomised, wait-list controlled trial to investigate the effects of MBSR on a preselected, Dutch university student population with elevated stress levels. Clinical symptoms are measured at baseline, post-treatment, and three months after training. Our primary clinical symptom is perceived stress, with additional measures of depressive and anxiety symptoms, alcohol use, stress resilience, positive mental health, and stress reactivity in daily life. We investigate the effects of MBSR on stress regulation in terms of behaviour, self-report measures, physiology, and brain activity. Repetitive negative thinking, cognitive reactivity, emotional allowance, mindfulness skills, and self-compassion will be tested as potential mediating factors for the clinical effects of MBSR. Childhood trauma, personality traits and baseline brain activity patterns will be tested as potential moderators of the clinical outcomes. Discussion This study aims to provide valuable insights into the effectiveness of MBSR in reducing stress-related symptoms in a susceptible student population and crucially, to investigate its effects on stress regulation, and to identify who may benefit most from the intervention. Trial registration Registered on September 15, 2022, at clinicaltrials.gov, NCT05541263 .

Regional emulation tools based on statistical relationships, such as pattern scaling, provide a computationally inexpensive way of projecting ocean dynamic sea-level change for a broad range of climate change scenarios. Such approaches usually require a careful selection of one or more predictor variables of climate change so that the statistical model is properly optimized. Even when appropriate predictors have been selected, spatiotemporal oscillations driven by internal climate variability can be a large source of statistical model error. Using pattern recognition techniques that exploit spatial covariance information can effectively reduce internal variability in simulations of ocean dynamic sea level, significantly reducing random errors in regional emulation tools. Here, we test two pattern recognition methods based on empirical orthogonal functions (EOFs), namely signal-to-noise maximizing EOF pattern filtering and low-frequency component analysis, for their ability to reduce errors in pattern scaling of ocean dynamic sea-level change. We use the Max Planck Institute Grand Ensemble (MPI-GE) as a test bed for both methods, as it is a type of initial-condition large ensemble designed for an optimal characterization of the externally forced response. We show that the two methods tested here more efficiently reduce errors than conventional approaches such as a simple ensemble average. For instance, filtering only two realizations by characterizing their common response to external forcing reduces the random error by almost 60 %, a reduction that is only achieved by averaging at least 12 realizations. We further investigate the applicability of both methods to single-realization modeling experiments, including four CMIP5 simulations for comparison with previous regional emulation analyses. Pattern filtering leads to a varying degree of error reduction depending on the model and scenario, ranging from more than 20 % to about 70 % reduction in global-mean root mean squared error compared with unfiltered simulations. Our results highlight the relevance of pattern recognition methods as a tool to reduce errors in regional emulation tools of ocean dynamic sea-level change, especially when one or only a few realizations are available. Removing internal variability prior to tuning regional emulation tools can optimize the performance of the statistical model, leading to substantial differences in emulated dynamic sea level compared to unfiltered simulations.

This study evaluated the relationship between apparent diffusion coefficient (ADC) values in pancreatic ductal adenocarcinoma (PDAC) and tumor grades based on WHO, Adsay, and Kalimuthu classifications, using whole-mount pancreatectomy specimens. If glandular formation plays a key role in the degree of diffusion restriction, diffusion-weighted imaging could facilitate non-invasive grading of PDAC. A freehand region of interest (ROI) was drawn along tumor borders on the preoperative ADC map in each tumor-containing slice. Resection specimens were retrospectively graded according to WHO, Adsay, and Kalimuthu classifications and correlated with overall survival and the 10th percentile of whole-volume ADC values. Findings from 40 patients (23 male, median age 67) showed no correlation between ADC p10 values and WHO differentiation (p = 0.050), Adsay grade (p = 0.955), or Kalimuthu patterns (p = 0.117). There was no association between ADC p10 and overall survival (p = 0.082) and other clinicopathological variables. Survival was significantly lower for poor tumor differentiation (p = 0.046) and non-glandular Kalimuthu patterns (p = 0.016) and there was a trend towards inferior survival for Adsay G3 (p = 0.090) after correction for age, tumor location, and stage. Preoperative ADC measurements for determining PDAC aggressiveness had limited clinical utility, as there was no correlation with histological parameters or overall survival in resectable PDAC.

BackgroundIncreasing efforts toward the prevention of stress-related mental disorders have created a need for unobtrusive real-life monitoring of stress-related symptoms. Wearable devices have emerged as a possible solution to aid in this process, but their use in real-life stress detection has not been systematically investigated. ObjectiveWe aimed to determine the utility of ecological momentary assessments (EMA) and physiological arousal measured through wearable devices in detecting ecologically relevant stress states. MethodsUsing EMA combined with wearable biosensors for ecological physiological assessments (EPA), we investigated the impact of an ecological stressor (ie, a high-stakes examination week) on physiological arousal and affect compared to a control week without examinations in first-year medical and biomedical science students (51/83, 61.4% female). We first used generalized linear mixed-effects models with maximal fitting approaches to investigate the impact of examination periods on subjective stress exposure, mood, and physiological arousal. We then used machine learning models to investigate whether we could use EMA, wearable biosensors, or the combination of both to classify momentary data (ie, beeps) as belonging to examination or control weeks. We tested both individualized models using a leave-one-beep-out approach and group-based models using a leave-one-subject-out approach. ResultsDuring stressful high-stakes examination (versus control) weeks, participants reported increased negative affect and decreased positive affect. Intriguingly, physiological arousal decreased on average during the examination week. Time-resolved analyses revealed peaks in physiological arousal associated with both momentary self-reported stress exposure and self-reported positive affect. Mediation models revealed that the decreased physiological arousal in the examination week was mediated by lower positive affect during the same period. We then used machine learning to show that while individualized EMA outperformed EPA in its ability to classify beeps as originating from examinations or from control weeks (1603/4793, 33.45% and 1648/4565, 36.11% error rates, respectively), a combination of EMA and EPA yields optimal classification (1363/4565, 29.87% error rate). Finally, when comparing individualized models to group-based models, we found that the individualized models significantly outperformed the group-based models across all 3 inputs (EMA, EPA, and the combination). ConclusionsThis study underscores the potential of wearable biosensors for stress-related mental health monitoring. However, it emphasizes the necessity of psychological context in interpreting physiological arousal captured by these devices, as arousal can be related to both positive and negative contexts. Moreover, our findings support a personalized approach in which momentary stress is optimally detected when referenced against an individual’s own data.

BackgroundCross-sectional relationships between psychosocial resilience factors (RFs) and resilience, operationalized as the outcome of low mental health reactivity to stressor exposure (low “stressor reactivity” [SR]), were reported during the first wave of the COVID-19 pandemic in 2020. ObjectiveExtending these findings, we here examined prospective relationships and weekly dynamics between the same RFs and SR in a longitudinal sample during the aftermath of the first wave in several European countries. MethodsOver 5 weeks of app-based assessments, participants reported weekly stressor exposure, mental health problems, RFs, and demographic data in 1 of 6 different languages. As (partly) preregistered, hypotheses were tested cross-sectionally at baseline (N=558), and longitudinally (n=200), using mixed effects models and mediation analyses. ResultsRFs at baseline, including positive appraisal style (PAS), optimism (OPT), general self-efficacy (GSE), perceived good stress recovery (REC), and perceived social support (PSS), were negatively associated with SR scores, not only cross-sectionally (baseline SR scores; all P

Attribution of sea-level change to its different drivers is typically done using a sea-level budget approach. While the global mean sea-level budget is considered closed, closing the budget on a finer spatial scale is more complicated due to, for instance, limitations in our observational system and the spatial processes contributing to regional sea-level change. Consequently, the regional budget has been mainly analysed on a basin-wide scale. Here we investigate the sea-level budget at sub-basin scales, using two machine learning techniques to extract domains of coherent sea-level variability: a neural network approach (self-organizing map, SOM) and a network detection approach (δ-MAPS). The extracted domains provide more spatial detail within the ocean basins and indicate how sea-level variability is connected among different regions. Using these domains we can close, within 1σ uncertainty, the sub-basin regional sea-level budget from 1993–2016 in 100 % and 76 % of the SOM and δ-MAPS regions, respectively. Steric variations dominate the temporal sea-level variability and determine a significant part of the total regional change. Sea-level change due to mass exchange between ocean and land has a relatively homogeneous contribution to all regions. In highly dynamic regions (e.g. the Gulf Stream region) the dynamic mass redistribution is significant. Regions where the budget cannot be closed highlight processes that are affecting sea level but are not well captured by the observations, such as the influence of western boundary currents. The use of the budget approach in combination with machine learning techniques leads to new insights into regional sea-level variability and its drivers.

Abstract Specific approaches are needed to reach and support people with a lower socioeconomic position (SEP) to achieve healthier eating behaviours. There is a growing body of evidence suggesting that digital health tools exhibit potential to address these needs because of its specific features that enable application of various behaviour change techniques (BCTs). The aim of this scoping review is to identify the BCTs that are used in diet-related digital interventions targeted at people with a low SEP, and which of these BCTs coincide with improved eating behaviour. The systematic search was performed in 3 databases, using terms related to e/m-health, diet quality and socioeconomic position. A total of 17 full text papers were included. The average number of BCTs per intervention was 6.9 (ranged 3–15). BCTs from the cluster ‘Goals and planning’ were applied most often (25x), followed by the clusters ‘Shaping knowledge’ (18x) and ‘Natural consequences’ (18x). Other frequently applied BCT clusters were ‘Feedback and monitoring’ (15x) and ‘Comparison of behaviour’ (13x). Whereas some BCTs were frequently applied, such as goal setting, others were rarely used, such as social support. Most studies (n = 13) observed a positive effect of the intervention on eating behaviour (e.g. having breakfast) in the low SEP group, but this was not clearly associated with the number or type of applied BCTs. In conclusion, more intervention studies focused on people with a low SEP are needed to draw firm conclusions as to which BCTs are effective in improving their diet quality. Also, further research should investigate combinations of BCTs, the intervention design and context, and the use of multicomponent approaches. We encourage intervention developers and researchers to describe interventions more thoroughly, following the systematics of a behaviour change taxonomy, and to select BCTs knowingly.

Ocean mass change is one of the main drivers of present-day sea-level change (SLC). Also known as barystatic SLC, ocean mass change is caused by the exchange of freshwater between the land and the ocean, such as melting of continental ice from glaciers and ice sheets, and variations in land water storage. While many studies have quantified the present-day barystatic contribution to global mean SLC, fewer works have looked into regional changes. This study provides an analysis of regional patterns of contemporary mass redistribution associated with barystatic SLC since 1993 (the satellite altimetry era), with a focus on the uncertainty budget. We consider three types of uncertainties: intrinsic (the uncertainty from the data/model itself), temporal (related to the temporal variability in the time series) and spatial–structural (related to the spatial distribution of the mass change sources). Regional patterns (fingerprints) of barystatic SLC are computed from a range of estimates of the individual freshwater sources and used to analyze the different types of uncertainty. Combining all contributions, we find that regional sea-level trends range from −0.4 to 3.3 mm yr−1 for 2003–2016 and from −0.3 to 2.6 mm yr−1 for 1993–2016, considering the 5–95th percentile range across all grid points and depending on the choice of dataset. When all types of uncertainties from all contributions are combined, the total barystatic uncertainties regionally range from 0.6 to 1.3 mm yr−1 for 2003–2016 and from 0.4 to 0.8 mm yr−1 for 1993–2016, also depending on the dataset choice. We find that the temporal uncertainty dominates the budget, responsible on average for 65 % of the total uncertainty, followed by the spatial–structural and intrinsic uncertainties, which contribute on average 16 % and 18 %, respectively. The main source of uncertainty is the temporal uncertainty from the land water storage contribution, which is responsible for 35 %–60 % of the total uncertainty, depending on the region of interest. Another important contribution comes from the spatial–structural uncertainty from Antarctica and land water storage, which shows that different locations of mass change can lead to trend deviations larger than 20 %. As the barystatic SLC contribution and its uncertainty vary significantly from region to region, better insights into regional SLC are important for local management and adaptation planning.

BackgroundStress-related mental disorders are highly prevalent and pose a substantial burden on individuals and society. Improving strategies for the prevention and treatment of mental disorders requires a better understanding of their risk and resilience factors. This multicenter study aims to contribute to this endeavor by investigating psychological resilience in healthy but susceptible young adults over 9 months. Resilience is conceptualized in this study as the maintenance of mental health or quick recovery from mental health perturbations upon exposure to stressors, assessed longitudinally via frequent monitoring of stressors and mental health. ObjectiveThis study aims to investigate the factors predicting mental resilience and adaptive processes and mechanisms contributing to mental resilience and to provide a methodological and evidence-based framework for later intervention studies. MethodsIn a multicenter setting, across 5 research sites, a sample with a total target size of 250 young male and female adults was assessed longitudinally over 9 months. Participants were included if they reported at least 3 past stressful life events and an elevated level of (internalizing) mental health problems but were not presently affected by any mental disorder other than mild depression. At baseline, sociodemographic, psychological, neuropsychological, structural, and functional brain imaging; salivary cortisol and α-amylase levels; and cardiovascular data were acquired. In a 6-month longitudinal phase 1, stressor exposure, mental health problems, and perceived positive appraisal were monitored biweekly in a web-based environment, while ecological momentary assessments and ecological physiological assessments took place once per month for 1 week, using mobile phones and wristbands. In a subsequent 3-month longitudinal phase 2, web-based monitoring was reduced to once a month, and psychological resilience and risk factors were assessed again at the end of the 9-month period. In addition, samples for genetic, epigenetic, and microbiome analyses were collected at baseline and at months 3 and 6. As an approximation of resilience, an individual stressor reactivity score will be calculated. Using regularized regression methods, network modeling, ordinary differential equations, landmarking methods, and neural net–based methods for imputation and dimension reduction, we will identify the predictors and mechanisms of stressor reactivity and thus be able to identify resilience factors and mechanisms that facilitate adaptation to stressors. ResultsParticipant inclusion began in October 2020, and data acquisition was completed in June 2022. A total of 249 participants were assessed at baseline, 209 finished longitudinal phase 1, and 153 finished longitudinal phase 2. ConclusionsThe Dynamic Modelling of Resilience–Observational Study provides a methodological framework and data set to identify predictors and mechanisms of mental resilience, which are intended to serve as an empirical foundation for future intervention studies. International Registered Report Identifier (IRRID)DERR1-10.2196/39817

Summary: Therapeutic neoantigen cancer vaccines have limited clinical efficacy to date. Here, we identify a heterologous prime-boost vaccination strategy using a self-assembling peptide nanoparticle TLR-7/8 agonist (SNP) vaccine prime and a chimp adenovirus (ChAdOx1) vaccine boost that elicits potent CD8 T cells and tumor regression. ChAdOx1 administered intravenously (i.v.) had 4-fold higher antigen-specific CD8 T cell responses than mice boosted by the intramuscular (i.m.) route. In the therapeutic MC38 tumor model, i.v. heterologous prime-boost vaccination enhances regression compared with ChAdOx1 alone. Remarkably, i.v. boosting with a ChAdOx1 vector encoding an irrelevant antigen also mediates tumor regression, which is dependent on type I IFN signaling. Single-cell RNA sequencing of the tumor myeloid compartment shows that i.v. ChAdOx1 reduces the frequency of immunosuppressive Chil3 monocytes and activates cross-presenting type 1 conventional dendritic cells (cDC1s). The dual effect of i.v. ChAdOx1 vaccination enhancing CD8 T cells and modulating the TME represents a translatable paradigm for enhancing anti-tumor immunity in humans.

Abstract The stressful extrauterine environment following premature birth likely has far-reaching and persistent adverse consequences. The effects of early “third-trimester” ex utero stress on large-scale brain networks’ covariance patterns may provide a potential avenue to understand how early-life stress following premature birth increases risk or resilience. We evaluated the impact of early-life stress exposure (e.g., quantification of invasive procedures) on maturational covariance networks (MCNs) between 30 and 40 weeks of gestational age in 180 extremely preterm-born infants (

The preoperative prediction of resectability pancreatic ductal adenocarcinoma (PDAC) is challenging. This retrospective single-center study examined tumor and vessel radiomics to predict the resectability of PDAC in chemo-naïve patients. The tumor and adjacent arteries and veins were segmented in the portal-venous phase of contrast-enhanced CT scans, and radiomic features were extracted. Features were selected via stability and collinearity testing, and least absolute shrinkage and selection operator application (LASSO). Three models, using tumor features, vessel features, and a combination of both, were trained with the training set (N = 86) to predict resectability. The results were validated with the test set (N = 15) and compared to the multidisciplinary team’s (MDT) performance. The vessel-features-only model performed best, with an AUC of 0.92 and sensitivity and specificity of 97% and 73%, respectively. Test set validation showed a sensitivity and specificity of 100% and 88%, respectively. The combined model was as good as the vessel model (AUC = 0.91), whereas the tumor model showed poor performance (AUC = 0.76). The MDT’s prediction reached a sensitivity and specificity of 97% and 84% for the training set and 88% and 100% for the test set, respectively. Our clinician-independent vessel-based radiomics model can aid in predicting resectability and shows performance comparable to that of the MDT. With these encouraging results, improved, automated, and generalizable models can be developed that reduce workload and can be applied in non-expert hospitals.

BackgroundAlthough energy balance-related parenting practices are regarded critical components in the prevention of childhood obesity, most programs targeting parenting practices with respect to a wide range of energy balance-related behaviors were not aimed at high-risk families with a lower socioeconomic position (SEP).ObjectiveThe Samen Happie! app-based program aimed to stimulate healthy child weight development especially among families with a lower SEP, by encouraging healthy energy balance-related parenting practices.MethodsA two-armed randomized controlled trial examined the process and effectiveness of the Samen Happie! program on child zBMI outcomes at 6- and 12-months follow-up. In total, 357 Dutch parents with infants aged 5–15 months old at baseline participated. Parents in the app condition (n = 179) received access to the Samen Happie! app and were compared to a waitlist-control condition (n = 178). Changes in zBMI were examined through linear mixed-effects models based on intention-to-treat and exploratory per-protocol principles.ResultsProcess data showed low levels of sustained app use and moderate app acceptability. A general increase in child zBMI was observed in both conditions after 6 and 12 months. Intention-to-treat analyses using multiple imputations showed several statistically significant differences between conditions and high-risk subgroups. Specifically, at 6-months follow-up, zBMI increase was least pronounced in the app condition among children of parents with lower educational level. These findings were supported by exploratory per-protocol analyses including only frequent app users. In addition, per-protocol analyses showed benefits of app use at 6-months follow-up for children of parents with higher BMI. However, these effects were reversed at 12-months follow-up in both intention-to-treat and per-protocol analyses, where children of parents in the app condition in general increased the most in zBMI.ConclusionsThis study suggests that the Samen Happie! program might prevent zBMI increases after 6 months among children of parents with lower educational level, and children of parents with higher BMI who more frequently use the app. However, the app did not prevent increases in zBMI after 12 months. Future research should investigate strategies to increase sustained app use and engagement in mHealth parenting programs for childhood obesity as well as options to combine app-based programs with additional support strategies aimed at high-risk families.Trial registrationNetherlands trial register (ID: NTR6938), https://trialsearch.who.int/Trial2.aspx?TrialID=NTR6938.

Abstract Background Over the past decades, health care services for pancreatic surgery were reorganized. Volume norms were applied with the result that only a limited number of expert centers perform pancreatic surgery. As a result of this centralization of pancreatic surgery, the patient journey of patients with pancreatic tumors has become multi-institutional. To illustrate, patients are referred to a center of expertise for pancreatic surgery whereas other parts of pancreatic care, such as chemotherapy, take place in local hospitals. This fragmentation of health care services could affect continuity of care (COC). The aim of this study was to assess COC perceived by patients in a pancreatic care network and investigate correlations with patient-and care-related characteristics. Methods This is a pilot study in which patients with (pre) malignant pancreatic tumors discussed in a multidisciplinary tumor board in a Dutch tertiary hospital were asked to participate. Patients were asked to fill out the Nijmegen Continuity of Care-questionnaire (NCQ) (5-point Likert scale). Additionally, their patient-and care-related data were retrieved from medical records. Correlations of NCQ score and patient-and care-related characteristics were calculated with Spearman’s correlation coefficient. Results In total, 44 patients were included (92% response rate). Pancreatic cancer was the predominant diagnosis (32%). Forty percent received a repetition of diagnostic investigations in the tertiary hospital. Mean scores for personal continuity were 3.55 ± 0.74 for GP, 3.29 ± 0.91 for the specialist and 3.43 ± 0.65 for collaboration between GPs and specialists. Overall COC was scored with a mean 3.38 ± 0.72. No significant correlations were observed between NCQ score and certain patient-or care-related characteristics. Conclusion Continuity of care perceived by patients with pancreatic tumors was scored as moderate. This outcome supports the need to improve continuity of care within multi-institutional pancreatic care networks.

Abstract The SARS-CoV-2 pandemic is not only a threat to physical health but is also having severe impacts on mental health. Although increases in stress-related symptomatology and other adverse psycho-social outcomes, as well as their most important risk factors have been described, hardly anything is known about potential protective factors. Resilience refers to the maintenance of mental health despite adversity. To gain mechanistic insights about the relationship between described psycho-social resilience factors and resilience specifically in the current crisis, we assessed resilience factors, exposure to Corona crisis-specific and general stressors, as well as internalizing symptoms in a cross-sectional online survey conducted in 24 languages during the most intense phase of the lockdown in Europe (22 March to 19 April) in a convenience sample of N = 15,970 adults. Resilience, as an outcome, was conceptualized as good mental health despite stressor exposure and measured as the inverse residual between actual and predicted symptom total score. Preregistered hypotheses (osf.io/r6btn) were tested with multiple regression models and mediation analyses. Results confirmed our primary hypothesis that positive appraisal style (PAS) is positively associated with resilience (p

Abstract Background At the time of surgery, approximately 10–20% of the patients with pancreatic cancer are considered unresectable because of unexpected liver metastasis, peritoneal carcinomatosis or locally advanced disease. This leads to futile surgical treatment with all the associated morbidity, mortality and costs. More than 50% of all liver metastases develop in the first six months postoperatively. These (subcentimeter) liver metastases are most likely already present at the time of diagnosis and have not been identified pre-operatively, due to the poor sensitivity of routine preoperative contrast-enhanced CT (CECT). Methods The DIA-PANC study is a prospective, international, multicenter, diagnostic cohort study investigating diffusion-weighted, contrast-enhanced MRI for the detection of liver metastases in patients with all stages of pancreatic cancer. Indeterminate or malignant liver lesions on MRI will be further investigated histopathologically. For patients with suspected liver lesions without histopathological proof, follow up imaging with paired CT and MRI at 3-, 6- and 12-months will serve as an alternative reference standard. Discussion The DIA-PANC trial is expected to report high-level evidence of the diagnostic accuracy of MRI for the detection of liver metastases, resulting in significant value for clinical decision making, guideline development and improved stratification for treatment strategies and future trials. Furthermore, DIA-PANC will contribute to our knowledge of liver metastases regarding incidence, imaging characteristics, their number and extent, and their change in time with or without treatment. It will enhance the worldwide implementation of MRI and consequently improve personalized treatment of patients with suspected pancreatic ductal adenocarcinoma. Trial registration ClinicalTrials.gov Identifier: NCT03469726 . Registered on March 19th 2018 - Retrospectively registered.

Abstract Metal soap formation is one of the most wide-spread degradation mechanisms observed in historical oil paintings, affecting works of art from museum collections worldwide. Metal soaps develop from a chemical reaction between metal ions present in the pigments and saturated fatty acids, which are released by the oil binder. The presence of large metal soap crystals inside paint layers or at the paint surface can be detrimental for the visual appearance of artworks. Moreover, metal soaps can possibly trigger mechanical damage, ultimately resulting in flaking of the paint. This paper departs from a recently proposed computational model to predict chemo-mechanical degradation in historical oil paintings, as presented in Eumelen et al. (J Mech Phys Solids 132:103683, 2019). The model describes metal soap formation and growth, which are phenomena that are driven by the diffusion of saturated fatty acids and proceed by a nucleation process from a crystalline nucleus of small size. This results into a chemically-induced strain in the paint, which may promote crack nucleation and propagation. The proposed model is here used to investigate the effects of saturated fatty acid concentration and initial nucleus geometry on the amount of chemo-mechanical damage generated. Numerical simulations show that both factors have a marginal influence on the growth rate of the metal soap crystal, but play a significant role on the extent of fracture induced in the paint.

Objectives:. Compare total tumor volume (TTV) response after systemic treatment to Response Evaluation Criteria in Solid Tumors (RECIST1.1) and assess the prognostic value of TTV change and RECIST1.1 for recurrence-free survival (RFS) in patients with colorectal liver-only metastases (CRLM). Background:. RECIST1.1 provides unidimensional criteria to evaluate tumor response to systemic therapy. Those criteria are accepted worldwide but are limited by interobserver variability and ignore potentially valuable information about TTV. Methods:. Patients with initially unresectable CRLM receiving systemic treatment from the randomized, controlled CAIRO5 trial (NCT02162563) were included. TTV response was assessed using software specifically developed together with SAS analytics. Baseline and follow-up computed tomography (CT) scans were used to calculate RECIST1.1 and TTV response to systemic therapy. Different thresholds (10%, 20%, 40%) were used to define response of TTV as no standard currently exists. RFS was assessed in a subgroup of patients with secondarily resectable CRLM after induction treatment. Results:. A total of 420 CT scans comprising 7820 CRLM in 210 patients were evaluated. In 30% to 50% (depending on chosen TTV threshold) of patients, discordance was observed between RECIST1.1 and TTV change. A TTV decrease of >40% was observed in 47 (22%) patients who had stable disease according to RECIST1.1. In 118 patients with secondarily resectable CRLM, RFS was shorter for patients with less than 10% TTV decrease compared with patients with more than 10% TTV decrease (P = 0.015), while RECIST1.1 was not prognostic (P = 0.821). Conclusions:. TTV response assessment shows prognostic potential in the evaluation of systemic therapy response in patients with CRLM.

It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance – a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience.

Abstract Background Eating rate is a basic determinant of appetite regulation: people who eat more slowly feel sated earlier and eat less. A high eating rate contributes to overeating and potentially to weight gain. Previous studies showed that an augmented fork that delivers real-time feedback on eating rate is a potentially effective intervention to decrease eating rate in naturalistic settings. This study assessed the impact of using the augmented fork during a 15-week period on eating rate and body weight. Methods In a parallel randomized controlled trial, 141 participants with overweight (age: 49.2 ± 12.3 y; BMI: 31.5 ± 4.48 kg/m2) were randomized to intervention groups (VFC, n = 51 or VFC+, n = 44) or control group (NFC, n = 46). First, we measured bite rate and success ratio on five consecutive days with the augmented fork without feedback (T1). The intervention groups (VFC, VFC+) then used the same fork, but now received vibrotactile feedback when they ate more than one bite per 10 s. Participants in VFC+ had additional access to a web portal with visual feedback. In the control group (NFC), participants ate with the fork without either feedback. The intervention period lasted four weeks, followed by a week of measurements only (T2) and another measurement week after eight weeks (T3). Body weight was assessed at T1, T2, and T3. Results Participants in VFC and VFC+ had a lower bite rate (p

Summary: Polymorphism in the microglial receptor CD33 gene has been linked to late-onset Alzheimer disease (AD), and reduced expression of the CD33 sialic acid-binding domain confers protection. Thus, CD33 inhibition might be an effective therapy against disease progression. Progress toward discovery of selective CD33 inhibitors has been hampered by the absence of an atomic resolution structure. We report here the crystal structures of CD33 alone and bound to a subtype-selective sialic acid mimetic called P22 and use them to identify key binding residues by site-directed mutagenesis and binding assays to reveal the molecular basis for its selectivity toward sialylated glycoproteins and glycolipids. We show that P22, when presented on microparticles, increases uptake of the toxic AD peptide, amyloid-β (Aβ), into microglial cells. Thus, the sialic acid-binding site on CD33 is a promising pharmacophore for developing therapeutics that promote clearance of the Aβ peptide that is thought to cause AD. : Molecular Structure; Neuroscience; Molecular Neuroscience; Components of the Immune System; Protein Structure Aspects Subject Areas: Molecular Structure, Neuroscience, Molecular Neuroscience, Components of the Immune System, Protein Structure Aspects

Abstract Purpose Metabolic parameters are increasingly being used to characterize tumors. Motion artifacts due to patient respiration introduce uncertainties in quantification of metabolic parameters during positron emission tomography (PET) image acquisition. The present study investigates the impact of amplitude-based optimal respiratory gating (ORG) on quantification of PET-derived image features in patients with pancreatic ductal adenocarcinoma (PDAC), in correlation with overall survival (OS). Methods Sixty-nine patients with histologically proven primary PDAC underwent 2′-deoxy-2′-[18F]fluoroglucose ([18F]FDG) PET/CT imaging during diagnostic work-up. Standard image acquisition and reconstruction was performed in accordance with the EANM guidelines and ORG images were reconstructed with a duty cycle of 35%. PET-derived image features, including standard parameters, first- and second-order texture features, were calculated from the standard and corresponding ORG images, and correlation with OS was assessed. Results ORG significantly impacts the quantification of nearly all features; values of single-voxel parameters (e.g., SUVmax) showed a wider range, volume-based parameters (e.g., SUVmean) were reduced, and texture features were significantly changed. After correction for motion artifacts using ORG, some features that describe intra-tumoral heterogeneity were more strongly correlated to OS. Conclusions Correction for respiratory motion artifacts using ORG impacts the quantification of metabolic parameters in PDAC lesions. The correlation of metabolic parameters with OS was significantly affected, in particular parameters that describe intra-tumor heterogeneity. Therefore, interpretation of single-voxel or average metabolic parameters in relation to clinical outcome should be done cautiously. Furthermore, ORG is a valuable tool to improve quantification of intra-tumoral heterogeneity in PDAC.

Abstract Introduction Hypercholesterolemia is a well-known risk factor for developing atherosclerosis and subsequently for the risk of a myocardial infarction (MI). Moreover, it might also be related to the extent of damaged myocardium in the event of a MI. The aim of this study was to evaluate the association of baseline low density lipoprotein-cholesterol (LDL-c) level with infarct size in patients with ST-segment elevation myocardial infarction (STEMI) after primary percutaneously coronary intervention (pPCI). Methods Baseline blood samples were obtained from all patients admitted between 2004 and 2014 with STEMI who underwent pPCI. Patients were excluded in case of out of hospital cardiac arrest, treatment delay of at least 10 h or no complete reperfusion after pPCI in the culprit vessel. Peak creatine kinase (CK) level was used for infarct size estimation, defined as the maximal value during admission. Results A total of 2248 patients were included in this study (mean age 61.8 ± 12.2 years; 25.0% female). Mean LDL-c level was 3.6 ± 1.1 mmol/L and median peak CK level was 1275 U/L (IQR 564–2590 U/L). Baseline LDL-c level [β = 0.041; (95% CI 0.019–0.062); p

Abstract Introduction To investigate the additive prognostic value of growth differentiation factor (GDF-15) levels in ST-segment elevation myocardial infarction (STEMI) patients treated with primary percutaneously coronary intervention (pPCI) with 10-year mortality on top of clinical characteristics and known cardiac biomarkers. Methods Baseline serum GDF-15 levels were measured in 290 STEMI patients treated with pPCI in the MISSION! intervention trial conducted from February 1, 2004 through October 31, 2006. The incremental prognostic value of GDF-15 and NTproBNP levels was evaluated on top of clinical characteristics using Cox proportional hazards analysis, Chi-square models and C-index. Outcome was 10-year all-cause mortality. Results Mean age was 59.0 ± 11.5 years and 65 (22.4) patients were female. A total of 37 patients died during a follow-up of 9.4 (IQR 8.8–10.0) years. Multivariable Cox regression revealed GDF-15 and NTproBNP levels above median to be independently associated with 10-year all-cause mortality [HR GDF-15, 2.453 (95% CI 1.064–5.658), P = 0.04; HR NTproBNP, 2.413 (95% CI 1.043–5.564), P = 0.04] after correction for other clinical variables. Stratified by median GDF-15 (37.78 pmol/L) and NTproBNP (11.74 pmol/L) levels, Kaplan–Meier curves showed significant better survival for patients with GDF-15 and NTproBNP levels below the median versus above the median. The likelihood ratio test showed a significant incremental value of GDF-15 (P = 0.03) as compared with a model with clinically important variables and NTproBNP. The C-statistics for this model improved from 0.82 to 0.84 when adding GDF-15. Conclusion GDF-15 levels at admission in STEMI patients are independently associated with 10-year all-cause mortality rates and could improve risk stratification on top of clinical variables and other cardiac biomarkers.

IntroductionTargeted therapy against tumor angiogenesis is widely used in clinical practice for patients with colorectal liver metastases (CRLM). Possible predictive biomarkers for tumor angiogenesis, such as, microvessel density (MVD), hypoxia and cell proliferation, can be determined using immunohistochemical staining. However, patients ineligible for surgical treatment need to undergo invasive diagnostic interventions in order to determine these biomarkers. CT perfusion (CTP) is an emerging functional imaging technique, which can non-invasively determine vascular properties of solid tumors. The purpose of this study was to evaluate CTP with histological biomarkers in CRLM.Material and methodsPatients with CRLM underwent CTP one day before liver surgery. CTP analysis was performed on the entire volume of the largest metastases in each patient. Dual-input maximum slope analysis was used and data concerning arterial flow (AF), portal flow (PF) and perfusion index (PI) were recorded. Immunohistochemical staining with CD34, M75/CA-IX and MIB-1 was performed on the rim in the midsection of the tumor to determine respectively MVD, hypoxia and cell proliferation.ResultsTwenty CRLM in 20 patients were studied. Mean size of the largest CRLM was 37 mm (95% CI 21-54 mm). Mean AF and PF were respectively 64 ml/min/100ml (95% CI 48-79) and 30 ml/min/100ml (95% CI 22-38). Mean PI was 68% (95% CI 62-73). No significant correlation was found between tumor growth patterns and CTP (p = 0.95). MVD did not significantly correlate to AF (r = 0.05; p = 0.84), PF (r = 0.17; p = 0.47) and PI (r = -0.12; p = 0.63). Cell proliferation also did not significantly correlate to AF (r = 0.07; p = 0.78), PF (r = -0.01; p = 0.95) and PI (r = 0.15; p = 0.52). Hypoxia did not significantly correlate to AF (r = -0.05; p = 0.83), however, significantly to PF (r = 0.51; p = 0.02) and a trend to negative correlation with PF (r = -0.43; p = 0.06). However, after controlling the false discovery rate, no significant correlation between CTP and used immunohistochemical biomarkers was found.ConclusionIn conclusion, this feasibility study found a trend to negative correlation between PI and hypoxia, CTP might therefore possibly evaluate this prognostic marker in CRLM non-invasively. However, CTP is not an appropriate technique for the assessment of microvessels or cell proliferation in CRLM.

Background: In various cancer types, the first step towards extended metastatic disease is the presence of lymph node metastases. Imaging methods with sufficient diagnostic accuracy are required to personalize treatment. Lymph node metastases can be detected with ultrasmall superparamagnetic iron oxide (USPIO)-enhanced magnetic resonance imaging (MRI), but this method needs validation. Here, a workflow is presented, which is designed to compare MRI-visible lymph nodes on a node-to-node basis with histopathology. Methods: In patients with prostate, rectal, periampullary, esophageal, and head-and-neck cancer, in vivo USPIO-enhanced MRI was performed to detect lymph nodes suspicious of harboring metastases. After lymphadenectomy, but before histopathological assessment, a 7 Tesla preclinical ex vivo MRI of the surgical specimen was performed, and in vivo MR images were radiologically matched to ex vivo MR images. Lymph nodes were annotated on the ex vivo MRI for an MR-guided pathological examination of the specimens. Results: Matching lymph nodes of ex vivo MRI to pathology was feasible in all cancer types. The annotated ex vivo MR images enabled a comparison between USPIO-enhanced in vivo MRI and histopathology, which allowed for analyses on a nodal, or at least on a nodal station, basis. Conclusions: A workflow was developed to validate in vivo USPIO-enhanced MRI with histopathology. Guiding the pathologist towards lymph nodes in the resection specimens during histopathological work-up allowed for the analysis at a nodal basis, or at least nodal station basis, of in vivo suspicious lymph nodes with corresponding histopathology, providing direct information for validation of in vivo USPIO-enhanced, MRI-detected lymph nodes.

Using contextual information to predict aversive events is a critical ability that protects from generalizing fear responses to safe contexts. Animal models have demonstrated the importance of spatial context representations within the hippocampal formation in contextualization of fear learning. The ventromedial prefrontal cortex (vmPFC) is known to play an important role in safety learning, possibly also through the incorporation of context information. However, if contextual representations are related to context-dependent expression of fear memory in humans remains unclear. Twenty-one healthy participants underwent functional MRI combined with a cue-context conditioning paradigm within a self-navigated virtual reality environment. The environment included two buildings (Threat and Safe context), which had distinct features outside but were identical inside. Within each context, participants saw two cues (CS+, CS-). The CS+ was consistently (100% reinforcement rate) paired with an electric shock in the Threat context, but never in the Safe context. The CS- was never paired with a shock. We found robust differential skin conductance responses (SCRs; CS+ ​> ​CS-) in the Threat context, but also within the Safe context, indicating fear generalization. Within the Safe context, vmPFC responses to the CS+ were larger than those in the Threat context. We furthermore found environment-specific representations for the two contexts in the training paradigm (i.e., before conditioning took place) in the hippocampus to be related to fear expression and generalization. Namely, participants with a weak context representation (z-score 1.65). Thus, a weak neural representation strength of spatial context may explain overgeneralization of memory to safe contexts. In addition, our findings demonstrate that context-dependent regulation of fear expression engages ventromedial prefrontal pathways suggesting this involves a similar mechanism that is known to be involved in retrieval of extinction memory.

Endoscopic ultrasound (EUS) with fine needle aspiration (FNA) or biopsy (FNB) to diagnose lesions in the gastrointestinal tract is common. Demand for histology sampling to identify treatment-specific targets is increasing. Various core biopsy FNB needles to obtain tissue for histology are currently available, however, with variable (37–97%) histology yields. In this multicenter study, we evaluated performance, safety, and user experience of a novel device (the puncture biopsy forceps (PBF) needle). Twenty-four procedures with the PBF needle were performed in 24 patients with a suspected pancreatic lesion (n = 10), subepithelial lesion (n = 10), lymph node (n = 3), or pararectal mass (n = 1). In 20/24 (83%) procedures, the PBF needle yielded sufficient material for interpretation (sample adequacy). In 17/24 (71%), a correct diagnosis was made with the material from the PBF needle (diagnostic accuracy). All participating endoscopists experienced a learning curve. (Per)procedural technical issues occurred in four cases (17%), but there were no adverse events. The PBF needle is a safe and potentially useful device to obtain an EUS-guided biopsy specimen. As the design of the PBF needle is different to core biopsy FNB needles, specific training will likely further improve the performance of the PBF needle. Furthermore, the design of the needle needs further improvement to make it more robust in clinical practice.

Introduction: It has been proposed that the use of cue-reminders may increase the effectiveness of interventions that aim to prevent health-risk behaviors (i.e., having unsafe sex, unhealthy dietary intake, lack of physical activity, and substance use). The aim of this systematic review was to explore whether there is evidence supporting this proposition, and to explore how cue-reminders are applied in health-risk behavior interventions to date.Method: We systemically reviewed (non-) randomized trials that examine differences in health-risk behaviors between an experimental group receiving an intervention with exposure to a cue-reminder and a control group receiving the intervention without such cue.Results: Six studies were eligible for inclusion. The studies differed in sample and research design, and how the cue-reminder was applied. One study demonstrated a positive and small effect, and one study found a negative medium effect of the cue-reminder. In the remaining studies, the effect sizes were positive but non-significant.Discussion: It is unclear whether complementing health-risk behavior interventions with cue-reminders increases the effectiveness of these interventions. Further investigation and experimentation into the efficiency and effectiveness of cue-reminders is needed before health-risk behavior interventions are complemented with cue-reminders.