Search

Emotional stimuli are preferentially processed in the visual system, in particular, fearful faces. Evidence comes from unimodal studies with emotional faces, although real-life emotional encounters typically involve input from multiple sensory channels, such as a face paired with a voice. Therefore, in this study, we investigated how emotional voices influence preferential processing of co-occurring emotional faces. To investigate early visual processing, we used the breaking continuous flash suppression paradigm (b-CFS): We presented fearful, happy, or neutral faces to one eye, which were initially inaccessible to conscious awareness due to the predominant perception of a dynamic mask presented to the other eye. Faces were presented either unimodally or paired with non-linguistic vocalizations (fearful, happy, neutral). Thirty-six healthy participants were asked to respond as soon as the faces reached conscious awareness. We replicated earlier findings that fearful faces broke suppression faster overall, supporting a threat bias. Moreover, all faces broke suppression faster when paired with voices. Interestingly, faces paired with neutral and happy voices broke suppression the fastest, followed by faces with fearful voices. Thus, in addition to supporting a threat bias in unimodally presented fearful faces, we found evidence for crossmodal facilitation.

Abstract BackgroundMobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as major depressive disorder. The aim of this study was to learn if machine learning (ML) models based on longitudinal speech data are helpful in predicting momentary depression severity. Data analyses were based on a dataset including 30 inpatients during an acute depressive episode receiving sleep deprivation therapy in stationary care, an intervention inducing a rapid change in depressive symptoms in a relatively short period of time. Using an ambulatory assessment approach, we captured speech samples and assessed concomitant depression severity via self-report questionnaire over the course of 3 weeks (before, during, and after therapy). We extracted 89 speech features from the speech samples using the Extended Geneva Minimalistic Acoustic Parameter Set from the Open-Source Speech and Music Interpretation by Large-Space Extraction (audEERING) toolkit and the additional parameter speech rate. ObjectiveWe aimed to understand if a multiparameter ML approach would significantly improve the prediction compared to previous statistical analyses, and, in addition, which mechanism for splitting training and test data was most successful, especially focusing on the idea of personalized prediction. MethodsTo do so, we trained and evaluated a set of >500 ML pipelines including random forest, linear regression, support vector regression, and Extreme Gradient Boosting regression models and tested them on 5 different train-test split scenarios: a group 5-fold nested cross-validation at the subject level, a leave-one-subject-out approach, a chronological split, an odd-even split, and a random split. ResultsIn the 5-fold cross-validation, the leave-one-subject-out, and the chronological split approaches, none of the models were statistically different from random chance. The other two approaches produced significant results for at least one of the models tested, with similar performance. In total, the superior model was an Extreme Gradient Boosting in the odd-even split approach (RP ConclusionsOverall, our analyses highlight that ML fails to predict depression scores of unseen patients, but prediction performance increased strongly compared to our previous analyses with multilevel models. We conclude that future personalized ML models might improve prediction performance even more, leading to better patient management and care.

Background: Vaccination against Streptococcus pneumoniae is advised for transplant recipients to reduce morbidity and mortality associated with invasive pneumococcal disease. However, data on alloantibodies after sequential vaccination (with a pneumococcal conjugate vaccine followed by a polysaccharide vaccine) are still lacking. Methods: In the current study, we determined HLA class I and II and major histocompatibility class I-related chain A (MICA) antibodies in 41 clinically stable kidney transplant recipients. These antibodies were measured prior to and post sequential pneumococcal vaccination over a period of 12 months. Alloantibodies were measured by Luminex bead-based assays, and pneumococcal IgG antibodies were measured by ELISA. Results: Over a 12-month period, the sequential analysis revealed no significant change in alloantibodies. One patient developed de novo donor-specific antibodies (DSA) 1.5 months after the first vaccination, with mean fluorescence intensities of up to 2300. These DSA became undetectable in the follow-up, and the patient showed no signs of allograft rejection. Another patient experienced a biopsy-proven borderline rejection 7 months after the first vaccination but did not develop de novo DSA. Both maintained stable kidney function. As expected, the pneumococcal antibodies increased significantly after vaccination (p < 0.0001). Conclusions: Given the overall risk of alloimmune responses in transplant recipients, we would not attribute the two noticeable patient courses to vaccination. Thus, we consider sequential vaccination immunologically safe.

Abstract Background Wearable technology is used by consumers and researchers worldwide for continuous activity monitoring in daily life. Results of high-quality laboratory-based validation studies enable us to make a guided decision on which study to rely on and which device to use. However, reviews in adults that focus on the quality of existing laboratory studies are missing. Methods We conducted a systematic review of wearable validation studies with adults. Eligibility criteria were: (i) study under laboratory conditions with humans (age ≥ 18 years); (ii) validated device outcome must belong to one dimension of the 24-hour physical behavior construct (i.e., intensity, posture/activity type, and biological state); (iii) study protocol must include a criterion measure; (iv) study had to be published in a peer-reviewed English language journal. Studies were identified via a systematic search in five electronic databases as well as back- and forward citation searches. The risk of bias was assessed based on the QUADAS-2 tool with eight signaling questions. Results Out of 13,285 unique search results, 545 published articles between 1994 and 2022 were included. Most studies (73.8% (N = 420)) validated an intensity measure outcome such as energy expenditure; only 14% (N = 80) and 12.2% (N = 70) of studies validated biological state or posture/activity type outcomes, respectively. Most protocols validated wearables in healthy adults between 18 and 65 years. Most wearables were only validated once. Further, we identified six wearables (i.e., ActiGraph GT3X+, ActiGraph GT9X, Apple Watch 2, Axivity AX3, Fitbit Charge 2, Fitbit, and GENEActiv) that had been used to validate outcomes from all three dimensions, but none of them were consistently ranked with moderate to high validity. Risk of bias assessment resulted in 4.4% (N = 24) of all studies being classified as “low risk”, while 16.5% (N = 90) were classified as “some concerns” and 79.1% (N = 431) as “high risk”. Conclusion Laboratory validation studies of wearables assessing physical behaviour in adults are characterized by low methodological quality, large variability in design, and a focus on intensity. Future research should more strongly aim at all components of the 24-hour physical behaviour construct, and strive for standardized protocols embedded in a validation framework.

Objective: To examine the daily course of, and factors associated with, momentary fatigue after subarachnoid haemorrhage, and to explore subgroups of patients with distinct diurnal patterns of fatigue. Design: Observational study using ecological momentary assessment. Subjects: A total of 41 participants with subarachnoid haemorrhage. Methods: Patients with fatigue were included within one year post-onset. Momentary fatigue (scale 1–7) was assessed with repeated measurements (10–11 times/day) during 7 consecutive days. Multilevel-mixed-model analyses and latent-class trajectory modelling were conducted. Results: Mean (standard deviation; SD) age of the group was 53.9 (13.0) years, 56% female, and mean (SD) time post-subarachnoid haemorrhage onset was 9.3 (3.2) months. Mean (SD) momentary fatigue over all days was 3.22 (1.47). Fatigue increased significantly (p