The added value of ferritin levels and genetic markers for the prediction of haemoglobin deferral.

Background And Objectives: On-site haemoglobin deferral for blood donors is sometimes necessary for donor health but demotivating for donors and inefficient for the blood bank. Deferral rates could be reduced by accurately predicting donors' haemoglobin status before they visit the blood bank. Although such predictive models have been published, there is ample room for improvement in predictive performance.

Defining migraine days, based on longitudinal E-diary data.

Background: There is a need for standardization of the definition of a migraine day for clinical and research purposes.

Methods: We prospectively compared different definitions of a migraine day with E-diary data of n = 1494 patients with migraine. We used a baseline definition based on migraine characteristics with a duration of ≥4 hours OR triptan intake (independently from its effect) OR (visual) aura lasting 5-60 minutes.

Generating synthetic mixed discrete-continuous health records with mixed sum-product networks.

Objective: Privacy is a concern whenever individual patient health data is exchanged for scientific research. We propose using mixed sum-product networks (MSPNs) as private representations of data and take samples from the network to generate synthetic data that can be shared for subsequent statistical analysis. This anonymization method was evaluated with respect to privacy and information loss.

Explainable haemoglobin deferral predictions using machine learning models: Interpretation and consequences for the blood supply.

Background And Objectives: Accurate predictions of haemoglobin (Hb) deferral for whole-blood donors could aid blood banks in reducing deferral rates and increasing efficiency and donor motivation. Complex models are needed to make accurate predictions, but predictions must also be explainable. Before the implementation of a prediction model, its impact on the blood supply should be estimated to avoid shortages.

Associations Between Symptoms, Donor Characteristics and IgG Antibody Response in 2082 COVID-19 Convalescent Plasma Donors.

Many studies already reported on the association between patient characteristics on the severity of COVID-19 disease outcome, but the relation with SARS-CoV-2 antibody levels is less clear. To investigate this in more detail, we performed a retrospective observational study in which we used the IgG antibody response from 11,118 longitudinal antibody measurements of 2,082 unique COVID convalescent plasma donors. COVID-19 symptoms and donor characteristics were obtained by a questionnaire.

Evaluating privacy of individuals in medical data.

Although data protection is compulsory when personal data is shared, there is no systematic method available to evaluate to what extent each individual is at risk of a privacy breach. We use a collection of measures that quantify how much information is needed to uncover sensitive information. Combined with visualization techniques, our approach can be used to perform a detailed privacy analysis of medical data.

First results of a ferritin-based blood donor deferral policy in the Netherlands.

Background: Whole blood donors are at risk of becoming iron deficient. To monitor iron stores, Sanquin implemented a new deferral policy based on ferritin levels, in addition to the traditional hemoglobin measurements.

Methods: Ferritin levels are determined in every fifth donation, as well as in all first-time donors.

Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms.

Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables.

Simultaneous discovery of cancer subtypes and subtype features by molecular data integration.

Motivation: Subtyping cancer is key to an improved and more personalized prognosis/treatment. The increasing availability of tumor related molecular data provides the opportunity to identify molecular subtypes in a data-driven way. Molecular subtypes are defined as groups of samples that have a similar molecular mechanism at the origin of the carcinogenesis.

A KNIME-Based Analysis of the Zebrafish Photomotor Response Clusters the Phenotypes of 14 Classes of Neuroactive Molecules.

Recently, the photomotor response (PMR) of zebrafish embryos was reported as a robust behavior that is useful for high-throughput neuroactive drug discovery and mechanism prediction. Given the complexity of the PMR, there is a need for rapid and easy analysis of the behavioral data. In this study, we developed an automated analysis workflow using the KNIME Analytics Platform and made it freely accessible.

Authority defied: need for cognitive closure influences regulatory control when resisting authority.

The present studies examined whether differences in need for cognitive closure (NCC) were related to differences in regulatory control when confronted with authority. In two studies, levels of regulatory control were measured when participants resisted (Study 1; N = 46) or prepared to resist the influence attempt of an authority figure (Study 2; N = 50). Results showed that resisting the influence attempt from a high-authority figure was more depleting for participants higher in NCC compared to individuals lower in NCC.

Empathy is a beautiful thing: empathy predicts imitation only for attractive others.

Research shows that we spontaneously imitate people. Moreover, empathy predicts the degree of this non-conscious imitation. Little is known, however, if or how this expression of empathy is influenced by stable physical characteristics of our interaction-partners.

Assessment of implicit sexual associations in non-incarcerated pedophiles.

Offences committed by pedophiles are crimes that evoke serious public concern and outrage. Although recent research using implicit measures has shown promise in detecting deviant sexual associations, the discriminatory and predictive quality of implicit tasks has not yet surpassed traditional assessment methods such as questionnaires and phallometry. The current research extended previous findings by examining whether a combination of two implicit tasks, the Implicit Association Task (IAT) and the Picture Association Task (PAT), was capable of differentiating pedophiles from non-pedophiles, and whether the PAT, which allows separate analysis for male, female, boy and girl stimulus categories, was more sensitive to specific sexual associations in pedophiles than the IAT.

Attentional and affective processing of sexual stimuli in women with hypoactive sexual desire disorder.

Hypoactive sexual desire disorder (HSDD) is the most common sexual problem in women. From an incentive motivation perspective, HSDD may be the result of a weak association between sexual stimuli and rewarding experiences. As a consequence, these stimuli may either lose or fail to acquire a positive meaning, resulting in a limited number of incentives that have the capacity to elicit a sexual response.

Effects of a priori liking on the elicitation of mimicry.

Mimicry and prosocial feelings are generally thought to be positively related. However, the conditions under which mimicry and liking are related largely remain unspecified. We advance this specification by examining the relationship between mimicry and liking more thoroughly.

Executive functioning and imitation: Increasing working memory load facilitates behavioural imitation.

If perceptual and bodily states are closely linked and if perceiving action automatically leads to corresponding activations in one's own motor system, then why do not we imitate all the time? There is evidence suggesting that executive functioning (EF) may play a moderating role in inhibiting overt imitation [e.g. Luria, A.