The significance of learning opportunities in teacher training courses and their individual use for the development of educational-scientific knowledge

Der Beitrag thematisiert das Wirkungsgefüge zwischen Eingangsvoraussetzungen, Lernangebot und individueller Nutzung des Lernangebots sowie dem Aufbau des bildungswissenschaftlichen Wissens im universitären Lehramtsstudium. Die Datengrundlage bildet eine Vollerhebung von 3.273 Referendar(inn)en in Nordrhein-Westfalen zu Beginn des Referendariats, bei der das bildungswissenschaftliche Wissen mit einem standardisierten Wissenstest erfasst wurde. Für die generelle Wirkung des Studiums auf den Aufbau des bildungswissenschaftlichen Wissens sprechen substanzielle Wissensunterschiede zwischen Lehramtsstudierenden und Quereinsteigern. Es zeigen sich jedoch nur geringe Effekte formeller universitärer Faktoren (Spezifität der Institution bzw. Art der Studienstrukturen) für den Wissensaufbau im Lehramtsstudium (DIPF/Orig.)The article topicalizes the interdependencies between enrollment prerequisites, study programs offered, and the individual use of the courses, as well as the structure and development of educational-scientific knowledge in the field of university-based teacher training. The study is based on data collected through a census survey carried out in North Rhine-Westphalia among 3.273 trainee teachers at the beginning of their internship; in this survey, a standardized test of knowledge was used to capture the educational-scientific knowledge. Substantial differences in knowledge between student teachers and lateral entrants confirm the general effect of study courses on the development of educational-scientific knowledge. However, formal university-related factors (specificity of the institution or type of study-course structure) are of only moderate effect on the development of knowledge in the course of university-based teacher training. (DIPF/Orig.

Effects of Phytoestrogen Extracts Isolated from Elder Flower on Hormone Production and Receptor Expression of Trophoblast Tumor Cells JEG-3 and BeWo, as well as MCF7 Breast Cancer Cells

Hereinwe investigated the effect of elderflower extracts (EFE) and of enterolactone/enterodiol on hormone production and proliferation of trophoblast tumor cell lines JEG-3 and BeWo, as well as MCF7 breast cancer cells. The EFE was analyzed by mass spectrometry. Cells were incubated with various concentrations of EFE. Untreated cells served as controls. Supernatants were tested for estradiol production with an ELISA method. Furthermore, the effect of the EFE on ER alpha/ER beta /PR expression was assessed by immunocytochemistry. EFE contains a substantial amount of lignans. Estradiol production was inhibited in all cells in a concentration-dependent manner. EFE upregulated ER alpha in JEG-3 cell lines. In MCF7 cells, a significant ER alpha downregulation and PR upregulation were observed. The control substances enterolactone and enterodiol in contrast inhibited the expression of both ER and of PR in MCF7 cells. In addition, the production of estradiol was upregulated in BeWo and MCF7 cells in a concentration dependent manner. The downregulating effect of EFE on ER alpha expression and the upregulation of the PR expression in MFC-7 cells are promising results. Therefore, additional unknown substances might be responsible for ER alpha downregulation and PR upregulation. These findings suggest potential use of EFE in breast cancer prevention and/or treatment and warrant further investigation

Specific protein antigen delivery to human Langerhans cells in intact skin

Immune modulating therapies and vaccines are in high demand, not least to the recent global spread of SARS-CoV2. To achieve efficient activation of the immune system, professional antigen presenting cells have proven to be key coordinators of such responses. Especially targeted approaches, actively directing antigens to specialized dendritic cells, promise to be more effective and accompanied by reduced payload due to less off-target effects. Although antibody and glycan-based targeting of receptors on dendritic cells have been employed, these are often expensive and time-consuming to manufacture or lack sufficient specificity. Thus, we applied a small-molecule ligand that specifically binds Langerin, a hallmark receptor on Langerhans cells, conjugated to a model protein antigen. Via microneedle injection, this construct was intradermally administered into intact human skin explants, selectively loading Langerhans cells in the epidermis. The ligand-mediated cellular uptake outpaces protein degradation resulting in intact antigen delivery. Due to the pivotal role of Langerhans cells in induction of immune responses, this approach of antigen-targeting of tissue-resident immune cells offers a novel way to deliver highly effective vaccines with minimally invasive administration

A Remote Secondary Binding Pocket Promotes Heteromultivalent Targeting of DC-SIGN

Dendritic cells (DC) are antigen-presenting cells coordinating the interplay of the innate and the adaptive immune response. The endocytic C-type lectin receptors DC-SIGN and Langerin display expression profiles restricted to distinct DC subtypes and have emerged as prime targets for next-generation immunotherapies and anti-infectives. Using heteromultivalent liposomes copresenting mannosides bearing aromatic aglycones with natural glycan ligands, we serendipitously discovered striking cooperativity effects for DC-SIGN+ but not for Langerin+ cell lines. Mechanistic investigations combining NMR spectroscopy with molecular docking and molecular dynamics simulations led to the identification of a secondary binding pocket for the glycomimetics. This pocket, located remotely of DC-SIGN’s carbohydrate bindings site, can be leveraged by heteromultivalent avidity enhancement. We further present preliminary evidence that the aglycone allosterically activates glycan recognition and thereby contributes to DC-SIGN-specific cell targeting. Our findings have important implications for both translational and basic glycoscience, showcasing heteromultivalent targeting of DCs to improve specificity and supporting potential allosteric regulation of DC-SIGN and CLRs in general

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

<p>Abstract</p> <p>Background</p> <p>Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2).</p> <p>Methods</p> <p>A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances.</p> <p>Results</p> <p>The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity.</p> <p>Conclusions</p> <p>Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.</p

Home monitoring and decision support for international liver transplant children

Complications may occur after a liver transplantation, therefore proper monitoring and care in the post-operation phase plays a very important role. Sometimes, monitoring and care for patients from abroad is difficult due to a variety of reasons, e.g., different care facilities. The objective of our research for this paper is to design, implement and evaluate a home monitoring and decision support infrastructure for international children who underwent liver transplant operation. A point-of-care device and the PedsQL questionnaire were used in patients’ home environment for measuring the blood parameters and assessing quality of life. By using a tablet PC and a specially developed software, the measured results were able to be transmitted to the health care providers via internet. So far, the developed infrastructure has been evaluated with four international patients/families transferring 38 records of blood test. The evaluation showed that the home monitoring and decision support infrastructure is technically feasible and is able to give timely alarm in case of abnormal situation as well as may increase parent’s feeling of safety for their children

Clinical evaluation of a mobile sensor-based gait analysis method for outcome measurement after knee arthroplasty

Clinical scores and motion-capturing gait analysis are today’s gold standard for outcome measurement after knee arthroplasty, although they are criticized for bias and their ability to reflect patients’ actual quality of life has been questioned. In this context, mobile gait analysis systems have been introduced to overcome some of these limitations. This study used a previously developed mobile gait analysis system comprising three inertial sensor units to evaluate daily activities and sports. The sensors were taped to the lumbosacral junction and the thigh and shank of the affected limb. The annotated raw data was evaluated using our validated proprietary software. Six patients undergoing knee arthroplasty were examined the day before and 12 months after surgery. All patients reported a satisfactory outcome, although four patients still had limitations in their desired activities. In this context, feasible running speed demonstrated a good correlation with reported impairments in sports-related activities. Notably, knee flexion angle while descending stairs and the ability to stop abruptly when running exhibited good correlation with the clinical stability and proprioception of the knee. Moreover, fatigue effects were displayed in some patients. The introduced system appears to be suitable for outcome measurement after knee arthroplasty and has the potential to overcome some of the limitations of stationary gait labs while gathering additional meaningful parameters regarding the force limits of the knee

Wearable sensors in healthcare and sensor-enhanced health information systems: all our tomorrows?

Wearable sensor systems which allow for remote or self-monitoring of health-related parameters are regarded as one means to alleviate the consequences of demographic change. This paper aims to summarize current research in wearable sensors as well as in sensor-enhanced health information systems. Wearable sensor technologies are already advanced in terms of their technical capabilities and are frequently used for cardio-vascular monitoring. Epidemiologic predictions suggest that neuro-psychiatric diseases will have a growing impact on our health systems and thus should be addressed more intensively. Two current project examples demonstrate the benefit of wearable sensor technologies: long-term, objective measurement under daily-life, unsupervised conditions. Finally, up-to-date approaches for the implementation of sensor-enhanced health information systems are outlined. Wearable sensors are an integral part of future pervasive, ubiquitous and person-centered health care delivery. Future challenges include their integration into sensor-enhanced health information systems and sound evaluation studies involving measures of workload reduction and costs