A5061902374- COVID-19 and Mental Health
- Long-Term Effects of COVID-19
- Olfactory and Sensory Function Studies
- Patient-Provider Communication in Healthcare
- Biomedical and Engineering Education
- Pancreatic and Hepatic Oncology Research
- Functional Brain Connectivity Studies
- Phonocardiography and Auscultation Techniques
- Mobile Health and mHealth Applications
- Innovative Teaching Methods
- Hemispheric Asymmetry in Neuroscience
- Viral Infections and Outbreaks Research
- Health Literacy and Information Accessibility
- Advanced Neuroimaging Techniques and Applications
- Lung Cancer Research Studies
- Neuroendocrine Tumor Research Advances
Philipps University of Marburg
2021-2023
University Medical Center Hamburg-Eppendorf
2023
Universität Hamburg
2023
Abstract Background The increasing popularity and availability of tablet computers raises questions regarding clinical scenarios. This pilot study examined the patient’s satisfaction when using a tablet-based digital questionnaire as tool for obtaining medical history in an emergency department to what extent gender, age, technical competence mother tongue influence user satisfaction. Patients were asked complete three consecutive questionnaires: first collected basic epidemiological data...
Pancreatic neuroendocrine neoplasms (pNEN) are rare and heterogeneous tumors. Previous investigations have shown that autophagy can be a target for cancer therapy. This study aimed to determine the association between expression of autophagy-associated gene transcripts clinical parameters in pNEN. In total, 54 pNEN specimens were obtained from our human biobank. The patient characteristics retrieved medical record. RT-qPCR was performed assess autophagic
Assessment of regional language lateralization is crucial in many scenarios, but not all populations are suited for its evaluation via task-functional magnetic resonance imaging (fMRI). In this study, the utility structural connectome features classification anterior temporal lobes (ATLs) was investigated. Laterality indices semantic processing ATL were computed from task-fMRI 1038 subjects Human Connectome Project who labeled as stronger rightward lateralized (RL) or leftward to bilaterally...
Objective: This work proposes a semi-supervised training approach for detecting lung and heart sounds simultaneously with only one trained model in invariance to the auscultation point. Methods: We use open-access data from 2016 Physionet/CinC Challenge, 2022 George Moody sound database HF_V1. first train specialist single-task models using foreground ground truth (GT) labels different databases identify background events respective databases. The pseudo-labels generated this way were...
The COVID-19 pandemic confronted the medical community worldwide with numerous challenges, not only respect to care, but also for teaching next generation of physicians. To minimize risk infections patient-unrelated classes can be held digitally. Here we present a student initiated, web-based approach, called “From symptom diagnosis”. In this seminar case reports rare diseases were presented audience in symptom-focused manner. patients´ most significant symptoms presented, followed by an...
Pandemic scenarios like SARS-Cov-2 require rapid information aggregation. In the age of eHealth and data-driven medicine, publicly available symptom tracking tools offer efficient scalable means collecting analyzing large amounts data. As a result, gains can be communicated to front-line providers. We have developed such an application in less than month reached more 500 thousand users within 48 hours. The dataset contains on basic epidemiological parameters, symptoms, risk factors details...
Abstract Pandemic scenarios like SARS-Cov-2 require rapid information aggregation. In the age of eHealth and data-driven medicine, publicly available symptom tracking tools offer efficient scalable means collecting analyzing large amounts data. As a result, gains can be communicated to front-line providers. We have developed such an application in less than month reached more 500 thousand users within 48 hours. The dataset contains on basic epidemiological parameters, symptoms, risk factors...