Publications by authors named "Sebastian Sitaru"

Sarcoidosis is a rare and elusive chronic inflammatory disease. It can manifest itself in any organ, but preferentially affects the lungs and the skin. Our case of an elderly woman with cutaneous and pulmonary sarcoidosis presented with exacerbated itchy, scaly skin changes to our department.

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Background: Current digital medical databases record systematically coded diagnoses, but many legacy databases are full of hand-written, free text diagnoses, which can only be meaningfully analysed after mapping them to a coding system. While diagnoses can be extracted from full medical notes with good accuracy, no algorithm using only an unstructured free text diagnosis with no additional data has been published to date.

Objectives/methods: Therefore, we sought to create an algorithm which maps hand-written German diagnoses from our clinical photography database to ICD-10 diagnosis codes, validate its output manually by dermatologists and analyse diagnosis counts over time as a proof-of-concept of its application.

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Background: Chronic spontaneous urticaria (CSU) significantly impairs patients' quality of life. Despite advances in diagnosis and therapy, treatment is still unsatisfactory. Telemedicine offers a promising solution to improve treatment.

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Background: The integration of artificial intelligence and chatbot technology in health care has attracted significant attention due to its potential to improve patient care and streamline history-taking. As artificial intelligence-driven conversational agents, chatbots offer the opportunity to revolutionize history-taking, necessitating a comprehensive examination of their impact on medical practice.

Objective: This systematic review aims to assess the role, effectiveness, usability, and patient acceptance of chatbots in medical history-taking.

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Background: Malassezia yeasts are almost universally present on human skin worldwide. While they can cause diseases such as pityriasis versicolor, their implication in skin homeostasis and pathophysiology of other dermatoses is still unclear. Their analysis using native microscopy of skin tape strips is operator dependent and requires skill, training and significant amounts of hands-on time.

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Background: The complexity, high prevalence, and substantial personal and socioeconomic burden collectively render atopic dermatitis (AD) a major public health concern. Using crowdsourced Internet data has the potential to provide unique insights into this concern, as demonstrated by several previous studies. However, a comprehensive comparison across European countries remains lacking.

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Background: Dermatological conditions are prevalent across all population sub-groups. The affected body part is of importance to their diagnosis, therapy, and research. The automatic identification of body parts in dermatological clinical pictures could therefore improve clinical care by providing additional information for clinical decision-making algorithms, discovering hard-to-treat areas, and research by identifying new patterns of disease.

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Mounting experimental evidence from in vitro and in vivo animal studies points to an essential role of the CXCL8-CXCR1/2 axis in neutrophils in the pathophysiology of inflammatory and autoimmune diseases. In addition, the pathogenetic involvement of neutrophils and the CXCL8-CXCR1/2 axis in cancer progression and metastasis is increasingly recognized. Consequently, therapeutic targeting of CXCR1/2 or CXCL8 has been intensively investigated in recent years using a wide array of in vitro and animal disease models.

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Background: Asthma and its main phenotype allergic asthma are prevalent, chronic, and complex diseases affecting 4% of the population. One main trigger for allergic asthma exacerbations is pollen. Online health information search behavior by people is increasing, and analysis of web-search data can provide valuable insight into disease burden and risk factors of a population.

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Introduction: The pathogenesis of atopic diseases is highly complex, and the exact mechanisms leading to atopic dermatitis (AD) onset in infants remain mostly enigmatic. In addition to an interdependent network of components of skin development in young age and skin barrier dysfunction underlying AD development that is only partially understood, a complex interplay between environmental factors and lifestyle habits with skin barrier and immune dysregulation is suspected to contribute to AD onset. This study aims to comprehensively evaluate individual microbiome and immune responses in the context of environmental determinants related the risk of developing AD in the first 4 years of a child's life.

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Background: Artificial intelligence (AI) and convolutional neural networks (CNNs) represent rising trends in modern medicine. However, comprehensive data on the performance of AI practices in clinical dermatologic images are non-existent. Furthermore, the role of professional data selection for training remains unknown.

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Autoimmune phenomena occur in healthy individuals, but when self-tolerance fails, the autoimmune response may result in specific pathology. According to Witebsky's postulates, one of the criteria in diagnosing a disease as autoimmune is the reproduction of the disease in experimental animals by the passive transfer of autoantibodies. For epidermolysis bullosa acquisita (EBA), a prototypic organ-specific autoimmune disease of skin and mucous membranes, several experimental models were recently established.

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