Die Rolle des Proteinase-aktivierten Rezeptors-2 (PAR2) in der Leishmania major (L. major) Infektion

Ziel der Arbeit war es, die Bedeutung des PAR2-Rezeptors für den Infektionsverlauf der experimentellen Leishmaniasis bei PAR+/+ und PAR-/- Mäusen zu analysieren. Es wurden die Mechanismen einer koordinierten Immunantwort, die bei einer L. major spezifischen Th1-Antwort zu einer Resistenz und mittels Ausbildung einer Th2-Differenzierung zur Suszeptibilität gegenüber einer Infektion führen, untersucht. Die infektionsbedingte Expression epidermaler PAR2-Liganden führt zur konsekutiven NO-Freisetzung in den phagozytierenden Makrophagen, einer vermehrten Th1-Differenzierung und somit zur Erregerresistenz bei PAR2+/+ Mäusen. Demgegenüber zeigt sich in Abwesenheit von PAR2 bei Ausbildung einer Th2-Antwort eine verstärkte Suszeptibilität der PAR-/- Mäuse gegenüber einer L. major Infektion. Das vorliegende Modell lässt die Bedeutung von PAR2 bei infektiösen Hauterkrankungen und der Immunantwort erkennen

Economic analysis of the costs associated with Hidradenitis suppurativa at a German University Hospital

Background and objectives Hidradenitis suppurativa (HS) significantly affects the patient`s quality of life and leads to multiple medical consultations. Aim of this study was to assess the utilization of medical care of HS patients. Patients and methods All patients presenting in 2017 for an outpatient, day patient and / or inpatient treatment with leading claim type HS at the Department of Dermatology, University Hospital Würzburg, were included. Primary outcome was the economic burden of HS patients, measured by resource utilization in €. Results The largest share of the direct medical costs for HS were the inpatient costs with a leading surgical diagnosis-related group (DRG). Antiseptics were the predominant topical prescription. While doxycycline was the most frequently prescribed systemic therapy, adalimumab was the main cost driver. The difference between in-patient (€ 110.25) and outpatient (€ 26.34) direct non-medical costs was statistically significant (p < 0.001). With regards to indirect medical costs, a statistically significantly higher loss of gross value added (inpatient mean € 1,827.00; outpatient mean € 203.00) and loss of production (inpatient mean € 1,026.00; outpatient mean € 228.00) could be noted (p < 0.001), respectively. Conclusions The present study on disease-specific costs of HS confirms that the hospital care of patients with this disease is cost-intensive. However, the primary goal of physicians is not and should not be to save costs regarding their patients`treatment, but rather the premise to utilize the existing resources as efficient as possible. Reducing the use of costly therapeutics and inpatient stays therefore requires more effective therapy options with an improved cost-benefit profile

Switching from Adalimumab Originator to Biosimilar in Patients with Hidradenitis Suppurativa Results in Losses of Response&mdash;Data from the German HS Registry HSBest

Since 2021, adalimumab biosimilar ABP 501 can be used alternatively to adalimumab originator (ADAO) in the treatment of hidradenitis suppurativa (HS). Effectiveness and safety data remain scarce. We investigated the impact of switching from ADAO to ABP 501 on disease severity and the occurrence of adverse events (AEs) in patients with HS. We analyzed clinical data on patients enrolled in the German HSBest registry. Evaluation outcomes were assessed at three time points (baseline of originator (t0), prior to switching to biosimilar (t1) and 12 to 14 weeks after switching (t2)) and included patient-reported AEs and disease severity using the International Hidradenitis Suppurativa Severity Score System (IHS4) score. In total, 94 patients were switched from ADAO to ABP 501. Overall, 33.3% (n = 31/94) of the patients developed AEs and/or loss of response (LoR) within 12 to 14 weeks after switching. Of these, 61.3% (n = 19/31) experienced LoR but no AEs, 22.6% (n = 7/31) LoR combined with AEs and 16.1% (n = 5/31) AEs only. Our study showed that switching HS patients from ADAO to ABP 501 does significantly affect treatment effectiveness. Switching patients who are on remission maintenance therapy should be viewed critically

Superior skin cancer classification by the combination of human and artificial intelligence

Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification). Findings: Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5% Interpretation: Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems. (C) 2019 The Author(s). Published by Elsevier Ltd

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

Background: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Methods: Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Findings: Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Interpretation: Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001). (C) 2019 The Author(s). Published by Elsevier Ltd