German evidence- and consensus-based guideline on the management of penile urethritis.

Urethritis is a common condition predominantly caused by sexually transmitted pathogens such as Chlamydia trachomatis, Neisseria gonorrhoeae, and Mycoplasma genitalium. It is not possible to differentiate with certainty between pathogens on the basis of clinical characteristics alone. However, empirical antibiotic therapy is often initiated in clinical practice.

Isolated inflammatory involvement of the occipital artery in giant cell arteritis and polymyalgia rheumatica: findings from a retrospective analysis and the critical role of MRI in diagnosis.

Background: Diagnosis of Giant Cell Arteritis (GCA) and Polymyalgia rheumatica (PMR) may be challenging as many patients present with non-specific symptoms. Superficial cranial arteries are predilection sites of inflammatory affection. Ultrasound is typically the diagnostic tool of first choice supplementary to clinical and laboratory examination.

C-X-C motif chemokine receptor 4-directed PET signal in the arterial tree is not consistently linked to calcified plaque burden and cardiovascular risk.

To establish the extent, distribution and frequency of in-vivo vessel wall [Ga]Ga-PentixaFor uptake and to determine its relationship with calcified atherosclerotic plaque burden (CAP) and cardiovascular risk factors (CVRF). 65 oncological patients undergoing [Ga]Ga-PentixaFor PET/CT were assessed. Radiotracer uptake (target-to-background ratio [TBR]) and CAP burden (including number of CAP sites, calcification circumference and thickness) in seven major vessel segments per patient were determined.

Transferable automatic hematological cell classification: Overcoming data limitations with self-supervised learning.

Background And Objective: Classification of peripheral blood and bone marrow cells is critical in the diagnosis and monitoring of hematological disorders. The development of robust and reliable automatic classification systems is hampered by data scarcity and limited model generalizability across laboratories. The present study proposes the integration of self-supervised learning (SSL) into cell classification pipelines to address these challenges.