- Prof Navarini to speak at Inflammatory Skin Disease Summit in New York (19 November 2016)
- Fantastic “Future Leaders in Dermatological Research” Meeting in Madrid, Prof Navarini spoke about Investigator Initiated Trials and mentored four young investigators (11 November 2016)
- Personalized treatment study of Acrodermatitis continua suppurativa Hallopeau just accepted in JAMA Dermatology! (27 October 2016)
- URTICANA study patient recruitment complete! (05 October 2016)
- White scale sign paper accepted in JAMA Dermatology (22 September 2016)
- PRUVAB paper accepted in JEADV (21 September 2016)
- ERASPEN Meeting in Munich: >150 patients included! (09 September 2016)
- CTLA4 mutation paper accepted in JACI (12 August 2016)
Genetic architecture and resulting phenotype
The Navarini Lab uses a two-pronged approach of analysing genetic and visual data to understand skin conditions.
This page aims to inform and update you about our activities. If you have similar interests, we are always looking for collaborations. Also, the page is a home for the reference assistant that we consider a significant advance for scientific writing - why don't you try it yourself?
Utilizing exome-wide next generation sequencing allows us to detect disease driving genetic alterations. These are then taken forward for functional analysis, which sometimes can lead to novel therapeutic approaches. Our main focus of conditions are the neutrophil-mediated inflammatory diseases (NMID) including pustular psoriasis, pyoderma gangrenosum, Sweet syndrome and others. Other projects include additional immune-mediated conditions.
Our Vision-Team also seeks to extract objective insights out of imaging, which are the dermatologists' most valuable data. We use systematic evaluation of common skin conditions with new visual tools such as dermoscopy as well as machine-learning techniques that enabled us to develop eczema-detecting algorithms.
With these two central themes, we aim towards optimised genotype-phenotype correlations and clinical insights that could be useful for future decision support systems.