表皮水疱症

Severe epidermolysis bullosa simplex is a skin fragility disorder characterized by blistering caused by cytolysis within basal keratinocytes, resulting in compromised epidermal integrity. Here we report the generation of the human induced pluripotent stem cell (hiPSC) line MLi002-A-1, an isogenic control derived from patient-specific MLi002-A line carrying the KRT5 c.1424A > G (p.E475G) mutation. Genome editing restored the wild-type sequence without detectable changes at top-predicted off-target sites. The edited line exhibits a normal karyotype, typical pluripotent morphology, robust pluripotency marker expression, and trilineage differentiation potential. This genetically matched control enables mutation-specific studies and in vitro modeling of epidermolysis bullosa simplex.

Epidermolysis bullosa (EB) is a heterogeneous group of inherited disorders characterised by skin fragility, caused by pathogenic variants in genes encoding structural components of the dermo-epidermal junction. With the advent of next-generation sequencing (NGS), the diagnostic paradigm has shifted from a morphological to a genotype-oriented approach. This review summarises the genetic architecture of EB, the types of mutations and genotype-phenotype relationships, the challenges in interpreting variants of unknown significance (VUS), and therapeutic strategies targeting specific mutational mechanisms, including read-through approaches, exon skipping and genome editing. The role of modifier genes and epigenetic factors in clinical variability is also discussed. The focus is on the translational potential of genomics for personalized therapy in EB. Overall, this review synthesizes the molecular basis of all four major EB types across 16+ classical genes, highlights the paradigm shift where NGS achieves a diagnostic yield exceeding 90%, and critically assesses recent therapeutic milestones-ranging from the first FDA-approved topical gene therapy to precision RNA and genome-editing modalities.

Epidermolysis bullosa (EB) defines a group of rare, inherited and currently incurable genetic disorders characterized by excessive skin fragility, with blistering and wounding of skin and mucous membranes upon minor mechanical trauma. Accurate wound assessment is imperative for measuring and longitudinal monitoring of disease activity and for determining the most accurate treatment. In this work, we describe the annotation of clinical images from EB patients and the subsequent training of both classical convolutional neural networks and state-of-the-art transformer-based architectures for the segmentation of various categories of EB skin wounds. Utilising our dataset of 260 EB images from 18 patients and the corresponding 536 expert-annotated segmentation masks, we train and evaluate five different model architectures and compare their performance against human inter-annotator agreement. External evaluation was not possible because no comparable annotated EB datasets are available. Our results demonstrate that transformer-based models, particularly Mask2Former, achieve near-expert-level segmentation accuracy in five of the seven categories and even surpass human inter-annotator agreement in two. Models based on convolutional neural networks perform noticeably worse and generally fail to accurately segment rarely occurring classes. Averaged over all categories, Mask2Former achieved a mean dice similarity coefficient (DSC) of 52.9%, compared to an inter-annotator agreement of 55.9%. These results indicate that Mask2Former has the potential to serve as a clinical decision support system to improve wound segmentation and categorization. Furthermore, this model provides a foundation for planned future applications in automated wound measurement, wound progression monitoring and documentation and the development of a smartphone-based telemedicine application.