表皮水疱症

Bart syndrome is characterized by the triad of aplasia cutis congenita (ACC), epidermolysis bullosa (EB), and nail abnormalities. We report a rare case of Bart syndrome presenting with ACC, nail abnormalities, and congenital brachydactyly of the left great toe in a neonate with genetically confirmed dominant dystrophic EB. The diagnosis was supported by next-generation sequencing, which identified a heterozygous COL7A1 c.6007G>A (p.Gly2003Arg) variant. The patient was managed conservatively with topical antibiotics, nonadherent dressings, and temporary keratinocyte allograft application, resulting in spontaneous epithelialization without major complications. This case underscores congenital brachydactyly as a rare extracutaneous association of Bart syndrome and highlights the diagnostic value of molecular testing, as well as the potential effectiveness of conservative management in achieving a favorable outcome.

Type VII collagen (COL7), a major component of anchoring fibrils, is essential for dermal-epidermal adhesion and pathogenic variants in COL7A1, encoding COL7, cause dystrophic epidermolysis bullosa (DEB). In normal skin, COL7 is localised just beneath the lamina densa. In contrast, previous immunoelectron microscopy (IEM) studies of DEB skin with residual COL7 expression demonstrated aberrant localisation of COL7 above the lamina densa and hemidesmosomes of epidermal basal keratinocytes, possibly reflecting impaired secretion of defective COL7. However, conventional microscopy techniques lack sufficient spatial resolution to clearly visualise individual protein distributions and resolve this finding. Here, we revisit the abnormal localisation of COL7 using two higher-resolution approaches: structured illumination microscopy (SIM) and expansion microscopy (ExM). In normal human skin, the COL7 NC1 domain colocalised with type IV collagen (COL4), a surrogate marker of the lamina densa and was consistently detected beneath integrin α6 (ITGA6), a hemidesmosomal marker. In DEB skin with residual COL7 expression, COL7 was detected above ITGA6 within basal keratinocytes, recapitulating previous IEM observations. SIM quantitatively confirmed significant BMZ disorganisation in DEB, whereas ExM showed a consistent but non-significant trend, likely due to variability in expansion factors. These findings demonstrate that high-resolution fluorescence imaging can reproduce classic IEM observations of COL7 mislocalisation, suggesting its potential as a more accessible complementary approach for morphologic assessment of DEB.

Epidermolysis bullosa (EB) is a group of genetic diseases characterized by skin fragility. Although therapeutic options aim to accelerate wound-healing, improvement is needed; therefore, birch bark and propolis were investigated due to their beneficial biological properties. A representative ethanolic extract was analyzed by reversed-phase high-performance liquid chromatography with diode array detection (RP-HPLC-DAD) for chemical profiling of the raw materials. A hydrophobic natural deep eutectic solvent (HNaDES) for birch bark extraction, as well as a hydrogel and a bigel enriched with propolis and birch bark extract, were prepared and characterized by Fourier transform infrared (FT-IR) spectroscopy. Cytotoxicity and wound-healing potential were evaluated using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and scratch assays in six human keratinocyte cell lines: two from healthy individuals, two from recessive dystrophic ΕΒ patients (RDEB), and two from laminin-332-deficient junctional EB patients (JEB). RP-HPLC-DAD revealed the presence of phenolic compounds (e.g., chrysin, pinocembrin, pinobanksin) and pentacyclic triterpenes (e.g., betulin and betulinic acid), characteristic of propolis and birch bark, respectively. FT-IR confirmed HNaDES formation and indicated physical interactions within the gels. All systems exhibited no cytotoxicity at 1 μg/mL and increased cell vitality. Moreover, in keratinocytes derived from JEB patients, hydrogel improved wound- healing significantly at 24 h, whereas bigel showed significant improvement at 8 h. The developed systems could be promising topical treatments.

Epidermolysis bullosa (EB) comprises a group of rare inherited genodermatoses characterized by fragility and blistering of the skin and mucous membranes, chronic wounding, and significant morbidity including increased risk of squamous cell carcinoma in severe subtypes. Key unmet priorities include reducing diagnostic latency, establishing objective wound monitoring, enabling early detection of malignant transformation within chronic ulcerations, and developing therapies that durably modify disease progression. Artificial intelligence (AI) encompassing machine learning (ML), and deep learning (DL) is increasingly integrated into EB research and clinical practice to address these unmet needs. This structured narrative review synthesises current evidence on AI applications in EB spanning genetic diagnostics, wound assessment, inflammatory endotyping, drug repurposing, and emerging therapeutic technologies, and integrates evidence from registered clinical trials. In genomics, DL-based splicing prediction models and variant prioritisation frameworks accelerate pathogenic variant detection and reduce diagnostic latency. In wound care, convolutional neural networks-based platforms enable automated lesion segmentation and remote monitoring, while multimodal AI models predict healing trajectories and support stratification of wounds by chronicity. Computational transcriptomic analyses have identified candidate repurposing agents by reversing pathogenic gene expression signatures in EB tissue. Emerging convergence of AI with biosensors-integrated wound dressings and three-dimensional bioprinting of genetically corrected skin substitutes represents a transformative future direction. Translational barriers include limited EB-specific training datasets, algorithmic bias across diverse skin phototypes, the interpretability deficit of DL systems, and evolving regulatory frameworks for AI as a medical device. Expansion of internationally interoperable EB disease registries with standardised wound imaging protocols is identified as the single most impactful intervention to accelerate AI adoption. A minimum endpoint set for AI-assisted EB wound assessment, incorporating wound area trajectory, wound type classification, tissue composition, and paired patient-reported pain and itch scores, is proposed to standardise outcome reporting across future studies.