BACKGROUND Rosacea is a chronic inflammatory disease with variable clinical presentations including transient flushing, fixed erythema, papules, pustules and phymatous changes on the central face. Owing to the diversity of clinical manifestations, the lack of objective biochemical examinations and non-specificity of histopathology, accurate identification of rosacea is a big challenge. Artificial intelligence has emerged as a potential tool in the identification and evaluation of some skin diseases such as melanoma, basal cell carcinoma and psoriasis. OBJECTIVE In this work, we utilized convolution neural networks (CNN) to identify the clinical photos (from three different angles) of patients with rosacea and other diseases that would be easily confused with rosacea (such as acne, seborrheic dermatitis and eczema). METHODS In this work, we utilized convolution neural networks (CNN) to identify the clinical photos (from three different angles) of patients with rosacea and other diseases that would be easily confused with rosacea (such as acne, seborrheic dermatitis and eczema). RESULTS The CNN in our study achieved an overall accuracy and precision of 0.914 and 0.898, with an area under the receiver operating characteristic curve (AUROC) of 0.972 for the detection of rosacea. The accuracy of classifying the three subtypes of rosacea, ETR, PPR, PhR was 83.9%, 74.3% and 80.0%, respectively. Moreover, the accuracy and precision of our CNN to distinguish rosacea from acne reached 0.931 and 0.893, respectively. For the identificaiton between rosacea, seborrheic dermatitis and eczema, the overall accuracy was 0.757 and the precision was 0.667. Finally, by comparing the CNN with different levels of dermatologists, we showed that our CNN system is capable of identifying rosacea with a performance superior to resident doctors or attending physicians and comparable to experienced specialists. CONCLUSIONS In conclusion, by assessing clinical images, the CNN system in our study performed at dermatologist-level in the identification of rosacea. CLINICALTRIAL None
Cutaneous wound healing, an integral part for protection of skin barrier, is a complex biological process and intimately associated with keratinocyte migration. However, mechanisms regulating keratinocyte migration in the process of cutaneous wound repair remain largely unknown. Here, we found that N-acetyltransferase 10 (NAT10) is essential for cutaneous wound repair in an in vivo skin wound healing model – a significant delay of wound repair in Nat10 haploinsufficient mice and a remarkable inhibition of keratinocyte migration by NAT10 knockdown in an in vitro keratinocyte migration model. We further demonstrate that loss of NAT10 expression attenuates the wound-induced IL-6/IL-8 expression through inhibiting NF-κB/p65 activity in keratinocytes. By deeply digging, silencing NAT10 compromises the level of nuclear p65 by facilitating its poly-ubiquitination, thus accelerates its degradation in the nucleus. Notably, we detected a strong positive correlation between the expression of NAT10 and relevant NF-kB/p65-IL6 signaling activity in mouse wound skin tissues. Overall, our study reveals an important role of NAT10 on cutaneous wound repair by potentiating NF-κB/p65-IL-6/8-STAT3 signaling. Targeting NAT10 might be a potential strategy for the treatment of skin wound dysfunctions and related diseases.
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