Localization and quantification of intact, undamaged right-handed double-stranded B-DNA, and denatured single-stranded DNA in normal human epidermis and its effects on apoptosis and terminal differentiation (denucleation)

Novel B-DNA dermatophyte assay for demonstration of canonical DNA in dermatophytes: Histopathologic characterization by artificial intelligence

We describe a novel assay and artificial intelligence-driven histopathologic approach identifying dermatophytes in human skin tissue sections (ie, B-DNA dermatophyte assay) and demonstrate, for the first time, the presence of dermatophytes in tissue using immunohistochemistry to detect canonical right-handed double-stranded (ds) B-DNA. Immunohistochemistry was performed using anti-ds-B-DNA monoclonal antibodies with formalin-fixed paraffin-embedded tissues to determine the presence of dermatophytes. The B-DNA assay resulted in a more accurate identification of dermatophytes, nuclear morphology, dimensions, and gene expression of dermatophytes (ie, optical density values) than periodic acid-Schiff (PAS), Grocott methenamine silver (GMS), or hematoxylin and eosin (H&E) stains. The novel assay guided by artificial intelligence allowed for efficient identification of different types of dermatophytes (eg, hyphae, microconidia, macroconidia, and arthroconidia). Using the B-DNA dermatophyte assay as a clinical tool for diagnosing dermatophytes is an alternative to PAS, GMS, and H&E as a fast and inexpensive way to accurately detect dermatophytosis and reduce the number of false negatives. Our assay resulted in superior identification, sensitivity, life cycle stages, and morphology compared to H&E, PAS, and GMS stains. This method detects a specific structural marker (ie, ds-B-DNA), which can assist with diagnosis of dermatophytes. It represents a significant advantage over methods currently in use.

Modelling epidermis homoeostasis and psoriasis pathogenesis

We present a computational model to study the spatio-temporal dynamics of epidermis homoeostasis under normal and pathological conditions. The model consists of a population kinetics model of the central transition pathway of keratinocyte proliferation, differentiation and loss and an agent-based model that propagates cell movements and generates the stratified epidermis. The model recapitulates observed homoeostatic cell density distribution, the epidermal turnover time and the multilayered tissue structure. We extend the model to study the onset, recurrence and phototherapy-induced remission of psoriasis. The model considers psoriasis as a parallel homoeostasis of normal and psoriatic keratinocytes originated from a shared stem cell (SC) niche environment and predicts two homoeostatic modes of psoriasis: a disease mode and a quiescent mode. Interconversion between the two modes can be controlled by interactions between psoriatic SCs and the immune system and by normal and psoriatic SCs competing for growth niches. The prediction of a quiescent state potentially explains the efficacy of multi-episode UVB irradiation therapy and recurrence of psoriasis plaques, which can further guide designs of therapeutics that specifically target the immune system and/or the keratinocytes.