Inflammatory memory in psoriasis: From remission to recurrence.

The routine use of targeted systemic immunomodulatory therapies has transformed outcomes for people with severe psoriasis, with skin clearance (clinical remission) rates up to 60% at 1 year of biologic treatment. However, psoriasis may recur following drug withdrawal, and as a result, patients tend to continue receiving costly treatment indefinitely. Here, we review research into the "inflammatory memory" in resolved psoriasis skin and the potential mechanisms leading to psoriasis recurrence following drug withdrawal. Research has implicated immune cells such as tissue resident memory T cells, Langerhans cells, and dermal dendritic cells, and there is growing interest in keratinocytes and fibroblasts. A better understanding of the interactions between these cell populations, enabled by single cell technologies, will help to elucidate the events underpinning the shift from remission to recurrence. This may inform the development of personalized strategies for sustaining remission while reducing long-term drug burden.

Q94 is not a selective modulator of proteinase-activated receptor 1 (PAR1) in platelets.

Thrombin is a potent platelet activator, acting through proteinase-activated receptors -1 and -4 (PAR1 and PAR4). Of these, PAR-1 is activated more rapidly and by lower thrombin concentrations. Consequently, PAR-1 has been extensively investigated as a target for anti-platelet drugs to prevent myocardial infarction. Q94 has been reported to act as an allosteric modulator of PAR1, potently and selectively inhibiting PAR1-Gαq coupling in multiple cell lines, but its effects on human platelet activation have not been previously studied. Platelet Ca2+ signaling, integrin αIIbß3 activation and α-granule secretion were monitored following stimulation by a PAR1-activating peptide (PAR1-AP). Although Q94 inhibited these responses, its potency was low compared to other PAR1 antagonists. In addition, αIIbß3 activation and α-granule secretion in response to other platelet activators were also inhibited with similar potency. Finally, in endothelial cells, Q94 did not inhibit PAR1-dependent Ca2+ signaling. Our data suggest that Q94 may have PAR1-independent off-target effects in platelets, precluding its use as a selective PAR1 allosteric modulator.

Single-cell analysis of psoriasis resolution demonstrates an inflammatory fibroblast state targeted by IL-23 blockade.

Biologic therapies targeting the IL-23/IL-17 axis have transformed the treatment of psoriasis. However, the early mechanisms of action of these drugs remain poorly understood. Here, we perform longitudinal single-cell RNA-sequencing in affected individuals receiving IL-23 inhibitor therapy. By profiling skin at baseline, day 3 and day 14 of treatment, we demonstrate that IL-23 blockade causes marked gene expression shifts, with fibroblast and myeloid populations displaying the most extensive changes at day 3. We also identify a transient WNT5A+/IL24+ fibroblast state, which is only detectable in lesional skin. In-silico and in-vitro studies indicate that signals stemming from these WNT5A+/IL24+ fibroblasts upregulate multiple inflammatory genes in keratinocytes. Importantly, the abundance of WNT5A+/IL24+ fibroblasts is significantly reduced after treatment. This observation is validated in-silico, by deconvolution of multiple transcriptomic datasets, and experimentally, by RNA in-situ hybridization. These findings demonstrate that the evolution of inflammatory fibroblast states is a key feature of resolving psoriasis skin.

Systematic review of deep learning image analyses for the diagnosis and monitoring of skin disease.

Skin diseases affect one-third of the global population, posing a major healthcare burden. Deep learning may optimise healthcare workflows through processing skin images via neural networks to make predictions. A focus of deep learning research is skin lesion triage to detect cancer, but this may not translate to the wider scope of >2000 other skin diseases. We searched for studies applying deep learning to skin images, excluding benign/malignant lesions (1/1/2000-23/6/2022, PROSPERO CRD42022309935). The primary outcome was accuracy of deep learning algorithms in disease diagnosis or severity assessment. We modified QUADAS-2 for quality assessment. Of 13,857 references identified, 64 were included. The most studied diseases were acne, psoriasis, eczema, rosacea, vitiligo, urticaria. Deep learning algorithms had high specificity and variable sensitivity in diagnosing these conditions. Accuracy of algorithms in diagnosing acne (median 94%, IQR 86-98; n = 11), rosacea (94%, 90-97; n = 4), eczema (93%, 90-99; n = 9) and psoriasis (89%, 78-92; n = 8) was high. Accuracy for grading severity was highest for psoriasis (range 93-100%, n = 2), eczema (88%, n = 1), and acne (67-86%, n = 4). However, 59 (92%) studies had high risk-of-bias judgements and 62 (97%) had high-level applicability concerns. Only 12 (19%) reported participant ethnicity/skin type. Twenty-four (37.5%) evaluated the algorithm in an independent dataset, clinical setting or prospectively. These data indicate potential of deep learning image analysis in diagnosing and monitoring common skin diseases. Current research has important methodological/reporting limitations. Real-world, prospectively-acquired image datasets with external validation/testing will advance deep learning beyond the current experimental phase towards clinically-useful tools to mitigate rising health and cost impacts of skin disease.