Effects on peripheral and central nervous system of key inflammatory intercellular signalling peptides and proteins in psoriasis.

Psoriasis is a chronic systemic inflammatory cutaneous disease. Where the immune system plays an important role in its pathogenesis, with key inflammatory intercellular signalling peptides and proteins including IL-17 and IL-23. The psychoneurological system also figures prominently in development of psoriasis. There is a high prevalence of comorbidity between psoriasis and mental health disorders such as depression, anxiety and mania. Patients with psoriasis often suffer from pathological pain in the lesions, and their neurological accidents could improve the lesions in innervated areas. The immune system and the psychoneurological system interact closely in the pathogenesis of psoriasis. Patients with psoriasis exhibit abnormal levels of neuropeptides both in circulating and localized lesion, acting as immunomodulators involved in the inflammatory response. Moreover, receptors for inflammatory factors are expressed in both peripheral and central nervous systems (CNSs), suggesting that nervous system can receive and be influenced by signals from immune system. Key inflammatory intercellular signalling peptides and proteins in psoriasis, such as IL-17 and IL-23, can be involved in sensory signalling and may affect synaptic plasticity and the blood-brain barrier of CNS through the circulation. This review provides an overview of the multiple effects on the peripheral and CNS under conditions of systemic inflammation in psoriasis, providing a framework and inspiration for in-depth studies of neuroimmunomodulation in psoriasis.

Human-multimodal deep learning collaboration in 'precise' diagnosis of lupus erythematosus subtypes and similar skin diseases.

BACKGROUND: Lupus erythematosus (LE) is a spectrum of autoimmune diseases. Due to the complexity of cutaneous LE (CLE), clinical skin image-based artificial intelligence is still experiencing difficulties in distinguishing subtypes of LE. OBJECTIVES: We aim to develop a multimodal deep learning system (MMDLS) for human-AI collaboration in diagnosis of LE subtypes. METHODS: This is a multi-centre study based on 25 institutions across China to assist in diagnosis of LE subtypes, other eight similar skin diseases and healthy subjects. In total, 446 cases with 800 clinical skin images, 3786 multicolor-immunohistochemistry (multi-IHC) images and clinical data were collected, and EfficientNet-B3 and ResNet-18 were utilized in this study. RESULTS: In the multi-classification task, the overall performance of MMDLS on 13 skin conditions is much higher than single or dual modals (Sen = 0.8288, Spe = 0.9852, Pre = 0.8518, AUC = 0.9844). Further, the MMDLS-based diagnostic-support help improves the accuracy of dermatologists from 66.88% ± 6.94% to 81.25% ± 4.23% (p = 0.0004). CONCLUSIONS: These results highlight the benefit of human-MMDLS collaborated framework in telemedicine by assisting dermatologists and rheumatologists in the differential diagnosis of LE subtypes and similar skin diseases.