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Abstract Objective Our study sought to investigate the clinical influencing factors of psoriasis patients with depression, and analyze whether the content of monoamine neurotransmitters in plasma was correlated with depression incidence among psoriasis patients. Methods Ninety patients with psoriasis and 40 healthy volunteers (aged from18 to 60) were recruited and interviewed with a piloted questionnaire in both groups to obtain relevant information. The catecholamine in plasma from the two groups was analyzed by radioimmunoassay. The data were analyzed by SPSS statistical software. Results The mean Hamilton Depression Scale (HAMD) and mean Athens Insomnia Scale (AIS) scores of the psoriasis patients were higher than the control group. Dopamine content in the plasma was lower (comparing psoriasis patients without depression and the control group, and was negatively correlated with HAMD, AIS, and Psoriasis Area and Severity Index (PASI) scores in the psoriasis patients with depression. There was no significant difference in the epinephrine and norepinephrine contents in all groups. PASI scores were positively correlated with HAMD scores in psoriasis patients. The low dopamine content, Dermatology Life Quality Index, and high PASI scores were the risk factors for depression among the psoriasis patients. Conclusion Psoriasis patients have a significantly higher risk of depression than healthy people, and higher PASI scores were linked to a higher incidence of depression. The dopamine levels of patients were influenced by both psoriasis and depression. The risk factors for depression in psoriasis patients are low dopamine levels in the plasma, severe skin lesions, and lower quality of life.

This paper intends to investigate the risk management of inclusive digital financial platforms. First, it explains the idea of smart cities, their function, and inclusive financial risk control technologies based on big data. The varieties of digital inclusive financial platforms and their risk profiles are next examined. The Back Propagation (BP) neural network is used to build a BP-KMV model based on the KMV model. Finally, utilizing M Company as a case study, this paper uses the BP-KMV model to examine the credit risk and risk management of unlisted enterprises on the digital inclusive financial platform. The results show that of the four unlisted companies, L Company has the greatest default rate (7.35%), while J Company has the lowest default rate (4.82%). The highest research and development (R&D) spending rate is 14.1% for J company, while the highest patent ownership rate is 43.09% for L company. The data demonstrates a negative correlation between the percentage of R&D expenditures and the default rate of unlisted enterprises. In other words, a larger default risk is associated with lower R&D expense rates. Additionally, there is a correlation between patent ownership and default rates that is positive, suggesting that higher patent ownership rates are linked to higher default rates. Additionally, the risk management technologies of M business can complement one another. The theoretical research of comprehensive digital inclusive finance risk control can be enriched by the risk analysis of digital inclusive financial platforms utilizing the BP-KMV model in the context of smart cities. [ABSTRACT FROM AUTHOR]