During inflammation, chemokines play a central role by mediating the activation of inflammatory
cascade responses in tissue injury. Among more than 200 chemokines, CX3CL1 is a special
chemotactic factor existing in both membrane-bound and soluble forms. Its only receptor, CX3CR1, is a
member of the G protein-coupled receptor superfamily. The CX3CL1/CX3CR1 axis can affect many
inflammatory processes by communicating with different inflammatory signaling pathways, such as
JAK-STAT, Toll-like receptor, MAPK, AKT, NF-κB, Wnt/β-catenin, as well as others. These inflammatory
networks are involved in much pathology. Determining the crosstalk between the
CX3CL1/CX3CR1 axis and these inflammatory signaling pathways could contribute to solving problems
in tissue injury, and the CX3CL1/CX3CR1 axis may be a better therapeutic target than inflammatory
signaling pathways for preventing tissue injury due to the complexity of inflammatory signaling
networks.
Psoriatic arthritis (PsA) is associated with psoriasis, featured by its irreversible joint symptoms. Despite the significant impact on the healthcare system, it is still challenging to leverage machine learning or statistical models to predict PsA and its progression, or analyze drug efficacy. With 3961 patients’ clinical records, we developed a machine learning model for PsA diagnosis and analysis of PsA progression risk, respectively. Furthermore, general additive models (GAMs) and the Kaplan–Meier (KM) method were applied to analyze the efficacy of various drugs on psoriasis treatment and inhibiting PsA progression. The independent experiment on the PsA prediction model demonstrates outstanding prediction performance with an AUC score of 0.87 and an AUPR score of 0.89, and the Jackknife validation test on the PsA progression prediction model also suggests the superior performance with an AUC score of 0.80 and an AUPR score of 0.83, respectively. We also identified that interleukin-17 inhibitors were the more effective drug for severe psoriasis compared to other drugs, and methotrexate had a lower effect in inhibiting PsA progression. The results demonstrate that machine learning and statistical approaches enable accurate early prediction of PsA and its progression, and analysis of drug efficacy.
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