Background: Due to the lower reliability of laboratory tests, skin diseases are more suitable for diagnosis with AI models. There are limited AI dermatology diagnostic models combining images and text; few of these are for Asian populations, and few cover the most common types of diseases.Methods: Leveraging a dataset sourced from Asia comprising over 200,000 images and 220,000 medical records, we explored a deep learning-based system for Dual-channel images and extracted text for the diagnosis of skin diseases model DIET-AI to diagnose 31 skin diseases, which covers the majority of common skin diseases. From 1 September to 1 December 2021, we prospectively collected images from 6,043 cases and medical records from 15 hospitals in seven provinces in China. Then the performance of DIET-AI was compared with that of six doctors of different seniorities in the clinical dataset.Results: The average performance of DIET-AI in 31 diseases was not less than that of all the doctors of different seniorities. By comparing the area under the curve, sensitivity, and specificity, we demonstrate that the DIET-AI model is effective in clinical scenarios. In addition, medical records affect the performance of DIET-AI and physicians to varying degrees.Conclusion: This is the largest dermatological dataset for the Chinese demographic. For the first time, we built a Dual-channel image classification model on a non-cancer dermatitis dataset with both images and medical records and achieved comparable diagnostic performance to senior doctors about common skin diseases. It provides references for exploring the feasibility and performance evaluation of DIET-AI in clinical use afterward.

Objective: The second messenger inositol triphosphate (IP3) is involved in signal transduction in multiple cell types. We evaluated the effects of high-dose levocetirizine on chronic spontaneous urticaria (CSU) and examined the significance of serum IP3 level in the pathogenesis of CSU. Methods: Fifteen patients with refractory CSU were given oral levocetirizine at a dose of 15 mg once daily for 7 days, and treatment efficacy was determined using the Urticaria Activity Score and by evaluating wheal-and-erythema reactions and itching. The serum concentration of IP3 at specific time points was determined by enzyme-linked immunosorbent assay. Results: The mean serum concentration of IP3 was 43.54 ± 41.97 pg/mL prior to treatment, 18.40 ± 17.53 pg/mL after treatment, and 1.31 ± 0.92 pg/mL in a healthy control group. The mean concentration of IP3 was significantly higher before treatment than after treatment, and the level of IP3 in the patient group before and after treatment was significantly higher than that in the control group. Conclusion: High-dose levocetirizine was shown to be effective in the treatment of CSU. The level of serum IP3 was positively correlated with CSU activity, indicating that IP3 may play an important role in the pathogenesis of this condition.

Chronic spontaneous urticaria (CSU) is an immune-mediated skin disorder marked by recurrent hives and angioedema, affecting a large proportion of the global population. Traditional immunotherapy often fails, particularly in difficult-to-treat cases, highlighting the need for more precise, immune cell-targeted treatments. Nanomaterials offer unique advantages, such as size tuning, surface functionalization, and controlled release, allowing precise targeting of immune cell receptors. This is crucial for enhancing treatment efficacy and minimizing systemic side effects. This review explores the potential of nanomaterials in CSU immunotherapy, focusing on their ability to modulate immune cells—such as mast cells, eosinophils, basophils, and T lymphocytes—to restore immune balance and alleviate symptoms. However, challenges remain in the clinical application of nanomaterials, including concerns about immunotoxicity, long-term safety, and the need for stronger evidence of efficacy in human trials. Additionally, while AI in optimizing nanomaterial design shows promise, it is still in early development and requires further exploration to enhance its practical applications. Current nanomaterial applications in allergic diseases, such as food allergies and asthma, show promise but differ from CSU in immune mechanisms like mast cell-eosinophil interactions and neuroimmune pathways. This approach is novel in that nanomaterials could provide a more precise, targeted solution for CSU, overcoming the limitations of current therapies. Future research should focus on optimizing nanomaterial formulations, integrating multi-omics analyses, and developing AI-driven strategies for personalized treatment. These advances could pave the way for safer, more effective immunotherapies in CSU and other immune-mediated diseases.