Background: Psoriasis is one of the most frequent inflammatory skin conditions and could be treated via tele-dermatology, provided that the current lack of reliable tools for objective severity assessments is overcome. Psoriasis Area and Severity Index (PASI) has a prominent level of subjectivity and is rarely used in real practice, although it is the most widely accepted metric for measuring psoriasis severity currently. Objective: This study aimed to develop an image–artificial intelligence (AI)–based validated system for severity assessment with the explicit intention of facilitating long-term management of patients with psoriasis. Methods: A deep learning system was trained to estimate the PASI score by using 14,096 images from 2367 patients with psoriasis. We used 1962 patients from January 2015 to April 2021 to train the model and the other 405 patients from May 2021 to July 2021 to validate it. A multiview feature enhancement block was designed to combine vision features from different perspectives to better simulate the visual diagnostic method in clinical practice. A classification header along with a regression header was simultaneously applied to generate PASI scores, and an extra cross-teacher header after these 2 headers was designed to revise their output. The mean average error (MAE) was used as the metric to evaluate the accuracy of the predicted PASI score. By making the model minimize the MAE value, the model becomes closer to the target value. Then, the proposed model was compared with 43 experienced dermatologists. Finally, the proposed model was deployed into an app named SkinTeller on the WeChat platform. Results: The proposed image-AI–based PASI-estimating model outperformed the average performance of 43 experienced dermatologists with a 33.2% performance gain in the overall PASI score. The model achieved the smallest MAE of 2.05 at 3 input images by the ablation experiment. In other words, for the task of psoriasis severity assessment, the severity score predicted by our model was close to the PASI score diagnosed by experienced dermatologists. The SkinTeller app has been used 3369 times for PASI scoring in 1497 patients from 18 hospitals, and its excellent performance was confirmed by a feedback survey of 43 dermatologist users. Conclusions: An image-AI–based psoriasis severity assessment model has been proposed to automatically calculate PASI scores in an efficient, objective, and accurate manner. The SkinTeller app may be a promising alternative for dermatologists’ accurate assessment in the real world and chronic disease self-management in patients with psoriasis.

Keloids are benign fibroproliferative tumors that severely diminish the quality of life due to discomfort, dysfunction, and disfigurement. Recently, ultrasound technology as a noninvasive adjuvant therapy is developed to optimize treatment protocols. However, the biophysical mechanisms have not yet been fully elucidated. Here, it is proposed that piezo‐type mechanosensitive ion channel component 1 (Piezo1) plays an important role in low‐frequency sonophoresis (LFS) induced mechanical transduction pathways that trigger downstream cellular signaling processes. It is demonstrated that patient‐derived primary keloid fibroblasts (PKF), NIH 3T3, and HFF‐1 cell migration are inhibited, and PKF apoptosis is significantly increased by LFS stimulation. And the effects of LFS is diminished by the application of GsMTx‐4, the selective inhibitor of Piezo1, and the knockdown of Piezo1. More importantly, the effects of LFS can be imitated by Yoda1, an agonist of Piezo1 channels. Establishing a patient‐derived xenograft keloid implantation mouse model further verified these results, as LFS significantly decreased the volume and weight of the keloids. Moreover, blocking the Piezo1 channel impaired the effectiveness of LFS treatment. These results suggest that LFS inhibits the malignant characteristics of keloids by activating the Piezo1 channel, thus providing a theoretical basis for improving the clinical treatment of keloids.

Over millions of years of evolution, species in nature have gradually adopted bio-structures. These structures have specific mechanical, hydrodynamic, optical, and electrical properties, which provide humans with valuable abilities to design and fabricate high-performance components and devices. However, traditional fabrication technologies cannot accurately reproduce or imitate the complicated and exquisite bioinspired structures, which restrict the development and application of biomimetic study. Due to the emergence and development of 3D printing technologies, more bioinspired structures can now be designed and fabricated. Recent progress in the 3D printing of structures inspired by species in nature, such as springtails, filefish, abalone shell, conch shell, wheat awn, plant stem, and wood, are reviewed in this paper, which also discusses current challenges and potential future developments in 3D printing for bioinspired structures.

Three-dimensional (3D) cell culture has revolutionized life sciences, particularly in organoid technologies. Traditional bioscaffold materials, however, complicate the detachment of tumor organoids and hamper the routine use of organoid–immune cell cocultures. Here, we show an acoustic virtual 3D scaffold (AV-Scaf) method to achieve 3D tumor organoid culture, enabling a direct-interacting tumor organoid–immune cell coculture system. The self-organization process of tumor cells is facilitated by a vortex acoustic field, which enables the cell bioassembly and ion channel activation. This approach can significantly enhance the influx of calcium ions, thereby accelerating intercellular interactions of cellular assemblies. We established scaffold-free melanoma and breast cancer organoids using AV-Scaf and cocultured melanoma organoids with T cells. We found that our coculture system resulted in a high activation state of T cells, characterized by notable up-regulation of granzyme B (2.82 to 17.5%) and interferon-γ (1.36 to 16%). AV-Scaf offers an efficient method for tumor organoid–immune cell studies, advancing cancer research and immunotherapy development.

Although microfluidic approaches for liposomes preparation have been developed, fabricating microfluidic devices remains expensive and time-consuming. Also, owing to the traditional layout of microchannels, the volumetric throughput of microfluidics has been greatly limited. Herein an ultra-high volumetric throughput nanoliposome preparation method using 3D printed microfluidic chips is presented. A high-resolution projection micro stereolithography (PμSL) 3D printer is applied to produce microfluidic chips with critical dimensions of 400 µm. The microchannels of the microfluidic chip adopt a three-layer layout, achieving the total flow rate (TFR) up to 474 ml min⁻¹, which is remarkably higher than those in the reported literature. The liposome size can be as small as 80 nm. The state of flows in microchannels and the effect of turbulence on liposome formation are explored. The experimental results demonstrate that the 3D printed integrated microfluidic chip enables ultra-high volumetric throughput nanoliposome preparation and can control size efficiently, which has great potential in targeting drug delivery systems.